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Sample records for regression analysis explained

  1. Common pitfalls in statistical analysis: Linear regression analysis

    Directory of Open Access Journals (Sweden)

    Rakesh Aggarwal

    2017-01-01

    Full Text Available In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

  2. Understanding logistic regression analysis

    OpenAIRE

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

  3. Logistic Regression: Concept and Application

    Science.gov (United States)

    Cokluk, Omay

    2010-01-01

    The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

  4. Data to support "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations & Biological Condition"

    Data.gov (United States)

    U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...

  5. Understanding logistic regression analysis.

    Science.gov (United States)

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

  6. 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

  7. Explaining the heterogeneous scrapie surveillance figures across Europe: a meta-regression approach

    Directory of Open Access Journals (Sweden)

    Ru Giuseppe

    2007-06-01

    Full Text Available Abstract Background Two annual surveys, the abattoir and the fallen stock, monitor the presence of scrapie across Europe. A simple comparison between the prevalence estimates in different countries reveals that, in 2003, the abattoir survey appears to detect more scrapie in some countries. This is contrary to evidence suggesting the greater ability of the fallen stock survey to detect the disease. We applied meta-analysis techniques to study this apparent heterogeneity in the behaviour of the surveys across Europe. Furthermore, we conducted a meta-regression analysis to assess the effect of country-specific characteristics on the variability. We have chosen the odds ratios between the two surveys to inform the underlying relationship between them and to allow comparisons between the countries under the meta-regression framework. Baseline risks, those of the slaughtered populations across Europe, and country-specific covariates, available from the European Commission Report, were inputted in the model to explain the heterogeneity. Results Our results show the presence of significant heterogeneity in the odds ratios between countries and no reduction in the variability after adjustment for the different risks in the baseline populations. Three countries contributed the most to the overall heterogeneity: Germany, Ireland and The Netherlands. The inclusion of country-specific covariates did not, in general, reduce the variability except for one variable: the proportion of the total adult sheep population sampled as fallen stock by each country. A large residual heterogeneity remained in the model indicating the presence of substantial effect variability between countries. Conclusion The meta-analysis approach was useful to assess the level of heterogeneity in the implementation of the surveys and to explore the reasons for the variation between countries.

  8. Multiple regression analysis of Jominy hardenability data for boron treated steels

    International Nuclear Information System (INIS)

    Komenda, J.; Sandstroem, R.; Tukiainen, M.

    1997-01-01

    The relations between chemical composition and their hardenability of boron treated steels have been investigated using a multiple regression analysis method. A linear model of regression was chosen. The free boron content that is effective for the hardenability was calculated using a model proposed by Jansson. The regression analysis for 1261 steel heats provided equations that were statistically significant at the 95% level. All heats met the specification according to the nordic countries producers classification. The variation in chemical composition explained typically 80 to 90% of the variation in the hardenability. In the regression analysis elements which did not significantly contribute to the calculated hardness according to the F test were eliminated. Carbon, silicon, manganese, phosphorus and chromium were of importance at all Jominy distances, nickel, vanadium, boron and nitrogen at distances above 6 mm. After the regression analysis it was demonstrated that very few outliers were present in the data set, i.e. data points outside four times the standard deviation. The model has successfully been used in industrial practice replacing some of the necessary Jominy tests. (orig.)

  9. Linear regression analysis: part 14 of a series on evaluation of scientific publications.

    Science.gov (United States)

    Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

    2010-11-01

    Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication. This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience. After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately. The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.

  10. A systematic review and meta-regression analysis of mivacurium for tracheal intubation

    NARCIS (Netherlands)

    Vanlinthout, L.E.H.; Mesfin, S.H.; Hens, N.; Vanacker, B.F.; Robertson, E.N.; Booij, L.H.D.J.

    2014-01-01

    We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent

  11. Survival analysis II: Cox regression

    NARCIS (Netherlands)

    Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.

    2011-01-01

    In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the

  12. Principal component regression analysis with SPSS.

    Science.gov (United States)

    Liu, R X; Kuang, J; Gong, Q; Hou, X L

    2003-06-01

    The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

  13. Health care: necessity or luxury good? A meta-regression analysis

    OpenAIRE

    Iordache, Ioana Raluca

    2014-01-01

    When estimating the influence income per capita exerts on health care expenditure, the research in the field offers mixed results. Studies employ different data, estimation techniques and models, which brings about the question whether these differences in research design play any part in explaining the heterogeneity of reported outcomes. By employing meta-regression analysis, the present paper analyzes 220 estimates of health spending income elasticity collected from 54 studies and finds tha...

  14. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  15. Applied regression analysis a research tool

    CERN Document Server

    Pantula, Sastry; Dickey, David

    1998-01-01

    Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...

  16. Regression Analysis by Example. 5th Edition

    Science.gov (United States)

    Chatterjee, Samprit; Hadi, Ali S.

    2012-01-01

    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 and thoroughly…

  17. A primer for biomedical scientists on how to execute model II linear regression analysis.

    Science.gov (United States)

    Ludbrook, John

    2012-04-01

    1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

  18. Regression analysis with categorized regression calibrated exposure: some interesting findings

    Directory of Open Access Journals (Sweden)

    Hjartåker Anette

    2006-07-01

    Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

  19. PATH ANALYSIS WITH LOGISTIC REGRESSION MODELS : EFFECT ANALYSIS OF FULLY RECURSIVE CAUSAL SYSTEMS OF CATEGORICAL VARIABLES

    OpenAIRE

    Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University

    2001-01-01

    This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.

  20. Explaining variation in nascent entrepreneurship

    NARCIS (Netherlands)

    A.J. van Stel (André); A.R.M. Wennekers (Sander); P. Reynolds (Paul); A.R. Thurik (Roy)

    2004-01-01

    textabstractThis paper aims at explaining cross-country variation in nascent entrepreneurship. Regression analysis is applied using various explanatory variables derived from three different approaches. We make use of the Global Entrepreneurship Monitor database, including nascent entrepreneurship

  1. Explaining nascent entrepreneurship across countries

    NARCIS (Netherlands)

    A.R. Thurik (Roy); A.J. van Stel (André); A.R.M. Wennekers (Sander); P. Reynolds (Paul)

    2003-01-01

    textabstractThis paper aims at explaining cross-country variation in nascent entrepreneurship. Regression analysis is applied using various explanatory variables derived from three different approaches. We make use of the Global Entrepreneurship Monitor database, including nascent entrepreneurship

  2. Gaussian process regression analysis for functional data

    CERN Document Server

    Shi, Jian Qing

    2011-01-01

    Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime

  3. Femoral anteversion and tibial torsion only explain 25% of variance in regression analysis of foot progression angle in children with diplegic cerebral palsy

    Science.gov (United States)

    2013-01-01

    Background The relationship between torsional bony deformities and rotational gait parameters has not been sufficiently investigated. This study was to investigate the degree of contribution of torsional bony deformities to rotational gait parameters in patients with diplegic cerebral palsy (CP). Methods Thirty three legs from 33 consecutive ambulatory patients (average age 9.5 years, SD 6.9 years; 20 males and 13 females) with diplegic CP who underwent preoperative three dimensional gait analysis, foot radiographs, and computed tomography (CT) were included. Adjusted foot progression angle (FPA) was retrieved from gait analysis by correcting pelvic rotation from conventional FPA, which represented the rotational gait deviation of the lower extremity from the tip of the femoral head to the foot. Correlations between rotational gait parameters (FPA, adjusted FPA, average pelvic rotation, average hip rotation, and average knee rotation) and radiologic measurements (acetabular version, femoral anteversion, knee torsion, tibial torsion, and anteroposteriortalo-first metatarsal angle) were analyzed. Multiple regression analysis was performed to identify significant contributing radiographic measurements to adjusted FPA. Results Adjusted FPA was significantly correlated with FPA (r=0.837, pregression analysis, femoral anteversion (p=0.026) and tibial torsion (p=0.034) were found to be the significant contributing structural deformities to the adjusted FPA (R2=0.247). Conclusions Femoral anteversion and tibial torsion were found to be the significant structural deformities that could affect adjusted FPA in patients with diplegic CP. Femoral anteversion and tibial torsion could explain only 24.7% of adjusted FPA. PMID:23767833

  4. Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

    International Nuclear Information System (INIS)

    Jafri, Y.Z.; Kamal, L.

    2007-01-01

    Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

  5. Regression Analysis and the Sociological Imagination

    Science.gov (United States)

    De Maio, Fernando

    2014-01-01

    Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.

  6. Polylinear regression analysis in radiochemistry

    International Nuclear Information System (INIS)

    Kopyrin, A.A.; Terent'eva, T.N.; Khramov, N.N.

    1995-01-01

    A number of radiochemical problems have been formulated in the framework of polylinear regression analysis, which permits the use of conventional mathematical methods for their solution. The authors have considered features of the use of polylinear regression analysis for estimating the contributions of various sources to the atmospheric pollution, for studying irradiated nuclear fuel, for estimating concentrations from spectral data, for measuring neutron fields of a nuclear reactor, for estimating crystal lattice parameters from X-ray diffraction patterns, for interpreting data of X-ray fluorescence analysis, for estimating complex formation constants, and for analyzing results of radiometric measurements. The problem of estimating the target parameters can be incorrect at certain properties of the system under study. The authors showed the possibility of regularization by adding a fictitious set of data open-quotes obtainedclose quotes from the orthogonal design. To estimate only a part of the parameters under consideration, the authors used incomplete rank models. In this case, it is necessary to take into account the possibility of confounding estimates. An algorithm for evaluating the degree of confounding is presented which is realized using standard software or regression analysis

  7. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

    Science.gov (United States)

    Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  8. 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.

  9. Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.

    Science.gov (United States)

    Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty

    2014-06-17

    The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.

  10. Preface to Berk's "Regression Analysis: A Constructive Critique"

    OpenAIRE

    de Leeuw, Jan

    2003-01-01

    It is pleasure to write a preface for the book ”Regression Analysis” of my fellow series editor Dick Berk. And it is a pleasure in particular because the book is about regression analysis, the most popular and the most fundamental technique in applied statistics. And because it is critical of the way regression analysis is used in the sciences, in particular in the social and behavioral sciences. Although the book can be read as an introduction to regression analysis, it can also be read as a...

  11. Multicollinearity and Regression Analysis

    Science.gov (United States)

    Daoud, Jamal I.

    2017-12-01

    In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

  12. Hierarchical regression analysis in structural Equation Modeling

    NARCIS (Netherlands)

    de Jong, P.F.

    1999-01-01

    In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main

  13. Multivariate Regression Analysis and Slaughter Livestock,

    Science.gov (United States)

    AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

  14. Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis.

    Science.gov (United States)

    Oguntunde, Philip G; Lischeid, Gunnar; Dietrich, Ottfried

    2018-03-01

    This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease (P  1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

  15. Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

    Science.gov (United States)

    Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

    2015-01-01

    Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

  16. Bayesian logistic regression analysis

    NARCIS (Netherlands)

    Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

    2012-01-01

    In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

  17. Regression analysis using dependent Polya trees.

    Science.gov (United States)

    Schörgendorfer, Angela; Branscum, Adam J

    2013-11-30

    Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

  18. RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

    Science.gov (United States)

    This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

  19. Multiple regression and beyond an introduction to multiple regression and structural equation modeling

    CERN Document Server

    Keith, Timothy Z

    2014-01-01

    Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

  20. Gaussian Process Regression Model in Spatial Logistic Regression

    Science.gov (United States)

    Sofro, A.; Oktaviarina, A.

    2018-01-01

    Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

  1. Moderation analysis using a two-level regression model.

    Science.gov (United States)

    Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

    2014-10-01

    Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

  2. 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.

  3. Two Paradoxes in Linear Regression Analysis

    Science.gov (United States)

    FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

    2016-01-01

    Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

  4. Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

    Science.gov (United States)

    Azen, Razia; Traxel, Nicole

    2009-01-01

    This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

  5. Linear regression and sensitivity analysis in nuclear reactor design

    International Nuclear Information System (INIS)

    Kumar, Akansha; Tsvetkov, Pavel V.; McClarren, Ryan G.

    2015-01-01

    Highlights: • Presented a benchmark for the applicability of linear regression to complex systems. • Applied linear regression to a nuclear reactor power system. • Performed neutronics, thermal–hydraulics, and energy conversion using Brayton’s cycle for the design of a GCFBR. • Performed detailed sensitivity analysis to a set of parameters in a nuclear reactor power system. • Modeled and developed reactor design using MCNP, regression using R, and thermal–hydraulics in Java. - Abstract: The paper presents a general strategy applicable for sensitivity analysis (SA), and uncertainity quantification analysis (UA) of parameters related to a nuclear reactor design. This work also validates the use of linear regression (LR) for predictive analysis in a nuclear reactor design. The analysis helps to determine the parameters on which a LR model can be fit for predictive analysis. For those parameters, a regression surface is created based on trial data and predictions are made using this surface. A general strategy of SA to determine and identify the influential parameters those affect the operation of the reactor is mentioned. Identification of design parameters and validation of linearity assumption for the application of LR of reactor design based on a set of tests is performed. The testing methods used to determine the behavior of the parameters can be used as a general strategy for UA, and SA of nuclear reactor models, and thermal hydraulics calculations. A design of a gas cooled fast breeder reactor (GCFBR), with thermal–hydraulics, and energy transfer has been used for the demonstration of this method. MCNP6 is used to simulate the GCFBR design, and perform the necessary criticality calculations. Java is used to build and run input samples, and to extract data from the output files of MCNP6, and R is used to perform regression analysis and other multivariate variance, and analysis of the collinearity of data

  6. Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

  7. Prevalence of treponema species detected in endodontic infections: systematic review and meta-regression analysis.

    Science.gov (United States)

    Leite, Fábio R M; Nascimento, Gustavo G; Demarco, Flávio F; Gomes, Brenda P F A; Pucci, Cesar R; Martinho, Frederico C

    2015-05-01

    This systematic review and meta-regression analysis aimed to calculate a combined prevalence estimate and evaluate the prevalence of different Treponema species in primary and secondary endodontic infections, including symptomatic and asymptomatic cases. The MEDLINE/PubMed, Embase, Scielo, Web of Knowledge, and Scopus databases were searched without starting date restriction up to and including March 2014. Only reports in English were included. The selected literature was reviewed by 2 authors and classified as suitable or not to be included in this review. Lists were compared, and, in case of disagreements, decisions were made after a discussion based on inclusion and exclusion criteria. A pooled prevalence of Treponema species in endodontic infections was estimated. Additionally, a meta-regression analysis was performed. Among the 265 articles identified in the initial search, only 51 were included in the final analysis. The studies were classified into 2 different groups according to the type of endodontic infection and whether it was an exclusively primary/secondary study (n = 36) or a primary/secondary comparison (n = 15). The pooled prevalence of Treponema species was 41.5% (95% confidence interval, 35.9-47.0). In the multivariate model of meta-regression analysis, primary endodontic infections (P apical abscess, symptomatic apical periodontitis (P < .001), and concomitant presence of 2 or more species (P = .028) explained the heterogeneity regarding the prevalence rates of Treponema species. Our findings suggest that Treponema species are important pathogens involved in endodontic infections, particularly in cases of primary and acute infections. Copyright © 2015 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  8. Design and analysis of experiments classical and regression approaches with SAS

    CERN Document Server

    Onyiah, Leonard C

    2008-01-01

    Introductory Statistical Inference and Regression Analysis Elementary Statistical Inference Regression Analysis Experiments, the Completely Randomized Design (CRD)-Classical and Regression Approaches Experiments Experiments to Compare Treatments Some Basic Ideas Requirements of a Good Experiment One-Way Experimental Layout or the CRD: Design and Analysis Analysis of Experimental Data (Fixed Effects Model) Expected Values for the Sums of Squares The Analysis of Variance (ANOVA) Table Follow-Up Analysis to Check fo

  9. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  10. The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard

    and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

  11. Simulation Experiments in Practice: Statistical Design and Regression Analysis

    OpenAIRE

    Kleijnen, J.P.C.

    2007-01-01

    In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic DOE and regression analysis assume a single simulation response that is normally and independen...

  12. Relationship between the curve of Spee and craniofacial variables: A regression analysis.

    Science.gov (United States)

    Halimi, Abdelali; Benyahia, Hicham; Azeroual, Mohamed-Faouzi; Bahije, Loubna; Zaoui, Fatima

    2018-06-01

    The aim of this regression analysis was to identify the determining factors, which impact the curve of Spee during its genesis, its therapeutic reconstruction, and its stability, within a continuously evolving craniofacial morphology throughout life. We selected a total of 107 patients, according to the inclusion criteria. A morphological and functional clinical examination was performed for each patient: plaster models, tracing of the curve of Spee, crowding, Angle's classification, overjet and overbite were thus recorded. Then, we made a cephalometric analysis based on the standardized lateral cephalograms. In the sagittal dimension, we measured the values of angles ANB, SNA, SNB, SND, I/i; and the following distances: AoBo, I/NA, i/NB, SE and SL. In the vertical dimension, we measured the values of angles FMA, GoGn/SN, the occlusal plane, and the following distances: SAr, ArD, Ar/Con, Con/Gn, GoPo, HFP, HFA and IF. The statistical analysis was performed using the SPSS software with a significance level of 0.05. Our sample including 107 subjects was composed of 77 female patients (71.3%) and 30 male patients (27.8%) 7 hypodivergent patients (6.5%), 56 hyperdivergent patients (52.3%) and 44 normodivergent patients (41.1%). Patients' mean age was 19.35±5.95 years. The hypodivergent patients presented more pronounced curves of Spee compared to the normodivergent and the hyperdivergent populations; patients in skeletal Class I presented less pronounced curves of Spee compared to patients in skeletal Class II and Class III. These differences were non significant (P>0.05). The curve of Spee was positively and moderately correlated with Angle's classification, overjet, overbite, sellion-articulare distance, and breathing type (P0.05). Seventy five percent (75%) of the hyperdivergent patients with an oral breathing presented an overbite of 3mm, which is quite excessive given the characteristics often admitted for this typology; this parameter could explain the overbite

  13. Meta-regression analyses to explain statistical heterogeneity in a systematic review of strategies for guideline implementation in primary health care.

    Directory of Open Access Journals (Sweden)

    Susanne Unverzagt

    Full Text Available This study is an in-depth-analysis to explain statistical heterogeneity in a systematic review of implementation strategies to improve guideline adherence of primary care physicians in the treatment of patients with cardiovascular diseases. The systematic review included randomized controlled trials from a systematic search in MEDLINE, EMBASE, CENTRAL, conference proceedings and registers of ongoing studies. Implementation strategies were shown to be effective with substantial heterogeneity of treatment effects across all investigated strategies. Primary aim of this study was to explain different effects of eligible trials and to identify methodological and clinical effect modifiers. Random effects meta-regression models were used to simultaneously assess the influence of multimodal implementation strategies and effect modifiers on physician adherence. Effect modifiers included the staff responsible for implementation, level of prevention and definition pf the primary outcome, unit of randomization, duration of follow-up and risk of bias. Six clinical and methodological factors were investigated as potential effect modifiers of the efficacy of different implementation strategies on guideline adherence in primary care practices on the basis of information from 75 eligible trials. Five effect modifiers were able to explain a substantial amount of statistical heterogeneity. Physician adherence was improved by 62% (95% confidence interval (95% CI 29 to 104% or 29% (95% CI 5 to 60% in trials where other non-medical professionals or nurses were included in the implementation process. Improvement of physician adherence was more successful in primary and secondary prevention of cardiovascular diseases by around 30% (30%; 95% CI -2 to 71% and 31%; 95% CI 9 to 57%, respectively compared to tertiary prevention. This study aimed to identify effect modifiers of implementation strategies on physician adherence. Especially the cooperation of different health

  14. 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)

  15. On logistic regression analysis of dichotomized responses.

    Science.gov (United States)

    Lu, Kaifeng

    2017-01-01

    We study the properties of treatment effect estimate in terms of odds ratio at the study end point from logistic regression model adjusting for the baseline value when the underlying continuous repeated measurements follow a multivariate normal distribution. Compared with the analysis that does not adjust for the baseline value, the adjusted analysis produces a larger treatment effect as well as a larger standard error. However, the increase in standard error is more than offset by the increase in treatment effect so that the adjusted analysis is more powerful than the unadjusted analysis for detecting the treatment effect. On the other hand, the true adjusted odds ratio implied by the normal distribution of the underlying continuous variable is a function of the baseline value and hence is unlikely to be able to be adequately represented by a single value of adjusted odds ratio from the logistic regression model. In contrast, the risk difference function derived from the logistic regression model provides a reasonable approximation to the true risk difference function implied by the normal distribution of the underlying continuous variable over the range of the baseline distribution. We show that different metrics of treatment effect have similar statistical power when evaluated at the baseline mean. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. On two flexible methods of 2-dimensional regression analysis

    Czech Academy of Sciences Publication Activity Database

    Volf, Petr

    2012-01-01

    Roč. 18, č. 4 (2012), s. 154-164 ISSN 1803-9782 Grant - others:GA ČR(CZ) GAP209/10/2045 Institutional support: RVO:67985556 Keywords : regression analysis * Gordon surface * prediction error * projection pursuit Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/SI/volf-on two flexible methods of 2-dimensional regression analysis.pdf

  17. Explaining differences between hospitals in number of organ donors.

    NARCIS (Netherlands)

    Friele, R.D.; Coppen, R.; Marquet, R.L.; Gevers, J.K.M.

    2006-01-01

    The shortage of donor organs calls for a careful examination of all improvement options. In this study, 80 Dutch hospitals were compared. They provided 868 donors in a 5-year period, constituting 91% of all donors in that period in The Netherlands. Multilevel regression analysis was used to explain

  18. Orthodontic bracket bonding without previous adhesive priming: A meta-regression analysis.

    Science.gov (United States)

    Altmann, Aline Segatto Pires; Degrazia, Felipe Weidenbach; Celeste, Roger Keller; Leitune, Vicente Castelo Branco; Samuel, Susana Maria Werner; Collares, Fabrício Mezzomo

    2016-05-01

    To determine the consensus among studies that adhesive resin application improves the bond strength of orthodontic brackets and the association of methodological variables on the influence of bond strength outcome. In vitro studies were selected to answer whether adhesive resin application increases the immediate shear bond strength of metal orthodontic brackets bonded with a photo-cured orthodontic adhesive. Studies included were those comparing a group having adhesive resin to a group without adhesive resin with the primary outcome measurement shear bond strength in MPa. A systematic electronic search was performed in PubMed and Scopus databases. Nine studies were included in the analysis. Based on the pooled data and due to a high heterogeneity among studies (I(2)  =  93.3), a meta-regression analysis was conducted. The analysis demonstrated that five experimental conditions explained 86.1% of heterogeneity and four of them had significantly affected in vitro shear bond testing. The shear bond strength of metal brackets was not significantly affected when bonded with adhesive resin, when compared to those without adhesive resin. The adhesive resin application can be set aside during metal bracket bonding to enamel regardless of the type of orthodontic adhesive used.

  19. Resting-state functional magnetic resonance imaging: the impact of regression analysis.

    Science.gov (United States)

    Yeh, Chia-Jung; Tseng, Yu-Sheng; Lin, Yi-Ru; Tsai, Shang-Yueh; Huang, Teng-Yi

    2015-01-01

    To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear. Copyright © 2014 by the American Society of Neuroimaging.

  20. Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata

    Directory of Open Access Journals (Sweden)

    Oguntunji Abel Olusegun

    2017-01-01

    Full Text Available Morphometric parameters and their indices are central to the understanding of the type and function of livestock. The present study was conducted to predict body weight (BWT of adult Nigerian Muscovy ducks from nine (9 morphometric parameters and seven (7 body indices and also to identify the most important predictor of BWT among them using regression tree analysis (RTA. The experimental birds comprised of 1,020 adult male and female Nigerian Muscovy ducks randomly sampled in Rain Forest (203, Guinea Savanna (298 and Derived Savanna (519 agro-ecological zones. Result of RTA revealed that compactness; body girth and massiveness were the most important independent variables in predicting BWT and were used in constructing RT. The combined effect of the three predictors was very high and explained 91.00% of the observed variation of the target variable (BWT. The optimal regression tree suggested that Muscovy ducks with compactness >5.765 would be fleshy and have highest BWT. The result of the present study could be exploited by animal breeders and breeding companies in selection and improvement of BWT of Muscovy ducks.

  1. Development of a User Interface for a Regression Analysis Software Tool

    Science.gov (United States)

    Ulbrich, Norbert Manfred; Volden, Thomas R.

    2010-01-01

    An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.

  2. Method for nonlinear exponential regression analysis

    Science.gov (United States)

    Junkin, B. G.

    1972-01-01

    Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

  3. Regression of uveal malignant melanomas following cobalt-60 plaque. Correlates between acoustic spectrum analysis and tumor regression

    International Nuclear Information System (INIS)

    Coleman, D.J.; Lizzi, F.L.; Silverman, R.H.; Ellsworth, R.M.; Haik, B.G.; Abramson, D.H.; Smith, M.E.; Rondeau, M.J.

    1985-01-01

    Parameters derived from computer analysis of digital radio-frequency (rf) ultrasound scan data of untreated uveal malignant melanomas were examined for correlations with tumor regression following cobalt-60 plaque. Parameters included tumor height, normalized power spectrum and acoustic tissue type (ATT). Acoustic tissue type was based upon discriminant analysis of tumor power spectra, with spectra of tumors of known pathology serving as a model. Results showed ATT to be correlated with tumor regression during the first 18 months following treatment. Tumors with ATT associated with spindle cell malignant melanoma showed over twice the percentage reduction in height as those with ATT associated with mixed/epithelioid melanomas. Pre-treatment height was only weakly correlated with regression. Additionally, significant spectral changes were observed following treatment. Ultrasonic spectrum analysis thus provides a noninvasive tool for classification, prediction and monitoring of tumor response to cobalt-60 plaque

  4. Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

    Science.gov (United States)

    Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

    2017-09-01

    This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

  5. Detection of epistatic effects with logic regression and a classical linear regression model.

    Science.gov (United States)

    Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

    2014-02-01

    To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

  6. An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

    Directory of Open Access Journals (Sweden)

    Wen-Tsao Pan

    2016-01-01

    Full Text Available Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.

  7. Intermediate and advanced topics in multilevel logistic regression analysis.

    Science.gov (United States)

    Austin, Peter C; Merlo, Juan

    2017-09-10

    Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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

    Directory of Open Access Journals (Sweden)

    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. 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.

  10. Ridge Regression Signal Processing

    Science.gov (United States)

    Kuhl, Mark R.

    1990-01-01

    The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.

  11. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

    2013-01-01

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  12. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng

    2013-11-05

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  13. Analysis of Relationship Between Personality and Favorite Places with Poisson Regression Analysis

    Directory of Open Access Journals (Sweden)

    Yoon Song Ha

    2018-01-01

    Full Text Available A relationship between human personality and preferred locations have been a long conjecture for human mobility research. In this paper, we analyzed the relationship between personality and visiting place with Poisson Regression. Poisson Regression can analyze correlation between countable dependent variable and independent variable. For this analysis, 33 volunteers provided their personality data and 49 location categories data are used. Raw location data is preprocessed to be normalized into rates of visit and outlier data is prunned. For the regression analysis, independent variables are personality data and dependent variables are preprocessed location data. Several meaningful results are found. For example, persons with high tendency of frequent visiting to university laboratory has personality with high conscientiousness and low openness. As well, other meaningful location categories are presented in this paper.

  14. application of multilinear regression analysis in modeling of soil

    African Journals Online (AJOL)

    Windows User

    Accordingly [1, 3] in their work, they applied linear regression ... (MLRA) is a statistical technique that uses several explanatory ... order to check this, they adopted bivariate correlation analysis .... groups, namely A-1 through A-7, based on their relative expected ..... Multivariate Regression in Gorgan Province North of Iran” ...

  15. Background stratified Poisson regression analysis of cohort data.

    Science.gov (United States)

    Richardson, David B; Langholz, Bryan

    2012-03-01

    Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

  16. Linear regression methods a ccording to objective functions

    OpenAIRE

    Yasemin Sisman; Sebahattin Bektas

    2012-01-01

    The aim of the study is to explain the parameter estimation methods and the regression analysis. The simple linear regressionmethods grouped according to the objective function are introduced. The numerical solution is achieved for the simple linear regressionmethods according to objective function of Least Squares and theLeast Absolute Value adjustment methods. The success of the appliedmethods is analyzed using their objective function values.

  17. Poisson Regression Analysis of Illness and Injury Surveillance Data

    Energy Technology Data Exchange (ETDEWEB)

    Frome E.L., Watkins J.P., Ellis E.D.

    2012-12-12

    The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra

  18. A Quality Assessment Tool for Non-Specialist Users of Regression Analysis

    Science.gov (United States)

    Argyrous, George

    2015-01-01

    This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…

  19. Background stratified Poisson regression analysis of cohort data

    International Nuclear Information System (INIS)

    Richardson, David B.; Langholz, Bryan

    2012-01-01

    Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. (orig.)

  20. Valuing avoided morbidity using meta-regression analysis: what can health status measures and QALYs tell us about WTP?

    Science.gov (United States)

    Van Houtven, George; Powers, John; Jessup, Amber; Yang, Jui-Chen

    2006-08-01

    Many economists argue that willingness-to-pay (WTP) measures are most appropriate for assessing the welfare effects of health changes. Nevertheless, the health evaluation literature is still dominated by studies estimating nonmonetary health status measures (HSMs), which are often used to assess changes in quality-adjusted life years (QALYs). Using meta-regression analysis, this paper combines results from both WTP and HSM studies applied to acute morbidity, and it tests whether a systematic relationship exists between HSM and WTP estimates. We analyze over 230 WTP estimates from 17 different studies and find evidence that QALY-based estimates of illness severity--as measured by the Quality of Well-Being (QWB) Scale--are significant factors in explaining variation in WTP, as are changes in the duration of illness and the average income and age of the study populations. In addition, we test and reject the assumption of a constant WTP per QALY gain. We also demonstrate how the estimated meta-regression equations can serve as benefit transfer functions for policy analysis. By specifying the change in duration and severity of the acute illness and the characteristics of the affected population, we apply the regression functions to predict average WTP per case avoided. Copyright 2006 John Wiley & Sons, Ltd.

