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Sample records for survey multiple regression

  1. Multiple linear regression analysis

    Science.gov (United States)

    Edwards, T. R.

    1980-01-01

    Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

  2. Business applications of multiple regression

    CERN Document Server

    Richardson, Ronny

    2015-01-01

    This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta

  3. Multiple Regression and Its Discontents

    Science.gov (United States)

    Snell, Joel C.; Marsh, Mitchell

    2012-01-01

    Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.

  4. Multiple Regression and Its Discontents

    Science.gov (United States)

    Snell, Joel C.; Marsh, Mitchell

    2012-01-01

    Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.

  5. Correlation Weights in Multiple Regression

    Science.gov (United States)

    Waller, Niels G.; Jones, Jeff A.

    2010-01-01

    A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…

  6. Multiple Instance Regression with Structured Data

    Science.gov (United States)

    Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

    2008-01-01

    This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.

  7. Some Simple Computational Formulas for Multiple Regression

    Science.gov (United States)

    Aiken, Lewis R., Jr.

    1974-01-01

    Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)

  8. Assumptions of Multiple Regression: Correcting Two Misconceptions

    Directory of Open Access Journals (Sweden)

    Matt N. Williams

    2013-09-01

    Full Text Available In 2002, an article entitled - Four assumptions of multiple regression that researchers should always test- by.Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times.(as of August 2013, and it is one of the first results displayed in a Google search for - regression.assumptions- . While Osborne and Waters' efforts in raising awareness of the need to check assumptions.when using regression are laudable, we note that the original article contained at least two fairly important.misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the.assumption of normally distributed variables; and secondly, that measurement errors necessarily cause.underestimation of simple regression coefficients. In this article, we clarify that multiple regression models.estimated using ordinary least squares require the assumption of normally distributed errors in order for.trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or.predictor variables. Secondly, we point out that regression coefficients in simple regression models will be.biased (toward zero estimates of the relationships between variables of interest when measurement error is.uncorrelated across those variables, but that when correlated measurement error is present, regression.coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of.the assumptions of multiple regression when using ordinary least squares.

  9. Practical Session: Multiple Linear Regression

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    Clausel, M.; Grégoire, G.

    2014-12-01

    Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).

  10. Multiple linear regression model for bromate formation based on the survey data of source waters from geographically different regions across China.

    Science.gov (United States)

    Yu, Jianwei; Liu, Juan; An, Wei; Wang, Yongjing; Zhang, Junzhi; Wei, Wei; Su, Ming; Yang, Min

    2015-01-01

    A total of 86 source water samples from 38 cities across major watersheds of China were collected for a bromide (Br(-)) survey, and the bromate (BrO3 (-)) formation potentials (BFPs) of 41 samples with Br(-) concentration >20 μg L(-1) were evaluated using a batch ozonation reactor. Statistical analyses indicated that higher alkalinity, hardness, and pH of water samples could lead to higher BFPs, with alkalinity as the most important factor. Based on the survey data, a multiple linear regression (MLR) model including three parameters (alkalinity, ozone dose, and total organic carbon (TOC)) was established with a relatively good prediction performance (model selection criterion = 2.01, R (2) = 0.724), using logarithmic transformation of the variables. Furthermore, a contour plot was used to interpret the influence of alkalinity and TOC on BrO3 (-) formation with prediction accuracy as high as 71 %, suggesting that these two parameters, apart from ozone dosage, were the most important ones affecting the BFPs of source waters with Br(-) concentration >20 μg L(-1). The model could be a useful tool for the prediction of the BFPs of source water.

  11. Uso de regressões logísticas múltiplas para mapeamento digital de solos no Planalto Médio do RS Multiple logistic regression applied to soil survey in rio grande do sul state, Brazil

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    Samuel Ribeiro Figueiredo

    2008-12-01

    hydrographic variables (distance to rivers, flow length, topographical wetness index, and stream power index. Multiple logistic regressions were established between the soil classes mapped on the basis of a traditional survey at a scale of 1:80.000 and the land variables calculated using the DEM. The regressions were used to calculate the probability of occurrence of each soil class. The final estimated soil map was drawn by assigning the soil class with highest probability of occurrence to each cell. The general accuracy was evaluated at 58 % and the Kappa coefficient at 38 % in a comparison of the original soil map with the map estimated at the original scale. A legend simplification had little effect to increase the general accuracy of the map (general accuracy of 61 % and Kappa coefficient of 39 %. It was concluded that multiple logistic regressions have a predictive potential as tool of supervised soil mapping.

  12. Incremental Net Effects in Multiple Regression

    Science.gov (United States)

    Lipovetsky, Stan; Conklin, Michael

    2005-01-01

    A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

  13. Multiple-Instance Regression with Structured Data

    Science.gov (United States)

    Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

    2008-01-01

    We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

  14. The M Word: Multicollinearity in Multiple Regression.

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    Morrow-Howell, Nancy

    1994-01-01

    Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

  15. Multiple Kernel Spectral Regression for Dimensionality Reduction

    Directory of Open Access Journals (Sweden)

    Bing Liu

    2013-01-01

    Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.

  16. The Geometry of Enhancement in Multiple Regression.

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    Waller, Niels G

    2011-10-01

    In linear multiple regression, "enhancement" is said to occur when R (2)=b'r>r'r, where b is a p×1 vector of standardized regression coefficients and r is a p×1 vector of correlations between a criterion y and a set of standardized regressors, x. When p=1 then b≡r and enhancement cannot occur. When p=2, for all full-rank R xx≠I, R xx=E[xx']=V Λ V' (where V Λ V' denotes the eigen decomposition of R xx; λ 1>λ 2), the set [Formula: see text] contains four vectors; the set [Formula: see text]; [Formula: see text] contains an infinite number of vectors. When p≥3 (and λ 1>λ 2>⋯>λ p ), both sets contain an uncountably infinite number of vectors. Geometrical arguments demonstrate that B 1 occurs at the intersection of two hyper-ellipsoids in ℝ (p) . Equations are provided for populating the sets B 1 and B 2 and for demonstrating that maximum enhancement occurs when b is collinear with the eigenvector that is associated with λ p (the smallest eigenvalue of the predictor correlation matrix). These equations are used to illustrate the logic and the underlying geometry of enhancement in population, multiple-regression models. R code for simulating population regression models that exhibit enhancement of any degree and any number of predictors is included in Appendices A and B.

  17. A Dirty Model for Multiple Sparse Regression

    CERN Document Server

    Jalali, Ali; Sanghavi, Sujay

    2011-01-01

    Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recovered. A natural question in this setting is whether one can use the sharing to further decrease the overall number of samples required. A line of recent research has studied the use of \\ell_1/\\ell_q norm block-regularizations with q>1 for such problems; however these could actually perform worse in sample complexity -- vis a vis solving each problem separately ignoring sharing -- depending on the level of sharing. We present a new method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. A very simple idea: we decompose the parameters into two components and regularize these differently. We show both theore...

  18. Interpretation of Standardized Regression Coefficients in Multiple Regression.

    Science.gov (United States)

    Thayer, Jerome D.

    The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…

  19. Remaining Phosphorus Estimate Through Multiple Regression Analysis

    Institute of Scientific and Technical Information of China (English)

    M. E. ALVES; A. LAVORENTI

    2006-01-01

    The remaining phosphorus (Prem), P concentration that remains in solution after shaking soil with 0.01 mol L-1 CaCl2 containing 60 μg mL-1 P, is a very useful index for studies related to the chemistry of variable charge soils. Although the Prem determination is a simple procedure, the possibility of estimating accurate values of this index from easily and/or routinely determined soil properties can be very useful for practical purposes. The present research evaluated the Premestimation through multiple regression analysis in which routinely determined soil chemical data, soil clay content and soil pH measured in 1 mol L-1 NaF (pHNaF) figured as Prem predictor variables. The Prem can be estimated with acceptable accuracy using the above-mentioned approach, and PHNaF not only substitutes for clay content as a predictor variable but also confers more accuracy to the Prem estimates.

  20. Multiple Surveys of Students and Survey Fatigue

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    Porter, Stephen R.; Whitcomb, Michael E.; Weitzer, William H.

    2004-01-01

    This chapter reviews the literature on survey fatigue and summarizes a research project that indicates that administering multiple surveys in one academic year can significantly suppress response rates in later surveys. (Contains 4 tables.)

  1. Relationship between Multiple Regression and Selected Multivariable Methods.

    Science.gov (United States)

    Schumacker, Randall E.

    The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

  2. Fuzzy multiple linear regression: A computational approach

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    Juang, C. H.; Huang, X. H.; Fleming, J. W.

    1992-01-01

    This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

  3. Entrepreneurial intention modeling using hierarchical multiple regression

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    Marina Jeger

    2014-12-01

    Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.

  4. Abnormal behavior of the least squares estimate of multiple regression

    Institute of Scientific and Technical Information of China (English)

    陈希孺; 安鸿志

    1997-01-01

    An example is given to reveal the abnormal behavior of the least squares estimate of multiple regression. It is shown that the least squares estimate of the multiple linear regression may be "improved in the sense of weak consistency when nuisance parameters are introduced into the model. A discussion on the implications of this finding is given.

  5. General Nature of Multicollinearity in Multiple Regression Analysis.

    Science.gov (United States)

    Liu, Richard

    1981-01-01

    Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

  6. Enhance-Synergism and Suppression Effects in Multiple Regression

    Science.gov (United States)

    Lipovetsky, Stan; Conklin, W. Michael

    2004-01-01

    Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

  7. A Multiple Regression Approach to Normalization of Spatiotemporal Gait Features.

    Science.gov (United States)

    Wahid, Ferdous; Begg, Rezaul; Lythgo, Noel; Hass, Chris J; Halgamuge, Saman; Ackland, David C

    2016-04-01

    Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson's disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 normalization using the multiple regression method reduced these correlations to weak values (|r| normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.

  8. Research and analyze of physical health using multiple regression analysis

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    T. S. Kyi

    2014-01-01

    Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

  9. Direction of Effects in Multiple Linear Regression Models.

    Science.gov (United States)

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  10. Strategies for Identification and Detection of Outliers in Multiple Regression.

    Science.gov (United States)

    Vannoy, Martha

    Outliers are frequently found in data sets and can cause problems for researchers if not addressed. Failure to identify and deal with outliers in an appropriate manner may lead researchers to report erroneous results. Using a multiple regression context, this paper examines some of the reasons for the presence of outliers and simple methods for…

  11. Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

    Science.gov (United States)

    Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim

    2012-01-01

    Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…

  12. Multiple regression for physiological data analysis: the problem of multicollinearity.

    Science.gov (United States)

    Slinker, B K; Glantz, S A

    1985-07-01

    Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

  13. Simple and multiple linear regression: sample size considerations.

    Science.gov (United States)

    Hanley, James A

    2016-11-01

    The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Multiple Retrieval Models and Regression Models for Prior Art Search

    CERN Document Server

    Lopez, Patrice

    2009-01-01

    This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend.

  15. A comparison of random forest regression and multiple linear regression for prediction in neuroscience.

    Science.gov (United States)

    Smith, Paul F; Ganesh, Siva; Liu, Ping

    2013-10-30

    Regression is a common statistical tool for prediction in neuroscience. However, linear regression is by far the most common form of regression used, with regression trees receiving comparatively little attention. In this study, the results of conventional multiple linear regression (MLR) were compared with those of random forest regression (RFR), in the prediction of the concentrations of 9 neurochemicals in the vestibular nucleus complex and cerebellum that are part of the l-arginine biochemical pathway (agmatine, putrescine, spermidine, spermine, l-arginine, l-ornithine, l-citrulline, glutamate and γ-aminobutyric acid (GABA)). The R(2) values for the MLRs were higher than the proportion of variance explained values for the RFRs: 6/9 of them were ≥ 0.70 compared to 4/9 for RFRs. Even the variables that had the lowest R(2) values for the MLRs, e.g. ornithine (0.50) and glutamate (0.61), had much lower proportion of variance explained values for the RFRs (0.27 and 0.49, respectively). The RSE values for the MLRs were lower than those for the RFRs in all but two cases. In general, MLRs seemed to be superior to the RFRs in terms of predictive value and error. In the case of this data set, MLR appeared to be superior to RFR in terms of its explanatory value and error. This result suggests that MLR may have advantages over RFR for prediction in neuroscience with this kind of data set, but that RFR can still have good predictive value in some cases. Copyright © 2013 Elsevier B.V. All rights reserved.

  16. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

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    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  17. A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

    Science.gov (United States)

    Shen, Jianzhao; Gao, Sujuan

    2008-10-01

    In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

  18. Two SPSS programs for interpreting multiple regression results.

    Science.gov (United States)

    Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

    2010-02-01

    When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

  19. MULTIPLE REGRESSION ANALYSIS OF MAIN ECONOMIC INDICATORS IN TOURISM

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    Erika KULCSÁR

    2009-12-01

    Full Text Available This paper analysis the measure between GDP dependent variable in the sector of hotels and restaurants and the following independent variables: overnight stays in the establishments of touristic reception, arrivals in the establishments of touristic reception and investments in hotels and restaurants sector in the period of analysis 1995-2007. With the multiple regression analysis I found that investments and tourist arrivals are significant predictors for the GDP dependent variable. Based on these results, I identified those components of the marketing mix, which in my opinion require investment, which could contribute to the positive development of tourist arrivals in the establishments of touristic reception.

  20. Forecasting relativistic electron flux using dynamic multiple regression models

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    H.-L. Wei

    2011-02-01

    Full Text Available The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach is exploited to deduce a dynamic multiple regression model that can be used to predict the daily maximum of high energy electron fluxes at geosynchronous orbit from data. It is shown that the model developed provides reliable predictions.

  1. MULTIPLE REGRESSION ANALYSIS OF MAIN ECONOMIC INDICATORS IN TOURISM

    Directory of Open Access Journals (Sweden)

    Erika KULCSÁR

    2009-12-01

    Full Text Available This paper analysis the measure between GDP dependent variable in the sector of hotels and restaurants and the following independent variables: overnight stays in the establishments of touristic reception, arrivals in the establishments of touristic reception and investments in hotels and restaurants sector in the period of analysis 1995-2007. With the multiple regression analysis I found that investments and tourist arrivals are significant predictors for the GDP dependent variable. Based on these results, I identified those components of the marketing mix, which in my opinion require investment, which could contribute to the positive development of tourist arrivals in the establishments of touristic reception.

  2. Interpret with caution: multicollinearity in multiple regression of cognitive data.

    Science.gov (United States)

    Morrison, Catriona M

    2003-08-01

    Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

  3. Hot Resistance Estimation for Dry Type Transformer Using Multiple Variable Regression, Multiple Polynomial Regression and Soft Computing Techniques

    Directory of Open Access Journals (Sweden)

    M. Srinivasan

    2012-01-01

    Full Text Available Problem statement: This study presents a novel method for the determination of average winding temperature rise of transformers under its predetermined field operating conditions. Rise in the winding temperature was determined from the estimated values of winding resistance during the heat run test conducted as per IEC standard. Approach: The estimation of hot resistance was modeled using Multiple Variable Regression (MVR, Multiple Polynomial Regression (MPR and soft computing techniques such as Artificial Neural Network (ANN and Adaptive Neuro Fuzzy Inference System (ANFIS. The modeled hot resistance will help to find the load losses at any load situation without using complicated measurement set up in transformers. Results: These techniques were applied for the hot resistance estimation for dry type transformer by using the input variables cold resistance, ambient temperature and temperature rise. The results are compared and they show a good agreement between measured and computed values. Conclusion: According to our experiments, the proposed methods are verified using experimental results, which have been obtained from temperature rise test performed on a 55 kVA dry-type transformer.

  4. Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition

    Science.gov (United States)

    Andriana, A. S.; Prihatmanto, D.; Hidaya, E. M. I.; Supriana, I.; Machbub, C.

    2017-01-01

    Understanding images by recognizing its objects is still a challenging task. Face elements detection has been developed by researchers but not yet shows enough information (low resolution in information) needed for recognizing objects. Available face recognition methods still have error in classification and need a huge amount of examples which may still be incomplete. Another approach which is still rare in understanding images uses pattern structures or syntactic grammars describing shape detail features. Image pixel values are also processed as signal patterns which are approximated by mathematical function curve fitting. This paper attempts to add contiguous uniform deviation method to curve fitting algorithm to increase applicability in image recognition system related to object movement. The combination of multiple linear regression and contiguous uniform deviation method are applied to the function of image pixel values, and show results in higher resolution (more information) of visual object detail description in object movement.

  5. Vehicle Travel Time Predication based on Multiple Kernel Regression

    Directory of Open Access Journals (Sweden)

    Wenjing Xu

    2014-07-01

    Full Text Available With the rapid development of transportation and logistics economy, the vehicle travel time prediction and planning become an important topic in logistics. Travel time prediction, which is indispensible for traffic guidance, has become a key issue for researchers in this field. At present, the prediction of travel time is mainly short term prediction, and the predication methods include artificial neural network, Kaman filter and support vector regression (SVR method etc. However, these algorithms still have some shortcomings, such as highcomputationcomplexity, slow convergence rate etc. This paper exploits the learning ability of multiple kernel learning regression (MKLR in nonlinear prediction processing characteristics, logistics planning based on MKLR for vehicle travel time prediction. The method for Vehicle travel time prediction includes the following steps: (1 preprocessing historical data; (2 selecting appropriate kernel function, training the historical data and performing analysis ;(3 predicting the vehicle travel time based on the trained model. The experimental results show that, through the analysis of using different methods for prediction, the vehicle travel time prediction method proposed in this paper, archives higher accuracy than other methods. It also illustrates the feasibility and effectiveness of the proposed prediction method.

  6. [Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

    Science.gov (United States)

    Ling, Ru; Liu, Jiawang

    2011-12-01

    To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

  7. Influencing Academic Library Use in Tanzania: A Multiple Regression Analysis

    Directory of Open Access Journals (Sweden)

    Leocardia L Juventus

    2016-12-01

    Full Text Available Library use is influenced by many factors. This study uses a multiple regression analysis to ascertain the connection between the level of library use and a few of these factors based on the questionnaire responses from 158 undergraduate students who use academic libraries in two Tanzania’s universities: Muhimbili University of Health and Allied Sciences (MUHAS, and Hubert Kairuki Memorial University (HKMU. It has been discovered that users of academic libraries in Tanzania are influenced by the need to: search and access online materials, check for new books or other resources, check out books and other materials, and enjoy a friendly environment for study. However, their library use is not influenced by either the free wireless network, or consultation from librarians. It is argued that, academic libraries need to devise and implement plans that can make these libraries better learning environment and platforms to drive socio-economic developmentparticularly in developing nations such as Tanzania. It is further argued that, this can be enhanced through investment in modern academic library infrastructures.

  8. Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Jaber Almedeij

    2012-01-01

    Full Text Available Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values.

  9. Overcoming multicollinearity in multiple regression using correlation coefficient

    Science.gov (United States)

    Zainodin, H. J.; Yap, S. J.

    2013-09-01

    Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

  10. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction

    Science.gov (United States)

    Kuhn, David; Parida, Laxmi

    2016-01-01

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. Availability and implementation: The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. Contact: dhe@us.ibm.com PMID:27307640

  11. Forecasting Gold Prices Using Multiple Linear Regression Method

    Directory of Open Access Journals (Sweden)

    Z. Ismail

    2009-01-01

    Full Text Available Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the melt-down of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted model of MLR will be used to predict the future gold prices. A naive model known as “forecast-1” was considered to be a benchmark model in order to evaluate the performance of the model. Results: Many factors determine the price of gold and based on “a hunch of experts”, several economic factors had been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB; USD/Euro Foreign Exchange Rate (EUROUSD; Inflation rate (INF; Money Supply (M1; New York Stock Exchange (NYSE; Standard and Poor 500 (SPX; Treasury Bill (T-BILL and US Dollar index (USDX were considered to

  12. Regression Discontinuity Designs with Multiple Rating-Score Variables

    Science.gov (United States)

    Reardon, Sean F.; Robinson, Joseph P.

    2012-01-01

    In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those…

  13. Variable selection in multiple linear regression: The influence of ...

    African Journals Online (AJOL)

    Akaike's information criterion, influential data cases, Mallows' Cp criterion, multiple ... In this paper we introduce two new measures of the selection influence of an ..... [1] Akaike H, 1973, Information theory and an extension of the maximum ...

  14. The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

    Science.gov (United States)

    Jaccard, James; And Others

    1990-01-01

    Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

  15. Multiple linear regression with correlations among the predictor variables. Theory and computer algorithm ridge (FORTRAN 77)

    Science.gov (United States)

    van Gaans, P. F. M.; Vriend, S. P.

    Application of ridge regression in geoscience usually is a more appropriate technique than ordinary least-squares regression, especially in the situation of highly intercorrelated predictor variables. A FORTRAN 77 program RIDGE for ridged multiple linear regression is presented. The theory of linear regression and ridge regression is treated, to allow for a careful interpretation of the results and to understand the structure of the program. The program gives various parameters to evaluate the extent of multicollinearity within a given regression problem, such as the correlation matrix, multiple correlations among the predictors, variance inflation factors, eigenvalues, condition number, and the determinant of the predictors correlation matrix. The best method for the optimum choice of the ridge parameter with ridge regression has not been established yet. Estimates of the ridge bias, ridged variance inflation factors, estimates, and norms for the ridge parameter therefore are given as output by RIDGE and should complement inspection of the ridge traces. Application within the earth sciences is discussed.

  16. Tightness of M-estimators for multiple linear regression in time series

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We show tightness of a general M-estimator for multiple linear regression in time series. The positive criterion function for the M-estimator is assumed lower semi-continuous and sufficiently large for large argument: Particular cases are the Huber-skip and quantile regression. Tightness requires...

  17. Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

    Science.gov (United States)

    Beckstead, Jason W.

    2012-01-01

    The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

  18. Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

    Science.gov (United States)

    Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

    2014-12-01

    This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

  19. The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

    Science.gov (United States)

    Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

    2014-09-01

    In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

  20. An improved multiple linear regression and data analysis computer program package

    Science.gov (United States)

    Sidik, S. M.

    1972-01-01

    NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

  1. Prediction on adsorption ratio of carbon dioxide to methane on coals with multiple linear regression

    Institute of Scientific and Technical Information of China (English)

    YU Hong-guan; MENG Xian-ming; FAN Wei-tang; YE Jian-ping

    2007-01-01

    The multiple linear regression equations for adsorption ratio of CO2/CH4 and its coal quality indexes were built with SPSS software on basis of existing coal quality data and its adsorption amount of CO2 and CH4.The regression equations built were tested with data collected from some S,and the influences of coal quality indexes on adsorption ratio of CO2/CH4 were studied with investigation of regression equations.The study results show that the regression equation for adsorption ratio of CO2/CH4 and volatile matter,ash and moisture in coal can be Obtained with multiple linear regression analysis,that the influence of same coal quality index with the degree of metamorphosis or influence of coal quality indexes for same coal rank on adsorption ratio is not consistent.

  2. Simple multiple regression model for long range forecasting of Indian summer monsoon rainfall

    Digital Repository Service at National Institute of Oceanography (India)

    Sadhuram, Y.; Murthy, T.V.R.

    ) and ISMR is found to be 0.62. The multiple correlation using the above two parameters is 0.85 which explains 72% variance in ISMR. Using the above two parameters a linear multiple regression model to predict ISMR is developed. The results are comparable...

  3. Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

    Science.gov (United States)

    Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

    2010-01-01

    Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

  4. A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators Multiple Causes

    Science.gov (United States)

    Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

    2016-01-01

    Background: Measurement equivalence is an essential prerequisite for making valid comparisons in mental health questionnaires across groups. In most methods used for assessing measurement equivalence, which is known as Differential Item Functioning (DIF), latent variables are assumed to be continuous. Objective: To compare a new method called Latent Class Regression (LCR) designed for discrete latent variable with the multiple indicators multiple cause (MIMIC) as a continuous latent variable technique to assess the measurement equivalence of the 12-item General Health Questionnaire (GHQ-12), which is a cross deferent subgroup of Iranian nurses. Methods: A cross-sectional survey was conducted in 2014 among 771 nurses working in the hospitals of Fars and Bushehr provinces of southern Iran. To identify the Minor Psychiatric Disorders (MPD), the nurses completed self-report GHQ-12 questionnaires and sociodemographic questions. Two uniform-DIF detection methods, LCR and MIMIC, were applied for comparability when the GHQ-12 score was assumed to be discrete and continuous, respectively. Results: The result of fitting LCR with 2 classes indicated that 27.4% of the nurses had MPD. Gender was identified as an influential factor of the level of MPD.LCR and MIMIC agree with detection of DIF and DIF-free items by gender, age, education and marital status in 83.3, 100.0, 91.7 and 83.3% cases, respectively. Conclusions: The results indicated that the GHQ-12 is to a great degree, an invariant measure for the assessment of MPD among nurses. High convergence between the two methods suggests using the LCR approach in cases of discrete latent variable, e.g. GHQ-12 and adequate sample size. PMID:27482129

  5. Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis

    Science.gov (United States)

    Lin, Jing-Fung; Wu, Jyh-Shyang; Sheu, Jer-Jia

    2015-03-01

    The citric acid (CA) coated Fe3O4 ferrofluids are prepared by a co-precipitation method and the magneto-optical retardance property is measured by a Stokes polarimeter. Optimization and multiple regression of retardance in ferrofluids are executed by combining Taguchi method and Excel. From the nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, volume of CA, and coating temperature, influence sequence and excellent program are found. Multiple regression analysis and F-test on the significance of regression equation are performed. It is found that the model F value is much larger than Fcritical and significance level P <0.0001. So it can be concluded that the regression model has statistically significant predictive ability. Substituting excellent program into equation, retardance is obtained as 32.703°, higher than the highest value in tests by 11.4%.

  6. Tools to support interpreting multiple regression in the face of multicollinearity.

    Science.gov (United States)

    Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

    2012-01-01

    While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

  7. On asymptotics of t-type regression estimation in multiple linear model

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    We consider a robust estimator (t-type regression estimator) of multiple linear regression model by maximizing marginal likelihood of a scaled t-type error t-distribution.The marginal likelihood can also be applied to the de-correlated response when the withinsubject correlation can be consistently estimated from an initial estimate of the model based on the independent working assumption. This paper shows that such a t-type estimator is consistent.

  8. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

    Science.gov (United States)

    Cheng, Anyu; Jiang, Xiao; Li, Yongfu; Zhang, Chao; Zhu, Hao

    2017-01-01

    This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi-dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short-term traffic flow prediction in terms of the accuracy and timeliness.

  9. Multiple Logistic Regression Analysis of Cigarette Use among High School Students

    Science.gov (United States)

    Adwere-Boamah, Joseph

    2011-01-01

    A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

  10. A case study found that a regression tree outperformed multiple linear regression in predicting the relationship between impairments and Social and Productive Activities scores.

    Science.gov (United States)

    Allore, Heather; Tinetti, Mary E; Araujo, Katy L B; Hardy, Susan; Peduzzi, Peter

    2005-02-01

    Many important physiologic and clinical predictors are continuous. Clinical investigators and epidemiologists' interest in these predictors lies, in part, in the risk they pose for adverse outcomes, which may be continuous as well. The relationship between continuous predictors and a continuous outcome may be complex and difficult to interpret. Therefore, methods to detect levels of a predictor variable that predict the outcome and determine the threshold for clinical intervention would provide a beneficial tool for clinical investigators and epidemiologists. We present a case study using regression tree methodology to predict Social and Productive Activities score at 3 years using five modifiable impairments. The predictive ability of regression tree methodology was compared with multiple linear regression using two independent data sets, one for development and one for validation. The regression tree approach and the multiple linear regression model provided similar fit (model deviances) on the development cohort. In the validation cohort, the deviance of the multiple linear regression model was 31% greater than the regression tree approach. Regression tree analysis developed a better model of impairments predicting Social and Productive Activities score that may be more easily applied in research settings than multiple linear regression alone.

  11. A Modified Gauss-Jordan Procedure as an Alternative to Iterative Procedures in Multiple Regression.

    Science.gov (United States)

    Roscoe, John T.; Kittleson, Howard M.

    Correlation matrices involving linear dependencies are common in educational research. In such matrices, there is no unique solution for the multiple regression coefficients. Although computer programs using iterative techniques are used to overcome this problem, these techniques possess certain disadvantages. Accordingly, a modified Gauss-Jordan…

  12. Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

    Science.gov (United States)

    Li, Spencer D.

    2011-01-01

    Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

  13. A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

    Science.gov (United States)

    Cooper, Paul D.

    2010-01-01

    A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

  14. A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

    Science.gov (United States)

    Cooper, Paul D.

    2010-01-01

    A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

  15. Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe

    NARCIS (Netherlands)

    Mazvimavi, D.; Meijerink, A.M.J.; Savenije, H.H.G.; Stein, A.

    2005-01-01

    The feasibility of predicting flow characteristics from basin descriptors using multiple regression and neural networks has been investigated on 52 basins in Zimbabwe. Flow characteristics considered were average annual runoff, base flow index, flow duration curve, and average monthly runoff . Mean

  16. The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data.

    Science.gov (United States)

    Enders, Craig K.

    2001-01-01

    Examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data using Monte Carlo simulation and considering the effects of four independent variables. Results indicate that FIML estimation was superior to that of three ad hoc techniques, with less bias and less…

  17. A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

    Science.gov (United States)

    Martin, David

    2008-01-01

    This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

  18. Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

    Science.gov (United States)

    Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

    1999-01-01

    A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

  19. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

    Science.gov (United States)

    Li, Yanming; Nan, Bin; Zhu, Ji

    2015-06-01

    We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study.

  20. Multiple-time correlation functions for non-Markovian interaction: Beyond the Quantum Regression Theorem

    CERN Document Server

    Alonso, D; Alonso, Daniel; Vega, In\\'es de

    2004-01-01

    Multiple time correlation functions are found in the dynamical description of different phenomena. They encode and describe the fluctuations of the dynamical variables of a system. In this paper we formulate a theory of non-Markovian multiple-time correlation functions (MTCF) for a wide class of systems. We derive the dynamical equation of the {\\it reduced propagator}, an object that evolve state vectors of the system conditioned to the dynamics of its environment, which is not necessarily at the vacuum state at the initial time. Such reduced propagator is the essential piece to obtain multiple-time correlation functions. An average over the different environmental histories of the reduced propagator permits us to obtain the evolution equations of the multiple-time correlation functions. We also study the evolution of MTCF within the weak coupling limit and it is shown that the multiple-time correlation function of some observables satisfy the Quantum Regression Theorem (QRT), whereas other correlations do no...

  1. Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

    Science.gov (United States)

    Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

    2011-11-01

    SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

  2. Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

    Science.gov (United States)

    Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

    2012-01-01

    Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

  3. On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.

    Science.gov (United States)

    Pek, Jolynn; Chalmers, R Philip; Monette, Georges

    2016-01-01

    When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.

  4. Simultaneous Multiple Response Regression and Inverse Covariance Matrix Estimation via Penalized Gaussian Maximum Likelihood.

    Science.gov (United States)

    Lee, Wonyul; Liu, Yufeng

    2012-10-01

    Multivariate regression is a common statistical tool for practical problems. Many multivariate regression techniques are designed for univariate response cases. For problems with multiple response variables available, one common approach is to apply the univariate response regression technique separately on each response variable. Although it is simple and popular, the univariate response approach ignores the joint information among response variables. In this paper, we propose three new methods for utilizing joint information among response variables. All methods are in a penalized likelihood framework with weighted L(1) regularization. The proposed methods provide sparse estimators of conditional inverse co-variance matrix of response vector given explanatory variables as well as sparse estimators of regression parameters. Our first approach is to estimate the regression coefficients with plug-in estimated inverse covariance matrices, and our second approach is to estimate the inverse covariance matrix with plug-in estimated regression parameters. Our third approach is to estimate both simultaneously. Asymptotic properties of these methods are explored. Our numerical examples demonstrate that the proposed methods perform competitively in terms of prediction, variable selection, as well as inverse covariance matrix estimation.

  5. Land Streamer Surveying Using Multiple Sources

    KAUST Repository

    Mahmoud, Sherif

    2014-12-11

    Various examples are provided for land streamer seismic surveying using multiple sources. In one example, among others, a method includes disposing a land streamer in-line with first and second shot sources. The first shot source is at a first source location adjacent to a proximal end of the land streamer and the second shot source is at a second source location separated by a fixed length corresponding to a length of the land streamer. Shot gathers can be obtained when the shot sources are fired. In another example, a system includes a land streamer including a plurality of receivers, a first shot source located adjacent to the proximal end of the land streamer, and a second shot source located in-line with the land streamer and the first shot source. The second shot source is separated from the first shot source by a fixed overall length corresponding to the land streamer.

  6. Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Ghiasi Majid; Askarnejad Nematollah; Dindarloo Saeid R.; Shamsoddini Hamed

    2016-01-01

    The most important objective of blasting in open pit mines is rock fragmentation. Prediction of produced boulders (oversized crushed rocks) is a key parameter in designing blast patterns. In this study, the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine, Iran was pre-dicted via multiple regression method and artificial neural networks. Results of 33 blasts in the mine were collected for modeling. Input variables were: joints spacing, density and uniaxial compressive strength of the intact rock, burden, spacing, stemming, bench height to burden ratio, and specific charge. The dependent variable was ratio of boulder volume to pattern volume. Both techniques were successful in predicting the ratio. In this study, the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19, respectively.

  7. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

    Science.gov (United States)

    Agha, Salah R; Alnahhal, Mohammed J

    2012-11-01

    The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study.

  8. COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2015-08-01

    Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

  9. Multiple Linear Regression Application on the Inter-Network Settlement of Internet

    Institute of Scientific and Technical Information of China (English)

    YANG Qing-feng; ZHANG Qi-xiang; L(U) Ting-jie

    2006-01-01

    This paper develops an analytical framework to explain the Internet interconnection settlement issues. The paper shows that multiple linear regression can be used in assessing the network value of Internet Backbone Providers (IBPs).By using the exchange rate of each network, we can define a rate of network value, which reflects the contribution of each network to interconnection and the interconnected network resource usage by each of the network.

  10. FORECASTING THE FINANCIAL RETURNS FOR USING MULTIPLE REGRESSION BASED ON PRINCIPAL COMPONENT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Nop Sopipan

    2013-01-01

    Full Text Available The aim of this study was to forecast the returns for the Stock Exchange of Thailand (SET Index by adding some explanatory variables and stationary Autoregressive order p (AR (p in the mean equation of returns. In addition, we used Principal Component Analysis (PCA to remove possible complications caused by multicollinearity. Results showed that the multiple regressions based on PCA, has the best performance.

  11. Multivariate Multiple Regression Models for a Big Data-Empowered SON Framework in Mobile Wireless Networks

    Directory of Open Access Journals (Sweden)

    Yoonsu Shin

    2016-01-01

    Full Text Available In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs and related network parameters (NPs using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.

  12. 中小学生自感课业负担的理论解释--基于北京调查样本的Logistic多项回归模型分析%The Theoretical Study On Self-perceived Academic Burden of Primary and Middle School Students A Multiple Logistic Regression Model Analysis Based on the Sample of Beijing Survey

    Institute of Scientific and Technical Information of China (English)

    王东

    2016-01-01

    “客观说”和“建构说”是研究课业负担原因的两类取向。基于2014年北京中小学生的实证调查数据,建立了囊括“客观说”“建构说”的Logistic多项回归模型。模型的结果证实了基于“客观说”的一些假设,例如学习成绩好的学生自感负担程度较低;教师质量高,学生自感负担轻;学校课程选择性高,学生课业负担也相对较轻。同时,Logistic多项回归模型也证实了“建构说”的一些假设,例如学生预期学历水平越高,自感负担越重;应试压力较强的学生,自感负担较重;体现学习态度的学习价值感、学习快乐感也对学生自感负担程度有显著影响。Logistic多项回归结果表明,作为一种客观“实在”,“课业负担”在中小学生中确实存在(客观说);然而学生对此的感受则会有强弱差异(建构说)。基于此,笔者提出“课业负担感”的概念,试图整合“客观说”和“建构说”两种观点。相比于传统的“课业负担”概念,笔者认为“课业负担感”这一概念提供了更为广阔的理论研究空间,对于“政策”导向的减负策略研究也富有价值。%There are two kind of theoretical hypothesis to explain the causes of students academic burden.One of them is the objective theory;another one is the construction theory.Based on the empirical survey data of primary and middle school students in Beijing in 2014,a multiple logistic regression model is established,which includes “objective theory”and “construction theory”.The results of this model was confirmed based on the “objective”of some of the assumptions,such as learning good grades students’ self-perceived burden level is low;the quality of teachers,students’ self-perceived burden light;high school curriculum selectivity,academic burden is also relatively light.At the same time,the multiple logistic regression model also

  13. MULTIPLE LOGISTIC REGRESSION MODEL TO PREDICT RISK FACTORS OF ORAL HEALTH DISEASES

    Directory of Open Access Journals (Sweden)

    Parameshwar V. Pandit

    2012-06-01

    Full Text Available Purpose: To analysis the dependence of oral health diseases i.e. dental caries and periodontal disease on considering the number of risk factors through the applications of logistic regression model. Method: The cross sectional study involves a systematic random sample of 1760 permanent dentition aged between 18-40 years in Dharwad, Karnataka, India. Dharwad is situated in North Karnataka. The mean age was 34.26±7.28. The risk factors of dental caries and periodontal disease were established by multiple logistic regression model using SPSS statistical software. Results: The factors like frequency of brushing, timings of cleaning teeth and type of toothpastes are significant persistent predictors of dental caries and periodontal disease. The log likelihood value of full model is –1013.1364 and Akaike’s Information Criterion (AIC is 1.1752 as compared to reduced regression model are -1019.8106 and 1.1748 respectively for dental caries. But, the log likelihood value of full model is –1085.7876 and AIC is 1.2577 followed by reduced regression model are -1019.8106 and 1.1748 respectively for periodontal disease. The area under Receiver Operating Characteristic (ROC curve for the dental caries is 0.7509 (full model and 0.7447 (reduced model; the ROC for the periodontal disease is 0.6128 (full model and 0.5821 (reduced model. Conclusions: The frequency of brushing, timings of cleaning teeth and type of toothpastes are main signifi cant risk factors of dental caries and periodontal disease. The fitting performance of reduced logistic regression model is slightly a better fit as compared to full logistic regression model in identifying the these risk factors for both dichotomous dental caries and periodontal disease.

  14. Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis: Empirical Evidence from Bosnia and Herzegovina

    Directory of Open Access Journals (Sweden)

    Deni Memić

    2015-01-01

    Full Text Available This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska. Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit and multiple discriminant analysis (MDA and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.

  15. Research on the multiple linear regression in non-invasive blood glucose measurement.

    Science.gov (United States)

    Zhu, Jianming; Chen, Zhencheng

    2015-01-01

    A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.

  16. Proximate analysis based multiple regression models for higher heating value estimation of low rank coals

    Energy Technology Data Exchange (ETDEWEB)

    Akkaya, Ali Volkan [Department of Mechanical Engineering, Yildiz Technical University, 34349 Besiktas, Istanbul (Turkey)

    2009-02-15

    In this paper, multiple nonlinear regression models for estimation of higher heating value of coals are developed using proximate analysis data obtained generally from the low rank coal samples as-received basis. In this modeling study, three main model structures depended on the number of proximate analysis parameters, which are named the independent variables, such as moisture, ash, volatile matter and fixed carbon, are firstly categorized. Secondly, sub-model structures with different arrangements of the independent variables are considered. Each sub-model structure is analyzed with a number of model equations in order to find the best fitting model using multiple nonlinear regression method. Based on the results of nonlinear regression analysis, the best model for each sub-structure is determined. Among them, the models giving highest correlation for three main structures are selected. Although the selected all three models predicts HHV rather accurately, the model involving four independent variables provides the most accurate estimation of HHV. Additionally, when the chosen model with four independent variables and a literature model are tested with extra proximate analysis data, it is seen that that the developed model in this study can give more accurate prediction of HHV of coals. It can be concluded that the developed model is effective tool for HHV estimation of low rank coals. (author)

  17. Early cost estimating for road construction projects using multiple regression techniques

    Directory of Open Access Journals (Sweden)

    Ibrahim Mahamid

    2011-12-01

    Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.

  18. FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Hirpa G. Lemu

    2017-03-01

    Full Text Available This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables determination of the friction coefficient value under a wide range of friction conditions without performing time-consuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

  19. Multivariate quantiles and multiple-output regression quantiles: From $L_1$ optimization to halfspace depth

    CERN Document Server

    Hallin, Marc; Šiman, Miroslav; 10.1214/09-AOS723

    2010-01-01

    A new multivariate concept of quantile, based on a directional version of Koenker and Bassett's traditional regression quantiles, is introduced for multivariate location and multiple-output regression problems. In their empirical version, those quantiles can be computed efficiently via linear programming techniques. Consistency, Bahadur representation and asymptotic normality results are established. Most importantly, the contours generated by those quantiles are shown to coincide with the classical halfspace depth contours associated with the name of Tukey. This relation does not only allow for efficient depth contour computations by means of parametric linear programming, but also for transferring from the quantile to the depth universe such asymptotic results as Bahadur representations. Finally, linear programming duality opens the way to promising developments in depth-related multivariate rank-based inference.

  20. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

    Science.gov (United States)

    Yoo, Yun Joo; Sun, Lei; Poirier, Julia G.; Paterson, Andrew D.

    2016-01-01

    ABSTRACT By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. PMID:27885705

  1. Optimization of rheological parameter for micro-bubble drilling fluids by multiple regression experimental design

    Institute of Scientific and Technical Information of China (English)

    郑力会; 王金凤; 李潇鹏; 张燕; 李都

    2008-01-01

    In order to optimize plastic viscosity of 18 mPa·s circulating micro-bubble drilling fluid formula,orthogonal and uniform experimental design methods were applied,and the plastic viscosities of 36 and 24 groups of agent were tested,respectively.It is found that these two experimental design methods show drawbacks,that is,the amount of agent is difficult to determine,and the results are not fully optimized.Therefore,multiple regression experimental method was used to design experimental formula.By randomly selecting arbitrary agent with the amount within the recommended range,17 groups of drilling fluid formula were designed,and the plastic viscosity of each experiment formula was measured.Set plastic viscosity as the objective function,through multiple regressions,then quadratic regression model is obtained,whose correlation coefficient meets the requirement.Set target values of plastic viscosity to be 18,20 and 22 mPa·s,respectively,with the trial method,5 drilling fluid formulas are obtained with accuracy of 0.000 3,0.000 1 and 0.000 3.Arbitrarily select target value of each of the two groups under the formula for experimental verification of drilling fluid,then the measurement errors between theoretical and tested plastic viscosity are less than 5%,confirming that regression model can be applied to optimizing the circulating of plastic-foam drilling fluid viscosity.In accordance with the precision of different formulations of drilling fluid for other constraints,the methods result in the optimization of the circulating micro-bubble drilling fluid parameters.

  2. Multiple Regression Analysis of Unconfined Compression Strength of Mine Tailings Matrices

    Directory of Open Access Journals (Sweden)

    Mahmood Ali A.

    2017-01-01

    Full Text Available As part of a novel approach of sustainable development of mine tailings, experimental and numerical analysis is carried out on newly formulated tailings matrices. Several physical characteristic tests are carried out including the unconfined compression strength test to ascertain the integrity of these matrices when subjected to loading. The current paper attempts a multiple regression analysis of the unconfined compressive strength test results of these matrices to investigate the most pertinent factors affecting their strength. Results of this analysis showed that the suggested equation is reasonably applicable to the range of binder combinations used.

  3. Variable selection in multiple linear regression: The influence of individual cases

    Directory of Open Access Journals (Sweden)

    SJ Steel

    2007-12-01

    Full Text Available The influence of individual cases in a data set is studied when variable selection is applied in multiple linear regression. Two different influence measures, based on the C_p criterion and Akaike's information criterion, are introduced. The relative change in the selection criterion when an individual case is omitted is proposed as the selection influence of the specific omitted case. Four standard examples from the literature are considered and the selection influence of the cases is calculated. It is argued that the selection procedure may be improved by taking the selection influence of individual data cases into account.

  4. Analysis of aromatic constituents in multicomponent hydrocarbon mixtures by infrared spectroscopy using multiple linear regression

    Science.gov (United States)

    Vesnin, V. L.; Muradov, V. G.

    2012-09-01

    Absorption spectra of multicomponent hydrocarbon mixtures based on n-heptane and isooctane with addition of benzene (up to 1%) and toluene and o-xylene (up to 20%) were investigated experimentally in the region of the first overtones of the hydrocarbon groups (λ = 1620-1780 nm). It was shown that their concentrations could be determined separately by using a multiple linear regression method. The optimum result was obtained by including four wavelengths at 1671, 1680, 1685, and 1695 nm, which took into account absorption of CH groups in benzene, toluene, and o-xylene and CH3 groups, respectively.

  5. Multiple regression technique for Pth degree polynominals with and without linear cross products

    Science.gov (United States)

    Davis, J. W.

    1973-01-01

    A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

  6. A note on the use of multiple linear regression in molecular ecology.

    Science.gov (United States)

    Frasier, Timothy R

    2016-03-01

    Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information.

  7. Problems of correlations between explanatory variables in multiple regression analyses in the dental literature.

    Science.gov (United States)

    Tu, Y-K; Kellett, M; Clerehugh, V; Gilthorpe, M S

    2005-10-01

    Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can give rise to spurious results, have in the past frequently been disregarded in dental research. This article illustrates and explains the problems which may be encountered, in the hope of increasing awareness and understanding of these issues, thereby improving the quality of the statistical analyses undertaken in dental research. Three examples from different clinical dental specialties are used to demonstrate how to diagnose the problem of collinearity/multicollinearity in multiple regression analyses and to illustrate how collinearity/multicollinearity can seriously distort the model development process. Lack of awareness of these problems can give rise to misleading results and erroneous interpretations. Multivariable analysis is a useful tool for dental research, though only if its users thoroughly understand the assumptions and limitations of these methods. It would benefit evidence-based dentistry enormously if researchers were more aware of both the complexities involved in multiple regression when using these methods and of the need for expert statistical consultation in developing study design and selecting appropriate statistical methodologies.

  8. Performance Evaluation of Button Bits in Coal Measure Rocks by Using Multiple Regression Analyses

    Science.gov (United States)

    Su, Okan

    2016-02-01

    Electro-hydraulic and jumbo drills are commonly used for underground coal mines and tunnel drives for the purpose of blasthole drilling and rock bolt installations. Not only machine parameters but also environmental conditions have significant effects on drilling. This study characterizes the performance of button bits during blasthole drilling in coal measure rocks by using multiple regression analyses. The penetration rate of jumbo and electro-hydraulic drills was measured in the field by employing bits in different diameters and the specific energy of the drilling was calculated at various locations, including highway tunnels and underground roadways of coal mines. Large block samples were collected from each location at which in situ drilling measurements were performed. Then, the effects of rock properties and machine parameters on the drilling performance were examined. Multiple regression models were developed for the prediction of the specific energy of the drilling and the penetration rate. The results revealed that hole area, impact (blow) energy, blows per minute of the piston within the drill, and some rock properties, such as the uniaxial compressive strength (UCS) and the drilling rate index (DRI), influence the drill performance.

  9. New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey

    CERN Document Server

    Way, M J; Gazis, P R; Srivastava, A N

    2009-01-01

    Expanding upon the work of Way & Srivastava 2006 we demonstrate how the use of training sets of comparable size continue to make Gaussian Process Regression a competitive and in many ways a superior approach to that of Neural Networks and other least-squares fitting methods. This is possible via new matrix inversion techniques developed for Gaussian Processes that do not require that the kernel matrix be sparse. This development, combined with a neural-network kernel function appears to give superior results for this problem. We demonstrate that there appears to be a minimum number of training set galaxies needed to obtain the optimal fit when using our Gaussian Process Regression rank-reduction methods. We also find that morphological information included with many photometric surveys appears, for the most part, to make the photometric redshift evaluation slightly worse rather than better. This would indicate that morphological information simply adds noise from the Gaussian Process point of view. In add...

  10. Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Balachandran, K.K.; Jayalakshmy, K.V.; Laluraj, C.M.; Nair, M.; Joseph, T.; Sheeba, P.

    The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients...

  11. Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

    Science.gov (United States)

    Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

    2013-01-01

    This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

  12. Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

    Science.gov (United States)

    Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

    2013-01-01

    This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

  13. Multiple linear regression models of urban runoff pollutant load and event mean concentration considering rainfall variables.

    Science.gov (United States)

    Maniquiz, Marla C; Lee, Soyoung; Kim, Lee-Hyung

    2010-01-01

    Rainfall is an important factor in estimating the event mean concentration (EMC) which is used to quantify the washed-off pollutant concentrations from non-point sources (NPSs). Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression (MLR) to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long-term monitoring is needed to gather more data that can be used for the development of estimation models.