  1. Regression modeling methods, theory, and computation with SAS

    CERN Document Server

    Panik, Michael

    2009-01-01

    Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

  2. Retro-regression--another important multivariate regression improvement.

    Science.gov (United States)

    Randić, M

    2001-01-01

    We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

  3. Management of Industrial Performance Indicators: Regression Analysis and Simulation

    Directory of Open Access Journals (Sweden)

    Walter Roberto Hernandez Vergara

    2017-11-01

    Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.

  4. Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

    Science.gov (United States)

    Hecht, Jeffrey B.

    The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

  5. A Latent Class Regression Analysis of Men's Conformity to Masculine Norms and Psychological Distress

    Science.gov (United States)

    Wong, Y. Joel; Owen, Jesse; Shea, Munyi

    2012-01-01

    How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse…

  6. Spatial and Inter-temporal Sources of Poverty, Inequality and Gender Disparities in Cameroon: a Regression-Based Decomposition Analysis

    OpenAIRE

    Boniface Ngah Epo; Francis Menjo Baye; Nadine Teme Angele Manga

    2011-01-01

    This study applies the regression-based inequality decomposition technique to explain poverty and inequality trends in Cameroon. We also identify gender related factors which explain income disparities and discrimination based on the 2001 and 2007 Cameroon household consumption surveys. The results show that education, health, employment in the formal sector, age cohorts, household size, gender, ownership of farmland and urban versus rural residence explain household economic wellbeing; dispa...

  7. Predicting Dropouts of University Freshmen: A Logit Regression Analysis.

    Science.gov (United States)

    Lam, Y. L. Jack

    1984-01-01

    Stepwise discriminant analysis coupled with logit regression analysis of freshmen data from Brandon University (Manitoba) indicated that six tested variables drawn from research on university dropouts were useful in predicting attrition: student status, residence, financial sources, distance from home town, goal fulfillment, and satisfaction with…

  8. Simulation Experiments in Practice : Statistical Design and Regression Analysis

    NARCIS (Netherlands)

    Kleijnen, J.P.C.

    2007-01-01

    In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

  9. COMPARISON OF PARTIAL LEAST SQUARES REGRESSION METHOD ALGORITHMS: NIPALS AND PLS-KERNEL AND AN APPLICATION

    Directory of Open Access Journals (Sweden)

    ELİF BULUT

    2013-06-01

    Full Text Available Partial Least Squares Regression (PLSR is a multivariate statistical method that consists of partial least squares and multiple linear regression analysis. Explanatory variables, X, having multicollinearity are reduced to components which explain the great amount of covariance between explanatory and response variable. These components are few in number and they don’t have multicollinearity problem. Then multiple linear regression analysis is applied to those components to model the response variable Y. There are various PLSR algorithms. In this study NIPALS and PLS-Kernel algorithms will be studied and illustrated on a real data set.

  10. Quality of life in breast cancer patients--a quantile regression analysis.

    Science.gov (United States)

    Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma

    2008-01-01

    Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.

  11. Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

    Science.gov (United States)

    Zarb, Francis; McEntee, Mark F; Rainford, Louise

    2015-06-01

    To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

  12. Regression analysis of radiological parameters in nuclear power plants

    International Nuclear Information System (INIS)

    Bhargava, Pradeep; Verma, R.K.; Joshi, M.L.

    2003-01-01

    Indian Pressurized Heavy Water Reactors (PHWRs) have now attained maturity in their operations. Indian PHWR operation started in the year 1972. At present there are 12 operating PHWRs collectively producing nearly 2400 MWe. Sufficient radiological data are available for analysis to draw inferences which may be utilised for better understanding of radiological parameters influencing the collective internal dose. Tritium is the main contributor to the occupational internal dose originating in PHWRs. An attempt has been made to establish the relationship between radiological parameters, which may be useful to draw inferences about the internal dose. Regression analysis have been done to find out the relationship, if it exist, among the following variables: A. Specific tritium activity of heavy water (Moderator and PHT) and tritium concentration in air at various work locations. B. Internal collective occupational dose and tritium release to environment through air route. C. Specific tritium activity of heavy water (Moderator and PHT) and collective internal occupational dose. For this purpose multivariate regression analysis has been carried out. D. Tritium concentration in air at various work location and tritium release to environment through air route. For this purpose multivariate regression analysis has been carried out. This analysis reveals that collective internal dose has got very good correlation with the tritium activity release to the environment through air route. Whereas no correlation has been found between specific tritium activity in the heavy water systems and collective internal occupational dose. The good correlation has been found in case D and F test reveals that it is not by chance. (author)

  13. Comparison of cranial sex determination by discriminant analysis and logistic regression.

    Science.gov (United States)

    Amores-Ampuero, Anabel; Alemán, Inmaculada

    2016-04-05

    Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

  14. Regression Analysis: Instructional Resource for Cost/Managerial Accounting

    Science.gov (United States)

    Stout, David E.

    2015-01-01

    This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…

  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. Robust Mediation Analysis Based on Median Regression

    Science.gov (United States)

    Yuan, Ying; MacKinnon, David P.

    2014-01-01

    Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925

  17. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

    Science.gov (United States)

    Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

    2012-05-01

    Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  18. A comparative study of multiple regression analysis and back ...

    Indian Academy of Sciences (India)

    Abhijit Sarkar

    artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding ... Keywords. Submerged arc welding (SAW); multi-regression analysis (MRA); artificial neural network ..... Degree of freedom.

  19. Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

    Science.gov (United States)

    Hu, Yi-Chung

    2014-01-01

    On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

  20. Exploratory regression analysis: a tool for selecting models and determining predictor importance.

    Science.gov (United States)

    Braun, Michael T; Oswald, Frederick L

    2011-06-01

    Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at http://dl.dropbox.com/u/2480715/ERA.xlsm?dl=1) . The program investigates all 2(p) - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits.

  1. Regression analysis for LED color detection of visual-MIMO system

    Science.gov (United States)

    Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

    2018-04-01

    Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

  2. Evaluation of syngas production unit cost of bio-gasification facility using regression analysis techniques

    Energy Technology Data Exchange (ETDEWEB)

    Deng, Yangyang; Parajuli, Prem B.

    2011-08-10

    Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.

  3. A systematic review and meta-regression analysis of mivacurium for tracheal intubation.

    Science.gov (United States)

    Vanlinthout, L E H; Mesfin, S H; Hens, N; Vanacker, B F; Robertson, E N; Booij, L H D J

    2014-12-01

    We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent intubation conditions: mivacurium dose, 24.4%; opioid use, 29.9%; time to intubation and age together, 18.6%. The odds ratio (95% CI) for excellent intubation was 3.14 (1.65-5.73) for doubling the mivacurium dose, 5.99 (2.14-15.18) for adding opioids to the intubation sequence, and 6.55 (6.01-7.74) for increasing the delay between mivacurium injection and airway insertion from 1 to 2 min in subjects aged 25 years and 2.17 (2.01-2.69) for subjects aged 70 years, p < 0.001 for all. We conclude that good conditions for tracheal intubation are more likely by delaying laryngoscopy after injecting a higher dose of mivacurium with an opioid, particularly in older people. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

  4. Logic regression and its extensions.

    Science.gov (United States)

    Schwender, Holger; Ruczinski, Ingo

    2010-01-01

    Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

  5. External Tank Liquid Hydrogen (LH2) Prepress Regression Analysis Independent Review Technical Consultation Report

    Science.gov (United States)

    Parsons, Vickie s.

    2009-01-01

    The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.

  6. Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

    Science.gov (United States)

    Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

    2017-04-01

    To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

  7. Non-stationary hydrologic frequency analysis using B-spline quantile regression

    Science.gov (United States)

    Nasri, B.; Bouezmarni, T.; St-Hilaire, A.; Ouarda, T. B. M. J.

    2017-11-01

    Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic and water resources systems under the assumption of stationarity. However, with increasing evidence of climate change, it is possible that the assumption of stationarity, which is prerequisite for traditional frequency analysis and hence, the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extremes based on B-Spline quantile regression which allows to model data in the presence of non-stationarity and/or dependence on covariates with linear and non-linear dependence. A Markov Chain Monte Carlo (MCMC) algorithm was used to estimate quantiles and their posterior distributions. A coefficient of determination and Bayesian information criterion (BIC) for quantile regression are used in order to select the best model, i.e. for each quantile, we choose the degree and number of knots of the adequate B-spline quantile regression model. The method is applied to annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in the variable of interest and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for an annual maximum and minimum discharge with high annual non-exceedance probabilities.

  8. Alternative Methods of Regression

    CERN Document Server

    Birkes, David

    2011-01-01

    Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

  9. On macroeconomic values investigation using fuzzy linear regression analysis

    Directory of Open Access Journals (Sweden)

    Richard Pospíšil

    2017-06-01

    Full Text Available The theoretical background for abstract formalization of the vague phenomenon of complex systems is the fuzzy set theory. In the paper, vague data is defined as specialized fuzzy sets - fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters. To identify the fuzzy coefficients of the model, the genetic algorithm is used. The linear approximation of the vague function together with its possibility area is analytically and graphically expressed. A suitable application is performed in the tasks of the time series fuzzy regression analysis. The time-trend and seasonal cycles including their possibility areas are calculated and expressed. The examples are presented from the economy field, namely the time-development of unemployment, agricultural production and construction respectively between 2009 and 2011 in the Czech Republic. The results are shown in the form of the fuzzy regression models of variables of time series. For the period 2009-2011, the analysis assumptions about seasonal behaviour of variables and the relationship between them were confirmed; in 2010, the system behaved fuzzier and the relationships between the variables were vaguer, that has a lot of causes, from the different elasticity of demand, through state interventions to globalization and transnational impacts.

  10. REGRESSION ANALYSIS OF SEA-SURFACE-TEMPERATURE PATTERNS FOR THE NORTH PACIFIC OCEAN.

    Science.gov (United States)

    SEA WATER, *SURFACE TEMPERATURE, *OCEANOGRAPHIC DATA, PACIFIC OCEAN, REGRESSION ANALYSIS , STATISTICAL ANALYSIS, UNDERWATER EQUIPMENT, DETECTION, UNDERWATER COMMUNICATIONS, DISTRIBUTION, THERMAL PROPERTIES, COMPUTERS.

  11. Regression analysis understanding and building business and economic models using Excel

    CERN Document Server

    Wilson, J Holton

    2012-01-01

    The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe

  12. Regression models for explaining and predicting concentrations of organochlorine pesticides in fish from streams in the United States

    Science.gov (United States)

    Nowell, Lisa H.; Crawford, Charles G.; Gilliom, Robert J.; Nakagaki, Naomi; Stone, Wesley W.; Thelin, Gail; Wolock, David M.

    2009-01-01

    Empirical regression models were developed for estimating concentrations of dieldrin, total chlordane, and total DDT in whole fish from U.S. streams. Models were based on pesticide concentrations measured in whole fish at 648 stream sites nationwide (1992-2001) as part of the U.S. Geological Survey's National Water Quality Assessment Program. Explanatory variables included fish lipid content, estimates (or surrogates) representing historical agricultural and urban sources, watershed characteristics, and geographic location. Models were developed using Tobit regression methods appropriate for data with censoring. Typically, the models explain approximately 50 to 70% of the variability in pesticide concentrations measured in whole fish. The models were used to predict pesticide concentrations in whole fish for streams nationwide using the U.S. Environmental Protection Agency's River Reach File 1 and to estimate the probability that whole-fish concentrations exceed benchmarks for protection of fish-eating wildlife. Predicted concentrations were highest for dieldrin in the Corn Belt, Texas, and scattered urban areas; for total chlordane in the Corn Belt, Texas, the Southeast, and urbanized Northeast; and for total DDT in the Southeast, Texas, California, and urban areas nationwide. The probability of exceeding wildlife benchmarks for dieldrin and chlordane was predicted to be low for most U.S. streams. The probability of exceeding wildlife benchmarks for total DDT is higher but varies depending on the fish taxon and on the benchmark used. Because the models in the present study are based on fish data collected during the 1990s and organochlorine pesticide residues in the environment continue to decline decades after their uses were discontinued, these models may overestimate present-day pesticide concentrations in fish. ?? 2009 SETAC.

  13. Nonlinear regression analysis for evaluating tracer binding parameters using the programmable K1003 desk computer

    International Nuclear Information System (INIS)

    Sarrach, D.; Strohner, P.

    1986-01-01

    The Gauss-Newton algorithm has been used to evaluate tracer binding parameters of RIA by nonlinear regression analysis. The calculations were carried out on the K1003 desk computer. Equations for simple binding models and its derivatives are presented. The advantages of nonlinear regression analysis over linear regression are demonstrated

  14. Regression analysis for the social sciences

    CERN Document Server

    Gordon, Rachel A

    2010-01-01

    The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

  15. Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

    Science.gov (United States)

    Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

    2017-12-01

    The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

  16. Regression methods for medical research

    CERN Document Server

    Tai, Bee Choo

    2013-01-01

    Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

  17. Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

    Science.gov (United States)

    Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

    2017-09-20

    Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  18. Treating experimental data of inverse kinetic method by unitary linear regression analysis

    International Nuclear Information System (INIS)

    Zhao Yusen; Chen Xiaoliang

    2009-01-01

    The theory of treating experimental data of inverse kinetic method by unitary linear regression analysis was described. Not only the reactivity, but also the effective neutron source intensity could be calculated by this method. Computer code was compiled base on the inverse kinetic method and unitary linear regression analysis. The data of zero power facility BFS-1 in Russia were processed and the results were compared. The results show that the reactivity and the effective neutron source intensity can be obtained correctly by treating experimental data of inverse kinetic method using unitary linear regression analysis and the precision of reactivity measurement is improved. The central element efficiency can be calculated by using the reactivity. The result also shows that the effect to reactivity measurement caused by external neutron source should be considered when the reactor power is low and the intensity of external neutron source is strong. (authors)

  19. Regression analysis of informative current status data with the additive hazards model.

    Science.gov (United States)

    Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

    2015-04-01

    This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

  20. Logistic regression models

    CERN Document Server

    Hilbe, Joseph M

    2009-01-01

    This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

  1. 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....

  2. Explaining the judicial independence of international courts: a comparative analysis

    DEFF Research Database (Denmark)

    Beach, Derek

    What factors allow some international courts (ICs) to rule against the express preferences of powerful member states, whereas others routinely defer to governments? While judicial independence is not the only factor explaining the strength of a given international institution, it is a necessary...... condition. The paper first develops three sets of competing explanatory variables that potentially can explain variations in the judicial independence of ICs. The causal effects of these explanatory variables upon variance in judicial independence are investigated in a comparative analysis of the ACJ, ECJ...

  3. MULGRES: a computer program for stepwise multiple regression analysis

    Science.gov (United States)

    A. Jeff Martin

    1971-01-01

    MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

  4. Real-time regression analysis with deep convolutional neural networks

    OpenAIRE

    Huerta, E. A.; George, Daniel; Zhao, Zhizhen; Allen, Gabrielle

    2018-01-01

    We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

  5. [Comparison of application of Cochran-Armitage trend test and linear regression analysis for rate trend analysis in epidemiology study].

    Science.gov (United States)

    Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H

    2017-05-10

    We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P valuelinear regression P value). The statistical power of CAT test decreased, while the result of linear regression analysis remained the same when population size was reduced by 100 times and AMI incidence rate remained unchanged. The two statistical methods have their advantages and disadvantages. It is necessary to choose statistical method according the fitting degree of data, or comprehensively analyze the results of two methods.

  6. The evolution of GDP in USA using cyclic regression analysis

    OpenAIRE

    Catalin Angelo IOAN; Gina IOAN

    2013-01-01

    Based on the four major types of economic cycles (Kondratieff, Juglar, Kitchin, Kuznet), the paper aims to determine their actual length (for the U.S. economy) using cyclic regressions based on Fourier analysis.

  7. Quantile regression for the statistical analysis of immunological data with many non-detects.

    Science.gov (United States)

    Eilers, Paul H C; Röder, Esther; Savelkoul, Huub F J; van Wijk, Roy Gerth

    2012-07-07

    Immunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects. Quantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects. Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

  8. Optimal choice of basis functions in the linear regression analysis

    International Nuclear Information System (INIS)

    Khotinskij, A.M.

    1988-01-01

    Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs

  9. Exploring emergency department 4-hour target performance and cancelled elective operations: a regression analysis of routinely collected and openly reported NHS trust data.

    Science.gov (United States)

    Keogh, Brad; Culliford, David; Guerrero-Ludueña, Richard; Monks, Thomas

    2018-05-24

    To quantify the effect of intrahospital patient flow on emergency department (ED) performance targets and indicate if the expectations set by the National Health Service (NHS) England 5-year forward review are realistic in returning emergency services to previous performance levels. Linear regression analysis of routinely reported trust activity and performance data using a series of cross-sectional studies. NHS trusts in England submitting routine nationally reported measures to NHS England. 142 acute non-specialist trusts operating in England between 2012 and 2016. The primary outcome measures were proportion of 4-hour waiting time breaches and cancelled elective operations. Univariate and multivariate linear regression models were used to show relationships between the outcome measures and various measures of trust activity including empty day beds, empty night beds, day bed to night bed ratio, ED conversion ratio and delayed transfers of care. Univariate regression results using the outcome of 4-hour breaches showed clear relationships with empty night beds and ED conversion ratio between 2012 and 2016. The day bed to night bed ratio showed an increasing ability to explain variation in performance between 2015 and 2016. Delayed transfers of care showed little evidence of an association. Multivariate model results indicated that the ability of patient flow variables to explain 4-hour target performance had reduced between 2012 and 2016 (19% to 12%), and had increased in explaining cancelled elective operations (7% to 17%). The flow of patients through trusts is shown to influence ED performance; however, performance has become less explainable by intratrust patient flow between 2012 and 2016. Some commonly stated explanatory factors such as delayed transfers of care showed limited evidence of being related. The results indicate some of the measures proposed by NHS England to reduce pressure on EDs may not have the desired impact on returning services to previous

  10. Regression analysis for the social sciences

    CERN Document Server

    Gordon, Rachel A

    2015-01-01

    Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

  11. A method for nonlinear exponential regression analysis

    Science.gov (United States)

    Junkin, B. G.

    1971-01-01

    A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

  12. Analysis of Functional Data with Focus on Multinomial Regression and Multilevel Data

    DEFF Research Database (Denmark)

    Mousavi, Seyed Nourollah

    Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects of application...... and methodological development. Our main Functional data analysis (FDA) is a fast growing area in statistical research with increasingly diverse range of application from economics, medicine, agriculture, chemometrics, etc. Functional regression is an area of FDA which has received the most attention both in aspects...

  13. Defense styles explain psychiatric symptoms: an empirical study.

    Science.gov (United States)

    Holi, M M; Sammallahti, P R; Aalberg, V A

    1999-11-01

    To examine the relation between psychiatric symptoms and defense mechanisms, we administered two questionnaires, the Symptom Check-list 90 (SCL-90) and the Defense Style Questionnaire (DSQ) to 122 psychiatric out-patients and to a community sample of 337 subjects. Using regression analysis, we found that 51.8% of the variation in subject's Global Severity Index value could be explained by his defense style. Of the three defense styles, the immature style explained most of the variation in the symptoms. We found little overall evidence for specific connections between particular defenses and symptoms. Projection and dissociation were central in most of the symptom dimensions. We compared patients and controls with the same level of general symptom severity and found that patients used significantly more devaluation and splitting, and controls used significantly more altruism and idealization. Whether defenses predispose to certain symptomatology or are one of its aspects is discussed.

  14. Regression analysis of a chemical reaction fouling model

    International Nuclear Information System (INIS)

    Vasak, F.; Epstein, N.

    1996-01-01

    A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

  15. [A SAS marco program for batch processing of univariate Cox regression analysis for great database].

    Science.gov (United States)

    Yang, Rendong; Xiong, Jie; Peng, Yangqin; Peng, Xiaoning; Zeng, Xiaomin

    2015-02-01

    To realize batch processing of univariate Cox regression analysis for great database by SAS marco program. We wrote a SAS macro program, which can filter, integrate, and export P values to Excel by SAS9.2. The program was used for screening survival correlated RNA molecules of ovarian cancer. A SAS marco program could finish the batch processing of univariate Cox regression analysis, the selection and export of the results. The SAS macro program has potential applications in reducing the workload of statistical analysis and providing a basis for batch processing of univariate Cox regression analysis.

  16. Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

    NARCIS (Netherlands)

    Kleijnen, J.P.C.

    1995-01-01

    This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

  17. Explained variation and predictive accuracy in general parametric statistical models: the role of model misspecification

    DEFF Research Database (Denmark)

    Rosthøj, Susanne; Keiding, Niels

    2004-01-01

    When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give a ...... a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis....

  18. Application of multilinear regression analysis in modeling of soil ...

    African Journals Online (AJOL)

    The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...

  19. 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.

  20. Bias due to two-stage residual-outcome regression analysis in genetic association studies.

    Science.gov (United States)

    Demissie, Serkalem; Cupples, L Adrienne

    2011-11-01

    Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

  1. 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.

  2. An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy

    DEFF Research Database (Denmark)

    Merlo, Juan; Wagner, Philippe; Ghith, Nermin

    2016-01-01

    BACKGROUND AND AIM: Many multilevel logistic regression analyses of "neighbourhood and health" focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that disting...

  3. Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis.

    Science.gov (United States)

    Ribaroff, G A; Wastnedge, E; Drake, A J; Sharpe, R M; Chambers, T J G

    2017-06-01

    Animal models of maternal high fat diet (HFD) demonstrate perturbed offspring metabolism although the effects differ markedly between models. We assessed studies investigating metabolic parameters in the offspring of HFD fed mothers to identify factors explaining these inter-study differences. A total of 171 papers were identified, which provided data from 6047 offspring. Data were extracted regarding body weight, adiposity, glucose homeostasis and lipidaemia. Information regarding the macronutrient content of diet, species, time point of exposure and gestational weight gain were collected and utilized in meta-regression models to explore predictive factors. Publication bias was assessed using Egger's regression test. Maternal HFD exposure did not affect offspring birthweight but increased weaning weight, final bodyweight, adiposity, triglyceridaemia, cholesterolaemia and insulinaemia in both female and male offspring. Hyperglycaemia was found in female offspring only. Meta-regression analysis identified lactational HFD exposure as a key moderator. The fat content of the diet did not correlate with any outcomes. There was evidence of significant publication bias for all outcomes except birthweight. Maternal HFD exposure was associated with perturbed metabolism in offspring but between studies was not accounted for by dietary constituents, species, strain or maternal gestational weight gain. Specific weaknesses in experimental design predispose many of the results to bias. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

  4. Regression Benchmarking: An Approach to Quality Assurance in Performance

    OpenAIRE

    Bulej, Lubomír

    2005-01-01

    The paper presents a short summary of our work in the area of regression benchmarking and its application to software development. Specially, we explain the concept of regression benchmarking, the requirements for employing regression testing in a software project, and methods used for analyzing the vast amounts of data resulting from repeated benchmarking. We present the application of regression benchmarking on a real software project and conclude with a glimpse at the challenges for the fu...

  5. Advanced statistics: linear regression, part I: simple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

  6. Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

    International Nuclear Information System (INIS)

    Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

    2007-01-01

    Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

  7. The Resilience of Dependency Effects in Explaining Income Inequality in the Global Economy: A Cross National Analysis, 1975-1995

    Directory of Open Access Journals (Sweden)

    Linda Beer

    2015-08-01

    Full Text Available The contemporary era is one of both accelerated economic globalization and rising inequality. There is an increasing awareness among both academic scholars and development professionals that globalization puts certain populations at risk. However, there has been inadequate theoretical analysis and a lack of up to date empirical studies that explain just how contemporary globalization a?ects inequality and the well being of individuals. This study explores the conditions under which TNC penetration and other globalization processes in?uence change in domestic income distribution. Its aim is to investigate whether theoretical models that have proven successful in explaining di?erences in income inequality cross-sectionally also allow for an understanding of the dynamics of income distribution during the 1980s and early 1990s, an era characterized by a dramatic acceleration of globalization. We present an analysis of change in national income distribution using linear regression models with a panel design. This study suggests that dependence on foreign investment as a development strategy, especially compared to domestic and human capital investment, may be misguided for nations concerned with equality. Net of other factors, foreign investment dependence bene?ts the elite segments of the income-earning population over the poorer eighty percent. Our analysis provides evidence of a shift in capital/labor relations brought about by globalization that has signi? cantly contributed to the rise in income inequality seen throughout the world.

  8. Forecasting urban water demand: A meta-regression analysis.

    Science.gov (United States)

    Sebri, Maamar

    2016-12-01

    Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike. Copyright © 2016. Published by Elsevier Ltd.

  9. A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

    Science.gov (United States)

    Anderson, Carl A; McRae, Allan F; Visscher, Peter M

    2006-07-01

    Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

  10. The use of cognitive ability measures as explanatory variables in regression analysis.

    Science.gov (United States)

    Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

    2012-12-01

    Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

  11. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    Science.gov (United States)

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  12. Temporal trends in sperm count: a systematic review and meta-regression analysis.

    Science.gov (United States)

    Levine, Hagai; Jørgensen, Niels; Martino-Andrade, Anderson; Mendiola, Jaime; Weksler-Derri, Dan; Mindlis, Irina; Pinotti, Rachel; Swan, Shanna H

    2017-11-01

    Reported declines in sperm counts remain controversial today and recent trends are unknown. A definitive meta-analysis is critical given the predictive value of sperm count for fertility, morbidity and mortality. To provide a systematic review and meta-regression analysis of recent trends in sperm counts as measured by sperm concentration (SC) and total sperm count (TSC), and their modification by fertility and geographic group. PubMed/MEDLINE and EMBASE were searched for English language studies of human SC published in 1981-2013. Following a predefined protocol 7518 abstracts were screened and 2510 full articles reporting primary data on SC were reviewed. A total of 244 estimates of SC and TSC from 185 studies of 42 935 men who provided semen samples in 1973-2011 were extracted for meta-regression analysis, as well as information on years of sample collection and covariates [fertility group ('Unselected by fertility' versus 'Fertile'), geographic group ('Western', including North America, Europe Australia and New Zealand versus 'Other', including South America, Asia and Africa), age, ejaculation abstinence time, semen collection method, method of measuring SC and semen volume, exclusion criteria and indicators of completeness of covariate data]. The slopes of SC and TSC were estimated as functions of sample collection year using both simple linear regression and weighted meta-regression models and the latter were adjusted for pre-determined covariates and modification by fertility and geographic group. Assumptions were examined using multiple sensitivity analyses and nonlinear models. SC declined significantly between 1973 and 2011 (slope in unadjusted simple regression models -0.70 million/ml/year; 95% CI: -0.72 to -0.69; P regression analysis reports a significant decline in sperm counts (as measured by SC and TSC) between 1973 and 2011, driven by a 50-60% decline among men unselected by fertility from North America, Europe, Australia and New Zealand. Because

  13. Neighborhood social capital and crime victimization: comparison of spatial regression analysis and hierarchical regression analysis.

    Science.gov (United States)

    Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro

    2012-11-01

    Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan. Copyright

  14. 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

  15. Analysis of γ spectra in airborne radioactivity measurements using multiple linear regressions

    International Nuclear Information System (INIS)

    Bao Min; Shi Quanlin; Zhang Jiamei

    2004-01-01

    This paper describes the net peak counts calculating of nuclide 137 Cs at 662 keV of γ spectra in airborne radioactivity measurements using multiple linear regressions. Mathematic model is founded by analyzing every factor that has contribution to Cs peak counts in spectra, and multiple linear regression function is established. Calculating process adopts stepwise regression, and the indistinctive factors are eliminated by F check. The regression results and its uncertainty are calculated using Least Square Estimation, then the Cs peak net counts and its uncertainty can be gotten. The analysis results for experimental spectrum are displayed. The influence of energy shift and energy resolution on the analyzing result is discussed. In comparison with the stripping spectra method, multiple linear regression method needn't stripping radios, and the calculating result has relation with the counts in Cs peak only, and the calculating uncertainty is reduced. (authors)

  16. Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

    Science.gov (United States)

    Ulbrich, N.; Volden, T.

    2018-01-01

    Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.

  17. Error analysis of dimensionless scaling experiments with multiple points using linear regression

    International Nuclear Information System (INIS)

    Guercan, Oe.D.; Vermare, L.; Hennequin, P.; Bourdelle, C.

    2010-01-01

    A general method of error estimation in the case of multiple point dimensionless scaling experiments, using linear regression and standard error propagation, is proposed. The method reduces to the previous result of Cordey (2009 Nucl. Fusion 49 052001) in the case of a two-point scan. On the other hand, if the points follow a linear trend, it explains how the estimated error decreases as more points are added to the scan. Based on the analytical expression that is derived, it is argued that for a low number of points, adding points to the ends of the scanned range, rather than the middle, results in a smaller error estimate. (letter)

  18. Clinical evaluation of a novel population-based regression analysis for detecting glaucomatous visual field progression.

    Science.gov (United States)

    Kovalska, M P; Bürki, E; Schoetzau, A; Orguel, S F; Orguel, S; Grieshaber, M C

    2011-04-01

    The distinction of real progression from test variability in visual field (VF) series may be based on clinical judgment, on trend analysis based on follow-up of test parameters over time, or on identification of a significant change related to the mean of baseline exams (event analysis). The aim of this study was to compare a new population-based method (Octopus field analysis, OFA) with classic regression analyses and clinical judgment for detecting glaucomatous VF changes. 240 VF series of 240 patients with at least 9 consecutive examinations available were included into this study. They were independently classified by two experienced investigators. The results of such a classification served as a reference for comparison for the following statistical tests: (a) t-test global, (b) r-test global, (c) regression analysis of 10 VF clusters and (d) point-wise linear regression analysis. 32.5 % of the VF series were classified as progressive by the investigators. The sensitivity and specificity were 89.7 % and 92.0 % for r-test, and 73.1 % and 93.8 % for the t-test, respectively. In the point-wise linear regression analysis, the specificity was comparable (89.5 % versus 92 %), but the sensitivity was clearly lower than in the r-test (22.4 % versus 89.7 %) at a significance level of p = 0.01. A regression analysis for the 10 VF clusters showed a markedly higher sensitivity for the r-test (37.7 %) than the t-test (14.1 %) at a similar specificity (88.3 % versus 93.8 %) for a significant trend (p = 0.005). In regard to the cluster distribution, the paracentral clusters and the superior nasal hemifield progressed most frequently. The population-based regression analysis seems to be superior to the trend analysis in detecting VF progression in glaucoma, and may eliminate the drawbacks of the event analysis. Further, it may assist the clinician in the evaluation of VF series and may allow better visualization of the correlation between function and structure owing to VF

  19. Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

    Science.gov (United States)

    Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

  20. Determining Balıkesir’s Energy Potential Using a Regression Analysis Computer Program

    Directory of Open Access Journals (Sweden)

    Bedri Yüksel

    2014-01-01

    Full Text Available Solar power and wind energy are used concurrently during specific periods, while at other times only the more efficient is used, and hybrid systems make this possible. When establishing a hybrid system, the extent to which these two energy sources support each other needs to be taken into account. This paper is a study of the effects of wind speed, insolation levels, and the meteorological parameters of temperature and humidity on the energy potential in Balıkesir, in the Marmara region of Turkey. The relationship between the parameters was studied using a multiple linear regression method. Using a designed-for-purpose computer program, two different regression equations were derived, with wind speed being the dependent variable in the first and insolation levels in the second. The regression equations yielded accurate results. The computer program allowed for the rapid calculation of different acceptance rates. The results of the statistical analysis proved the reliability of the equations. An estimate of identified meteorological parameters and unknown parameters could be produced with a specified precision by using the regression analysis method. The regression equations also worked for the evaluation of energy potential.

  1. Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

    Science.gov (United States)

    Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

    2014-01-01

    In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

  2. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  3. Statistical methods in regression and calibration analysis of chromosome aberration data

    International Nuclear Information System (INIS)

    Merkle, W.

    1983-01-01

    The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)

  4. Marginal longitudinal semiparametric regression via penalized splines

    KAUST Repository

    Al Kadiri, M.

    2010-08-01

    We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

  5. Marginal longitudinal semiparametric regression via penalized splines

    KAUST Repository

    Al Kadiri, M.; Carroll, R.J.; Wand, M.P.

    2010-01-01

    We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.

  6. Length bias correction in gene ontology enrichment analysis using logistic regression.

    Science.gov (United States)

    Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

    2012-01-01

    When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

  7. Analysis of designed experiments by stabilised PLS Regression and jack-knifing

    DEFF Research Database (Denmark)

    Martens, Harald; Høy, M.; Westad, F.

    2001-01-01

    Pragmatical, visually oriented methods for assessing and optimising bi-linear regression models are described, and applied to PLS Regression (PLSR) analysis of multi-response data from controlled experiments. The paper outlines some ways to stabilise the PLSR method to extend its range...... the reliability of the linear and bi-linear model parameter estimates. The paper illustrates how the obtained PLSR "significance" probabilities are similar to those from conventional factorial ANOVA, but the PLSR is shown to give important additional overview plots of the main relevant structures in the multi....... An Introduction, Wiley, Chichester, UK, 2001]....

  8. Replica analysis of overfitting in regression models for time-to-event data

    Science.gov (United States)

    Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

    2017-09-01

    Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

  9. Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

    Science.gov (United States)

    Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

    2017-05-01

    This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

  10. 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.

  11. Exploring factors associated with traumatic dental injuries in preschool children: a Poisson regression analysis.

    Science.gov (United States)

    Feldens, Carlos Alberto; Kramer, Paulo Floriani; Ferreira, Simone Helena; Spiguel, Mônica Hermann; Marquezan, Marcela

    2010-04-01

    This cross-sectional study aimed to investigate the factors associated with dental trauma in preschool children using Poisson regression analysis with robust variance. The study population comprised 888 children aged 3- to 5-year-old attending public nurseries in Canoas, southern Brazil. Questionnaires assessing information related to the independent variables (age, gender, race, mother's educational level and family income) were completed by the parents. Clinical examinations were carried out by five trained examiners in order to assess traumatic dental injuries (TDI) according to Andreasen's classification. One of the five examiners was calibrated to assess orthodontic characteristics (open bite and overjet). Multivariable Poisson regression analysis with robust variance was used to determine the factors associated with dental trauma as well as the strengths of association. Traditional logistic regression was also performed in order to compare the estimates obtained by both methods of statistical analysis. 36.4% (323/888) of the children suffered dental trauma and there was no difference in prevalence rates from 3 to 5 years of age. Poisson regression analysis showed that the probability of the outcome was almost 30% higher for children whose mothers had more than 8 years of education (Prevalence Ratio = 1.28; 95% CI = 1.03-1.60) and 63% higher for children with an overjet greater than 2 mm (Prevalence Ratio = 1.63; 95% CI = 1.31-2.03). Odds ratios clearly overestimated the size of the effect when compared with prevalence ratios. These findings indicate the need for preventive orientation regarding TDI, in order to educate parents and caregivers about supervising infants, particularly those with increased overjet and whose mothers have a higher level of education. Poisson regression with robust variance represents a better alternative than logistic regression to estimate the risk of dental trauma in preschool children.

  12. Application of range-test in multiple linear regression analysis in ...

    African Journals Online (AJOL)

    Application of range-test in multiple linear regression analysis in the presence of outliers is studied in this paper. First, the plot of the explanatory variables (i.e. Administration, Social/Commercial, Economic services and Transfer) on the dependent variable (i.e. GDP) was done to identify the statistical trend over the years.

  13. Study Heterogeneity and Estimation of Prevalence of Primary Aldosteronism: A Systematic Review and Meta-Regression Analysis.

    Science.gov (United States)

    Käyser, Sabine C; Dekkers, Tanja; Groenewoud, Hans J; van der Wilt, Gert Jan; Carel Bakx, J; van der Wel, Mark C; Hermus, Ad R; Lenders, Jacques W; Deinum, Jaap

    2016-07-01

    For health care planning and allocation of resources, realistic estimation of the prevalence of primary aldosteronism is necessary. Reported prevalences of primary aldosteronism are highly variable, possibly due to study heterogeneity. Our objective was to identify and explain heterogeneity in studies that aimed to establish the prevalence of primary aldosteronism in hypertensive patients. PubMed, EMBASE, Web of Science, Cochrane Library, and reference lists from January 1, 1990, to January 31, 2015, were used as data sources. Description of an adult hypertensive patient population with confirmed diagnosis of primary aldosteronism was included in this study. Dual extraction and quality assessment were the forms of data extraction. Thirty-nine studies provided data on 42 510 patients (nine studies, 5896 patients from primary care). Prevalence estimates varied from 3.2% to 12.7% in primary care and from 1% to 29.8% in referral centers. Heterogeneity was too high to establish point estimates (I(2) = 57.6% in primary care; 97.1% in referral centers). Meta-regression analysis showed higher prevalences in studies 1) published after 2000, 2) from Australia, 3) aimed at assessing prevalence of secondary hypertension, 4) that were retrospective, 5) that selected consecutive patients, and 6) not using a screening test. All studies had minor or major flaws. This study demonstrates that it is pointless to claim low or high prevalence of primary aldosteronism based on published reports. Because of the significant impact of a diagnosis of primary aldosteronism on health care resources and the necessary facilities, our findings urge for a prevalence study whose design takes into account the factors identified in the meta-regression analysis.

  14. Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

    Science.gov (United States)

    Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

    2013-09-01

    Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p regression analysis showed a greater likelihood for minimum frontal breadth (p regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

  15. Understanding poisson regression.

    Science.gov (United States)

    Hayat, Matthew J; Higgins, Melinda

    2014-04-01

    Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

  16. Regularized principal covariates regression and its application to finding coupled patterns in climate fields

    Science.gov (United States)

    Fischer, M. J.

    2014-02-01

    There are many different methods for investigating the coupling between two climate fields, which are all based on the multivariate regression model. Each different method of solving the multivariate model has its own attractive characteristics, but often the suitability of a particular method for a particular problem is not clear. Continuum regression methods search the solution space between the conventional methods and thus can find regression model subspaces that mix the attractive characteristics of the end-member subspaces. Principal covariates regression is a continuum regression method that is easily applied to climate fields and makes use of two end-members: principal components regression and redundancy analysis. In this study, principal covariates regression is extended to additionally span a third end-member (partial least squares or maximum covariance analysis). The new method, regularized principal covariates regression, has several attractive features including the following: it easily applies to problems in which the response field has missing values or is temporally sparse, it explores a wide range of model spaces, and it seeks a model subspace that will, for a set number of components, have a predictive skill that is the same or better than conventional regression methods. The new method is illustrated by applying it to the problem of predicting the southern Australian winter rainfall anomaly field using the regional atmospheric pressure anomaly field. Regularized principal covariates regression identifies four major coupled patterns in these two fields. The two leading patterns, which explain over half the variance in the rainfall field, are related to the subtropical ridge and features of the zonally asymmetric circulation.

  17. Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

    Science.gov (United States)

    Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

    2014-01-01

    It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

  18. Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

    Science.gov (United States)

    Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

    2014-01-01

    It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

  19. Analysis of the Influence of Quantile Regression Model on Mainland Tourists’ Service Satisfaction Performance

    Directory of Open Access Journals (Sweden)

    Wen-Cheng Wang

    2014-01-01

    Full Text Available It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

  20. Econometric analysis of realised covariation: high frequency covariance, regression and correlation in financial economics

    OpenAIRE

    Ole E. Barndorff-Nielsen; Neil Shephard

    2002-01-01

    This paper analyses multivariate high frequency financial data using realised covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis and covariance. It will be based on a fixed interval of time (e.g. a day or week), allowing the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions and covariances change through time. In particular w...

  1. The M Word: Multicollinearity in Multiple Regression.

    Science.gov (United States)

    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…

  2. X-ray CT-Derived Soil Characteristics Explain Varying Air, Water, and Solute Transport Properties across a Loamy Field

    DEFF Research Database (Denmark)

    Paradelo Pérez, Marcos; Katuwal, Sheela; Møldrup, Per

    2016-01-01

    -derived parameters by using a best subsets regression analysis. The regression coefficients improved using CTmatrix, limiting macroporosity, and genus density, while the best model for t0.05 used CTmatrix only. The scanning resolution and the time for soil structure development after mechanical activities could......The characterization of soil pore space geometry is important for explaining fluxes of air, water, and solutes through soil and understanding soil hydrogeochemical functions. X-ray computed tomography (CT) can be applied for this characterization, and in this study CT-derived parameters were used...... to explain water, air, and solute transport through soil. Forty-five soil columns (20 by 20 cm) were collected from an agricultural field in Estrup, Denmark, and subsequently scanned using a medical CT scanner. Nonreactive tracer leaching experiments were performed in the laboratory along with measurements...

  3. Prediction of radiation levels in residences: A methodological comparison of CART [Classification and Regression Tree Analysis] and conventional regression

    International Nuclear Information System (INIS)

    Janssen, I.; Stebbings, J.H.

    1990-01-01

    In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and ∼200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs

  4. A rotor optimization using regression analysis

    Science.gov (United States)

    Giansante, N.

    1984-01-01

    The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

  5. Regression and local control rates after radiotherapy for jugulotympanic paragangliomas: Systematic review and meta-analysis

    International Nuclear Information System (INIS)

    Hulsteijn, Leonie T. van; Corssmit, Eleonora P.M.; Coremans, Ida E.M.; Smit, Johannes W.A.; Jansen, Jeroen C.; Dekkers, Olaf M.

    2013-01-01

    The primary treatment goal of radiotherapy for paragangliomas of the head and neck region (HNPGLs) is local control of the tumor, i.e. stabilization of tumor volume. Interestingly, regression of tumor volume has also been reported. Up to the present, no meta-analysis has been performed giving an overview of regression rates after radiotherapy in HNPGLs. The main objective was to perform a systematic review and meta-analysis to assess regression of tumor volume in HNPGL-patients after radiotherapy. A second outcome was local tumor control. Design of the study is systematic review and meta-analysis. PubMed, EMBASE, Web of Science, COCHRANE and Academic Search Premier and references of key articles were searched in March 2012 to identify potentially relevant studies. Considering the indolent course of HNPGLs, only studies with ⩾12 months follow-up were eligible. Main outcomes were the pooled proportions of regression and local control after radiotherapy as initial, combined (i.e. directly post-operatively or post-embolization) or salvage treatment (i.e. after initial treatment has failed) for HNPGLs. A meta-analysis was performed with an exact likelihood approach using a logistic regression with a random effect at the study level. Pooled proportions with 95% confidence intervals (CI) were reported. Fifteen studies were included, concerning a total of 283 jugulotympanic HNPGLs in 276 patients. Pooled regression proportions for initial, combined and salvage treatment were respectively 21%, 33% and 52% in radiosurgery studies and 4%, 0% and 64% in external beam radiotherapy studies. Pooled local control proportions for radiotherapy as initial, combined and salvage treatment ranged from 79% to 100%. Radiotherapy for jugulotympanic paragangliomas results in excellent local tumor control and therefore is a valuable treatment for these types of tumors. The effects of radiotherapy on regression of tumor volume remain ambiguous, although the data suggest that regression can

  6. Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

    Energy Technology Data Exchange (ETDEWEB)

    Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels (Belgium); Shabbir, A. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium); Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany); Hornung, G. [Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)

    2016-11-15

    Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

  7. Mechanisms of neuroblastoma regression

    Science.gov (United States)

    Brodeur, Garrett M.; Bagatell, Rochelle

    2014-01-01

    Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179

  8. Vector regression introduced

    Directory of Open Access Journals (Sweden)

    Mok Tik

    2014-06-01

    Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

  9. Identification of Key Beliefs Explaining Male Circumcision Motivation Among Adolescent Boys in Zimbabwe: Targets for Behavior Change Communication.

    Science.gov (United States)

    Kasprzyk, Danuta; Tshimanga, Mufuta; Hamilton, Deven T; Gorn, Gerald J; Montaño, Daniel E

    2018-02-01

    Male circumcision (MC) significantly reduces HIV acquisition among men, leading WHO/UNAIDS to recommend high HIV and low MC prevalence countries circumcise 80% of adolescents and men age 15-49. Despite significant investment to increase MC capacity only 27% of the goal has been achieved in Zimbabwe. To increase adoption, research to create evidence-based messages is greatly needed. The Integrated Behavioral Model (IBM) was used to investigate factors affecting MC motivation among adolescents. Based on qualitative elicitation study results a survey was designed and administered to a representative sample of 802 adolescent boys aged 13-17 in two urban and two rural areas in Zimbabwe. Multiple regression analysis found all six IBM constructs (2 attitude, 2 social influence, 2 personal agency) significantly explained MC intention (R 2  = 0.55). Stepwise regression analysis of beliefs underlying each IBM belief-based construct found 9 behavioral, 6 injunctive norm, 2 descriptive norm, 5 efficacy, and 8 control beliefs significantly explained MC intention. A final stepwise regression of all the significant IBM construct beliefs identified 12 key beliefs best explaining intention. Similar analyses were carried out with subgroups of adolescents by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group. This study demonstrates the application of theory-driven research to identify evidence-based targets for the design of effective MC messages for interventions to increase adolescents' motivation. Incorporating these findings into communication campaigns is likely to improve demand for MC.

  10. Repeated Results Analysis for Middleware Regression Benchmarking

    Czech Academy of Sciences Publication Activity Database

    Bulej, Lubomír; Kalibera, T.; Tůma, P.

    2005-01-01

    Roč. 60, - (2005), s. 345-358 ISSN 0166-5316 R&D Projects: GA ČR GA102/03/0672 Institutional research plan: CEZ:AV0Z10300504 Keywords : middleware benchmarking * regression benchmarking * regression testing Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.756, year: 2005

  11. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    Science.gov (United States)

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  12. Bayesian Analysis for Penalized Spline Regression Using WinBUGS

    Directory of Open Access Journals (Sweden)

    Ciprian M. Crainiceanu

    2005-09-01

    Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

  13. Forecasting municipal solid waste generation using prognostic tools and regression analysis.

    Science.gov (United States)

    Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

    2016-11-01

    For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Myoung Keon [Agency for Defense Development, Daejeon (Korea, Republic of); Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon [Chungnam Nat’l Univ., Daejeon (Korea, Republic of)

    2016-10-15

    This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

  15. Development of Compressive Failure Strength for Composite Laminate Using Regression Analysis Method

    International Nuclear Information System (INIS)

    Lee, Myoung Keon; Lee, Jeong Won; Yoon, Dong Hyun; Kim, Jae Hoon

    2016-01-01

    This paper provides the compressive failure strength value of composite laminate developed by using regression analysis method. Composite material in this document is a Carbon/Epoxy unidirection(UD) tape prepreg(Cycom G40-800/5276-1) cured at 350°F(177°C). The operating temperature is –60°F~+200°F(-55°C - +95°C). A total of 56 compression tests were conducted on specimens from eight (8) distinct laminates that were laid up by standard angle layers (0°, +45°, –45° and 90°). The ASTM-D-6484 standard was used for test method. The regression analysis was performed with the response variable being the laminate ultimate fracture strength and the regressor variables being two ply orientations (0° and ±45°)

  16. Standards for Standardized Logistic Regression Coefficients

    Science.gov (United States)

    Menard, Scott

    2011-01-01

    Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

  17. Explaining the differential distribution of Clean Development Mechanism projects across host countries

    International Nuclear Information System (INIS)

    Winkelman, Andrew G.; Moore, Michael R.

    2011-01-01

    The Clean Development Mechanism (CDM) of the Kyoto Protocol represents an opportunity to involve all developing countries in the effort to reduce greenhouse gas emissions while also promoting sustainable development. To date, however, the majority of CDM projects have gone to emerging markets such as China, India, Brazil, and Mexico, while very few least developed countries have hosted projects. This paper investigates the differential distribution of CDM activities across countries. We develop a conceptual model for project profitability, which helps to identify potential country-level determinants of CDM activity. These potential determinants are employed as explanatory variables in regression analysis to explain the actual distribution of projects. Human capital and greenhouse gas emission levels influenced which countries have hosted projects and the amount of certified emission reductions (CER) created. Countries that offered growing markets for CDM co-products, such as electricity, were more likely to be CDM hosts, while economies with higher carbon intensity levels had greater CER production. These findings work against the least developed countries and help to explain their lack of CDM activity. - Research Highlights: → Regression models are used to explain the inter-country distribution of CDM projects. → Emissions and human capital are significant for hosting projects and CER creation. → An economy's emissions intensity is significant in determining CERs created. → Capacity building and electricity sector growth are significant in hosting projects. → The experience level for host countries in the CDM is significant for CER creation.

  18. CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions

    Science.gov (United States)

    Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.

  19. Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

    Science.gov (United States)

    Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

    2016-01-01

    Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

  20. Multiple regression models for energy use in air-conditioned office buildings in different climates

    International Nuclear Information System (INIS)

    Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.

    2010-01-01

    An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

  1. Inferring gene expression dynamics via functional regression analysis

    Directory of Open Access Journals (Sweden)

    Leng Xiaoyan

    2008-01-01

    Full Text Available Abstract Background Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other. Results We demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles. Conclusion Our findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.

  2. Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

    Science.gov (United States)

    Agga, Getahun E; Scott, H Morgan

    2015-10-01

    Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

  3. Regression analysis of growth responses to water depth in three wetland plant species

    DEFF Research Database (Denmark)

    Sorrell, Brian K; Tanner, Chris C; Brix, Hans

    2012-01-01

    depths from 0 – 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth...

  4. A SOCIOLOGICAL ANALYSIS OF THE CHILDBEARING COEFFICIENT IN THE ALTAI REGION BASED ON METHOD OF FUZZY LINEAR REGRESSION

    Directory of Open Access Journals (Sweden)

    Sergei Vladimirovich Varaksin

    2017-06-01

    Full Text Available Purpose. Construction of a mathematical model of the dynamics of childbearing change in the Altai region in 2000–2016, analysis of the dynamics of changes in birth rates for multiple age categories of women of childbearing age. Methodology. A auxiliary analysis element is the construction of linear mathematical models of the dynamics of childbearing by using fuzzy linear regression method based on fuzzy numbers. Fuzzy linear regression is considered as an alternative to standard statistical linear regression for short time series and unknown distribution law. The parameters of fuzzy linear and standard statistical regressions for childbearing time series were defined with using the built in language MatLab algorithm. Method of fuzzy linear regression is not used in sociological researches yet. Results. There are made the conclusions about the socio-demographic changes in society, the high efficiency of the demographic policy of the leadership of the region and the country, and the applicability of the method of fuzzy linear regression for sociological analysis.

  5. 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…

  6. LOGISTIC REGRESSION AS A TOOL FOR DETERMINATION OF THE PROBABILITY OF DEFAULT FOR ENTERPRISES

    Directory of Open Access Journals (Sweden)

    Erika SPUCHLAKOVA

    2017-12-01

    Full Text Available In a rapidly changing world it is necessary to adapt to new conditions. From a day to day approaches can vary. For the proper management of the company it is essential to know the financial situation. Assessment of the company financial health can be carried out by financial analysis which provides a number of methods how to evaluate the company financial health. Analysis indicators are often included in the company assessment, in obtaining bank loans and other financial resources to ensure the functioning of the company. As company focuses on the future and its planning, it is essential to forecast the future financial situation. According to the results of company´s financial health prediction, the company decides on the extension or limitation of its business. It depends mainly on the capabilities of company´s management how they will use information obtained from financial analysis in practice. The findings of logistic regression methods were published firstly in the 60s, as an alternative to the least squares method. The essence of logistic regression is to determine the relationship between being explained (dependent variable and explanatory (independent variables. The basic principle of this static method is based on the regression analysis, but unlike linear regression, it can predict the probability of a phenomenon that has occurred or not. The aim of this paper is to determine the probability of bankruptcy enterprises.

  7. THE PROGNOSIS OF RUSSIAN DEFENSE INDUSTRY DEVELOPMENT IMPLEMENTED THROUGH REGRESSION ANALYSIS

    Directory of Open Access Journals (Sweden)

    L.M. Kapustina

    2007-03-01

    Full Text Available The article illustrates the results of investigation the major internal and external factors which influence the development of the defense industry, as well as the results of regression analysis which quantitatively displays the factorial contribution in the growth rate of Russian defense industry. On the basis of calculated regression dependences the authors fulfilled the medium-term prognosis of defense industry. Optimistic and inertial versions of defense product growth rate for the period up to 2009 are based on scenario conditions in Russian economy worked out by the Ministry of economy and development. In conclusion authors point out which factors and conditions have the largest impact on successful and stable operation of Russian defense industry.

  8. 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

  9. Nutrient analysis explained for non-chemists by using interactive e-learning material

    NARCIS (Netherlands)

    Busstra, M.C.; Hulshof, P.J.M.; Houwen, J.; Elburg, L.; Hollman, P.C.H.

    2012-01-01

    The diverse educational and professional background of individuals involved in food composition data work presents challenges in their training. In particular, it is difficult to explain chemical analysis of nutrients to individuals lacking a background in chemistry. Therefore an interactive

  10. Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

    Science.gov (United States)

    Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

    2011-08-01

    In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

  11. 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.

  12. Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

    Science.gov (United States)

    Erdoğan, Sinem B; Tong, Yunjie; Hocke, Lia M; Lindsey, Kimberly P; deB Frederick, Blaise

    2016-01-01

    Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, "dynamic global signal regression" (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional "static" global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.

  13. The Regression Analysis of Individual Financial Performance: Evidence from Croatia

    OpenAIRE

    Bahovec, Vlasta; Barbić, Dajana; Palić, Irena

    2017-01-01

    Background: A large body of empirical literature indicates that gender and financial literacy are significant determinants of individual financial performance. Objectives: The purpose of this paper is to recognize the impact of the variable financial literacy and the variable gender on the variation of the financial performance using the regression analysis. Methods/Approach: The survey was conducted using the systematically chosen random sample of Croatian financial consumers. The cross sect...

  14. Can neighborhoods explain racial/ethnic differences in adolescent inactivity?

    Science.gov (United States)

    Richmond, Tracy K; Field, Alison E; Rich, Michael

    2007-01-01

    To determine if neighborhoods and their attributes contribute to racial/ethnic disparities in adolescent inactivity. We undertook a cross-sectional analysis of the National Longitudinal Study of Adolescent Health (n = 17,007), a nationally representative school-based study in the United States. Stratifying by gender, we used multivariate linear regression and multi-level modeling to determine whether neighborhood of residence may partially explain racial/ethnic disparities in adolescent physical inactivity, defined as hours viewing television or videos/DVDs and/or playing computer/video games each week. Participants lived in largely segregated communities. Black and Hispanic adolescent girls reported higher levels of inactivity than White adolescent girls (21 vs. 15 vs. 13 hours/week, respectively, p violent crime in the neighborhood was associated with inactivity, despite the individual's perception of his/her neighborhood as safe not being predictive. Although inactivity varies by race/ethnicity and gender, only in Hispanic adolescent girls does neighborhood fully explain the differential use. Our findings suggest that approaches other than changing neighborhood characteristics are needed to eliminate racial/ethnic disparities in adolescent inactivity.

  15. Genetic factors explain half of all variance in serum eosinophil cationic protein

    DEFF Research Database (Denmark)

    Elmose, Camilla; Sverrild, Asger; van der Sluis, Sophie

    2014-01-01

    with variation in serum ECP and to determine the relative proportion of the variation in ECP due to genetic and non-genetic factors, in an adult twin sample. METHODS: A sample of 575 twins, selected through a proband with self-reported asthma, had serum ECP, lung function, airway responsiveness to methacholine......, exhaled nitric oxide, and skin test reactivity, measured. Linear regression analysis and variance component models were used to study factors associated with variation in ECP and the relative genetic influence on ECP levels. RESULTS: Sex (regression coefficient = -0.107, P ... was statistically non-significant (r = -0.11, P = 0.50). CONCLUSION: Around half of all variance in serum ECP is explained by genetic factors. Serum ECP is influenced by sex, BMI, and airway responsiveness. Serum ECP and airway responsiveness seem not to share genetic variance....

  16. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

    Science.gov (United States)

    van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

    2016-11-24

    Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

  17. A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

    Science.gov (United States)

    Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

    2015-12-01

    This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. Code Red: Explaining Average Age of Death in the City of Hamilton

    Directory of Open Access Journals (Sweden)

    Patrick F. DeLuca

    2015-11-01

    Full Text Available The aim of this study is to identify the underlying factors that explain the average age of death in the City of Hamilton, Ontario, Canada, as identified in the Code Red Series of articles that were published in the city's local newspaper in 2010. Using a combination of data from the Canadian Census, the Government of Ontario and the Canadian Institute for Health Information, factor analysis was performed yielding three factors relating to poverty, working class, and health and aging. In a regression analysis these factors account for 42% of the total variability in the average ages of death observed at the census tract level of geography within the city.

  19. Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

    Science.gov (United States)

    Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

    2016-08-01

    This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

  20. Evidence-based identification of key beliefs explaining adult male circumcision motivation in Zimbabwe: targets for behavior change messaging.

    Science.gov (United States)

    Montaño, Daniel E; Kasprzyk, Danuta; Hamilton, Deven T; Tshimanga, Mufuta; Gorn, Gerald

    2014-05-01

    Male circumcision (MC) reduces HIV acquisition among men, leading WHO/UNAIDS to recommend a goal to circumcise 80 % of men in high HIV prevalence countries. Significant investment to increase MC capacity in priority countries was made, yet only 5 % of the goal has been achieved in Zimbabwe. The integrated behavioral model (IBM) was used as a framework to investigate the factors affecting MC motivation among men in Zimbabwe. A survey instrument was designed based on elicitation study results, and administered to a representative household-based sample of 1,201 men aged 18-30 from two urban and two rural areas in Zimbabwe. Multiple regression analysis found all five IBM constructs significantly explained MC Intention. Nearly all beliefs underlying the IBM constructs were significantly correlated with MC Intention. Stepwise regression analysis of beliefs underlying each construct respectively found that 13 behavioral beliefs, 5 normative beliefs, 4 descriptive norm beliefs, 6 efficacy beliefs, and 10 control beliefs were significant in explaining MC Intention. A final stepwise regression of the five sets of significant IBM construct beliefs identified 14 key beliefs that best explain Intention. Similar analyses were carried out with subgroups of men by urban-rural and age. Different sets of behavioral, normative, efficacy, and control beliefs were significant for each sub-group, suggesting communication messages need to be targeted to be most effective for sub-groups. Implications for the design of effective MC demand creation messages are discussed. This study demonstrates the application of theory-driven research to identify evidence-based targets for intervention messages to increase men's motivation to get circumcised and thereby improve demand for male circumcision.

  1. How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach.

    Science.gov (United States)

    Mujasi, Paschal N; Asbu, Eyob Z; Puig-Junoy, Jaume

    2016-07-08

    Hospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year. This was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency. The average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p Uganda.

  2. A multiple regression analysis for accurate background subtraction in 99Tcm-DTPA renography

    International Nuclear Information System (INIS)

    Middleton, G.W.; Thomson, W.H.; Davies, I.H.; Morgan, A.

    1989-01-01

    A technique for accurate background subtraction in 99 Tc m -DTPA renography is described. The technique is based on a multiple regression analysis of the renal curves and separate heart and soft tissue curves which together represent background activity. It is compared, in over 100 renograms, with a previously described linear regression technique. Results show that the method provides accurate background subtraction, even in very poorly functioning kidneys, thus enabling relative renal filtration and excretion to be accurately estimated. (author)

  3. Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis

    International Nuclear Information System (INIS)

    Fang, Xiande; Xu, Yu

    2011-01-01

    The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination R c 2 ≥ 0.96 and the mean absolute percentage deviation = 1.19% for the three turbines. -- Highlights: → Performed a critical review of empirical models of turbine efficiency. → Developed an empirical model in the desired form for air cycle refrigeration, using the Taylor expansion and regression analysis. → Verified the method for developing the empirical model. → Verified the model.

  4. Econometric analysis of realized covariation: high frequency based covariance, regression, and correlation in financial economics

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Shephard, N.

    2004-01-01

    This paper analyses multivariate high frequency financial data using realized covariation. We provide a new asymptotic distribution theory for standard methods such as regression, correlation analysis, and covariance. It will be based on a fixed interval of time (e.g., a day or week), allowing...... the number of high frequency returns during this period to go to infinity. Our analysis allows us to study how high frequency correlations, regressions, and covariances change through time. In particular we provide confidence intervals for each of these quantities....