  14. Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

    Directory of Open Access Journals (Sweden)

    Avval Zhila Mohajeri

    2015-01-01

    Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

  15. Multiple Regression Prediction Model for Cutting Forces in Turning Carbon-Reinforced PEEK CF30

    Directory of Open Access Journals (Sweden)

    Francisco Mata

    2010-01-01

    Full Text Available Among the thermoplastic polymers available, the reinforced polyetheretherketone with 30% of carbon fibres (PEEK CF 30 demonstrates a particularly good combination of strength, rigidity, and hardness, which prove ideal for industrial applications. Considering these properties and potential areas of application, it is necessary to investigate the machining of PEEK CF30. In this study, response surface methodology was applied to predict the cutting forces in turning operations using TiN-coated cutting tools under dry conditions where the machining parameters are cutting speed ranges, feed rate, and depth of cut. For this study, the experiments have been conducted using full factorial design in the design of experiments (DOEs on CNC turning machine. Based on statistical analysis, multiple quadratic regression model for cutting forces was derived with satisfactory 2-squared correlation. This model proved to be highly preferment for predicting cutting forces.

  16. Melanin and blood concentration in human skin studied by multiple regression analysis: experiments

    Science.gov (United States)

    Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

    2001-09-01

    Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

  17. Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques.

    Science.gov (United States)

    Ghazali, Nurul Adyani; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri M D; Sansuddin, Nurulilyana; Al Madhoun, Wesam Ahmed

    2010-06-01

    Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

  18. Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes.

    Science.gov (United States)

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.

  19. Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

    Directory of Open Access Journals (Sweden)

    Fengfeng Wang

    2014-01-01

    Full Text Available Background. MicroRNA (miRNA is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC, and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI and chromosomal instability (CIN signaling pathways. Results. A regression model was adopted to identify the significantly associated miRNAs targeting a set of candidate genes frequently involved in colorectal cancer MSI and CIN pathways. Multiple linear regression analysis was used to construct the model and find the significant mRNA-miRNA associations. We identified three significantly associated mRNA-miRNA pairs: BCL2 was positively associated with miR-16 and SMAD4 was positively associated with miR-567 in the CRC tissue, while MSH6 was positively associated with miR-142-5p in the normal tissue. As for the whole model, BCL2 and SMAD4 models were not significant, and MSH6 model was significant. The significant associations were different in the normal and the CRC tissues. Conclusion. Our results have laid down a solid foundation in exploration of novel CRC mechanisms, and identification of miRNA roles as oncomirs or tumor suppressor mirs in CRC.

  20. Multiple linear and principal component regressions for modelling ecotoxicity bioassay response.

    Science.gov (United States)

    Gomes, Ana I; Pires, José C M; Figueiredo, Sónia A; Boaventura, Rui A R

    2014-01-01

    The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response. With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

  1. Thermodynamic Analysis of Simple Gas Turbine Cycle with Multiple Regression Modelling and Optimization

    Directory of Open Access Journals (Sweden)

    Abdul Ghafoor Memon

    2014-03-01

    Full Text Available In this study, thermodynamic and statistical analyses were performed on a gas turbine system, to assess the impact of some important operating parameters like CIT (Compressor Inlet Temperature, PR (Pressure Ratio and TIT (Turbine Inlet Temperature on its performance characteristics such as net power output, energy efficiency, exergy efficiency and fuel consumption. Each performance characteristic was enunciated as a function of operating parameters, followed by a parametric study and optimization. The results showed that the performance characteristics increase with an increase in the TIT and a decrease in the CIT, except fuel consumption which behaves oppositely. The net power output and efficiencies increase with the PR up to certain initial values and then start to decrease, whereas the fuel consumption always decreases with an increase in the PR. The results of exergy analysis showed the combustion chamber as a major contributor to the exergy destruction, followed by stack gas. Subsequently, multiple regression models were developed to correlate each of the response variables (performance characteristic with the predictor variables (operating parameters. The regression model equations showed a significant statistical relationship between the predictor and response variables.

  2. Random regressions models to describe the genetic variation of milk yield over multiple parities in Buffaloes

    Directory of Open Access Journals (Sweden)

    H. Tonhati

    2010-02-01

    Full Text Available The objectives of this study were to estimate (covariance functions for additive genetic and permanent environmental effects, as well as the genetic parameters for milk yield over multiple parities, using random regressions models (RRM. Records of 4,757 complete lactations of Murrah breed buffaloes from 12 herds were analyzed. Ages at calving were between 2 and 11 years. The model included the additive genetic and permanent environmental random effects and the fixed effects of contemporary groups (herd, year and calving season and milking frequency (1 or 2. A cubic regression on Legendre orthogonal polynomials of ages was used to model the mean trend. The additive genetic and permanent environmental effects were modeled by Legendre orthogonal polynomials. Residual variances were considered homogenous or heterogeneous, modeled through variance functions or step functions with 5, 7 or 10 classes. Results from Akaike’s and Schwarz’s Bayesian information criterion indicated that a RRM considering a third order polynomial for the additive genetic and permanent environmental effects and a step function with 5 classes for residual variances fitted best. Heritability estimates obtained by this model varied from 0.10 to 0.28. Genetic correlations were high between consecutive ages, but decreased when intervals between ages increased

  3. Modeling the Philippines' real gross domestic product: A normal estimation equation for multiple linear regression

    Science.gov (United States)

    Urrutia, Jackie D.; Tampis, Razzcelle L.; Mercado, Joseph; Baygan, Aaron Vito M.; Baccay, Edcon B.

    2016-02-01

    The objective of this research is to formulate a mathematical model for the Philippines' Real Gross Domestic Product (Real GDP). The following factors are considered: Consumers' Spending (x1), Government's Spending (x2), Capital Formation (x3) and Imports (x4) as the Independent Variables that can actually influence in the Real GDP in the Philippines (y). The researchers used a Normal Estimation Equation using Matrices to create the model for Real GDP and used α = 0.01.The researchers analyzed quarterly data from 1990 to 2013. The data were acquired from the National Statistical Coordination Board (NSCB) resulting to a total of 96 observations for each variable. The data have undergone a logarithmic transformation particularly the Dependent Variable (y) to satisfy all the assumptions of the Multiple Linear Regression Analysis. The mathematical model for Real GDP was formulated using Matrices through MATLAB. Based on the results, only three of the Independent Variables are significant to the Dependent Variable namely: Consumers' Spending (x1), Capital Formation (x3) and Imports (x4), hence, can actually predict Real GDP (y). The regression analysis displays that 98.7% (coefficient of determination) of the Independent Variables can actually predict the Dependent Variable. With 97.6% of the result in Paired T-Test, the Predicted Values obtained from the model showed no significant difference from the Actual Values of Real GDP. This research will be essential in appraising the forthcoming changes to aid the Government in implementing policies for the development of the economy.

  4. Reconstruction the Missing Pixels for Landsat ETM+SLC-off Images Using Multiple Linear Regression Model

    Directory of Open Access Journals (Sweden)

    Asmaa S. Abdul Jabar

    2016-09-01

    Full Text Available On 31 May 2003, the scan line corrector (SLC of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+ sensor which compensates for the forward motion of the satellite in the imagery acquired failed permanently, resulting in loss of the ability to scan about 20% of the pixels in each Landsat 7 SLC-off image. This permanent failure has seriously hampered the scientific applications of ETM+ images. In this study, an innovative gap filling approach has been introduced to recover the missing pixels in the SLC-off images using multi-temporal ETM+ SLC-off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two fill images in parallel using the multiple linear regressions (MLR model. Simulated and actual SLC-off ETM+ images were used to assess the performance of the proposed method by comparing with multi-temporal data based methods, the LLHM method which is based on simple linear regression (SLR model. The qualitative and quantitative evaluations indicate that the proposed method can recover the value of un-scanned pixels accurately, especially in heterogeneous landscape and even with more temporally distant fill images.

  5. Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

    KAUST Repository

    Abdul Jameel, Abdul Gani

    2016-09-14

    An improved model for the prediction of ignition quality of hydrocarbon fuels has been developed using 1H nuclear magnetic resonance (NMR) spectroscopy and multiple linear regression (MLR) modeling. Cetane number (CN) and derived cetane number (DCN) of 71 pure hydrocarbons and 54 hydrocarbon blends were utilized as a data set to study the relationship between ignition quality and molecular structure. CN and DCN are functional equivalents and collectively referred to as D/CN, herein. The effect of molecular weight and weight percent of structural parameters such as paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic CH–CH2 groups, naphthenic CH–CH2 groups, and aromatic C–CH groups on D/CN was studied. A particular emphasis on the effect of branching (i.e., methyl substitution) on the D/CN was studied, and a new parameter denoted as the branching index (BI) was introduced to quantify this effect. A new formula was developed to calculate the BI of hydrocarbon fuels using 1H NMR spectroscopy. Multiple linear regression (MLR) modeling was used to develop an empirical relationship between D/CN and the eight structural parameters. This was then used to predict the DCN of many hydrocarbon fuels. The developed model has a high correlation coefficient (R2 = 0.97) and was validated with experimentally measured DCN of twenty-two real fuel mixtures (e.g., gasolines and diesels) and fifty-nine blends of known composition, and the predicted values matched well with the experimental data.

  6. Evidencing the association between swimming capacities and performance indicators in water polo: a multiple regression study.

    Science.gov (United States)

    Kontic, Dean; Zenic, Natasa; Uljevic, Ognjen; Sekulic, Damir; Lesnik, Blaz

    2017-06-01

    Swimming capacities are hypothesized to be important determinants of water polo performance but there is an evident lack of studies examining different swimming capacities in relation to specific offensive and defensive performance variables in this sport. The aim of this study was to determine the relationship between five swimming capacities and six performance determinants in water polo. The sample comprised 79 high-level youth water polo players (all males, 17-18 years of age). The variables included six performance-related variables (agility in offence and defense, efficacy in offence and defense, polyvalence in offence and defense), and five swimming-capacity tests (water polo sprint test [15 m], swimming sprint test [25 m], short-distance [100 m], aerobic endurance [400 m] and an anaerobic lactate endurance test [4× 50 m]). First, multiple regressions were calculated for one-half of the sample of subjects which were then validated with the remaining half of the sample. The 25-m swim was not included in the regression analyses due to the multicollinearity with other predictors. The originally calculated regression models were validated for defensive agility (R=0.67 and R=0.55 for the original regression calculation and validation subsample, respectively) offensive agility (R=0.59 and R=0.61), and offensive efficacy (R=0.64 and R=0.58). Anaerobic lactate endurance is a significant predictor of offensive and defensive agility, while 15 m sprint significantly contributes to offensive efficacy. Swimming capacities are not found to be related to the polyvalence of the players. The most superior offensive performance can be expected from those players with a high level of anaerobic lactate endurance and advanced sprinting capacity, while anaerobic lactate endurance is recognized as most important quality in defensive duties. Future studies should observe players' polyvalence in relation to (theoretical) knowledge of technical and tactical tasks. Results reinforce

  7. A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis

    CERN Document Server

    Taneja, Abhishek

    2011-01-01

    The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like mean square error (MSE), R-square, R-Square adjusted, condition number, root mean square error(RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear re...

  8. A critical assessment of shrinkage-based regression approaches for estimating the adverse health effects of multiple air pollutants

    Science.gov (United States)

    Roberts, Steven; Martin, Michael

    Most investigations of the adverse health effects of multiple air pollutants analyse the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. Concerns have been raised about this type of analysis, and it has been stated that new methodology or models should be developed for investigating the adverse health effects of multiple air pollutants. In this paper, we introduce the use of the lasso for this purpose and compare its statistical properties to those of ridge regression and the Poisson log-linear model. Ridge regression has been used in time series analyses on the adverse health effects of multiple air pollutants but its properties for this purpose have not been investigated. A series of simulation studies was used to compare the performance of the lasso, ridge regression, and the Poisson log-linear model. In these simulations, realistic mortality time series were generated with known air pollution mortality effects permitting the performance of the three models to be compared. Both the lasso and ridge regression produced more accurate estimates of the adverse health effects of the multiple air pollutants than those produced using the Poisson log-linear model. This increase in accuracy came at the expense of increased bias. Ridge regression produced more accurate estimates than the lasso, but the lasso produced more interpretable models. The lasso and ridge regression offer a flexible way of obtaining more accurate estimation of pollutant effects than that provided by the standard Poisson log-linear model.

  9. Statistical Downscaling: A Comparison of Multiple Linear Regression and k-Nearest Neighbor Approaches

    Science.gov (United States)

    Gangopadhyay, S.; Clark, M. P.; Rajagopalan, B.

    2002-12-01

    The success of short term (days to fortnight) streamflow forecasting largely depends on the skill of surface climate (e.g., precipitation and temperature) forecasts at local scales in the individual river basins. The surface climate forecasts are used to drive the hydrologic models for streamflow forecasting. Typically, Medium Range Forecast (MRF) models provide forecasts of large scale circulation variables (e.g. pressures, wind speed, relative humidity etc.) at different levels in the atmosphere on a regular grid - which are then used to "downscale" to the surface climate at locations within the model grid box. Several statistical and dynamical methods are available for downscaling. This paper compares the utility of two statistical downscaling methodologies: (1) multiple linear regression (MLR) and (2) a nonparametric approach based on k-nearest neighbor (k-NN) bootstrap method, in providing local-scale information of precipitation and temperature at a network of stations in the Upper Colorado River Basin. Downscaling to the stations is based on output of large scale circulation variables (i.e. predictors) from the NCEP Medium Range Forecast (MRF) database. Fourteen-day six hourly forecasts are developed using these two approaches, and their forecast skill evaluated. A stepwise regression is performed at each location to select the predictors for the MLR. The k-NN bootstrap technique resamples historical data based on their "nearness" to the current pattern in the predictor space. Prior to resampling a Principal Component Analysis (PCA) is performed on the predictor set to identify a small subset of predictors. Preliminary results using the MLR technique indicate a significant value in the downscaled MRF output in predicting runoff in the Upper Colorado Basin. It is expected that the k-NN approach will match the skill of the MLR approach at individual stations, and will have the added advantage of preserving the spatial co-variability between stations, capturing

  10. [Multiple dependent variables LS-SVM regression algorithm and its application in NIR spectral quantitative analysis].

    Science.gov (United States)

    An, Xin; Xu, Shuo; Zhang, Lu-Da; Su, Shi-Guang

    2009-01-01

    In the present paper, on the basis of LS-SVM algorithm, we built a multiple dependent variables LS-SVM (MLS-SVM) regression model whose weights can be optimized, and gave the corresponding algorithm. Furthermore, we theoretically explained the relationship between MLS-SVM and LS-SVM. Sixty four broomcorn samples were taken as experimental material, and the sample ratio of modeling set to predicting set was 51 : 13. We first selected randomly and uniformly five weight groups in the interval [0, 1], and then in the way of leave-one-out (LOO) rule determined one appropriate weight group and parameters including penalizing parameters and kernel parameters in the model according to the criterion of the minimum of average relative error. Then a multiple dependent variables quantitative analysis model was built with NIR spectrum and simultaneously analyzed three chemical constituents containing protein, lysine and starch. Finally, the average relative errors between actual values and predicted ones by the model of three components for the predicting set were 1.65%, 6.47% and 1.37%, respectively, and the correlation coefficients were 0.9940, 0.8392 and 0.8825, respectively. For comparison, LS-SVM was also utilized, for which the average relative errors were 1.68%, 6.25% and 1.47%, respectively, and the correlation coefficients were 0.9941, 0.8310 and 0.8800, respectively. It is obvious that MLS-SVM algorithm is comparable to LS-SVM algorithm in modeling analysis performance, and both of them can give satisfying results. The result shows that the model with MLS-SVM algorithm is capable of doing multi-components NIR quantitative analysis synchronously. Thus MLS-SVM algorithm offers a new multiple dependent variables quantitative analysis approach for chemometrics. In addition, the weights have certain effect on the prediction performance of the model with MLS-SVM, which is consistent with our intuition and is validated in this study. Therefore, it is necessary to optimize

  11. Optimization of end-members used in multiple linear regression geochemical mixing models

    Science.gov (United States)

    Dunlea, Ann G.; Murray, Richard W.

    2015-11-01

    Tracking marine sediment provenance (e.g., of dust, ash, hydrothermal material, etc.) provides insight into contemporary ocean processes and helps construct paleoceanographic records. In a simple system with only a few end-members that can be easily quantified by a unique chemical or isotopic signal, chemical ratios and normative calculations can help quantify the flux of sediment from the few sources. In a more complex system (e.g., each element comes from multiple sources), more sophisticated mixing models are required. MATLAB codes published in Pisias et al. solidified the foundation for application of a Constrained Least Squares (CLS) multiple linear regression technique that can use many elements and several end-members in a mixing model. However, rigorous sensitivity testing to check the robustness of the CLS model is time and labor intensive. MATLAB codes provided in this paper reduce the time and labor involved and facilitate finding a robust and stable CLS model. By quickly comparing the goodness of fit between thousands of different end-member combinations, users are able to identify trends in the results that reveal the CLS solution uniqueness and the end-member composition precision required for a good fit. Users can also rapidly check that they have the appropriate number and type of end-members in their model. In the end, these codes improve the user's confidence that the final CLS model(s) they select are the most reliable solutions. These advantages are demonstrated by application of the codes in two case studies of well-studied datasets (Nazca Plate and South Pacific Gyre).

  12. Multiple linear regression model for predicting biomass digestibility from structural features.

    Science.gov (United States)

    Zhu, Li; O'Dwyer, Jonathan P; Chang, Vincent S; Granda, Cesar B; Holtzapple, Mark T

    2010-07-01

    A total of 147 model lignocellulose samples with a broad spectrum of structural features (lignin contents, acetyl contents, and crystallinity indices) were hydrolyzed with a wide range of cellulase loadings during 1-, 6-, and 72-h hydrolysis periods. Carbohydrate conversions at 1, 6, and 72 h were linearly proportional to the logarithm of cellulase loadings from approximately 10% to 90% conversion, indicating that the simplified HCH-1 model is valid for predicting lignocellulose digestibility. The HCH-1 model is a modified Michaelis-Menton model that accounts for the fraction of insoluble substrate available to bind with enzyme. The slopes and intercepts of a simplified HCH-1 model were correlated with structural features using multiple linear regression (MLR) models. The agreement between the measured and predicted 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses indicate that lignin content, acetyl content, and cellulose crystallinity are key factors that determine biomass digestibility. The 1-, 6-, and 72-h glucan, xylan, and total sugar conversions predicted from structural features using MLR models and the simplified HCH-1 model fit satisfactorily with the measured data (R(2) approximately 1.0). The parameter selection suggests that lignin content and cellulose crystallinity more strongly affect on digestibility than acetyl content. Cellulose crystallinity has greater influence during short hydrolysis periods whereas lignin content has more influence during longer hydrolysis periods. Cellulose crystallinity shows more influence on glucan hydrolysis whereas lignin content affects xylan hydrolysis to a greater extent.

  13. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

    Science.gov (United States)

    Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

    2016-10-01

    We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two (13) C atoms ((13) C2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of (13) C2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% (13) C2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  14. MANUFACTURING AND CONTINUOUS IMPROVEMENT AREAS USING PARTIAL LEAST SQUARE PATH MODELING WITH MULTIPLE REGRESSION COMPARISON

    Directory of Open Access Journals (Sweden)

    Carlos Monge Perry

    2014-07-01

    Full Text Available Structural equation modeling (SEM has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique.  This paper shows the application of structural equation modeling using partial least square (PLS-SEM, in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40.  The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM.  This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research

  15. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions

    Directory of Open Access Journals (Sweden)

    Abolghasem Beheshti

    2016-05-01

    Full Text Available A quantitative structure–activity relationship (QSAR was performed to analyze antimalarial activities of 68 urea derivatives using multiple linear regressions (MLR. QSAR analyses were performed on the available 68 IC50 oral data based on theoretical molecular descriptors. A suitable set of molecular descriptors were calculated to represent the molecular structures of compounds, such as constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors. The important descriptors were selected with the aid of the genetic algorithm (GA method. The obtained model was validated using leave-one-out (LOO cross-validation; external test set and Y-randomization test. The root mean square errors (RMSE of the training set, and the test set for GA–MLR model were calculated to be 0.314 and 0.486, the square of correlation coefficients (R2 were obtained 0.801 and 0.803, respectively. Results showed that the predictive ability of the model was satisfactory, and it can be used for designing similar group of antimalarial compounds.

  16. QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions

    Indian Academy of Sciences (India)

    Eslam Pourbasheer; Saadat Vahdani; Reza Aalizadeh; Alireza Banaei; Mohammad Reza Ganjali

    2015-07-01

    The predictive analysis based on quantitative structure activity relationships (QSAR) on benzim-idazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation.

  17. Determination of useful ranges of mixing conditions for glycerin Fatty Acid ester by multiple regression analysis.

    Science.gov (United States)

    Uchimoto, Takeaki; Iwao, Yasunori; Hattori, Hiroaki; Noguchi, Shuji; Itai, Shigeru

    2013-01-01

    The interaction of the effects of the triglycerin full behenate (TR-FB) concentration and the mixing time on lubrication and tablet properties were analyzed under a two-factor central composite design, and compared with those of magnesium stearate (Mg-St). Various amounts of lubricant (0.07-3.0%) were added to granules and mixed for 1-30 min. A multiple linear regression analysis was performed to identify the effect of the mixing conditions on each physicochemical property. The mixing conditions did not significantly affect the lubrication properties of TR-FB. For tablet properties, tensile strength decreased and disintegration time increased when the lubricant concentration and the mixing time were increased for Mg-St. The direct interaction of the Mg-St concentration and the mixing time had a significant negative effect on the disintegration time. In contrast, any mixing conditions of TR-FB did not affect the tablet properties. In addition, the range of mixing conditions which satisfied the lubrication and tablet property criteria was broader for TR-FB than that for Mg-St, suggesting that TR-FB allows tablets with high quality attributes to be produced consistently. Therefore, TR-FB is a potential lubricant alternative to Mg-St.

  18. [Clinical research XX. From clinical judgment to multiple logistic regression model].

    Science.gov (United States)

    Berea-Baltierra, Ricardo; Rivas-Ruiz, Rodolfo; Pérez-Rodríguez, Marcela; Palacios-Cruz, Lino; Moreno, Jorge; Talavera, Juan O

    2014-01-01

    The complexity of the causality phenomenon in clinical practice implies that the result of a maneuver is not solely caused by the maneuver, but by the interaction among the maneuver and other baseline factors or variables occurring during the maneuver. This requires methodological designs that allow the evaluation of these variables. When the outcome is a binary variable, we use the multiple logistic regression model (MLRM). This multivariate model is useful when we want to predict or explain, adjusting due to the effect of several risk factors, the effect of a maneuver or exposition over the outcome. In order to perform an MLRM, the outcome or dependent variable must be a binary variable and both categories must mutually exclude each other (i.e. live/death, healthy/ill); on the other hand, independent variables or risk factors may be either qualitative or quantitative. The effect measure obtained from this model is the odds ratio (OR) with 95 % confidence intervals (CI), from which we can estimate the proportion of the outcome's variability explained through the risk factors. For these reasons, the MLRM is used in clinical research, since one of the main objectives in clinical practice comprises the ability to predict or explain an event where different risk or prognostic factors are taken into account.

  19. Tests of Simple Slopes in Multiple Regression Models with an Interaction: Comparison of Four Approaches.

    Science.gov (United States)

    Liu, Yu; West, Stephen G; Levy, Roy; Aiken, Leona S

    2017-01-01

    In multiple regression researchers often follow up significant tests of the interaction between continuous predictors X and Z with tests of the simple slope of Y on X at different sample-estimated values of the moderator Z (e.g., ±1 SD from the mean of Z). We show analytically that when X and Z are randomly sampled from the population, the variance expression of the simple slope at sample-estimated values of Z differs from the traditional variance expression obtained when the values of X and Z are fixed. A simulation study using randomly sampled predictors compared four approaches: (a) the Aiken and West ( 1991 ) test of simple slopes at fixed population values of Z, (b) the Aiken and West test at sample-estimated values of Z, (c) a 95% percentile bootstrap confidence interval approach, and (d) a fully Bayesian approach with diffuse priors. The results showed that approach (b) led to inflated Type 1 error rates and 95% confidence intervals with inadequate coverage rates, whereas other approaches maintained acceptable Type 1 error rates and adequate coverage of confidence intervals. Approach (c) had asymmetric rejection rates at small sample sizes. We used an empirical data set to illustrate these approaches.

  20. Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression

    Science.gov (United States)

    Beh, W. L.; Yong, M. K. Au

    2017-04-01

    This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.

  1. Performance Prediction Modelling for Flexible Pavement on Low Volume Roads Using Multiple Linear Regression Analysis

    Directory of Open Access Journals (Sweden)

    C. Makendran

    2015-01-01

    Full Text Available Prediction models for low volume village roads in India are developed to evaluate the progression of different types of distress such as roughness, cracking, and potholes. Even though the Government of India is investing huge quantum of money on road construction every year, poor control over the quality of road construction and its subsequent maintenance is leading to the faster road deterioration. In this regard, it is essential that scientific maintenance procedures are to be evolved on the basis of performance of low volume flexible pavements. Considering the above, an attempt has been made in this research endeavor to develop prediction models to understand the progression of roughness, cracking, and potholes in flexible pavements exposed to least or nil routine maintenance. Distress data were collected from the low volume rural roads covering about 173 stretches spread across Tamil Nadu state in India. Based on the above collected data, distress prediction models have been developed using multiple linear regression analysis. Further, the models have been validated using independent field data. It can be concluded that the models developed in this study can serve as useful tools for the practicing engineers maintaining flexible pavements on low volume roads.

  2. Multiple regression as a preventive tool for determining the risk of Legionella spp.

    Directory of Open Access Journals (Sweden)

    Enrique Gea-Izquierdo

    2012-04-01

    Full Text Available To determine the interrelationship between health & hygiene conditions for prevention of legionellosis, the compositionof materials used in water distribution systems, the water origin and Legionella pneumophila risk. Material and methods. Include adescriptive study and multiple regression analysis on a sample of golf course sprinkler irrigation systems (n=31 pertaining to hotelslocated on the Costa del Sol (Malaga, Spain. The study was carried out in 2009. Results. Presented a significant lineal relation, withall the independent variables contributing significantly (p<0.05 to the model’s fit. The relationship between water type and the risk ofLegionella, as well as the material composition and the latter, is lineal and positive. In contrast, the relationship between health-hygieneconditions and Legionella risk is lineal and negative. Conclusion. The characterization of Legionella pneumophila concentration, asdefined by the risk in water and through use of the predictive method, can contribute to the consideration of new influence variables inthe development of the agent, resulting in improved control and prevention of the disease.

  3. Multiple regression method to determine aerosol optical depth in atmospheric column in Penang, Malaysia

    Science.gov (United States)

    Tan, F.; Lim, H. S.; Abdullah, K.; Yoon, T. L.; Zubir Matjafri, M.; Holben, B.

    2014-02-01

    Aerosol optical depth (AOD) from AERONET data has a very fine resolution but air pollution index (API), visibility and relative humidity from the ground truth measurements are coarse. To obtain the local AOD in the atmosphere, the relationship between these three parameters was determined using multiple regression analysis. The data of southwest monsoon period (August to September, 2012) taken in Penang, Malaysia, was used to establish a quantitative relationship in which the AOD is modeled as a function of API, relative humidity, and visibility. The highest correlated model was used to predict AOD values during southwest monsoon period. When aerosol is not uniformly distributed in the atmosphere then the predicted AOD can be highly deviated from the measured values. Therefore these deviated data can be removed by comparing between the predicted AOD values and the actual AERONET data which help to investigate whether the non uniform source of the aerosol is from the ground surface or from higher altitude level. This model can accurately predict AOD if only the aerosol is uniformly distributed in the atmosphere. However, further study is needed to determine this model is suitable to use for AOD predicting not only in Penang, but also other state in Malaysia or even global.

  4. A factor analysis-multiple regression model for source apportionment of suspended particulate matter

    Science.gov (United States)

    Okamoto, Shin'ichi; Hayashi, Masayuki; Nakajima, Masaomi; Kainuma, Yasutaka; Shiozawa, Kiyoshige

    A factor analysis-multiple regression (FA-MR) model has been used for a source apportionment study in the Tokyo metropolitan area. By a varimax rotated factor analysis, five source types could be identified: refuse incineration, soil and automobile, secondary particles, sea salt and steel mill. Quantitative estimations using the FA-MR model corresponded to the calculated contributing concentrations determined by using a weighted least-squares CMB model. However, the source type of refuse incineration identified by the FA-MR model was similar to that of biomass burning, rather than that produced by an incineration plant. The estimated contributions of sea salt and steel mill by the FA-MR model contained those of other sources, which have the same temporal variation of contributing concentrations. This symptom was caused by a multicollinearity problem. Although this result shows the limitation of the multivariate receptor model, it gives useful information concerning source types and their distribution by comparing with the results of the CMB model. In the Tokyo metropolitan area, the contributions from soil (including road dust), automobile, secondary particles and refuse incineration (biomass burning) were larger than industrial contributions: fuel oil combustion and steel mill. However, since vanadium is highly correlated with SO 42- and other secondary particle related elements, a major portion of secondary particles is considered to be related to fuel oil combustion.

  5. Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

    Science.gov (United States)

    Choi, Jae-Seok; Kim, Munchurl

    2017-03-01

    Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

  6. Energy production through organic fraction of municipal solid waste-A multiple regression modeling approach.

    Science.gov (United States)

    Ramesh, N; Ramesh, S; Vennila, G; Abdul Bari, J; MageshKumar, P

    2016-12-01

    In the 21st century, people migrated from rural to urban areas for several reasons. As a result, the populations of Indian cities are increasing day by day. On one hand, the country is developing in the field of science and technology and on the other hand, it is encountering a serious problem called 'Environmental degradation'. Due to increase in population, the generation of solid waste is also increased and is being disposed in open dumps and landfills which lead to air and land pollution. This study is attempted to generate energy out of organic solid waste by the bio- fermentation process. The study was conducted for a period of 7 months at Erode, Tamilnadu and the reading on various parameters like Hydraulic retention time, organic loading rate, sludge loading rate, influent pH, effluent pH, inlet volatile acids, out let volatile fatty acids, inlet VSS/TS ratio, outlet VSS/TS ratio, influent COD, effluent COD and % of COD removal are recorded for every 10 days. The aim of the present study is to develop a model through multiple linear regression analysis with COD as dependent variable and various parameters like HRT, OLR, SLR, influent, effluent, VSS/TS ratio, influent COD, effluent COD, etc as independent variables and to analyze the impact of these parameters on COD. The results of the model developed through step-wise regression method revealed that only four parameters Influent COD, effluent COD, VSS/TS and Influent/pH were main influencers of COD removal. The parameters influent COD and VSS/TS have positive impact on COD removal and the parameters effluent COD and Influent/pH have negative impact. The parameter Influent COD has the highest order of impact, followed by effluent COD, VSS/TS and influent pH. The other parameters HRT, OLR, SLR, INLET VFA and OUTLET VFA were not significantly contributing to the removal of COD. The implementation of the process suggested through this study might bring in dual benefit to the community, viz treatment of solid

  7. DESIGNING A FORECAST MODEL FOR ECONOMIC GROWTH OF JAPAN USING COMPETITIVE (HYBRID ANN VS MULTIPLE REGRESSION MODELS

    Directory of Open Access Journals (Sweden)

    Ahmet DEMIR

    2015-07-01

    Full Text Available Artificial neural network models have been already used on many different fields successfully. However, many researches show that ANN models provide better optimum results than other competitive models in most of the researches. But does it provide optimum solutions in case ANN is proposed as hybrid model? The answer of this question is given in this research by using these models on modelling a forecast for GDP growth of Japan. Multiple regression models utilized as competitive models versus hybrid ANN (ANN + multiple regression models. Results have shown that hybrid model gives better responds than multiple regression models. However, variables, which were significantly affecting GDP growth, were determined and some of the variables, which were assumed to be affecting GDP growth of Japan, were eliminated statistically.

  8. Modeling of Soil Aggregate Stability using Support Vector Machines and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Ali Asghar Besalatpour

    2016-02-01

    Full Text Available Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010. Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA, mean weight diameter (MWD, geometric mean diameter (GMD of aggregates, and water-dispersible clay (WDC content (Calero et al., 2008. Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010. Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014. The main objective of this study is to investigate the potential use of support vector machines (SVMs method for estimating soil aggregate stability (as quantified by GMD as compared to multiple linear regression approach. Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E, which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM content was determined by the Walkley-Black method (Nelson & Sommers, 1986. Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand were measured using the procedure described by Gee & Bauder (1986 and calcium carbonate equivalent (CCE content was determined by the back-titration method (Nelson, 1982. The modified Kemper & Rosenau (1986 method was used to determine wet-aggregate stability (GMD. The topographic attributes of elevation, slope, and aspect were characterized using a 20-m

  9. Source apportionment based on an atmospheric dispersion model and multiple linear regression analysis

    Science.gov (United States)

    Fushimi, Akihiro; Kawashima, Hiroto; Kajihara, Hideo

    Understanding the contribution of each emission source of air pollutants to ambient concentrations is important to establish effective measures for risk reduction. We have developed a source apportionment method based on an atmospheric dispersion model and multiple linear regression analysis (MLR) in conjunction with ambient concentrations simultaneously measured at points in a grid network. We used a Gaussian plume dispersion model developed by the US Environmental Protection Agency called the Industrial Source Complex model (ISC) in the method. Our method does not require emission amounts or source profiles. The method was applied to the case of benzene in the vicinity of the Keiyo Central Coastal Industrial Complex (KCCIC), one of the biggest industrial complexes in Japan. Benzene concentrations were simultaneously measured from December 2001 to July 2002 at sites in a grid network established in the KCCIC and the surrounding residential area. The method was used to estimate benzene emissions from the factories in the KCCIC and from automobiles along a section of a road, and then the annual average contribution of the KCCIC to the ambient concentrations was estimated based on the estimated emissions. The estimated contributions of the KCCIC were 65% inside the complex, 49% at 0.5-km sites, 35% at 1.5-km sites, 20% at 3.3-km sites, and 9% at a 5.6-km site. The estimated concentrations agreed well with the measured values. The estimated emissions from the factories and the road were slightly larger than those reported in the first Pollutant Release and Transfer Register (PRTR). These results support the reliability of our method. This method can be applied to other chemicals or regions to achieve reasonable source apportionments.

  10. Oral health-related risk behaviours and attitudes among Croatian adolescents--multiple logistic regression analysis.

    Science.gov (United States)

    Spalj, Stjepan; Spalj, Vedrana Tudor; Ivanković, Luida; Plancak, Darije

    2014-03-01

    The aim of this study was to explore the patterns of oral health-related risk behaviours in relation to dental status, attitudes, motivation and knowledge among Croatian adolescents. The assessment was conducted in the sample of 750 male subjects - military recruits aged 18-28 in Croatia using the questionnaire and clinical examination. Mean number of decayed, missing and filled teeth (DMFT) and Significant Caries Index (SIC) were calculated. Multiple logistic regression models were crated for analysis. Although models of risk behaviours were statistically significant their explanatory values were quite low. Five of them--rarely toothbrushing, not using hygiene auxiliaries, rarely visiting dentist, toothache as a primary reason to visit dentist, and demand for tooth extraction due to toothache--had the highest explanatory values ranging from 21-29% and correctly classified 73-89% of subjects. Toothache as a primary reason to visit dentist, extraction as preferable therapy when toothache occurs, not having brushing education in school and frequent gingival bleeding were significantly related to population with high caries experience (DMFT > or = 14 according to SiC) producing Odds ratios of 1.6 (95% CI 1.07-2.46), 2.1 (95% CI 1.29-3.25), 1.8 (95% CI 1.21-2.74) and 2.4 (95% CI 1.21-2.74) respectively. DMFT> or = 14 model had low explanatory value of 6.5% and correctly classified 83% of subjects. It can be concluded that oral health-related risk behaviours are interrelated. Poor association was seen between attitudes concerning oral health and oral health-related risk behaviours, indicating insufficient motivation to change lifestyle and habits. Self-reported oral hygiene habits were not strongly related to dental status.

  11. Watershed Regressions for Pesticides (WARP) models for predicting stream concentrations of multiple pesticides

    Science.gov (United States)

    Stone, Wesley W.; Crawford, Charles G.; Gilliom, Robert J.

    2013-01-01

    Watershed Regressions for Pesticides for multiple pesticides (WARP-MP) are statistical models developed to predict concentration statistics for a wide range of pesticides in unmonitored streams. The WARP-MP models use the national atrazine WARP models in conjunction with an adjustment factor for each additional pesticide. The WARP-MP models perform best for pesticides with application timing and methods similar to those used with atrazine. For other pesticides, WARP-MP models tend to overpredict concentration statistics for the model development sites. For WARP and WARP-MP, the less-than-ideal sampling frequency for the model development sites leads to underestimation of the shorter-duration concentration; hence, the WARP models tend to underpredict 4- and 21-d maximum moving-average concentrations, with median errors ranging from 9 to 38% As a result of this sampling bias, pesticides that performed well with the model development sites are expected to have predictions that are biased low for these shorter-duration concentration statistics. The overprediction by WARP-MP apparent for some of the pesticides is variably offset by underestimation of the model development concentration statistics. Of the 112 pesticides used in the WARP-MP application to stream segments nationwide, 25 were predicted to have concentration statistics with a 50% or greater probability of exceeding one or more aquatic life benchmarks in one or more stream segments. Geographically, many of the modeled streams in the Corn Belt Region were predicted to have one or more pesticides that exceeded an aquatic life benchmark during 2009, indicating the potential vulnerability of streams in this region.

  12. Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

    Directory of Open Access Journals (Sweden)

    Faridah Hani Mohamed Salleh

    2017-01-01

    Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.

  13. Analyzing Regression-Discontinuity Designs with Multiple Assignment Variables: A Comparative Study of Four Estimation Methods

    Science.gov (United States)

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2013-01-01

    In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a single cutoff location along the assignment variable. This article introduces the multivariate regression-discontinuity design (MRDD), where multiple…

  14. Simreg: a Software Including Some New Developments in Multiple Comparison and Simultaneous Confidence Bands for Linear Regression Models

    Directory of Open Access Journals (Sweden)

    Mortaza Jamshidian

    2005-01-01

    Full Text Available The problem of simultaneous inference and multiple comparison for comparing means of k( ≥ 3 populations has been long studied in the statistics literature and is widely available in statistics literature. However to-date, the problem of multiple comparison of regression models has not found its way to the software. It is only recently that the computational aspects of this problem have been resolved in a general setting. SimReg employs this new methodology and provides users with software for multiple regression of several regression models. The comparisons can be among any set of pairs, and moreover any number of predictors can be included in the model. More importantly predictors can be constrained to their natural boundaries, if known. Computational methods for the problem of simultaneous confidence bands when predictors are constrained to intervals has also recently been addressed. SimReg utilizes this recent development to offer simultaneous confidence bands for regression models with any number of predictor variables. Again, the predictors can be constrained to their natural boundaries which results in narrower bands, as compared to the case where no restriction is imposed. A by-product of these confidence bands is a new method for comparing two regression surfaces, that is more informative than the usual partial F test.

  15. The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

    Science.gov (United States)

    Le, Huy; Marcus, Justin

    2012-01-01

    This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

  16. Multiple linear regression to develop strength scaled equations for knee and elbow joints based on age, gender and segment mass

    DEFF Research Database (Denmark)

    D'Souza, Sonia; Rasmussen, John; Schwirtz, Ansgar

    2012-01-01

    and valuable ergonomic tool. Objective: To investigate age and gender effects on the torque-producing ability in the knee and elbow in older adults. To create strength scaled equations based on age, gender, upper/lower limb lengths and masses using multiple linear regression. To reduce the number of dependent...

  17. Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

    Science.gov (United States)

    Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

    2015-01-01

    Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

  18. The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

    Science.gov (United States)

    Le, Huy; Marcus, Justin

    2012-01-01

    This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

  19. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

    Science.gov (United States)

    Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D

    2015-05-01

    Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

  20. Improving the accuracies of bathymetric models based on multiple regression for calibration (case study: Sarca River, Italy)

    Science.gov (United States)

    Niroumand-Jadidi, Milad; Vitti, Alfonso

    2016-10-01

    The optical imagery has the potential for extraction of spatially and temporally explicit bathymetric information in inland/coastal waters. Lyzenga's model and optimal band ratio analysis (OBRA) are main bathymetric models which both provide linear relations with water depths. The former model is sensitive and the latter is quite robust to substrate variability. The simple regression is the widely used approach for calibration of bathymetric models either Lyzenga's model or OBRA model. In this research, a multiple regression is examined for empirical calibration of the models in order to take the advantage of all spectral channels of the imagery. This method is applied on both Lyzenga's model and OBRA model for the bathymetry of a shallow Alpine river in Italy, using WorldView-2 (WV-2) and GeoEye images. Insitu depths are recorded using RTK GPS in two reaches. One-half of the data is used for calibration of models and the remaining half as independent check-points for accuracy assessment. In addition, radiative transfer model is used to simulate a set of spectra in a range of depths, substrate types, and water column properties. The simulated spectra are convolved to the sensors' spectral bands for further bathymetric analysis. Investigating the simulated spectra, it is concluded that the multiple regression improves the robustness of the Lyzenga's model with respect to the substrate variability. The improvements of multiple regression approach are much more pronounced for the Lyzenga's model rather than the OBRA model. This is in line with findings from real imagery; for instance, the multiple regression applied for calibration of Lyzenga's and OBRA models demonstrated, respectively, 22% and 9% higher determination coefficients (R2) as well as 3 cm and 1 cm better RMSEs compared to the simple regression using the WV-2 image.

  1. SPECIFICS OF THE APPLICATIONS OF MULTIPLE REGRESSION MODEL IN THE ANALYSES OF THE EFFECTS OF GLOBAL FINANCIAL CRISES

    Directory of Open Access Journals (Sweden)

    Željko V. Račić

    2010-12-01

    Full Text Available This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand and the credit cards, i.e. indebtedness of the population on this basis (on the other hand, in the USA (from 1999. to 2008. We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program.

  2. QSAR study of the non-peptidic inhibitors of procollagen C-proteinase based on Multiple linear regression, principle component regression, and partial least squares

    Directory of Open Access Journals (Sweden)

    Ardeshir Khazaei

    2017-09-01

    Full Text Available The quantitative structure–activity relationship (QSAR analyses were carried out in a series of novel sulfonamide derivatives as the procollagen C-proteinase inhibitors for treatment of fibrotic conditions. Sphere exclusion method was used to classify data set into categories of train and test set at different radii ranging from 0.9 to 0.5. Multiple linear regression (MLR, principal component regression (PCR and partial least squares (PLS were used as the regression methods and stepwise, Genetic algorithm (GA, and simulated annealing (SA were used as the feature selection methods. Three of the statistically best significant models were chosen from the results for discussion. Model 1 was obtained by MLR–SA methodology at a radius of 1.6. This model with a coefficient of determination (r2 = 0.71 can well predict the real inhibitor activities. Cross-validated q2 of this model, 0.64, indicates good internal predictive power of the model. External validation of the model (pred_r2 = 0.85 showed that the model can well predict activity of novel PCP inhibitors. The model 2 which developed using PLS–SW explains 72% (r2 = 0.72 of the total variance in the training set as well as it has internal (q2 and external (pred_r2 predictive ability of ∼67% and ∼71% respectively. The last developed model by PCR–SA has a correlation coefficient (r2 of 0.68 which can explains 68% of the variance in the observed activity values. In this case internal and external validations are 0.61 and 0.75, respectively. Alignment Independent (AI and atomic valence connectivity index (chiv have the greatest effect on the biological activities. Developed models can be useful in designing and synthesis of effective and optimized novel PCP inhibitors which can be used for treatment of fibrotic conditions.

  3. A Survey of Health Care Models that Encompass Multiple Departments

    NARCIS (Netherlands)

    Vanberkel, Peter T.; Boucherie, Richardus J.; Hans, Elias W.; Hurink, Johann L.; Litvak, Nelli

    2009-01-01

    In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by

  4. A survey of health care models that encompass multiple departments

    NARCIS (Netherlands)

    Vanberkel, P.T.; Boucherie, Richardus J.; Hans, Elias W.; Hurink, Johann L.; Litvak, Nelli

    2009-01-01

    In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by

  5. Multiple regression models of δ13C and δ15N for fish populations in the eastern Gulf of Mexico

    Science.gov (United States)

    Radabaugh, Kara R.; Peebles, Ernst B.

    2014-08-01

    Multiple regression models were created to explain spatial and temporal variation in the δ13C and δ15N values of fish populations on the West Florida Shelf (eastern Gulf of Mexico, USA). Extensive trawl surveys from three time periods were used to acquire muscle samples from seven groundfish species. Isotopic variation (δ13Cvar and δ15Nvar) was calculated as the deviation from the isotopic mean of each fish species. Static spatial data and dynamic water quality parameters were used to create models predicting δ13Cvar and δ15Nvar in three fish species that were caught in the summers of 2009 and 2010. Additional data sets were then used to determine the accuracy of the models for predicting isotopic variation (1) in a different time period (fall 2010) and (2) among four entirely different fish species that were collected during summer 2009. The δ15Nvar model was relatively stable and could be applied to different time periods and species with similar accuracy (mean absolute errors 0.31-0.33‰). The δ13Cvar model had a lower predictive capability and mean absolute errors ranged from 0.42 to 0.48‰. δ15N trends are likely linked to gradients in nitrogen fixation and Mississippi River influence on the West Florida Shelf, while δ13C trends may be linked to changes in algal species, photosynthetic fractionation, and abundance of benthic vs. planktonic basal resources. These models of isotopic variability may be useful for future stable isotope investigations of trophic level, basal resource use, and animal migration on the West Florida Shelf.

  6. FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

    Science.gov (United States)

    Lorenzo-Seva, Urbano; Ferrando, Pere J

    2011-03-01

    We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

  7. Investigations upon the indefinite rolls quality assurance in multiple regression analysis

    Directory of Open Access Journals (Sweden)

    Kiss, I.

    2012-04-01

    Full Text Available The rolling rolls quality has been enhanced mainly due to the improvements of the chemical compositions of rolls materials. The realization of an optimal chemical composition can constitute a technical efficient mode to assure the exploitation properties, the material from which the rolling mills rolls are manufactured having a higher importance in this sense. This paper continues to present the scientifically results of our experimental research in the area of the rolling rolls. The basic research contains concrete elements of immediate practical utilities in the metallurgical enterprises, for the quality improvements of rolls, having in last as the aim the durability growth and the safety in exploitation. This paper presents an analysis of the chemical composition, the influences upon the mechanical properties of the indefinite cast iron rolls. We present some mathematical correlations and graphical interpretations between the hardness (on the working surface and on necks and the chemical composition. Using the double and triple correlations which is really helpful in the foundry practice, as it allows us to determine variation boundaries for the chemical composition, in view the obtaining the optimal values of the hardness. We suggest a mathematical interpretation of the influence of the chemical composition over the hardness of these indefinite rolling rolls. In this sense we use the multiple regression analysis which can be an important statistical tool for the investigation of relationships between variables. The enunciation of some mathematically modeling results can be described through a number of multi-component equations determined for the spaces with 3 and 4 dimensions. Also, the regression surfaces, curves of levels and volumes of variations can be represented and interpreted by technologists considering these as correlation diagrams between the analyzed variables. In this sense, these researches results can be used in the engineers

  8. Estimating Driver Performance Using Multiple Electroencephalography (EEG)-Based Regression Algorithms

    Science.gov (United States)

    2014-09-01

    driving simulation and ecologically valid subject pool to which the simple linear regression algorithm was applied. Table 2 Average squared...Bones PJ, Jones RD. Detection of lapses in responsiveness from the EEG. Journal of Neural Engineering. 2011;8(1):1–15. Perez CA, Palma A, Holzmann

  9. Use of Structure Coefficients in Published Multiple Regression Articles: Beta Is Not Enough.

    Science.gov (United States)

    Courville, Troy; Thompson, Bruce

    2001-01-01

    Reviewed articles published in the "Journal of Applied Psychology" (JAP) to determine how interpretations might have differed if standardized regression coefficients and structure coefficients (or bivariate "r"s of predictors with the criterion) had been interpreted. Summarizes some dramatic misinterpretations or incomplete…

  10. Multiple regression models for the prediction of the maximum obtainable thermal efficiency of organic Rankine cycles

    DEFF Research Database (Denmark)

    Larsen, Ulrik; Pierobon, Leonardo; Wronski, Jorrit;

    2014-01-01

    to power. In this study we propose four linear regression models to predict the maximum obtainable thermal efficiency for simple and recuperated ORCs. A previously derived methodology is able to determine the maximum thermal efficiency among many combinations of fluids and processes, given the boundary...