  5. Distance Based Root Cause Analysis and Change Impact Analysis of Performance Regressions

    Directory of Open Access Journals (Sweden)

    Junzan Zhou

    2015-01-01

    Full Text Available Performance regression testing is applied to uncover both performance and functional problems of software releases. A performance problem revealed by performance testing can be high response time, low throughput, or even being out of service. Mature performance testing process helps systematically detect software performance problems. However, it is difficult to identify the root cause and evaluate the potential change impact. In this paper, we present an approach leveraging server side logs for identifying root causes of performance problems. Firstly, server side logs are used to recover call tree of each business transaction. We define a novel distance based metric computed from call trees for root cause analysis and apply inverted index from methods to business transactions for change impact analysis. Empirical studies show that our approach can effectively and efficiently help developers diagnose root cause of performance problems.

  6. Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

    Science.gov (United States)

    Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

    2017-01-01

    The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

  7. Advanced statistics: linear regression, part II: multiple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

  8. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

    Directory of Open Access Journals (Sweden)

    Maarten van Smeden

    2016-11-01

    Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

  9. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

    2015-01-01

    predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

  10. Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

    Science.gov (United States)

    Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

    2008-04-01

    Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

  11. Multivariate regression analysis for determining short-term values of radon and its decay products from filter measurements

    International Nuclear Information System (INIS)

    Kraut, W.; Schwarz, W.; Wilhelm, A.

    1994-01-01

    A multivariate regression analysis is applied to decay measurements of α-resp. β-filter activcity. Activity concentrations for Po-218, Pb-214 and Bi-214, resp. for the Rn-222 equilibrium equivalent concentration are obtained explicitly. The regression analysis takes into account properly the variances of the measured count rates and their influence on the resulting activity concentrations. (orig.) [de

  12. An Econometric Analysis of Modulated Realised Covariance, Regression and Correlation in Noisy Diffusion Models

    DEFF Research Database (Denmark)

    Kinnebrock, Silja; Podolskij, Mark

    This paper introduces a new estimator to measure the ex-post covariation between high-frequency financial time series under market microstructure noise. We provide an asymptotic limit theory (including feasible central limit theorems) for standard methods such as regression, correlation analysis...... process can be relaxed and how our method can be applied to non-synchronous observations. We also present an empirical study of how high-frequency correlations, regressions and covariances change through time....

  13. A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

    Science.gov (United States)

    Karabatsos, George

    2017-02-01

    Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

  14. Regression analysis of mixed recurrent-event and panel-count data.

    Science.gov (United States)

    Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

    2014-07-01

    In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  15. [Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

    Science.gov (United States)

    Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

    2011-11-01

    To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

  16. The crux of the method: assumptions in ordinary least squares and logistic regression.

    Science.gov (United States)

    Long, Rebecca G

    2008-10-01

    Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

  17. Use of multiple linear regression and logistic regression models to investigate changes in birthweight for term singleton infants in Scotland.

    Science.gov (United States)

    Bonellie, Sandra R

    2012-10-01

    To illustrate the use of regression and logistic regression models to investigate changes over time in size of babies particularly in relation to social deprivation, age of the mother and smoking. Mean birthweight has been found to be increasing in many countries in recent years, but there are still a group of babies who are born with low birthweights. Population-based retrospective cohort study. Multiple linear regression and logistic regression models are used to analyse data on term 'singleton births' from Scottish hospitals between 1994-2003. Mothers who smoke are shown to give birth to lighter babies on average, a difference of approximately 0.57 Standard deviations lower (95% confidence interval. 0.55-0.58) when adjusted for sex and parity. These mothers are also more likely to have babies that are low birthweight (odds ratio 3.46, 95% confidence interval 3.30-3.63) compared with non-smokers. Low birthweight is 30% more likely where the mother lives in the most deprived areas compared with the least deprived, (odds ratio 1.30, 95% confidence interval 1.21-1.40). Smoking during pregnancy is shown to have a detrimental effect on the size of infants at birth. This effect explains some, though not all, of the observed socioeconomic birthweight. It also explains much of the observed birthweight differences by the age of the mother.   Identifying mothers at greater risk of having a low birthweight baby as important implications for the care and advice this group receives. © 2012 Blackwell Publishing Ltd.

  18. Explaining variation in the uptake of HPV vaccination in England

    Directory of Open Access Journals (Sweden)

    Whynes David K

    2011-03-01

    Full Text Available Abstract Background In England, two national programmes of HPV vaccination for girls have been instituted, a routine programme for 12- and 13-year-olds and a catch-up programme for 17- and 18-year-olds. Uptake rates across the country have been far from uniform, and this research sought to identify factors explaining the variation in uptake by locality. Methods An association between uptake, deprivation and ethnic background had been established in pilot research. The present analysis was conducted at an aggregate, Primary Care Trust (PCT, level for the first year of the programmes. Published measures of HPV vaccination uptake, material deprivation, ethnic composition of PCT populations, primary care quality, and uptake of cervical screening and of other childhood immunisations were collated. Strong evidence of collinearity amongst the explanatory variables required a factor analysis to be undertaken. This provided four independent factors, used thereafter in regression models to explain uptake by PCT. Results The factor analysis revealed that ethnic composition was associated with attitudes towards cervical screening and other childhood vaccinations, whilst material deprivation and quality of primary care were orthogonal. Ethnic composition, early childhood vaccination, cervical screening and primary care quality were found to be influential in predicting uptake in both the routine and the catch-up cohorts, although with a lower degree of confidence in the case of the last two independent variables. Lower primary care quality was significant in explaining a greater fall in vaccination uptake between the first two doses in the catch-up cohort. Greater deprivation was a significant explanatory factor for both uptake and the fall in uptake between doses for the catch-up cohort but not for uptake in the routine cohort. Conclusion These results for uptake of the first year of the national programme using aggregate data corroborate findings from

  19. Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis

    Science.gov (United States)

    Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae

    The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.

  20. An Additive-Multiplicative Cox-Aalen Regression Model

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

  1. Marital status integration and suicide: A meta-analysis and meta-regression.

    Science.gov (United States)

    Kyung-Sook, Woo; SangSoo, Shin; Sangjin, Shin; Young-Jeon, Shin

    2018-01-01

    Marital status is an index of the phenomenon of social integration within social structures and has long been identified as an important predictor suicide. However, previous meta-analyses have focused only on a particular marital status, or not sufficiently explored moderators. A meta-analysis of observational studies was conducted to explore the relationships between marital status and suicide and to understand the important moderating factors in this association. Electronic databases were searched to identify studies conducted between January 1, 2000 and June 30, 2016. We performed a meta-analysis, subgroup analysis, and meta-regression of 170 suicide risk estimates from 36 publications. Using random effects model with adjustment for covariates, the study found that the suicide risk for non-married versus married was OR = 1.92 (95% CI: 1.75-2.12). The suicide risk was higher for non-married individuals aged analysis by gender, non-married men exhibited a greater risk of suicide than their married counterparts in all sub-analyses, but women aged 65 years or older showed no significant association between marital status and suicide. The suicide risk in divorced individuals was higher than for non-married individuals in both men and women. The meta-regression showed that gender, age, and sample size affected between-study variation. The results of the study indicated that non-married individuals have an aggregate higher suicide risk than married ones. In addition, gender and age were confirmed as important moderating factors in the relationship between marital status and suicide. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Renfu Jia

    2016-01-01

    Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

  3. A regression analysis of the effect of energy use in agriculture

    International Nuclear Information System (INIS)

    Karkacier, Osman; Gokalp Goktolga, Z.; Cicek, Adnan

    2006-01-01

    This study investigates the impacts of energy use on productivity of Turkey's agriculture. It reports the results of a regression analysis of the relationship between energy use and agricultural productivity. The study is based on the analysis of the yearbook data for the period 1971-2003. Agricultural productivity was specified as a function of its energy consumption (TOE) and gross additions of fixed assets during the year. Least square (LS) was employed to estimate equation parameters. The data of this study comes from the State Institute of Statistics (SIS) and The Ministry of Energy of Turkey

  4. Important variables in explaining real-time peak price in the independent power market of Ontario

    International Nuclear Information System (INIS)

    Rueda, I.E.A.; Marathe, A.

    2005-01-01

    This paper uses support vector machines (SVM) based learning algorithm to select important variables that help explain the real-time peak electricity price in the Ontario market. The Ontario market was opened to competition only in May 2002. Due to the limited number of observations available, finding a set of variables that can explain the independent power market of Ontario (IMO) real-time peak price is a significant challenge for the traders and analysts. The kernel regressions of the explanatory variables on the IMO real-time average peak price show that non-linear dependencies exist between the explanatory variables and the IMO price. This non-linear relationship combined with the low variable-observation ratio rule out conventional statistical analysis. Hence, we use an alternative machine learning technique to find the important explanatory variables for the IMO real-time average peak price. SVM sensitivity analysis based results find that the IMO's predispatch average peak price, the actual import peak volume, the peak load of the Ontario market and the net available supply after accounting for load (energy excess) are some of the most important variables in explaining the real-time average peak price in the Ontario electricity market. (author)

  5. 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.

  6. Regression of environmental noise in LIGO data

    International Nuclear Information System (INIS)

    Tiwari, V; Klimenko, S; Mitselmakher, G; Necula, V; Drago, M; Prodi, G; Frolov, V; Yakushin, I; Re, V; Salemi, F; Vedovato, G

    2015-01-01

    We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the GW channel from the PEM measurements. One of the most promising regression methods is based on the construction of Wiener–Kolmogorov (WK) filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the WK method has been extended, incorporating banks of Wiener filters in the time–frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we present the first results on regression of the bi-coherent noise in the LIGO data. (paper)

  7. [Multiple linear regression analysis of X-ray measurement and WOMAC scores of knee osteoarthritis].

    Science.gov (United States)

    Ma, Yu-Feng; Wang, Qing-Fu; Chen, Zhao-Jun; Du, Chun-Lin; Li, Jun-Hai; Huang, Hu; Shi, Zong-Ting; Yin, Yue-Shan; Zhang, Lei; A-Di, Li-Jiang; Dong, Shi-Yu; Wu, Ji

    2012-05-01

    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. 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. There was statistical significance in the regression equation of AP X-rays value and WOMAC scores (Pregression equation of lateral X-ray value and WOMAC scores (P>0.05). 1) 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. 3) The correlation between tibial angle,joint gap angle on antero-posterior X-ray and WOMAC scores is significant, which can be used to assess the functional recovery of patients before and after treatment.

  8. Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

    Science.gov (United States)

    Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

    2006-01-01

    Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

  9. Finding determinants of audit delay by pooled OLS regression analysis

    OpenAIRE

    Vuko, Tina; Čular, Marko

    2014-01-01

    The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days) from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011). We use pooled OLS regression analysis, mode...

  10. Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

    Science.gov (United States)

    Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

    2003-06-01

    To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

  11. A Seemingly Unrelated Poisson Regression Model

    OpenAIRE

    King, Gary

    1989-01-01

    This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

  12. Dose-Dependent Effects of Statins for Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Regression Analysis.

    Science.gov (United States)

    To, Minh-Son; Prakash, Shivesh; Poonnoose, Santosh I; Bihari, Shailesh

    2018-05-01

    The study uses meta-regression analysis to quantify the dose-dependent effects of statin pharmacotherapy on vasospasm, delayed ischemic neurologic deficits (DIND), and mortality in aneurysmal subarachnoid hemorrhage. Prospective, retrospective observational studies, and randomized controlled trials (RCTs) were retrieved by a systematic database search. Summary estimates were expressed as absolute risk (AR) for a given statin dose or control (placebo). Meta-regression using inverse variance weighting and robust variance estimation was performed to assess the effect of statin dose on transformed AR in a random effects model. Dose-dependence of predicted AR with 95% confidence interval (CI) was recovered by using Miller's Freeman-Tukey inverse. The database search and study selection criteria yielded 18 studies (2594 patients) for analysis. These included 12 RCTs, 4 retrospective observational studies, and 2 prospective observational studies. Twelve studies investigated simvastatin, whereas the remaining studies investigated atorvastatin, pravastatin, or pitavastatin, with simvastatin-equivalent doses ranging from 20 to 80 mg. Meta-regression revealed dose-dependent reductions in Freeman-Tukey-transformed AR of vasospasm (slope coefficient -0.00404, 95% CI -0.00720 to -0.00087; P = 0.0321), DIND (slope coefficient -0.00316, 95% CI -0.00586 to -0.00047; P = 0.0392), and mortality (slope coefficient -0.00345, 95% CI -0.00623 to -0.00067; P = 0.0352). The present meta-regression provides weak evidence for dose-dependent reductions in vasospasm, DIND and mortality associated with acute statin use after aneurysmal subarachnoid hemorrhage. However, the analysis was limited by substantial heterogeneity among individual studies. Greater dosing strategies are a potential consideration for future RCTs. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. Quantile regression theory and applications

    CERN Document Server

    Davino, Cristina; Vistocco, Domenico

    2013-01-01

    A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and

  14. Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

    Science.gov (United States)

    Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

    2018-04-01

    Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

  15. Beyond the mean estimate: a quantile regression analysis of inequalities in educational outcomes using INVALSI survey data

    Directory of Open Access Journals (Sweden)

    Antonella Costanzo

    2017-09-01

    Full Text Available Abstract The number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students’ performance. Analogous results emerged pertaining to the difference in students’ performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education.

  16. Weighted functional linear regression models for gene-based association analysis.

    Science.gov (United States)

    Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

    2018-01-01

    Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

  17. Data analysis and approximate models model choice, location-scale, analysis of variance, nonparametric regression and image analysis

    CERN Document Server

    Davies, Patrick Laurie

    2014-01-01

    Introduction IntroductionApproximate Models Notation Two Modes of Statistical AnalysisTowards One Mode of Analysis Approximation, Randomness, Chaos, Determinism ApproximationA Concept of Approximation Approximation Approximating a Data Set by a Model Approximation Regions Functionals and EquivarianceRegularization and Optimality Metrics and DiscrepanciesStrong and Weak Topologies On Being (almost) Honest Simulations and Tables Degree of Approximation and p-values ScalesStability of Analysis The Choice of En(α, P) Independence Procedures, Approximation and VaguenessDiscrete Models The Empirical Density Metrics and Discrepancies The Total Variation Metric The Kullback-Leibler and Chi-Squared Discrepancies The Po(λ) ModelThe b(k, p) and nb(k, p) Models The Flying Bomb Data The Student Study Times Data OutliersOutliers, Data Analysis and Models Breakdown Points and Equivariance Identifying Outliers and Breakdown Outliers in Multivariate Data Outliers in Linear Regression Outliers in Structured Data The Location...

  18. Regression analysis of mixed panel count data with dependent terminal events.

    Science.gov (United States)

    Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L

    2017-05-10

    Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  19. CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

    Science.gov (United States)

    Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

    2016-02-01

    The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

  20. Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site.

    Science.gov (United States)

    Ndiath, Mansour M; Cisse, Badara; Ndiaye, Jean Louis; Gomis, Jules F; Bathiery, Ousmane; Dia, Anta Tal; Gaye, Oumar; Faye, Babacar

    2015-11-18

    In Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance. This study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis. From Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of -0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R(2) = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast

  1. Analysis of sparse data in logistic regression in medical research: A newer approach

    Directory of Open Access Journals (Sweden)

    S Devika

    2016-01-01

    Full Text Available Background and Objective: In the analysis of dichotomous type response variable, logistic regression is usually used. However, the performance of logistic regression in the presence of sparse data is questionable. In such a situation, a common problem is the presence of high odds ratios (ORs with very wide 95% confidence interval (CI (OR: >999.999, 95% CI: 999.999. In this paper, we addressed this issue by using penalized logistic regression (PLR method. Materials and Methods: Data from case-control study on hyponatremia and hiccups conducted in Christian Medical College, Vellore, Tamil Nadu, India was used. The outcome variable was the presence/absence of hiccups and the main exposure variable was the status of hyponatremia. Simulation dataset was created with different sample sizes and with a different number of covariates. Results: A total of 23 cases and 50 controls were used for the analysis of ordinary and PLR methods. The main exposure variable hyponatremia was present in nine (39.13% of the cases and in four (8.0% of the controls. Of the 23 hiccup cases, all were males and among the controls, 46 (92.0% were males. Thus, the complete separation between gender and the disease group led into an infinite OR with 95% CI (OR: >999.999, 95% CI: 999.999 whereas there was a finite and consistent regression coefficient for gender (OR: 5.35; 95% CI: 0.42, 816.48 using PLR. After adjusting for all the confounding variables, hyponatremia entailed 7.9 (95% CI: 2.06, 38.86 times higher risk for the development of hiccups as was found using PLR whereas there was an overestimation of risk OR: 10.76 (95% CI: 2.17, 53.41 using the conventional method. Simulation experiment shows that the estimated coverage probability of this method is near the nominal level of 95% even for small sample sizes and for a large number of covariates. Conclusions: PLR is almost equal to the ordinary logistic regression when the sample size is large and is superior in small cell

  2. Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

    Science.gov (United States)

    Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

    2017-02-06

    Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

  3. What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

    Science.gov (United States)

    Thomas, Emily H.; Galambos, Nora

    2004-01-01

    To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

  4. 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. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

  5. Robust Regression and its Application in Financial Data Analysis

    OpenAIRE

    Mansoor Momeni; Mahmoud Dehghan Nayeri; Ali Faal Ghayoumi; Hoda Ghorbani

    2010-01-01

    This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from th...

  6. Economic and Health Predictors of National Postpartum Depression Prevalence: A Systematic Review, Meta-analysis, and Meta-Regression of 291 Studies from 56 Countries

    Directory of Open Access Journals (Sweden)

    Jennifer Hahn-Holbrook

    2018-02-01

    Full Text Available BackgroundPostpartum depression (PPD poses a major global public health challenge. PPD is the most common complication associated with childbirth and exerts harmful effects on children. Although hundreds of PPD studies have been published, we lack accurate global or national PPD prevalence estimates and have no clear account of why PPD appears to vary so dramatically between nations. Accordingly, we conducted a meta-analysis to estimate the global and national prevalence of PPD and a meta-regression to identify economic, health, social, or policy factors associated with national PPD prevalence.MethodsWe conducted a systematic review of all papers reporting PPD prevalence using the Edinburgh Postnatal Depression Scale. PPD prevalence and methods were extracted from each study. Random effects meta-analysis was used to estimate global and national PPD prevalence. To test for country level predictors, we drew on data from UNICEF, WHO, and the World Bank. Random effects meta-regression was used to test national predictors of PPD prevalence.Findings291 studies of 296284 women from 56 countries were identified. The global pooled prevalence of PPD was 17.7% (95% confidence interval: 16.6–18.8%, with significant heterogeneity across nations (Q = 16,823, p = 0.000, I2 = 98%, ranging from 3% (2–5% in Singapore to 38% (35–41% in Chile. Nations with significantly higher rates of income inequality (R2 = 41%, maternal mortality (R2 = 19%, infant mortality (R2 = 16%, or women of childbearing age working ≥40 h a week (R2 = 31% have higher rates of PPD. Together, these factors explain 73% of the national variation in PPD prevalence.InterpretationThe global prevalence of PPD is greater than previously thought and varies dramatically by nation. Disparities in wealth inequality and maternal-child-health factors explain much of the national variation in PPD prevalence.

  7. Support vector methods for survival analysis: a comparison between ranking and regression approaches.

    Science.gov (United States)

    Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

    2011-10-01

    To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

  8. Regression Analysis for Multivariate Dependent Count Data Using Convolved Gaussian Processes

    OpenAIRE

    Sofro, A'yunin; Shi, Jian Qing; Cao, Chunzheng

    2017-01-01

    Research on Poisson regression analysis for dependent data has been developed rapidly in the last decade. One of difficult problems in a multivariate case is how to construct a cross-correlation structure and at the meantime make sure that the covariance matrix is positive definite. To address the issue, we propose to use convolved Gaussian process (CGP) in this paper. The approach provides a semi-parametric model and offers a natural framework for modeling common mean structure and covarianc...

  9. Explaining Match Outcome During The Men’s Basketball Tournament at The Olympic Games

    Science.gov (United States)

    Leicht, Anthony S.; Gómez, Miguel A.; Woods, Carl T.

    2017-01-01

    In preparation for the Olympics, there is a limited opportunity for coaches and athletes to interact regularly with team performance indicators providing important guidance to coaches for enhanced match success at the elite level. This study examined the relationship between match outcome and team performance indicators during men’s basketball tournaments at the Olympic Games. Twelve team performance indicators were collated from all men’s teams and matches during the basketball tournament of the 2004-2016 Olympic Games (n = 156). Linear and non-linear analyses examined the relationship between match outcome and team performance indicator characteristics; namely, binary logistic regression and a conditional interference (CI) classification tree. The most parsimonious logistic regression model retained ‘assists’, ‘defensive rebounds’, ‘field-goal percentage’, ‘fouls’, ‘fouls against’, ‘steals’ and ‘turnovers’ (delta AIC winning (93.2%). Match outcome during the men’s basketball tournaments at the Olympic Games was identified by a unique combination of performance indicators. Despite the average model accuracy being marginally higher for the logistic regression analysis, the CI classification tree offered a greater practical utility for coaches through its resolution of non-linear phenomena to guide team success. Key points A unique combination of team performance indicators explained 93.2% of winning observations in men’s basketball at the Olympics. Monitoring of these team performance indicators may provide coaches with the capability to devise multiple game plans or strategies to enhance their likelihood of winning. Incorporation of machine learning techniques with team performance indicators may provide a valuable and strategic approach to explain patterns within multivariate datasets in sport science. PMID:29238245

  10. Robust best linear estimation for regression analysis using surrogate and instrumental variables.

    Science.gov (United States)

    Wang, C Y

    2012-04-01

    We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the calibration sample, we assume that there is an instrumental variable (IV) that is available for all study subjects. An IV is correlated with the unobserved true exposure variable and hence can be useful in the estimation of the regression coefficients. We propose a robust best linear estimator that uses all the available data, which is the most efficient among a class of consistent estimators. The proposed estimator is shown to be consistent and asymptotically normal under very weak distributional assumptions. For Poisson or linear regression, the proposed estimator is consistent even if the measurement error from the surrogate or IV is heteroscedastic. Finite-sample performance of the proposed estimator is examined and compared with other estimators via intensive simulation studies. The proposed method and other methods are applied to a bladder cancer case-control study.

  11. Entrepreneurial intention modeling using hierarchical multiple regression

    Directory of Open Access Journals (Sweden)

    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.

  12. Sensitivity of Microstructural Factors Influencing the Impact Toughness of Hypoeutectoid Steels with Ferrite-Pearlite Structure using Multiple Regression Analysis

    International Nuclear Information System (INIS)

    Lee, Seung-Yong; Lee, Sang-In; Hwang, Byoung-chul

    2016-01-01

    In this study, the effect of microstructural factors on the impact toughness of hypoeutectoid steels with ferrite-pearlite structure was quantitatively investigated using multiple regression analysis. Microstructural analysis results showed that the pearlite fraction increased with increasing austenitizing temperature and decreasing transformation temperature which substantially decreased the pearlite interlamellar spacing and cementite thickness depending on carbon content. The impact toughness of hypoeutectoid steels usually increased as interlamellar spacing or cementite thickness decreased, although the impact toughness was largely associated with pearlite fraction. Based on these results, multiple regression analysis was performed to understand the individual effect of pearlite fraction, interlamellar spacing, and cementite thickness on the impact toughness. The regression analysis results revealed that pearlite fraction significantly affected impact toughness at room temperature, while cementite thickness did at low temperature.

  13. Explaining public support for space exploration funding in America: A multivariate analysis

    Science.gov (United States)

    Nadeau, François

    2013-05-01

    Recent studies have identified the need to understand what shapes public attitudes toward space policy. I address this gap in the literature by developing a multivariate regression model explaining why many Americans support government spending on space exploration. Using pooled data from the 2006 and 2008 General Social Surveys, the study reveals that spending preferences on space exploration are largely apolitical and associated instead with knowledge and opinions about science. In particular, the odds of wanting to increase funding for space exploration are significantly higher for white, male Babyboomers with a higher socio-economic status, a fondness for organized science, and a post-secondary science education. As such, I argue that public support for NASA's spending epitomizes what Launius termed "Apollo Nostalgia" in American culture. That is, Americans benefitting most from the old social order of the 1960s developed a greater fondness for science that makes them more likely to lament the glory days of space exploration. The article concludes with suggestions for how to elaborate on these findings in future studies.

  14. Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

    OpenAIRE

    Geroukis, Asterios; Brorson, Erik

    2014-01-01

    In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

  15. A REVIEW ON THE USE OF REGRESSION ANALYSIS IN STUDIES OF AUDIT QUALITY

    Directory of Open Access Journals (Sweden)

    Agung Dodit Muliawan

    2015-07-01

    Full Text Available This study aimed to review how regression analysis has been used in studies of abstract phenomenon, such as audit quality, an importance concept in the auditing practice (Schroeder et al., 1986, yet is not well defined. The articles reviewed were the research articles that include audit quality as research variable, either as dependent or independent variables. The articles were purposefully selected to represent balance combination between audit specific and more general accounting journals and between Anglo Saxon and Anglo American journals. The articles were published between 1983-2011 and from the A/A class journal based on ERA 2010’s classifications. The study found that most of the articles reviewed used multiple regression analysis and treated audit quality as dependent variable and measured it by using a proxy. This study also highlights the size of data sample used and the lack of discussions about the assumptions of the statistical analysis used in most of the articles reviewed. This study concluded that the effectiveness and validity of multiple regressions do not only depends on its application by the researchers but also on how the researchers communicate their findings to the audience. KEYWORDS Audit quality, regression analysis ABSTRAK Kajian ini bertujuan untuk mereviu bagaimana analisa regresi digunakan dalam suatu fenomena abstrak seperti kualitas audit, suatu konsep yang penting dalam praktik audit (Schroeder et al., 1986 namun belum terdefinisi dengan jelas. Artikel yang direviu dalam kajian ini adalah artikel penelitian yang memasukkan kualitas audit sebagai variabel penelitian, baik sebagai variabel independen maupun dependen. Artikel-artikel tersebut dipilih dengan cara purposif sampling untuk mendapatkan keterwakilan yang seimbang antara artikel jurnal khusus audit dan akuntansi secara umum, serta mewakili jurnal Anglo Saxon dan Anglo American. Artikel yang direviu diterbitkan pada periode 1983-2011 oleh jurnal yang

  16. Logistic regression applied to natural hazards: rare event logistic regression with replications

    OpenAIRE

    Guns, M.; Vanacker, Veerle

    2012-01-01

    Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logisti...

  17. Climate change but not unemployment explains the changing suicidality in Thessaloniki Greece (2000-2012).

    Science.gov (United States)

    Fountoulakis, Konstantinos N; Savopoulos, Christos; Zannis, Prodromos; Apostolopoulou, Martha; Fountoukidis, Ilias; Kakaletsis, Nikolaos; Kanellos, Ilias; Dimellis, Dimos; Hyphantis, Thomas; Tsikerdekis, Athanasios; Pompili, Maurizio; Hatzitolios, Apostolos I

    2016-03-15

    Recently there was a debate concerning the etiology behind attempts and completed suicides. The aim of the current study was to search for possible correlations between the rates of attempted and completed suicide and climate variables and regional unemployment per year in the county of Thessaloniki, Macedonia, northern Greece, for the years 2000-12. The regional rates of suicide and attempted suicide as well as regional unemployment were available from previous publications of the authors. The climate variables were calculated from the daily E-OBS gridded dataset which is based on observational data Only the male suicide rates correlate significantly with high mean annual temperature but not with unemployment. The multiple linear regression analysis results suggest that temperature is the only variable that determines male suicides and explains 51% of their variance. Unemployment fails to contribute significantly to the model. There seems to be a seasonal distribution for attempts with mean rates being higher for the period from May to October and the rates clearly correlate with temperature. The highest mean rates were observed during May and August and the lowest during December and February. Multiple linear regression analysis suggests that temperature also determines the female attempts rate although the explained variable is significant but very low (3-5%) Climate variables and specifically high temperature correlate both with suicide and attempted suicide rates but with a different way between males and females. The climate effect was stronger than the effect of unemployment. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Differentiating regressed melanoma from regressed lichenoid keratosis.

    Science.gov (United States)

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  19. Substitution elasticities between GHG-polluting and nonpolluting inputs in agricultural production: A meta-regression

    International Nuclear Information System (INIS)

    Liu, Boying; Richard Shumway, C.

    2016-01-01

    This paper reports meta-regressions of substitution elasticities between greenhouse gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. We treat energy, fertilizer, and manure collectively as the “polluting input” and labor, land, and capital as nonpolluting inputs. We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input. Much of the heterogeneity of Morishima elasticities can be explained by type of primal or dual function, functional form, type and observational level of data, input categories, number of outputs, type of output, time period, and country categories. Each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. These findings imply that life-cycle analysis based on fixed proportion production functions could provide grossly inaccurate measures of GHG of biofuel. - Highlights: • This paper reports meta-regressions of substitution elasticities between greenhouse-gas (GHG) polluting and nonpolluting inputs in agricultural production, which is the main feedstock source for biofuel in the U.S. • We estimate meta-regressions for samples of Morishima substitution elasticities for labor, land, and capital vs. the polluting input based on 65 primary studies. • We found that each estimated long-run elasticity for the reference case, which is most relevant for assessing GHG emissions through life-cycle analysis, is greater than 1.0 and significantly different from zero. Most predicted long-run elasticities remain significantly different from zero at the data means. • These findings imply that life-cycle analysis based on fixed proportion production functions could

  20. High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

    Science.gov (United States)

    Laurens, L M L; Wolfrum, E J

    2013-12-18

    One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

  1. Identification of cotton properties to improve yarn count quality by using regression analysis

    International Nuclear Information System (INIS)

    Amin, M.; Ullah, M.; Akbar, A.

    2014-01-01

    Identification of raw material characteristics towards yarn count variation was studied by using statistical techniques. Regression analysis is used to meet the objective. Stepwise regression is used for mode) selection, and coefficient of determination and mean squared error (MSE) criteria are used to identify the contributing factors of cotton properties for yam count. Statistical assumptions of normality, autocorrelation and multicollinearity are evaluated by using probability plot, Durbin Watson test, variance inflation factor (VIF), and then model fitting is carried out. It is found that, invisible (INV), nepness (Nep), grayness (RD), cotton trash (TR) and uniformity index (VI) are the main contributing cotton properties for yarn count variation. The results are also verified by Pareto chart. (author)

  2. Explaining Interaction Effects within and across Levels of Analysis

    DEFF Research Database (Denmark)

    Andersson, Ulf; Cuervo-Cazurra, Alvaro; Nielsen, Bo Bernhard

    2014-01-01

    Many manuscripts submitted to the Journal of International Business Studies propose an interaction effect in their models in an effort to explain the complexity and contingency of relationships across borders. In this article, we provide guidance on how best to explain the interaction effects...

  3. 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

  4. A tandem regression-outlier analysis of a ligand cellular system for key structural modifications around ligand binding.

    Science.gov (United States)

    Lin, Ying-Ting

    2013-04-30

    A tandem technique of hard equipment is often used for the chemical analysis of a single cell to first isolate and then detect the wanted identities. The first part is the separation of wanted chemicals from the bulk of a cell; the second part is the actual detection of the important identities. To identify the key structural modifications around ligand binding, the present study aims to develop a counterpart of tandem technique for cheminformatics. A statistical regression and its outliers act as a computational technique for separation. A PPARγ (peroxisome proliferator-activated receptor gamma) agonist cellular system was subjected to such an investigation. Results show that this tandem regression-outlier analysis, or the prioritization of the context equations tagged with features of the outliers, is an effective regression technique of cheminformatics to detect key structural modifications, as well as their tendency of impact to ligand binding. The key structural modifications around ligand binding are effectively extracted or characterized out of cellular reactions. This is because molecular binding is the paramount factor in such ligand cellular system and key structural modifications around ligand binding are expected to create outliers. Therefore, such outliers can be captured by this tandem regression-outlier analysis.

  5. Regression analysis: An evaluation of the inuences behindthe pricing of beer

    OpenAIRE

    Eriksson, Sara; Häggmark, Jonas

    2017-01-01

    This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find si...

  6. Few crystal balls are crystal clear : eyeballing regression

    International Nuclear Information System (INIS)

    Wittebrood, R.T.

    1998-01-01

    The theory of regression and statistical analysis as it applies to reservoir analysis was discussed. It was argued that regression lines are not always the final truth. It was suggested that regression lines and eyeballed lines are often equally accurate. The many conditions that must be fulfilled to calculate a proper regression were discussed. Mentioned among these conditions were the distribution of the data, hidden variables, knowledge of how the data was obtained, the need for causal correlation of the variables, and knowledge of the manner in which the regression results are going to be used. 1 tab., 13 figs

  7. Role of regression analysis and variation of rheological data in calculation of pressure drop for sludge pipelines.

    Science.gov (United States)

    Farno, E; Coventry, K; Slatter, P; Eshtiaghi, N

    2018-06-15

    Sludge pumps in wastewater treatment plants are often oversized due to uncertainty in calculation of pressure drop. This issue costs millions of dollars for industry to purchase and operate the oversized pumps. Besides costs, higher electricity consumption is associated with extra CO 2 emission which creates huge environmental impacts. Calculation of pressure drop via current pipe flow theory requires model estimation of flow curve data which depends on regression analysis and also varies with natural variation of rheological data. This study investigates impact of variation of rheological data and regression analysis on variation of pressure drop calculated via current pipe flow theories. Results compare the variation of calculated pressure drop between different models and regression methods and suggest on the suitability of each method. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. The effect of hospital mergers on long-term sickness absence among hospital employees: a fixed effects multivariate regression analysis using panel data.

    Science.gov (United States)

    Kjekshus, Lars Erik; Bernstrøm, Vilde Hoff; Dahl, Espen; Lorentzen, Thomas

    2014-02-03

    Hospitals are merging to become more cost-effective. Mergers are often complex and difficult processes with variable outcomes. The aim of this study was to analyze the effect of mergers on long-term sickness absence among hospital employees. Long-term sickness absence was analyzed among hospital employees (N = 107 209) in 57 hospitals involved in 23 mergers in Norway between 2000 and 2009. Variation in long-term sickness absence was explained through a fixed effects multivariate regression analysis using panel data with years-since-merger as the independent variable. We found a significant but modest effect of mergers on long-term sickness absence in the year of the merger, and in years 2, 3 and 4; analyzed by gender there was a significant effect for women, also for these years, but only in year 4 for men. However, men are less represented among the hospital workforce; this could explain the lack of significance. Mergers has a significant effect on employee health that should be taken into consideration when deciding to merge hospitals. This study illustrates the importance of analyzing the effects of mergers over several years and the need for more detailed analyses of merger processes and of the changes that may occur as a result of such mergers.

  9. 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.

  10. Linear regression in astronomy. II

    Science.gov (United States)

    Feigelson, Eric D.; Babu, Gutti J.

    1992-01-01

    A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

  11. Análise de fatores e regressão bissegmentada em estudos de estratificação ambiental e adaptabilidade em milho Factor analysis and bissegmented regression for studies about environmental stratification and maize adaptability

    Directory of Open Access Journals (Sweden)

    Deoclécio Domingos Garbuglio

    2007-02-01

    Full Text Available O objetivo deste trabalho foi verificar possíveis divergências entre os resultados obtidos nas avaliações da adaptabilidade de 27 genótipos de milho (Zea mays L., e na estratificação de 22 ambientes no Estado do Paraná, por meio de técnicas baseadas na análise de fatores e regressão bissegmentada. As estratificações ambientais foram feitas por meio do método tradicional e por análise de fatores, aliada ao porcentual da porção simples da interação GxA (PS%. As análises de adaptabilidade foram realizadas por meio de regressão bissegmentada e análise de fatores. Pela análise de regressão bissegmentada, os genótipos estudados apresentaram alta performance produtiva; no entanto, não foi constatado o genótipo considerado como ideal. A adaptabilidade dos genótipos, analisada por meio de plotagens gráficas, apresentou respostas diferenciadas quando comparada à regressão bissegmentada. A análise de fatores mostrou-se eficiente nos processos de estratificação ambiental e adaptabilidade dos genótipos de milho.The objective of this work was to verify possible divergences among results obtained on adaptability evaluations of 27 maize genotypes (Zea mays L., and on stratification of 22 environments on Paraná State, Brazil, through techniques of factor analysis and bissegmented regression. The environmental stratifications were made through the traditional methodology and by factor analysis, allied to the percentage of the simple portion of GxE interaction (PS%. Adaptability analyses were carried out through bissegmented regression and factor analysis. By the analysis of bissegmented regression, studied genotypes had presented high productive performance; however, it was not evidenced the genotype considered as ideal. The adaptability of the genotypes, analyzed through graphs, presented different answers when compared to bissegmented regression. Factor analysis was efficient in the processes of environment stratification and

  12. Determinants of orphan drugs prices in France: a regression analysis.

    Science.gov (United States)

    Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher

    2017-04-21

    The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The

  13. Logistic regression applied to natural hazards: rare event logistic regression with replications

    Science.gov (United States)

    Guns, M.; Vanacker, V.

    2012-06-01

    Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

  14. Stepwise versus Hierarchical Regression: Pros and Cons

    Science.gov (United States)

    Lewis, Mitzi

    2007-01-01

    Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

  15. Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements

    KAUST Repository

    Ryu, Duchwan

    2010-09-28

    We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.

  16. Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales

    Czech Academy of Sciences Publication Activity Database

    Krištoufek, Ladislav

    2015-01-01

    Roč. 91, č. 1 (2015), 022802-1-022802-5 ISSN 1539-3755 R&D Projects: GA ČR(CZ) GP14-11402P Grant - others:GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : Detrended cross-correlation analysis * Regression * Scales Subject RIV: AH - Economics Impact factor: 2.288, year: 2014 http://library.utia.cas.cz/separaty/2015/E/kristoufek-0452315.pdf

  17. MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY

    OpenAIRE

    Chayalakshmi C.L

    2018-01-01

    MULTIPLE LINEAR REGRESSION ANALYSIS FOR PREDICTION OF BOILER LOSSES AND BOILER EFFICIENCY ABSTRACT Calculation of boiler efficiency is essential if its parameters need to be controlled for either maintaining or enhancing its efficiency. But determination of boiler efficiency using conventional method is time consuming and very expensive. Hence, it is not recommended to find boiler efficiency frequently. The work presented in this paper deals with establishing the statistical mo...

  18. Statistical learning method in regression analysis of simulated positron spectral data

    International Nuclear Information System (INIS)

    Avdic, S. Dz.

    2005-01-01

    Positron lifetime spectroscopy is a non-destructive tool for detection of radiation induced defects in nuclear reactor materials. This work concerns the applicability of the support vector machines method for the input data compression in the neural network analysis of positron lifetime spectra. It has been demonstrated that the SVM technique can be successfully applied to regression analysis of positron spectra. A substantial data compression of about 50 % and 8 % of the whole training set with two and three spectral components respectively has been achieved including a high accuracy of the spectra approximation. However, some parameters in the SVM approach such as the insensitivity zone e and the penalty parameter C have to be chosen carefully to obtain a good performance. (author)

  19. Use of generalized regression models for the analysis of stress-rupture data

    International Nuclear Information System (INIS)

    Booker, M.K.

    1978-01-01

    The design of components for operation in an elevated-temperature environment often requires a detailed consideration of the creep and creep-rupture properties of the construction materials involved. Techniques for the analysis and extrapolation of creep data have been widely discussed. The paper presents a generalized regression approach to the analysis of such data. This approach has been applied to multiple heat data sets for types 304 and 316 austenitic stainless steel, ferritic 2 1 / 4 Cr-1 Mo steel, and the high-nickel austenitic alloy 800H. Analyses of data for single heats of several materials are also presented. All results appear good. The techniques presented represent a simple yet flexible and powerful means for the analysis and extrapolation of creep and creep-rupture data

  20. Explaining pathological changes in axonal excitability through dynamical analysis of conductance-based models

    Science.gov (United States)

    Coggan, Jay S.; Ocker, Gabriel K.; Sejnowski, Terrence J.; Prescott, Steven A.

    2011-10-01

    Neurons rely on action potentials, or spikes, to relay information. Pathological changes in spike generation likely contribute to certain enigmatic features of neurological disease, like paroxysmal attacks of pain and muscle spasm. Paroxysmal symptoms are characterized by abrupt onset and short duration, and are associated with abnormal spiking although the exact pathophysiology remains unclear. To help decipher the biophysical basis for 'paroxysmal' spiking, we replicated afterdischarge (i.e. continued spiking after a brief stimulus) in a minimal conductance-based axon model. We then applied nonlinear dynamical analysis to explain the dynamical basis for initiation and termination of afterdischarge. A perturbation could abruptly switch the system between two (quasi-)stable attractor states: rest and repetitive spiking. This bistability was a consequence of slow positive feedback mediated by persistent inward current. Initiation of afterdischarge was explained by activation of the persistent inward current forcing the system to cross a saddle point that separates the basins of attraction associated with each attractor. Termination of afterdischarge was explained by the attractor associated with repetitive spiking being destroyed. This occurred when ultra-slow negative feedback, such as intracellular sodium accumulation, caused the saddle point and stable limit cycle to collide; in that regard, the active attractor is not truly stable when the slowest dynamics are taken into account. The model also explains other features of paroxysmal symptoms, including temporal summation and refractoriness.

  1. Regression: The Apple Does Not Fall Far From the Tree.

    Science.gov (United States)

    Vetter, Thomas R; Schober, Patrick

    2018-05-15

    Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

  2. Regression Analysis

    CERN Document Server

    Freund, Rudolf J; Sa, Ping

    2006-01-01

    The book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design

  3. Systematic review, meta-analysis, and meta-regression: Successful second-line treatment for Helicobacter pylori.

    Science.gov (United States)

    Muñoz, Neus; Sánchez-Delgado, Jordi; Baylina, Mireia; Puig, Ignasi; López-Góngora, Sheila; Suarez, David; Calvet, Xavier

    2018-06-01

    Multiple Helicobacter pylori second-line schedules have been described as potentially useful. It remains unclear, however, which are the best combinations, and which features of second-line treatments are related to better cure rates. The aim of this study was to determine that second-line treatments achieved excellent (>90%) cure rates by performing a systematic review and when possible a meta-analysis. A meta-regression was planned to determine the characteristics of treatments achieving excellent cure rates. A systematic review for studies evaluating second-line Helicobacter pylori treatment was carried out in multiple databases. A formal meta-analysis was performed when an adequate number of comparative studies was found, using RevMan5.3. A meta-regression for evaluating factors predicting cure rates >90% was performed using Stata Statistical Software. The systematic review identified 115 eligible studies, including 203 evaluable treatment arms. The results were extremely heterogeneous, with 61 treatment arms (30%) achieving optimal (>90%) cure rates. The meta-analysis favored quadruple therapies over triple (83.2% vs 76.1%, OR: 0.59:0.38-0.93; P = .02) and 14-day quadruple treatments over 7-day treatments (91.2% vs 81.5%, OR; 95% CI: 0.42:0.24-0.73; P = .002), although the differences were significant only in the per-protocol analysis. The meta-regression did not find any particular characteristics of the studies to be associated with excellent cure rates. Second-line Helicobacter pylori treatments achieving>90% cure rates are extremely heterogeneous. Quadruple therapy and 14-day treatments seem better than triple therapies and 7-day ones. No single characteristic of the treatments was related to excellent cure rates. Future approaches suitable for infectious diseases-thus considering antibiotic resistances-are needed to design rescue treatments that consistently achieve excellent cure rates. © 2018 John Wiley & Sons Ltd.

  4. Regression Association Analysis of Yield-Related Traits with RAPD Molecular Markers in Pistachio (Pistacia vera L.

    Directory of Open Access Journals (Sweden)

    Saeid Mirzaei

    2017-10-01

    Full Text Available Introduction: The pistachio (Pistacia vera, a member of the cashew family, is a small tree originating from Central Asia and the Middle East. The tree produces seeds that are widely consumed as food. Pistacia vera often is confused with other species in the genus Pistacia that are also known as pistachio. These other species can be distinguished by their geographic distributions and their seeds which are much smaller and have a soft shell. Continual advances in crop improvement through plant breeding are driven by the available genetic diversity. Therefore, the recognition and measurement of such diversity is crucial to breeding programs. In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL identification and marker assisted selection (MAS. The germplasm-regression-combined association studies not only allow mapping of genes/QTLs with higher level of confidence, but also allow detection of genes/QTLs, which will otherwise escape detection in linkage-based QTL studies based on the planned populations. The development of the marker-based technology offers a fast, reliable, and easy way to perform multiple regression analysis and comprise an alternative approach to breeding in diverse species of plants. The availability of many makers and morphological traits can help to regression analysis between these markers and morphological traits. Materials and Methods: In this study, 20 genotypes of Pistachio were studied and yield related traits were measured. Young well-expanded leaves were collected for DNA extraction and total genomic DNA was extracted. Genotyping was performed using 15 RAPD primers and PCR amplification products were visualized by gel electrophoresis. The reproducible RAPD fragments were scored on the basis of present (1 or absent (0 bands and a binary matrix constructed using each molecular marker. Association analysis between

  5. Regression models of reactor diagnostic signals

    International Nuclear Information System (INIS)

    Vavrin, J.

    1989-01-01

    The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

  6. Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

    Science.gov (United States)

    Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

    2009-11-01

    Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

  7. Comparison of Classical Linear Regression and Orthogonal Regression According to the Sum of Squares Perpendicular Distances

    OpenAIRE

    KELEŞ, Taliha; ALTUN, Murat

    2016-01-01

    Regression analysis is a statistical technique for investigating and modeling the relationship between variables. The purpose of this study was the trivial presentation of the equation for orthogonal regression (OR) and the comparison of classical linear regression (CLR) and OR techniques with respect to the sum of squared perpendicular distances. For that purpose, the analyses were shown by an example. It was found that the sum of squared perpendicular distances of OR is smaller. Thus, it wa...

  8. Online Statistical Modeling (Regression Analysis) for Independent Responses

    Science.gov (United States)

    Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

    2017-06-01

    Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

  9. A Simple Linear Regression Method for Quantitative Trait Loci Linkage Analysis With Censored Observations

    OpenAIRE

    Anderson, Carl A.; McRae, Allan F.; Visscher, Peter M.

    2006-01-01

    Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using...

  10. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression.

    Science.gov (United States)

    Hadayeghi, Alireza; Shalaby, Amer S; Persaud, Bhagwant N

    2010-03-01

    A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models. Copyright 2009 Elsevier Ltd. All rights reserved.

  11. Logistic regression applied to natural hazards: rare event logistic regression with replications

    Directory of Open Access Journals (Sweden)

    M. Guns

    2012-06-01

    Full Text Available Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

  12. Healthcare Expenditures Associated with Depression Among Individuals with Osteoarthritis: Post-Regression Linear Decomposition Approach.

    Science.gov (United States)

    Agarwal, Parul; Sambamoorthi, Usha

    2015-12-01

    Depression is common among individuals with osteoarthritis and leads to increased healthcare burden. The objective of this study was to examine excess total healthcare expenditures associated with depression among individuals with osteoarthritis in the US. Adults with self-reported osteoarthritis (n = 1881) were identified using data from the 2010 Medical Expenditure Panel Survey (MEPS). Among those with osteoarthritis, chi-square tests and ordinary least square regressions (OLS) were used to examine differences in healthcare expenditures between those with and without depression. Post-regression linear decomposition technique was used to estimate the relative contribution of different constructs of the Anderson's behavioral model, i.e., predisposing, enabling, need, personal healthcare practices, and external environment factors, to the excess expenditures associated with depression among individuals with osteoarthritis. All analysis accounted for the complex survey design of MEPS. Depression coexisted among 20.6 % of adults with osteoarthritis. The average total healthcare expenditures were $13,684 among adults with depression compared to $9284 among those without depression. Multivariable OLS regression revealed that adults with depression had 38.8 % higher healthcare expenditures (p regression linear decomposition analysis indicated that 50 % of differences in expenditures among adults with and without depression can be explained by differences in need factors. Among individuals with coexisting osteoarthritis and depression, excess healthcare expenditures associated with depression were mainly due to comorbid anxiety, chronic conditions and poor health status. These expenditures may potentially be reduced by providing timely intervention for need factors or by providing care under a collaborative care model.

  13. Robust Methods for Moderation Analysis with a Two-Level Regression Model.

    Science.gov (United States)

    Yang, Miao; Yuan, Ke-Hai

    2016-01-01

    Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

  14. Oil and gas pipeline construction cost analysis and developing regression models for cost estimation

    Science.gov (United States)

    Thaduri, Ravi Kiran

    In this study, cost data for 180 pipelines and 136 compressor stations have been analyzed. On the basis of the distribution analysis, regression models have been developed. Material, Labor, ROW and miscellaneous costs make up the total cost of a pipeline construction. The pipelines are analyzed based on different pipeline lengths, diameter, location, pipeline volume and year of completion. In a pipeline construction, labor costs dominate the total costs with a share of about 40%. Multiple non-linear regression models are developed to estimate the component costs of pipelines for various cross-sectional areas, lengths and locations. The Compressor stations are analyzed based on the capacity, year of completion and location. Unlike the pipeline costs, material costs dominate the total costs in the construction of compressor station, with an average share of about 50.6%. Land costs have very little influence on the total costs. Similar regression models are developed to estimate the component costs of compressor station for various capacities and locations.

  15. Social cognitive theories used to explain physical activity behavior in adolescents: a systematic review and meta-analysis.

    Science.gov (United States)

    Plotnikoff, Ronald C; Costigan, Sarah A; Karunamuni, Nandini; Lubans, David R

    2013-05-01

    To systematically review and examine the explanatory power of key social-cognitive theories used to explain physical activity (PA) intention and behavior, among adolescents. A systematic review and meta-analysis of the literature was performed using the electronic databases Medline, Cumulative Index to Nursing and Allied Health Literature, SPORTdiscus, EBSCO and Education Resources Information Center, Proquest Education Journals Collection, Science Direct, Web of Science and Scopus for social-cognitive theories (i.e., Health Promotion Model, Theory of Planned Behavior, Theory of Reasoned Action, Protection Motivation Theory, Social Cognitive Theory/Self-Efficacy Theory, Health Belief Model, Self-Determination Theory, Transtheoretical Model) used to explain PA intention and behavior. Related keywords in titles, abstracts, or indexing fields were searched. Twenty-three studies satisfied the inclusion criteria and were retained for data extraction and analysis; 16 were cross-sectional studies and seven were longitudinal studies. Most studies employed self-report measures. In general, the models explained greater proportions of variance for intention compared to behavior. The meta-analyses revealed 33% and 48% of the variance respectively for PA and intention were explained by social cognitive models. Few studies have tested the predictive capacity of social cognitive theories to explain objectively measured PA. The majority of PA variance remains unexplained and more theoretical research is needed. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Weibull and lognormal Taguchi analysis using multiple linear regression

    International Nuclear Information System (INIS)

    Piña-Monarrez, Manuel R.; Ortiz-Yañez, Jesús F.

    2015-01-01

    The paper provides to reliability practitioners with a method (1) to estimate the robust Weibull family when the Taguchi method (TM) is applied, (2) to estimate the normal operational Weibull family in an accelerated life testing (ALT) analysis to give confidence to the extrapolation and (3) to perform the ANOVA analysis to both the robust and the normal operational Weibull family. On the other hand, because the Weibull distribution neither has the normal additive property nor has a direct relationship with the normal parameters (µ, σ), in this paper, the issues of estimating a Weibull family by using a design of experiment (DOE) are first addressed by using an L_9 (3"4) orthogonal array (OA) in both the TM and in the Weibull proportional hazard model approach (WPHM). Then, by using the Weibull/Gumbel and the lognormal/normal relationships and multiple linear regression, the direct relationships between the Weibull and the lifetime parameters are derived and used to formulate the proposed method. Moreover, since the derived direct relationships always hold, the method is generalized to the lognormal and ALT analysis. Finally, the method’s efficiency is shown through its application to the used OA and to a set of ALT data. - Highlights: • It gives the statistical relations and steps to use the Taguchi Method (TM) to analyze Weibull data. • It gives the steps to determine the unknown Weibull family to both the robust TM setting and the normal ALT level. • It gives a method to determine the expected lifetimes and to perform its ANOVA analysis in TM and ALT analysis. • It gives a method to give confidence to the extrapolation in an ALT analysis by using the Weibull family of the normal level.

  17. Framing an Nuclear Emergency Plan using Qualitative Regression Analysis

    International Nuclear Information System (INIS)

    Amy Hamijah Abdul Hamid; Ibrahim, M.Z.A.; Deris, S.R.

    2014-01-01

    Since the arising on safety maintenance issues due to post-Fukushima disaster, as well as, lack of literatures on disaster scenario investigation and theory development. This study is dealing with the initiation difficulty on the research purpose which is related to content and problem setting of the phenomenon. Therefore, the research design of this study refers to inductive approach which is interpreted and codified qualitatively according to primary findings and written reports. These data need to be classified inductively into thematic analysis as to develop conceptual framework related to several theoretical lenses. Moreover, the framing of the expected framework of the respective emergency plan as the improvised business process models are abundant of unstructured data abstraction and simplification. The structural methods of Qualitative Regression Analysis (QRA) and Work System snapshot applied to form the data into the proposed model conceptualization using rigorous analyses. These methods were helpful in organising and summarizing the snapshot into an ' as-is ' work system that being recommended as ' to-be' w ork system towards business process modelling. We conclude that these methods are useful to develop comprehensive and structured research framework for future enhancement in business process simulation. (author)

  18. A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

    International Nuclear Information System (INIS)

    Kumar, Akansha; Tsvetkov, Pavel V.

    2015-01-01

    Highlights: • This paper presents a new method useful for the optimization of complex dynamic systems. • The method uses the strengths of; genetic algorithms (GA), and regression splines. • The method is applied to the design of a gas cooled fast breeder reactor design. • Tools like Java, R, and codes like MCNP, Matlab are used in this research. - Abstract: A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor. GA simulates natural evolution to perform optimization, and is widely used in recent times by the scientific community. The GA fits a population of random solutions to the optimized solution of a specific problem. In this work, we have developed a genetic algorithm to determine the values for a set of nuclear reactor parameters to design a gas cooled fast breeder reactor core including a basis thermal–hydraulics analysis, and energy transfer. Multivariate regression is implemented using regression splines (RS). Reactor designs are usually complex and a simulation needs a significantly large amount of time to execute, hence the implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using RS in conjunction with GA. Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The reactor parameters are given by the, radius of a fuel pin cell, isotopic enrichment of the fissile material in the fuel, mass flow rate of the coolant, and temperature of the coolant at the core inlet. And, the optimization objectives for the reactor core are, high breeding of U-233 and Pu-239 in

  19. Spatial stochastic regression modelling of urban land use

    International Nuclear Information System (INIS)

    Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A

    2014-01-01

    Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable

  20. Meta-analysis of institutional-economic factors explaining the environmental performance of payments for watershed services

    NARCIS (Netherlands)

    Brouwer, R.; Tesfaye, A.; Pauw, W.P.

    2011-01-01

    Payments for ecosystem services (PES) are a relatively new economic policy instrument, and the factors that drive and explain their environmental performance are poorly understood. Here a meta-analysis of causal relationships between the institutional design and environmental performance of 47

  1. Robust estimation for homoscedastic regression in the secondary analysis of case-control data

    KAUST Repository

    Wei, Jiawei; Carroll, Raymond J.; Mü ller, Ursula U.; Keilegom, Ingrid Van; Chatterjee, Nilanjan

    2012-01-01

    Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

  2. Robust estimation for homoscedastic regression in the secondary analysis of case-control data

    KAUST Repository

    Wei, Jiawei

    2012-12-04

    Primary analysis of case-control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case-control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case-control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case-control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.

  3. Variable and subset selection in PLS regression

    DEFF Research Database (Denmark)

    Høskuldsson, Agnar

    2001-01-01

    The purpose of this paper is to present some useful methods for introductory analysis of variables and subsets in relation to PLS regression. We present here methods that are efficient in finding the appropriate variables or subset to use in the PLS regression. The general conclusion...... is that variable selection is important for successful analysis of chemometric data. An important aspect of the results presented is that lack of variable selection can spoil the PLS regression, and that cross-validation measures using a test set can show larger variation, when we use different subsets of X, than...

  4. Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

    Science.gov (United States)

    Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

    2017-01-01

    The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

  5. Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats—Multiple factorial regression analysis

    International Nuclear Information System (INIS)

    Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

    2017-01-01

    The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200–240 g for 28 days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15 mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000 mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight

  6. Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.

    Science.gov (United States)

    Vatcheva, Kristina P; Lee, MinJae; McCormick, Joseph B; Rahbar, Mohammad H

    2016-04-01

    The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.

  7. Testing contingency hypotheses in budgetary research: An evaluation of the use of moderated regression analysis

    NARCIS (Netherlands)

    Hartmann, Frank G.H.; Moers, Frank

    1999-01-01

    In the contingency literature on the behavioral and organizational effects of budgeting, use of the Moderated Regression Analysis (MRA) technique is prevalent. This technique is used to test contingency hypotheses that predict interaction effects between budgetary and contextual variables. This

  8. Body Composition Explains Greater Variance in Weight-for-Length Z-scores than Mid-Upper Arm Circumference during Infancy - A Secondary Data Analysis

    International Nuclear Information System (INIS)

    Grijalva-Eternod, Carlos; Andersen, Gregers Stig; Girma, Tsinuel; Admassu, Bitiya; Kæstel, Pernille; Michaelsen, Kim F; Friis, Henrik; Wells, Jonathan CK

    2014-01-01

    Full text: Background: Traditionally, weight-for-length/height z-score (WLZ) was used to assess wasting (a mortality risk factor) in children 0-59 months. A recent consultation reached a majority position that mid-upper arm circumference (MUAC) is a better mortality predictor than WLZ in children 6-59 months. In addition, MUAC collected at the ages of 6-14 weeks has shown to identify infants more likely to die before reaching one year. To understand which body compartment is most affected by undernutrition, associations between anthropometric indicators and body composition data have been studied in children aged 6-59 months. To our knowledge, no such study has been done in children aged 0-6 months. We aimed to study these associations. Methods: Weight, length, MUAC, and lean and fat mass (LM and FM, respectively) obtained by air-displacement plethysmography of infants aged 0-6 months were obtained from an Ethiopian birth cohort study. The data, originally used to construct body composition reference data, measured infants at birth, 1.5, 2.5, 3.5, 4.5, and 6 months of age. A complete set of measurements available for 2506 out of a total of 2777 child measurements (563/598, 403/436, 414/444, 413/446, 368/415, and 345/441 in each age, respectively) was used for this analys, E-mail: is. Weight and length data were transformed to sex-and age-specific weight-for-length z-score (WLZ) values using the 2006 WHO growth standards. To remove the confounding positive association between LM or FM and length, we calculated sex- and age-specific standardised residuals values obtained from regressing LM or FM on length, separately by sex and age of measurement. The associations between MUAC, WLZ, length, and body composition residuals were assessed using correlation analysis. We used regression analysis to assess the independent contribution of body composition residuals to MUAC and WLZ. All analyses were done separately by age. Results: MUAC was strongly and positively correlated

  9. Modified Regression Correlation Coefficient for Poisson Regression Model

    Science.gov (United States)

    Kaengthong, Nattacha; Domthong, Uthumporn

    2017-09-01

    This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

  10. Linear regression and the normality assumption.

    Science.gov (United States)

    Schmidt, Amand F; Finan, Chris

    2017-12-16

    Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Economist intelligence unit democracy index in relation to health services accessibility: a regression analysis.