  11. The Generalized Regression Discontinuity Design: Using Multiple Assignment Variables and Cutoffs to Estimate Treatment Effects

    Science.gov (United States)

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2009-01-01

    This paper introduces a generalization of the regression-discontinuity design (RDD). Traditionally, RDD is considered in a two-dimensional framework, with a single assignment variable and cutoff. Treatment effects are measured at a single location along the assignment variable. However, this represents a specialized (and straight-forward)…

  12. Analyzing Regression-Discontinuity Designs with Multiple Assignment Variables: A Comparative Study of Four Estimation Methods

    Science.gov (United States)

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2012-01-01

    In a traditional regression-discontinuity design (RDD), units are assigned to treatment and comparison conditions solely on the basis of a single cutoff score on a continuous assignment variable. The discontinuity in the functional form of the outcome at the cutoff represents the treatment effect, or the average treatment effect at the cutoff.…

  13. Point Estimates and Confidence Intervals for Variable Importance in Multiple Linear Regression

    Science.gov (United States)

    Thomas, D. Roland; Zhu, PengCheng; Decady, Yves J.

    2007-01-01

    The topic of variable importance in linear regression is reviewed, and a measure first justified theoretically by Pratt (1987) is examined in detail. Asymptotic variance estimates are used to construct individual and simultaneous confidence intervals for these importance measures. A simulation study of their coverage properties is reported, and an…

  14. Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

    Science.gov (United States)

    Petrocelli, John V.

    2003-01-01

    A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

  15. A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

    Science.gov (United States)

    Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

    2011-01-01

    We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

  16. Spontaneous Regression of Hepatocellular Carcinoma with Multiple Lung Metastases: A Case Report and Review of the Literature.

    Science.gov (United States)

    Pectasides, Eirini; Miksad, Rebecca; Pyatibrat, Sergey; Srivastava, Amogh; Bullock, Andrea

    2016-09-01

    Spontaneous regression of hepatocellular carcinoma (HCC) is a rare event. Here we present a case of spontaneous regression of metastatic HCC. A 53-year-old man with hepatitis C and alcoholic cirrhosis was found to have a large liver mass consistent with HCC based on its radiographic features. Imaging also revealed left portal and hepatic vein thrombosis, as well as multiple lung nodules concerning for metastases. Approximately 2 months after the initial diagnosis, both the primary liver lesion and the lung metastases decreased in size and eventually resolved without any intervention. Thereafter, the left hepatic vein thrombus progressed into the inferior vena cava and the right atrium, and the patient died due to right heart failure. In this case report and literature review, we discuss the potential mechanisms for and review the literature on spontaneous regression of metastatic HCC.

  17. Multiple Regression Analysis of mRNA-miRNA Associations in Colorectal Cancer Pathway

    OpenAIRE

    Fengfeng Wang; S. C. Cesar Wong; Lawrence W. C. Chan; Cho, William C. S.; S. P. Yip; Yung, Benjamin Y. M.

    2014-01-01

    Background. MicroRNA (miRNA) is a short and endogenous RNA molecule that regulates posttranscriptional gene expression. It is an important factor for tumorigenesis of colorectal cancer (CRC), and a potential biomarker for diagnosis, prognosis, and therapy of CRC. Our objective is to identify the related miRNAs and their associations with genes frequently involved in CRC microsatellite instability (MSI) and chromosomal instability (CIN) signaling pathways. Results. A regression model was adopt...

  18. A methodology for the design of experiments in computational intelligence with multiple regression models.

    Science.gov (United States)

    Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  19. A methodology for the design of experiments in computational intelligence with multiple regression models

    Directory of Open Access Journals (Sweden)

    Carlos Fernandez-Lozano

    2016-12-01

    Full Text Available The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  20. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease.

    Science.gov (United States)

    Zhang, Daoqiang; Shen, Dinggang

    2012-01-16

    Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely multi-modal multi-task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of 'AD', 'MCI' or 'HC'), from the

  1. Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

    Science.gov (United States)

    NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

    2017-08-01

    Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

  2. Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

    Science.gov (United States)

    Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia

    2014-05-01

    Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets.

  3. Using Multiple Regression in Estimating (semi) VOC Emissions and Concentrations at the European Scale

    DEFF Research Database (Denmark)

    Fauser, Patrik; Thomsen, Marianne; Pistocchi, Alberto

    2010-01-01

    for an in-depth risk assessment. Uncertainty measures are not available for the RAR data; however, uncertainties for the applied regression models are given in the paper. Evaluation of the methods reveals that between 79% and 93% of all emission and PEC estimates are within one order of magnitude...... of the reported RAR values. Bearing in mind that the domain of the method comprises organic industrial high-production volume chemicals, four chemicals, prioritized in the Water Framework Directive and the Stockholm Convention on Persistent Organic Pollutants, were used to test the method for estimated emissions...

  4. Prediction of cavity growth rate during underground coal gasification using multiple regression analysis

    Institute of Scientific and Technical Information of China (English)

    Mehdi Najafi; Seyed Mohammad Esmaiel Jalali; Reza KhaloKakaie; Farrokh Forouhandeh

    2015-01-01

    During underground coal gasification (UCG), whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate (CGR) or the moving rate of the gasification face is affected by controllable (operation pressure, gasification time, geometry of UCG panel) and uncontrollable (coal seam properties) factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 11 UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

  5. The AstraLux Large M-dwarf Multiplicity Survey

    CERN Document Server

    Janson, Markus; Bergfors, Carolina; Brandner, Wolfgang; Hippler, Stefan; Daemgen, Sebastian; Kudryavtseva, Natalia; Schmalzl, Eva; Schnupp, Carolin; Henning, Thomas

    2012-01-01

    We present the results of an extensive high-resolution imaging survey of M-dwarf multiplicity using the Lucky Imaging technique. The survey made use of the AstraLux Norte camera at the Calar Alto 2.2m telescope and the AstraLux Sur camera at the ESO New Technology Telescope in order to cover nearly the full sky. In total, 761 stars were observed (701 M-type and 60 late K-type), among which 182 new and 37 previously known companions were detected in 205 systems. Most of the targets have been observed during two or more epochs, and could be confirmed as physical companions through common proper motion, often with orbital motion being confirmed in addition. After accounting for various bias effects, we find a total M-dwarf multiplicity fraction of 27+/-3% within the AstraLux detection range of 0.08-6" (semi-major axes of ~3-227 AU at a median distance of 30 pc). We examine various statistical multiplicity properties within the sample, such as the trend of multiplicity fraction with stellar mass and the semi-majo...

  6. Auxiliary variables in multiple imputation in regression with missing X: a warning against including too many in small sample research

    Science.gov (United States)

    2012-01-01

    Background Multiple imputation is becoming increasingly popular. Theoretical considerations as well as simulation studies have shown that the inclusion of auxiliary variables is generally of benefit. Methods A simulation study of a linear regression with a response Y and two predictors X1 and X2 was performed on data with n = 50, 100 and 200 using complete cases or multiple imputation with 0, 10, 20, 40 and 80 auxiliary variables. Mechanisms of missingness were either 100% MCAR or 50% MAR + 50% MCAR. Auxiliary variables had low (r=.10) vs. moderate correlations (r=.50) with X’s and Y. Results The inclusion of auxiliary variables can improve a multiple imputation model. However, inclusion of too many variables leads to downward bias of regression coefficients and decreases precision. When the correlations are low, inclusion of auxiliary variables is not useful. Conclusion More research on auxiliary variables in multiple imputation should be performed. A preliminary rule of thumb could be that the ratio of variables to cases with complete data should not go below 1 : 3. PMID:23216665

  7. Multiple race reporting for children in a national health survey.

    Science.gov (United States)

    Parker, J D; Lucas, J B

    2000-01-01

    The 1997 standard for race and ethnicity data from the Office of Management and Budget requires the collection of data for multiple race groups. The aims of this study were to compare characteristics of multiple race children and describe race reporting for children within interracial and multiple race families. Descriptive statistics were estimated using the 1993-1995 National Health Interview Surveys. In this time period, 2.6% of children had more than one race reported. Multiple race children were a diverse group who differed from each other and their single race counterparts. For example, the percent of children reported as both Black and White who lived in a two-parent household (58.9%), was significantly less than the corresponding percents for other multiple race children (65.8%-79.6%), and between the corresponding percents for single race Black (42.7%) and single race White children (83.2%). The relationships between parental race and child's race varied. Although 3.1% of children in two-parent households lived with interracial parents, fewer than half of these children had more than one race reported. Sociodemographic variables were not associated with child's reported race among interracial families. These findings indicate that generalizations about multiple race children for research or policy purposes will be problematic.

  8. Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

    Science.gov (United States)

    Bulcock, J. W.

    The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

  9. Predicting agility performance with other performance variables in pubescent boys: a multiple-regression approach.

    Science.gov (United States)

    Sekulic, Damir; Spasic, Miodrag; Esco, Michael R

    2014-04-01

    The goal was to investigate the influence of balance, jumping power, reactive-strength, speed, and morphological variables on five different agility performances in early pubescent boys (N = 71). The predictors included body height and mass, countermovement and broad jumps, overall stability index, 5 m sprint, and bilateral side jumps test of reactive strength. Forward stepwise regressions calculated on 36 randomly selected participants explained 47% of the variance in performance of the forward-backward running test, 50% of the 180 degrees turn test, 55% of the 20 yd. shuttle test, 62% of the T-shaped course test, and 44% of the zig-zag test, with the bilateral side jumps as the single best predictor. Regression models were cross-validated using the second half of the sample (n = 35). Correlation between predicted and achieved scores did not provide statistically significant validation statistics for the continuous-movement zig-zag test. Further study is needed to assess other predictors of agility in early pubescent boys.

  10. Digital soil mapping using multiple logistic regression on terrain parameters in southern Brazil Mapeamento digital de solos utilizando regressões logísticas múltiplas e parâmetros do terreno no sul do Brasil

    Directory of Open Access Journals (Sweden)

    Elvio Giasson

    2006-06-01

    Full Text Available Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.Os levantamentos de solos são fontes de informação necessárias para o planejamento de uso das terras, entretanto eles nem sempre estão disponíveis. Este estudo propõe o uso de regressões logísticas múltiplas na predição de ocorrência de classes de solos a partir de áreas de referência. Baseado no mapa original de solos em formato digital e parâmetros do terreno derivados do modelo numérico do terreno em ambiente ArcView, vários conjuntos de regressões logísticas múltiplas foram definidas usando o programa estatístico Minitab, estabelecendo relações entre as variáveis do terreno independentes e tipos de solos, usando tanto a legenda original como uma legenda simplificada, e usando ou não estratificação da área de estudo por classes de drenagem. Os parâmetros do terreno como elevação, distância dos rios, acúmulo de fluxo e índice de umidade topográfica foram as variáveis que melhor explicaram a distribuição das classes de

  11. Treatment adherence in multiple sclerosis: a survey of Belgian neurologists

    Directory of Open Access Journals (Sweden)

    Decoo D

    2015-11-01

    Full Text Available Danny Decoo,1 Mathieu Vokaer2 1Department of Neurology and Neurorehab, AZ Alma, Sijsele, Belgium; 2Multiple Sclerosis Clinic, Edith Cavell Hospital, CHIREC group, Brussels, Belgium Background: Poor treatment adherence is common among patients with multiple sclerosis (MS. This survey evaluated neurologists’ perception of treatment adherence among MS patients.Materials and methods: This questionnaire-based survey of Belgian neurologists treating MS patients was conducted between June and July 2014. Face-to-face interviews with the neurologists were based on a semistructured questionnaire containing questions regarding the perception of the treatment-adherence level.Results: A total of 41 neurologists participated in the survey. Of these, 88% indicated frequent discussions about treatment adherence as beneficial for treatment efficacy. The mean time spent on the treatment-adherence discussion during the initial consultation was 11 minutes, with 24% of doctors spending 5 minutes and 24% of doctors spending 10 minutes discussing this issue. The majority of neurologists (56% perceived the adherence level in MS as good, and 12% perceived it as excellent. The majority of neurologists (64% indicated intolerance as a main cause of poor adherence, and all neurologists reported insufficient efficacy as a consequence of nonadherence. The importance of adherence in the neurologists’ practice was evaluated on a scale of 1–10, with 1= “not very important” and 10= “very important”: 44% of doctors indicated a score of 10, and the mean score was 9.0.Conclusion: Belgian neurologists consider treatment adherence in MS as essential for the benefits of therapies. However, although neurologists are aware of the consequences of nonadherence, they generally spend limited time discussing the importance of treatment adherence with their patients. Keywords: multiple sclerosis, treatment adherence, physician survey

  12. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  13. [Inversion of the lake total nitrogen concentration by multiple regression kriging model based on hyperspectral data of HJ-1A].

    Science.gov (United States)

    Pan, Bang-long; Yi, Wei-ning; Wang, Xian-hua; Qin, Hui-ping; Wang, Jia-cheng; Qiao, Yan-li

    2011-07-01

    The content of total nitrogen in the waters is an important index to measure lake water quality, and the technique of remote sensing plays a large role in quantitatively monitoring the dynamic change and timely grasping the status of lake pollution. Taking Chaohu as an example, quantitative inversion models of total nitrogen were established by multivariable regression Kriging under analyzing of an correlation between total nitrogen and chlorophyll-a or suspended solids by HIS hyperspectral remote sensing data of HJ-1A satellite. The result shows that the correlation of 0.76 was discovered between total nitrogen and the multiple combination with band 72, band 79 and band 97, while the correlation could be increased to 0.83 by applying combined model of multiple linear regression and ordinary Kriging. The optimization of the residuals of the conventional regression model can improve the accuracy of the inversion effectively. These results also provide useful exploration for further establishing a common model of quantitative inversion of lake total nitrogen concentration.

  14. Multiple trait model combining random regressions for daily feed intake with single measured performance traits of growing pigs

    Directory of Open Access Journals (Sweden)

    Künzi Niklaus

    2002-01-01

    Full Text Available Abstract A random regression model for daily feed intake and a conventional multiple trait animal model for the four traits average daily gain on test (ADG, feed conversion ratio (FCR, carcass lean content and meat quality index were combined to analyse data from 1 449 castrated male Large White pigs performance tested in two French central testing stations in 1997. Group housed pigs fed ad libitum with electronic feed dispensers were tested from 35 to 100 kg live body weight. A quadratic polynomial in days on test was used as a regression function for weekly means of daily feed intake and to escribe its residual variance. The same fixed (batch and random (additive genetic, pen and individual permanent environmental effects were used for regression coefficients of feed intake and single measured traits. Variance components were estimated by means of a Bayesian analysis using Gibbs sampling. Four Gibbs chains were run for 550 000 rounds each, from which 50 000 rounds were discarded from the burn-in period. Estimates of posterior means of covariance matrices were calculated from the remaining two million samples. Low heritabilities of linear and quadratic regression coefficients and their unfavourable genetic correlations with other performance traits reveal that altering the shape of the feed intake curve by direct or indirect selection is difficult.

  15. Early sexual initiation and multiple sexual partners among Vietnamese women: analysis from the Multiple Indicator Cluster Survey, 2011

    Directory of Open Access Journals (Sweden)

    Dinh Thai Son

    2016-02-01

    Full Text Available Introduction: Under current HIV transmission mechanisms operating in Vietnam, women are seen as victims of their male partners. Having multiple sexual partners is one of the well-known risk factors for HIV infection. However, little is known about women's risky sexual behaviour and their vulnerability to HIV in Vietnam. This study aims to explore association between early sexual initiation and the number of lifetime sexual partners in Vietnamese women. Although the Vietnamese culture is socially conservative in this area, identifying women's risky sexual behaviour is important for the protection of women at risk of HIV and other sexually transmitted diseases. Design: A total of 8,791 women, who reported having had sexual intercourse, were included in this analysis of data from the 2011 Multiple Indicator Cluster Survey in Vietnam. Data were collected using two-stage strata sampling, first at the national level and second across six geographical regions (n=8,791. Multivariable logistic regressions describe association between early initiation of a sexual activity and lifetime multiple sexual partners. Results: Early sexual intercourse was significantly associated with having lifetime multiple sexual partners. Women who were aged 19 or younger at first sexual intercourse were over five times more likely to have multiple sexual partners, compared with women whose first sexual intercourse was after marriage; aged 10–14 years (OR=5.9; 95% CI=1.9–18.8 at first intercourse; and aged 15–19 years (OR=5.4; 95% CI=4.0–7.2 at first intercourse. There was significant association with having multiple sexual partners for women of lower household wealth and urban residence, but the association with educational attainment was not strong. Conclusions: The study results call for health and education policies to encourage the postponement of early sexual activity in young Vietnamese women as protection against risky sexual behaviour later in life.

  16. Cosmological constraints from multiple tracers in spectroscopic surveys

    CERN Document Server

    Alarcon, Alex; Gaztañaga, Enrique

    2016-01-01

    We use the Fisher matrix formalism to study the expansion and growth history of the Universe using galaxy clustering with 2D angular cross-correlation tomography in spectroscopic or high resolution photometric redshift surveys. The radial information is contained in the cross correlations between narrow redshift bins. We show how multiple tracers with redshift space distortions cancel sample variance and arbitrarily improve the constraints on the dark energy equation of state $\\omega(z)$ and the growth parameter $\\gamma$ in the noiseless limit. The improvement for multiple tracers quickly increases with the bias difference between the tracers, up to a factor $\\sim4$ in $\\text{FoM}_{\\gamma\\omega}$. We model a magnitude limited survey with realistic density and bias using a conditional luminosity function, finding a factor 1.3-9.0 improvement in $\\text{FoM}_{\\gamma\\omega}$ -- depending on global density -- with a split in a halo mass proxy. Partly overlapping redshift bins improve the constraints in multiple tr...

  17. Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study.

    Science.gov (United States)

    Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

    2015-10-01

    The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200-400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset.

  18. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

    Science.gov (United States)

    Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

    2012-01-01

    The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

  19. INFLUENCE OF TOURISM SECTOR IN ALBANIAN GDP: STIMATION USING MULTIPLE REGRESSION METHOD

    Directory of Open Access Journals (Sweden)

    Eglantina HYSA

    2012-06-01

    Full Text Available During last years, tourism sector has significantly increased in Albania, since after year 1990 Albania has passed from a centralized economy to a liberal one. Tourism sector plays an important role in economic and social development. The contributions of this sector reflect directly into the generation of national income. The two main components matching the tourism movements are the number of tourists and the number of overnights in hotels. Investments done in this sector could be expected to have high positive influence in the country's GDP. This study seeks to identify the influence of tourists, their overnights in hotels and capital investment spending by all sectors directly involved in tourism sector on tourism total contribution to gross domestic product of Albania during 1996-2009. A regression analysis has been performed taking as dependent variable GDP generated by tourism sector and as independent variables, capital investment, tourist number and overnights in hotels. Even if all the variables have been found to be positivlye related, the variable ‘overnights of foreigners and Albanians in hotels' have beenfound insignificant.

  20. A calibration method of Argo floats based on multiple regression analysis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Argo floats are free-moving floats that report vertical profiles of salinity, temperature and pressure at regular time intervals. These floats give good measurements of temperature and pressure, but salinity measurements may show significant sensor drifting with time. It is found that sensor drifting with time is not purely linear as presupposed by Wong (2003). A new method is developed to calibrate conductivity data measured by Argo floats. In this method, Wong's objective analysis method was adopted to estimate the background climatological salinity field on potential temperature surfaces from nearby historical data in WOD01. Furthermore, temperature and time factors are taken into account, and stepwise regression was used for a time-varying or temperature-varying slope in potential conductivity space to correct the drifting in these profiling float salinity data. The result shows salinity errors using this method are smaller than that of Wong's method, the quantitative and qualitative analysis of the conductivity sensor can be carried out with our method.

  1. Detection and parameter estimation for quantitative trait loci using regression models and multiple markers

    Directory of Open Access Journals (Sweden)

    Schook Lawrence B

    2000-07-01

    Full Text Available Abstract A strategy of multi-step minimal conditional regression analysis has been developed to determine the existence of statistical testing and parameter estimation for a quantitative trait locus (QTL that are unaffected by linked QTLs. The estimation of marker-QTL recombination frequency needs to consider only three cases: 1 the chromosome has only one QTL, 2 one side of the target QTL has one or more QTLs, and 3 either side of the target QTL has one or more QTLs. Analytical formula was derived to estimate marker-QTL recombination frequency for each of the three cases. The formula involves two flanking markers for case 1, two flanking markers plus a conditional marker for case 2, and two flanking markers plus two conditional markers for case 3. Each QTL variance and effect, and the total QTL variance were also estimated using analytical formulae. Simulation data show that the formulae for estimating marker-QTL recombination frequency could be a useful statistical tool for fine QTL mapping. With 1 000 observations, a QTL could be mapped to a narrow chromosome region of 1.5 cM if no linked QTL is present, and to a 2.8 cM chromosome region if either side of the target QTL has at least one linked QTL.

  2. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

    Science.gov (United States)

    Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

    2013-10-01

    In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

  3. Prediction of Rotor Spun Yarn Strength Using Adaptive Neuro-fuzzy Inference System and Linear Multiple Regression Methods

    Institute of Scientific and Technical Information of China (English)

    NURWAHA Deogratias; WANG Xin-hou

    2008-01-01

    This paper presents a comparison study of two models for predicting the strength of rotor spun cotton yarns from fiber properties. The adaptive neuro-fuzzy system inference (ANFIS) and Multiple Linear Regression models are used to predict the rotor spun yarn strength. Fiber properties and yarn count are used as inputs to train the two models and the count-strength-product (CSP) was the target. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better predictive power in comparison with linear multipleregression model. The impact of each fiber property is also illustrated.

  4. Assessment of the expected construction company’s net profit using neural network and multiple regression models

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

    This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

  5. Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

    Science.gov (United States)

    Greensmith, David J

    2014-01-01

    Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow.

  6. Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

    Science.gov (United States)

    Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

    2015-01-01

    Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, prandom forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse.

  7. Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

    Directory of Open Access Journals (Sweden)

    Dongdong eLin

    2014-10-01

    Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed

  8. Multiple logistic regression model of signalling practices of drivers on urban highways

    Science.gov (United States)

    Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana

    2015-05-01

    Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.

  9. Using Meta-Regression to Explore Moderating Effects in Surveys of International Achievement

    Science.gov (United States)

    Benton, Tom

    2014-01-01

    This article demonstrates how meta-analytic techniques, that have typically been used to synthesize findings across numerous studies, can also be applied to examine the reasons why relationships between background characteristics and outcomes may vary across different locations in a single multi-site survey. This application is particularly…

  10. Multiple Regression Analysis for Grading and Prognosis of Cubital Tunnel Syndrome:Assessment of Akahori’s Classification

    Directory of Open Access Journals (Sweden)

    Nishida,Keiichiro

    2013-02-01

    Full Text Available The purpose of this study was to quantitatively evaluate Akahori's preoperative classification of cubital tunnel syndrome. We analyzed the results for 57 elbows that were treated by a simple decompression procedure from 1997 to 2004. The relationship between each item of Akahori's preoperative classification and clinical stage was investigated based on the parameter distribution. We evaluated Akahori's classification system using multiple regression analysis, and investigated the association between the stage and treatment results. The usefulness of the regression equation was evaluated by analysis of variance of the expected and observed scores. In the parameter distribution, each item of Akahori's classification was mostly associated with the stage, but it was difficult to judge the severity of palsy. In the mathematical evaluation, the most effective item in determining the stage was sensory conduction velocity. It was demonstrated that the established regression equation was highly reliable (R=0.922. Akahori's preoperative classification can also be used in postoperative classification, and this classification was correlated with postoperative prognosis. Our results indicate that Akahori's preoperative classification is a suitable system. It is reliable, reproducible and well-correlated with the postoperative prognosis. In addition, the established prediction formula is useful to reduce the diagnostic complexity of Akahori's classification.

  11. ESTIMATE OF CO2 EFFLUX OF SOIL, OF A TRANSITION FOREST IN NORTHWEST OF MATO GROSSO STATE, USING MULTIPLE REGRESSION

    Directory of Open Access Journals (Sweden)

    Carla Maria Abido Valentini

    2008-03-01

    Full Text Available Many research groups have being studying the contribution of tropical forests to the global carbon cycle, and theclimatic consequences of substituting the forests for pastures. Considering that soil CO2 efflux is the greater component of the carboncycle of the biosphere, this work found an equation for estimating the soil CO2 efflux of an area of the Transition Forest, using a modelof multiple regression for time series data of temperature and soil moisture. The study was carried out in the northwest of MatoGrosso, Brazil (11°24.75’S; 55°19.50’W, in a transition forest between cerrado and AmazonForest, 50 km far from Sinop county.Each month, throughout one year, it was measured soil CO2 efflux, temperature and soil moisture. The annual average of soil CO2 efflux was 7.5 ± 0.6 (mean ± SE ì mol m-2 s-1, the annual mean soil temperature was 25,06 ± 0.12 (mean ± SE ºC. The study indicatedthat the humidity had high influence on soil CO2 efflux; however the results were more significant using a multiple regression modelthat estimated the logarithm of soil CO2 efflux, considering time, soil moisture and the interaction between time duration and theinverse of soil temperature. .

  12. QSRR Study of GC Retention Indices of Volatile Compounds Emitted from Mosla chinensis Maxim by Multiple Linear Regression%QSRR Study of GC Retention Indices of Volatile Compounds Emitted from Mosla chinensis Maxim by Multiple Linear Regression

    Institute of Scientific and Technical Information of China (English)

    曹慧; 李祖光; 陈小珍

    2011-01-01

    The volatile compounds emitted from Mosla chinensis Maxim were analyzed by headspace solid-phase micro- extraction (HS-SPME) and headspace liquid-phase microextraction (HS-LPME) combined with gas chromatography-mass spectrometry (GC-MS). The main volatiles from Mosla chinensis Maxim were studied in this paper. It can be seen that 61 compounds were separated and identified. Forty-nine volatile compounds were identified by SPME method, mainly including myrcene, a-terpinene, p-cymene, (E)-ocimene, thymol, thymol acetate and (E)-fl-farnesene. Forty-five major volatile compounds were identified by LPME method, including a-thujene, a-pinene, camphene, butanoic acid, 2-methylpropyl ester, myrcene, butanoic acid, butyl ester, a-terpinene, p-cymene, (E)-ocimene, butane, 1,1-dibutoxy-, thymol, thymol acetate and (E)-fl-farnesene. After analyzing the volatile compounds, multiple linear regression (MLR) method was used for building the regression model. Then the quantitative structure-retention relationship (QSRR) model was validated by predictive-ability test. The prediction results were in good agreement with the experimental values. The results demonstrated that headspace SPME-GC-MS and LPME-GC-MS are the simple, rapid and easy sample enrichment technique suitable for analysis of volatile compounds. This investigation provided an effective method for predicting the retention indices of new compounds even in the absence of the standard candidates.

  13. EMPIRICAL STUDY OF DIFFERENT FACTORS EFFECTS ON ARTICLES PUBLICATION REGARDING SURVEY INTERVIEWER CHARACTERISTICS USING MULTILEVEL REGRESSION MODEL

    Directory of Open Access Journals (Sweden)

    Alina MOROŞANU

    2013-06-01

    Full Text Available The purpose of this research work is to evaluate the effects which some factors could have on articles publication regarding survey interviewer characteristics. For this, the author studied the existing literature from the various fields in which articles on survey interviewer characteristics has been published and which can be found in online articles database. The analysis was performed on 243 articles achieved by researchers in the time period 1949-2012. Using statistical software R and applying multilevel regression model, the results showed that the time period when the studied articles are made and the interaction between the number of authors and the number of pages affect the most their publication in journals with a certain level of impact factor.

  14. Understanding Why Students Participate in Multiple Surveys: Who are the Hard-Core Responders?

    Science.gov (United States)

    Porter, Stephen R.; Whitcomb, Michael E.

    2004-01-01

    What causes a student to participate in a survey? This paper looks at survey response across multiple surveys to understand who the hard-core survey responders and non-responders are. Students at a selective liberal arts college were administered four different surveys throughout the 2002-2003 academic year, and we use the number of surveys…

  15. Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

    Science.gov (United States)

    Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

    2017-07-01

    This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

  16. Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

    Science.gov (United States)

    Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

    2016-01-01

    Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

  17. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

    Science.gov (United States)

    Barrett, C. A.

    1985-01-01

    Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

  18. Effect size and power in assessing moderating effects of categorical variables using multiple regression: a 30-year review.

    Science.gov (United States)

    Aguinis, Herman; Beaty, James C; Boik, Robert J; Pierce, Charles A

    2005-01-01

    The authors conducted a 30-year review (1969-1998) of the size of moderating effects of categorical variables as assessed using multiple regression. The median observed effect size (f(2)) is only .002, but 72% of the moderator tests reviewed had power of .80 or greater to detect a targeted effect conventionally defined as small. Results suggest the need to minimize the influence of artifacts that produce a downward bias in the observed effect size and put into question the use of conventional definitions of moderating effect sizes. As long as an effect has a meaningful impact, the authors advise researchers to conduct a power analysis and plan future research designs on the basis of smaller and more realistic targeted effect sizes.

  19. FORECASTING RETURNS FOR THE STOCK EXCHANGE OF THAILAND INDEX USING MULTIPLE REGRESSION BASED ON PRINCIPAL COMPONENT ANALYSIS

    Directory of Open Access Journals (Sweden)

    Nop Sopipan

    2013-01-01

    Full Text Available The aim of this study was to forecast the returns for the Stock Exchange of Thailand (SET Index by adding some explanatory variables and stationary Autoregressive Moving-Average order p and q (ARMA (p, q in the mean equation of returns. In addition, we used Principal Component Analysis (PCA to remove possible complications caused by multicollinearity. Afterwards, we forecast the volatility of the returns for the SET Index. Results showed that the ARMA (1,1, which includes multiple regression based on PCA, has the best performance. In forecasting the volatility of returns, the GARCH model performs best for one day ahead; and the EGARCH model performs best for five days, ten days and twenty-two days ahead.

  20. Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

    Science.gov (United States)

    Suresh, Arumuganainar; Choi, Hong Lim

    2011-10-01

    Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (Pspecific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. Multiple linear regression models for shear strength prediction and design of simplysupported deep beams subjected to symmetrical point loads

    Directory of Open Access Journals (Sweden)

    Panatchai Chetchotisak

    2015-09-01

    Full Text Available Because of nonlinear strain distributions caused either by abrupt changes in geometry or in loading in deep beam, the approach for conventional beams is not applicable. Consequently, strut-and-tie model (STM has been applied as the most rational and simple method for strength prediction and design of reinforced concrete deep beams. A deep beam is idealized by the STM as a truss-like structure consisting of diagonal concrete struts and tension ties. There have been numerous works proposing the STMs for deep beams. However, uncertainty and complexity in shear strength computations of deep beams can be found in some STMs. Therefore, improvement of methods for predicting the shear strengths of deep beams are still needed. By means of a large experimental database of 406 deep beam test results covering a wide range of influencing parameters, several shapes and geometry of STM and six state-of-the-art formulation of the efficiency factors found in the design codes and literature, the new STMs for predicting the shear strength of simply supported reinforced concrete deep beams using multiple linear regression analysis is proposed in this paper. Furthermore, the regression diagnostics and the validation process are included in this study. Finally, two numerical examples are also provided for illustration.

  2. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

    Science.gov (United States)

    Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

    2013-01-01

    The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

  3. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

    Science.gov (United States)

    Lee, L.; Helsel, D.

    2005-01-01

    Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

  4. Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.

    Science.gov (United States)

    Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan

    2017-02-20

    The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

  5. Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models.

    Science.gov (United States)

    Oliveira, H R; Silva, F F; Siqueira, O H G B D; Souza, N O; Junqueira, V S; Resende, M D V; Borquis, R R A; Rodrigues, M T

    2016-05-01

    We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from -0.58 to 0.03, -0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.

  6. Examination of Parameters Affecting the House Prices by Multiple Regression Analysis and its Contributions to Earthquake-Based Urban Transformation

    Science.gov (United States)

    Denli, H. H.; Durmus, B.

    2016-12-01

    The purpose of this study is to examine the factors which may affect the apartment prices with multiple linear regression analysis models and visualize the results by value maps. The study is focused on a county of Istanbul - Turkey. Totally 390 apartments around the county Umraniye are evaluated due to their physical and locational conditions. The identification of factors affecting the price of apartments in the county with a population of approximately 600k is expected to provide a significant contribution to the apartment market.Physical factors are selected as the age, number of rooms, size, floor numbers of the building and the floor that the apartment is positioned in. Positional factors are selected as the distances to the nearest hospital, school, park and police station. Totally ten physical and locational parameters are examined by regression analysis.After the regression analysis has been performed, value maps are composed from the parameters age, price and price per square meters. The most significant of the composed maps is the price per square meters map. Results show that the location of the apartment has the most influence to the square meter price information of the apartment. A different practice is developed from the composed maps by searching the ability of using price per square meters map in urban transformation practices. By marking the buildings older than 15 years in the price per square meters map, a different and new interpretation has been made to determine the buildings, to which should be given priority during an urban transformation in the county.This county is very close to the North Anatolian Fault zone and is under the threat of earthquakes. By marking the apartments older than 15 years on the price per square meters map, both older and expensive square meters apartments list can be gathered. By the help of this list, the priority could be given to the selected higher valued old apartments to support the economy of the country

  7. Land use regression modeling of intra-urban residential variability in multiple traffic-related air pollutants

    Directory of Open Access Journals (Sweden)

    Baxter Lisa K

    2008-05-01

    Full Text Available Abstract Background There is a growing body of literature linking GIS-based measures of traffic density to asthma and other respiratory outcomes. However, no consensus exists on which traffic indicators best capture variability in different pollutants or within different settings. As part of a study on childhood asthma etiology, we examined variability in outdoor concentrations of multiple traffic-related air pollutants within urban communities, using a range of GIS-based predictors and land use regression techniques. Methods We measured fine particulate matter (PM2.5, nitrogen dioxide (NO2, and elemental carbon (EC outside 44 homes representing a range of traffic densities and neighborhoods across Boston, Massachusetts and nearby communities. Multiple three to four-day average samples were collected at each home during winters and summers from 2003 to 2005. Traffic indicators were derived using Massachusetts Highway Department data and direct traffic counts. Multivariate regression analyses were performed separately for each pollutant, using traffic indicators, land use, meteorology, site characteristics, and central site concentrations. Results PM2.5 was strongly associated with the central site monitor (R2 = 0.68. Additional variability was explained by total roadway length within 100 m of the home, smoking or grilling near the monitor, and block-group population density (R2 = 0.76. EC showed greater spatial variability, especially during winter months, and was predicted by roadway length within 200 m of the home. The influence of traffic was greater under low wind speed conditions, and concentrations were lower during summer (R2 = 0.52. NO2 showed significant spatial variability, predicted by population density and roadway length within 50 m of the home, modified by site characteristics (obstruction, and with higher concentrations during summer (R2 = 0.56. Conclusion Each pollutant examined displayed somewhat different spatial patterns

  8. Low-dose biplanar skeletal survey versus digital skeletal survey in multiple myeloma

    Energy Technology Data Exchange (ETDEWEB)

    Boutry, Nathalie [University Hospital of Jeanne de Flandre and University of Lille 2, Departments of Pediatric and Musculoskeletal Imaging, Lille (France); University Hospital of Jeanne de Flandre and University of Lille 2, Department of Pediatric Imaging, Lille (France); Hopital Jeanne de Flandre, Service de Radiopediatrie, Lille (France); Dutouquet, Bastien; Cotten, Anne [University Hospital of Roger Salengro and University of Lille 2, Department of Musculoskeletal Imaging, Lille (France); Leleu, Xavier [University Hospital of Claude Huriez and University of Lille 2, Clinical Hematology Department, Lille (France); Vieillard, Marie-Helene [University Hospital of Roger Salengro and University of Lille 2, Rheumatology Department, Lille (France); Duhamel, Alain [University of Lille 2, Department of Medical Statistics, Lille (France)

    2013-08-15

    To evaluate the low-dose biplanar (LDB) skeletal survey (SS) for the assessment of focal bone involvement in patients with multiple myeloma (MM) as compared with digital SS and to compare the two techniques in terms of image quality, patient comfort and radiation exposure. Fifty-six consecutive patients with newly diagnosed or first relapsed MM underwent LDB and digital SS on the same day. These were assessed by two radiologists for the detection of focal bone lesions. In the case of discordance, whole-body MR imaging was performed. Image quality, patient comfort and radiation dose were also assessed. Fifty-six patients (M:30, F:26, mean age, 62 years) with newly diagnosed (n = 21) or first relapse MM (n = 35) were enrolled. A total of 473 bone lesions in 46 patients (82 %) were detected. Out of that total, digital SS detected significantly more lesions than LDB SS (451 [95.35 %] versus 467 [98.73 %]), especially in osteopenic and obese patients. Overall patient satisfaction was greater with LDB SS (48.6 %) compared with digital SS (2.7 %). The radiation dose was significantly reduced (by a factor of 7.8) with the LDB X-ray device. Low-dose biplanar skeletal surveys cannot replace digital SS in all patients suffering from multiple myeloma. (orig.)

  9. Logistic regression.

    Science.gov (United States)

    Nick, Todd G; Campbell, Kathleen M

    2007-01-01

    The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.

  10. Estimation of the retention behaviour of s-triazine derivatives applying multiple regression analysis of selected molecular descriptors

    Directory of Open Access Journals (Sweden)

    Jevrić Lidija R.

    2013-01-01

    Full Text Available The estimation of retention factors by correlation equations with physico-chemical properties can be of great helpl in chromatographic studies. The retention factors were experimentally measured by RP-HPTLC on impregnated silica gel with paraffin oil using two-component solvent systems. The relationships between solute retention and modifier concentration were described by Snyder’s linear equation. A quantitative structure-retention relationship was developed for a series of s-triazine compounds by the multiple linear regression (MLR analysis. The MLR procedure was used to model the relationships between the molecular descriptors and retention of s-triazine derivatives. The physicochemical molecular descriptors were calculated from the optimized structures. The physico-chemical properties were the lipophilicity (log P, connectivity indices (χ, total energy (Et, water solubility (log W, dissociation constant (pKa, molar refractivity (MR, and Gibbs energy (GibbsE of s-triazines. A high agreement between the experimental and predicted retention parameters was obtained when the dissociation constant and the hydrophilic-lipophilic balance were used as the molecular descriptors. The empirical equations may be successfully used for the prediction of the various chromatographic characteristics of substances, with a similar chemical structure. [Projekat Ministarstva nauke Republike Srbije, br. 31055, br. 172012, br. 172013 i br. 172014

  11. 2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Fereshteh Shiri

    2010-08-01

    Full Text Available In the present work, support vector machines (SVMs and multiple linear regression (MLR techniques were used for quantitative structure–property relationship (QSPR studies of retention time (tR in standardized liquid chromatography–UV–mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLRand SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD. The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r2 and q2 are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William’s plot. The effects of different descriptors on the retention times are described.

  12. Comparing Effects of Biologic Agents in Treating Patients with Rheumatoid Arthritis: A Multiple Treatment Comparison Regression Analysis.

    Directory of Open Access Journals (Sweden)

    Ingunn Fride Tvete

    Full Text Available Rheumatoid arthritis patients have been treated with disease modifying anti-rheumatic drugs (DMARDs and the newer biologic drugs. We sought to compare and rank the biologics with respect to efficacy. We performed a literature search identifying 54 publications encompassing 9 biologics. We conducted a multiple treatment comparison regression analysis letting the number experiencing a 50% improvement on the ACR score be dependent upon dose level and disease duration for assessing the comparable relative effect between biologics and placebo or DMARD. The analysis embraced all treatment and comparator arms over all publications. Hence, all measured effects of any biologic agent contributed to the comparison of all biologic agents relative to each other either given alone or combined with DMARD. We found the drug effect to be dependent on dose level, but not on disease duration, and the impact of a high versus low dose level was the same for all drugs (higher doses indicated a higher frequency of ACR50 scores. The ranking of the drugs when given without DMARD was certolizumab (ranked highest, etanercept, tocilizumab/ abatacept and adalimumab. The ranking of the drugs when given with DMARD was certolizumab (ranked highest, tocilizumab, anakinra/rituximab, golimumab/ infliximab/ abatacept, adalimumab/ etanercept [corrected]. Still, all drugs were effective. All biologic agents were effective compared to placebo, with certolizumab the most effective and adalimumab (without DMARD treatment and adalimumab/ etanercept (combined with DMARD treatment the least effective. The drugs were in general more effective, except for etanercept, when given together with DMARDs.

  13. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

    Science.gov (United States)

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  14. Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

    Directory of Open Access Journals (Sweden)

    Yuehjen E. Shao

    2013-01-01

    Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

  15. Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models

    Science.gov (United States)

    Linard, Joshua I.

    2013-01-01

    Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.

  16. Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

    Science.gov (United States)

    Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming

    2017-09-07

    Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017. Published by Elsevier B.V.

  17. A parallel implementation of the network identification by multiple regression (NIR algorithm to reverse-engineer regulatory gene networks.

    Directory of Open Access Journals (Sweden)

    Francesco Gregoretti

    Full Text Available The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes--as is the case in biological networks--due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.

  18. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error.

    Science.gov (United States)

    Jäntschi, Lorentz; Bálint, Donatella; Bolboacă, Sorana D

    2016-01-01

    Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

  19. Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

    Science.gov (United States)

    Armaghani, Danial Jahed; Mahdiyar, Amir; Hasanipanah, Mahdi; Faradonbeh, Roohollah Shirani; Khandelwal, Manoj; Amnieh, Hassan Bakhshandeh

    2016-09-01

    Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

  20. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  1. A comparison on parameter-estimation methods in multiple regression analysis with existence of multicollinearity among independent variables

    Directory of Open Access Journals (Sweden)

    Hukharnsusatrue, A.

    2005-11-01

    Full Text Available The objective of this research is to compare multiple regression coefficients estimating methods with existence of multicollinearity among independent variables. The estimation methods are Ordinary Least Squares method (OLS, Restricted Least Squares method (RLS, Restricted Ridge Regression method (RRR and Restricted Liu method (RL when restrictions are true and restrictions are not true. The study used the Monte Carlo Simulation method. The experiment was repeated 1,000 times under each situation. The analyzed results of the data are demonstrated as follows. CASE 1: The restrictions are true. In all cases, RRR and RL methods have a smaller Average Mean Square Error (AMSE than OLS and RLS method, respectively. RRR method provides the smallest AMSE when the level of correlations is high and also provides the smallest AMSE for all level of correlations and all sample sizes when standard deviation is equal to 5. However, RL method provides the smallest AMSE when the level of correlations is low and middle, except in the case of standard deviation equal to 3, small sample sizes, RRR method provides the smallest AMSE.The AMSE varies with, most to least, respectively, level of correlations, standard deviation and number of independent variables but inversely with to sample size.CASE 2: The restrictions are not true.In all cases, RRR method provides the smallest AMSE, except in the case of standard deviation equal to 1 and error of restrictions equal to 5%, OLS method provides the smallest AMSE when the level of correlations is low or median and there is a large sample size, but the small sample sizes, RL method provides the smallest AMSE. In addition, when error of restrictions is increased, OLS method provides the smallest AMSE for all level, of correlations and all sample sizes, except when the level of correlations is high and sample sizes small. Moreover, the case OLS method provides the smallest AMSE, the most RLS method has a smaller AMSE than

  2. Relating relapse and T2 lesion changes to disability progression in multiple sclerosis: a systematic literature review and regression analysis

    Science.gov (United States)

    2013-01-01

    Background In the treatment of multiple sclerosis (MS), the most important therapeutic aim of disease-modifying treatments (DMTs) is to prevent or postpone long-term disability. Given the typically slow progression observed in the majority of relapsing-remitting MS (RRMS) patients, the primary endpoint for most randomized clinical trials (RCTs) is a reduction in relapse rate. It is widely assumed that reducing relapse rate will slow disability progression. Similarly, MRI studies suggest that reducing T2 lesions will be associated with slowing long-term disability in MS. The objective of this study was to evaluate the relationship between treatment effects on relapse rates and active T2 lesions to differences in disease progression (as measured by the Expanded Disability Status Scale [EDSS]) in trials evaluating patients with clinically isolated syndrome (CIS), RRMS, and secondary progressive MS (SPMS). Methods A systematic literature review was conducted in Medline, Embase, CENTRAL, and PsycINFO to identify randomized trials published in English from January 1, 1993-June 3, 2013 evaluating DMTs in adult MS patients using keywords for CIS, RRMS, and SPMS combined with keywords for relapse and recurrence. Eligible studies were required to report outcomes of relapse and T2 lesion changes or disease progression in CIS, RRMS, or SPMS patients receiving DMTs and have a follow-up duration of at least 22 months. Ultimately, 40 studies satisfied these criteria for inclusion. Regression analyses were conducted on RCTs to relate differences between the effect of treatments on relapse rates and on active T2 lesions to differences between the effects of treatments on disease progression (as measured by EDSS). Results Regression analysis determined there is a substantive clinically and statistically significant association between concurrent treatment effects in relapse rate and EDSS; p EDSS measures also were found (p < 0.05), with some suggestion that the strength of

  3. Explaining marital patterns and trends in namibia: a regression analysis of 1992, 2000 and 2006 demographic and survey data.

    Directory of Open Access Journals (Sweden)

    Lillian Pazvakawambwa

    Full Text Available BACKGROUND: Marriage is a significant event in life-course of individuals, and creates a system that characterizes societal and economic structures. Marital patterns and dynamics over the years have changed a lot, with decreasing proportions of marriage, increased levels of divorce and co-habitation in developing countries. Although, such changes have been reported in African societies including Namibia, they have largely remained unexplained. OBJECTIVES AND METHODS: IN THIS PAPER, WE EXAMINED TRENDS AND PATTERNS OF MARITAL STATUS OF WOMEN OF MARRIAGEABLE AGE: 15 to 49 years, in Namibia using the 1992, 2000 and 2006 Demographic and Health Survey (DHS data. Trends were established for selected demographic variables. Two binary logistic regression models for ever-married versus never married, and cohabitation versus married were fitted to establish factors associated with such nuptial systems. Further a multinomial logistic regression models, adjusted for bio-demographic and socio-economic variables, were fitted separately for each year, to establish determinants of type of union (never married, married and cohabitation. RESULTS AND CONCLUSIONS: Findings indicate a general change away from marriage, with a shift in singulate mean age at marriage. Cohabitation was prevalent among those less than 30 years of age, the odds were higher in urban areas and increased since 1992. Be as it may marriage remained a persistent nuptiality pattern, and common among the less educated and employed, but lower odds in urban areas. Results from multinomial model suggest that marital status was associated with age at marriage, total children born, region, place of residence, education level and religion. We conclude that marital patterns have undergone significant transformation over the past two decades in Namibia, with a coexistence of traditional marriage framework with co-habitation, and sizeable proportion remaining unmarried to the late 30s. A shift in the

  4. Student Ratings of the Importance of Survey Items, Multiplicative Factor Analysis, and the Validity of the Community of Inquiry Survey

    Science.gov (United States)

    Diaz, Sebastian R.; Swan, Karen; Ice, Philip; Kupczynski, Lori

    2010-01-01

    This research builds upon prior validation studies of the Community of Inquiry (CoI) survey by utilizing multiple rating measures to validate the survey's tripartite structure (teaching presence, social presence, and cognitive presence). In prior studies exploring the construct validity of these 3 subscales, only respondents' course ratings were…

  5. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

    Science.gov (United States)

    Kokaly, R.F.; Clark, R.N.

    1999-01-01

    We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using

  6. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression

    Directory of Open Access Journals (Sweden)

    Faezehossadat Khademi

    2016-12-01

    Full Text Available Compressive strength of concrete, recognized as one of the most significant mechanical properties of concrete, is identified as one of the most essential factors for the quality assurance of concrete. In the current study, three different data-driven models, i.e., Artificial Neural Network (ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS, and Multiple Linear Regression (MLR were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC. Recycled aggregate is the current need of the hour owing to its environmental pleasant aspect of re-using the wastes due to construction. 14 different input parameters, including both dimensional and non-dimensional parameters, were used in this study for predicting the 28 days compressive strength of concrete. The present study concluded that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANN and ANFIS in comparison to MLR. In other words, comparing the test step of all the three models, it can be concluded that the MLR model is better to be utilized for preliminary mix design of concrete, and ANN and ANFIS models are suggested to be used in the mix design optimization and in the case of higher accuracy necessities. In addition, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated. Finally, the effect of number of input parameters on 28 days compressive strength of concrete is examined.

  7. Predicting punching acceleration from selected strength and power variables in elite karate athletes: a multiple regression analysis.

    Science.gov (United States)

    Loturco, Irineu; Artioli, Guilherme Giannini; Kobal, Ronaldo; Gil, Saulo; Franchini, Emerson

    2014-07-01

    This study investigated the relationship between punching acceleration and selected strength and power variables in 19 professional karate athletes from the Brazilian National Team (9 men and 10 women; age, 23 ± 3 years; height, 1.71 ± 0.09 m; and body mass [BM], 67.34 ± 13.44 kg). Punching acceleration was assessed under 4 different conditions in a randomized order: (a) fixed distance aiming to attain maximum speed (FS), (b) fixed distance aiming to attain maximum impact (FI), (c) self-selected distance aiming to attain maximum speed, and (d) self-selected distance aiming to attain maximum impact. The selected strength and power variables were as follows: maximal dynamic strength in bench press and squat-machine, squat and countermovement jump height, mean propulsive power in bench throw and jump squat, and mean propulsive velocity in jump squat with 40% of BM. Upper- and lower-body power and maximal dynamic strength variables were positively correlated to punch acceleration in all conditions. Multiple regression analysis also revealed predictive variables: relative mean propulsive power in squat jump (W·kg-1), and maximal dynamic strength 1 repetition maximum in both bench press and squat-machine exercises. An impact-oriented instruction and a self-selected distance to start the movement seem to be crucial to reach the highest acceleration during punching execution. This investigation, while demonstrating strong correlations between punching acceleration and strength-power variables, also provides important information for coaches, especially for designing better training strategies to improve punching speed.

  8. International Differences in Multiple Sclerosis Health Outcomes and Associated Factors in a Cross-sectional Survey

    Directory of Open Access Journals (Sweden)

    Grace D. Reilly

    2017-05-01

    Full Text Available Multiple sclerosis (MS is a major cause of disability and poor quality of life (QOL. Previous studies have shown differences in MS health outcomes between countries. This study aimed to examine the associations between international regions and health outcomes in people with MS. Self-reported data were taken from the Health Outcomes and Lifestyle In a Sample of people with Multiple Sclerosis online survey collected in 2012. The 2,401 participants from 37 countries were categorized into three regions: Australasia, Europe, and North America. Differences were observed between regions in disability, physical and mental health QOL, fatigue, and depression, but most of these disappeared after adjusting for sociodemographic, disease, and lifestyle factors in multivariable regression models. However, adjusted odds for disability were higher in Europe [odds ratio (OR: 2.17, 95% confidence interval (CI: 1.28 to 3.67] and North America (OR: 1.79, 95% CI: 1.28 to 2.51 compared to Australasia. There may be other unmeasured factors that vary between regions, including differences in access and quality of healthcare services, determining disability in MS. When assessing differences in MS health outcomes, lifestyle factors and medication use should be taken into consideration.

  9. An Investigation of the Relationship of Intellective and Personality Variables to Success in an Independent Study Science Course Through the Use of a Modified Multiple Regression Model.

    Science.gov (United States)

    Szabo, Michael; Feldhusen, John F.

    This is an empirical study of selected learner characteristics and their relation to academic success, as indicated by course grades, in a structured independent study learning program. This program, called the Audio-Tutorial System, was utilized in an undergraduate college course in the biological sciences. By use of multiple regression analysis,…

  10. Regressão múltipla stepwise e hierárquica em Psicologia Organizacional: aplicações, problemas e soluções Stepwise and hierarchical multiple regression in organizational psychology: Applications, problemas and solutions

    Directory of Open Access Journals (Sweden)

    Gardênia Abbad

    2002-01-01

    Full Text Available Este artigo discute algumas aplicações das técnicas de análise de regressão múltipla stepwise e hierárquica, as quais são muito utilizadas em pesquisas da área de Psicologia Organizacional. São discutidas algumas estratégias de identificação e de solução de problemas relativos à ocorrência de erros do Tipo I e II e aos fenômenos de supressão, complementaridade e redundância nas equações de regressão múltipla. São apresentados alguns exemplos de pesquisas nas quais esses padrões de associação entre variáveis estiveram presentes e descritas as estratégias utilizadas pelos pesquisadores para interpretá-los. São discutidas as aplicações dessas análises no estudo de interação entre variáveis e na realização de testes para avaliação da linearidade do relacionamento entre variáveis. Finalmente, são apresentadas sugestões para lidar com as limitações das análises de regressão múltipla (stepwise e hierárquica.This article discusses applications of stepwise and hierarchical multiple regression analyses to research in organizational psychology. Strategies for identifying type I and II errors, and solutions to potential problems that may arise from such errors are proposed. In addition, phenomena such as suppression, complementarity, and redundancy are reviewed. The article presents examples of research where these phenomena occurred, and the manner in which they were explained by researchers. Some applications of multiple regression analyses to studies involving between-variable interactions are presented, along with tests used to analyze the presence of linearity among variables. Finally, some suggestions are provided for dealing with limitations implicit in multiple regression analyses (stepwise and hierarchical.

  11. Isokinetic knee strength qualities as predictors of jumping performance in high-level volleyball athletes: multiple regression approach.

    Science.gov (United States)

    Sattler, Tine; Sekulic, Damir; Spasic, Miodrag; Osmankac, Nedzad; Vicente João, Paulo; Dervisevic, Edvin; Hadzic, Vedran

    2016-01-01

    Previous investigations noted potential importance of isokinetic strength in rapid muscular performances, such as jumping. This study aimed to identify the influence of isokinetic-knee-strength on specific jumping performance in volleyball. The secondary aim of the study was to evaluate reliability and validity of the two volleyball-specific jumping tests. The sample comprised 67 female (21.96±3.79 years; 68.26±8.52 kg; 174.43±6.85 cm) and 99 male (23.62±5.27 years; 84.83±10.37 kg; 189.01±7.21 cm) high- volleyball players who competed in 1st and 2nd National Division. Subjects were randomly divided into validation (N.=55 and 33 for males and females, respectively) and cross-validation subsamples (N.=54 and 34 for males and females, respectively). Set of predictors included isokinetic tests, to evaluate the eccentric and concentric strength capacities of the knee extensors, and flexors for dominant and non-dominant leg. The main outcome measure for the isokinetic testing was peak torque (PT) which was later normalized for body mass and expressed as PT/Kg. Block-jump and spike-jump performances were measured over three trials, and observed as criteria. Forward stepwise multiple regressions were calculated for validation subsamples and then cross-validated. Cross validation included correlations between and t-test differences between observed and predicted scores; and Bland Altman graphics. Jumping tests were found to be reliable (spike jump: ICC of 0.79 and 0.86; block-jump: ICC of 0.86 and 0.90; for males and females, respectively), and their validity was confirmed by significant t-test differences between 1st vs. 2nd division players. Isokinetic variables were found to be significant predictors of jumping performance in females, but not among males. In females, the isokinetic-knee measures were shown to be stronger and more valid predictors of the block-jump (42% and 64% of the explained variance for validation and cross-validation subsample, respectively

  12. A C*-based Extended Multiple Linear Regression Method to Determine Decadal Changes in Anthropogenic CO2 in the Ocean

    Science.gov (United States)

    Clement, Dominic; Gruber, Nicolas

    2017-04-01

    Major progress has been made by the international community (e.g., GO-SHIP, IOCCP, IMBER/SOLAS carbon working groups) in recent years by collecting and providing homogenized datasets for carbon and other biogeochemical variables in the surface ocean (SOCAT) and interior ocean (GLODAPv2). Together with previous efforts, this has enabled the community to develop methods to assess changes in the ocean carbon cycle through time. Of particular interest is the determination of the decadal change in the anthropogenic CO2 inventory solely based on in-situ measurements from at least two time periods in the interior ocean. However, all such methods face the difficulty of a scarce dataset in both space and time, making the use of appropriate interpolation techniques in time and space a crucial element of any method. Here we present a new method based on the parameter C*, whose variations reflect the total change in dissolved inorganic carbon (DIC) driven by the exchange of CO2 across the air-sea interface. We apply the extended Multiple Linear Regression method (Friis et al., 2005) on C* in order (1) to calculate the change in anthropogenic CO2 from the original DIC/C* measurements, and (2) to interpolate the result onto a spatial grid using other biogeochemical variables (T,S,AOU, etc.). These calculations are made on isopycnal slabs across whole ocean basins. In combination with the transient steady state assumption (Tanhua et al., 2007) providing a temporal correction factor, we address the spatial and temporal interpolation challenges. Using synthetic data from a hindcast simulation with a global ocean biogeochemistry model (NCAR-CCSM with BEC), we tested the method for robustness and accuracy in determining ΔCant. We will present data-based results for all ocean basins, with the most recent estimate of an global uptake of 32±6 Pg C between 1994 and 2007, indicating an uptake rate 2.5±0.5 Pg C yr-1 for this time period. These results are compared with regional and

  13. 5 CFR 591.216 - How does OPM combine survey data for the DC area and for COLA areas with multiple survey areas?

    Science.gov (United States)

    2010-01-01

    ... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false How does OPM combine survey data for the DC area and for COLA areas with multiple survey areas? 591.216 Section 591.216 Administrative... combine survey data for the DC area and for COLA areas with multiple survey areas? (a) Washington,...

  14. Linear regression

    CERN Document Server

    Olive, David J

    2017-01-01

    This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...

  15. Partial least squares regression can aid in detecting differential abundance of multiple features in sets of metagenomic samples

    Directory of Open Access Journals (Sweden)

    Ondrej eLibiger

    2015-12-01

    Full Text Available It is now feasible to examine the composition and diversity of microbial communities (i.e., `microbiomes‘ that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology 'Metastats‘ across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency

  16. Relationship of push-ups and sit-ups tests to selected anthropometric variables and performance results: a multiple regression study.

    Science.gov (United States)

    Esco, Michael R; Olson, Michele S; Williford, Henry

    2008-11-01

    The purpose of this study was to explore whether selected anthropometric measures such as specific skinfold sites, along with weight, height, body mass index (BMI), waist and hip circumferences, and waist/hip ratio (WHR) were associated with sit-ups (SU) and push-ups (PU) performance, and to build a regression model for SU and PU tests. One hundred apparently healthy adults (40 men and 60 women) served as the subjects for test validation. The subjects performed 60-second SU and PU tests. The variables analyzed via multiple regression included weight, height, BMI, hip and waist circumferences, WHR, skinfolds at the abdomen (SFAB), thigh (SFTH), and subscapularis (SFSS), and sex. An additional cohort of 40 subjects (17 men and 23 women) was used to cross-validate the regression models. Validity was confirmed by correlation and paired t-tests. The regression analysis yielded a four-variable (PU, height, SFAB, and SFTH) multiple regression equation for estimating SU (R2 = 0.64, SEE = 7.5 repetitions). For PU, only SU was loaded into the regression equation (R2 = 0.43, SEE = 9.4 repetitions). Thus, the variables in the regression models accounted for 64% and 43% of the variation in SU and PU, respectively. The cross-validation sample elicited a high correlation for SU (r = 0.87) and PU (r = 0.79) scores. Moreover, paired-samples t-tests revealed that there were no significant differences between actual and predicted SU and PU scores. Therefore, this study shows that there are a number of selected, health-related anthropometric variables that account significantly for, and are predictive of, SU and PU tests.

  17. Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

    Science.gov (United States)

    Denli, H. H.; Koc, Z.

    2015-12-01

    Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

  18. Multiple Regression with Varying Levels of Correlation among Predictors: Monte Carlo Sampling from Normal and Non-Normal Populations.

    Science.gov (United States)

    Vasu, Ellen Storey

    1978-01-01

    The effects of the violation of the assumption of normality in the conditional distributions of the dependent variable, coupled with the condition of multicollinearity upon the outcome of testing the hypothesis that the regression coefficient equals zero, are investigated via a Monte Carlo study. (Author/JKS)

  19. Reconciling catch differences from multiple fishery independent gill net surveys

    Science.gov (United States)

    Kraus, Richard T.; Vandergoot, Christopher; Kocovsky, Patrick M.; Rogers, Mark W.; Cook, H. Andrew; Brenden, Travis O.

    2017-01-01

    Fishery independent gill net surveys provide valuable demographic information for population assessment and resource management, but relative to net construction, the effects of ancillary species, and environmental variables on focal species catch rates are poorly understood. In response, we conducted comparative deployments with three unique, inter-agency, survey gill nets used to assess walleye Sander vitreus in Lake Erie. We used an information-theoretic approach with Akaike’s second-order information criterion (AICc) to evaluate linear mixed models of walleye catch as a function of net type (multifilament and two types of monofilament netting), mesh size (categorical), Secchi depth, temperature, water depth, catch of ancillary species, and interactions among selected variables. The model with the greatest weight of evidence showed that walleye catches were positively associated with potential prey and intra-guild predators and negatively associated with water depth and temperature. In addition, the multifilament net had higher average walleye catches than either of the two monofilament nets. Results from this study both help inform decisions about proposed gear changes to stock assessment surveys in Lake Erie, and advance our understanding of how multispecies associations explain variation in gill net catches. Of broader interest to fishery-independent gill net studies, effects of abiotic variables and ancillary species on focal specie’s catch rates were small in comparison with net characteristics of mesh size or twine type.

  20. Multiple Regression and Mediator Variables can be used to Avoid Double Counting when Economic Values are Derived using Stochastic Herd Simulation

    DEFF Research Database (Denmark)

    Østergaard, Søren; Ettema, Jehan Frans; Hjortø, Line

    Multiple regression and model building with mediator variables was addressed to avoid double counting when economic values are estimated from data simulated with herd simulation modeling (using the SimHerd model). The simulated incidence of metritis was analyzed statistically as the independent...... variable, while using the traits representing the direct effects of metritis on yield, fertility and occurrence of other diseases as mediator variables. The economic value of metritis was estimated to be €78 per 100 cow-years for each 1% increase of metritis in the period of 1-100 days in milk...... in multiparous cows. The merit of using this approach was demonstrated since the economic value of metritis was estimated to be 81% higher when no mediator variables were included in the multiple regression analysis...

  1. A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding

    Indian Academy of Sciences (India)

    ABHIJIT SARKAR; PRASENJIT DEY; R N RAI; SUBHAS CHANDRA SAHA

    2016-05-01

    Weld bead plays an important role in determining the quality of welding particularly in high heat input processes. This research paper presents the development of multiple regression analysis (MRA) and artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arcwelding process. Design of experiments is based on Taguchi’s L16 orthogonal array by varying wire feed rate,transverse speed and stick out to develop a multiple regression model, which has been checked for adequacy andsignificance. Also, ANN model was accomplished with the back propagation approach in MATLAB program to predict bead geometry and HAZ width. Finally, the results of two prediction models were compared and analyzed. It is found that the error related to the prediction of bead geometry and HAZ width is smaller in ANN than MRA.

  2. Guide to using Multiple Regression in Excel (MRCX v.1.1) for Removal of River Stage Effects from Well Water Levels

    Energy Technology Data Exchange (ETDEWEB)

    Mackley, Rob D.; Spane, Frank A.; Pulsipher, Trenton C.; Allwardt, Craig H.

    2010-09-01

    A software tool was created in Fiscal Year 2010 (FY11) that enables multiple-regression correction of well water levels for river-stage effects. This task was conducted as part of the Remediation Science and Technology project of CH2MHILL Plateau Remediation Company (CHPRC). This document contains an overview of the correction methodology and a user’s manual for Multiple Regression in Excel (MRCX) v.1.1. It also contains a step-by-step tutorial that shows users how to use MRCX to correct river effects in two different wells. This report is accompanied by an enclosed CD that contains the MRCX installer application and files used in the tutorial exercises.

  3. Literature Survey on Operator Performance using Multiple Displays

    Science.gov (United States)

    2006-03-01

    research. DRDC Atlantic CR 2005-209 17 5. References 1. Burns, C., Greenley , M., Roth, E., Barone, A., Brooks, J. (2002). Literature Survey for...Obermayer...(2004) 1. Burns, Greenley ...(2002) 2. Layman, Weatherly (2004) MILITARY 1. Hopper (1998) 2. Hopper (1999) 3. Hopper (2000) 4. Hopper...2 12 Burdea (2003) 1 2 2 2 1 2 2 12 Burns, Greenley ...(2002) 2 2 2 2 2 2 3 15 Chapanis (1996) 1 2 2 2 1 2 2 12 Cole, Kentridge (2004) 1 2 2 2 1 2

  4. Gene-based multiple regression association testing for combined examination of common and low frequency variants in quantitative trait analysis

    Directory of Open Access Journals (Sweden)

    Yun Joo eYoo

    2013-11-01

    Full Text Available Multi-marker methods for genetic association analysis can be performed for common and low frequency SNPs to improve power. Regression models are an intuitive way to formulate multi-marker tests. In previous studies we evaluated regression-based multi-marker tests for common SNPs, and through identification of bins consisting of correlated SNPs, developed a multi-bin linear combination (MLC test that is a compromise between a 1df linear combination test and a multi-df global test. Bins of SNPs in high linkage disequilibrium (LD are identified, and a linear combination of individual SNP statistics is constructed within each bin. Then association with the phenotype is represented by an overall statistic with df as many or few as the number of bins. In this report we evaluate multi-marker tests for SNPs that occur at low frequencies. There are many linear and quadratic multi-marker tests that are suitable for common or low frequency variant analysis. We compared the performance of the MLC tests with various linear and quadratic statistics in joint or marginal regressions. For these comparisons, we performed a simulation study of genotypes and quantitative traits for 85 genes with many low frequency SNPs based on HapMap Phase III. We compared the tests using 1 set of all SNPs in a gene, 2 set of common SNPs in a gene (MAF≥5%, 3 set of low frequency SNPs (1%≤MAF

  5. Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

    Science.gov (United States)

    Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

    2017-01-01

    In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

  6. Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand

    Directory of Open Access Journals (Sweden)

    Piyawat Wuttichaikitcharoen

    2014-08-01

    Full Text Available Predicting sediment yield is necessary for good land and water management in any river basin. However, sometimes, the sediment data is either not available or is sparse, which renders estimating sediment yield a daunting task. The present study investigates the factors influencing suspended sediment yield using the principal component analysis (PCA. Additionally, the regression relationships for estimating suspended sediment yield, based on the selected key factors from the PCA, are developed. The PCA shows six components of key factors that can explain at least up to 86.7% of the variation of all variables. The regression models show that basin size, channel network characteristics, land use, basin steepness and rainfall distribution are the key factors affecting sediment yield. The validation of regression relationships for estimating suspended sediment yield shows the error of estimation ranging from −55% to +315% and −59% to +259% for suspended sediment yield and for area-specific suspended sediment yield, respectively. The proposed relationships may be considered useful for predicting suspended sediment yield in ungauged basins of Northern Thailand that have geologic, climatic and hydrologic conditions similar to the study area.

  7. Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease

    Science.gov (United States)

    Zhang, Daoqiang; Shen, Dinggang

    2011-01-01

    Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Recently, rather than predicting categorical variables as in classification, several pattern regression methods have also been used to estimate continuous clinical variables from brain images. However, most existing regression methods focus on estimating multiple clinical variables separately and thus cannot utilize the intrinsic useful correlation information among different clinical variables. On the other hand, in those regression methods, only a single modality of data (usually only the structural MRI) is often used, without considering the complementary information that can be provided by different modalities. In this paper, we propose a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Here, the variables include not only the clinical variables used for regression but also the categorical variable used for classification, with different tasks corresponding to prediction of different variables. Specifically, our method contains two key components, i.e., (1) a multi-task feature selection which selects the common subset of relevant features for multiple variables from each modality, and (2) a multi-modal support vector machine which fuses the above-selected features from all modalities to predict multiple (regression and classification) variables. To validate our method, we perform two sets of experiments on ADNI baseline MRI, FDG-PET, and cerebrospinal fluid (CSF) data from 45 AD patients, 91 MCI patients, and 50 healthy controls (HC). In the first set of experiments, we estimate two clinical variables such as Mini Mental State Examination (MMSE) and Alzheimer’s Disease Assessment Scale - Cognitive Subscale (ADAS-Cog), as well as one categorical variable (with value of ‘AD’, ‘MCI’ or

  8. Multiple vulnerabilities and maternal healthcare in Vietnam: findings from the Multiple Indicator Cluster Surveys, 2000, 2006, and 2011

    Directory of Open Access Journals (Sweden)

    Hoang Van Minh

    2016-02-01

    Full Text Available Background: Knowledge of the aggregate effects of multiple socioeconomic vulnerabilities is important for shedding light on the determinants of growing health inequalities and inequities in maternal healthcare. Objective: This paper describes patterns of inequity in maternal healthcare utilization and analyzes associations between inequity and multiple socioeconomic vulnerabilities among women in Vietnam. Design: This is a repeated cross-sectional study using data from the Vietnam Multiple Indicator Cluster Surveys 2000, 2006, and 2011. Two maternal healthcare indicators were selected: (1 skilled antenatal care and (2 skilled delivery care. Four types of socioeconomic vulnerabilities – low education, ethnic minority, poverty, and rural location – were assessed both as separate explanatory variables and as composite indicators (combinations of three and four vulnerabilities. Pairwise comparisons and adjusted odds ratios were used to assess socioeconomic inequities in maternal healthcare. Results: In all three surveys, there were increases across the survey years in both the proportions of women who received antenatal care by skilled staff (68.6% in 2000, 90.8% in 2006, and 93.7% in 2011 and the proportions of women who gave birth with assistance from skilled staff (69.9% in 2000, 87.7% in 2006, and 92.9% in 2011. The receipt of antenatal care by skilled staff and birth assistance from skilled health personnel were less common among vulnerable women, especially those with multiple vulnerabilities. Conclusions: Even though Vietnam has improved its coverage of maternal healthcare on average, policies should target maternal healthcare utilization among women with multiple socioeconomic vulnerabilities. Both multisectoral social policies and health policies are needed to tackle multiple vulnerabilities more effectively by identifying those who are poor, less educated, live in rural areas, and belong to ethnic minority groups.

  9. Survey on Factors Affecting the Recurrence of Multiple sclerosis Attacks

    Directory of Open Access Journals (Sweden)

    Hasanzadeh F

    2015-02-01

    Full Text Available Background and Objective: Multiple sclerosis (MS is a chronic and progressive disease of the central nervous system. The identification of effective factors and patient education on the prevention of exposure to these factors can be effective in reducing relapses. Therefore, the present study aimed to investigate the factors that affect recurrence in patients with multiple sclerosis. Materials and Method: This cross-sectional study was conducted on 402 MS patients referred to the MS Association of Mashhad, Iran, in 2013. The study participants were selected using purposive sampling method. Data collection tools included a demographic questionnaire and a 40-item researcher-made questionnaire concerning factors affecting the recurrence of MS. The 40-item questionnaire consisted of 4 domains personal factors, medical condition, pharmaceutical conditions, and environmental factors. Data analysis was performed using SPSS software version 21 and Mann Whitney, Pearson correlation, Kruskal Wallis, and chi-square statistical tests. Results: Among the patients, 21/4% were male and 78/6% female. Most patients were 20-29 years old. The highest prevalence of recurrence of MS was observed in the summer (70.8% and in August (50.3%. There was no significant relationship between patient’s gender and their views about the relationship between infections, fatigue, and heat and recurrence of MS. However, a higher rate of women believed stress (P < 0.029 and sexual development (P < 0.001 to be effective on the incidence of MS recurrence, in comparison to men. Conclusion: Most patients with MS are women, and they believe stress to be more effective on the incidence of MS attack recurrence. Therefore, nurses, as the primary care educators, can improve the patients’ capability, disease management, and quality of life through training of stress management techniques.

  10. Optimization of stratification scheme for a fishery-independent survey with multiple objectives

    Institute of Scientific and Technical Information of China (English)

    XU Binduo; REN Yiping; CHEN Yong; XUE Ying; ZHANG Chongliang; WAN Rong

    2015-01-01

    Fishery-independent surveys are often used for collecting high quality biological and ecological data to support fisheries management. A careful optimization of fishery-independent survey design is necessary to improve the precision of survey estimates with cost-effective sampling efforts. We developed a simulation approach to evaluate and optimize the stratification scheme for a fishery-independent survey with multiple goals including estimation of abundance indices of individual species and species diversity indices. We compared the performances of the sampling designs with different stratification schemes for different goals over different months. Gains in precision of survey estimates from the stratification schemes were acquired compared to simple random sampling design for most indices. The stratification scheme with five strata performed the best. This study showed that the loss of precision of survey estimates due to the reduction of sampling efforts could be compensated by improved stratification schemes, which would reduce the cost and negative impacts of survey trawling on those species with low abundance in the fishery-independent survey. This study also suggests that optimization of a survey design differed with different survey objectives. A post-survey analysis can improve the stratification scheme of fishery-independent survey designs.

  11. Multivariate Regression Approach To Integrate Multiple Satellite And Tide Gauge Data For Real Time Sea Level Prediction

    DEFF Research Database (Denmark)

    Cheng, Yongcun; Andersen, Ole Baltazar; Knudsen, Per

    2010-01-01

    The Sea Level Thematic Assembly Center in the EUFP7 MyOcean project aims at build a sea level service for multiple satellite sea level observations at a European level for GMES marine applications. It aims to improve the sea level related products to guarantee the sustainability and the quality o...... stations with satellite altimetry....

  12. Investigating the Quantitative Structure-Activity Relationships for Antibody Recognition of Two Immunoassays for Polycyclic Aromatic Hydrocarbons by Multiple Regression Methods

    Directory of Open Access Journals (Sweden)

    Yan-Feng Zhang

    2012-07-01

    Full Text Available Polycyclic aromatic hydrocarbons (PAHs are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (Sm and valence molecular connectivity index (3χv are the most important factors. The obtained regression equations for RisC kit are all statistically significant (p < 0.005 and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05 and not suitable for predicting. It is probably because that the RisC immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on the CR values, cross-reaction potential (CRP is calculated and used as a complement of CR for evaluation of cross-reactions in immunoassays. Only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays.

  13. Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

    Science.gov (United States)

    Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

    2016-03-01

    Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

  14. Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Chelgani, S. Chehreh; Jorjani, E.; Mesroghli, Sh.; Bagherieh, A.H. [Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak Tehran (Iran); Hower, James C. [Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511 (United States)

    2008-01-15

    The effects of proximate and ultimate analysis, maceral content, and coal rank (R{sub max}) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen + nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and R{sub max} input sets with HGI in linear condition can achieve the correlation coefficients (R{sup 2}) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and R{sub max} can be used as the best predictor for the estimation of HGI on multivariable regression (R{sup 2} = 0.81) and also artificial neural network methods (R{sup 2} = 0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction. (author)

  15. Evaluation of heat transfer mathematical models and multiple linear regression to predict the inside variables in semi-solar greenhouse

    Directory of Open Access Journals (Sweden)

    M Taki

    2017-05-01

    Full Text Available Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Some works were done in past to 2015 year to simulation and predict the inside variables in different greenhouse structures. Usually simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. The main objective of this paper is comparison between heat transfer and regression models to evaluate them to predict inside air and roof temperature in a semi-solar greenhouse in Tabriz University. Materials and Methods In this study, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level. In this research, shape and orientation of the greenhouse, selected between some greenhouses common shapes and according to receive maximum solar radiation whole the year. Also internal thermal screen and cement north wall was used to store and prevent of heat lost during the cold period of year. So we called this structure, ‘semi-solar’ greenhouse. It was covered with glass (4 mm thickness. It occupies a surface of approximately 15.36 m2 and 26.4 m3. The orientation of this greenhouse was East–West and perpendicular to the direction of the wind prevailing

  16. Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia atlantica wood.

    Science.gov (United States)

    Ghaedi, M; Rahimi, Mahmoud Reza; Ghaedi, A M; Tyagi, Inderjeet; Agarwal, Shilpi; Gupta, Vinod Kumar

    2016-01-01

    Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015 g and 0.08 g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250 mg g(-1) and 125 mg g(-1) respectively.

  17. Survey of Multiple Information Hiding Techniques using Visual Cryptography

    Directory of Open Access Journals (Sweden)

    Bijoy Chhetri

    2015-10-01

    Full Text Available Information now a day‟s seems to have become abundant and the secure transmission and visualization of it has been a challenge. The major security concerns are of Authentication, Confidentiality and Data Integrity.  In regard to this, the various security methodologies have been introduced and Cryptography is one of the schemes where the information is transferred in the disguise form and only authentic user can reveal the exact information. Various Cryptographic techniques has played a very vital role in this regard, among which Visual Cryptographic System(VCS is one of such kind  where the secret data (image, text etc is encoded into multiple images and decoded using Human Visual System(HVS without having to tedious calculations and sound  knowledge of Cryptography. VC  is one of such methodology where the secret information is bifurcated into many disguise images and on super imposing these images, the original secret information is revealed, using Human Visual System(HVS unlike the traditional cryptography where lot of complex mathematical and time consuming calculation are to be performed. In this paper study of various VC techniques has been done based on number of shares, number of secret messages and types of shares in the cases of Grayscale Image.

  18. Impact of pomalidomide therapy in multiple myeloma: a recent survey.

    Science.gov (United States)

    Kumar, Arvind; Porwal, Mayur; Verma, Ankita; Mishra, Arun K

    2014-12-01

    Pomalidomide (Pomalyst(®)) is a synthetic compound derived by modifying the chemical structure of thalidomide to improve its potency and reduce its side effects and third drug in the class of immunomodulatory drugs. Pomalidomide is under global development with Celgene Corporation, was approved by the U.S. Food and Drug Administration on February 8, 2013 to treat patients with relapsed and refractory multiple myeloma (MM) who have received at least two prior therapies including bortezomib and lenalidomide. In October 2009, it has found orphan designation for the treatment of relapsed and refractory MM by the EMA, and on August 2013, marketing authorization has issued in Europe by gaining a positive response. It inhibits myeloma cell growth and angiogenesis directly. Pomalidomide is the latest myeloma cell growth inhibitor to be approved in both USA and EU. The predominant side effects are thrombocytopenia, neuropathy, and deep vein thrombosis. Pomalidomide is also being investigated in patients with amyloidosis, prostate cancer, small cell lung cancer, pancreatic cancer, graft-versus-host disease, and Waldenstrom's macroglobulinemia. This article reviews the available information on pomalidomide with respect to its clinical pharmacology, mechanism of action, pharmacokinetics, pharmacodynamics, metabolism, pre-clinical studies, and clinical trials.

  19. Melanin and blood concentration in a human skin model studied by multiple regression analysis: assessment by Monte Carlo simulation

    Science.gov (United States)

    Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

    2001-09-01

    Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.

  20. Quantitative structure-property relationship modeling of water-to-wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network.

    Science.gov (United States)

    Dashtbozorgi, Zahra; Golmohammadi, Hassan

    2010-12-01

    The main aim of this study was the development of a quantitative structure-property relationship method using an artificial neural network (ANN) for predicting the water-to-wet butyl acetate partition coefficients of organic solutes. As a first step, a genetic algorithm-multiple linear regression model was developed; the descriptors appearing in this model were considered as inputs for the ANN. These descriptors are principal moment of inertia C (I(C)), area-weighted surface charge of hydrogen-bonding donor atoms (HACA-2), Kier and Hall index (order 2) ((2)χ), Balaban index (J), minimum bond order of a C atom (P(C)) and relative negative-charged SA (RNCS). Then a 6-4-1 neural network was generated for the prediction of water-to-wet butyl acetate partition coefficients of 76 organic solutes. By comparing the results obtained from multiple linear regression and ANN models, it can be seen that statistical parameters (Fisher ratio, correlation coefficient and standard error) of the ANN model are better than that regression model, which indicates that nonlinear model can simulate the relationship between the structural descriptors and the partition coefficients of the investigated molecules more accurately.

  1. Application of single-step genomic best linear unbiased prediction with a multiple-lactation random regression test-day model for Japanese Holsteins.

    Science.gov (United States)

    Baba, Toshimi; Gotoh, Yusaku; Yamaguchi, Satoshi; Nakagawa, Satoshi; Abe, Hayato; Masuda, Yutaka; Kawahara, Takayoshi

    2017-08-01

    This study aimed to evaluate a validation reliability of single-step genomic best linear unbiased prediction (ssGBLUP) with a multiple-lactation random regression test-day model and investigate an effect of adding genotyped cows on the reliability. Two data sets for test-day records from the first three lactations were used: full data from February 1975 to December 2015 (60 850 534 records from 2 853 810 cows) and reduced data cut off in 2011 (53 091 066 records from 2 502 307 cows). We used marker genotypes of 4480 bulls and 608 cows. Genomic enhanced breeding values (GEBV) of 305-day milk yield in all the lactations were estimated for at least 535 young bulls using two marker data sets: bull genotypes only and both bulls and cows genotypes. The realized reliability (R(2) ) from linear regression analysis was used as an indicator of validation reliability. Using only genotyped bulls, R(2) was ranged from 0.41 to 0.46 and it was always higher than parent averages. The very similar R(2) were observed when genotyped cows were added. An application of ssGBLUP to a multiple-lactation random regression model is feasible and adding a limited number of genotyped cows has no significant effect on reliability of GEBV for genotyped bulls. © 2016 Japanese Society of Animal Science.

  2. Asthma outcomes in children and adolescents with multiple morbidities: Findings from the National Health Interview Survey.

    Science.gov (United States)

    Patel, Minal R; Leo, Harvey L; Baptist, Alan P; Cao, Yanyun; Brown, Randall W

    2015-06-01

    More Americans are managing multiple chronic conditions (MCCs), and trends are particularly alarming in youth. The purpose of this study was to examine the prevalence and distribution of 9 chronic conditions in children and adolescents with and without asthma, and adverse asthma outcomes associated with having MCCs. Cross-sectional interview data from the National Health Interview Survey were analyzed (N = 66,790) between 2007 and 2012 in youth 0 to 17 years of age. Bivariate analysis methods and multivariate generalized linear regression were used to examine associations. Five percent of children with asthma had 1 or more coexisting health conditions. The prevalence of 1 or more comorbidities was greater among those with asthma than those without (5.07% [95% CI: 4.5-5.6] vs. 2.73% [95% CI: 2.6-2.9]). Those with asthma were twice as likely to have co-occurring hypertension (prevalence ratio [PR] = 2.2 [95% CI: 1.5-3.2]) and arthritis (PR = 2.7 [95% CI: 1.8-4.0]) compared with those without asthma. Every additional chronic condition with asthma was associated with a greater likelihood of an asthma attack (PR = 1.1 [95% CI: 1.0-1.2]), all-cause emergency department visits (PR = 1.3 [95% CI: 1.1-1.5]), and missed school days (PR = 2.3 [95% CI: 1.7-3.2]). Children and adolescents with asthma in the US who suffer from MCCs have increased asthma symptoms, missed school days, and all-cause emergency department visits. Further research on optimal management strategies for this group is needed. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

  3. Multiple-output support vector machine regression with feature selection for arousal/valence space emotion assessment.

    Science.gov (United States)

    Torres-Valencia, Cristian A; Álvarez, Mauricio A; Orozco-Gutiérrez, Alvaro A

    2014-01-01

    Human emotion recognition (HER) allows the assessment of an affective state of a subject. Until recently, such emotional states were described in terms of discrete emotions, like happiness or contempt. In order to cover a high range of emotions, researchers in the field have introduced different dimensional spaces for emotion description that allow the characterization of affective states in terms of several variables or dimensions that measure distinct aspects of the emotion. One of the most common of such dimensional spaces is the bidimensional Arousal/Valence space. To the best of our knowledge, all HER systems so far have modelled independently, the dimensions in these dimensional spaces. In this paper, we study the effect of modelling the output dimensions simultaneously and show experimentally the advantages in modeling them in this way. We consider a multimodal approach by including features from the Electroencephalogram and a few physiological signals. For modelling the multiple outputs, we employ a multiple output regressor based on support vector machines. We also include an stage of feature selection that is developed within an embedded approach known as Recursive Feature Elimination (RFE), proposed initially for SVM. The results show that several features can be eliminated using the multiple output support vector regressor with RFE without affecting the performance of the regressor. From the analysis of the features selected in smaller subsets via RFE, it can be observed that the signals that are more informative into the arousal and valence space discrimination are the EEG, Electrooculogram/Electromiogram (EOG/EMG) and the Galvanic Skin Response (GSR).

  4. A comparison between Joint Regression Analysis and the Additive Main and Multiplicative Interaction model: the robustness with increasing amounts of missing data

    Directory of Open Access Journals (Sweden)

    Paulo Canas Rodrigues

    2011-12-01

    Full Text Available This paper joins the main properties of joint regression analysis (JRA, a model based on the Finlay-Wilkinson regression to analyse multi-environment trials, and of the additive main effects and multiplicative interaction (AMMI model. The study compares JRA and AMMI with particular focus on robustness with increasing amounts of randomly selected missing data. The application is made using a data set from a breeding program of durum wheat (Triticum turgidum L., Durum Group conducted in Portugal. The results of the two models result in similar dominant cultivars (JRA and winner of mega-environments (AMMI for the same environments. However, JRA had more stable results with the increase in the incidence rates of missing values.

  5. Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma.

    Science.gov (United States)

    Lehrer, Michael; Bhadra, Anindya; Ravikumar, Visweswaran; Chen, James Y; Wintermark, Max; Hwang, Scott N; Holder, Chad A; Huang, Erich P; Fevrier-Sullivan, Brenda; Freymann, John B; Rao, Arvind

    2017-05-01

    Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

  6. The effect of multiple reminders on response patterns in a Danish health survey

    DEFF Research Database (Denmark)

    Christensen, Anne I; Ekholm, Ola; Kristensen, Peter L;

    2015-01-01

    BACKGROUND: Reminders are routinely applied in surveys to increase response rates and reduce the possibility of bias. This study examines the effect of multiple reminders on the response rate, non-response bias, prevalence estimates and exposure-outcome relations in a national self......-administered health survey. METHODS: Data derive from the Danish National Health Survey 2010, in which 298 550 individuals (16 years of age or older) were invited to participate in a cross-sectional survey using a mixed-mode approach (paper and web questionnaires). At least two reminders were sent to non-respondents......, and 177 639 individuals completed the questionnaire (59.5%). Response patterns were compared between four groups of individuals (first mailing respondents, second mailing respondents, third mailing respondents and non-respondents). RESULTS: Multiple reminders led to an increase in response rate from 36...

  7. Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches

    Institute of Scientific and Technical Information of China (English)

    Hejun KANG; Shelley M.ALEXANDER

    2009-01-01

    We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS) -based approaches: logistic regression and Akaike's Information Criterion (AIC),Multiple Criteria Evaluation (MCE),and Bayesian Analysis (specifically Dempster-Shafer theory). We used lynx Lynx canadensis as our focal species,and developed our environment relationship model using track data collected in Banff National Park,Alberta,Canada,during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy),the failure to predict a species where it occurred (omission error) and the prediction of presence where there was absence (commission error). Our overall accuracy showed the logistic regression approach was the most accurate (74.51% ). The multiple criteria evaluation was intermediate (39.22%),while the Dempster-Shafer (D-S) theory model was the poorest (29.90%). However,omission and commission error tell us a different story: logistic regression had the lowest commission error,while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least,the logistic regression model is optimal. However,where sample size is small or the species is very rare,it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer) that would over-predict,protect more sites,and thereby minimize the risk of missing critical habitat in conservation plans.

  8. Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

    Science.gov (United States)

    Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

    2017-05-01

    The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for

  9. Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches

    Directory of Open Access Journals (Sweden)

    Shelley M. ALEXANDER

    2009-02-01

    Full Text Available We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS-based approaches: logistic regression and Akaike’s Information Criterion (AIC, Multiple Criteria Evaluation (MCE, and Bayesian Analysis (specifically Dempster-Shafer theory. We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy, the failure to predict a species where it occurred (omission error and the prediction of presence where there was absence (commission error. Our overall accuracy showed the logistic regression approach was the most accurate (74.51%. The multiple criteria evaluation was intermediate (39.22%, while the Dempster-Shafer (D-S theory model was the poorest (29.90%. However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans[Current Zoology 55(1: 28 – 40, 2009].

  10. Radiologic assessment of third molar tooth and spheno-occipital synchondrosis for age estimation: a multiple regression analysis study.

    Science.gov (United States)

    Demirturk Kocasarac, Husniye; Sinanoglu, Alper; Noujeim, Marcel; Helvacioglu Yigit, Dilek; Baydemir, Canan

    2016-05-01

    For forensic age estimation, radiographic assessment of third molar mineralization is important between 14 and 21 years which coincides with the legal age in most countries. The spheno-occipital synchondrosis (SOS) is an important growth site during development, and its use for age estimation is beneficial when combined with other markers. In this study, we aimed to develop a regression model to estimate and narrow the age range based on the radiologic assessment of third molar and SOS in a Turkish subpopulation. Panoramic radiographs and cone beam CT scans of 349 subjects (182 males, 167 females) with age between 8 and 25 were evaluated. Four-stage system was used to evaluate the fusion degree of SOS, and Demirjian's eight stages of development for calcification for third molars. The Pearson correlation indicated a strong positive relationship between age and third molar calcification for both sexes (r = 0.850 for females, r = 0.839 for males, P age and SOS fusion for females (r = 0.814), but a moderate relationship was found for males (r = 0.599), P age determination formula using these scores was established.

  11. The Multiplicity of Massive Stars: A High Angular Resolution Survey With The HST Fine Guidance Sensor

    Science.gov (United States)

    2015-01-01

    to their place of birth have relatively more companions, consistent with the idea that stars ejected from clusters are preferentially single objects...THE MULTIPLICITY OF MASSIVE STARS : A HIGH ANGULAR RESOLUTION SURVEY WITH THE HST FINE GUIDANCE SENSOR* E. J. Aldoretta1,2, S. M. Caballero-Nieves3, D...all-sky survey made with the Fine Guidance Sensor on the Hubble Space Telescope to search for angularly resolved binary systems among massive stars . The

  12. Quantitative structure-property relationship study of acidity constants of some 9,10-anthraquinone derivatives using multiple linear regression and partial least-squares procedures.

    Science.gov (United States)

    Shamsipur, M; Hemmateenejad, B; Akhond, M; Sharghi, H

    2001-07-06

    A quantitative structure-property relationship study is suggested for the prediction of acidity constants of some recently synthesized 9,10-anthraquinone derivatives in binary methanol-water mixtures. Modeling of the acidity constant of the anthraquinones as a function of physicochemical parameters and mole fraction of methanol was established by means of the partial least-squares algorithm based on singular value decomposition (PLS-SVD) and multiple linear regression. The PLS-SVD procedure resulted in a better prediction ability and was found to be insensitive to noneffective descriptors. The classification of anthraquinones by the calculated descriptors was established.

  13. Prediction of acute in vivo toxicity of some amine and amide drugs to rats by multiple linear regression, partial least squares and an artificial neural network.

    Science.gov (United States)

    Mahani, Mohamad Khayatzadeh; Chaloosi, Marzieh; Maragheh, Mohamad Ghanadi; Khanchi, Ali Reza; Afzali, Daryoush

    2007-09-01

    The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.

  14. Multiple Regression Analysis of Reading Performance Data from Twin Pairs with Reading Difficulties and Non-twin Siblings: The Augmented Model

    OpenAIRE

    Wadsworth, S J; Olson, R. K.; Willcutt, E.G.; DeFries, J. C.

    2012-01-01

    The augmented multiple regression model for the analysis of data from selected twin pairs was extended to facilitate analyses of data from twin pairs and non-twin siblings. Fitting this extended model to data from both selected twin pairs and siblings yields direct estimates of heritability (h2) and the difference between environmental influences shared by members of twin pairs and those of sib or twin/sib pairs [i.e., c2(t) − c2(s)]. When this model was fitted to reading performance data fro...

  15. Investigating Multiple Household Water Sources and Uses with a Computer-Assisted Personal Interviewing (CAPI Survey

    Directory of Open Access Journals (Sweden)

    Morgan C. MacDonald

    2016-12-01

    Full Text Available The investigation of multiple sources in household water management is considered overly complicated and time consuming using paper and pen interviewing (PAPI. We assess the advantages of computer-assisted personal interviewing (CAPI in Pacific Island Countries (PICs. We adapted an existing PAPI survey on multiple water sources and expanded it to incorporate location of water use and the impacts of extreme weather events using SurveyCTO on Android tablets. We then compared the efficiency and accuracy of data collection using the PAPI version (n = 44 with the CAPI version (n = 291, including interview duration, error rate and trends in interview duration with enumerator experience. CAPI surveys facilitated high-quality data collection and were an average of 15.2 min faster than PAPI. CAPI survey duration decreased by 0.55% per survey delivered (p < 0.0001, whilst embedded skip patterns and answer lists lowered data entry error rates, relative to PAPI (p < 0.0001. Large-scale household surveys commonly used in global monitoring and evaluation do not differentiate multiple water sources and uses. CAPI equips water researchers with a quick and reliable tool to address these knowledge gaps and advance our understanding of development research priorities.

  16. Diplotype Trend Regression Analysis of the ADH Gene Cluster and the ALDH2 Gene: Multiple Significant Associations with Alcohol Dependence

    Science.gov (United States)

    Luo, Xingguang; Kranzler, Henry R.; Zuo, Lingjun; Wang, Shuang; Schork, Nicholas J.; Gelernter, Joel

    2006-01-01

    The set of alcohol-metabolizing enzymes has considerable genetic and functional complexity. The relationships between some alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) genes and alcohol dependence (AD) have long been studied in many populations, but not comprehensively. In the present study, we genotyped 16 markers within the ADH gene cluster (including the ADH1A, ADH1B, ADH1C, ADH5, ADH6, and ADH7 genes), 4 markers within the ALDH2 gene, and 38 unlinked ancestry-informative markers in a case-control sample of 801 individuals. Associations between markers and disease were analyzed by a Hardy-Weinberg equilibrium (HWE) test, a conventional case-control comparison, a structured association analysis, and a novel diplotype trend regression (DTR) analysis. Finally, the disease alleles were fine mapped by a Hardy-Weinberg disequilibrium (HWD) measure (J). All markers were found to be in HWE in controls, but some markers showed HWD in cases. Genotypes of many markers were associated with AD. DTR analysis showed that ADH5 genotypes and diplotypes of ADH1A, ADH1B, ADH7, and ALDH2 were associated with AD in European Americans and/or African Americans. The risk-influencing alleles were fine mapped from among the markers studied and were found to coincide with some well-known functional variants. We demonstrated that DTR was more powerful than many other conventional association methods. We also found that several ADH genes and the ALDH2 gene were susceptibility loci for AD, and the associations were best explained by several independent risk genes. PMID:16685648

  17. APPLICATION OF GENETIC ALGORITHM-MULTIPLE LINEAR REGRESSION (GA-MLR FOR PREDICTION OF ANTI-FUNGAL ACTIVITY

    Directory of Open Access Journals (Sweden)

    Stephen Eyije Abechi

    2016-04-01

    Full Text Available Aim: To develop good and rational Quantitative Structure Activity Relationship (QSAR mathematical models that can predict to a significant level the anti-tyrosinase and anti-Candida Albicans Minimum inhibitory concentration (MIC of ketone and tetra- etone derivatives. Place and Duration of Study: Department of Chemistry (Mathieson Laboratory (3-Physical Chemistry unit, Ahmadu Bello University, Zaria, Nigeria, between December 2015 and March 2016. Methodology: A set of 44 ketone and tetra-ketone derivatives with their anti-tyrosinase and anti-Candida Albicans activities in terms of minimum inhibitory concentration (MIC against the gram-positive fungal and hyperpigmentation were selected for 1D-3D quantitative structure activity relationship (QSAR analysis using the parameterization method 6 (PM6 basis set. The computed descriptors were correlated with their experimental MIC. Genetic Function Approximation (GFA method and Multi-Linear Regression analysis (MLR were used to derive the most statistically significant QSAR model. Results: The result obtained indicates that the most statistically significant QSAR model was a five- parametric linear equation with the squared correlation coefficient (R2 value of 0.9914, adjusted squared correlation coefficient (R 2 adj value of 0.9896 and Leave one out (LOO cross validation coefficient (Q2 value of 0.9853. An external set was used for confirming the predictive power of the model, its R2 pred = 0.9618 and rm^2 = 0.8981. Conclusion: The QSAR results reveal that molecular mass, atomic mass, polarity, electronic and topological predominantly influence the anti-tyrosinase and anti-Candida Albicans activity of the complexes. The wealth of information in this study will provide an insight to designing novel bioactive ketones and tetra-ketones compound that will curb the emerging trend of multi-drug resistant strain of fungal and hyperpigmentation

  18. [Multiple imputation and complete case analysis in logistic regression models: a practical assessment of the impact of incomplete covariate data].

    Science.gov (United States)

    Camargos, Vitor Passos; César, Cibele Comini; Caiaffa, Waleska Teixeira; Xavier, Cesar Coelho; Proietti, Fernando Augusto

    2011-12-01

    Researchers in the health field often deal with the problem of incomplete databases. Complete Case Analysis (CCA), which restricts the analysis to subjects with complete data, reduces the sample size and may result in biased estimates. Based on statistical grounds, Multiple Imputation (MI) uses all collected data and is recommended as an alternative to CCA. Data from the study Saúde em Beagá, attended by 4,048 adults from two of nine health districts in the city of Belo Horizonte, Minas Gerais State, Brazil, in 2008-2009, were used to evaluate CCA and different MI approaches in the context of logistic models with incomplete covariate data. Peculiarities in some variables in this study allowed analyzing a situation in which the missing covariate data are recovered and thus the results before and after recovery are compared. Based on the analysis, even the more simplistic MI approach performed better than CCA, since it was closer to the post-recovery results.