    Science.gov (United States)

    Walker, Mary Ellen; Anonson, June; Szafron, Michael

    2015-01-01

    The relationship between political environment and health services accessibility (HSA) has not been the focus of any specific studies. The purpose of this study was to address this gap in the literature by examining the relationship between political environment and HSA. This relationship that HSA indicators (physicians, nurses and hospital beds per 10 000 people) has with political environment was analyzed with multiple least-squares regression using the components of democracy (electoral processes and pluralism, functioning of government, political participation, political culture, and civil liberties). The components of democracy were represented by the 2011 Economist Intelligence Unit Democracy Index (EIUDI) sub-scores. The EIUDI sub-scores and the HSA indicators were evaluated for significant relationships with multiple least-squares regression. While controlling for a country's geographic location and level of democracy, we found that two components of a nation's political environment: functioning of government and political participation, and their interaction had significant relationships with the three HSA indicators. These study findings are of significance to health professionals because they examine the political contexts in which citizens access health services, they come from research that is the first of its kind, and they help explain the effect political environment has on health. © The Author 2014. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  12. Clinical benefit from pharmacological elevation of high-density lipoprotein cholesterol: meta-regression analysis.

    Science.gov (United States)

    Hourcade-Potelleret, F; Laporte, S; Lehnert, V; Delmar, P; Benghozi, Renée; Torriani, U; Koch, R; Mismetti, P

    2015-06-01

    Epidemiological evidence that the risk of coronary heart disease is inversely associated with the level of high-density lipoprotein cholesterol (HDL-C) has motivated several phase III programmes with cholesteryl ester transfer protein (CETP) inhibitors. To assess alternative methods to predict clinical response of CETP inhibitors. Meta-regression analysis on raising HDL-C drugs (statins, fibrates, niacin) in randomised controlled trials. 51 trials in secondary prevention with a total of 167,311 patients for a follow-up >1 year where HDL-C was measured at baseline and during treatment. The meta-regression analysis showed no significant association between change in HDL-C (treatment vs comparator) and log risk ratio (RR) of clinical endpoint (non-fatal myocardial infarction or cardiac death). CETP inhibitors data are consistent with this finding (RR: 1.03; P5-P95: 0.99-1.21). A prespecified sensitivity analysis by drug class suggested that the strength of relationship might differ between pharmacological groups. A significant association for both statins (p<0.02, log RR=-0.169-0.0499*HDL-C change, R(2)=0.21) and niacin (p=0.02, log RR=1.07-0.185*HDL-C change, R(2)=0.61) but not fibrates (p=0.18, log RR=-0.367+0.077*HDL-C change, R(2)=0.40) was shown. However, the association was no longer detectable after adjustment for low-density lipoprotein cholesterol for statins or exclusion of open trials for niacin. Meta-regression suggested that CETP inhibitors might not influence coronary risk. The relation between change in HDL-C level and clinical endpoint may be drug dependent, which limits the use of HDL-C as a surrogate marker of coronary events. Other markers of HDL function may be more relevant. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  13. Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.

    Science.gov (United States)

    Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge

    2015-06-01

    Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight

  14. Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression

    Science.gov (United States)

    Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi

    2016-01-01

    The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590

  15. Explaining socio-demographic differences in disengagement from sports in adolescence.

    Science.gov (United States)

    Prins, Richard G; Kamphuis, Carlijn B M; van Empelen, Pepijn; Beenackers, Mariëlle A; Brug, Johannes; Mackenbach, Johan P; Oenema, Anke

    2013-10-01

    The purpose of this longitudinal study is to identify risk groups for disengagement from sports during adolescence. In addition, it will be explored whether cognitive and environmental factors can explain socio-demographic differences in disengagement from sports. Data were obtained from the Environmental Determinants of Obesity in Rotterdam Schoolchildren study, and 357 adolescents were eligible for analysis. Socio-demographics (gender, ethnicity, education), individual cognitions and neighbourhood perceptions were assessed at baseline (2005/2006), and sports participation at baseline and at follow-up (2007/2008). Two dichotomous outcome variables were constructed: (i) disengagement from sports (yes/no) and (ii) ceased compliance with the fitnorm (i.e. cease engaging in sports ≥3 times/wk) (yes/no). In logistic regression and mediation analyses, we identified socio-demographic differences in the two outcomes. Subsequently, we applied mediation analyses to identify the contribution of cognitive and environmental explanatory factors of the socio-demographic differences. Girls [odds ratio (OR): 2.5, 95% confidence interval (CI): 1.5-4.5] were more likely than boys to disengage from sports. Girls (OR: 2.5, 95% CI: 1.4-4.2), adolescents of non-Western background (OR: 1.8, 95% CI: 1.0-3.0) and those in lower educational levels (OR: 1.7, 95% CI: 1.0-2.9) were more likely to cease compliance with the fitnorm. Perceived neighbourhood safety partly explained gender differences in disengagement from sports (8%). Intention partly explained ethnical (32%) and educational differences (37%) in ceasing compliance with the fitnorm. Girls, lower-educated adolescents and those with a non-Western background showed more pronounced reductions in sports participation and compliance with the fitnorm. Intention and perceived neighbourhood safety could partially explain these differences.

  16. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis

    Directory of Open Access Journals (Sweden)

    BUDIMAN

    2012-01-01

    Full Text Available Budiman, Arisoesilaningsih E. 2012. Predictive model of Amorphophallus muelleri growth in some agroforestry in East Java by multiple regression analysis. Biodiversitas 13: 18-22. The aims of this research was to determine the multiple regression models of vegetative and corm growth of Amorphophallus muelleri Blume in some age variations and habitat conditions of agroforestry in East Java. Descriptive exploratory research method was conducted by systematic random sampling at five agroforestries on four plantations in East Java: Saradan, Bojonegoro, Nganjuk and Blitar. In each agroforestry, we observed A. muelleri vegetative and corm growth on four growing age (1, 2, 3 and 4 years old respectively as well as environmental variables such as altitude, vegetation, climate and soil conditions. Data were analyzed using descriptive statistics to compare A. muelleri habitat in five agroforestries. Meanwhile, the influence and contribution of each environmental variable to the growth of A. muelleri vegetative and corm were determined using multiple regression analysis of SPSS 17.0. The multiple regression models of A. muelleri vegetative and corm growth were generated based on some characteristics of agroforestries and age showed high validity with R2 = 88-99%. Regression model showed that age, monthly temperatures, percentage of radiation and soil calcium (Ca content either simultaneously or partially determined the growth of A. muelleri vegetative and corm. Based on these models, the A. muelleri corm reached the optimal growth after four years of cultivation and they will be ready to be harvested. Additionally, the soil Ca content should reach 25.3 me.hg-1 as Sugihwaras agroforestry, with the maximal radiation of 60%.

  17. WASP (Write a Scientific Paper) using Excel - 13: Correlation and Regression.

    Science.gov (United States)

    Grech, Victor

    2018-07-01

    Correlation and regression measure the closeness of association between two continuous variables. This paper explains how to perform these tests in Microsoft Excel and their interpretation, as well as how to apply these tests dynamically using Excel's functions. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Does Discrimination Explain High Risk of Depression among High-Income African American Men?

    Science.gov (United States)

    Assari, Shervin; Lankarani, Maryam Moghani; Caldwell, Cleopatra Howard

    2018-04-19

    Background: Higher socioeconomic status is known to decrease the risk for poor mental health overall. However, African American males of higher socioeconomic status (SES) are at an increased risk for having a major depressive episode (MDE). It is not known whether perceived discrimination (PD) explains this risk. The current study used nationally representative data to explore the role of PD in explaining the association between high-SES and having MDE among African American men. Methods: The National Survey of American Life (NSAL), 2003, included 4461 American adults including 1271 African American men. SES indicators (i.e., household income, educational attainment, employment status, and marital status) were the independent variables. 12-month MDE measured using the Composite International Diagnostic Interview (CIDI) was the outcome. Age, gender, and region were the covariates. PD was the potential mediator. For data analysis, we used logistic regression. Results: Among African American men, household income was positively associated with odds of 12-month MDE. The positive association between household income and odds of MDE remained unchanged after adding PD to the model, suggesting that PD may not explain why high-income African American men are at a higher risk of MDE. Conclusions: Perceived discrimination does not explain the increased risk for depression among African American males of higher SES. Future research should explore the role of other potential mechanisms such as stress, coping, social isolation, and/or negative social interaction that may increase psychological costs of upward social mobility for African American males.

  19. The number of subjects per variable required in linear regression analyses.

    Science.gov (United States)

    Austin, Peter C; Steyerberg, Ewout W

    2015-06-01

    To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Interpreting Bivariate Regression Coefficients: Going beyond the Average

    Science.gov (United States)

    Halcoussis, Dennis; Phillips, G. Michael

    2010-01-01

    Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

  1. Statistical analysis of sediment toxicity by additive monotone regression splines

    NARCIS (Netherlands)

    Boer, de W.J.; Besten, den P.J.; Braak, ter C.J.F.

    2002-01-01

    Modeling nonlinearity and thresholds in dose-effect relations is a major challenge, particularly in noisy data sets. Here we show the utility of nonlinear regression with additive monotone regression splines. These splines lead almost automatically to the estimation of thresholds. We applied this

  2. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Steed, Chad A [ORNL; SwanII, J. Edward [Mississippi State University (MSU); Fitzpatrick, Patrick J. [Mississippi State University (MSU); Jankun-Kelly, T.J. [Mississippi State University (MSU)

    2012-02-01

    New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.

  3. Spatial-Temporal Variations of Turbidity and Ocean Current Velocity of the Ariake Sea Area, Kyushu, Japan Through Regression Analysis with Remote Sensing Satellite Data

    OpenAIRE

    Yuichi Sarusawa; Kohei Arai

    2013-01-01

    Regression analysis based method for turbidity and ocean current velocity estimation with remote sensing satellite data is proposed. Through regressive analysis with MODIS data and measured data of turbidity and ocean current velocity, regressive equation which allows estimation of turbidity and ocean current velocity is obtained. With the regressive equation as well as long term MODIS data, turbidity and ocean current velocity trends in Ariake Sea area are clarified. It is also confirmed tha...

  4. Characterization of sonographically indeterminate ovarian tumors with MR imaging. A logistic regression analysis

    International Nuclear Information System (INIS)

    Yamashita, Y.; Hatanaka, Y.; Torashima, M.; Takahashi, M.; Miyazaki, K.; Okamura, H.

    1997-01-01

    Purpose: The goal of this study was to maximize the discrimination between benign and malignant masses in patients with sonographically indeterminate ovarian lesions by means of unenhanced and contrast-enhanced MR imaging, and to develop a computer-assisted diagnosis system. Material and Methods: Findings in precontrast and Gd-DTPA contrast-enhanced MR images of 104 patients with 115 sonographically indeterminate ovarian masses were analyzed, and the results were correlated with histopathological findings. Of 115 lesions, 65 were benign (23 cystadenomas, 13 complex cysts, 11 teratomas, 6 fibrothecomas, 12 others) and 50 were malignant (32 ovarian carcinomas, 7 metastatic tumors of the ovary, 4 carcinomas of the fallopian tubes, 7 others). A logistic regression analysis was performed to discriminate between benign and malignant lesions, and a model of a computer-assisted diagnosis was developed. This model was prospectively tested in 75 cases of ovarian tumors found at other institutions. Results: From the univariate analysis, the following parameters were selected as significant for predicting malignancy (p≤0.05): A solid or cystic mass with a large solid component or wall thickness greater than 3 mm; complex internal architecture; ascites; and bilaterality. Based on these parameters, a model of a computer-assisted diagnosis system was developed with the logistic regression analysis. To distinguish benign from malignant lesions, the maximum cut-off point was obtained between 0.47 and 0.51. In a prospective application of this model, 87% of the lesions were accurately identified as benign or malignant. (orig.)

  5. Mixed kernel function support vector regression for global sensitivity analysis

    Science.gov (United States)

    Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

    2017-11-01

    Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

  6. Examination of influential observations in penalized spline regression

    Science.gov (United States)

    Türkan, Semra

    2013-10-01

    In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

  7. 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. PMID:24125908

  8. Nonparametric Mixture of Regression Models.

    Science.gov (United States)

    Huang, Mian; Li, Runze; Wang, Shaoli

    2013-07-01

    Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

  9. Evaluation of Visual Field Progression in Glaucoma: Quasar Regression Program and Event Analysis.

    Science.gov (United States)

    Díaz-Alemán, Valentín T; González-Hernández, Marta; Perera-Sanz, Daniel; Armas-Domínguez, Karintia

    2016-01-01

    To determine the sensitivity, specificity and agreement between the Quasar program, glaucoma progression analysis (GPA II) event analysis and expert opinion in the detection of glaucomatous progression. The Quasar program is based on linear regression analysis of both mean defect (MD) and pattern standard deviation (PSD). Each series of visual fields was evaluated by three methods; Quasar, GPA II and four experts. The sensitivity, specificity and agreement (kappa) for each method was calculated, using expert opinion as the reference standard. The study included 439 SITA Standard visual fields of 56 eyes of 42 patients, with a mean of 7.8 ± 0.8 visual fields per eye. When suspected cases of progression were considered stable, sensitivity and specificity of Quasar, GPA II and the experts were 86.6% and 70.7%, 26.6% and 95.1%, and 86.6% and 92.6% respectively. When suspected cases of progression were considered as progressing, sensitivity and specificity of Quasar, GPA II and the experts were 79.1% and 81.2%, 45.8% and 90.6%, and 85.4% and 90.6% respectively. The agreement between Quasar and GPA II when suspected cases were considered stable or progressing was 0.03 and 0.28 respectively. The degree of agreement between Quasar and the experts when suspected cases were considered stable or progressing was 0.472 and 0.507. The degree of agreement between GPA II and the experts when suspected cases were considered stable or progressing was 0.262 and 0.342. The combination of MD and PSD regression analysis in the Quasar program showed better agreement with the experts and higher sensitivity than GPA II.

  10. Brightness-normalized Partial Least Squares Regression for hyperspectral data

    International Nuclear Information System (INIS)

    Feilhauer, Hannes; Asner, Gregory P.; Martin, Roberta E.; Schmidtlein, Sebastian

    2010-01-01

    Developed in the field of chemometrics, Partial Least Squares Regression (PLSR) has become an established technique in vegetation remote sensing. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions. To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data (400-2510 nm) to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry. Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry (up to 3.6% additional explained variance in validation) compared to conventional PLSR. Little improvement was made on effects due to variable leaf area index, while minor improvement (mostly not significant) was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios. Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data.

  11. Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

    Science.gov (United States)

    Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

    2015-03-01

    Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.

  12. Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

    International Nuclear Information System (INIS)

    Yang, Jianhong; Yi, Cancan; Xu, Jinwu; Ma, Xianghong

    2015-01-01

    A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine. - Highlights: • Both training and testing samples are considered for analytical lines selection. • The analytical lines are auto-selected based on the built-in characteristics of spectral lines. • The new method can achieve better prediction accuracy and modeling robustness. • Model predictions are given with confidence interval of probabilistic distribution

  13. Regression modeling of ground-water flow

    Science.gov (United States)

    Cooley, R.L.; Naff, R.L.

    1985-01-01

    Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

  14. Predictions of biochar production and torrefaction performance from sugarcane bagasse using interpolation and regression analysis.

    Science.gov (United States)

    Chen, Wei-Hsin; Hsu, Hung-Jen; Kumar, Gopalakrishnan; Budzianowski, Wojciech M; Ong, Hwai Chyuan

    2017-12-01

    This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275°C for 60min or at 300°C for 30min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300°C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

    Science.gov (United States)

    Ng, Kar Yong; Awang, Norhashidah

    2018-01-06

    Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

  16. Influence diagnostics in meta-regression model.

    Science.gov (United States)

    Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

    2017-09-01

    This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

  17. Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

    Science.gov (United States)

    Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

    2017-02-01

    The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism

  18. Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

    Science.gov (United States)

    Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

    2011-03-20

    Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the

  19. Applied Regression Modeling A Business Approach

    CERN Document Server

    Pardoe, Iain

    2012-01-01

    An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

  20. A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

    Science.gov (United States)

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

    2006-08-01

    A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

  1. Mediation analysis for logistic regression with interactions: Application of a surrogate marker in ophthalmology

    DEFF Research Database (Denmark)

    Jensen, Signe Marie; Hauger, Hanne; Ritz, Christian

    2018-01-01

    Mediation analysis is often based on fitting two models, one including and another excluding a potential mediator, and subsequently quantify the mediated effects by combining parameter estimates from these two models. Standard errors of such derived parameters may be approximated using the delta...... method. For a study evaluating a treatment effect on visual acuity, a binary outcome, we demonstrate how mediation analysis may conveniently be carried out by means of marginally fitted logistic regression models in combination with the delta method. Several metrics of mediation are estimated and results...

  2. Predictive market segmentation model: An application of logistic regression model and CHAID procedure

    Directory of Open Access Journals (Sweden)

    Soldić-Aleksić Jasna

    2009-01-01

    Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

  3. Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

    Science.gov (United States)

    Ulbrich, N.

    2015-01-01

    An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

  4. Classification of Effective Soil Depth by Using Multinomial Logistic Regression Analysis

    Science.gov (United States)

    Chang, C. H.; Chan, H. C.; Chen, B. A.

    2016-12-01

    Classification of effective soil depth is a task of determining the slopeland utilizable limitation in Taiwan. The "Slopeland Conservation and Utilization Act" categorizes the slopeland into agriculture and husbandry land, land suitable for forestry and land for enhanced conservation according to the factors including average slope, effective soil depth, soil erosion and parental rock. However, sit investigation of the effective soil depth requires a cost-effective field work. This research aimed to classify the effective soil depth by using multinomial logistic regression with the environmental factors. The Wen-Shui Watershed located at the central Taiwan was selected as the study areas. The analysis of multinomial logistic regression is performed by the assistance of a Geographic Information Systems (GIS). The effective soil depth was categorized into four levels including deeper, deep, shallow and shallower. The environmental factors of slope, aspect, digital elevation model (DEM), curvature and normalized difference vegetation index (NDVI) were selected for classifying the soil depth. An Error Matrix was then used to assess the model accuracy. The results showed an overall accuracy of 75%. At the end, a map of effective soil depth was produced to help planners and decision makers in determining the slopeland utilizable limitation in the study areas.

  5. Regression and kriging analysis for grid power factor estimation

    Directory of Open Access Journals (Sweden)

    Rajesh Guntaka

    2014-12-01

    Full Text Available The measurement of power factor (PF in electrical utility grids is a mainstay of load balancing and is also a critical element of transmission and distribution efficiency. The measurement of PF dates back to the earliest periods of electrical power distribution to public grids. In the wide-area distribution grid, measurement of current waveforms is trivial and may be accomplished at any point in the grid using a current tap transformer. However, voltage measurement requires reference to ground and so is more problematic and measurements are normally constrained to points that have ready and easy access to a ground source. We present two mathematical analysis methods based on kriging and linear least square estimation (LLSE (regression to derive PF at nodes with unknown voltages that are within a perimeter of sample nodes with ground reference across a selected power grid. Our results indicate an error average of 1.884% that is within acceptable tolerances for PF measurements that are used in load balancing tasks.

  6. Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

    Science.gov (United States)

    Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

    2006-01-01

    Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

  7. Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: A multiscale joinpoint regression analysis

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2011-12-01

    Full Text Available Abstract Background Although prostate cancer-related incidence and mortality have declined recently, striking racial/ethnic differences persist in the United States. Visualizing and modelling temporal trends of prostate cancer late-stage incidence, and how they vary according to geographic locations and race, should help explaining such disparities. Joinpoint regression is increasingly used to identify the timing and extent of changes in time series of health outcomes. Yet, most analyses of temporal trends are aspatial and conducted at the national level or for a single cancer registry. Methods Time series (1981-2007 of annual proportions of prostate cancer late-stage cases were analyzed for non-Hispanic Whites and non-Hispanic Blacks in each county of Florida. Noise in the data was first filtered by binomial kriging and results were modelled using joinpoint regression. A similar analysis was also conducted at the state level and for groups of metropolitan and non-metropolitan counties. Significant racial differences were detected using tests of parallelism and coincidence of time trends. A new disparity statistic was introduced to measure spatial and temporal changes in the frequency of racial disparities. Results State-level percentage of late-stage diagnosis decreased 50% since 1981; a decline that accelerated in the 90's when Prostate Specific Antigen (PSA screening was introduced. Analysis at the metropolitan and non-metropolitan levels revealed that the frequency of late-stage diagnosis increased recently in urban areas, and this trend was significant for white males. The annual rate of decrease in late-stage diagnosis and the onset years for significant declines varied greatly among counties and racial groups. Most counties with non-significant average annual percent change (AAPC were located in the Florida Panhandle for white males, whereas they clustered in South-eastern Florida for black males. The new disparity statistic indicated

  8. Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: a multiscale joinpoint regression analysis.

    Science.gov (United States)

    Goovaerts, Pierre; Xiao, Hong

    2011-12-05

    Although prostate cancer-related incidence and mortality have declined recently, striking racial/ethnic differences persist in the United States. Visualizing and modelling temporal trends of prostate cancer late-stage incidence, and how they vary according to geographic locations and race, should help explaining such disparities. Joinpoint regression is increasingly used to identify the timing and extent of changes in time series of health outcomes. Yet, most analyses of temporal trends are aspatial and conducted at the national level or for a single cancer registry. Time series (1981-2007) of annual proportions of prostate cancer late-stage cases were analyzed for non-Hispanic Whites and non-Hispanic Blacks in each county of Florida. Noise in the data was first filtered by binomial kriging and results were modelled using joinpoint regression. A similar analysis was also conducted at the state level and for groups of metropolitan and non-metropolitan counties. Significant racial differences were detected using tests of parallelism and coincidence of time trends. A new disparity statistic was introduced to measure spatial and temporal changes in the frequency of racial disparities. State-level percentage of late-stage diagnosis decreased 50% since 1981; a decline that accelerated in the 90's when Prostate Specific Antigen (PSA) screening was introduced. Analysis at the metropolitan and non-metropolitan levels revealed that the frequency of late-stage diagnosis increased recently in urban areas, and this trend was significant for white males. The annual rate of decrease in late-stage diagnosis and the onset years for significant declines varied greatly among counties and racial groups. Most counties with non-significant average annual percent change (AAPC) were located in the Florida Panhandle for white males, whereas they clustered in South-eastern Florida for black males. The new disparity statistic indicated that the spatial extent of racial disparities reached a

  9. Drug treatment rates with beta-blockers and ACE-inhibitors/angiotensin receptor blockers and recurrences in takotsubo cardiomyopathy: A meta-regression analysis.

    Science.gov (United States)

    Brunetti, Natale Daniele; Santoro, Francesco; De Gennaro, Luisa; Correale, Michele; Gaglione, Antonio; Di Biase, Matteo

    2016-07-01

    In a recent paper Singh et al. analyzed the effect of drug treatment on recurrence of takotsubo cardiomyopathy (TTC) in a comprehensive meta-analysis. The study found that recurrence rates were independent of clinic utilization of BB prescription, but inversely correlated with ACEi/ARB prescription: authors therefore conclude that ACEi/ARB rather than BB may reduce risk of recurrence. We aimed to re-analyze data reported in the study, now weighted for populations' size, in a meta-regression analysis. After multiple meta-regression analysis, we found a significant regression between rates of prescription of ACEi and rates of recurrence of TTC; regression was not statistically significant for BBs. On the bases of our re-analysis, we confirm that rates of recurrence of TTC are lower in populations of patients with higher rates of treatment with ACEi/ARB. That could not necessarily imply that ACEi may prevent recurrence of TTC, but barely that, for example, rates of recurrence are lower in cohorts more compliant with therapy or more prescribed with ACEi because more carefully followed. Randomized prospective studies are surely warranted. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. Vectors, a tool in statistical regression theory

    NARCIS (Netherlands)

    Corsten, L.C.A.

    1958-01-01

    Using linear algebra this thesis developed linear regression analysis including analysis of variance, covariance analysis, special experimental designs, linear and fertility adjustments, analysis of experiments at different places and times. The determination of the orthogonal projection, yielding

  11. 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.

  12. Sub-pixel estimation of tree cover and bare surface densities using regression tree analysis

    Directory of Open Access Journals (Sweden)

    Carlos Augusto Zangrando Toneli

    2011-09-01

    Full Text Available Sub-pixel analysis is capable of generating continuous fields, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

  13. Dual Regression

    OpenAIRE

    Spady, Richard; Stouli, Sami

    2012-01-01

    We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

  14. A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

    Science.gov (United States)

    Saunders, Christina T; Blume, Jeffrey D

    2017-10-26

    Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.

  15. A Simulation Investigation of Principal Component Regression.

    Science.gov (United States)

    Allen, David E.

    Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

  16. Prevalence of rapid eye movement sleep behavior disorder (RBD) in Parkinson's disease: a meta and meta-regression analysis.

    Science.gov (United States)

    Zhang, Xiaona; Sun, Xiaoxuan; Wang, Junhong; Tang, Liou; Xie, Anmu

    2017-01-01

    Rapid eye movement sleep behavior disorder (RBD) is thought to be one of the most frequent preceding symptoms of Parkinson's disease (PD). However, the prevalence of RBD in PD stated in the published studies is still inconsistent. We conducted a meta and meta-regression analysis in this paper to estimate the pooled prevalence. We searched the electronic databases of PubMed, ScienceDirect, EMBASE and EBSCO up to June 2016 for related articles. STATA 12.0 statistics software was used to calculate the available data from each research. The prevalence of RBD in PD patients in each study was combined to a pooled prevalence with a 95 % confidence interval (CI). Subgroup analysis and meta-regression analysis were performed to search for the causes of the heterogeneity. A total of 28 studies with 6869 PD cases were deemed eligible and included in our meta-analysis based on the inclusion and exclusion criteria. The pooled prevalence of RBD in PD was 42.3 % (95 % CI 37.4-47.1 %). In subgroup analysis and meta-regression analysis, we found that the important causes of heterogeneity were the diagnosis criteria of RBD and age of PD patients (P = 0.016, P = 0.019, respectively). The results indicate that nearly half of the PD patients are suffering from RBD. Older age and longer duration are risk factors for RBD in PD. We can use the minimal diagnosis criteria for RBD according to the International Classification of Sleep Disorders to diagnose RBD patients in our daily work if polysomnography is not necessary.

  17. Does Discrimination Explain High Risk of Depression among High-Income African American Men?

    Directory of Open Access Journals (Sweden)

    Shervin Assari

    2018-04-01

    Full Text Available Background: Higher socioeconomic status is known to decrease the risk for poor mental health overall. However, African American males of higher socioeconomic status (SES are at an increased risk for having a major depressive episode (MDE. It is not known whether perceived discrimination (PD explains this risk. The current study used nationally representative data to explore the role of PD in explaining the association between high-SES and having MDE among African American men. Methods: The National Survey of American Life (NSAL, 2003, included 4461 American adults including 1271 African American men. SES indicators (i.e., household income, educational attainment, employment status, and marital status were the independent variables. 12-month MDE measured using the Composite International Diagnostic Interview (CIDI was the outcome. Age, gender, and region were the covariates. PD was the potential mediator. For data analysis, we used logistic regression. Results: Among African American men, household income was positively associated with odds of 12-month MDE. The positive association between household income and odds of MDE remained unchanged after adding PD to the model, suggesting that PD may not explain why high-income African American men are at a higher risk of MDE. Conclusions: Perceived discrimination does not explain the increased risk for depression among African American males of higher SES. Future research should explore the role of other potential mechanisms such as stress, coping, social isolation, and/or negative social interaction that may increase psychological costs of upward social mobility for African American males.

  18. Can Equity Volatility Explain the Global Loan Pricing Puzzle?

    OpenAIRE

    Lewis Gaul; Pinar Uysal

    2013-01-01

    This paper examines whether unobservable differences in firm volatility are responsible for the global loan pricing puzzle, which is the observation that corporate loan interest rates appear to be lower in Europe than in the United States. We analyze whether equity volatility, an error prone measure of firm volatility, can explain this difference in loan spreads. We show that using equity volatility in OLS regressions will result in biased and inconsistent estimates of the difference in U.S. ...

  19. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis.

    Science.gov (United States)

    Rodríguez-Barranco, Miguel; Tobías, Aurelio; Redondo, Daniel; Molina-Portillo, Elena; Sánchez, María José

    2017-03-17

    Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.

  20. Regression-based statistical mediation and moderation analysis in clinical research: Observations, recommendations, and implementation.

    Science.gov (United States)

    Hayes, Andrew F; Rockwood, Nicholas J

    2017-11-01

    There have been numerous treatments in the clinical research literature about various design, analysis, and interpretation considerations when testing hypotheses about mechanisms and contingencies of effects, popularly known as mediation and moderation analysis. In this paper we address the practice of mediation and moderation analysis using linear regression in the pages of Behaviour Research and Therapy and offer some observations and recommendations, debunk some popular myths, describe some new advances, and provide an example of mediation, moderation, and their integration as conditional process analysis using the PROCESS macro for SPSS and SAS. Our goal is to nudge clinical researchers away from historically significant but increasingly old school approaches toward modifications, revisions, and extensions that characterize more modern thinking about the analysis of the mechanisms and contingencies of effects. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Choosing of mode and calculation of multiple regression equation parameters in X-ray radiometric analysis

    International Nuclear Information System (INIS)

    Mamikonyan, S.V.; Berezkin, V.V.; Lyubimova, S.V.; Svetajlo, Yu.N.; Shchekin, K.I.

    1978-01-01

    A method to derive multiple regression equations for X-ray radiometric analysis is described. Te method is realized in the form of the REGRA program in an algorithmic language. The subprograms included in the program are describe. In analyzing cement for Mg, Al, Si, Ca and Fe contents as an example, the obtainment of working equations in the course of calculations by the program is shown to simpliy the realization of computing devices in instruments for X-ray radiometric analysis

  2. Analysis of dental caries using generalized linear and count regression models

    Directory of Open Access Journals (Sweden)

    Javali M. Phil

    2013-11-01

    Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

  3. Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

    Science.gov (United States)

    Sun, Jianguo; Feng, Yanqin; Zhao, Hui

    2015-01-01

    Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

  4. Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

    Science.gov (United States)

    Li, Jiangtong; Luo, Yongdao; Dai, Honglin

    2018-01-01

    Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

  5. Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

    Science.gov (United States)

    Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

    2007-09-01

    Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

  6. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha

    2014-12-08

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  7. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha; Huang, Jianhua Z.