  19. Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

    Science.gov (United States)

    Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

    2014-12-01

    This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

  20. Development of a regression model to predict copper toxicity to Daphnia magna and site-specific copper criteria across multiple surface-water drainages in an arid landscape.

    Science.gov (United States)

    Fulton, Barry A; Meyer, Joseph S

    2014-08-01

    The water effect ratio (WER) procedure developed by the US Environmental Protection Agency is commonly used to derive site-specific criteria for point-source metal discharges into perennial waters. However, experience is limited with this method in the ephemeral and intermittent systems typical of arid climates. The present study presents a regression model to develop WER-based site-specific criteria for a network of ephemeral and intermittent streams influenced by nonpoint sources of Cu in the southwestern United States. Acute (48-h) Cu toxicity tests were performed concurrently with Daphnia magna in site water samples and hardness-matched laboratory waters. Median effect concentrations (EC50s) for Cu in site water samples (n=17) varied by more than 12-fold, and the range of calculated WER values was similar. Statistically significant (α=0.05) univariate predictors of site-specific Cu toxicity included (in sequence of decreasing significance) dissolved organic carbon (DOC), hardness/alkalinity ratio, alkalinity, K, and total dissolved solids. A multiple-regression model developed from a combination of DOC and alkalinity explained 85% of the toxicity variability in site water samples, providing a strong predictive tool that can be used in the WER framework when site-specific criteria values are derived. The biotic ligand model (BLM) underpredicted toxicity in site waters by more than 2-fold. Adjustments to the default BLM parameters improved the model's performance but did not provide a better predictive tool compared with the regression model developed from DOC and alkalinity.

  1. Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Lee-Jane W [Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX 77555-1109 (United States); Nishino, Thomas K [Department of Radiology, University of Texas Medical Branch, Galveston, TX 77555-0709 (United States); Khamapirad, Tuenchit [Department of Radiology, University of Texas Medical Branch, Galveston, TX 77555-0709 (United States); Grady, James J [Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX 77555-1109 (United States); Jr, Morton H Leonard [Department of Radiology, University of Texas Medical Branch, Galveston, TX 77555-0709 (United States); Brunder, Donald G [Department of Academic Computing/Academic Resources, University of Texas Medical Branch, Galveston, TX 77555-1035 (United States)

    2007-08-21

    Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R{sup 2} = 0.93) and %-density (R{sup 2} = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.

  2. Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

    Science.gov (United States)

    Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J.; Leonard, Morton H., Jr.; Brunder, Donald G.

    2007-08-01

    Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor-intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2 = 0.93) and %-density (R2 = 0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies.

  3. A comparative study between the use of artificial neural networks and multiple linear regression for caustic concentration prediction in a stage of alumina production

    Directory of Open Access Journals (Sweden)

    Giovanni Leopoldo Rozza

    2015-09-01

    Full Text Available With world becoming each day a global village, enterprises continuously seek to optimize their internal processes to hold or improve their competitiveness and make better use of natural resources. In this context, decision support tools are an underlying requirement. Such tools are helpful on predicting operational issues, avoiding cost risings, loss of productivity, work-related accident leaves or environmental disasters. This paper has its focus on the prediction of spent liquor caustic concentration of Bayer process for alumina production. Caustic concentration measuring is essential to keep it at expected levels, otherwise quality issues might arise. The organization requests caustic concentration by chemical analysis laboratory once a day, such information is not enough to issue preventive actions to handle process inefficiencies that will be known only after new measurement on the next day. Thereby, this paper proposes using Multiple Linear Regression and Artificial Neural Networks techniques a mathematical model to predict the spent liquor´s caustic concentration. Hence preventive actions will occur in real time. Such models were built using software tool for numerical computation (MATLAB and a statistical analysis software package (SPSS. The models output (predicted caustic concentration were compared with the real lab data. We found evidence suggesting superior results with use of Artificial Neural Networks over Multiple Linear Regression model. The results demonstrate that replacing laboratorial analysis by the forecasting model to support technical staff on decision making could be feasible.

  4. Regressing Multiple Viral Plaques and Skin Fragility Syndrome in a Cat Coinfected with FcaPV2 and FcaPV3

    Directory of Open Access Journals (Sweden)

    Alberto Alberti

    2015-01-01

    Full Text Available Feline viral plaques are uncommon skin lesions clinically characterized by multiple, often pigmented, and slightly raised lesions. Numerous reports suggest that papillomaviruses (PVs are involved in their development. Immunosuppressed and immunocompetent cats are both affected, the biological behavior is variable, and the regression is possible but rarely documented. Here we report a case of a FIV-positive cat with skin fragility syndrome and regressing multiple viral plaques in which the contemporary presence of two PV types (FcaPV2 and FcaPV3 was demonstrated by combining a quantitative molecular approach to histopathology. The cat, under glucocorticoid therapy for stomatitis and pruritus, developed skin fragility and numerous grouped slightly raised nonulcerated pigmented macules and plaques with histological features of epidermal thickness, mild dysplasia, and presence of koilocytes. Absolute quantification of the viral DNA copies (4555 copies/microliter of FcaPV2 and 8655 copies/microliter of FcaPV3 was obtained. Eighteen months after discontinuation of glucocorticoid therapy skin fragility and viral plaques had resolved. The role of the two viruses cannot be established and it remains undetermined how each of the viruses has contributed to the onset of VP; the spontaneous remission of skin lesions might have been induced by FIV status change over time due to glucocorticoid withdraw and by glucocorticoids withdraw itself.

  5. The VLT-FLAMES Tarantula Survey. XXII. Multiplicity properties of the B-type stars

    NARCIS (Netherlands)

    Dunstall, P.R.; Dufton, P.L.; Sana, H.; Evans, C.J.; Howarth, I.D.; Simón-Díaz, S.; de Mink, S.E.; Langer, N.; Maíz Apellániz, J.; Taylor, W.D.

    2015-01-01

    We investigate the multiplicity properties of 408 B-type stars observed in the 30 Doradus region of the Large Magellanic Cloud with multi-epoch spectroscopy from the VLT-FLAMES Tarantula Survey (VFTS). We use a cross-correlation method to estimate relative radial velocities from the helium and metal

  6. Quantile regression

    CERN Document Server

    Hao, Lingxin

    2007-01-01

    Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao

  7. Large-scale survey of Chinese precollege students' epistemological beliefs about physics: A progression or a regression?

    Science.gov (United States)

    Zhang, Ping; Ding, Lin

    2013-06-01

    This paper reports a cross-grade comparative study of Chinese precollege students’ epistemological beliefs about physics by using the Colorado Learning Attitudes Survey about Sciences (CLASS). Our students of interest are middle and high schoolers taking traditional lecture-based physics as a mandatory science course each year from the 8th grade to the 12th grade in China. The original CLASS was translated into Mandarin through a rigorous transadaption process, and then it was administered as a pencil-and-paper in-class survey to a total of 1318 students across all the five grade levels (8-12). Our results showed that although in general student epistemological beliefs became less expertlike after receiving more years of traditional instruction (a trend consistent with what was reported in the previous literature), the cross-grade change was not a monotonous decrease. Instead, students at grades 9 and 12 showed a slight positive shift in their beliefs measured by CLASS. Particularly, when compared to the 8th graders, students at the 9th grade demonstrated a significant increase in their views about the conceptual nature of physics and problem-solving sophistication. We hypothesize that both pedagogical and nonpedagogical factors may have contributed to these positive changes. Our results cast light on the complex nature of the relationship between formal instruction and student epistemological beliefs.

  8. Univariate and multiple linear regression analyses for 23 single nucleotide polymorphisms in 14 genes predisposing to chronic glomerular diseases and IgA nephropathy in Han Chinese

    Directory of Open Access Journals (Sweden)

    Hui Wang

    2014-01-01

    Full Text Available Immunoglobulin A nephropathy (IgAN is a complex trait regulated by the inter-action among multiple physiologic regulatory systems and probably involving numerous genes, which leads to inconsistent findings in genetic studies. One possibility of failure to replicate some single-locus results is that the underlying genetics of IgAN nephropathy is based on multiple genes with minor effects. To learn the association between 23 single nucleotide polymorphisms (SNPs in 14 genes predisposing to chronic glomerular diseases and IgAN in Han males, the 23 SNPs genotypes of 21 Han males were detected and analyzed with a BaiO gene chip, and their asso-ciations were analyzed with univariate analysis and multiple linear regression analysis. Analysis showed that CTLA4 rs231726 and CR2 rs1048971 revealed a significant association with IgAN. These findings support the multi-gene nature of the etiology of IgAN and propose a potential gene-gene interactive model for future studies.

  9. Biplots in Reduced-Rank Regression

    NARCIS (Netherlands)

    Braak, ter C.J.F.; Looman, C.W.N.

    1994-01-01

    Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c

  10. The VLA Nascent Disk and Multiplicity Survey of Perseus Protostars (VANDAM). II. Multiplicity of Protostars in the Perseus Molecular Cloud

    CERN Document Server

    Tobin, John J; Li, Zhi-Yun; Chandler, Claire J; Dunham, Michael M; Segura-Cox, Dominique; Sadavoy, Sarah I; Melis, Carl; Harris, Robert J; Kratter, Kaitlin; Perez, Laura

    2016-01-01

    We present a multiplicity study of all known protostars (94) in the Perseus molecular cloud from a Karl G. Jansky Very Large Array (VLA) survey at Ka-band (8 mm and 1 cm) and C-band (4 cm and 6.6 cm). The observed sample has a bolometric luminosity range between 0.1 L$_{\\odot}$ and $\\sim$33 L$_{\\odot}$, with a median of 0.7 L$_{\\odot}$. This multiplicity study is based on the Ka-band data, having a best resolution of $\\sim$0.065" (15 AU) and separations out to $\\sim$43" (10000 AU) can be probed. The overall multiplicity fraction (MF) is found to be of 0.40$\\pm$0.06 and the companion star fraction (CSF) is 0.71$\\pm$0.06. The MF and CSF of the Class 0 protostars are 0.57$\\pm$0.09 and 1.2$\\pm$0.2, and the MF and CSF of Class I protostars are both 0.23$\\pm$0.08. The distribution of companion separations appears bi-modal, with a peak at $\\sim$75 AU and another peak at $\\sim$3000 AU. Turbulent fragmentation is likely the dominant mechanism on $>$1000 AU scales and disk fragmentation is likely to be the dominant mec...

  11. The severity of Minamata disease declined in 25 years: temporal profile of the neurological findings analyzed by multiple logistic regression model.

    Science.gov (United States)

    Uchino, Makoto; Hirano, Teruyuki; Satoh, Hiroshi; Arimura, Kimiyoshi; Nakagawa, Masanori; Wakamiya, Jyunji

    2005-01-01

    Minamata disease (MD) was caused by ingestion of seafood from the methylmercury-contaminated areas. Although 50 years have passed since the discovery of MD, there have been only a few studies on the temporal profile of neurological findings in certified MD patients. Thus, we evaluated changes in neurological symptoms and signs of MD using discriminants by multiple logistic regression analysis. The severity of predictive index declined in 25 years in most of the patients. Only a few patients showed aggravation of neurological findings, which was due to complications such as spino-cerebellar degeneration. Patients with chronic MD aged over 45 years had several concomitant diseases so that their clinical pictures were complicated. It was difficult to differentiate chronic MD using statistically established discriminants based on sensory disturbance alone. In conclusion, the severity of MD declined in 25 years along with the modification by age-related concomitant disorders.

  12. Multiple Regression Analysis of the Variable Component in the Near-Infrared Region for Type 1 AGN MCG+08-11-011

    CERN Document Server

    Tomita, H; Kobayashi, Y; Minezaki, T; Enya, K; Suganuma, M; Aoki, T; Koshida, S; Yamauchi, M; Tomita, Hiroyuki; Yoshii, Yuzuru; Kobayashi, Yukiyasu; Minezaki, Takeo; Enya, Keigo; Suganuma, Masahiro; Aoki, Tsutomu; Koshida, Shintaro; Yamauchi, Masahiro

    2006-01-01

    We propose a new method of analysing a variable component for type 1 active galactic nuclei (AGNs) in the near-infrared wavelength region. This analysis uses a multiple regression technique and divides the variable component into two components originating in the accretion disk at the center of AGNs and from the dust torus that far surrounds the disk. Applying this analysis to the long-term $VHK$ monitoring data of MCG+08-11-011 that were obtained by the MAGNUM project, we found that the $(H-K)$-color temperature of the dust component is $T = 1635$K $\\pm20$K, which agrees with the sublimation temperature of dust grains, and that the time delay of $K$ to $H$ variations is $\\Delta t\\approx 6$ days, which indicates the existence of a radial temperature gradient in the dust torus. As for the disk component, we found that the power-law spectrum of $f_\

  13. A comparative analysis of the effects of instructional design factors on student success in e-learning: multiple-regression versus neural networks

    Directory of Open Access Journals (Sweden)

    Halil Ibrahim Cebeci

    2009-12-01

    Full Text Available This study explores the relationship between the student performance and instructional design. The research was conducted at the E-Learning School at a university in Turkey. A list of design factors that had potential influence on student success was created through a review of the literature and interviews with relevant experts. From this, the five most import design factors were chosen. The experts scored 25 university courses on the extent to which they demonstrated the chosen design factors. Multiple-regression and supervised artificial neural network (ANN models were used to examine the relationship between student grade point averages and the scores on the five design factors. The results indicated that there is no statistical difference between the two models. Both models identified the use of examples and applications as the most influential factor. The ANN model provided more information and was used to predict the course-specific factor values required for a desired level of success.

  14. Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

    Science.gov (United States)

    Whitlock, C. H., III

    1977-01-01

    Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

  15. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

    Science.gov (United States)

    Azadi, Sama; Karimi-Jashni, Ayoub

    2016-02-01

    Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate.

  16. A modified parallel constitutive model for elevated temperature flow behavior of Ti-6Al-4V alloy based on multiple regression

    Energy Technology Data Exchange (ETDEWEB)

    Cai, Jun; Shi, Jiamin; Wang, Kuaishe; Wang, Wen; Wang, Qingjuan; Liu, Yingying [Xi' an Univ. of Architecture and Technology, Xi' an (China). School of Metallurgical Engineering; Li, Fuguo [Northwestern Polytechnical Univ., Xi' an (China). School of Materials Science and Engineering

    2017-07-15

    Constitutive analysis for hot working of Ti-6Al-4V alloy was carried out by using experimental stress-strain data from isothermal hot compression tests. A new kind of constitutive equation called a modified parallel constitutive model was proposed by considering the independent effects of strain, strain rate and temperature. The predicted flow stress data were compared with the experimental data. Statistical analysis was introduced to verify the validity of the developed constitutive equation. Subsequently, the accuracy of the proposed constitutive equations was evaluated by comparing with other constitutive models. The results showed that the developed modified parallel constitutive model based on multiple regression could predict flow stress of Ti-6Al-4V alloy with good correlation and generalization.

  17. Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

    Science.gov (United States)

    Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

    2016-11-01

    The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

  18. Quantifying TiO2 Abundance of Lunar Soils:Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect

    Institute of Scientific and Technical Information of China (English)

    Lin Li

    2011-01-01

    Partial least squares (PLS) regression was applied to the Lunar Soil Characterization Consortium (LSCC) dataset for spectral estimation of TiO2.The LSCC dataset was split into a number of subsets including the low-Ti,high-Ti,total mare soils,total highland,Apollo 16,and Apollo 14 soils to investigete the effects of interfering minerals and nonlinearity on the PLS performance.The PLS weight loading vectors were analyzed through stepwise multiple regression analysis (SMRA) to identify mineral species driving and interfering the PLS performance.PLS exhibits high performance for estimating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together.The results suggest that while the dominant TiO2-bearing minerals are few,additional PLS factors are required to compensate the effects on the important PLS factors of minerals that are not highly corrected to TiO2,to accommodate nonlinear relationships between reflectance and TiO2,and to correct inconsistent mineral-TiO2 correlations between the high-Ti and iow-Ti mare samples.Analysis of the LSCC highland soil samples indicates that the Apollo 16 soils are responsible for the large errors of TiO2 estimates when the soils are modeled with other subgroups.For the LSCC Apollo 16 samples,the dominant spectral effects of plagioclase over other dark minerals are primarily responsible for large errors of estimated TiO2.For the Apollo 14 soils,more accurate estimation for TiO2 is attributed to the positive correlation between a major TiO2-bearing component and TiO2,explaining why the Apollo 14 soils follow the regression trend when analyzed with other soils groups.

  19. Assessing the Determinants of Renewable Electricity Acceptance Integrating Meta-Analysis Regression and a Local Comprehensive Survey

    Directory of Open Access Journals (Sweden)

    Simona Bigerna

    2015-08-01

    Full Text Available In dealing with renewable electricity (RE, individuals are involved both as end-consumers on the demand side and as stakeholders (citizens in the local production process on the supply side. Empirical evidence shows that in many countries, consumers are willing to pay a significant amount to facilitate adoption of RE. By contrast, environmental externalities are often the cause of strong opposition to RE adoption if local communities are involved as stakeholders in wind, solar or biomass investment projects. Looking at the literature on willingness to pay and on willingness to accept, we have investigated RE acceptance mechanisms. First, we have used the meta-analysis to assess the major determinants of RE acceptance on both demand and supply sides. Meta-analysis has provided some insights useful for managing field research on an onshore wind farm enlargement project located in the Umbria region. Meta-analysis and survey results confirm that the local community plays a central role in local RE acceptance. Furthermore, people who have previous experience with windmills require less compensation, or are willing to pay more, for RE development. Results suggest that these attributes should be included in future research to improve understanding of determinants of RE acceptance.

  20. Multiple Linear Regression Analysis Indicates Association of P-Glycoprotein Substrate or Inhibitor Character with Bitterness Intensity, Measured with a Sensor.

    Science.gov (United States)

    Yano, Kentaro; Mita, Suzune; Morimoto, Kaori; Haraguchi, Tamami; Arakawa, Hiroshi; Yoshida, Miyako; Yamashita, Fumiyoshi; Uchida, Takahiro; Ogihara, Takuo

    2015-09-01

    P-glycoprotein (P-gp) regulates absorption of many drugs in the gastrointestinal tract and their accumulation in tumor tissues, but the basis of substrate recognition by P-gp remains unclear. Bitter-tasting phenylthiocarbamide, which stimulates taste receptor 2 member 38 (T2R38), increases P-gp activity and is a substrate of P-gp. This led us to hypothesize that bitterness intensity might be a predictor of P-gp-inhibitor/substrate status. Here, we measured the bitterness intensity of a panel of P-gp substrates and nonsubstrates with various taste sensors, and used multiple linear regression analysis to examine the relationship between P-gp-inhibitor/substrate status and various physical properties, including intensity of bitter taste measured with the taste sensor. We calculated the first principal component analysis score (PC1) as the representative value of bitterness, as all taste sensor's outputs shared significant correlation. The P-gp substrates showed remarkably greater mean bitterness intensity than non-P-gp substrates. We found that Km value of P-gp substrates were correlated with molecular weight, log P, and PC1 value, and the coefficient of determination (R(2) ) of the linear regression equation was 0.63. This relationship might be useful as an aid to predict P-gp substrate status at an early stage of drug discovery.

  1. Development and application of a multiple linear regression model to consider the impact of weekly waste container capacity on the yield from kerbside recycling programmes in Scotland.

    Science.gov (United States)

    Baird, Jim; Curry, Robin; Reid, Tim

    2013-03-01

    This article describes the development and application of a multiple linear regression model to identify how the key elements of waste and recycling infrastructure, namely container capacity and frequency of collection, affect the yield from municipal kerbside recycling programmes. The overall aim of the research was to gain an understanding of the factors affecting the yield from municipal kerbside recycling programmes in Scotland with an underlying objective to evaluate the efficacy of the model as a decision-support tool for informing the design of kerbside recycling programmes. The study isolates the principal kerbside collection service offered by all 32 councils across Scotland, eliminating those recycling programmes associated with flatted properties or multi-occupancies. The results of the regression analysis model have identified three principal factors which explain 80% of the variability in the average yield of the principal dry recyclate services: weekly residual waste capacity, number of materials collected and the weekly recycling capacity. The use of the model has been evaluated and recommendations made on ongoing methodological development and the use of the results in informing the design of kerbside recycling programmes. We hope that the research can provide insights for the further development of methods to optimise the design and operation of kerbside recycling programmes.

  2. An improved approach for measuring the impact of multiple CO2 conductances on the apparent photorespiratory CO2 compensation point through slope-intercept regression.

    Science.gov (United States)

    Walker, Berkley J; Skabelund, Dane C; Busch, Florian A; Ort, Donald R

    2016-06-01

    Biochemical models of leaf photosynthesis, which are essential for understanding the impact of photosynthesis to changing environments, depend on accurate parameterizations. One such parameter, the photorespiratory CO2 compensation point can be measured from the intersection of several CO2 response curves measured under sub-saturating illumination. However, determining the actual intersection while accounting for experimental noise can be challenging. Additionally, leaf photosynthesis model outcomes are sensitive to the diffusion paths of CO2 released from the mitochondria. This diffusion path of CO2 includes both chloroplastic as well as cell wall resistances to CO2 , which are not readily measurable. Both the difficulties of determining the photorespiratory CO2 compensation point and the impact of multiple intercellular resistances to CO2 can be addressed through application of slope-intercept regression. This technical report summarizes an improved framework for implementing slope-intercept regression to evaluate measurements of the photorespiratory CO2 compensation point. This approach extends past work to include the cases of both Rubisco and Ribulose-1,5-bisphosphate (RuBP)-limited photosynthesis. This report further presents two interactive graphical applications and a spreadsheet-based tool to allow users to apply slope-intercept theory to their data.

  3. Hand washing behavior and associated factors in Vietnam based on the Multiple Indicator Cluster Survey, 2010-2011.

    Science.gov (United States)

    To, Kien Gia; Lee, Jong-Koo; Nam, You-Seon; Trinh, Oanh Thi Hoang; Van Do, Dung

    2016-01-01

    Handwashing is a cost-effective way of preventing communicable diseases such as respiratory and food-borne illnesses. However, handwashing rates are low in developing countries. Target 7C of the seventh Millennium Development Goals was to increase by half the proportion of people with sustainable access to safe drinking water and basic sanitation by 2015. Studies have found that better access to improved water sources and sanitation is associated with higher rates of handwashing. Our goal was to describe handwashing behaviour and identify the associated factors in Vietnamese households. Data from 12,000 households participating in the Vietnam Multiple Indicator Cluster Survey 2011 were used. The survey used a multistage sampling method to randomly select 100 clusters and 20 households per cluster. Self-administered questionnaires were used to collect data from a household representative. Demographic variables, the presence of a specific place for handwashing, soap and water, access to improved sanitation, and access to improved water sources were tested for association with handwashing behaviour in logistic regression. Almost 98% of households had a specific place for handwashing, and 85% had cleansing materials and water at such a place. The prevalence of handwashing in the sample was almost 85%. Educational level, ethnicity of the household head, and household wealth were factors associated with handwashing practice (psoap and water at handwashing sites should be increased and practical teaching programs should be deployed in order to increase handwashing rates.

  4. Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

    Science.gov (United States)

    Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

    2015-01-01

    Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

  5. Exploring the equity of GP practice prescribing rates for selected coronary heart disease drugs: a multiple regression analysis with proxies of healthcare need

    Directory of Open Access Journals (Sweden)

    St Leger Antony S

    2005-02-01

    Full Text Available Abstract Background There is a small, but growing body of literature highlighting inequities in GP practice prescribing rates for many drug therapies. The aim of this paper is to further explore the equity of prescribing for five major CHD drug groups and to explain the amount of variation in GP practice prescribing rates that can be explained by a range of healthcare needs indicators (HCNIs. Methods The study involved a cross-sectional secondary analysis in four primary care trusts (PCTs 1–4 in the North West of England, including 132 GP practices. Prescribing rates (average daily quantities per registered patient aged over 35 years and HCNIs were developed for all GP practices. Analysis was undertaken using multiple linear regression. Results Between 22–25% of the variation in prescribing rates for statins, beta-blockers and bendrofluazide was explained in the multiple regression models. Slightly more variation was explained for ACE inhibitors (31.6% and considerably more for aspirin (51.2%. Prescribing rates were positively associated with CHD hospital diagnoses and procedures for all drug groups other than ACE inhibitors. The proportion of patients aged 55–74 years was positively related to all prescribing rates other than aspirin, where they were positively related to the proportion of patients aged >75 years. However, prescribing rates for statins and ACE inhibitors were negatively associated with the proportion of patients aged >75 years in addition to the proportion of patients from minority ethnic groups. Prescribing rates for aspirin, bendrofluazide and all CHD drugs combined were negatively associated with deprivation. Conclusion Although around 25–50% of the variation in prescribing rates was explained by HCNIs, this varied markedly between PCTs and drug groups. Prescribing rates were generally characterised by both positive and negative associations with HCNIs, suggesting possible inequities in prescribing rates on the basis

  6. Persistence of Multiple Tumor-Specific T-Cell Clones Is Associated with Complete Tumor Regression in a Melanoma Patient Receiving Adoptive Cell Transfer Therapy

    Science.gov (United States)

    Zhou, Juhua; Dudley, Mark E.; Rosenberg, Steven A.; Robbins, Paul F.

    2007-01-01

    Summary The authors recently reported that adoptive immunotherapy with autologous tumor-reactive tumor infiltrating lymphocytes (TILs) immediately following a conditioning nonmyeloablative chemotherapy regimen resulted in an enhanced clinical response rate in patients with metastatic melanoma. These observations led to the current studies, which are focused on a detailed analysis of the T-cell antigen reactivity as well as the in vivo persistence of T cells in melanoma patient 2098, who experienced a complete regression of all metastatic lesions in lungs and soft tissues following therapy. Screening of an autologous tumor cell cDNA library using transferred TILs resulted in the identification of novel mutated growth arrest-specific gene 7 (GAS7) and glyceral-dehyde-3-phosphate dehydrogenase (GAPDH) gene transcripts. Direct sequence analysis of the expressed T-cell receptor beta chain variable regions showed that the transferred TILs contained multiple T-cell clonotypes, at least six of which persisted in peripheral blood for a month or more following transfer. The persistent T cells recognized both the mutated GAS7 and GAPDH. These persistent tumor-reactive T-cell clones were detected in tumor cell samples obtained from the patient following adoptive cell transfer and appeared to be represented at higher levels in the tumor sample obtained 1 month following transfer than in the peripheral blood obtained at the same time. Overall, these results indicate that multiple tumor-reactive T cells can persist in the peripheral blood and at the tumor site for prolonged times following adoptive transfer and thus may be responsible for the complete tumor regression in this patient. PMID:15614045

  7. A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

    Directory of Open Access Journals (Sweden)

    Chunggil Jung

    2017-08-01

    Full Text Available This study attempts to estimate spatial soil moisture in South Korea (99,000 km2 from January 2013 to December 2015 using a multiple linear regression (MLR model and the Terra moderate-resolution imaging spectroradiometer (MODIS land surface temperature (LST and normalized distribution vegetation index (NDVI data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs of the Korea Meteorological Administration (KMA, and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM technique and observed LST data from 71 KMA stations. The coefficient of determination (R2 of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI can be used to better understand the severity of droughts with the

  8. The transition to early fatherhood: National estimates based on multiple surveys

    Directory of Open Access Journals (Sweden)

    H. Elizabeth Peters

    2008-04-01

    Full Text Available This study provides systematic information about the prevalence of early male fertility and the relationship between family background characteristics and early parenthood across three widely used data sources: the 1979 and 1997 National Longitudinal Surveys of Youth and the 2002 National Survey of Family Growth. We provide descriptive statistics on early fertility by age, sex, race, cohort, and data set. Because each data set includes birth cohorts with varying early fertility rates, prevalence estimates for early male fertility are relatively similar across data sets. Associations between background characteristics and early fertility in regression models are less consistent across data sets. We discuss the implications of these findings for scholars doing research on early male fertility.

  9. Determining the Relationship between U.S. County-Level Adult Obesity Rate and Multiple Risk Factors by PLS Regression and SVM Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Chau-Kuang Chen

    2015-02-01

    Full Text Available Data from the Center for Disease Control (CDC has shown that the obesity rate doubled among adults within the past two decades. This upsurge was the result of changes in human behavior and environment. Partial least squares (PLS regression and support vector machine (SVM models were conducted to determine the relationship between U.S. county-level adult obesity rate and multiple risk factors. The outcome variable was the adult obesity rate. The 23 risk factors were categorized into four domains of the social ecological model including biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the top eight significant risk factors with high normalized importance were identified including physical inactivity, natural amenity, percent of households receiving SNAP benefits, and percent of all restaurants being fast food. The study results were consistent with those in the literature. The study showed that adult obesity rate was influenced by biological/behavioral factor, socioeconomic status, food environment, and physical environment embedded in the social ecological theory. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.

  10. Regression Basics

    CERN Document Server

    Kahane, Leo H

    2007-01-01

    Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:

  11. The AstraLux Multiplicity Survey: Extension to Late M-dwarfs

    CERN Document Server

    Janson, Markus; Brandner, Wolfgang; Kudryavtseva, Natalia; Hormuth, Felix; Hippler, Stefan; Henning, Thomas

    2014-01-01

    The distribution of multiplicity among low-mass stars is a key issue to understanding the formation of stars and brown dwarfs, and recent surveys have yielded large enough samples of nearby low-mass stars to study this issue statistically to good accuracy. Previously, we have presented a multiplicity study of ~700 early/mid M-type stars observed with the AstraLux high-resolution Lucky Imaging cameras. Here, we extend the study of multiplicity in M-type stars through studying 286 nearby mid/late M-type stars, bridging the gap between our previous study and multiplicity studies of brown dwarfs. Most of the targets have been observed more than once, allowing us to assess common proper motion to confirm companionship. We detect 68 confirmed or probable companions in 66 systems, of which 41 were previously undiscovered. Detections are made down to the resolution limit of ~100 mas of the instrument. The raw multiplicity in the AstraLux sensitivity range is 17.9%, leading to a total multiplicity fraction of 21-27% d...

  12. THE VLA NASCENT DISK AND MULTIPLICITY SURVEY OF PERSEUS PROTOSTARS (VANDAM). II. MULTIPLICITY OF PROTOSTARS IN THE PERSEUS MOLECULAR CLOUD

    Energy Technology Data Exchange (ETDEWEB)

    Tobin, John J.; Harris, Robert J. [Leiden Observatory, Leiden University, P.O. Box 9513, 2300-RA Leiden (Netherlands); Looney, Leslie W.; Segura-Cox, Dominique [Department of Astronomy, University of Illinois, Urbana, IL 61801 (United States); Li, Zhi-Yun [Department of Astronomy, University of Virginia, Charlottesville, VA 22903 (United States); Chandler, Claire J.; Perez, Laura [National Radio Astronomy Observatory, P.O. Box O, Socorro, NM 87801 (United States); Dunham, Michael M. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS 78, Cambridge, MA 02138 (United States); Sadavoy, Sarah I. [Max-Planck-Institut für Astronomie, D-69117 Heidelberg (Germany); Melis, Carl [Center for Astrophysics and Space Sciences, University of California, San Diego, CA 92093 (United States); Kratter, Kaitlin, E-mail: tobin@strw.leidenuniv.nl [University of Arizona, Steward Observatory, Tucson, AZ 85721 (United States)

    2016-02-10

    We present a multiplicity study of all known protostars (94) in the Perseus molecular cloud from a Karl G. Jansky Very Large Array survey at Ka-band (8 mm and 1 cm) and C-band (4 and 6.6 cm). The observed sample has a bolometric luminosity range between 0.1 L{sub ⊙} and ∼33 L{sub ⊙}, with a median of 0.7 L{sub ⊙}. This multiplicity study is based on the Ka-band data, having a best resolution of ∼0.″065 (15 au) and separations out to ∼43″ (10,000 au) can be probed. The overall multiplicity fraction (MF) is found to be 0.40 ± 0.06 and the companion star fraction (CSF) is 0.71 ± 0.06. The MF and CSF of the Class 0 protostars are 0.57 ± 0.09 and 1.2 ± 0.2, and the MF and CSF of Class I protostars are both 0.23 ± 0.08. The distribution of companion separations appears bi-modal, with a peak at ∼75 au and another peak at ∼3000 au. Turbulent fragmentation is likely the dominant mechanism on >1000 au scales and disk fragmentation is likely to be the dominant mechanism on <200 au scales. Toward three Class 0 sources we find companions separated by <30 au. These systems have the smallest separations of currently known Class 0 protostellar binary systems. Moreover, these close systems are embedded within larger (50–400 au) structures and may be candidates for ongoing disk fragmentation.

  13. 车削温度多元回归模型的试验研究%Testing study of multiple regression model based on turning temperature

    Institute of Scientific and Technical Information of China (English)

    杨睿; 李顺才; 袁冠雷; 吴明明

    2016-01-01

    Using control variable method,the turning tests were conducted on the work-piece under different parameters,and the real-time temperatures near the turning point of the cutting tool were collected by using infrared thermometry. By the tests,the temperature-time curve and mean temperature under different parameters were obtained,and the multiple regression model of the mean temperature on turning speed,feed rate,back cutting depth was also established based on the least square method.This article analyzes the correlation between the mean turning temperatures with three parameters,and the measured temperature values are compared with the fitted values.The research shows that under the given condition,the regression model can better predict the mean cutting temperature based on the turning parameters.%采用控制变量法对工件进行不同车削参数下的车削试验,利用红外测温仪实时采集车刀刀尖附近的温度,得到不同车削参量下的温度-时间曲线及温度均值。基于最小二乘法拟合得到了车削温度均值关于车削速度、进给速度、背吃刀量的多元回归模型,分析了车削温度均值与3个车削参数的相关性。研究表明,在给定车削条件下该回归模型能基于车削参数较好地预测车刀温度均值。

  14. Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

    Science.gov (United States)

    Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

    2014-06-01

    Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

  15. Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

    Science.gov (United States)

    Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

    2016-06-01

    Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

  16. Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran

    Directory of Open Access Journals (Sweden)

    Maryam Khodadadi

    2016-06-01

    Full Text Available Background: Data mining (DM is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR and neural network (NN models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD5], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity as the regression input vectors. Models were adjusted to input attributes, effluent BOD5 (BODout and COD (CODout. The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC plot and other statistical measures such as relative absolute error (RAE. Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout indicate different performances and the former was better (P < 0.05 for the prediction of both effluent BOD5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better

  17. A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*. An Application in Joint Prediction of Appointment Miss-opportunities across Multiple Clinics.

    Science.gov (United States)

    Alaeddini, Adel; Hong, Seung Hee

    2017-08-11

    Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics. An extension of L1 / L2 regularization is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm. A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics. The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be

  18. Averaging kernel prediction from atmospheric and surface state parameters based on multiple regression for nadir-viewing satellite measurements of carbon monoxide and ozone

    Directory of Open Access Journals (Sweden)

    H. M. Worden

    2013-07-01

    Full Text Available A current obstacle to the observation system simulation experiments (OSSEs used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs. We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO and ozone (O3 based on actual retrievals from MOPITT (Measurements Of Pollution In The Troposphere on the Earth Observing System (EOS-Terra satellite and TES (Tropospheric Emission Spectrometer and OMI (Ozone Monitoring Instrument on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMRs, solar zenith angle, water vapor amount, etc. We first compute the singular value decomposition (SVD for individual cloud-free AKs and retain the first three ranked singular vectors in order to fit the most significant orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of test retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true retrieval AKs from the test set. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs

  19. Multiple sex partner behavior in female undergraduate students in China: A multi-campus survey

    Directory of Open Access Journals (Sweden)

    Zhang Miaoxuan

    2009-08-01

    Full Text Available Abstract Background China is realizing increases in women engaged in premarital sex and multiple sex partner behavior. Our aim was to examine prevalence and determinants of multiple sex partner behavior among female undergraduates in China. Methods Anonymously completed questionnaires were received from 4,769 unmarried female undergraduates, recruited using randomized cluster sampling by type of university and students' major and grade. Items captured demographic, family, peer and work influence, and student factors (major, academic performance, and sex-related knowledge and attitudes. To examine risk factors for sexual behaviors, we used multi-level logistic regression, yielding odds ratios (OR and 95% confidence intervals (95% CI. Results Of 4,769 female students, 863 (18.10% reported ever having sexual intercourse, and 5.31% reported having multiple sex partners (29.32% of all women having sexual intercourse. Several demographic, family, peer and work influences, and student factors (including major, performance, knowledge, and attitude toward sex were risk factors for ever having sex. However, risk factors for multiple sex partners only included working in a place of entertainment, having current close friends that were living with boyfriends, poor academic performance, and positive attitudes toward multiple partners. These women also were more likely to practice masturbation, start having sex at a younger age, have sex with married men and/or men not their "boyfriends" at first coitus, and not use condoms consistently. Conclusion A small but important subset of Chinese female undergraduates is engaged in unprotected sex with multiple sex partners. Interventions need to target at risk women, stressing the importance of consistent condom use.

  20. Dentist and practice characteristics associated with restorative treatment of enamel caries in permanent teeth: multiple-regression modeling of observational clinical data from The National Dental PBRN

    Science.gov (United States)

    Fellows, Jeffrey L; Gordan, Valeria V.; Gilbert, Gregg H.; Rindal, D. Brad; Qvist, Vibeke; Litaker, Mark S.; Benjamin, Paul; Flink, Håkan; Pihlstrom, Daniel J.; Johnson, Neil

    2014-01-01

    Purpose Current evidence in dentistry recommends non-surgical treatment to manage enamel caries lesions. However, surveyed practitioners report they would restore enamel lesions that are confined to the enamel. We used actual clinical data to evaluate patient, dentist, and practice characteristics associated with restoration of enamel caries, while accounting for other factors. Methods We combined data from a National Dental Practice-Based Research Network observational study of consecutive restorations placed in previously unrestored permanent tooth surfaces and practice/demographic data from 229 participating network dentists. Analysis of variance and logistic regression, using generalized estimating equations (GEE) and variable selection within blocks, were used to test the hypothesis that patient, dentist, and practice characteristics were associated with variations in enamel restorations of occlusal and proximal caries compared to dentin lesions, accounting for dentist and patient clustering. Results Network dentists from 5 regions placed 6,891 restorations involving occlusal and/or proximal caries lesions. Enamel restorations accounted for 16% of enrolled occlusal caries lesions and 6% of enrolled proximal caries lesions. Enamel occlusal restorations varied significantly (pEnamel proximal restorations varied significantly (penamel caries restorations can guide strategies to improve provider adherence to evidence-based clinical recommendations. PMID:25000667

  1. Prevalence and influential factors of erectile dysfunction in male renal transplant recipients: a multiple center survey

    Institute of Scientific and Technical Information of China (English)

    TIAN Ye; DU Lin-dong; AO Jian-hua; MA Lin-lin; CHEN Li-sheng; XU Yuan-cheng; JI Zheng-guo; TANG Ya-wang; ZHANG Lei; L(U) Wen-cheng; LIN Jun; GUO Hong-bo; XIE Ze-lin; SUN Wen

    2008-01-01

    .000, respectively). After Logistic regression analysis, only five factors, age, education level, interruption of arteria iliaca interna distal end, worrying transplanted kidney function impacted by sexual life and CsA-based immunosuppressive regimens sustained their significance.Conclusions Renal transplant has varying effects on erectile function. ED is highly prevalent among RTRs and its influential factors are multiple. Age, education level, interruption of arteria iliaca interna distal end, worrying transplanted kidney function impacted by sexual life, CsA-based immunosuppressive regimens are the main influential factors of ED in male RTRs.

  2. Application of Multiple Linear Regression and Extended Principal-Component Analysis to Determination of the Acid Dissociation Constant of 7-Hydroxycoumarin in Water/AOT/Isooctane Reverse Micelles.

    Science.gov (United States)

    Caselli; Daniele; Mangone; Paolillo

    2000-01-15

    The apparent pK(a) of dyes in water-in-oil microemulsions depends on the charge of the acid and base forms of the buffers present in the water pool. Extended principal-component analysis allows the precise determination of the apparent pK(a) and of the spectra of the acid and base forms of the dye. Combination with multiple linear regression increases the precision. The pK(a) of 7-hydroxycoumarin (umbelliferone) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at various different water/surfactant ratios. The spectra of the acid and base forms of the dye in the microemulsion are very similar to those in bulk water in the presence of Tris and ammonia. The presence of carbonate changes somewhat the spectrum of the acid form. Results are discussed taking into account the profile of the electrostatic potential drop in the water pool and the possible partition of umbelliferone between the aqueous core and the surfactant. The pK(a) values corrected for these effects are independent of w(0) and are close to the value of the pK(a) in bulk water. Copyright 2000 Academic Press.

  3. Elaborate ligand-based modeling coupled with multiple linear regression and k nearest neighbor QSAR analyses unveiled new nanomolar mTOR inhibitors.

    Science.gov (United States)

    Khanfar, Mohammad A; Taha, Mutasem O

    2013-10-28

    The mammalian target of rapamycin (mTOR) has an important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is a clinically validated target for cancer therapy. In this study, we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated an IC50 value of 48 nM.

  4. Metabolic activity of tree saps of different origin towards cultured human cells in the light of grade correspondence analysis and multiple regression modeling

    Directory of Open Access Journals (Sweden)

    Artur Wnorowski

    2017-06-01

    Full Text Available Tree saps are nourishing biological media commonly used for beverage and syrup production. Although the nutritional aspect of tree saps is widely acknowledged, the exact relationship between the sap composition, origin, and effect on the metabolic rate of human cells is still elusive. Thus, we collected saps from seven different tree species and conducted composition-activity analysis. Saps from trees of Betulaceae, but not from Salicaceae, Sapindaceae, nor Juglandaceae families, were increasing the metabolic rate of HepG2 cells, as measured using tetrazolium-based assay. Content of glucose, fructose, sucrose, chlorides, nitrates, sulphates, fumarates, malates, and succinates in sap samples varied across different tree species. Grade correspondence analysis clustered trees based on the saps’ chemical footprint indicating its usability in chemotaxonomy. Multiple regression modeling showed that glucose and fumarate present in saps from silver birch (Betula pendula Roth., black alder (Alnus glutinosa Gaertn., and European hornbeam (Carpinus betulus L. are positively affecting the metabolic activity of HepG2 cells.

  5. Determination of the acid dissociation constant of bromocresol green and cresol red in water/AOT/isooctane reverse micelles by multiple linear regression and extended principal component analysis.

    Science.gov (United States)

    Caselli, Maurizio; Mangone, Annarosa; Paolillo, Paola; Traini, Angela

    2002-01-01

    The pKa of 3',3",5',5"tetrabromo-m-cresolsulfonephtalein (Bromocresol Green) and o-cresolsulphonephtalein (Cresol Red) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at different water/surfactant ratios. Extended Principal Component Analysis was used for a precise determination of the apparent pKa and of the spectra of the acid and base forms of the dye. The apparent pKa of dyes in water-in-oil microemulsions depends on the charge of the acid and base forms of the buffers present in the water pool. Combination with multiple linear regression increases the precision. Results are discussed taking into account the profile of the electrostatic potential in the water pool and the possible partition of the indicator between the aqueous core and the surfactant. The pKa corrected for these effects are independent of w0 and are close to the value of the pKa in bulk water. On the basis of a tentative hypothesis it is possible to calculate the true pKa of the buffer in the pool.

  6. Using multiple regression, Bayesian networks and artificial neural networks for prediction of total egg production in European quails based on earlier expressed phenotypes.

    Science.gov (United States)

    Felipe, Vivian P S; Silva, Martinho A; Valente, Bruno D; Rosa, Guilherme J M

    2015-04-01

    The prediction of total egg production (TEP) potential in poultry is an important task to aid optimized management decisions in commercial enterprises. The objective of the present study was to compare different modeling approaches for prediction of TEP in meat type quails (Coturnix coturnix coturnix) using phenotypes such as weight, weight gain, egg production and egg quality measurements. Phenotypic data on 30 traits from two lines (L1, n=180; and L2, n=205) of quail were modeled to predict TEP. Prediction models included multiple linear regression and artificial neural network (ANN). Moreover, Bayesian network (BN) and a stepwise approach were used as variable selection methods. BN results showed that TEP is independent from other earlier expressed traits when conditioned on egg production from 35 to 80 days of age (EP1). In addition, the prediction accuracy was much lower when EP1 was not included in the model. The best predictive model was ANN, after feature selection, showing prediction correlations of r=0.792 and r=0.714 for L1 and L2, respectively. In conclusion, machine learning methods may be useful, but reasonable prediction accuracies are obtained only when partial egg production measurements are included in the model.

  7. Clearness index in cloudy days estimated with meteorological information by multiple regression analysis; Kisho joho wo riyoshita kaiki bunseki ni yoru dontenbi no seiten shisu no suitei

    Energy Technology Data Exchange (ETDEWEB)

    Nakagawa, S. [Maizuru National College of Technology, Kyoto (Japan); Kenmoku, Y.; Sakakibara, T. [Toyohashi University of Technology, Aichi (Japan); Kawamoto, T. [Shizuoka University, Shizuoka (Japan). Faculty of Engineering

    1996-10-27

    Study is under way for a more accurate solar radiation quantity prediction for the enhancement of solar energy utilization efficiency. Utilizing the technique of roughly estimating the day`s clearness index from forecast weather, the forecast weather (constituted of weather conditions such as `clear,` `cloudy,` etc., and adverbs or adjectives such as `afterward,` `temporary,` and `intermittent`) has been quantified relative to the clearness index. This index is named the `weather index` for the purpose of this article. The error high in rate in the weather index relates to cloudy days, which means a weather index falling in 0.2-0.5. It has also been found that there is a high correlation between the clearness index and the north-south wind direction component. A multiple regression analysis has been carried out, under the circumstances, for the estimation of clearness index from the maximum temperature and the north-south wind direction component. As compared with estimation of the clearness index on the basis only of the weather index, estimation using the weather index and maximum temperature achieves a 3% improvement throughout the year. It has also been learned that estimation by use of the weather index and north-south wind direction component enables a 2% improvement for summer and a 5% or higher improvement for winter. 2 refs., 6 figs., 4 tabs.

  8. Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

    Science.gov (United States)

    Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

    2016-12-01

    The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

  9. Effect of child marriage on girls' school dropout in Nepal: Analysis of data from the Multiple Indicator Cluster Survey 2014

    Science.gov (United States)

    Sekine, Kazutaka; Hodgkin, Marian Ellen

    2017-01-01

    School dropout and child marriage are interrelated outcomes that have an enormous impact on adolescent girls. However, the literature reveals gaps in the empirical evidence on the link between child marriage and the dropout of girls from school. This study identifies the ‘tipping point’ school grades in Nepal when the risk of dropout due to marriage is highest, measures the effect of child marriage on girls’ school dropout rates, and assesses associated risk factors. Weighted percentages were calculated to examine the grades at highest risk and the distribution of reasons for discontinuing school. Using the Nepal Multiple Indicator Cluster Survey (MICS) 2014 data, we estimated the effect of marriage on school attendance and dropout among girls aged 15–17 by constructing logistic regression models. A multivariate logistic regression model was used to assess risk factors of school dropout due to child marriage. It was found that early marriage is the most common reason given for leaving school. Overall, the risk of school dropout due to marriage heightens after girls complete the fifth or sixth grade. The risk of girls’ dropping out peaks in the seventh and eighth grades and remains noteworthy in the ninth and tenth grades. Married girls in Nepal are 10 times more likely to drop out than their unmarried peers. Little or no education of the household head, belonging to the Kirat religion, and membership of a traditionally disadvantaged social class each elevate the risk of school dropout due to early marriage. The findings underscore the need to delay girl’s marriage so as to reduce girls’ school dropout in Nepal. School-based programmes aimed at preventing child marriage should target girls from the fifth grade because they are at increased risk of dropping out, as well as prioritizing girls from disadvantaged groups. PMID:28727793

  10. Multiple imputation to evaluate the impact of an assay change in national surveys.

    Science.gov (United States)

    Sternberg, Maya

    2017-07-30

    National health surveys, such as the National Health and Nutrition Examination Survey, are used to monitor trends of nutritional biomarkers. These surveys try to maintain the same biomarker assay over time, but there are a variety of reasons why the assay may change. In these cases, it is important to evaluate the potential impact of a change so that any observed fluctuations in concentrations over time are not confounded by changes in the assay. To this end, a subset of stored specimens previously analyzed with the old assay is retested using the new assay. These paired data are used to estimate an adjustment equation, which is then used to 'adjust' all the old assay results and convert them into 'equivalent' units of the new assay. In this paper, we present a new way of approaching this problem using modern statistical methods designed for missing data. Using simulations, we compare the proposed multiple imputation approach with the adjustment equation approach currently in use. We also compare these approaches using real National Health and Nutrition Examination Survey data for 25-hydroxyvitamin D. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

  11. Multiple populations in Galactic globular clusters: a survey in the Strömgren system

    DEFF Research Database (Denmark)

    Alonso-García, J.; Catelan, M.; Amigo, P.

    2013-01-01

    We are coming to believe that stellar populations in globular clusters are not as simple as they were once thought to be. A growing amount of photometric and spectroscopic evidence shows that globular clusters host at least two different stellar populations. In our contribution to these proceedings...... we present the first results of a survey we are conducting to look for the presence of multiple populations in a significant number of Galactic globular clusters, using the Strömgren system. We intend to photometrically separate these populations and characterize their radial distributions...

  12. Autistic Regression

    Science.gov (United States)

    Matson, Johnny L.; Kozlowski, Alison M.

    2010-01-01

    Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…

  13. Symptoms and Association with Health Outcomes in Relapsing-Remitting Multiple Sclerosis: Results of a US Patient Survey

    Directory of Open Access Journals (Sweden)

    Angela E. Williams

    2014-01-01

    Full Text Available Background. A variety of symptoms have been reported, but the prevalence of specific symptoms in relapsing-remitting multiple sclerosis (RRMS, how they are related to one another, and their impact on patient reported outcomes is not well understood. Objective. To describe how symptoms of RRMS cooccur and their impact on patient-reported outcomes. Methods. Individuals who reported a physician diagnosis of RRMS in a large general health survey in the United States indicated the symptoms they experience because of RRMS and completed validated scales, including the work productivity and activity impairment questionnaire and either the SF-12v2 or SF-36v2. Symptom clusters were identified through hierarchical cluster analysis, and the relationship between clusters and outcomes was assessed through regression. Results. Fatigue, difficulty walking, and numbness were the most commonly reported symptoms. Seven symptom clusters were identified, and several were significantly related to patient reported outcomes. Pain, muscle spasms, and stiffness formed a cluster strongly related to physical quality of life; depression was strongly related to mental quality of life and cognitive difficulty was associated with work impairment. Conclusions. Symptoms in RRMS show a strong relationship with quality of life and should be taken into consideration in treatment decisions and evaluation of treatment success.

  14. The AstraLux Multiplicity Survey: Extension to Late M-dwarfs

    Science.gov (United States)

    Janson, Markus; Bergfors, Carolina; Brandner, Wolfgang; Kudryavtseva, Natalia; Hormuth, Felix; Hippler, Stefan; Henning, Thomas

    2014-07-01

    The distribution of multiplicity among low-mass stars is a key issue to understanding the formation of stars and brown dwarfs, and recent surveys have yielded large enough samples of nearby low-mass stars to study this issue statistically to good accuracy. Previously, we have presented a multiplicity study of ~700 early/mid M-type stars observed with the AstraLux high-resolution Lucky Imaging cameras. Here, we extend the study of multiplicity in M-type stars through studying 286 nearby mid/late M-type stars, bridging the gap between our previous study and multiplicity studies of brown dwarfs. Most of the targets have been observed more than once, allowing us to assess common proper motion to confirm companionship. We detect 68 confirmed or probable companions in 66 systems, of which 41 were previously undiscovered. Detections are made down to the resolution limit of ~100 mas of the instrument. The raw multiplicity in the AstraLux sensitivity range is 17.9%, leading to a total multiplicity fraction of 21%-27% depending on the mass ratio distribution, which is consistent with being flat down to mass ratios of ~0.4, but cannot be stringently constrained below this value. The semi-major axis distribution is well represented by a log-normal function with μa = 0.78 and σa = 0.47, which is narrower and peaked at smaller separations than for a Sun-like sample. This is consistent with a steady decrease in average semi-major axis from the highest-mass binary stars to the brown dwarf binaries. Based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck Institute for Astronomy and the Instituto de Astrofísica de Andalucía (CSIC).

  15. Gender-specific patterns of multiple concurrent sexual partnerships: a national cross sectional survey in Botswana.

    Science.gov (United States)

    Ho-Foster, Ari; Laetsang, Ditiro; Masisi, Mokgweetsi; Anderson, Marina; Tlhoiwe, Derrick; Cockcroft, Anne; Andersson, Neil

    2010-08-01

    A priority of AIDS prevention in Botswana is to reduce multiple concurrent sexual partnerships. We analysed data from interviews with people aged 16-60 years in a 2007 national stratified random cluster sample of communities across Botswana. Among 768 male and 1784 female respondents, 10% reported multiple sexual partners in the month prior to the survey (MP1); 19% of men and 6% of women. In a multivariate analysis, men were more likely to report MP1 if they had not completed primary education (adjusted Odds Ratio (ORa) 2.13, 95% confidence intervals with adjustment for clustering (CIca) 1.19-3.85), if they were single (ORa 2.29, 95% CIca 1.28-4.11), if they had experienced intimate partner violence in the last year (ORa 2.59, 95% CIca 1.51-4.45) and if they reported acquiescence to high risk sex (ORa 8.32, 95% CIca 3.38-20.46). Women who said they earned more or the same as their partner were also more likely to report MP1 (ORa 1.76, 95% CIca 1.21-2.56). The higher rate of MP1 among men with different forms of choice-disability shows an important potential multiplication of male risk factors. Women with higher income were more likely to have more partners, questioning the idea that multiple concurrent partners is mainly a question of male disposable income.

  16. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

    Science.gov (United States)

    Welp, Gerhard; Thiel, Michael

    2017-01-01

    Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of

  17. High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

    Science.gov (United States)

    Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

    2017-01-01

    Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness

  18. [First aid for multiple trauma patients: investigative survey in the Firenze-Bologna area].

    Science.gov (United States)

    Crescioli, G L; Donati, D; Federici, A; Rasero, L

    1999-01-01

    Overall mortality ascribable to multiple traumas, that in Italy is responsible for about 8,000 death/year, is strictly dependent on the function of the so called Trauma Care System. This study reports on an epidemiological survey conducted in the urban area of Florence along a 23-month period (from Jan 97 to Nov 99), with the aim to identify the typology of traumas and the first aid care delivered to the person until hospital admission. These data were compared to those collected in the urban area of Bologna because the composition of the first-aid team is different, being nurses, in Bologna, an integral component of the first aid system. On a total of 118 multiple traumas, 17% was represented by isolated head trauma, while in 72% involvement of other organs was present in addition to the head; 11% of cases were abdominal or thoracic traumas, 1% of lower extremities. In 46% the cause of trauma was a car accident. The complexity of care delivered to the person with trauma was less in the Florence survey, as indicated by the immobilization of patients, performed in only 11% of cases as compared to 47% in Bologna, by the application of the cervical collar, applied in 12% versus 62% of traumas. Although the two samples are not strictly comparable, these data suggest that the presence of nurses in the Trauma Care System can be one of the elements of improvement of the quality of delivered care.

  19. Long-Baseline Interferometric Multiplicity Survey of the Sco-Cen OB Association

    CERN Document Server

    Rizzuto, A C; Robertson, J G; Kok, Y; Tuthill, P G; Warrington, B A; Haubois, X; Tango, W J; Norris, B; Brummelaar, T ten; Kraus, A L; Jacob, A; Laliberte-Houdeville, C

    2013-01-01

    We present the first multiplicity-dedicated long baseline optical interferometric survey of the Scorpius-Centaurus-Lupus-Crux association. We used the Sydney University Stellar Interferometer to undertake a survey for new companions to 58 Sco-Cen B- type stars and have detected 24 companions at separations ranging from 7-130mas, 14 of which are new detections. Furthermore, we use a Bayesian analysis and all available information in the literature to determine the multiplicity distribution of the 58 stars in our sample, showing that the companion frequency is F = 1.35 and the mass ratio distribution is best described as a power law with exponent equal to -0.46, agreeing with previous Sco-Cen high mass work and differing significantly from lower-mass stars in Tau-Aur. Based on our analysis, we estimate that among young B-type stars in moving groups, up to 23% are apparently single stars. This has strong implications for the understanding of high-mass star formation, which requires angular momentum dispersal thr...

  20. A review of the most relevant multiple regression models for sales forecasting in gas stations; Uma revisao dos principais modelos de regressao multipla para previsao de vendas de postos de combustiveis

    Energy Technology Data Exchange (ETDEWEB)

    Wanke, Peter [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Instituto de Pesquisa e Pos-Graduacao em Administracao de Empresas (COPPEAD). Centro de Estudos em Logistica

    2004-07-01

    In this paper, the most relevant multiple regression models for sales forecasting of gas stations, developed over the past ten years, are reviewed. The most significant variables related to gas station sales, the types of the multiple regression models (linear or non-linear), the most common uses in supporting decision making and its limits are presented. The predictive power of each model and its impact on decision-making, such as sensitivity analysis and confidence intervals for independent variables, are also commented. Four models are presented, based on studies conducted in South Africa, Portugal and Brazil. In conclusion, suggestions for future developments are presented based on past developments. (author)

  1. Comparative investigation of superoxide trapping by cyclic nitrone spin traps: the use of singular value decomposition and multiple linear regression analysis.

    Science.gov (United States)

    Keszler, Agnes; Kalyanaraman, B; Hogg, Neil

    2003-11-01

    The kinetics of the reaction between superoxide and the spin trapping agents 5,5-dimethyl-1-pyrroline N-oxide (DMPO), 5-(diethoxyphosphoryl)-5-methyl-1-pyrroline N-oxide (DEPMPO), and 5-tert-butoxycarbonyl-5-methyl-1-pyrroline N-oxide (BMPO) were re-examined in the superoxide-generating xanthine/xanthine oxidase system, by competition with spontaneous dismutation. The approach used singular value decomposition (SVD), multiple linear regression, and spectral simulation. The experiments were carried out using a two-syringe mixing arrangement with fast scan acquisition of 100 consecutive EPR spectra. Using SVD analysis, the extraction of both temporal and spectral information could be obtained from in a single run. The superoxide spin adduct was the exclusive EPR active species in the case of DEPMPO and BMPO, and the major component when DMPO was used. In the latter case a very low concentration of hydroxyl adduct was also observed, which did not change during the decay of the DMPO-superoxide adduct. This indicates that the hydroxyl radical adduct is not formed from the spontaneous decay of the superoxide radical adduct, as has been previously suggested [correction]. It was established that in short-term studies (up to 100 s) DMPO was the superior spin trapping agent, but for reaction times longer than 100 s the other two spin traps were more advantageous. The second order rate constants for the spin trapping reaction were found to be DMPO (2.4 M(-1)s(-1)), DEPMPO (0.53 M(-1)s(-1)), and BMPO (0.24 M(-1)s(-1)) determined through competition with spontaneous dismutation of superoxide, at pH 7.4 and 20 degrees C.

  2. Investigation of the relationship between very warm days in Romania and large-scale atmospheric circulation using multiple linear regression approach

    Science.gov (United States)

    Barbu, N.; Cuculeanu, V.; Stefan, S.

    2016-10-01

    The aim of this study is to investigate the relationship between the frequency of very warm days (TX90p) in Romania and large-scale atmospheric circulation for winter (December-February) and summer (June-August) between 1962 and 2010. In order to achieve this, two catalogues from COST733Action were used to derive daily circulation types. Seasonal occurrence frequencies of the circulation types were calculated and have been utilized as predictors within the multiple linear regression model (MLRM) for the estimation of winter and summer TX90p values for 85 synoptic stations covering the entire Romania. A forward selection procedure has been utilized to find adequate predictor combinations and those predictor combinations were tested for collinearity. The performance of the MLRMs has been quantified based on the explained variance. Furthermore, the leave-one-out cross-validation procedure was applied and the root-mean-squared error skill score was calculated at station level in order to obtain reliable evidence of MLRM robustness. From this analysis, it can be stated that the MLRM performance is higher in winter compared to summer. This is due to the annual cycle of incoming insolation and to the local factors such as orography and surface albedo variations. The MLRM performances exhibit distinct variations between regions with high performance in wintertime for the eastern and southern part of the country and in summertime for the western part of the country. One can conclude that the MLRM generally captures quite well the TX90p variability and reveals the potential for statistical downscaling of TX90p values based on circulation types.

  3. Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

    Science.gov (United States)

    Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi

    2017-11-01

    In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression.

    Science.gov (United States)

    Hu, L; Liang, M; Mouraux, A; Wise, R G; Hu, Y; Iannetti, G D

    2011-12-01

    Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR(d)) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLR(d) method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLR(d) approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR(d) effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLR(d) can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.

  5. Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression

    Science.gov (United States)

    Hu, L.; Liang, M.; Mouraux, A.; Wise, R. G.; Hu, Y.

    2011-01-01

    Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLRd) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLRd method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLRd approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLRd effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLRd can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli. PMID:21880936

  6. Short communication: Genetic correlation and heritability of milk coagulation traits within and across lactations in Holstein cows using multiple-lactation random regression animal models.

    Science.gov (United States)

    Pretto, D; Vallas, M; Pärna, E; Tänavots, A; Kiiman, H; Kaart, T

    2014-12-01

    Genetic parameters of milk rennet coagulation time (RCT) and curd firmness (a30) among the first 3 lactations in Holstein cows were estimated. The data set included 39,960 test-day records from 5,216 Estonian Holstein cows (the progeny of 306 sires), which were recorded from April 2005 to May 2010 in 98 herds across the country. A multiple-lactation random regression animal model was used. Individual milk samples from each cow were collected during routine milk recording. These samples were analyzed for milk composition and coagulation traits with intervals of 2 to 3 mo in each lactation (7 to 305 DIM) and from first to third lactation. Mean heritabilities were 0.36, 0.32, and 0.28 for log-transformed RCT [ln(RCT)] and 0.47, 0.40, and 0.62 for a30 for parities 1, 2, and 3, respectively. Mean repeatabilities for ln(RCT) were 0.53, 0.55, and 0.56, but 0.59, 0.61, and 0.68 for a30 for parities 1, 2 and 3, respectively. Mean genetic correlations between ln(RCT) and a30 were -0.19, -0.14, and 0.02 for parities 1, 2, and 3, respectively. Mean genetic correlations were 0.91, 0.79, and 0.99 for ln(RCT), and 0.95, 0.94, and 0.94 for a30 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Due to these high genetic correlations, we concluded that for a proper genetic evaluation of milk coagulation properties it is sufficient to record RCT and a30 only in the first lactation. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  7. Fine Surveying and 3D Modeling Approach for Wooden Ancient Architecture via Multiple Laser Scanner Integration

    Directory of Open Access Journals (Sweden)

    Qingwu Hu

    2016-03-01

    Full Text Available A multiple terrestrial laser scanner (TLS integration approach is proposed for the fine surveying and 3D modeling of ancient wooden architecture in an ancient building complex of Wudang Mountains, which is located in very steep surroundings making it difficult to access. Three-level TLS with a scalable measurement distance and accuracy is presented for data collection to compensate for data missed because of mutual sheltering and scanning view limitations. A multi-scale data fusion approach is proposed for data registration and filtering of the different scales and separated 3D data. A point projection algorithm together with point cloud slice tools is designed for fine surveying to generate all types of architecture maps, such as plan drawings, facade drawings, section drawings, and doors and windows drawings. The section drawings together with slicing point cloud are presented for the deformation analysis of the building structure. Along with fine drawings and laser scanning data, the 3D models of the ancient architecture components are built for digital management and visualization. Results show that the proposed approach can achieve fine surveying and 3D documentation of the ancient architecture within 3 mm accuracy. In addition, the defects of scanning view and mutual sheltering can overcome to obtain the complete and exact structure in detail.

  8. Evaluating the effect of stellar multiplicity on the PSF of space-based weak lensing surveys

    CERN Document Server

    Kuntzer, T; Meylan, G

    2016-01-01

    The next generation of space-based telescopes used for weak lensing surveys will require exquisite point spread function (PSF) determination. Previously negligible effects may become important in the reconstruction of the PSF, in part because of the improved spatial resolution. In this paper, we show that unresolved multiple star systems can affect the ellipticity and size of the PSF and that this effect is not cancelled even when using many stars in the reconstruction process. We estimate the error in the reconstruction of the PSF due to the binaries in the star sample both analytically and with image simulations for different PSFs and stellar populations. The simulations support our analytical finding that the error on the size of the PSF is a function of the multiple stars distribution and of the intrinsic value of the size of the PSF, i.e. if all stars were single. Similarly, the modification of each of the complex ellipticity components (e1,e2) depends on the distribution of multiple stars and on the int...

  9. Ionic structure modeling of surface water from reservoirs in Metropolitan basin of Ceará State, Brazil using multiple linear regression

    Directory of Open Access Journals (Sweden)

    José Ribeiro de Araújo Neto

    2014-04-01

    Full Text Available The main goal of this work was to develop and validate multiple regression models to estimate the electrical conductivity of the surface water reservoir in the basin Metropolitan Ceará State, based on the concentration of the each investigated ion. The influence of ions on the values of EC formed by each group from a hierarchical cluster analysis – HCA was determined. The data were provided by the Company of Water Resources Management of Ceará and cover the period of 1998/2009. A total of 290 samples from seven reservoirs were used. The parameters evaluated were: Electrical conductivity of water (EC, Sodium (Na+, calcium (Ca+2, magnesium (Mg+2, chloride (Cl- and bicarbonate (HCO3-. The results showed that the HCA formed two distinct groups and the values of all parameters studied in the group 2 always presented with average highest than the group 1. The reservoirs in the group 1 (Castro and Pompeu Sobrinho have the highest level of salinity in the Metropolitan basin. Chloride was present in the both models developed and it was the main ion responsible for the ionic composition of the EC. The statistical models developed had simulated values very close to those observed and this indicates a good accuracy of the models. According to the indices applied, calibrated and validated models showed good accuracy with indices Trusts (c greater than 0.71, and with the indexes Willmott (id greater than 0.85. This fact show a good performance of the models applied in this work. Resumo - Este trabalho foi realizado com o objetivo de desenvolver e validar modelos de regressão múltipla em que a condutividade elétrica das águas superficiais de reservatórios na bacia Metropolitana do Ceará, pudesse ser estimada com base na concentração de cada íon pesquisado, determinando, assim, a ordem de influência dos íons nos valores da CE, isso para cada grupo formado a partir de uma análise multivariada de agrupamento hierárquico - AAH. Os dados utilizados

  10. Use of Multiple Linear Regression Method for Modelling Seasonal Changes in Stable Isotopes of 18O and 2H in 30 Pouns in Gilan Province

    Directory of Open Access Journals (Sweden)

    M.A. Mousavi Shalmani

    2014-08-01

    Full Text Available In order to assessment of water quality and characterize seasonal variation in 18O and 2H in relation with different chemical and physiographical parameters and modelling of effective parameters, an study was conducted during 2010 to 2011 in 30 different ponds in the north of Iran. Samples were collected at three different seasons and analysed for chemical and isotopic components. Data shows that highest amounts of δ18O and δ2H were recorded in the summer (-1.15‰ and -12.11‰ and the lowest amounts were seen in the winter (-7.50‰ and -47.32‰ respectively. Data also reveals that there is significant increase in d-excess during spring and summer in ponds 20, 21, 22, 24, 25 and 26. We can conclude that residual surface runoff (from upper lands is an important source of water to transfer soluble salts in to these ponds. In this respect, high retention time may be the main reason for movements of light isotopes in to the ponds. This has led d-excess of pond 12 even greater in summer than winter. This could be an acceptable reason for ponds 25 and 26 (Siyahkal county with highest amount of d-excess and lowest amounts of δ18O and δ2H. It seems light water pumped from groundwater wells with minor source of salt (originated from sea deep percolation in to the ponds, could may be another reason for significant decrease in the heavy isotopes of water (18O and 2H for ponds 2, 12, 14 and 25 from spring to summer. Overall conclusion of multiple linear regression test indicate that firstly from 30 variables (under investigation only a few cases can be used for identifying of changes in 18O and 2H by applications. Secondly, among the variables (studied, phytoplankton content was a common factor for interpretation of 18O and 2H during spring and summer, and also total period (during a year. Thirdly, the use of water in the spring was recommended for sampling, for 18O and 2H interpretation compared with other seasons. This is because of function can be

  11. New Insights into Trace Element Partitioning in Amphibole from Multiple Regression Analysis, with Application to the Magma Plumbing System of Mt. Lamington (Papua New Guinea)

    Science.gov (United States)

    Zhang, J.; Humphreys, M.; Cooper, G.; Davidson, J.; Macpherson, C.

    2015-12-01

    We present a new multiple regression (MR) analysis of published amphibole-melt trace element partitioning data, with the aim of retrieving robust relationships between amphibole crystal-chemical compositions and trace element partition coefficients (D). We examined experimental data for calcic amphiboles of kaersutite, pargasite, tschermakite (Tsch), magnesiohornblende (MgHbl) and magnesiohastingsite (MgHst) compositions crystallized from basanitic-rhyolitic melts (n = 150). The MR analysis demonstrates the varying significance of amphibole major element components assigned to different crystallographic sites (T, M1-3, M4, A) as independent variables in controlling D, and it allows us to retrieve statistically significant relationships for REE, Y, Rb, Sr, Pb, Ti, Zr, Nb (n > 25, R2 > 0.6, p-value < 0.05). For example, DLREE are controlled by SiT, M1-3 site components and CaM4, whereas DMREE-HREE are controlled solely by M1-3 site components. Our overall results for the REE are supported by application of the lattice strain model (Blundy & Wood, 1994). A significant advantage of our study over previous work linking D to melt polymerization (e.g. Tiepolo et al., 2007) is the ability to reconstruct melt compositions from in situ amphibole compositional analyses and published D data. We applied our MR analysis to Mt. Lamington (PNG), where Mg-Hst in quenched mafic enclaves are juxtaposed with MgHbl-Tsch phenocrysts from andesitic host lavas. The results indicate that MgHbl-Tsch are crystallized from a cool, rhyolitic melt (800-900±50 ºC, 70-77±5 wt % SiO2; Ridolfi & Renzulli 2012) with lower Rb and Sr and higher Pb, relative to a hot, andesitic-dacitic melt (950-1,000±50 ºC; 60-70±5 wt % SiO2) where MgHst are crystallized. REE and Nb contents are similar in both types of melts despite higher REE and Nb in MgHbl-Tsch. Therefore, the REE compositional disparity between MgHst and MgHbl-Tsch is driven by the difference in the DREE, rather than the melt REE

  12. Mechanical waves conceptual survey: Its modification and conversion to a standard multiple-choice test

    Science.gov (United States)

    Barniol, Pablo; Zavala, Genaro

    2016-06-01

    In this article we present several modifications of the mechanical waves conceptual survey, the most important test to date that has been designed to evaluate university students' understanding of four main topics in mechanical waves: propagation, superposition, reflection, and standing waves. The most significant changes are (i) modification of several test questions that had some problems in their original design, (ii) standardization of the number of options for each question to five, (iii) conversion of the two-tier questions to multiple-choice questions, and (iv) modification of some questions to make them independent of others. To obtain a final version of the test, we administered both the original and modified versions several times to students at a large private university in Mexico. These students were completing a course that covers the topics tested by the survey. The final modified version of the test was administered to 234 students. In this study we present the modifications for each question, and discuss the reasons behind them. We also analyze the results obtained by the final modified version and offer a comparison between the original and modified versions. In the Supplemental Material we present the final modified version of the test. It can be used by teachers and researchers to assess students' understanding of, and learning about, mechanical waves.

  13. The Multiplicity of Massive Stars: A High Angular Resolution Survey with the HST Fine Guidance Sensor

    CERN Document Server

    Aldoretta, E J; Gies, D R; Nelan, E P; Wallace, D J; Hartkopf, W I; Henry, T J; Jao, W -C; Apellániz, J Maíz; Mason, B D; Moffat, A F J; Norris, R P; Richardson, N D; Williams, S J

    2014-01-01

    We present the results of an all-sky survey made with the Fine Guidance Sensor on Hubble Space Telescope to search for angularly resolved binary systems among the massive stars. The sample of 224 stars is comprised mainly of Galactic O- and B-type stars and Luminous Blue Variables, plus a few luminous stars in the Large Magellanic Cloud. The FGS TRANS mode observations are sensitive to detection of companions with an angular separation between 0."01 and 1."0 and brighter than $\\triangle m = 5$. The FGS observations resolved 52 binary and 6 triple star systems and detected partially resolved binaries in 7 additional targets (43 of these are new detections). These numbers yield a companion detection frequency of 29% for the FGS survey. We also gathered literature results on the numbers of close spectroscopic binaries and wider astrometric binaries among the sample, and we present estimates of the frequency of multiple systems and the companion frequency for subsets of stars residing in clusters and associations...

  14. Surveying multiple health professional team members within institutional settings: an example from the nursing home industry.

    Science.gov (United States)

    Clark, Melissa A; Roman, Anthony; Rogers, Michelle L; Tyler, Denise A; Mor, Vincent

    2014-09-01

    Quality improvement and cost containment initiatives in health care increasingly involve interdisciplinary teams of providers. To understand organizational functioning, information is often needed from multiple members of a leadership team since no one person may have sufficient knowledge of all aspects of the organization. To minimize survey burden, it is ideal to ask unique questions of each member of the leadership team in areas of their expertise. However, this risks substantial missing data if all eligible members of the organization do not respond to the survey. Nursing home administrators (NHA) and directors of nursing (DoN) play important roles in the leadership of long-term care facilities. Surveys were administered to NHAs and DoNs from a random, nationally representative sample of U.S. nursing homes about the impact of state policies, market forces, and organizational factors that impact provider performance and residents' outcomes. Responses were obtained from a total of 2,686 facilities (response rate [RR] = 66.6%) in which at least one individual completed the questionnaire and 1,693 facilities (RR = 42.0%) in which both providers participated. No evidence of nonresponse bias was detected. A high-quality representative sample of two providers in a long-term care facility can be obtained. It is possible to optimize data collection by obtaining unique information about the organization from each provider while minimizing the number of items asked of each individual. However, sufficient resources must be available for follow-up to nonresponders with particular attention paid to lower resourced, lower quality facilities caring for higher acuity residents in highly competitive nursing home markets.

  15. The VLT-FLAMES Tarantula Survey XXII. Multiplicity properties of the B-type stars

    CERN Document Server

    Dunstall, P R; Sana, H; Evans, C J; Howarth, I D; Simón-Díaz, S; de Mink, S E; Langer, N; Apellániz, J Maíz; Taylor, W D

    2015-01-01

    We investigate the multiplicity properties of 408 B-type stars observed in the 30 Doradus region of the Large Magellanic Cloud with multi-epoch spectroscopy from the VLT-FLAMES Tarantula Survey (VFTS). We use a cross-correlation method to estimate relative radial velocities from the helium and metal absorption lines for each of our targets. Objects with significant radial-velocity variations (and with an amplitude larger than 16 km/s) are classified as spectroscopic binaries. We find an observed spectroscopic binary fraction (defined by periods of 0.1) for the B-type stars, f_B(obs) = 0.25 +/- 0.02, which appears constant across the field of view, except for the two older clusters (Hodge 301 and SL 639). These two clusters have significantly lower fractions of 0.08 +/- 0.08 and 0.10 +/- 0.09, respectively. Using synthetic populations and a model of our observed epochs and their potential biases, we constrain the intrinsic multiplicity properties of the dwarf and giant (i.e. relatively unevolved) B-type stars ...

  16. Dutch survey of congenital coronary artery fistulas in adults: coronary artery-left ventricular multiple micro-fistulas multi-center observational survey in the Netherlands.

    NARCIS (Netherlands)

    Said, S.A.M.; Werf, T. van der

    2006-01-01

    BACKGROUND: Congenital coronary artery-left ventricular multiple micro-fistulas (CA-LVMMFs) in adults are rare anomalies. They may cause angina pectoris and myocardial infarction in association with normal coronary arteries. METHODS AND RESULTS: From the medical databases of a Dutch Survey of corona

  17. Changes of Multiple Metal Accumulation (MMA in New Orleans Soil: Preliminary Evaluation of Differences between Survey I (1992 and Survey II (2000

    Directory of Open Access Journals (Sweden)

    Paul W. Mielke

    2005-08-01

    Full Text Available Soil metal surveys were conducted in Baltimore, MD (1976-1979, Minnesota (1981-1988 and most recently, New Orleans, LA (1989-present. The unique characteristic of New Orleans is that it has two surveys; Survey I was completed in 1992 and Survey II was completed in 2000. This paper seeks to determine if there is a perceptible change in the amount of metals during less than a decade that separated these surveys. The Survey I collection was 4,026 samples stratified by 283 census tracts. All samples were collected in residential neighborhoods at least one block from a busy street. The Survey II collection was 5,467 samples stratified by 286 census tracts (plus City Park. The Survey II collection included busy streets as a category of samples. For comparison, the busy street category of 1,078 samples was excluded from Survey II for a total of 4,388 samples. The extraction methods of the two surveys used the same protocol for strength of acid (1 M HNO3, shaker-time (2 hours, and room temperature (~22ºC. However, Survey II differed in amount of sample used in extraction. For Surveys I and II, 4.0g and 0.4g were used respectively. The same ICP-AES was used to measure 8 metals in both surveys. To evaluate the analytical results of the two methods, reference soil samples (n=36 from the Wageningen Evaluating Programs for Analytical Laboratories, International Soil-analytical Exchange (WEPAL; ISE were used. The relationship between the 4.0 and 0.4 g results were linear and the Survey I results were adjusted for sample:acid ratio. Further evaluation was done by creating interpolated Multiple Metal Accumulation (MMA maps based on the median MMA for each census tract. A new map was created by dividing Survey II MMA by Survey I MMA. The ratio indicates increases of soil metals in the inner city and decreases of soil metals in the outlying areas of Metropolitan New Orleans. Comparing fresh parent alluvium from the Mississippi River with urban soil metal

  18. KAP Surveys and Dengue Control in Colombia: Disentangling the Effect of Sociodemographic Factors Using Multiple Correspondence Analysis

    Science.gov (United States)

    Quintero, Juliana

    2016-01-01

    During the last few decades, several studies have analyzed and described knowledge, attitudes, and practices (KAP) of populations regarding dengue. However, few studies have applied geometric data analytic techniques to generate indices from KAP domains. Results of such analyses have not been used to determine the potential effects of sociodemographic variables on the levels of KAP. The objective was to determine the sociodemographic factors related to different levels of KAP regarding dengue in two hyper-endemic cities of Colombia, using a multiple correspondence analysis (MCA) technique. In the context of a cluster randomized trial, 3,998 households were surveyed in Arauca and Armenia between 2012 and 2013. To generate KAP indexes, we performed a MCA followed by a hierarchical cluster analysis to classify each score in different groups. A quantile regression for each of the score groups was conducted. KAP indexes explained 56.1%, 79.7%, and 83.2% of the variance, with means of 4.2, 1.4, and 3.2 and values that ranged from 1 to 7, 7 and 11, respectively. The highest values of the index denoted higher levels of knowledge and practices. The attitudes index did not show the same relationship and was excluded from the analysis. In the quantile regression, age (0.06; IC: 0.03, 0.09), years of education (0.14; IC: 0.06, 0.22), and history of dengue in the family (0.21; IC: 0.12, 0.31) were positively related to lower levels of knowledge regarding dengue. The effect of such factors gradually decreased or disappeared when knowledge was higher. The practices indexes did not evidence a correlation with sociodemographic variables. These results suggest that the transformation of categorical variables into a single index by the use of MCA is possible when analyzing knowledge and practices regarding dengue from KAP questionnaires. Additionally, the magnitude of the effect of socioeconomic variables on the knowledge scores varies according to the levels of knowledge, suggesting

  19. Surveying the Bright Stars by Optical Interferometry I: A Search for Multiplicity Among Stars of Spectral Types F - K

    CERN Document Server

    Hutter, Donald; Tycner, Christopher; Benson, James; Hummel, Christian; Sanborn, Jason; Franz, Otto G; Johnston, Kenneth

    2016-01-01

    We present the first results from an ongoing survey for multiplicity among the bright stars using the Navy Precision Optical Interferometer (NPOI). We first present a summary of NPOI observations of known multiple systems, including the first detection of the companion of $\\beta$ Scuti with precise relative astrometry, to illustrate the instrument's detection sensitivity for binaries at magnitude differences $\\Delta$$m$ $\\lessapprox$ 3 over the range of angular separation 3 - 860 milliarcseconds (mas). A limiting $\\Delta$$m_{700}$ $\\sim$ 3.5 is likely for binaries where the component spectral types differ by less than two. Model fits to these data show good agreement with published orbits, and we additionally present a new orbit solution for one of these stars, $\\sigma$ Her. We then discuss early results of the survey of bright stars at $\\delta$ $\\geq$ -20$\\deg$. This survey, which complements previous surveys of the bright stars by speckle interferometry, initially emphasizes bright stars of spectral types F...

  20. The Canadian survey of health, lifestyle and ageing with multiple sclerosis: methodology and initial results

    Science.gov (United States)

    Ploughman, Michelle; Beaulieu, Serge; Harris, Chelsea; Hogan, Stephen; Manning, Olivia J; Alderdice, Penelope W; Fisk, John D; Sadovnick, A Dessa; O'Connor, Paul; Morrow, Sarah A; Metz, Luanne M; Smyth, Penelope; Mayo, Nancy; Marrie, Ruth Ann; Knox, Katherine B; Stefanelli, Mark; Godwin, Marshall

    2014-01-01

    Objective People with multiple sclerosis (MS) are living longer so strategies to enhance long-term health are garnering more interest. We aimed to create a profile of ageing with MS in Canada by recruiting 1250 (5% of the Canadian population above 55 years with MS) participants and focusing data collection on health and lifestyle factors, disability, participation and quality of life to determine factors associated with healthy ageing. Design National multicentre postal survey. Setting Recruitment from Canadian MS clinics, MS Society of Canada chapters and newspaper advertisements. Participants People aged 55 years or older with MS symptoms more than 20 years. Outcome measures Validated outcome measures and custom-designed questions examining MS disease characteristics, living situation, disability, comorbid conditions, fatigue, health behaviours, mental health, social support, impact of MS and others. Results Of the 921 surveys, 743 were returned (80.7% response rate). Participants (mean age 64.6±6.2 years) reported living with MS symptoms for an average of 32.9±9.5 years and 28.6% were either wheelchair users or bedridden. There was only 5.4% missing data and 709 respondents provided optional qualitative information. According to data derived from the 2012 Canadian Community Health Survey of Canadians above 55 years of age, older people with MS from this survey sample are about eight times less likely to be employed full-time. Older people with MS were less likely to engage in regular physical activity (26.7%) compared with typical older Canadians (45.2%). However, they were more likely to abstain from alcohol and smoking. Conclusions Despite barriers to participation, we were able to recruit and gather detailed responses (with good data quality) from a large proportion of older Canadians with MS. The data suggest that this sample of older people with MS is less likely to be employed, are less active and more disabled than other older Canadians

  1. Managing multiple projects: a literature review of setting priorities and a pilot survey of healthcare researchers in an academic setting.

    Science.gov (United States)

    Hopkins, Robert Borden; Campbell, Kaitryn; O'Reilly, Daria; Tarride, Jean-Eric; Bowen, Jim; Blackhouse, Gord; Goerre, Ron

    2007-05-16

    To summarize and then assess with a pilot study the use of published best practice recommendations for priority setting during management of multiple healthcare research projects, in a resource-constrained environment. Medical, economic, business, and operations literature was reviewed to summarize and develop a survey to assess best practices for managing multiple projects. Fifteen senior healthcare research project managers, directors, and faculty at an urban academic institution were surveyed to determine most commonly used priority rules, ranking of rules, characteristics of their projects, and availability of resources. Survey results were compared to literature recommendations to determine use of best practices. Seven priority-setting rules were identified for managing multiple projects. Recommendations on assigning priorities by project characteristics are presented. In the pilot study, a large majority of survey respondents follow best practice recommendations identified in the research literature. However, priority rules such as Most Total Successors (MTS) and Resource Scheduling Method (RSM) were used "very often" by half of the respondents when better performing priority rules were available. Through experience, project managers learn to manage multiple projects under resource constraints. Best practice literature can assist project managers in priority setting by recommending the most appropriate priority given resource constraints and project characteristics. There is room for improvement in managing multiple projects.

  2. Hand washing behavior and associated factors in Vietnam based on the Multiple Indicator Cluster Survey, 2010–2011

    Directory of Open Access Journals (Sweden)

    Kien Gia To

    2016-02-01

    Full Text Available Background: Handwashing is a cost-effective way of preventing communicable diseases such as respiratory and food-borne illnesses. However, handwashing rates are low in developing countries. Target 7C of the seventh Millennium Development Goals was to increase by half the proportion of people with sustainable access to safe drinking water and basic sanitation by 2015. Studies have found that better access to improved water sources and sanitation is associated with higher rates of handwashing. Objective: Our goal was to describe handwashing behaviour and identify the associated factors in Vietnamese households. Design: Data from 12,000 households participating in the Vietnam Multiple Indicator Cluster Survey 2011 were used. The survey used a multistage sampling method to randomly select 100 clusters and 20 households per cluster. Self-administered questionnaires were used to collect data from a household representative. Demographic variables, the presence of a specific place for handwashing, soap and water, access to improved sanitation, and access to improved water sources were tested for association with handwashing behaviour in logistic regression. Results: Almost 98% of households had a specific place for handwashing, and 85% had cleansing materials and water at such a place. The prevalence of handwashing in the sample was almost 85%. Educational level, ethnicity of the household head, and household wealth were factors associated with handwashing practice (p<0.05. Those having access to an improved sanitation facility were more likely to practise handwashing [odds ratio (OR=1.69, 95% confidence interval (CI: 1.37–2.09, p<0.001], as were those with access to improved water sources (OR=1.74, 95% CI: 1.37–2.21, p<0.001. Conclusions: Households with low education, low wealth, belonging to ethnic minorities, and with low access to improved sanitation facilities and water sources should be targeted for interventions implementing handwashing

  3. Regression in autistic spectrum disorders.

    Science.gov (United States)

    Stefanatos, Gerry A

    2008-12-01

    A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

  4. Credit Scoring Problem Based on Regression Analysis

    OpenAIRE

    Khassawneh, Bashar Suhil Jad Allah

    2014-01-01

    ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....

  5. Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.

    Science.gov (United States)

    Maleki, Afshin; Daraei, Hiua; Alaei, Loghman; Faraji, Aram

    2014-01-01

    Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.

  6. Bridging a Survey Redesign Using Multiple Imputation: An Application to the 2014 CPS ASEC

    Directory of Open Access Journals (Sweden)

    Rothbaum Jonathan

    2017-03-01

    Full Text Available The Current Population Survey Annual Social and Economic Supplement (CPS ASEC serves as the data source for official income, poverty, and inequality statistics in the United States. In 2014, the CPS ASEC questionnaire was redesigned to improve data quality and to reduce misreporting, item nonresponse, and errors resulting from respondent fatigue. The sample was split into two groups, with nearly 70% receiving the traditional instrument and 30% receiving the redesigned instrument. Due to the relatively small redesign sample, analyses of changes in income and poverty between this and future years may lack sufficient power, especially for subgroups. The traditional sample is treated as if the responses were missing for income sources targeted by the redesign, and multiple imputation is used to generate plausible responses. A flexible imputation technique is used to place individuals into strata along two dimensions: 1 their probability of income recipiency and 2 their expected income conditional on recipiency for each income source. By matching on these two dimensions, this approach combines the ideas of propensity score matching and predictive means matching. In this article, this approach is implemented, the matching models are evaluated using diagnostics, and the results are analyzed.

  7. [Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

    Science.gov (United States)

    Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

    In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

  8. Genetic parameter estimation for milk yield over multiple parities and various lengths of lactation in Danish Jerseys by random regression models.

    Science.gov (United States)

    Guo, Z; Lund, M S; Madsen, P; Korsgaard, I; Jensen, J

    2002-06-01

    The objectives of this study were to test for heterogeneity of genetic and environmental variance among completed and extended records from different lactations or different days in milk (DIM) and to build a model that accounts for this heterogeneity. A total of 147,457 305-d milk yield records from Danish Jersey cows calving between 1984 and early 1999 from two regions of Denmark were used in this study. Results showed that DIM and parity influenced parameters estimated from an animal model with repeated records. Therefore, the data were analyzed using random-regression models that allow the covariance between measurements to change gradually with DIM and parity. Random regressions were fitted for additive genetic effects and permanent environmental effects using second- or third-order normalized Legendre polynomials for DIM and parity. Variances of random-regression coefficients associated with all orders of the polynomials were significant. Based on these parameter estimates, a covariance function (CF) was defined. The CF showed that the heritability decreases over parities, but within each parity heritability increases with DIM, whereas variance of permanent environmental effects increases over parities and decreases with DIM. Generally, genetic correlations were higher between records with similar DIM and parity. The results indicate that there are problems with the extension procedure used to predict 305-d milk yields. Using the covariance functions estimated in this study, breeding values could be predicted that take into account the covariance structure between records from different parities and different DIM.

  9. Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

    Science.gov (United States)

    McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

    2017-02-01

    Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

  10. Personal, social, and game-related correlates of active and non-active gaming among dutch gaming adolescents: survey-based multivariable, multilevel logistic regression analyses.

    Science.gov (United States)

    Simons, Monique; de Vet, Emely; Chinapaw, Mai Jm; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes

    2014-04-04

    Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games-active games-seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; Pgames (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; Pgaming and a little bit lower score on game engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; Pgaming (OR 3.3, CI 1.46-7.53; P=.004), and a more positive image of a non-active gamer (OR 2, CI 1.07-3.75; P=.03). Various factors were significantly associated with active gaming ≥1 h/wk and non-active gaming >7 h/wk. Active gaming is most strongly (negatively) associated with attitude with respect to non-active games, followed by observed active game behavior of brothers and sisters and attitude with respect to active gaming (positive associations). On the other hand, non

  11. Mobile Technology Use by People Experiencing Multiple Sclerosis Fatigue: Survey Methodology

    Science.gov (United States)

    Reay, Nicholas

    2017-01-01

    Background Fatigue is one of the most commonly reported symptoms of multiple sclerosis (MS). It has a profound impact on all spheres of life, for people with MS and their relatives. It is one of the key precipitants of early retirement. Individual, group, and Internet cognitive behavioral therapy–based approaches to supporting people with MS to manage their fatigue have been shown to be effective. Objective The aim of this project was to (1) survey the types of mobile devices and level of Internet access people with MS use or would consider using for a health intervention and (2) characterize the levels of fatigue severity and their impact experienced by the people in our sample to provide an estimate of fatigue severity of people with MS in New Zealand. The ultimate goal of this work was to support the future development of a mobile intervention for the management of fatigue for people with MS. Methods Survey methodology using an online questionnaire was used to assess people with MS. A total of 51 people with MS participated. The average age was 48.5 years, and the large majority of the sample (77%) was female. Results Participants reported significant levels of fatigue as measured with the summary score of the Neurological Fatigue Index (mean 31.4 [SD 5.3]). Most (84%) respondents scored on average more than 3 on the fatigue severity questions, reflecting significant fatigue. Mobile phone usage was high with 86% of respondents reporting having a mobile phone; apps were used by 75% of respondents. Most participants (92%) accessed the Internet from home. Conclusions New Zealand respondents with MS experienced high levels of both fatigue severity and fatigue impact. The majority of participants have a mobile device and access to the Internet. These findings, along with limited access to face-to-face cognitive behavioral therapy–based interventions, create an opportunity to develop a mobile technology platform for delivering a cognitive behavioral therapy

  12. Regression Methods for Virtual Metrology of Layer Thickness in Chemical Vapor Deposition

    DEFF Research Database (Denmark)

    Purwins, Hendrik; Barak, Bernd; Nagi, Ahmed

    2014-01-01

    predictive variable alone, the 3 most predictive variables, an expert selection, and full set. The following regression methods are compared: Simple Linear Regression, Multiple Linear Regression, Partial Least Square Regression, and Ridge Linear Regression utilizing the Partial Least Square Estimate......The quality of wafer production in semiconductor manufacturing cannot always be monitored by a costly physical measurement. Instead of measuring a quantity directly, it can be predicted by a regression method (Virtual Metrology). In this paper, a survey on regression methods is given to predict...... algorithm, and Support Vector Regression (SVR). On a test set, SVR outperforms the other methods by a large margin, being more robust towards changes in the production conditions. The method performs better on high-dimensional multivariate input data than on the most predictive variables alone. Process...

  13. Multiple aspects of sexual orientation: prevalence and sociodemographic correlates in a New Zealand national survey.

    Science.gov (United States)

    Wells, J Elisabeth; McGee, Magnus A; Beautrais, Annette L

    2011-02-01

    Sexual orientation consists of multiple components. This study investigated both sexual identity and same-sex sexual behavior. Data came from the New Zealand Mental Health Survey, a nationally representative community sample of New Zealanders aged 16 years or older, interviewed face-to-face (N = 12,992, 48% male). The response rate was 73.3%. Self-reported sexual identity was 98.0% heterosexual, 0.6% bisexual, 0.8% homosexual, 0.3% "Something else," and 0.1% "Not sure." Same-sex sexual behavior with a partner was more common: 3.2% reported same-sex sexual experience only and 1.9% reported both experience and a relationship. For analysis of childhood and lifecourse, five sexuality groups were investigated: homosexual, bisexual, and heterosexual divided into those with no same-sex sexual experience, experience only, and experience and relationship. The non-exclusively heterosexual groups were more likely to have experienced adverse events in childhood. Educational achievement and current equivalized household income did not differ systematically across the sexuality groups. Only 9.4% of the exclusively heterosexual lived alone, compared with 16.7% of bisexuals and 19.0% of homosexuals. Heterosexuals were more likely than bisexuals or homosexuals to have ever married or had biological children, with differences more marked for males than for females. Heterosexuals with no same-sex sexual experience were more likely to be currently married than the other two heterosexual groups. Restricting comparisons to heterosexual, bisexual, and homosexual identification ignores the diversity within heterosexuals. Differences between the bisexual and homosexual groups were small compared with the differences between these groups and the exclusively heterosexual group, except for sex (80.8% of bisexuals were female).

  14. Multiple Aspects of Sexual Orientation: Prevalence and Sociodemographic Correlates in a New Zealand National Survey

    Science.gov (United States)

    McGee, Magnus A.; Beautrais, Annette L.

    2010-01-01

    Sexual orientation consists of multiple components. This study investigated both sexual identity and same-sex sexual behavior. Data came from the New Zealand Mental Health Survey, a nationally representative community sample of New Zealanders aged 16 years or older, interviewed face-to-face (N = 12,992, 48% male). The response rate was 73.3%. Self-reported sexual identity was 98.0% heterosexual, 0.6% bisexual, 0.8% homosexual, 0.3% “Something else,” and 0.1% “Not sure.” Same-sex sexual behavior with a partner was more common: 3.2% reported same-sex sexual experience only and 1.9% reported both experience and a relationship. For analysis of childhood and lifecourse, five sexuality groups were investigated: homosexual, bisexual, and heterosexual divided into those with no same-sex sexual experience, experience only, and experience and relationship. The non-exclusively heterosexual groups were more likely to have experienced adverse events in childhood. Educational achievement and current equivalized household income did not differ systematically across the sexuality groups. Only 9.4% of the exclusively heterosexual lived alone, compared with 16.7% of bisexuals and 19.0% of homosexuals. Heterosexuals were more likely than bisexuals or homosexuals to have ever married or had biological children, with differences more marked for males than for females. Heterosexuals with no same-sex sexual experience were more likely to be currently married than the other two heterosexual groups. Restricting comparisons to heterosexual, bisexual, and homosexual identification ignores the diversity within heterosexuals. Differences between the bisexual and homosexual groups were small compared with the differences between these groups and the exclusively heterosexual group, except for sex (80.8% of bisexuals were female). PMID:20567892

  15. A Near-Infrared Multiplicity Survey of Class I/Flat-Spectrum Systems in Six Nearby Molecular Clouds

    CERN Document Server

    Haisch, K E; Barsony, M; Stahler, S W; Jr., Karl E. Haisch; Greene, Thomas P.; Barsony, Mary; Stahler, Steven W.

    2003-01-01

    We present new near-IR observations of 76 Class I/flat-spectrum objects in the nearby (d < 320 pc) Perseus, Taurus, Chamaeleon I and II, rho Ophiuchi, and Serpens dark clouds. These observations are part of a larger systematic infrared multiplicity survey of self-embedded objects in the nearest dark clouds. When combined with the results of our previously published near-infrared multiplicity survey, we find a restricted companion star fraction of 14/79 (18% +/- 4%) of the sources surveyed to be binary or higher order multiple systems over a separation range of ~300 - 2000 AU with a magnitude difference $\\Delta$ K <= 4, and with no correction for background contamination or completeness. This is consistent with the fraction of binary/multiple systems found among older pre-main-sequence T Tauri stars in each of the Taurus, rho Ophiuchi, and Chamaeleon star-forming regions over a similar separation range, as well as the combined companion star fraction for these regions. However, the companion star fractio...