    2014-01-01

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  8. Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

    Science.gov (United States)

    Koon, Sharon; Petscher, Yaacov

    2015-01-01

    The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

  9. Finding determinants of audit delay by pooled OLS regression analysis

    Directory of Open Access Journals (Sweden)

    Tina Vuko

    2014-03-01

    Full Text Available The aim of this paper is to investigate determinants of audit delay. Audit delay is measured as the length of time (i.e. the number of calendar days from the fiscal year-end to the audit report date. It is important to understand factors that influence audit delay since it directly affects the timeliness of financial reporting. The research is conducted on a sample of Croatian listed companies, covering the period of four years (from 2008 to 2011. We use pooled OLS regression analysis, modelling audit delay as a function of the following explanatory variables: audit firm type, audit opinion, profitability, leverage, inventory and receivables to total assets, absolute value of total accruals, company size and audit committee existence. Our results indicate that audit committee existence, profitability and leverage are statistically significant determinants of audit delay in Croatia.

  10. A Diagrammatic Exposition of Regression and Instrumental Variables for the Beginning Student

    Science.gov (United States)

    Foster, Gigi

    2009-01-01

    Some beginning students of statistics and econometrics have difficulty with traditional algebraic approaches to explaining regression and related techniques. For these students, a simple and intuitive diagrammatic introduction as advocated by Kennedy (2008) may prove a useful framework to support further study. The author presents a series of…

  11. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    Science.gov (United States)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  12. 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.

  13. A regression approach for Zircaloy-2 in-reactor creep constitutive equations

    International Nuclear Information System (INIS)

    Yung Liu, Y.; Bement, A.L.

    1977-01-01

    In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

  14. Application of nonlinear regression analysis for ammonium exchange by natural (Bigadic) clinoptilolite

    International Nuclear Information System (INIS)

    Gunay, Ahmet

    2007-01-01

    The experimental data of ammonium exchange by natural Bigadic clinoptilolite was evaluated using nonlinear regression analysis. Three two-parameters isotherm models (Langmuir, Freundlich and Temkin) and three three-parameters isotherm models (Redlich-Peterson, Sips and Khan) were used to analyse the equilibrium data. Fitting of isotherm models was determined using values of standard normalization error procedure (SNE) and coefficient of determination (R 2 ). HYBRID error function provided lowest sum of normalized error and Khan model had better performance for modeling the equilibrium data. Thermodynamic investigation indicated that ammonium removal by clinoptilolite was favorable at lower temperatures and exothermic in nature

  15. Estimating the causes of traffic accidents using logistic regression and discriminant analysis.

    Science.gov (United States)

    Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu

    2014-01-01

    Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.

  16. Factors explaining priority setting at community mental health centres: a quantitative analysis of referral assessments.

    Science.gov (United States)

    Grepperud, Sverre; Holman, Per Arne; Wangen, Knut Reidar

    2014-12-14

    Clinicians at Norwegian community mental health centres assess referrals from general practitioners and classify them into three priority groups (high priority, low priority, and refusal) according to need where need is defined by three prioritization criteria (severity, effect, and cost-effectiveness). In this study, we seek to operationalize the three criteria and analyze to what extent they have an effect on clinical-level priority setting after controlling for clinician characteristics and organisational factors. Twenty anonymous referrals were rated by 42 admission team members employed at 14 community mental health centres in the South-East Health Region of Norway. Intra-class correlation coefficients were calculated and logistic regressions were performed. Variation in clinicians' assessments of the three criteria was highest for effect and cost-effectiveness. An ordered logistic regression model showed that all three criteria for prioritization, three clinician characteristics (education, being a manager or not, and "guideline awareness"), and the centres themselves (fixed effects), explained priority decisions. The relative importance of the explanatory factors, however, depended on the priority decision studied. For the classification of all admitted patients into high- and low-priority groups, all clinician characteristics became insignificant. For the classification of patients, into those admitted and non-admitted, one criterion (effect) and "being a manager or not" became insignificant, while profession ("being a psychiatrist") became significant. Our findings suggest that variation in priority decisions can be reduced by: (i) reducing the disagreement in clinicians' assessments of cost-effectiveness and effect, and (ii) restricting priority decisions to clinicians with a similar background (education, being a manager or not, and "guideline awareness").

  17. Factors that could explain the increasing prevalence of type 2 diabetes among adults in a Canadian province: a critical review and analysis

    Directory of Open Access Journals (Sweden)

    Véronique Thibault

    2016-11-01

    Full Text Available Abstract Background The prevalence of diabetes has increased since the last decade in New Brunswick. Identifying factors contributing to the increase in diabetes prevalence will help inform an action plan to manage the condition. The objective was to describe factors that could explain the increasing prevalence of type 2 diabetes in New Brunswick since 2001. Methods A critical literature review was conducted to identify factors potentially responsible for an increase in prevalence of diabetes. Data from various sources were obtained to draw a repeated cross-sectional (2001–2014 description of these factors concurrently with changes in the prevalence of type 2 diabetes in New Brunswick. Linear regressions, Poisson regressions and Cochran Armitage analysis were used to describe relationships between these factors and time. Results Factors identified in the review were summarized in five categories: individual-level risk factors, environmental risk factors, evolution of the disease, detection effect and global changes. The prevalence of type 2 diabetes has increased by 120% between 2001 and 2014. The prevalence of obesity, hypertension, prediabetes, alcohol consumption, immigration and urbanization increased during the study period and the consumption of fruits and vegetables decreased which could represent potential factors of the increasing prevalence of type 2 diabetes. Physical activity, smoking, socioeconomic status and education did not present trends that could explain the increasing prevalence of type 2 diabetes. During the study period, the mortality rate and the conversion rate from prediabetes to diabetes decreased and the incidence rate increased. Suggestion of a detection effect was also present as the number of people tested increased while the HbA1c and the age at detection decreased. Period and birth cohort effect were also noted through a rise in the prevalence of type 2 diabetes across all age groups, but greater increases were

  18. Computer software for linear and nonlinear regression in organic NMR

    International Nuclear Information System (INIS)

    Canto, Eduardo Leite do; Rittner, Roberto

    1991-01-01

    Calculation involving two variable linear regressions, require specific procedures generally not familiar to chemist. For attending the necessity of fast and efficient handling of NMR data, a self explained and Pc portable software has been developed, which allows user to produce and use diskette recorded tables, containing chemical shift or any other substituent physical-chemical measurements and constants (σ T , σ o R , E s , ...)

  19. Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?

    Science.gov (United States)

    Kohler, Friedbert; Renton, Roger; Dickson, Hugh G; Estell, John; Connolly, Carol E

    2011-02-01

    We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes.

  20. BRGLM, Interactive Linear Regression Analysis by Least Square Fit

    International Nuclear Information System (INIS)

    Ringland, J.T.; Bohrer, R.E.; Sherman, M.E.

    1985-01-01

    1 - Description of program or function: BRGLM is an interactive program written to fit general linear regression models by least squares and to provide a variety of statistical diagnostic information about the fit. Stepwise and all-subsets regression can be carried out also. There are facilities for interactive data management (e.g. setting missing value flags, data transformations) and tools for constructing design matrices for the more commonly-used models such as factorials, cubic Splines, and auto-regressions. 2 - Method of solution: The least squares computations are based on the orthogonal (QR) decomposition of the design matrix obtained using the modified Gram-Schmidt algorithm. 3 - Restrictions on the complexity of the problem: The current release of BRGLM allows maxima of 1000 observations, 99 variables, and 3000 words of main memory workspace. For a problem with N observations and P variables, the number of words of main memory storage required is MAX(N*(P+6), N*P+P*P+3*N, and 3*P*P+6*N). Any linear model may be fit although the in-memory workspace will have to be increased for larger problems

  1. The non-condition logistic regression analysis of the reason of hypothyroidism after hyperthyroidism with 131I treatment

    International Nuclear Information System (INIS)

    Dang Yaping; Hu Guoying; Meng Xianwen

    1994-01-01

    There are many opinions on the reason of hypothyroidism after hyperthyroidism with 131 I treatment. In this respect, there are a few scientific analyses and reports. The non-condition logistic regression solved this problem successfully. It has a higher scientific value and confidence in the risk factor analysis. 748 follow-up patients' data were analysed by the non-condition logistic regression. The results shown that the half-life and 131 I dose were the main causes of the incidence of hypothyroidism. The degree of confidence is 92.4%

  2. The microcomputer scientific software series 2: general linear model--regression.

    Science.gov (United States)

    Harold M. Rauscher

    1983-01-01

    The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

  3. Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

    Science.gov (United States)

    Liu, Yan; Salvendy, Gavriel

    2009-05-01

    This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

  4. Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

    2018-03-01

    Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

  5. Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

    2017-01-01

    Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

  6. The use of Meta-Regression Analysis to harmonize LCA literature: an application to GHG emissions of 2. and 3. generation biofuels

    International Nuclear Information System (INIS)

    Menten, Fabio; Cheze, Benoit; Patouillard, Laure; Bouvart, Frederique

    2013-01-01

    This article presents the results of a literature review performs with a meta-regression analysis (MRA) that focuses on the estimates of advanced biofuel Greenhouse Gas (GHG) emissions assessed with a Life Cycle Assessment (LCA) approach. The mean GHG emissions of both second (G2) and third generation (G3) biofuels and the effects of factors influencing these estimates are identified and quantified by means of specific statistical methods. 47 LCA studies are included in the database, providing 593 estimates. Each study estimate of the database is characterized by i) technical data/characteristics, ii) author's methodological choices and iii) typology of the study under consideration. The database is composed of both the vector of these estimates - expressed in grams of CO 2 equivalent per MJ of biofuel (g CO 2 eq/MJ) - and a matrix containing vectors of predictor variables which can be continuous or dummy variables. The former is the dependent variable while the latter corresponds to the explanatory variables of the meta-regression model. Parameters are estimated by mean of econometrics methods. Our results clearly highlight a hierarchy between G3 and G2 biofuels: life cycle GHG emissions of G3 biofuels are statistically higher than those of Ethanol which, in turn, are superior to those of BtL. Moreover, this article finds empirical support for many of the hypotheses formulated in narrative literature surveys concerning potential factors which may explain estimates variations. Finally, the MRA results are used to address the harmonization issue in the field of advanced biofuels GHG emissions thanks to the technique of benefits transfer using meta-regression models. The range of values hence obtained appears to be lower than the fossil fuel reference (about 83.8 in g CO 2 eq/ MJ). However, only Ethanol and BtL do comply with the GHG emission reduction thresholds for biofuels defined in both the American and European directives. (authors)

  7. Better Autologistic Regression

    Directory of Open Access Journals (Sweden)

    Mark A. Wolters

    2017-11-01

    Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

  8. Explaining Innovation. An Empirical Analysis of Industry Data from Norway

    Directory of Open Access Journals (Sweden)

    Torbjørn Lorentzen

    2016-01-01

    Full Text Available The objective of the paper is to analyse why some firms innovate while others do not. The paper combines different theories of innovation by relating innovation to internal, firm specific assets and external, regional factors. Hypotheses are derived from theories and tested empirically by using logistic regression. The empirical analysis indicates that internal funding of R&D and size of the firm are the most important firm specific attributes for successful innovation. External, regional factors are also important. The analysis shows that firms located in large urban regions have significantly higher innovation rates than firms located in the periphery, and firms involved in regional networking are more likely to innovate compared to firms not involved in networking. The analysis contributes to a theoretical and empirical understanding of factors that influence on innovation and the role innovation plays in the market economy. Innovation policy should be targeted at developing a tax system and building infrastructure which give firms incentives to invest and allocate internal resources to R&D-activities and collaborate with others in innovation. From an economic policy perspective, consideration should be given to allocating more public resources to rural areas in order to compensate for the asymmetric distribution of resources between the centre and periphery. The paper contributes to the scientific literature of innovation by combining the firm oriented perspective with weight on firm specific, internal resources and a system perspective which focuses on external resources and networking as the most important determinants of innovation in firms.

  9. Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

    Science.gov (United States)

    Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

    2017-05-01

    Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

  10. Analysis of some methods for reduced rank Gaussian process regression

    DEFF Research Database (Denmark)

    Quinonero-Candela, J.; Rasmussen, Carl Edward

    2005-01-01

    While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent...... proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank...... Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning...

  11. SPSS explained

    CERN Document Server

    Hinton, Perry R; Brownlow, Charlotte

    2014-01-01

    SPSS Explained provides the student with all that they need to undertake statistical analysis using SPSS. It combines a step-by-step approach to each procedure with easy to follow screenshots at each stage of the process. A number of other helpful features are provided: regular advice boxes with tips specific to each test explanations divided into 'essential' and 'advanced' sections to suit readers at different levels frequently asked questions at the end of each chapter. The first edition of this popular book has been fully updated for IBM SPSS version 21 and also includes: chapters that expl

  12. The art of regression modeling in road safety

    CERN Document Server

    Hauer, Ezra

    2015-01-01

    This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...

  13. Spontaneous regression of a large hepatocellular carcinoma: case report

    Directory of Open Access Journals (Sweden)

    Alqutub, Adel

    2011-01-01

    Full Text Available The prognosis of untreated advanced hepatocellular carcinoma (HCC is grim with a median survival of less than 6 months. Spontaneous regression of HCC has been defined as the disappearance of the hepatic lesions in the absence of any specific therapy. The spontaneous regression of a very large HCC is very rare and limited data is available in the English literature. We describe spontaneous regression of hepatocellular carcinoma in a 65-year-old male who presented to our clinic with vague abdominal pain and weight loss of two months duration. He was found to have multiple hepatic lesions with elevation of serum alpha-fetoprotein (AFP level to 6,500 µg/L (normal <20 µg/L. Computed tomography revealed advanced HCC replacing almost 80% of the right hepatic lobe. Without any intervention the patient showed gradual improvement over a period of few months. Follow-up CT scan revealed disappearance of hepatic lesions with progressive decline of AFP levels to normal. Various mechanisms have been postulated to explain this rare phenomenon, but the exact mechanism remains a mystery.

  14. Analysis of quantile regression as alternative to ordinary least squares

    OpenAIRE

    Ibrahim Abdullahi; Abubakar Yahaya

    2015-01-01

    In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well ...

  15. Mathematical models for estimating earthquake casualties and damage cost through regression analysis using matrices

    International Nuclear Information System (INIS)

    Urrutia, J D; Bautista, L A; Baccay, E B

    2014-01-01

    The aim of this study was to develop mathematical models for estimating earthquake casualties such as death, number of injured persons, affected families and total cost of damage. To quantify the direct damages from earthquakes to human beings and properties given the magnitude, intensity, depth of focus, location of epicentre and time duration, the regression models were made. The researchers formulated models through regression analysis using matrices and used α = 0.01. The study considered thirty destructive earthquakes that hit the Philippines from the inclusive years 1968 to 2012. Relevant data about these said earthquakes were obtained from Philippine Institute of Volcanology and Seismology. Data on damages and casualties were gathered from the records of National Disaster Risk Reduction and Management Council. This study will be of great value in emergency planning, initiating and updating programs for earthquake hazard reduction in the Philippines, which is an earthquake-prone country.

  16. Experimental and regression analysis for multi cylinder diesel engine operated with hybrid fuel blends

    Directory of Open Access Journals (Sweden)

    Gopal Rajendiran

    2014-01-01

    Full Text Available The purpose of this research work is to build a multiple linear regression model for the characteristics of multicylinder diesel engine using multicomponent blends (diesel- pungamia methyl ester-ethanol as fuel. Nine blends were tested by varying diesel (100 to 10% by Vol., biodiesel (80 to 10% by vol. and keeping ethanol as 10% constant. The brake thermal efficiency, smoke, oxides of nitrogen, carbon dioxide, maximum cylinder pressure, angle of maximum pressure, angle of 5% and 90% mass burning were predicted based on load, speed, diesel and biodiesel percentage. To validate this regression model another multi component fuel comprising diesel-palm methyl ester-ethanol was used in same engine. Statistical analysis was carried out between predicted and experimental data for both fuel. The performance, emission and combustion characteristics of multi cylinder diesel engine using similar fuel blends can be predicted without any expenses for experimentation.

  17. 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.…

  18. ROLE OF PARENTS' ADJUSTMENT IN EXPLAINING PERCEPTION OF ADOLESCENTS' NEGATIVE INTERACTIONS WITH MOTHER AND FATHER

    Directory of Open Access Journals (Sweden)

    Tamara Efendić-Spahić

    2014-04-01

    Full Text Available The research was conducted with the aim of examining the contribution of facets of the adjustment of mother and father for explaining the adolescents’ perception of negative relations with parents. The following adjustment measures were used in this research: anxiety, hypersensitivity, inner coherence, interpersonal orientation and aggression of mother and father individually. The measures of negative interactions between adolescents and parents are conceptualized through the dimension of negative relations with parents, which includes adolescents’ assessment regarding the rejection by father and mother and the assessment of negative relations with father and mother. The research was conducted on a sample including 273 subjects in total: 47 female subjects, 44 male subjects and their parents. For testing the hypotheses, the multiple regression analysis was used. The obtained results show that adjustment facets are important predictors for explaining the perception of negative relations with father. The facet of aggression stands as the most significant predictor among adjustment factors for the group of fathers. For the group of mothers, adjustment did not prove a significant predictor for explaining perception of negative relations. Possible explanations for a modest contribution of mother’s adjustment can be found in the possibility for the quality of family interactions with mother is more explained by an emotional relation that is established between her and the child in early childhood and does not change its quality at later development stages.

  19. An examination of multilevel factors that may explain gender differences in children's physical activity.

    Science.gov (United States)

    Crespo, Noe C; Corder, Kirsten; Marshall, Simon; Norman, Gregory J; Patrick, Kevin; Sallis, Jim F; Elder, John P

    2013-09-01

    Girls are less physically active than boys, yet no single study has examined the factors that may explain gender differences in children's physical activity (PA). This study was a cross-sectional analysis of data from 116 caregivers and their children aged 5-8 years who participated in the MOVE study. Caregivers reported various factors that may relate to children's PA (eg, encouragement for child PA and PA equipment at home). Child PA was measured by 7-day accelerometry. Linear regression tested for the variance in moderate-to-vigorous physical activity (MVPA) explained by gender and several variables. Gender and ethnicity interactions were examined. Caregivers were mostly female (97%), mean age 38 ± 6 years, mean BMI 28 ± 6 (kg/m2). Child's mean age was 8.1 ± 0.7, 54% were female and 40% were overweight/obese. Girls were less physically active than boys (54.1 ± 19.7 vs. 65.2 ± 28.0 daily minutes of MVPA, respectively). Among girls, more days of PE/week was associated with greater MVPA. Among boys, greater parent support for PA, greater parent modeling for PA, and greater number of PA equipment in the home were associated with greater MVPA. This study supports that boys and girls have different correlates for MVPA, which may partly explain gender differences in PA.

  20. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.

    2016-02-09

    We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information.

  1. Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors.

    Science.gov (United States)

    Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

    2018-02-01

    The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (Pregression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher Mc

  2. Performance of an Axisymmetric Rocket Based Combined Cycle Engine During Rocket Only Operation Using Linear Regression Analysis

    Science.gov (United States)

    Smith, Timothy D.; Steffen, Christopher J., Jr.; Yungster, Shaye; Keller, Dennis J.

    1998-01-01

    The all rocket mode of operation is shown to be a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. An axisymmetric RBCC engine was used to determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and multiple linear regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inlet diameter ratio. A perfect gas computational fluid dynamics analysis, using both the Spalart-Allmaras and k-omega turbulence models, was performed with the NPARC code to obtain values of vacuum specific impulse. Results from the multiple linear regression analysis showed that for both the full flow and gas generator configurations increasing mixer-ejector area ratio and rocket area ratio increase performance, while increasing mixer-ejector inlet area ratio and mixer-ejector length-to-diameter ratio decrease performance. Increasing injected secondary flow increased performance for the gas generator analysis, but was not statistically significant for the full flow analysis. Chamber pressure was found to be not statistically significant.

  3. BOX-COX REGRESSION METHOD IN TIME SCALING

    Directory of Open Access Journals (Sweden)

    ATİLLA GÖKTAŞ

    2013-06-01

    Full Text Available Box-Cox regression method with λj, for j = 1, 2, ..., k, power transformation can be used when dependent variable and error term of the linear regression model do not satisfy the continuity and normality assumptions. The situation obtaining the smallest mean square error  when optimum power λj, transformation for j = 1, 2, ..., k, of Y has been discussed. Box-Cox regression method is especially appropriate to adjust existence skewness or heteroscedasticity of error terms for a nonlinear functional relationship between dependent and explanatory variables. In this study, the advantage and disadvantage use of Box-Cox regression method have been discussed in differentiation and differantial analysis of time scale concept.

  4. An Introduction to Graphical and Mathematical Methods for Detecting Heteroscedasticity in Linear Regression.

    Science.gov (United States)

    Thompson, Russel L.

    Homoscedasticity is an important assumption of linear regression. This paper explains what it is and why it is important to the researcher. Graphical and mathematical methods for testing the homoscedasticity assumption are demonstrated. Sources of homoscedasticity and types of homoscedasticity are discussed, and methods for correction are…

  5. Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

    Science.gov (United States)

    Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

    2013-03-01

    Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

  6. Quantifying the statistical importance of utilizing regression over classic energy intensity calculations for tracking efficiency improvements in industry

    Energy Technology Data Exchange (ETDEWEB)

    Nimbalkar, Sachin U. [ORNL; Wenning, Thomas J. [ORNL; Guo, Wei [ORNL

    2017-08-01

    In the United States, manufacturing facilities account for about 32% of total domestic energy consumption in 2014. Robust energy tracking methodologies are critical to understanding energy performance in manufacturing facilities. Due to its simplicity and intuitiveness, the classic energy intensity method (i.e. the ratio of total energy use over total production) is the most widely adopted. However, the classic energy intensity method does not take into account the variation of other relevant parameters (i.e. product type, feed stock type, weather, etc.). Furthermore, the energy intensity method assumes that the facilities’ base energy consumption (energy use at zero production) is zero, which rarely holds true. Therefore, it is commonly recommended to utilize regression models rather than the energy intensity approach for tracking improvements at the facility level. Unfortunately, many energy managers have difficulties understanding why regression models are statistically better than utilizing the classic energy intensity method. While anecdotes and qualitative information may convince some, many have major reservations about the accuracy of regression models and whether it is worth the time and effort to gather data and build quality regression models. This paper will explain why regression models are theoretically and quantitatively more accurate for tracking energy performance improvements. Based on the analysis of data from 114 manufacturing plants over 12 years, this paper will present quantitative results on the importance of utilizing regression models over the energy intensity methodology. This paper will also document scenarios where regression models do not have significant relevance over the energy intensity method.

  7. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  8. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  9. Deterministic rather than stochastic factors explain most of the variation in the expression of skin telangiectasia after radiotherapy

    International Nuclear Information System (INIS)

    Safwat, Akmal; Bentzen, Soeren M.; Turesson, Ingela; Hendry, Jolyon H.

    2002-01-01

    90% (with 95% confidence limits 65% and 100%) of the variance in radioresponsiveness in the right-sided field was explained by the radioresponsiveness on the left-sided field. Through the linear regression analysis between the AUC and the ID2, it was estimated that patients with a reaction that is 1 SD from the population mean would require a dose modification of approximately 23 Gy (from the group mean of 56 Gy) to give them a level of reaction similar to the group average. Similarly, the WAUC was significantly correlated with the ID2, and 81% (with 95% confidence limits 49% and 100%) of the variance in radioresponsiveness in the right-sided field was explained by the radioresponsiveness on the left-sided field. Patients with a reaction that is 1 SD from the population mean would require a dose modification of approximately 21 Gy (from the group mean of 56 Gy) to give them a level of reaction similar to the group average. Conclusion: For a given fractionation schedule, patient-related factors explain 81-90% of the patient-to-patient variation in telangiectasia level seen after radiotherapy. The remaining 10-19% are explained by stochastic effects. This observation encourages further research into genetic or phenotypic assays of normal tissue radioresponsiveness

  10. Exposure to Pre- and Perinatal Risk Factors Partially Explains Mean Differences in Self-Regulation between Races.

    Science.gov (United States)

    Barnes, J C; Boutwell, Brian B; Miller, J Mitchell; DeShay, Rashaan A; Beaver, Kevin M; White, Norman

    2016-01-01

    To examine whether differential exposure to pre- and perinatal risk factors explained differences in levels of self-regulation between children of different races (White, Black, Hispanic, Asian, and Other). Multiple regression models based on data from the Early Childhood Longitudinal Study, Birth Cohort (n ≈ 9,850) were used to analyze the impact of pre- and perinatal risk factors on the development of self-regulation at age 2 years. Racial differences in levels of self-regulation were observed. Racial differences were also observed for 9 of the 12 pre-/perinatal risk factors. Multiple regression analyses revealed that a portion of the racial differences in self-regulation was explained by differential exposure to several of the pre-/perinatal risk factors. Specifically, maternal age at childbirth, gestational timing, and the family's socioeconomic status were significantly related to the child's level of self-regulation. These factors accounted for a statistically significant portion of the racial differences observed in self-regulation. The findings indicate racial differences in self-regulation may be, at least partially, explained by racial differences in exposure to pre- and perinatal risk factors.

  11. Casemix funding for a specialist paediatrics hospital: a hedonic regression approach.

    Science.gov (United States)

    Bridges, J F; Hanson, R M

    2000-01-01

    This paper inquires into the effects that Diagnosis Related Groups (DRGs) have had on the ability to explain patient-level costs in a specialist paediatrics hospital. Two hedonic models are estimated using 1996/97 New Children's Hospital (NCH) patient level cost data, one with and one without a casemix index (CMI). The results show that the inclusion of a casemix index as an explanatory variable leads to a better accounting of cost. The full hedonic model is then used to simulate a funding model for the 1997/98 NCH cost data. These costs are highly correlated with the actual costs reported for that year. In addition, univariate regression indicates that there has been inflation in costs in the order of 4.8% between the two years. In conclusion, hedonic analysis can provide valuable evidence for the design of funding models that account for casemix.

  12. Stress Regression Analysis of Asphalt Concrete Deck Pavement Based on Orthogonal Experimental Design and Interlayer Contact

    Science.gov (United States)

    Wang, Xuntao; Feng, Jianhu; Wang, Hu; Hong, Shidi; Zheng, Supei

    2018-03-01

    A three-dimensional finite element box girder bridge and its asphalt concrete deck pavement were established by ANSYS software, and the interlayer bonding condition of asphalt concrete deck pavement was assumed to be contact bonding condition. Orthogonal experimental design is used to arrange the testing plans of material parameters, and an evaluation of the effect of different material parameters in the mechanical response of asphalt concrete surface layer was conducted by multiple linear regression model and using the results from the finite element analysis. Results indicated that stress regression equations can well predict the stress of the asphalt concrete surface layer, and elastic modulus of waterproof layer has a significant influence on stress values of asphalt concrete surface layer.

  13. A PANEL REGRESSION ANALYSIS OF HUMAN CAPITAL RELEVANCE IN SELECTED SCANDINAVIAN AND SE EUROPEAN COUNTRIES

    Directory of Open Access Journals (Sweden)

    Filip Kokotovic

    2016-06-01

    Full Text Available The study of human capital relevance to economic growth is becoming increasingly important taking into account its relevance in many of the Sustainable Development Goals proposed by the UN. This paper conducted a panel regression analysis of selected SE European countries and Scandinavian countries using the Granger causality test and pooled panel regression. In order to test the relevance of human capital on economic growth, several human capital proxy variables were identified. Aside from the human capital proxy variables, other explanatory variables were selected using stepwise regression while the dependant variable was GDP. This paper concludes that there are significant structural differences in the economies of the two observed panels. Of the human capital proxy variables observed, for the panel of SE European countries only life expectancy was statistically significant and it had a negative impact on economic growth, while in the panel of Scandinavian countries total public expenditure on education had a statistically significant positive effect on economic growth. Based upon these results and existing studies, this paper concludes that human capital has a far more significant impact on economic growth in more developed economies.

  14. Correlation and simple linear regression.

    Science.gov (United States)

    Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

    2003-06-01

    In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

  15. Use of multilevel logistic regression to identify the causes of differential item functioning.

    Science.gov (United States)

    Balluerka, Nekane; Gorostiaga, Arantxa; Gómez-Benito, Juana; Hidalgo, María Dolores

    2010-11-01

    Given that a key function of tests is to serve as evaluation instruments and for decision making in the fields of psychology and education, the possibility that some of their items may show differential behaviour is a major concern for psychometricians. In recent decades, important progress has been made as regards the efficacy of techniques designed to detect this differential item functioning (DIF). However, the findings are scant when it comes to explaining its causes. The present study addresses this problem from the perspective of multilevel analysis. Starting from a case study in the area of transcultural comparisons, multilevel logistic regression is used: 1) to identify the item characteristics associated with the presence of DIF; 2) to estimate the proportion of variation in the DIF coefficients that is explained by these characteristics; and 3) to evaluate alternative explanations of the DIF by comparing the explanatory power or fit of different sequential models. The comparison of these models confirmed one of the two alternatives (familiarity with the stimulus) and rejected the other (the topic area) as being a cause of differential functioning with respect to the compared groups.

  16. Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.

    Science.gov (United States)

    Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al; Wang, Shuang; Mohammed, Noman

    2018-03-05

    Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data. Our objective was to develop a hybrid cryptographic framework for performing regression analysis over distributed data in a secure and efficient way. Existing secure computation schemes are not suitable for processing the large-scale data that are used in cutting-edge machine learning applications. We designed, developed, and evaluated a hybrid cryptographic framework, which can securely perform regression analysis, a fundamental machine learning algorithm using somewhat homomorphic encryption and a newly introduced secure hardware component of Intel Software Guard Extensions (Intel SGX) to ensure both privacy and efficiency at the same time. Experimental results demonstrate that our proposed method provides a better trade-off in terms of security and efficiency than solely secure hardware-based methods. Besides, there is no approximation error. Computed model parameters are exactly similar to plaintext results. To the best of our knowledge, this kind of secure computation model using a hybrid cryptographic framework, which leverages both somewhat homomorphic encryption and Intel SGX, is not proposed or evaluated to this date. Our proposed framework ensures data security and computational efficiency at the same time. ©Md Nazmus Sadat, Xiaoqian Jiang, Md Momin Al Aziz, Shuang Wang, Noman Mohammed. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.03.2018.

  17. 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.