  16. Revolutionizing our View of Protostellar Multiplicity and Disks: The VLA Nascent Disk and Multiplicity (VANDAM) Survey of the Perseus Molecular Cloud

    CERN Document Server

    Tobin, John J; Li, Zhi-Yun; Chandler, Claire J; Dunham, Michael M; Segura-Cox, Dominique; Cox, Erin G; Harris, Robert J; Melis, Carl; Sadavoy, Sarah I; Pérez, Laura; Kratter, Kaitlin

    2016-01-01

    There is substantial evidence for disk formation taking place during the early stages of star formation and for most stars being born in multiple systems; however, protostellar multiplicity and disk searches have been hampered by low resolution, sample bias, and variable sensitivity. We have conducted an unbiased, high-sensitivity Karl G. Jansky Very Large Array (VLA) survey toward all known protostars (n = 94) in the Perseus molecular cloud (d~230 pc), with a resolution of ~15 AU (0.06") at 8 mm. We have detected candidate protostellar disks toward 17 sources (with 12 of those in the Class 0 stage) and we have found substructure on < 50AU scales for three Class 0 disk candidates, possibly evidence for disk fragmentation. We have discovered 16 new multiple systems (or new components) in this survey; the new systems have separations < 500 AU and 3 by < 30 AU. We also found a bi-modal distribution of separations, with peaks at ~75 AU and ~3000 AU, suggestive of formation through two distinct mechanisms...

  17. A survey of the literature on the multiple or parallel assembly Line balancing problem

    OpenAIRE

    Lusa García, Amaia

    2008-01-01

    This paper focuses on production systems that consist of multiple parallel assembly lines. The main literature contributions are briefly described and used to present a summary of the state of the art. The advantages and disadvantages of adopting multiple lines are discussed and the multiple assembly line balancing problem and its relevant characteristics are described. Peer Reviewed

  18. Beta-mapping and beta-regression for changes of ordinal-rating measurements on Likert scales: a comparison of the change scores among multiple treatment groups.

    Science.gov (United States)

    Zou, Kelly H; Carlsson, Martin O; Quinn, Sheila A

    2010-10-30

    Patient reported outcome and observer evaluative studies in clinical trials and post-hoc analyses often use instruments that measure responses on ordinal-rating or Likert scales. We propose a flexible distributional approach by modeling the change scores from the baseline to the end of the study using independent beta distributions. The two shape parameters of the fitted beta distributions are estimated by matching-moments. Covariates and the interaction terms are included in multivariate beta-regression analyses under generalized linear mixed models. These methods are illustrated on the treatment satisfaction data in an overactive bladder drug study with four treatment arms. Monte-Carlo simulations were conducted to compare the Type 1 errors and statistical powers using a beta likelihood ratio test of the proposed method against its fully nonparametric or parametric alternatives. Copyright © 2010 John Wiley & Sons, Ltd.

  19. Multiple Chronic Conditions and Use of Complementary and Alternative Medicine Among US Adults: Results From the 2012 National Health Interview Survey

    Science.gov (United States)

    Shi, Zaixing; Greenlee, Heather

    2016-01-01

    Introduction More than 25% of American adults report having 2 or more chronic conditions. People with chronic conditions often use complementary and alternative medicine (CAM) for self-care and disease management, despite a limited evidence base. Methods Data from the 2012 National Health Interview Survey (NHIS) (n = 33,557) were analyzed to assess associations between presence of multiple chronic conditions (n = 13) and CAM use, using multivariable relative risk and linear regressions weighted for complex NHIS sampling. CAM use was defined as self-reported use of one or more of 16 therapies in the previous 12 months. Results Chronic conditions were common. US adults reported one (22.3%) or 2 or more (33.8%) conditions. Many used at least one form of CAM. Multivitamins, multiminerals, or both (52.7%); vitamins (34.8%); and minerals (28.4%) were the most common. Compared with adults with no conditions, adults with 2 or more conditions were more likely to use multivitamins or multiminerals or both, vitamins, minerals, nonvitamins or herbs, mind–body therapies, chiropractic or osteopathic manipulation, massage, movement therapies, special diets, acupuncture, naturopathy, or some combination of these therapies (P <.003). Conclusion People with multiple chronic conditions have a high prevalence of CAM use. Longitudinal studies are needed to understand the association between CAM use and chronic disease prevention and treatment. PMID:27149072

  20. Assessing risk factors for periodontitis using regression

    Science.gov (United States)

    Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

    2013-10-01

    Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

  1. A new multiple regression model to identify multi-family houses with a high prevalence of sick building symptoms "SBS", within the healthy sustainable house study in Stockholm (3H).

    Science.gov (United States)

    Engvall, Karin; Hult, M; Corner, R; Lampa, E; Norbäck, D; Emenius, G

    2010-01-01

    The aim was to develop a new model to identify residential buildings with higher frequencies of "SBS" than expected, "risk buildings". In 2005, 481 multi-family buildings with 10,506 dwellings in Stockholm were studied by a new stratified random sampling. A standardised self-administered questionnaire was used to assess "SBS", atopy and personal factors. The response rate was 73%. Statistical analysis was performed by multiple logistic regressions. Dwellers owning their building reported less "SBS" than those renting. There was a strong relationship between socio-economic factors and ownership. The regression model, ended up with high explanatory values for age, gender, atopy and ownership. Applying our model, 9% of all residential buildings in Stockholm were classified as "risk buildings" with the highest proportion in houses built 1961-1975 (26%) and lowest in houses built 1985-1990 (4%). To identify "risk buildings", it is necessary to adjust for ownership and population characteristics.

  2. The VLT-FLAMES Tarantula Survey. VIII. Multiplicity properties of the O-type star population

    Science.gov (United States)

    Sana, H.; de Koter, A.; de Mink, S. E.; Dunstall, P. R.; Evans, C. J.; Hénault-Brunet, V.; Maíz Apellániz, J.; Ramírez-Agudelo, O. H.; Taylor, W. D.; Walborn, N. R.; Clark, J. S.; Crowther, P. A.; Herrero, A.; Gieles, M.; Langer, N.; Lennon, D. J.; Vink, J. S.

    2013-02-01

    Context. The Tarantula Nebula in the Large Magellanic Cloud is our closest view of a starburst region and is the ideal environment to investigate important questions regarding the formation, evolution and final fate of the most massive stars. Aims: We analyze the multiplicity properties of the massive O-type star population observed through multi-epoch spectroscopy in the framework of the VLT-FLAMES Tarantula Survey. With 360 O-type stars, this is the largest homogeneous sample of massive stars analyzed to date. Methods: We use multi-epoch spectroscopy and variability analysis to identify spectroscopic binaries. We also use a Monte-Carlo method to correct for observational biases. By modeling simultaneously the observed binary fraction, the distributions of the amplitudes of the radial velocity variations and the distribution of the time scales of these variations, we constrain the intrinsic current binary fraction and period and mass-ratio distributions. Results: We observe a spectroscopic binary fraction of 0.35 ± 0.03, which corresponds to the fraction of objects displaying statistically significant radial velocity variations with an amplitude of at least 20 km s-1. We compute the intrinsic binary fraction to be 0.51 ± 0.04. We adopt power-laws to describe the intrinsic period and mass-ratio distributions: f(log 10P/d) ~ (log 10P/d)π (with log 10P/d in the range 0.15-3.5) and f(q) ~ qκ with 0.1 ≤ q = M2/M1 ≤ 1.0. The power-law indexes that best reproduce the observed quantities are π = -0.45 ± 0.30 and κ = -1.0 ± 0.4. The period distribution that we obtain thus favours shorter period systems compared to an Öpik law (π = 0). The mass ratio distribution is slightly skewed towards low mass ratio systems but remains incompatible with a random sampling of a classical mass function (κ = -2.35). The binary fraction seems mostly uniform across the field of view and independent of the spectral types and luminosity classes. The binary fraction in the outer

  3. BP网络和多元回归在葡萄酒质量模型中的应用%Application of BP network and multiple regression in wine quality model

    Institute of Scientific and Technical Information of China (English)

    孙文兵; 曾祥燕; 杨立君

    2014-01-01

    In order to determine the independent variables of multiple linear regression and the input layer neurons of BP network, factor analysis is used to select out the 12 physical and chemical indicators with much impact on quality of wine as their independent variables and input layer neurons, respectively. Two models are established by using multiple linear regression and improved BP neural network, respectively, which show the relationships between the physical-chemical indi-cators and the quality of wine. The comparison of generalization performance for the both models, draws that average rela-tive error of multiple linear regression model for the prediction of new samples is 1.93%, while the average relative error of the BP neural network model is 0.37%. The simulations show that the generalization capability and stability of the BP neural network are better than those of the multiple regression model.%利用因子分析法筛选出对葡萄酒质量影响较大的12种理化指标,将其作为多元线性回归的自变量和BP网络输入层神经元,分别用多元线性回归和改进的BP神经网络两种方法建立葡萄酒和酿酒葡萄的主要理化指标与葡萄酒质量的关系模型。比较了两种模型的泛化能力,得出多元线性回归模型对新样本预测的平均相对误差是1.93%,而BP神经网络模型的平均相对误差是0.37%。仿真实验表明,BP神经网络的泛化能力和稳定性明显优于多元回归模型。

  4. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  5. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  6. Fungible weights in logistic regression.

    Science.gov (United States)

    Jones, Jeff A; Waller, Niels G

    2016-06-01

    In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record

  7. Handwashing in 51 Countries: Analysis of Proxy Measures of Handwashing Behavior in Multiple Indicator Cluster Surveys and Demographic and Health Surveys, 2010-2013.

    Science.gov (United States)

    Kumar, Swapna; Loughnan, Libbet; Luyendijk, Rolf; Hernandez, Orlando; Weinger, Merri; Arnold, Fred; Ram, Pavani K

    2017-08-01

    In 2009, a common set of questions addressing handwashing behavior was introduced into nationally representative Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), providing large amounts of comparable data from numerous countries worldwide. The objective of this analysis is to describe global handwashing patterns using two proxy indicators for handwashing behavior from 51 DHS and MICS surveys conducted in 2010-2013: availability of soap anywhere in the dwelling and access to a handwashing place with soap and water. Data were also examined across geographic regions, wealth quintiles, and rural versus urban settings. We found large disparities for both indicators across regions, and even among countries within the same World Health Organization region. Within countries, households in lower wealth quintiles and in rural areas were less likely to have soap anywhere in the dwelling and at designated handwashing locations than households in higher wealth quintiles and urban areas. In addition, disparities existed among various geographic regions within countries. This analysis demonstrates the need to promote access to handwashing materials and placement at handwashing locations in the dwelling, particularly in poorer, rural areas where children are more vulnerable to handwashing-preventable syndromes such as pneumonia and diarrhea.

  8. Multiple time series autoregressive method based on support vector regression%基于支持向量回归的多时间序列自回归方法

    Institute of Scientific and Technical Information of China (English)

    张伟; 柳先辉; 丁毅; 史德明

    2012-01-01

    能耗时间序列涉及多种能源,且各种能源间关系复杂,主要通过多个独立的单时间序列进行预报,这种方式忽略了多时间序列之间的依赖性.为了充分利用多时间序列之间的关联信息以提高预报的准确性,根据机器学习中的向量值函数学习和多任务学习理论,采用支持向量回归(SVR)算法建立了多时间序列的向量值自回归方法和多任务自回归方法.实验结果证明,与多个独立的单时间序列模型相比,通过这种方法建立的多时间序列自回归模型在焦化工序能耗预报中表现出了更好的性能.%Energy consumption time series involves a variety of energy and the relationship between different energy is complicated. Most existing consumption methods make prediction through multiple independent single time series respectively, which ignores dependencies between multiple time series. In order to take full advantage of the association between multiple time series and improve prediction accuracy, the vector-valued autoregressive method and multi-task autoregressive method based on Support Vector Regression (SVR) machines were proposed for multiple time series forecast according to vector-valued function learning and multi-task learning theory. The experimental results with energy consumption of coking process verify that multiple time series autoregressive models based on the proposed methods show better prediction performance.

  9. Bayesian Inference of a Multivariate Regression Model

    Directory of Open Access Journals (Sweden)

    Marick S. Sinay

    2014-01-01

    Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

  10. A Survey of Downlink Non-Orthogonal Multiple Access for 5G Wireless Communication Networks

    Institute of Scientific and Technical Information of China (English)

    WEI Zhiqiang; YUAN Jinhong; Derrick Wing Kwan Ng; Maged Elkashlan; DING Zhiguo

    2016-01-01

    Non⁃orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for the next generation cel⁃lular communication networks. In this paper, we first discuss a simple NOMA model with two users served by a single⁃carrier si⁃multaneously to illustrate its basic principles. Then, a more general model with multicarrier serving an arbitrary number of users on each subcarrier is also discussed. An overview of existing works on performance analysis, resource allocation, and multiple⁃in⁃put multiple⁃output NOMA are summarized and discussed. Furthermore, we discuss the key features of NOMA and its potential re⁃search challenges.

  11. Regression analysis by example

    National Research Council Canada - National Science Library

    Chatterjee, Samprit; Hadi, Ali S

    2012-01-01

    .... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...

  12. Multiple imputation of completely missing repeated measures data within person from a complex sample: application to accelerometer data in the National Health and Nutrition Examination Survey.

    Science.gov (United States)

    Liu, Benmei; Yu, Mandi; Graubard, Barry I; Troiano, Richard P; Schenker, Nathaniel

    2016-12-10

    The Physical Activity Monitor component was introduced into the 2003-2004 National Health and Nutrition Examination Survey (NHANES) to collect objective information on physical activity including both movement intensity counts and ambulatory steps. Because of an error in the accelerometer device initialization process, the steps data were missing for all participants in several primary sampling units, typically a single county or group of contiguous counties, who had intensity count data from their accelerometers. To avoid potential bias and loss in efficiency in estimation and inference involving the steps data, we considered methods to accurately impute the missing values for steps collected in the 2003-2004 NHANES. The objective was to come up with an efficient imputation method that minimized model-based assumptions. We adopted a multiple imputation approach based on additive regression, bootstrapping and predictive mean matching methods. This method fits alternative conditional expectation (ace) models, which use an automated procedure to estimate optimal transformations for both the predictor and response variables. This paper describes the approaches used in this imputation and evaluates the methods by comparing the distributions of the original and the imputed data. A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

  13. Outpatient Art Therapy with Multiple Personality Disorder: A Survey of Current Practice.

    Science.gov (United States)

    Mills, Anne

    1995-01-01

    Reports findings of a 1993 questionnaire completed by 46 North American art therapists that focuses on the outpatient treatment of multiple personality disorder. Includes information on role in diagnosing, fees and third-party payment, and therapeutic activities. Treatment issues include pacing and containment, and managing the client's chronic…

  14. Outpatient Art Therapy with Multiple Personality Disorder: A Survey of Current Practice.

    Science.gov (United States)

    Mills, Anne

    1995-01-01

    Reports findings of a 1993 questionnaire completed by 46 North American art therapists that focuses on the outpatient treatment of multiple personality disorder. Includes information on role in diagnosing, fees and third-party payment, and therapeutic activities. Treatment issues include pacing and containment, and managing the client's chronic…

  15. MILLIONS OF MULTIPLES: DETECTING AND CHARACTERIZING CLOSE-SEPARATION BINARY SYSTEMS IN SYNOPTIC SKY SURVEYS

    Energy Technology Data Exchange (ETDEWEB)

    Terziev, Emil; Law, Nicholas M. [Dunlap Institute for Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, Ontario M5S 3H4 (Canada); Arcavi, Iair [Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot 76100 (Israel); Baranec, Christoph; Bui, Khanh; Dekany, Richard G.; Kulkarni, S. R.; Riddle, Reed; Tendulkar, Shriharsh P. [Cahill Center for Astrophysics, California Institute of Technology, Pasadena, CA 91125 (United States); Bloom, Joshua S. [Department of Astronomy, University of California, Berkeley, CA 94720-3411 (United States); Burse, Mahesh P.; Chorida, Pravin; Das, H. K.; Punnadi, Sujit; Ramaprakash, A. N. [Inter-University Centre for Astronomy and Astrophysics, Ganeshkhind, Pune 411007 (India); Kraus, Adam L. [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Nugent, Peter [Computational Cosmology Center, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States); Ofek, Eran O. [Benoziyo Center for Astrophysics, Weizmann Institute of Science, 76100 Rehovot (Israel); Sullivan, Mark, E-mail: emil.terziev@utoronto.ca [Department of Physics (Astrophysics), University of Oxford, Keble Road, Oxford OX1 3RH (United Kingdom)

    2013-06-01

    The direct detection of binary systems in wide-field surveys is limited by the size of the stars' point-spread functions (PSFs). A search for elongated objects can find closer companions, but is limited by the precision to which the PSF shape can be calibrated for individual stars. Based on a technique from weak-lensing analysis, we have developed the BinaryFinder algorithm to search for close binaries by using precision measurements of PSF ellipticity across wide-field survey images. We show that the algorithm is capable of reliably detecting binary systems down to Almost-Equal-To 1/5 of the seeing limit, and can directly measure the systems' position angles, separations, and contrast ratios. To verify the algorithm's performance we evaluated 100,000 objects in Palomar Transient Factory (PTF) wide-field-survey data for signs of binarity, and then used the Robo-AO robotic laser adaptive optics system to verify the parameters of 44 high-confidence targets. We show that BinaryFinder correctly predicts the presence of close companions with a <11% false-positive rate, measures the detected binaries' position angles within 1 Degree-Sign to 4 Degree-Sign (depending on signal-to-noise ratio and separation), and separations within 25%, and weakly constrains their contrast ratios. When applied to the full PTF data set, we estimate that BinaryFinder will discover and characterize {approx}450,000 physically associated binary systems with separations <2 arcsec and magnitudes brighter than m{sub R} = 18. New wide-field synoptic surveys with high sensitivity and sub-arcsecond angular resolution, such as LSST, will allow BinaryFinder to reliably detect millions of very faint binary systems with separations as small as 0.1 arcsec.

  16. Millions of Multiples: Detecting and Characterizing Close-Separation Binary Systems in Synoptic Sky Surveys

    CERN Document Server

    Terziev, Emil; Arcavi, Iair; Baranec, Christoph; Bloom, Joshua S; Bui, Khanh; Burse, Mahesh P; Chorida, Pravin; Das, H K; Dekany, Richard G; Kraus, Adam L; Kulkarni, S R; Nugent, Peter; Ofek, Eran O; Punnadi, Sujit; Ramaprakash, A N; Riddle, Reed; Tendulkar, Shriharsh P

    2012-01-01

    The direct detection of binary systems in wide-field surveys is limited by the size of the stars' point-spread-functions (PSFs). A search for elongated objects can find closer companions, but is limited by the precision to which the PSF shape can be calibrated for individual stars. We have developed the BinaryFinder algorithm to search for close binaries by using precision measurements of PSF ellipticity across wide-field survey images. We show that the algorithm is capable of reliably detecting binary systems down to approximately 1/5 of the seeing limit, and can directly measure the systems' position angles, separations and contrast ratios. To verify the algorithm's performance we evaluated 100,000 objects in Palomar Transient Factory (PTF) wide-field-survey data for signs of binarity, and then used the Robo-AO robotic laser adaptive optics system to verify the parameters of 44 high-confidence targets. We show that BinaryFinder correctly predicts the presence of close companions with a <5% false-positive...

  17. Cactus: An Introduction to Regression

    Science.gov (United States)

    Hyde, Hartley

    2008-01-01

    When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…

  18. Quantitative structure-activity relationship (QSAR) study of interleukin-1 receptor associated kinase 4 (IRAK-4) inhibitor activity by the genetic algorithm and multiple linear regression (GA-MLR) method.

    Science.gov (United States)

    Pourbasheer, Eslam; Riahi, Siavash; Ganjali, Mohammad Reza; Norouzi, Parviz

    2010-12-01

    A linear quantitative structure-activity relationship (QSAR) model is presented for the modelling and prediction for the interleukin-1 receptor associated kinase 4 (IRAK-4) inhibition activity of amides and imidazo[1,2-α] pyridines. The model was produced using the multiple linear regression (MLR) technique on a database that consisted of 65 recently discovered amides and imidazo[1,2- α] pyridines. Among the different constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors that were considered as inputs to the model, seven variables were selected using the genetic algorithm subset selection method (GA). The accuracy of the proposed MLR model was illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomisation. The predictive ability of the model was found to be satisfactory and could be used for designing a similar group of compounds.

  19. Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Marami Milani

    2016-07-01

    Full Text Available This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new, and respiratory rate predictor RRP with three main components of cow’s milk (yield, fat, and protein for cows in Iran. The least absolute shrinkage selection operator (LASSO and the Akaike information criterion (AIC techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49 respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001 with R2 (0.69. For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

  20. 多元线性回归法在某隧洞地应力场反演中的应用%Application of multiple linear regression in inversion of tunnel ground stress

    Institute of Scientific and Technical Information of China (English)

    史存鹏; 王家祥; 陈长生; 王旺盛; 胡巍

    2015-01-01

    为弥补地应力实测点的不足,有必要通过多元线性回归方法反演地应力场。针对某深埋隧道,以水压致裂法实测地应力为依据,开展了该洞段地应力场的反演分析。在介绍反演基本原理和误差分析的基础上,给出了实例分析结果。研究结果表明,由多元线性回归计算得到的地应力值与实测结果规律一致,可用于评价工程洞段的地应力场分布;受水平构造应力、断裂构造等影响,该洞段地应力场存在一定空间变异性。%To overcome the drawbacks of insufficient measuring point of ground stress, it is necessary to invert the ground stress field by multiple linear regression method. Aiming at a deep-buried tunnel, according to the ground stress data measured by hydraulic fracturing method, the inversion of ground stress is carried out. Based on the introduction to the basic inversion theo-ry and error analysis, the inverted results of ground stress are provided. The results show that the calculated results by multiple linear regression is in accordance with the measured data, so it is feasible to be applied in the evaluation of ground stress in the presented tunnel section;due to the influence from horizontal tectonic stress and fracture structure, the ground stress in the pres-ented section reveals some spatial variability.

  1. Multiple regression analysis of urinary fluoride, s aliva and plaque fluoride levels of adolescents dental fluorosis%氟斑牙青少年尿氟与唾液氟及菌斑氟的相关性分析

    Institute of Scientific and Technical Information of China (English)

    于阳阳; 赵伟; 刘晓燕; 邹冬荣; 杨晓昀; 刘荣; 于晓峰; 营杰

    2016-01-01

    Objective The purpose of this study was to study the correlation between dental fluorosis, saliva and plaque fluoride levels and urinary fluoride values in adolescents dental fluorosis. Methods A middle school was chosen as a survey point in the study. Two hundred adolescents were examined the degree of dental fluorosis by Dean's method. These adolescents were divided into four groups according to the severity of fluorosis (n = 52, 40, 28 and 80). Fluoride ion specific electrode was used to measure the fluoride levels in dental plaque, saliva, urinary and drinking water. The differences were analyzed b y ANOVA. Correlation of the fluoride levels between dental plaque, saliva, urine and the degree of dental fluorosis were analyzed by the method of multiple linear regression. Results The average fluoride content of drinking water was (2.20 ± 0.40) mg/L. Compared with controls, the fluoride concentrations in dental plaque, saliva and urine were higher in light, medium and severe dental fluorosis groups [(1.55 ± 0.88), (1.94 ± 0.77), (2.74 ± 0.83) than (0.32 ± 0.20) mg/L; (4.44 ± 1.62), (8.09 ± 0.93), (10.72 ± 0.99) than (0.02 ± 0.01) mg/L;(31.77 ± 6.09), (57.98 ± 1.83), (65.98 ± 2.78) than (13.06 ± 2.11) μg/g, all P<0.05]. Urinary fluoride was correlated with fluoride in saliva and dental plaque (r=0.245, 0.440, all P<0.05). Saliva fluoride was correlated with fluoride in dental plaque (r=0.849, P<0.01). The degree of dental fluorosis was correlated with fluoride in urine and saliva (r = 0.497, 0.896, 0.924, all P< 0.01). The multiple linear regression equation between fluoride in urine and the degree of dent al fluorosis, fluoride in dental plaque and saliva was as follow: y = 1.357 + 1.618x1 + 0.001x2 - 0.331x3 ± 0.69. Conclusions The metabolism of fluoride in body is related with oral fluoride repository in adolescents dental fluorosis. Fluoride in urine is influenced by plaque fluoride level, saliva fluoride concentration and the degree of dental

  2. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...

  3. Information Management System Supporting a Multiple Property Survey Program with Legacy Radioactive Contamination.

    Science.gov (United States)

    Stager, Ron; Chambers, Douglas; Wiatzka, Gerd; Dupre, Monica; Callough, Micah; Benson, John; Santiago, Erwin; van Veen, Walter

    2017-04-01

    The Port Hope Area Initiative is a project mandated and funded by the Government of Canada to remediate properties with legacy low-level radioactive waste contamination in the Town of Port Hope, Ontario. The management and use of large amounts of data from surveys of some 4800 properties is a significant task critical to the success of the project. A large amount of information is generated through the surveys, including scheduling individual field visits to the properties, capture of field data laboratory sample tracking, QA/QC, property report generation and project management reporting. Web-mapping tools were used to track and display temporal progress of various tasks and facilitated consideration of spatial associations of contamination levels. The IM system facilitated the management and integrity of the large amounts of information collected, evaluation of spatial associations, automated report reproduction and consistent application and traceable execution for this project.x. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  4. Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices%实现logistic与Cox回归相乘相加交互作用的临床实践宏程序

    Institute of Scientific and Technical Information of China (English)

    聂志强; 欧艳秋; 庄建; 曲艳吉; 麦劲壮; 陈寄梅; 刘小清

    2016-01-01

    病例对照研究常采用条件或非条件logistic分析,生存资料分析常采用Cox比例模型,但多数文献仅纳入主效应模型,然而广义线性模型不同于一般线性模型,其交互作用分为相乘交互与相加交互作用,前者只有统计学意义而后者更符合生物学意义.笔者以SAS 9.4软件编写宏,在计算logistic与Cox相乘交互项同时计算交互对比度、归因比、交互作用指数指标及利用Wald、Delta、PL(profile likelihood)3种方法的可信区间评价相加交互作用,便于临床流行病学与遗传学大数据分析相乘相加交互作用时参考.%Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study,but Cox proportional hazard model is often used in survival data analysis.Most literature only refer to main effect model,however,generalized linear model differs from general linear model,and the interaction was composed of multiplicative interaction and additive interaction.The former is only statistical significant,but the latter has biological significance.In this paper,macros was written by using SAS 9.4 and the contrast ratio,attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions,and the confidence intervals of Wald,delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

  5. 奶牛产奶量与乳成分的多元回归分析%Multiple Regression Analysis on Milk Yield and Milk Composition of Dairy Cow

    Institute of Scientific and Technical Information of China (English)

    张巧娥; 吴学荣; 马水鱼; 邢燕

    2011-01-01

    通过SAS 8.2软件分析了20头胎次相同、泌乳期相近荷斯坦泌乳牛产奶量与乳成分中乳蛋白质率、乳脂率、干物质、体细胞数和乳中尿素氮的多元回归分析.结果表明:从产奶量与乳成分的单项指标回归分析表明,产奶量与乳脂率、体细胞数和干物质含量呈显著性的负相关,而与乳蛋白率和乳中尿素氮差异不显著;从产奶量与乳成分的多元回归分析表明,乳蛋白率、乳脂率和干物质含量对产奶量的影响高于体细胞数和乳中尿素氮,同时乳蛋白率、乳脂率、体细胞数和乳中尿素氮与产奶量成反比.%20 heads Holstein cattles of same matched plet and similar lactation period were selected. Multiple regression analysis between milk yield and protein ratio in milk, fat ration in milk, dry matter content,somatic cell count and urea nitrogen in milk were analyzed in this study by SAS 8.2. The result showed that the corelation between milk yield and fat ration in milk, somatic cell count, and dry matter content was significantly negative, while milk yield had no significant corelation with protein ratio in milk and urea nitrogen in milk according to single index regression analysis between milk yield and milk components. The effects of protein ratio in milk, fat ration in milk and dry matter content on milk yield were bigger than those of somatic cell count and urea nitrogen in milk, meanwhile, protein ratio in milk, fat ration in milk, somatic cell count and urea nitrogen in milk were inversely proportional to milk yield according to multiple regression analysis between milk yield and milk components.

  6. Varying-coefficient functional linear regression

    CERN Document Server

    Wu, Yichao; Müller, Hans-Georg; 10.3150/09-BEJ231

    2011-01-01

    Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite numb...

  7. ENCOMS: Argentinian survey in cost of illness and unmet needs in multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Celica Ysrraelit

    2014-05-01

    Full Text Available The objective of the study was to assess the cost of multiple sclerosis (MS patients in Argentina categorized by disease severity using a societal perspective. Method: Cross-sectional study including MS patients from 21 MS centers in 12 cities of Argentina. Patients were stratified by disease severity using the expanded disability status scale (EDSS (group 1 with EDSS score between 0 and 3; group 2 with EDSS >3 and 3 and <7, and USD 50,712 (95%CI 47,825-62,104 for patients with EDSS ≥7. Conclusions: This is the first Argentine study evaluating the costs of MS considering disease severity.

  8. Green Resource Allocation for Multiple OFDMA Based Networks:A Survey

    Institute of Scientific and Technical Information of China (English)

    A. S. Khan; J. Abdullah; H. Lenando; J. M. Nazim

    2016-01-01

    Abstract-Orthogonal frequency division multiple access (OFDMA) is a popular and widely accepted multiple access technique to provide high data rate services in a mobile environment in the area of wireless communications. OFDMA can provide better flexibility in allocating the radio spectra by utilizing subcarrier allocations, scheduling, and energy control to obtain multi-dimension diversity gains. Due to its resource allocation flexibility, OFDMA has been widely used as a green air interface technology for the emerging broadband wireless access networks. This paper extensively addresses the integration of green OFDMA to the future air interface technologies, for instance: two-tier cellular, multi radio access technologies (RATs), FemtoCell, and relay networks. The main focus of the paper is to review and analyze the current OFDMA techniques to address the green resource allocation in multiuser diversity, where the critical constraints are the computational complexity, energy efficiency, and the sub-channel assignment. The future trend of OFDMA based networks will aim to maximize the energy efficiency of the exclusive channel assignment through a joint sub-channel and power allocation to accommodate high data traffic networks specially the relay based 5G cellular networks.

  9. [An epidemiologic survey of multiple sclerosis in the Szczecin province in Poland].

    Science.gov (United States)

    Potemkowski, A

    2001-01-01

    The possibility of a focus of the province of Szczecin (Northwestern part of Poland) was first noticed in our survey covering the period of 1960-86. Starting from 1986 the incidence of MS in this region was closely followed. Seven communes with an area of 1202 km2 and 45,231 inhabitants, had 50 MS patients on Dec. 31st, 1995 and a prevalence rate of 110,54/100,000. This is twice the prevalence more than in surrounding area (52,89/100,000). New cases appeared irregularly, with four peaks in 1960, 1967-8, 1976-7 and 1981-2. The age at onset, duration of disease, life expectancy and natural history of the disease in the focus and rest of Szczecin region were similar. We conclude, that clustering seems to be due to the influence of environmental factors, such as peat soil, coniferous forest, quality of drinking water and nearby industrial plants.

  10. Anti-disturbance control theory for systems with multiple disturbances: a survey.

    Science.gov (United States)

    Guo, Lei; Cao, Songyin

    2014-07-01

    The problem of anti-disturbance control has been an eternal topic along with the development of the control theory. However, most methodologies can only deal with systems subject to a single equivalent disturbance which was merged by various types of uncertainties. In this paper, a review on anti-disturbance control is presented for systems with multiple disturbances. First, the classical control methods are briefly reviewed for disturbance attenuation or rejection problems. Then, recent advances in disturbance observer based control (DOBC) theory are introduced and especially, the composite hierarchical anti-disturbance control (CHADC) is firstly addressed. A comparison of different approaches is briefly carried out. Finally, focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques.

  11. Exploration of remote triggering: A survey of multiple fault structures in Haiti

    Science.gov (United States)

    Aiken, Chastity; Chao, Kevin; Gonzalez-Huizar, Hector; Douilly, Roby; Peng, Zhigang; Deschamps, Anne; Calais, Eric; Haase, Jennifer S.

    2016-12-01

    Triggering studies provide an important tool for understanding the fundamental physics of how faults slip and interact, and they also provide clues about the stress states of faults. In this study, we explore how seismic waves from the 27 February 2010 Mw8.8 Maule, Chile mainshock interact with the left lateral strike-slip Enriquillo-Plantain Garden Fault (EPGF) and surrounding reverse faults in the southern Haiti peninsula. The Chile mainshock occurred 6,000 km away and just 46 days after the 12 January 2010 Mw7.0 Haiti earthquake, a tragic event which activated multiple faults in the southern Haiti peninsula. During the surface waves of the Chile mainshock, several tectonic tremor signals were observed, originating from south of the EPGF trace. Cross-correlation of the triggered tremor and transient stresses resolved onto to the EPGF indicates that the Love wave of the Chile mainshock was the primary driving mechanism of the triggered deep shear slip and tremor signals, as opposed to dilatational stress changes generated by the Rayleigh wave. We also searched for any influence of transient stresses on Haiti aftershock activity by applying the matched filter technique to multiple days of seismic data around the time of the Chile mainshock. While we identified a slight increase in Haiti aftershock activity rate, the rate changes were significant only when small magnitude events were included in the significance tests. These observations are generally consistent with recent inferences that deep tectonic tremor is more sensitive than shallow earthquakes to external stress perturbations.

  12. The Hubble Space Telescope UV Legacy Survey of Galactic globular clusters - IX. The Atlas of multiple stellar populations

    Science.gov (United States)

    Milone, A. P.; Piotto, G.; Renzini, A.; Marino, A. F.; Bedin, L. R.; Vesperini, E.; D'Antona, F.; Nardiello, D.; Anderson, J.; King, I. R.; Yong, D.; Bellini, A.; Aparicio, A.; Barbuy, B.; Brown, T. M.; Cassisi, S.; Ortolani, S.; Salaris, M.; Sarajedini, A.; van der Marel, R. P.

    2017-01-01

    We use high-precision photometry of red-giant-branch (RGB) stars in 57 Galactic globular clusters (GCs), mostly from the `Hubble Space Telescope (HST) UV Legacy Survey of Galactic GCs', to identify and characterize their multiple stellar populations. For each cluster the pseudo-two-colour diagram (or `chromosome map') is presented, built with a suitable combination of stellar magnitudes in the F275W, F336W, F438W, and F814W filters that maximizes the separation between multiple populations. In the chromosome map of most GCs (type-I clusters), stars separate in two distinct groups that we identify with the first (1G) and the second generation (2G). This identification is further supported by noticing that 1G stars have primordial (oxygen-rich, sodium-poor) chemical composition, whereas 2G stars are enhanced in sodium and depleted in oxygen. This 1G-2G separation is not possible for a few GCs where the two sequences have apparently merged into an extended, continuous sequence. In some GCs (type-II clusters) the 1G and/or the 2G sequences appear to be split, hence displaying more complex chromosome maps. These clusters exhibit multiple subgiant branches (SGBs) also in purely optical colour-magnitude diagrams, with the fainter SGB joining into a red RGB which is populated by stars with enhanced heavy-element abundance. We measure the RGB width by using appropriate colours and pseudo-colours. When the metallicity dependence is removed, the RGB width correlates with the cluster mass. The fraction of 1G stars ranges from ˜8 per cent to ˜67 per cent and anticorrelates with the cluster mass, indicating that incidence and complexity of the multiple population phenomenon both increase with cluster mass.

  13. The M-dwarfs in Multiples (MinMs) survey - I. Stellar multiplicity among low-mass stars within 15 pc

    CERN Document Server

    Ward-Duong, K; De Rosa, R J; Bulger, J; Rajan, A; Goodwin, S P; Parker, Richard J; McCarthy, D W; Kulesa, C

    2015-01-01

    We present a large-scale, volume-limited companion survey of 245 late-K to mid-M (K7-M6) dwarfs within 15 pc. Infrared adaptive optics (AO) data were analysed from the Very Large Telescope, Subaru Telescope, Canada-France-Hawaii Telescope, and MMT Observatory to detect close companions to the sample from $\\sim$1 au to 100 au, while digitised wide-field archival plates were searched for wide companions from $\\sim$100 au to 10,000 au. With sensitivity to the bottom of the main sequence over a separation range of 3 au to 10,000 au, multiple AO and wide-field epochs allow us to confirm candidates with common proper motions, minimize background contamination, and enable a measurement of comprehensive binary statistics. We detected 65 co-moving stellar companions and find a companion star fraction of $23.5 \\pm 3.2$ per cent over the 3 au to 10,000 au separation range. The companion separation distribution is observed to rise to a higher frequency at smaller separations, peaking at closer separations than measured f...

  14. SURVEY

    DEFF Research Database (Denmark)

    SURVEY er en udbredt metode og benyttes inden for bl.a. samfundsvidenskab, humaniora, psykologi og sundhedsforskning. Også uden for forskningsverdenen er der mange organisationer som f.eks. konsulentfirmaer og offentlige institutioner samt marketingsafdelinger i private virksomheder, der arbejder...... med surveys. Denne bog gennemgår alle surveyarbejdets faser og giver en praktisk indføring i: • design af undersøgelsen og udvælgelse af stikprøver, • formulering af spørgeskemaer samt indsamling og kodning af data, • metoder til at analysere resultaterne...

  15. A Survey of Stellar Families: Multiplicity of Solar-Type Stars

    CERN Document Server

    Raghavan, Deepak; Henry, Todd J; Latham, David W; Marcy, Geoffrey W; Mason, Brian D; Gies, Douglas R; White, Russel J; Brummelaar, Theo A ten

    2010-01-01

    We present the results of a comprehensive assessment of companions to solar-type stars. A sample of 454 stars, including the Sun, was selected from the Hipparcos catalog with {\\pi} > 40 mas, {\\sigma}_{\\pi}/{\\pi} < 0.05, 0.5 < B - V < 1.0 (~ F6-K3), and constrained by absolute magnitude and color to exclude evolved stars. New observational aspects of this work include surveys for (1) very close companions with long-baseline interferometry at the CHARA Array, (2) close companions with speckle interferometry, and (3) wide proper motion companions identified by blinking multi-epoch archival images. In addition, we include the results from extensive radial-velocity monitoring programs and evaluate companion information from various catalogs. The overall observed fractions of single, double, triple, and higher order systems are 56% \\pm 2%, 33% \\pm 2%, 8% \\pm 1%, and 3% \\pm 1%, respectively, counting all confirmed stellar and brown dwarf companions. Our completeness analysis indicates that only a few undisc...

  16. Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis%膝关节骨性关节炎X线测量与WOMAC评分的多重线性回归分析

    Institute of Scientific and Technical Information of China (English)

    马玉峰; 阿迪力江; 董士宇; 吴忌; 王庆甫; 陈兆军; 杜春林; 李俊海; 黄沪; 时宗庭; 殷岳杉; 张雷

    2012-01-01

    Objective:To perform Multiple Linear Regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis, and to analyze their relationship with clinical and biomechanical concepts. Methods: From March 2011 to July 2011,140 patients (250 knees) were reviewed,including 132 knees in the left and 118 knees in the right;ranging in age from 40 to 71 years,with an average of 54.68 years. The MB-RULER measurement software was applied to measure femoral angle, tibial angle, femorotibial angle .joint gap angle from antero-posterir and lateral position of X-rays. The WOMAC scores were also collected. Then multiple regression equations was applied for the linear regression analysis of correlation between the X-ray measurement and WOMAC scores. Results:There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (P0.05). Conclusion :(D X-ray measurement of knee joint can reflect the WOMAC scores to a certain extent. ② It is necessary to measure the X-ray mechanical axis of knee, which is important for diagnosis and treatment of osteoarthritis. ③The correlation between libial angle Joint gap angle on antero-poslerior X-ray and WOMAC scores is significant,which can be used to assess the functional recovery of patients before and after treatment.%目的:进行膝关节骨性关节炎X线测量与WOMAC评分的多重线性回归分析,结合临床和生物力学分析两者的关系.方法:自2011年3月至2011年7月,膝关节骨性关节炎患者140例250膝,左侧132膝,右侧118膝;年龄40~71岁,平均54.68岁.应用MB-RULER测量软件测量患膝正侧位X线片股骨角、胫骨角、股胫角及关节间隙角等数值,并采集WOMAC评分,应用多重线性回归建立回归方程分析两者相关性.结果:应用多重线性回归分析正位X线片测量数值与WOMAC评分的回归方程有统计学意义(P<0.05),而侧位X线片测量数值与WOMAC评分的回归方程无统计学意义(P>0.05).结论:

  17. Complementary and Alternative Therapy Use in Multiple Sclerosis: A Cross-Sectional Survey

    Directory of Open Access Journals (Sweden)

    Muhteşem Gedizlioğlu

    2015-03-01

    Full Text Available OBJECTIVE: In this study, we aimed to document the use of alternative and complementary therapy (ACT in multiple sclerosis (MS patients, and the relationship with socio-demographic characteristics and clinical data of the ACT users. METHODS: We evaluated 101 MS patients followed in our outpatient clinic and living in Eagean district, mostly in central İzmir. All of them completed a questionnaire related to the use of ACT. Patients who had used any type of ACT were statistically compared with patients who had never used such a treatment in terms of their socio-demographic and clinical variables. RESULTS: Of the 101 patients reviewed, 65% were women. The majority of the patients had relapsing-remitting MS (90.1%. Sixty eight percent of participants reported never using ACT. Twenty six of the patients had tried an alternative treatment once. Only half of them reported some benefit. However, only six patients were still on this treatment. CONCLUSION: We conclude that our patients who were mostly living in the central city and had medium to high educational levels, did not have knowledge about ACT. The patients using such therapies mostly did not experience a meaningful benefit. No statistically meaningful difference was found between the alternative treatment users and non-users regarding EDSS scores, MS subtype, disease duration, age, educational level and residential district.

  18. The VLT-FLAMES Tarantula Survey VIII. Multiplicity properties of the O-type star population

    CERN Document Server

    Sana, H; de Mink, S E; Dunstall, P R; Evans, C J; Henault-Brunet, V; Apellaniz, J Maiz; Ramirez-Agudelo, O H; Taylor, W D; Walborn, N R; Clark, J S; Crowther, P A; Herrero, A; Gieles, M; Langer, N; Lennon, D J; Vink, J S

    2012-01-01

    Aims. We analyze the multiplicity properties of the massive O-type star population. With 360 O-type stars, this is the largest homogeneous sample of massive stars analyzed to date. Methods. We use multi-epoch spectroscopy and variability analysis to identify spectroscopic binaries. We also use a Monte-Carlo method to correct for observational biases. Results. We observe a spectroscopic binary fraction of 0.35\\pm0.03, which corresponds to the fraction of objects displaying statistically significant radial velocity variations with an amplitude of at least 20km/s. We compute the intrinsic binary fraction to be 0.51\\pm0.04. We adopt power-laws to describe the intrinsic period and mass-ratio distributions: f_P ~ (log P)^\\pi\\ (with 0.15 7.8', i.e. approx117 pc) and among the O9.7 I/II objects are however significantly lower than expected from statistical fluctuations. Conclusions. Using simple evolutionary considerations, we estimate that over 50% of the current O star population in 30 Dor will exchange mass with ...

  19. 多元回归与年龄移算法在老龄人口研究中的整合分析%The Meta-analysis of Multiple Regression and Age Shifting Algorithm in the Aging Population Research

    Institute of Scientific and Technical Information of China (English)

    吴启凡

    2015-01-01

    我国人口老龄化问题日趋明显,现阶段对人口老龄化的模型研究依然存在问题,在对我国人口老龄化情况的研究过程中,单纯运用多元回归的方法需考虑多重共线性问题,为避免此问题则要优选变量,但在逐步回归过程中又会将对其可能造成显著性影响的偏相关扰动项忽略,而且单纯运用回归模型进行预测将在长时间序列中造成较大误差,为此,结合年龄移算法对回归因子进行单项细度预测,再运用回归方程进行宏观计算,将大幅提高预测的精度。本文以男性人口、女性人口、城市人口、乡村人口等因素进行动态研究,先根据相关性分析,初步筛选影响因素,再通过多元线性回归找到人口老龄化与人口结构中相关因素的数量关系,这里通过逐步回归出恰好出现了偏相关扰动项无法接受检验的情况,我们运用两种标准化方法结合Mann-Whitney U检验进行验证分析,最终运用年龄移算模型和回归矩阵预测人口老龄化发展趋势,并根据预测结果进行相关分析,给出相应评价。%The problem of our aging population has become more evident ,the model for the study of population aging is still a problem at this stage. In the case of China’s aging population of the study,the issues of using a simple method (multiple re-gression multicollinearity) is to be considered,To avoid this problem may lead to the Multicollinearity,however they will be the likely cause of a significant impact which can be easily ignored. And use the simple regression model to predict the result in the long sequence may also give rise to more errors,so we need to combined with age-shift algorithm to return the individu-al factors fineness forecast,then use the macro regression equation to calculate,which will significantly improve the prediction accuracy. In this paper,According to correlation analysis,initial screening factors from

  20. 多元线性回归构建湖南省县医院卫生人力和床位预测模型%Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression

    Institute of Scientific and Technical Information of China (English)

    凌茹; 刘家望

    2011-01-01

    目的:应用多元线性回归构建湖南省县医院卫生人力和床位的预测模型.方法:分层随机抽样确立16个样本县,按照统一调查表进行调查,根据文献研究筛选20项指标进行多元线性回归分析.结果:进入卫生技术人员数多元线性回归模型的自变量包括:县域的城镇居民可支配收入、粗死亡率、县域医疗机构的实有床位、业务用房面积、专业设备总值、万元以上设备数、固定资产、长期负债、医疗收入、医疗支出、门急诊人次、出院人次、实际开放总床日、病床使用率;进入县医院床位数多元线性回归模型的自变量包括:县域的65岁及以上的人口数、城镇居民可支配收入,县域医疗机构的卫生技术人员、业务用房面积、专业设备总值、固定资产、长期负债、医疗收入、医疗支出、门急诊人次、出院人次、实际开放总床日、病床使用率、平均住院日.结论:预测模型解释性和拟合程度较好,可以用于短中期预测.%Objective To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. Methods We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires, and multiple linear regression analysis with 20 quotas selected by literature view was done. Results Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above

  1. Regression analysis by example

    CERN Document Server

    Chatterjee, Samprit

    2012-01-01

    Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

  2. Using an innovative multiple regression procedure in a cancer population (Part II: fever, depressive affect, and mobility problems clarify an influential symptom pair (pain–fatigue/weakness and cluster (pain–fatigue/weakness–sleep problems

    Directory of Open Access Journals (Sweden)

    Francoeur RB

    2014-12-01

    contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters. Keywords: depression, moderated regression, multicollinearity, sickness behavior, statistical interaction, symptom cluster

  3. Relapsing-remitting multiple sclerosis: patterns of response to disease-modifying therapies and associated factors: a national survey.

    Science.gov (United States)

    Sá, Maria José; de Sá, João; Sousa, Lívia

    2014-12-01

    Current treatments for relapsing-remitting multiple sclerosis (RRMS) are only partially effective. The objective of this study was to characterize treatment response in RRMS patients in Portugal to 12-month therapy with first-line disease-modifying therapies. In this retrospective study, neurologists at participating centers completed survey questionnaires using records of patients with RRMS who had received first-line treatment with one of five European Medicine Agency-approved agents in the 12 months prior to inclusion in the survey. Sub-optimal responders included patients treated for at least 1 year, and who had ≥1 relapse(s) or an increase of 1.5 points on the Expanded Disability Status Scale (EDSS; if baseline EDSS was 0) or an increase of ≥0.5 points (baseline EDSS ≥1). Optimal responders included patients treated for at least 1 year without relapse and who had an increase of EDSS (if baseline EDSS was 0) or no increase in EDSS (baseline EDSS ≥1). Data for 1,131 patients from 15 centers were analyzed. Twenty-six percent (95% confidence interval 23-28%) of patients had sub-optimal treatment response. Duration of therapy (P EDSS score (P EDSS score at baseline and did not differ among therapies. Neurologists should closely monitor patients to optimize treatment strategies and better control disease, improving prognosis.

  4. Unitary Response Regression Models

    Science.gov (United States)

    Lipovetsky, S.

    2007-01-01

    The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

  5. Flexible survival regression modelling

    DEFF Research Database (Denmark)

    Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben

    2009-01-01

    Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...

  6. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....

  7. Primary care approaches to musculoskeletal multiple-site joint pain pharmacological therapy: a survey of general practitioners.

    Science.gov (United States)

    Raja, Rafi; Kingsbury, Sarah R; Wise, Elspeth; Conaghan, Philip G

    2014-10-01

    Chronic multiple-site joint pain (MSJP) due to osteoarthritis and soft tissue disorders is common in people over 50 years old and associated with poor outcomes. This study examined current pharmacological approaches to MSJP management in primary care. One hundred and fifty general practitioners (GPs) attending an educational seminar participated in an electronic survey (mean response rate 96%). Most GPs reported treating multiple painful joints concurrently (78%) compared with focusing on a single joint (21%). The majority believed there was no difference in analgesia for different disorders when selecting paracetamol (84%), non-steroidal anti-inflammatory drugs (NSAID)/COX-2 inhibitors (57%) or opioids (70%). When optimising therapy, intra-class optimisation (increase NSAID dose 41%, change to another NSAID/COX-2 inhibitor 30%) was preferred to inter-class step up therapy (add opioid 23%, change to opioid 6%). For NSAID gastrointestinal intolerance, the preference was to add a gastro-protective agent (74%). There is a need to better characterise MSJP and examine optimal pharmacotherapy regimens.

  8. A survey paper on Ant Colony Optimization Routing algorithm for selecting Multiple Feasible Paths for Packet Switched Networks

    Directory of Open Access Journals (Sweden)

    Meenakshi R Patel

    2012-03-01

    Full Text Available ACO algorithms for datagram networks was given by Di Caro Dorigo, in year 1996. Basic mechanisms in typical ACO routing algorithms is Ant-like agents are proactively generated at the nodes to find/check paths toward assigned destinations Ants move hop-by-hop according to a exploratory routing policy based on the local routing .After reaching their destination, ants retrace their path and update nodes routing information according to the quality of the path. Routing information is statistical estimates of the time-to-go to the destination maintained in pheromone arrays. Data are probabilistically spread over the paths according to their estimated quality as stored in the pheromone variables. AntNet algorithms may cause the network congestion and stagnation as the routing table converges. In this paper we perform a survey on modified AntNet routing algorithm using Multiple Ant-Colony Optimization. Multiple ant colonies with different pheromone updating mechanism have different searching traits. By leveraging this feature, much of work is done by designing a set of adaptive rules to facilitate the collaboration between these colonies. This approach can balance the diversity and convergence of solutions generated by different ant colonies and also overcome the problem of Stagnation.

  9. The Hubble Space Telescope UV Legacy Survey of Galactic Globular Clusters. IX. The Atlas of Multiple Stellar Populations

    CERN Document Server

    Milone, A P; Renzini, A; Marino, A F; Bedin, L R; Vesperini, E; D'Antona, F; Nardiello, D; Anderson, J; King, I R; Yong, D; Bellini, A; Aparicio, A; Barbuy, B; Brown, T M; Cassisi, S; Ortolani, S; Salaris, M; Sarajedini, A; van der Marel, R P

    2016-01-01

    We use high-precision photometry of red-giant-branch (RGB) stars in 57 Galactic globular clusters (GCs), mostly from the `Hubble Space Telescope (HST) UV Legacy Survey of Galactic globular clusters', to identify and characterize their multiple stellar populations. For each cluster the pseudo two-color diagram (or `chromosome map') is presented, built with a suitable combination of stellar magnitudes in the F275W, F336W, F438W and F814W filters that maximizes the separation between multiple populations. In the chromosome map of most GCs (Type I clusters), stars separate in two distinct groups that we identify with the first (1G) and the second generation (2G). This identification is further supported by noticing that 1G stars have primordial (oxygen-rich, sodium-poor) chemical composition, whereas 2G stars are enhanced in sodium and depleted in oxygen. This 1G-2G separation is not possible for a few GCs where the two sequences have apparently merged into an extended, continuous sequence. In some GCs (Type II c...

  10. The Many Facets of Genetic Literacy: Assessing the Scalability of Multiple Measures for Broad Use in Survey Research.

    Directory of Open Access Journals (Sweden)

    Leah R Abrams

    Full Text Available To determine how three dimensions of genetic literacy (familiarity, skills, and factual knowledge fit the hierarchy of knowledge outlined in E.M. Rogers' Diffusion of Innovations to better conceptualize lay understandings of genomics.A consumer panel representing the US adult population (N = 1016 completed an electronic survey in November 2013. Adjusting for education, we used correlations, principle components analysis, Mokken Scale tests, and linear regressions to assess how scores on the three genetic literacy sub-dimensions fit an ordered scale.The three scores significantly loaded onto one factor, even when adjusting for education. Analyses revealed moderate strength in scaling (0.416, p<0.001 and a difficulty ordering that matched Rogers' hierarchy (knowledge more difficult than skills, followed by familiarity. Skills scores partially mediated the association between familiarity and knowledge with a significant indirect effect (0.241, p<0.001.We established an ordering in genetic literacy sub-dimensions such that familiarity with terminology precedes skills using information, which in turn precedes factual knowledge. This ordering is important to contextualizing previous findings, guiding measurement in future research, and identifying gaps in the understanding of genomics relevant to the demands of differing applications.

  11. Multiple information sources and consequences of conflicting information about medicine use during pregnancy: a multinational Internet-based survey.

    Science.gov (United States)

    Hämeen-Anttila, Katri; Nordeng, Hedvig; Kokki, Esa; Jyrkkä, Johanna; Lupattelli, Angela; Vainio, Kirsti; Enlund, Hannes

    2014-02-20

    A wide variety of information sources on medicines is available for pregnant women. When using multiple information sources, there is the risk that information will vary or even conflict. The objective of this multinational study was to analyze the extent to which pregnant women use multiple information sources and the consequences of conflicting information, and to investigate which maternal sociodemographic, lifestyle, and medical factors were associated with these objectives. An anonymous Internet-based questionnaire was made accessible during a period of 2 months, on 1 to 4 Internet websites used by pregnant women in 5 regions (Eastern Europe, Western Europe, Northern Europe, Americas, Australia). A total of 7092 responses were obtained (n=5090 pregnant women; n=2002 women with a child younger than 25 weeks). Descriptive statistics and logistic regression analysis were used. Of the respondents who stated that they needed information, 16.16% (655/4054) used one information source and 83.69% (3393/4054) used multiple information sources. Of respondents who used more than one information source, 22.62% (759/3355) stated that the information was conflicted. According to multivariate logistic regression analysis, factors significantly associated with experiencing conflict in medicine information included being a mother (OR 1.32, 95% CI 1.11-1.58), having university (OR 1.33, 95% CI 1.09-1.63) or other education (OR 1.49, 95% CI 1.09-2.03), residing in Eastern Europe (OR 1.52, 95% CI 1.22-1.89) or Australia (OR 2.28, 95% CI 1.42-3.67), use of 3 (OR 1.29, 95% CI 1.04-1.60) or >4 information sources (OR 1.82, 95% CI 1.49-2.23), and having ≥2 chronic diseases (OR 1.49, 95% CI 1.18-1.89). Because of conflicting information, 43.61% (331/759) decided not to use medication during pregnancy, 30.30% (230/759) sought a new information source, 32.67% (248/759) chose to rely on one source and ignore the conflicting one, 25.03% (190/759) became anxious, and 2.64% (20/759) did

  12. Trends and determinants for early initiation of and exclusive breastfeeding under six months in Vietnam: results from the Multiple Indicator Cluster Surveys, 2000–2011

    Directory of Open Access Journals (Sweden)

    Quyen Thi-Tu Bui

    2016-02-01

    Full Text Available Background: There is strong evidence that breastfeeding (BF significantly benefits mothers and infants in various ways. Yet the proportion of breastfed babies in Vietnam is low and continues to decline. This study fills an important evidence gap in BF practices in Vietnam. Objective: This paper examines the trend of early initiation of BF and exclusive BF from 2000 to 2011 in Vietnam and explores the determinants at individual and contextual levels. Design: Data from three waves of the Multiple Indicator Cluster Survey were combined to estimate crude and adjusted trends over time for two outcomes – early initiation of BF and exclusive BF. Three-level logistic regressions were fitted to examine the impacts of both individual and contextual characteristics on early initiation of BF and exclusive BF in the 2011 data. Results: Both types of BF showed a decreasing trend over time after controlling for individual-level characteristics but this trend was more evident for early initiation of BF. Apart from child's age, individual-level characteristics were not significant predictors of the BF outcomes, but provincial characteristics had a strong association. When controlling for individual-level characteristics, mothers living in provinces with a higher percentage of mothers with more than three children were more likely to have initiated early BF (odds ratio [OR]: 1.06; confidence interval [CI]: 1.02–1.11 but less likely to exclusively breastfeed their babies (OR: 0.94; CI: 0.88–1.01. Mothers living in areas with a higher poverty rate were more likely to breastfeed exclusively (OR: 1.07; CI: 1.02–1.13, and those who delivered by Caesarean section were less likely to initiate early BF. Conclusions: Our results suggest that environmental factors are becoming more important for determining BF practices in Vietnam. Intervention programs should therefore not only consider individual factors, but should also consider the potential impact of

  13. The MS Choices Survey: findings of a study assessing physician and patient perspectives on living with and managing multiple sclerosis

    Directory of Open Access Journals (Sweden)

    Riñon A

    2011-12-01

    Full Text Available Alberto Riñon1, Mandy Buch2, Derek Holley2, Elisabetta Verdun11Merck Serono S.A. – Geneva, Switzerland, a branch of Merck Serono S.A., Coinsins, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany; 2GfK Healthcare, London, UKBackground: Treatment of multiple sclerosis (MS with disease-modifying drugs (DMDs can reduce relapse frequency and delay disability progression. Although adherence to DMDs is difficult to measure accurately, evidence suggests that poor adherence is common and can compromise treatment success. There are likely to be multiple factors underlying poor adherence. To better understand these factors, the global MS Choices Survey investigated patient and physician perspectives regarding key aspects of MS diagnosis, treatment adherence and persistence, and disease management.Methods: The survey was conducted in seven countries and involved patients with MS (age 18–60 years; MS diagnosis for ≥1 year; current treatment with a DMD and physicians (neurologist for 3–30 years; treating ≥15 patients with MS per average month; >60% of time spent in clinical practice. Separate questionnaires were used for physicians and patients, each containing approximately 30 questions.Results: Questionnaires were completed by 331 patients and 280 physicians. Several differences were observed between the responses of patients and physicians, particularly for questions relating to treatment adherence. Overall, the proportion of patients reporting taking a treatment break (31% was almost twice that estimated by physicians (on average 17%. The reasons cited for poor adherence also differed between patients and physicians. For example, more physicians cited side effects as the main reason for poor patient adherence (82%, than responding patients (42%.Conclusions: Physicians may underestimate the scale of poor adherence to DMDs, which could impact on their assessment of treatment efficacy and result in inappropriate treatment escalation. In

  14. Neurocognitive deficits in schizophrenia are associated with alterations in blood levels of neurosteroids: a multiple regression analysis of findings from a double-blind, randomized, placebo-controlled, crossover trial with DHEA.

    Science.gov (United States)

    Ritsner, Michael S; Strous, Rael D

    2010-01-01

    While neurosteroids exert multiple effects in the central nervous system, their associations with neurocognitive deficits in schizophrenia are not yet fully understood. The purpose of this study was to identify the contribution of circulating levels of dehydroepiandrosterone (DHEA), its sulfate (DHEAS), androstenedione, and cortisol to neurocognitive deficits through DHEA administration in schizophrenia. Data regarding cognitive function, symptom severity, daily doses, side effects of antipsychotic agents and blood levels of DHEA, DHEAS, androstenedione and cortisol were collected among 55 schizophrenia patients in a double-blind, randomized, placebo-controlled, crossover trial with DHEA at three intervals: upon study entry, after 6weeks of DHEA administration (200mg/d), and after 6weeks of a placebo period. Multiple regression analysis was applied for predicting sustained attention, memory, and executive function scores across three examinations controlling for clinical, treatment and background covariates. Findings indicated that circulating DHEAS and androstenedione levels are shown as positive predictors of cognitive functioning, while DHEA level as negative predictor. Overall, blood neurosteroid levels and their molar ratios accounted for 16.5% of the total variance in sustained attention, 8-13% in visual memory tasks, and about 12% in executive functions. In addition, effects of symptoms, illness duration, daily doses of antipsychotic agents, side effects, education, and age of onset accounted for variability in cognitive functioning in schizophrenia. The present study suggests that alterations in circulating levels of neurosteroids and their molar ratios may reflect pathophysiological processes, which, at least partially, underlie cognitive dysfunction in schizophrenia. Copyright 2009 Elsevier Ltd. All rights reserved.

  15. Regression for economics

    CERN Document Server

    Naghshpour, Shahdad

    2012-01-01

    Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T

  16. On empirical analysis of factors affecting commercial house prices based on multiple linear regression%基于多元线性回归的商品房价格影响因素实证分析

    Institute of Scientific and Technical Information of China (English)

    刘枬; 梁晨

    2014-01-01

    According to Keynesianism and property-value bubble theory,the paper takes the statistical yearbooks in the period of 2000~2012 as the data sample,finds the three factors on house prices,that is,contemporary per capita income,newly increased housing areas,and property prices in previous year,discusses their influence on property prices by adopting the relevant analysis and multiple linear regression,figures out the main factors for the price fluctuation,and points out some suggestions for controlling the property prices.%依据凯恩斯理论和房地产泡沫理论,以统计年鉴2000年~2012年相关数据作样本,选取了当年年人均收入、新增住房面积、上一年商品房价格三个影响房价的因素,利用相关分析和多元线性回归分析测度其对房价的影响,找出了引起房价波动的主要因素,并提出了控制房价的建议。

  17. 基于人工鱼群算法的多元线性回归分析问题处理%Solution to problems concerning AFSA- based multiple linear regression analysis

    Institute of Scientific and Technical Information of China (English)

    李媛

    2011-01-01

    人工鱼群算法(AFsA)是一种基于动物行为的自治体寻优模式,依据鱼类活动特点构建的新型智能仿生算法.简要介绍了AFSA算法的基本原理,描述了使用AFSA算法解决多元线性回归分析问题的步骤和结果.仿真实验结果表明,AFSA算法在处理多元线性回归分析问题上是一种简单、高效的算法.%A brief introduction is made of the basic principles of Artificial Fish Swarm Algorithm (AFSA), a new algorithm with autonomous optimization mode according to the behavior of fish swarm. The steps are analyzed for the solution to problems concerning AFSA - based multiple linear regression analysis. The simulation experiment proves that it is simple and efficient.

  18. Impact of oral health in patients with multiple sclerosis and epilepsy: a survey in a neurology clinic.

    Science.gov (United States)

    Koffman, Boyd M; Khuder, Sadik; Mutgi, Sunil; Crooks, Ryan; Herial, Nabeel

    2012-01-01

    This study investigated the impact of oral health on neurological disorders using the Oral Health Impact Profile (OHIP). A total of 460 subjects completed the OHIP, including 141 control subjects who did not have any neurological conditions. Of the 319 subjects with a neurological diagnosis who were enrolled in the study, 31% had multiple sclerosis (MS), 34% had epilepsy, and 34% had other neurological conditions. Compared to the control group, mean age (p = .001), education (p = .003), and household income levels (p ≤ .001) were statistically significantly lower among subjects with epilepsy than in the other two groups. The majority of the study populations were Caucasian and the percentage was highest in those with MS (87%). Patients with any neurologic diagnosis had greater physical pain and disability than controls. Adjusting for demographic variables, the impact of physical disability was statistically significantly higher in patients with any neurological diagnosis (including MS and epilepsy) (OR = 4.49). In multinomial regression, the strongest association of physical disability impact was noted in patients with epilepsy (OR = 5.17). The physical disability domain of the OHIP is more commonly associated with a neurological diagnosis, including MS, and the association is strongest in patients with diagnosis of epilepsy. © 2012 Special Care Dentistry Association and Wiley Periodicals, Inc.

  19. XRA image segmentation using regression

    Science.gov (United States)

    Jin, Jesse S.

    1996-04-01

    Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.

  20. Autistic epileptiform regression.

    Science.gov (United States)

    Canitano, Roberto; Zappella, Michele

    2006-01-01

    Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.

  1. Scaled Sparse Linear Regression

    CERN Document Server

    Sun, Tingni

    2011-01-01

    Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...

  2. Rolling Regressions with Stata

    OpenAIRE

    Kit Baum

    2004-01-01

    This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...

  3. A SURVEY OF THE HIGH ORDER MULTIPLICITY OF NEARBY SOLAR-TYPE BINARY STARS WITH Robo-AO

    Energy Technology Data Exchange (ETDEWEB)

    Riddle, Reed L.; Bui, Khanh; Dekany, Richard G.; Kulkarni, Shrinivas; Tendulkar, Shriharsh P. [Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA 91125 (United States); Tokovinin, Andrei [Cerro Tololo Inter-American Observatory, Casilla 603, La Serena (Chile); Mason, Brian D.; Hartkopf, William I. [U.S. Naval Observatory, 3450 Massachusetts Avenue, Washington, DC 20392-5420 (United States); Roberts, Lewis C. Jr. [Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109 (United States); Baranec, Christoph [Institute for Astronomy, University of Hawai" i at Mānoa, Hilo, HI 96720-2700 (United States); Law, Nicholas M. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Burse, Mahesh P.; Das, H. K.; Punnadi, Sujit; Ramaprakash, A. N. [Inter-University Centre for Astronomy and Astrophysics, Ganeshkhind, Pune, 411007 (India)

    2015-01-20

    We conducted a survey of nearby binary systems composed of main sequence stars of spectral types F and G in order to improve our understanding of the hierarchical nature of multiple star systems. Using Robo-AO, the first robotic adaptive optics instrument, we collected high angular resolution images with deep and well-defined detection limits in the Sloan Digital Sky Survey i' band. A total of 695 components belonging to 595 systems were observed. We prioritized observations of faint secondary components with separations over 10'' to quantify the still poorly constrained frequency of their subsystems. Of the 214 secondaries observed, 39 contain such subsystems; 19 of those were discovered with Robo-AO. The selection-corrected frequency of secondary subsystems with periods from 10{sup 3.5} to 10{sup 5} days is 0.12 ± 0.03, the same as the frequency of such companions to the primary. Half of the secondary pairs belong to quadruple systems where the primary is also a close pair, showing that the presence of subsystems in both components of the outer binary is correlated. The relatively large abundance of 2+2 quadruple systems is a new finding, and will require more exploration of the formation mechanism of multiple star systems. We also targeted close binaries with periods less than 100 yr, searching for their distant tertiary components, and discovered 17 certain and 2 potential new triples. In a subsample of 241 close binaries, 71 have additional outer companions. The overall frequency of tertiary components is not enhanced, compared to all (non-binary) targets, but in the range of outer periods from 10{sup 6} to 10{sup 7.5} days (separations on the order of 500 AU), the frequency of tertiary components is 0.16 ± 0.03, exceeding the frequency of similar systems among all targets (0.09) by almost a factor of two. Measurements of binary stars with Robo-AO allowed us to compute first orbits for 9 pairs and to improve orbits of another 11 pairs.

  4. A Survey of the High Order Multiplicity of Nearby Solar-type Binary Stars with Robo-AO

    Science.gov (United States)

    Riddle, Reed L.; Tokovinin, Andrei; Mason, Brian D.; Hartkopf, William I.; Roberts, Lewis C., Jr.; Baranec, Christoph; Law, Nicholas M.; Bui, Khanh; Burse, Mahesh P.; Das, H. K.; Dekany, Richard G.; Kulkarni, Shrinivas; Punnadi, Sujit; Ramaprakash, A. N.; Tendulkar, Shriharsh P.

    2015-01-01

    We conducted a survey of nearby binary systems composed of main sequence stars of spectral types F and G in order to improve our understanding of the hierarchical nature of multiple star systems. Using Robo-AO, the first robotic adaptive optics instrument, we collected high angular resolution images with deep and well-defined detection limits in the Sloan Digital Sky Survey i' band. A total of 695 components belonging to 595 systems were observed. We prioritized observations of faint secondary components with separations over 10'' to quantify the still poorly constrained frequency of their subsystems. Of the 214 secondaries observed, 39 contain such subsystems; 19 of those were discovered with Robo-AO. The selection-corrected frequency of secondary subsystems with periods from 103.5 to 105 days is 0.12 ± 0.03, the same as the frequency of such companions to the primary. Half of the secondary pairs belong to quadruple systems where the primary is also a close pair, showing that the presence of subsystems in both components of the outer binary is correlated. The relatively large abundance of 2+2 quadruple systems is a new finding, and will require more exploration of the formation mechanism of multiple star systems. We also targeted close binaries with periods less than 100 yr, searching for their distant tertiary components, and discovered 17 certain and 2 potential new triples. In a subsample of 241 close binaries, 71 have additional outer companions. The overall frequency of tertiary components is not enhanced, compared to all (non-binary) targets, but in the range of outer periods from 106 to 107.5 days (separations on the order of 500 AU), the frequency of tertiary components is 0.16 ± 0.03, exceeding the frequency of similar systems among all targets (0.09) by almost a factor of two. Measurements of binary stars with Robo-AO allowed us to compute first orbits for 9 pairs and to improve orbits of another 11 pairs.

  5. Unbiased Quasi-regression

    Institute of Scientific and Technical Information of China (English)

    Guijun YANG; Lu LIN; Runchu ZHANG

    2007-01-01

    Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.

  6. Introduction to regression graphics

    CERN Document Server

    Cook, R Dennis

    2009-01-01

    Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

  7. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2005-01-01

    Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987

  8. Factors Influencing Marginal Fit of Crowns after Cementation: A multiple Linear Regression Analysis.%全冠边缘间隙影响因素的多元线性回归分析

    Institute of Scientific and Technical Information of China (English)

    章少萍; 马守治; 陈熙; 童新文; 李秀容; 张维文

    2011-01-01

    evaluated before and after cementation. Multiple linear regression analysis was used to determine whether the independent variables mentioned above had an impact on the MDAC. Results: Marginal discrepancies increased significantly after cementation.The backward multiple regression analysis showed that the FLP, TAAWP, HP, MDBC, and PLRC were jointly predictives of the MDAC. Conclusion: The FLP and MDBC may have a weak influence on the MDAC, while the TAAWP, HP and PLRC impact MDAC more significantly.

  9. Linkage of Multiple Sclerosis and Guillain-Barre Syndrome: A Population-Based Survey in Isfahan, Iran

    Directory of Open Access Journals (Sweden)

    Masoud Etemadifar

    2012-01-01

    Full Text Available Background. Multiple Sclerosis (MS and Guillain Barre Syndrome (GBS are autoimmune demyelinating disorders of Central and Peripheral Nervous system, respectively. The coexistence of these two syndromes in an individual's life span is rare. Objectives. To inspect throughout Isfahan MS society (IMSS records for MS cases who had history of documented GBS whether before the onset of MS or after it. Methods. This retrospective survey was carried out by analyzing the clinical records of 3,522 MS patients who were registered with IMSS, from April 2003 to July 2010. Eligible cases were requested to attend to IMSS for final clinical/paraclinical examinations. Results. Among 3,522 (2,716 women and 806 men MS subjects, we could identify seven patients (six females and one male with documented diagnosis of GBS. Six patients (five women and one man had developed MS within 6.5±7.0 (range: 1–16 years after being diagnosed with GBS and one (a woman had developed GBS three years after the diagnosis of MS. Conclusion. It seems that the development of MS in individuals with history of GBS is more than a simple incidental event.

  10. Extent of Fecal Contamination of Household Drinking Water in Nepal: Further Analysis of Nepal Multiple Indicator Cluster Survey 2014.

    Science.gov (United States)

    Kandel, Pragya; Kunwar, Ritu; Lamichhane, Prabhat; Karki, Surendra

    2017-02-08

    Water sources classified as "improved" may not necessarily provide safe drinking water for householders. We analyzed data from Nepal Multiple Indicator Cluster Survey 2014 to explore the extent of fecal contamination of household drinking water. Fecal contamination was detected in 81.2% (95% confidence interval [CI]: 77.9-84.2) household drinking water from improved sources and 89.6% (95% CI: 80.4-94.7) in water samples from unimproved sources. In adjusted analysis, there was no difference in odds of fecal contamination of household drinking water between improved and unimproved sources. We observed significantly lower odds of fecal contamination of drinking water in households in higher wealth quintiles, where soap and water were available for handwashing and in households employing water treatment. The extent of contamination of drinking water as observed in this study highlights the huge amount of effort required to ensure the provision of safely managed water in Nepal by 2030 as aimed in sustainable development goals.

  11. A survey of the high order multiplicity of nearby solar-type binary stars with Robo-AO

    CERN Document Server

    Riddle, Reed L; Mason, Brian D; Hartkopf, William I; Roberts,, Lewis C; Baranec, Christoph; Law, Nicholas M; Bui, Khanh; Burse, Mahesh P; Das, H K; Dekany, Richard G; Kulkarni, Shrinivas; Punnadi, Sujit; Ramaprakash, A N; Tendulkar, Shriharsh P

    2014-01-01

    We conducted a survey of nearby binary systems composed of main sequence stars of spectral types F and G in order to improve our understanding of the hierarchical nature of multiple star systems. Using Robo-AO, the first robotic adaptive optics instrument, we collected high angular resolution images with deep and well-defined detection limits in the SDSS $i'$ band. A total of 695 components belonging to 595 systems were observed. We prioritized observations of faint secondary components with separations over $10''$ to quantify the still poorly constrained frequency of their sub-systems. Of the 214 secondaries observed, 39 contain such subsystems; 19 of those were discovered with Robo-AO. The selection-corrected frequency of secondary sub-systems with periods from $10^{3.5}$ to $10^5$ days is 0.12$\\pm$0.03, the same as the frequency of such companions to the primary. Half of the secondary pairs belong to quadruple systems where the primary is also a close pair, showing that the presence of sub-systems in both ...

  12. Determination of vitamin B1 and B2 in compound vitamin Busing multiple lincar regression spectrophotometry%多元线性分光光度法测定复合维生素B片

    Institute of Scientific and Technical Information of China (English)

    杨敏; 刘惠军

    2001-01-01

    目的:建立复合维生素B片中维生素B1、维生素B2的含量测定方法。方法:采用多波长多元线性回归分光光度法,维生素B1的测定波长为245nm、248nm和251nm,维生素B2的测定波长为258nm、261nm和264nm,溶剂为盐酸液(9→1000)。结果:标准曲线在4.0μg/ml20.0μg/ml范围内呈良好线性关系。平均回收率维生素B1为99.8%,RSD小于2.21%;维生素B2为98.7%,RSD小于1.98%。结论:该法可准确测定复合维生素B中两组分的含量。%objective: To establish the determination of vitamins B1 and B2in compound vitamin B. Methods: The multiple linear regression spectrophotometry was used with wavelength of 245nm, 248nm, 251nm and 258nm, 261nm and 264nm. The solvent was HCL. Results: The calibration curve was linear between 4.0μg/ml to 20.0mg/ml. The relative standard deviations vitamins B1 and B2 were less than 1.1% and 0.7% at the three different concentrations. Their recovery rates was 101.4% and 99.4%. Conclusion: This method is accurate for the determination of vitamins B1 and B2 in the compound vitamin B.

  13. Morse–Smale Regression

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)

    2012-01-19

    This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.

  14. Regression to Causality

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...

  15. Effect of survey mode on response patterns

    DEFF Research Database (Denmark)

    Christensen, Anne Illemann; Ekholm, Ola; Glümer, Charlotte

    2014-01-01

    BACKGROUND: While face-to-face interviews are considered the gold standard of survey modes, self-administered questionnaires are often preferred for cost and convenience. This article examines response patterns in two general population health surveys carried out by face-to-face interview and self......-administered questionnaire, respectively. METHOD: Data derives from a health interview survey in the Region of Southern Denmark (face-to-face interview) and The Danish Health and Morbidity Survey 2010 (self-administered questionnaire). Identical questions were used in both surveys. Data on all individuals were obtained from...... administrative registers and linked to survey data at individual level. Multiple logistic regression analyses were used to examine the effect of survey mode on response patterns. RESULTS: The non-response rate was higher in the self-administered survey (37.9%) than in the face-to-face interview survey (23...

  16. Boosted beta regression.

    Directory of Open Access Journals (Sweden)

    Matthias Schmid

    Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

  17. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  18. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2013-01-01

    Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

  19. 学龄儿童心理行为特点与饮食行为关系多元线性回归分析%Multiple linear regression analysis of the correlation between psychological behaviors characteristics and diet behavior in the school-age children

    Institute of Scientific and Technical Information of China (English)

    沈洁; 张朋; 刘福康; 王婷婷; 孙桂菊; 刘江红

    2011-01-01

    Objective To analyze the effect of behavior characteristics of children on diet behavior, providing scientific knowledge for nutrition instruction. Methods A total of 302 fifth-grade primary school children from Jintan were selected. A questionnaire survey on nutritional behavior and psychological behavior was conducted among them from June to July 2010. The effect factors of diet behavior were analyzed with univariate linear regression. The variable (P < 0.05 in univariate regression model) was selected to establish multivariate regression model. Results The univariate linear regression analysis showed that anxious/depressed, social problems, thought problems, attention problems, aggressive behavior score and total score in boys and girls were negatively correlated with diet behavior score. Multiple linear regressions showed that attention problem scores in boys and thougHt problem scores in girls were negatively correlated with diet behavior score. Conclusion The findings demonstrate that psychological behaviors of school-age children are closely associated with diet behaviors. It is necessary to add health-related curriculum on risk behaviors prevention into quality education, carry out comprehensive behavior surveillance on psychology, nutrition and diet, and conduct early intervention in adolescents.%目的 分析学龄儿童心理行为特点对其饮食行为的影响,为有针对性地对其进行营养教育提供科学依据.方法 选择江苏省金坛市302名五年级儿童作为研究对象,于2010年5月至6月对其进行饮食行为和心理行为问题的问卷调查.利用单因素线性回归分析饮食行为的影响因素,从单因素分析结果中选择P< 0.05的变量建立多元线性回归模型.结果 单因素线性回归分析显示,不同性别间焦虑抑郁、社交问题、思维问题、注意缺陷、攻击行为因子分及总分与饮食量表得分负相关;多元线性回归分析显示,男性儿童注意缺陷

  20. Transductive Ordinal Regression

    CERN Document Server

    Seah, Chun-Wei; Ong, Yew-Soon

    2011-01-01

    Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...

  1. Study of Method for Predict Rate of Penetration Based of Multiple Regression Analysis%基于多元回归分析的钻速预测方法研究

    Institute of Scientific and Technical Information of China (English)

    李昌盛

    2013-01-01

    During the drilling process, drilling optimization technology can reduce drilling cost and construction accidents, and drilling rate prediction is the basis of optimum drilling technology. The current equations of drilling rate prediction consider different emphasis about drilling rate influencing factors, most of the equations are simplified so prediction accuracy can not meet the demand of the drilling operations. A few equations considering them comprehensively lead to poor operability and limited practicality because of complex structure. Based on the Bour-goyne & Youngs equation, 300 sets of mud log data from Yuanba drilling database are extracted and calculated the coefficients of drilling rate equation by multiple regression analysis. Differentiation of formation results in different regression coefficient. The drilling data of offset well is adopted to prove accuracy, the result is found that real drilling rate and predicted drilling rate are consistent and mean relative error is less than 13%. The method improves solving precision of coefficients of drilling rate equation, computing processes is relatively simple and data needed is easily obtained. Hence the method has important implications for popularizing drilling optimization techniques.%在钻井过程中,优化钻井技术可以降低钻井成本和减少施工事故,而钻速预测是优化钻井的基础.目前的钻速预测方程繁多,所考虑的钻速影响因素各有侧重,大多数方程做了诸多简化,预测精度不能满足现场需要;少数方程即便考虑比较全面,但由于结构复杂,可操作性差,实用性受到一定限制.以Bourgoyne &Youngs方程作为理论基础,从元坝地区钻井数据库抽取300组录井数据,利用多元回归分析方法进行钻速方程系数的求解,针对不同地层得到的回归系数也不相同.采用同区块井的钻井数据进行验证,发现其预测钻速与实际钻速基本相符,平均相对误差小于13%,说明所

  2. Nonparametric Predictive Regression

    OpenAIRE

    Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.

    2012-01-01

    A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...

  3. Trends in socioeconomic inequalities in child malnutrition in Vietnam: findings from the Multiple Indicator Cluster Surveys, 2000–2011

    Directory of Open Access Journals (Sweden)

    Vu Duy Kien

    2016-02-01

    Full Text Available Background: Child malnutrition is not only a major contributor to child mortality and morbidity, but it can also determine socioeconomic status in adult life. The rate of under-five child malnutrition in Vietnam has significantly decreased, but associated inequality issues still need attention. Objective: This study aims to explore trends, contributing factors, and changes in inequalities for under-five child malnutrition in Vietnam between 2000 and 2011. Design: Data were drawn from the Viet Nam Multiple Indicator Cluster Survey for the years 2000 and 2011. The dependent variables used for the study were stunting, underweight, and wasting of under-five children. The concentration index was calculated to see the magnitude of child malnutrition, and the inequality was decomposed to understand the contributions of determinants to child malnutrition. The total differential decomposition was used to identify and explore factors contributing to changes in child malnutrition inequalities. Results: Inequality in child malnutrition increased between 2000 and 2011, even though the overall rate declined. Most of the inequality in malnutrition was due to ethnicity and socioeconomic status. The total differential decomposition showed that the biggest and second biggest contributors to the changes in underweight inequalities were age and socioeconomic status, respectively. Socioeconomic status was the largest contributor to inequalities in stunting. Conclusions: Although the overall level of child malnutrition was improved in Vietnam, there were significant differences in under-five child malnutrition that favored those who were more advantaged in socioeconomic terms. The impact of socioeconomic inequalities in child malnutrition has increased over time. Multifaceted approaches, connecting several relevant ministries and sectors, may be necessary to reduce inequalities in childhood malnutrition.

  4. Trends in socioeconomic inequalities in child malnutrition in Vietnam: findings from the Multiple Indicator Cluster Surveys, 2000-2011.

    Science.gov (United States)

    Kien, Vu Duy; Lee, Hwa-Young; Nam, You-Seon; Oh, Juhwan; Giang, Kim Bao; Van Minh, Hoang

    2016-01-01

    Child malnutrition is not only a major contributor to child mortality and morbidity, but it can also determine socioeconomic status in adult life. The rate of under-five child malnutrition in Vietnam has significantly decreased, but associated inequality issues still need attention. This study aims to explore trends, contributing factors, and changes in inequalities for under-five child malnutrition in Vietnam between 2000 and 2011. Data were drawn from the Viet Nam Multiple Indicator Cluster Survey for the years 2000 and 2011. The dependent variables used for the study were stunting, underweight, and wasting of under-five children. The concentration index was calculated to see the magnitude of child malnutrition, and the inequality was decomposed to understand the contributions of determinants to child malnutrition. The total differential decomposition was used to identify and explore factors contributing to changes in child malnutrition inequalities. Inequality in child malnutrition increased between 2000 and 2011, even though the overall rate declined. Most of the inequality in malnutrition was due to ethnicity and socioeconomic status. The total differential decomposition showed that the biggest and second biggest contributors to the changes in underweight inequalities were age and socioeconomic status, respectively. Socioeconomic status was the largest contributor to inequalities in stunting. Although the overall level of child malnutrition was improved in Vietnam, there were significant differences in under-five child malnutrition that favored those who were more advantaged in socioeconomic terms. The impact of socioeconomic inequalities in child malnutrition has increased over time. Multifaceted approaches, connecting several relevant ministries and sectors, may be necessary to reduce inequalities in childhood malnutrition.

  5. Trends in socioeconomic inequalities in child malnutrition in Vietnam: findings from the Multiple Indicator Cluster Surveys, 2000–2011

    Science.gov (United States)

    Kien, Vu Duy; Lee, Hwa-Young; Nam, You-Seon; Oh, Juhwan; Giang, Kim Bao; Van Minh, Hoang

    2016-01-01

    Background Child malnutrition is not only a major contributor to child mortality and morbidity, but it can also determine socioeconomic status in adult life. The rate of under-five child malnutrition in Vietnam has significantly decreased, but associated inequality issues still need attention. Objective This study aims to explore trends, contributing factors, and changes in inequalities for under-five child malnutrition in Vietnam between 2000 and 2011. Design Data were drawn from the Viet Nam Multiple Indicator Cluster Survey for the years 2000 and 2011. The dependent variables used for the study were stunting, underweight, and wasting of under-five children. The concentration index was calculated to see the magnitude of child malnutrition, and the inequality was decomposed to understand the contributions of determinants to child malnutrition. The total differential decomposition was used to identify and explore factors contributing to changes in child malnutrition inequalities. Results Inequality in child malnutrition increased between 2000 and 2011, even though the overall rate declined. Most of the inequality in malnutrition was due to ethnicity and socioeconomic status. The total differential decomposition showed that the biggest and second biggest contributors to the changes in underweight inequalities were age and socioeconomic status, respectively. Socioeconomic status was the largest contributor to inequalities in stunting. Conclusions Although the overall level of child malnutrition was improved in Vietnam, there were significant differences in under-five child malnutrition that favored those who were more advantaged in socioeconomic terms. The impact of socioeconomic inequalities in child malnutrition has increased over time. Multifaceted approaches, connecting several relevant ministries and sectors, may be necessary to reduce inequalities in childhood malnutrition. PMID:26950558

  6. Wealth inequality and utilization of reproductive health services in the Republic of Vanuatu: insights from the multiple indicator cluster survey, 2007

    Directory of Open Access Journals (Sweden)

    Rahman Mosiur

    2011-12-01

    Full Text Available Abstract Background Although the Republic of Vanuatu has improved maternal indicators, more needs to be done to improve equity among the poorest in the use of reproductive health services to expedite the progress towards the Millennium Development Goal 5(MDG 5 target. While large developing country studies provide evidence of a rich-poor gap in reproductive health services utilization, not much is written in terms of Pacific Islands. Thus, this study aims to examine the degree of inequality in utilization of reproductive health services in a nationally representative sample of Vanuatu households. Methods This paper used data from the 2007 Vanuatu Multiple Indicator Cluster Survey (MICS. The analyses were based on responses from 615 ever married women, living with at least one child below two years of age. Outcomes included antenatal care (ANC and use of birth attendants at delivery, place of delivery, and counseling and testing for HIV/AIDS. Descriptive statistics and multivariate logistic regression methods were employed in the analysis. Results Findings revealed that the economic well-being status of the household to which women belong, played a crucial role in explaining the variation in service utilization. Inequality in utilization was found to be more pronounced between the poorest and richest groups within the wealth quintiles. In adjusted models, mothers in the richest bands of wealth were 5.50 (95% confidence interval [CI]: 1.34-22.47, 2.12 (95% CI: 1.02-3.42, 4.0 (95% CI 1.58-10.10, and 2.0 (95% CI 1.02-5.88 times more likely to have assisted delivery from medically trained personnel, have institutional deliveries, and have counseling and testing for HIV/AIDS. Conclusions Association between household wealth inequality and utilization of ANC and delivery assistance from medically trained personnel, institutional delivery, and counseling and testing for HIV/AIDS suggest that higher utilization of reproductive health care services in

  7. Household trends in access to improved water sources and sanitation facilities in Vietnam and associated factors: findings from the Multiple Indicator Cluster Surveys, 2000–2011

    Directory of Open Access Journals (Sweden)

    Tran Thi Tuyet-Hanh

    2016-02-01

    Full Text Available Background: Despite progress made by the Millennium Development Goal (MDG number 7.C, Vietnam still faces challenges with regard to the provision of access to safe drinking water and basic sanitation. Objective: This paper describes household trends in access to improved water sources and sanitation facilities separately, and analyses factors associated with access to improved water sources and sanitation facilities in combination. Design: Secondary data from the Vietnam Multiple Indicator Cluster Survey in 2000, 2006, and 2011 were analyzed. Descriptive statistics and tests of significance describe trends over time in access to water and sanitation by location, demographic and socio-economic factors. Binary logistic regressions (2000, 2006, and 2011 describe associations between access to water and sanitation, and geographic, demographic, and socio-economic factors. Results: There have been some outstanding developments in access to improved water sources and sanitation facilities from 2000 to 2011. In 2011, the proportion of households with access to improved water sources and sanitation facilities reached 90% and 77%, respectively, meeting the 2015 MDG targets for safe drinking water and basic sanitation set at 88% and 75%, respectively. However, despite these achievements, in 2011, only 74% of households overall had access to combined improved drinking water and sanitation facilities. There were also stark differences between regions. In 2011, only 47% of households had access to both improved water and sanitation facilities in the Mekong River Delta compared with 94% in the Red River Delta. In 2011, households in urban compared to rural areas were more than twice as likely (odds ratio [OR]: 2.2; 95% confidence interval [CI]: 1.9–2.5 to have access to improved water and sanitation facilities in combination, and households in the highest compared with the lowest wealth quintile were over 40 times more likely (OR: 42.3; 95% CI: 29.8–60

  8. Multiple regression analysis of the factors affecting the sinking per-formance of large-scale tuna purse seine%金枪鱼围网沉降性能影响因子的多元回归分析

    Institute of Scientific and Technical Information of China (English)

    周成; 许柳雄; 张新峰; 朱国平; 唐浩; 王学昉

    2013-01-01

    We evaluated the relationship between sinking depth and a number of factors using multiple regression to determine the external patterns of sinking a purse seine. We collected data on sinking depth, gear operation, and shooting duration(T), shooting velocity(S), current speed(Cs), current direction(Cd), and purse line(L) length from tuna purse seiners owned by Shanghai Fisheries General Corp. between September and December in 2011. Current direction and shooting velocity had no effect on sinking depth (P>0.05, df=53). Conversely, the shooting duration, current speed, and length of the purse line had a significant effect on sinking depth (P0.05, df=53),而放网时间、流速及括纲长度对网具沉降深度的影响均显著(P<0.05, df=53),最终得到沉降深度(D)和放网时间(T)、流速(Cs)及扩纲长度(L)的多元回归模型为D=0.069T−144.5Cs2+0.022L+158。主成分分析表明,中国金枪鱼围网作业在一定程度上没有利用有关渔场海况信息对捕捞操作进行有益的改变。在一般操作条件(放网时间为550 s,括纲长度为2000 m)时,模型预测表明:在无风无流的情况下,网具沉降深度平均值为236.78 m,95%置信区间为[211.51,262.04],而遇海流急促的情况(流速为1 kn)时,网具沉降深度平均值为92.27 m,95%置信区间为[60.56,123.97]。

  9. Testing for Association Using Multiple Response Survey Data: Approximate Procedures Based on the Rao-Scott Approach

    Science.gov (United States)

    Thomas, D. Roland; Decady, Yves J.

    2004-01-01

    This article discusses approximate tests of marginal association in 2-way tables in which one or both response variables admit multiple responses. Although multiple-response questions appear in all fields of research, including sociology, education, and marketing, the development of association tests that can be used with multiple-response data is…

  10. 城乡居民幸福感影响因素多重线性回归和路径分析%The Comparison between Multiple Linear Regression Analysis and Path Analysis on the Influencing Factors of Subjective Wellbeing among Urban and Rural Residents

    Institute of Scientific and Technical Information of China (English)

    徐曼; 柴云; 李涛; 卢丽; 刘冰

    2015-01-01

    目的:以多重线性回归和路径分析深入探讨城乡居民主观幸福感影响因素及其相互作用关系。方法:应用简单随机抽样方法选取1480名城乡居民运用Campbell主观幸福感指数量表进行问卷调查,数据采用多重线性回归和路径分析进行对比分析。结果:城乡居民总体幸福感指数平均得分为(11.17±1.99)。多重线性回归分析显示,未来目标、压力应对方式、自评健康状况、兴趣爱好、城乡居住地、休闲时间是幸福感指数的预测因素,标准化偏回归数分别为0.261、0.182、0.152、0.066、0.071、0.051。路径分析发现,未来目标、压力应对方式、自评健康状况直接作用于幸福感指数,路径系数为0.285、0.191、0.160,兴趣爱好、个人月收入、受教育程度、年龄间接作用于幸福感指数,间接效应为0.08、-0.04、0.10、-0.07。结论:主观幸福感与个体生理、心理、社会因素等多个内外部因素有关,多重线性回归和路径分析在探讨居民主观幸福感影响因素及其作用关系过程中各有侧重,相互补充。%Objective:To further explore the subjective wellbeing among urban and rural residents and their influencing factors and the factors'interactions by multiple linear regression analyses and path analy-ses .Methods:By simple random sampling method ,a questionnaire survey was conducted among 1480 ur-ban and rural residents using Compbell subjective well -being index Scale ,and data were compared and analyzed by multiple linear regression analyses and path analyses .Results:The average of urban and rural residents'general well-being index was(11.17 ±1.99).Multiple linear regression analysis showed that future goals,stress coping styles,self-rated health status,hobbies,place from urban and rural residence and leisure time were predictors of well -being index ,and their standardized partial regression

  11. [Understanding logistic regression].

    Science.gov (United States)

    El Sanharawi, M; Naudet, F

    2013-10-01

    Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.

  12. Constrained Sparse Galerkin Regression

    CERN Document Server

    Loiseau, Jean-Christophe

    2016-01-01

    In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.

  13. Practical Session: Logistic Regression

    Science.gov (United States)

    Clausel, M.; Grégoire, G.

    2014-12-01

    An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.

  14. Minimax Regression Quantiles

    DEFF Research Database (Denmark)

    Bache, Stefan Holst

    A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....

  15. Regression calibration with heteroscedastic error variance.

    Science.gov (United States)

    Spiegelman, Donna; Logan, Roger; Grove, Douglas

    2011-01-01

    The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation data containing the gold standard must be available. This estimator is a closed-form correction of the uncorrected primary regression coefficients, which may be of logistic or Cox proportional hazards model form, and is closely related to the version of regression calibration developed by Rosner et al. (1990). The primary regression model can include multiple covariates measured without error. The use of these estimators is illustrated in two data sets, one taken from occupational epidemiology (the ACE study) and one taken from nutritional epidemiology (the Nurses' Health Study). In both cases, although there was evidence of moderate heteroscedasticity, there was little difference in estimation or inference using this new procedure compared to standard regression calibration. It is shown theoretically that unless the relative risk is large or measurement error severe, standard regression calibration approximations will typically be adequate, even with moderate heteroscedasticity in the measurement error model variance. In a detailed simulation study, standard regression calibration performed either as well as or better than the new estimator. When the disease is rare and the errors normally distributed, or when measurement error is moderate, standard regression calibration remains the method of choice.

  16. Logistic regression analysis of risk factors of stress complications in patients with multiple trauma%多发伤患者应激并发症危险因素的logistic回归分析

    Institute of Scientific and Technical Information of China (English)

    徐文鹏; 应佑国; 方玉明; 秦宗和

    2014-01-01

    目的:探讨导致多发伤患者应激并发症相关的危险因素。方法:对64例多发伤患者应激并发症危险因素的相关指标进行单因素及多因素logistic 回归分析,收集血常规、C反应蛋白、血糖、血电解质、血气分析、肝肾功能等指标以及测定血清总三碘甲状腺原氨酸(TT3)、血清游离三碘甲状腺原氨酸(FT3)、血清总甲状腺素(TT4)、血清游离总甲状腺素、促甲状腺激素、促肾上腺皮质激素( ACTH)、血皮质醇( COR)、生长激素,细胞因子白介素( IL)-1、IL-6、IL-8、IL-10、肿瘤坏死因子( TNF-α)等。结果:64例出现应激性溃疡、急性呼吸窘迫综合征等应激并发症48例,出现应激并发症和无并发症患者,急性生理与慢性健康(APACH)Ⅱ评分、COR、ACTH、TT3、FT3、TT4、IL-6、IL-8、IL-10、TNF-α等差异均有统计学意义(P<0.05~P<0.01);多因素logistic分析表明,APACH Ⅱ评分、TT4、COR、IL-6和IL-10为早期应激并发症的独立危险因素(P<0.05~P<0.01)。结论:多发伤患者APACHⅡ评分以及创伤后TT4、COR、IL-6、IL-10等指标的变化与患者应激性消化道出血及呼吸窘迫综合征等应激并发症的出现密切相关。%Objective:To investigate the risk factors of stress complications in patients with multiple trauma. Methods:The clinical data of stress complications in 64 patients with multiple trauma were analyzed by univariate and multivariate binary Logistic regression. The data of routine blood,c-reactive protein,blood sugar,blood electrolytes,blood gas analysis,indexes of liver and kidney function, serum total iodine thyroid glycine(TT3),serum free thyroid original three iodine glycine(FT3),total serum thyroxine(TT4),serum free thyroid hormone,thyroid stimulating hormone,adrenocorticotropic hormone(ACTH),blood cortisol(COR),growth hormone,cytokine interleukin(IL)-1,IL-6,IL 8,IL-10 and tumor necrosis factor(TNF-α) in all cases were investigated. Results

  17. Nonlinear Regression with R

    CERN Document Server

    Ritz, Christian; Parmigiani, Giovanni

    2009-01-01

    R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.

  18. Adaptive metric kernel regression

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    2000-01-01

    regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

  19. Software Regression Verification

    Science.gov (United States)

    2013-12-11

    of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their

  20. Linear Regression Analysis

    CERN Document Server

    Seber, George A F

    2012-01-01

    Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.