  18. Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia

    Science.gov (United States)

    Li, Yue; Liang, Minggao; Zhang, Zhaolei

    2014-01-01

    Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML

  19. Grades, Gender, and Encouragement: A Regression Discontinuity Analysis

    Science.gov (United States)

    Owen, Ann L.

    2010-01-01

    The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…

  20. Progressive and Regressive Aspects of Information Technology in Society: A Third Sector Perspective

    Science.gov (United States)

    Miller, Kandace R.

    2009-01-01

    This dissertation explores the impact of information technology on progressive and regressive values in society from the perspective of one international foundation and four of its technology-related programs. Through a critical interpretive approach employing an instrumental multiple-case method, a framework to help explain the influence of…

  1. Financial analysis and forecasting of the results of small businesses performance based on regression model

    Directory of Open Access Journals (Sweden)

    Svetlana O. Musienko

    2017-03-01

    Full Text Available Objective to develop the economicmathematical model of the dependence of revenue on other balance sheet items taking into account the sectoral affiliation of the companies. Methods using comparative analysis the article studies the existing approaches to the construction of the company management models. Applying the regression analysis and the least squares method which is widely used for financial management of enterprises in Russia and abroad the author builds a model of the dependence of revenue on other balance sheet items taking into account the sectoral affiliation of the companies which can be used in the financial analysis and prediction of small enterprisesrsquo performance. Results the article states the need to identify factors affecting the financial management efficiency. The author analyzed scientific research and revealed the lack of comprehensive studies on the methodology for assessing the small enterprisesrsquo management while the methods used for large companies are not always suitable for the task. The systematized approaches of various authors to the formation of regression models describe the influence of certain factors on the company activity. It is revealed that the resulting indicators in the studies were revenue profit or the company relative profitability. The main drawback of most models is the mathematical not economic approach to the definition of the dependent and independent variables. Basing on the analysis it was determined that the most correct is the model of dependence between revenues and total assets of the company using the decimal logarithm. The model was built using data on the activities of the 507 small businesses operating in three spheres of economic activity. Using the presented model it was proved that there is direct dependence between the sales proceeds and the main items of the asset balance as well as differences in the degree of this effect depending on the economic activity of small

  2. A simplified procedure of linear regression in a preliminary analysis

    Directory of Open Access Journals (Sweden)

    Silvia Facchinetti

    2013-05-01

    Full Text Available The analysis of a statistical large data-set can be led by the study of a particularly interesting variable Y – regressed – and an explicative variable X, chosen among the remained variables, conjointly observed. The study gives a simplified procedure to obtain the functional link of the variables y=y(x by a partition of the data-set into m subsets, in which the observations are synthesized by location indices (mean or median of X and Y. Polynomial models for y(x of order r are considered to verify the characteristics of the given procedure, in particular we assume r= 1 and 2. The distributions of the parameter estimators are obtained by simulation, when the fitting is done for m= r + 1. Comparisons of the results, in terms of distribution and efficiency, are made with the results obtained by the ordinary least square methods. The study also gives some considerations on the consistency of the estimated parameters obtained by the given procedure.

  3. Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

    Science.gov (United States)

    Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

    2018-03-01

    Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

  4. Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes

    International Nuclear Information System (INIS)

    Faranda, Davide; Dubrulle, Bérengère; Daviaud, François; Pons, Flavio Maria Emanuele; Saint-Michel, Brice; Herbert, Éric; Cortet, Pierre-Philippe

    2014-01-01

    We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system

  5. Producing The New Regressive Left

    DEFF Research Database (Denmark)

    Crone, Christine

    members, this thesis investigates a growing political trend and ideological discourse in the Arab world that I have called The New Regressive Left. On the premise that a media outlet can function as a forum for ideology production, the thesis argues that an analysis of this material can help to trace...... the contexture of The New Regressive Left. If the first part of the thesis lays out the theoretical approach and draws the contextual framework, through an exploration of the surrounding Arab media-and ideoscapes, the second part is an analytical investigation of the discourse that permeates the programmes aired...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...

  6. Methods of Detecting Outliers in A Regression Analysis Model ...

    African Journals Online (AJOL)

    PROF. O. E. OSUAGWU

    2013-06-01

    Jun 1, 2013 ... especially true in observational studies .... Simple linear regression and multiple ... The simple linear ..... Grubbs,F.E (1950): Sample Criteria for Testing Outlying observations: Annals of ... In experimental design, the Relative.

  7. Principle Of Non-Regression And Social Security: Security And Trust Protection, Reservation Of The Possible And Non-Regression In The Fundamental Social Rights

    Directory of Open Access Journals (Sweden)

    Rodrigo Garcia Schwarz

    2016-12-01

    Full Text Available This paper aims to explain and analyze, from the perspective of the material and effective dimensions of fundamental social rights, questions that concern the relevance of security and trust for the social pact and, as a consequence, to the non-regression in the fundamental social rights in general and in the social security in particular. It reveals that, while the retrogression is not absolute, retrogression must be fully justified. The research is descriptive and explanatory, documentary-bibliographical.

  8. Diet influenced tooth erosion prevalence in children and adolescents: Results of a meta-analysis and meta-regression

    NARCIS (Netherlands)

    Salas, M.M.; Nascimento, G.G.; Vargas-Ferreira, F.; Tarquinio, S.B.; Huysmans, M.C.D.N.J.M.; Demarco, F.F.

    2015-01-01

    OBJECTIVE: The aim of the present study was to assess the influence of diet in tooth erosion presence in children and adolescents by meta-analysis and meta-regression. DATA: Two reviewers independently performed the selection process and the quality of studies was assessed. SOURCES: Studies

  9. Explaining the so-called 'price premium' in oil markets

    International Nuclear Information System (INIS)

    Merino, A.; Ortiz, A.

    2005-01-01

    This paper explores the information content of several variables on the so-called ''oil price premium over fundamentals''. We define this premium as the difference between the market oil price and the estimated price consistent with the OECD's relative industry stock level. By using Granger causality tests and extended regressions we test the systematic ability of a broad set of variables to explain the premium. We find that speculation in the oil market - measured by non-commercial long positions - can improve the traditional model, reducing the premium significantly during some parts of the sample. (author)

  10. Impact of Dobutamine in Patients With Septic Shock: A Meta-Regression Analysis.

    Science.gov (United States)

    Nadeem, Rashid; Sockanathan, Shivani; Singh, Mukesh; Hussain, Tamseela; Kent, Patrick; AbuAlreesh, Sarah

    2017-05-01

    Septic shock frequently requires vasopressor agents. Conflicting evidence exists for use of inotropes in patients with septic shock. Data from English studies on human adult septic shock patients were collected. A total of 83 studies were reviewed, while 11 studies with 21 data sets including 239 patients were pooled for meta-regression analysis. For VO2, pooled difference in means (PDM) was 0.274. For cardiac index (CI), PDM was 0.783. For delivery of oxygen, PDM was -0.890. For heart rate, PDM was -0.714. For left ventricle stroke work index, PDM was 0.375. For mean arterial pressure, PDM was -0.204. For mean pulmonary artery pressure, PDM was 0.085. For O2 extraction, PDM was 0.647. For PaCO2, PDM was -0.053. For PaO2, PDM was 0.282. For pulmonary artery occlusive pressure, PDM was 0.270. For pulmonary capillary wedge pressure, PDM was 0.300. For PVO2, PDM was -0.492. For right atrial pressure, PDM was 0.246. For SaO2, PDM was 0.604. For stroke volume index, PDM was 0.446. For SvO2, PDM was -0.816. For systemic vascular resistance, PDM was -0.600. For systemic vascular resistance index, PDM was 0.319. Meta-regression analysis was performed for VO2, DO2, CI, and O2 extraction. Age was found to be significant confounding factor for CI, DO2, and O2 extraction. APACHE score was not found to be a significant confounding factor for any of the parameters. Dobutamine seems to have a positive effect on cardiovascular parameters in patients with septic shock. Prospective studies with larger samples are required to further validate this observation.

  11. 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.

  12. Explaining Support Vector Machines: A Color Based Nomogram.

    Directory of Open Access Journals (Sweden)

    Vanya Van Belle

    Full Text Available Support vector machines (SVMs are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models.In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables.Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant. When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable.This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method.

  13. Introduction to the use of regression models in epidemiology.

    Science.gov (United States)

    Bender, Ralf

    2009-01-01

    Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

  14. Global Prevalence of Elder Abuse: A Meta-analysis and Meta-regression.

    Science.gov (United States)

    Ho, C Sh; Wong, S Y; Chiu, M M; Ho, R Cm

    2017-06-01

    Elder abuse is increasingly recognised as a global public health and social problem. There has been limited inter-study comparison of the prevalence and risk factors for elder abuse. This study aimed to estimate the pooled and subtype prevalence of elder abuse worldwide and identify significant associated risk factors. We conducted a meta-analysis and meta-regression of 34 population-based and 17 non-population-based studies. The pooled prevalences of elder abuse were 10.0% (95% confidence interval, 5.2%-18.6%) and 34.3% (95% confidence interval, 22.9%-47.8%) in population-based studies and third party- or caregiver-reported studies, respectively. Being in a marital relationship was found to be a significant moderator using random-effects model. This meta-analysis revealed that third parties or caregivers were more likely to report abuse than older abused adults. Subgroup analyses showed that females and those resident in non-western countries were more likely to be abused. Emotional abuse was the most prevalent elder abuse subtype and financial abuse was less commonly reported by third parties or caregivers. Heterogeneity in the prevalence was due to the high proportion of married older adults in the sample. Subgroup analysis showed that cultural factors, subtypes of abuse, and gender also contributed to heterogeneity in the pooled prevalence of elder abuse.

  15. Using Carl Rogers' person-centered model to explain interpersonal relationships at a school of nursing.

    Science.gov (United States)

    Bryan, Venise D; Lindo, Jascinth; Anderson-Johnson, Pauline; Weaver, Steve

    2015-01-01

    Faculty members are viewed as nurturers within the academic setting and may be able to influence students' behaviors through the formation of positive interpersonal relationships. Faculty members' attributes that best facilitated positive interpersonal relationships according to Carl Rogers' Person-Centered Model was studied. Students (n = 192) enrolled in a 3-year undergraduate nursing program in urban Jamaica were randomly selected to participate in this descriptive cross-sectional study. A 38-item questionnaire on interpersonal relationships with nursing faculty and students' perceptions of their teachers was utilized to collect data. Factor analysis was used to create factors of realness, prizing, and empathetic understanding. Multiple linear regression analysis on the interaction of the 3 factors and interpersonal relationship scores was performed while controlling for nursing students' study year and age. One hundred sixty-five students (mean age: 23.18 ± 4.51years; 99% female) responded. The regression model explained over 46% of the variance. Realness (β = 0.50, P < .001) was the only significant predictor of the interpersonal relationship scores assigned by the nursing students. Of the total number of respondents, 99 students (60%) reported satisfaction with the interpersonal relationships shared with faculty. Nursing students' perception of faculty members' realness appeared to be the most significant attribute in fostering positive interpersonal relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Estimating nonlinear selection gradients using quadratic regression coefficients: double or nothing?

    Science.gov (United States)

    Stinchcombe, John R; Agrawal, Aneil F; Hohenlohe, Paul A; Arnold, Stevan J; Blows, Mark W

    2008-09-01

    The use of regression analysis has been instrumental in allowing evolutionary biologists to estimate the strength and mode of natural selection. Although directional and correlational selection gradients are equal to their corresponding regression coefficients, quadratic regression coefficients must be doubled to estimate stabilizing/disruptive selection gradients. Based on a sample of 33 papers published in Evolution between 2002 and 2007, at least 78% of papers have not doubled quadratic regression coefficients, leading to an appreciable underestimate of the strength of stabilizing and disruptive selection. Proper treatment of quadratic regression coefficients is necessary for estimation of fitness surfaces and contour plots, canonical analysis of the gamma matrix, and modeling the evolution of populations on an adaptive landscape.

  17. Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

    Science.gov (United States)

    Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

    2013-01-01

    This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

  18. Explaining Away Intuitions

    Directory of Open Access Journals (Sweden)

    Jonathan Ichikawa

    2009-12-01

    Full Text Available What is it to explain away an intuition? Philosophers regularly attempt to explain intuitions away, but it is often unclear what the success conditions for their project consist in. I attempt to articulate some of these conditions, taking philosophical case studies as guides, and arguing that many attempts to explain away intuitions underestimate the challenge the project of explaining away involves. I will conclude, therefore, that explaining away intuitions is a more difficult task than has sometimes been appreciated; I also suggest, however, that the importance of explaining away intuitions has often been exaggerated.

  19. 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.

  20. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

    Science.gov (United States)

    Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

    2018-03-01

    Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

  1. Classification of Error-Diffused Halftone Images Based on Spectral Regression Kernel Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Zhigao Zeng

    2016-01-01

    Full Text Available This paper proposes a novel algorithm to solve the challenging problem of classifying error-diffused halftone images. We firstly design the class feature matrices, after extracting the image patches according to their statistics characteristics, to classify the error-diffused halftone images. Then, the spectral regression kernel discriminant analysis is used for feature dimension reduction. The error-diffused halftone images are finally classified using an idea similar to the nearest centroids classifier. As demonstrated by the experimental results, our method is fast and can achieve a high classification accuracy rate with an added benefit of robustness in tackling noise.

  2. Regression Models for Repairable Systems

    Czech Academy of Sciences Publication Activity Database

    Novák, Petr

    2015-01-01

    Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf

  3. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

    2009-10-15

    In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

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

    Directory of Open Access Journals (Sweden)

    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.

  5. A multiple linear regression analysis of factors affecting the simulated Basic Life Support (BLS) performance with Automated External Defibrillator (AED) in Flemish lifeguards.

    Science.gov (United States)

    Iserbyt, Peter; Schouppe, Gilles; Charlier, Nathalie

    2015-04-01

    Research investigating lifeguards' performance of Basic Life Support (BLS) with Automated External Defibrillator (AED) is limited. Assessing simulated BLS/AED performance in Flemish lifeguards and identifying factors affecting this performance. Six hundred and sixteen (217 female and 399 male) certified Flemish lifeguards (aged 16-71 years) performed BLS with an AED on a Laerdal ResusciAnne manikin simulating an adult victim of drowning. Stepwise multiple linear regression analysis was conducted with BLS/AED performance as outcome variable and demographic data as explanatory variables. Mean BLS/AED performance for all lifeguards was 66.5%. Compression rate and depth adhered closely to ERC 2010 guidelines. Ventilation volume and flow rate exceeded the guidelines. A significant regression model, F(6, 415)=25.61, p<.001, ES=.38, explained 27% of the variance in BLS performance (R2=.27). Significant predictors were age (beta=-.31, p<.001), years of certification (beta=-.41, p<.001), time on duty per year (beta=-.25, p<.001), practising BLS skills (beta=.11, p=.011), and being a professional lifeguard (beta=-.13, p=.029). 71% of lifeguards reported not practising BLS/AED. Being young, recently certified, few days of employment per year, practising BLS skills and not being a professional lifeguard are factors associated with higher BLS/AED performance. Measures should be taken to prevent BLS/AED performances from decaying with age and longer certification. Refresher courses could include a formal skills test and lifeguards should be encouraged to practise their BLS/AED skills. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. 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...

  7. Multiple linear regression analysis of bacterial deposition to polyurethane coatings after conditioning film formation in the marine environment

    NARCIS (Netherlands)

    Bakker, D.P.; Busscher, H.J.; Zanten, J. van; Vries, J. de; Klijnstra, J.W.; Mei, H.C. van der

    2004-01-01

    Many studies have shown relationships of substratum hydrophobicity, charge or roughness with bacterial adhesion, although bacterial adhesion is governed by interplay of different physico-chemical properties and multiple regression analysis would be more suitable to reveal mechanisms of bacterial

  8. Multiple linear regression analysis of bacterial deposition to polyurethane coating after conditioning film formation in the marine environment

    NARCIS (Netherlands)

    Bakker, Dewi P; Busscher, Henk J; van Zanten, Joyce; de Vries, Jacob; Klijnstra, Job W; van der Mei, Henny C

    Many studies have shown relationships of substratum hydrophobicity, charge or roughness with bacterial adhesion, although bacterial adhesion is governed by interplay of different physico-chemical properties and multiple regression analysis would be more suitable to reveal mechanisms of bacterial

  9. Characterization of breast masses by dynamic enhanced MR imaging. A logistic regression analysis

    International Nuclear Information System (INIS)

    Ikeda, O.; Morishita, S.; Kido, T.; Kitajima, M.; Yamashita, Y.; Takahashi, M.; Okamura, K.; Fukuda, S.

    1999-01-01

    Purpose: To identify features useful for differentiation between malignant and benign breast neoplasms using multivariate analysis of findings by MR imaging. Material and Methods: In a retrospective analysis, 61 patients with 64 breast masses underwent MR imaging and the time-signal intensity curves for precontrast dynamic postcontrast images were quantitatively analyzed. Statistical analysis was performed using a logistic regression model, which was prospectively tested in another 34 patients with suspected breast masses. Results: Univariate analysis revealed that the reliable indicators for malignancy were first the appearance of the tumor border, followed by the washout ratio, internal architecture after contrast enhancement, and peak time. The factors significantly associated with malignancy were irregular tumor border, followed by washout ratio, internal architecture, and peak time. For differentiation between benignity and malignancy, the maximum cut-off point was to be found between 0.47 and 0.51. In a prospective application of this model, 91% of the lesions were accurately discriminated as benign or malignant lesions. Conclusion: Combination of contrast-enhanced dynamic and postcontrast-enhanced MR imaging provided accurate data for the diagnosis of malignant neoplasms of the breast. The model had an accuracy of 91% (sensitivity 90%, specificity 93%). (orig.)

  10. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......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 modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  11. Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

    Science.gov (United States)

    Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

    2017-09-01

    To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

  12. Exposure to Pre- and Perinatal Risk Factors Partially Explains Mean Differences in Self-Regulation between Races.

    Directory of Open Access Journals (Sweden)

    J C Barnes

    Full Text Available To examine whether differential exposure to pre- and perinatal risk factors explained differences in levels of self-regulation between children of different races (White, Black, Hispanic, Asian, and Other.Multiple regression models based on data from the Early Childhood Longitudinal Study, Birth Cohort (n ≈ 9,850 were used to analyze the impact of pre- and perinatal risk factors on the development of self-regulation at age 2 years.Racial differences in levels of self-regulation were observed. Racial differences were also observed for 9 of the 12 pre-/perinatal risk factors. Multiple regression analyses revealed that a portion of the racial differences in self-regulation was explained by differential exposure to several of the pre-/perinatal risk factors. Specifically, maternal age at childbirth, gestational timing, and the family's socioeconomic status were significantly related to the child's level of self-regulation. These factors accounted for a statistically significant portion of the racial differences observed in self-regulation.The findings indicate racial differences in self-regulation may be, at least partially, explained by racial differences in exposure to pre- and perinatal risk factors.

  13. What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

    Science.gov (United States)

    Thomas, Emily H.; Galambos, Nora

    To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

  14. A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

    Science.gov (United States)

    Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

    2017-09-01

    Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  15. Determination of baroreflex sensitivity during the modified Oxford maneuver by trigonometric regressive spectral analysis.

    Directory of Open Access Journals (Sweden)

    Julia Gasch

    Full Text Available BACKGROUND: Differences in spontaneous and drug-induced baroreflex sensitivity (BRS have been attributed to its different operating ranges. The current study attempted to compare BRS estimates during cardiovascular steady-state and pharmacologically stimulation using an innovative algorithm for dynamic determination of baroreflex gain. METHODOLOGY/PRINCIPAL FINDINGS: Forty-five volunteers underwent the modified Oxford maneuver in supine and 60° tilted position with blood pressure and heart rate being continuously recorded. Drug-induced BRS-estimates were calculated from data obtained by bolus injections of nitroprusside and phenylephrine. Spontaneous indices were derived from data obtained during rest (stationary and under pharmacological stimulation (non-stationary using the algorithm of trigonometric regressive spectral analysis (TRS. Spontaneous and drug-induced BRS values were significantly correlated and display directionally similar changes under different situations. Using the Bland-Altman method, systematic differences between spontaneous and drug-induced estimates were found and revealed that the discrepancy can be as large as the gain itself. Fixed bias was not evident with ordinary least products regression. The correlation and agreement between the estimates increased significantly when BRS was calculated by TRS in non-stationary mode during the drug injection period. TRS-BRS significantly increased during phenylephrine and decreased under nitroprusside. CONCLUSIONS/SIGNIFICANCE: The TRS analysis provides a reliable, non-invasive assessment of human BRS not only under static steady state conditions, but also during pharmacological perturbation of the cardiovascular system.

  16. Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

    Science.gov (United States)

    Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

    2017-06-01

    We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. [Recommendations for the Stepwise Occupational Reintegration: Can the Characteristic of the Patients Explain the Differences Between the Rehabilitation Centers?].

    Science.gov (United States)

    Schmid, L; Jankowiak, S; Kaluscha, R; Krischak, G

    2016-06-01

    The first step to initiate a stepwise occupational reintegration (SOR) is the recommendation of the rehabilitation centers. Therefore rehabilitation centers have a significant impact on the use of SOR. There is evidence that the recommendation rate between the rehabilitation centers differs clearly. The present survey therefore analyses in detail the differences of the recommendation rate and examines which patient-related factors could explain the differences. This study is based on analysis of routine data provided by the German pension insurance in Baden-Württemberg (Rehabilitationsstatistikdatenbasis 2013; RSD). In the analyses rehabilitation measures were included if they were conducted by employed patients (18-64 years) with a muscular-skeletal system disease or a disorder of the connective tissue. Logistic regression models were performed to explain the differences in the recommendation rate of the rehabilitation centers. The data of 134 853 rehabilitation measures out of 32 rehabilitation centers were available. The recommendation rate differed between the rehabilitation centers from 1.36-18.53%. The logistic regression analysis showed that the period of working incapacity 12 month before the rehabilitation and the working capacity on the current job were the most important predictors for the recommendation of a SOR by the rehabilitation centers. Also the rehabilitation centers themselves have an important influence. The results of this survey indicate that the characteristic of the patients is an important factor for the recommendation of SOR. Additionally the rehabilitation centers themselves have an influence on the recommendation of SOR. The results point to the fact that the rehabilitation centers use different criteria by making a recommendation. © Georg Thieme Verlag KG Stuttgart · New York.

  18. Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits

    National Research Council Canada - National Science Library

    Gravier, Michael

    1999-01-01

    .... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...

  19. Logistic regression for risk factor modelling in stuttering research.

    Science.gov (United States)

    Reed, Phil; Wu, Yaqionq

    2013-06-01

    To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. [Application of detecting and taking overdispersion into account in Poisson regression model].

    Science.gov (United States)

    Bouche, G; Lepage, B; Migeot, V; Ingrand, P

    2009-08-01

    Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.

  1. A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim

    2014-01-24

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

  2. Towards explaining the speed of k-means

    NARCIS (Netherlands)

    Manthey, Bodo; van de Pol, Jan Cornelis; Raamsdonk, F.; Stoelinga, Mariëlle Ida Antoinette

    2011-01-01

    The $k$-means method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worst-case running-time. To explain the speed of the $k$-means method, a smoothed analysis has been conducted. We sketch this smoothed analysis and a generalization

  3. A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

    Directory of Open Access Journals (Sweden)

    Zhiming Song

    2015-01-01

    Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

  4. Regression analysis for bivariate gap time with missing first gap time data.

    Science.gov (United States)

    Huang, Chia-Hui; Chen, Yi-Hau

    2017-01-01

    We consider ordered bivariate gap time while data on the first gap time are unobservable. This study is motivated by the HIV infection and AIDS study, where the initial HIV contracting time is unavailable, but the diagnosis times for HIV and AIDS are available. We are interested in studying the risk factors for the gap time between initial HIV contraction and HIV diagnosis, and gap time between HIV and AIDS diagnoses. Besides, the association between the two gap times is also of interest. Accordingly, in the data analysis we are faced with two-fold complexity, namely data on the first gap time is completely missing, and the second gap time is subject to induced informative censoring due to dependence between the two gap times. We propose a modeling framework for regression analysis of bivariate gap time under the complexity of the data. The estimating equations for the covariate effects on, as well as the association between, the two gap times are derived through maximum likelihood and suitable counting processes. Large sample properties of the resulting estimators are developed by martingale theory. Simulations are performed to examine the performance of the proposed analysis procedure. An application of data from the HIV and AIDS study mentioned above is reported for illustration.

  5. Analysis of multi-layered films. [determining dye densities by applying a regression analysis to the spectral response of the composite transparency

    Science.gov (United States)

    Scarpace, F. L.; Voss, A. W.

    1973-01-01

    Dye densities of multi-layered films are determined by applying a regression analysis to the spectral response of the composite transparency. The amount of dye in each layer is determined by fitting the sum of the individual dye layer densities to the measured dye densities. From this, dye content constants are calculated. Methods of calculating equivalent exposures are discussed. Equivalent exposures are a constant amount of energy over a limited band-width that will give the same dye content constants as the real incident energy. Methods of using these equivalent exposures for analysis of photographic data are presented.

  6. Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

    Directory of Open Access Journals (Sweden)

    Mahdi Bashiri

    2012-02-01

    Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

  7. An evaluation of an operating BWR piping system damping during earthquake by applying auto regressive analysis

    International Nuclear Information System (INIS)

    Kitada, Y.; Makiguchi, M.; Komori, A.; Ichiki, T.

    1985-01-01

    The records of three earthquakes which had induced significant earthquake response to the piping system were obtained with the earthquake observation system. In the present paper, first, the eigenvalue analysis results for the natural piping system based on the piping support (boundary) conditions are described and second, the frequency and the damping factor evaluation results for each vibrational mode are described. In the present study, the Auto Regressive (AR) analysis method is used in the evaluation of natural frequencies and damping factors. The AR analysis applied here has a capability of direct evaluation of natural frequencies and damping factors from earthquake records observed on a piping system without any information on the input motions to the system. (orig./HP)

  8. Regression Phalanxes

    OpenAIRE

    Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

    2017-01-01

    Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

  9. Explaining Physics – What Skills does a good Explainer Need?

    CERN Multimedia

    CERN. Geneva; Bartels, Hauke

    2018-01-01

    Explaining physics in a way that it is both scientifically correct and comprehensible is a highly demanding practice. But are explanations an effective way to teach physics? Under which circumstances should a physics teacher explain – and is there such a thing as a guideline for effective instructional explanations? Of course, explaining is more than just presenting content knowledge in clear language – but what more? In our talk, we want to discuss empirical studies on instructional explanations from science education and psychology to address these questions. Among other things, we will refer to results from a large study aiming to research whether teacher education contributes to the development of explaining skills. Besides, we will give insights into a project that seeks to measure explaining skills with an interactive online test instrument.

  10. Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

    Science.gov (United States)

    Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

    2011-01-01

    Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

  11. Evidence-Based Identification of Key Beliefs Explaining Infant Male Circumcision Motivation Among Expectant Parents in Zimbabwe: Targets for Behavior Change Messaging.

    Science.gov (United States)

    Montaño, Daniel E; Tshimanga, Mufuta; Hamilton, Deven T; Gorn, Gerald; Kasprzyk, Danuta

    2018-02-01

    Slow adult male circumcision uptake is one factor leading some to recommend increased priority for infant male circumcision (IMC) in sub-Saharan African countries. This research, guided by the integrated behavioral model (IBM), was carried out to identify key beliefs that best explain Zimbabwean parents' motivation to have their infant sons circumcised. A quantitative survey, designed from qualitative elicitation study results, was administered to independent representative samples of 800 expectant mothers and 795 expectant fathers in two urban and two rural areas in Zimbabwe. Multiple regression analyses found IMC motivation among fathers was explained by instrumental attitude, descriptive norm and self-efficacy; while motivation among mothers was explained by instrumental attitude, injunctive norm, descriptive norm, self-efficacy, and perceived control. Regression analyses of beliefs underlying IBM constructs found some overlap but many differences in key beliefs explaining IMC motivation among mothers and fathers. We found differences in key beliefs among urban and rural parents. Urban fathers' IMC motivation was explained best by behavioral beliefs, while rural fathers' motivation was explained by both behavioral and efficacy beliefs. Urban mothers' IMC motivation was explained primarily by behavioral and normative beliefs, while rural mothers' motivation was explained mostly by behavioral beliefs. The key beliefs we identified should serve as targets for developing messages to improve demand and maximize parent uptake as IMC programs are rolled out. These targets need to be different among urban and rural expectant mothers and fathers.

  12. Personality disorders, violence, and antisocial behavior: a systematic review and meta-regression analysis.

    Science.gov (United States)

    Yu, Rongqin; Geddes, John R; Fazel, Seena

    2012-10-01

    The risk of antisocial outcomes in individuals with personality disorder (PD) remains uncertain. The authors synthesize the current evidence on the risks of antisocial behavior, violence, and repeat offending in PD, and they explore sources of heterogeneity in risk estimates through a systematic review and meta-regression analysis of observational studies comparing antisocial outcomes in personality disordered individuals with controls groups. Fourteen studies examined risk of antisocial and violent behavior in 10,007 individuals with PD, compared with over 12 million general population controls. There was a substantially increased risk of violent outcomes in studies with all PDs (random-effects pooled odds ratio [OR] = 3.0, 95% CI = 2.6 to 3.5). Meta-regression revealed that antisocial PD and gender were associated with higher risks (p = .01 and .07, respectively). The odds of all antisocial outcomes were also elevated. Twenty-five studies reported the risk of repeat offending in PD compared with other offenders. The risk of a repeat offense was also increased (fixed-effects pooled OR = 2.4, 95% CI = 2.2 to 2.7) in offenders with PD. The authors conclude that although PD is associated with antisocial outcomes and repeat offending, the risk appears to differ by PD category, gender, and whether individuals are offenders or not.

  13. Can trust in politicians explain individuals' support for climate policy? The case of CO2 tax

    International Nuclear Information System (INIS)

    Hammar, Henrik; Jagers, Sverker C.

    2006-01-01

    This article provides an analysis of the support for public policies from a trust perspective. The empirical focus is on the use of a tax on carbon dioxide (CO 2 ), a policy tool aimed at coordinating national emissions targets in Sweden. Among economists and experts in the field of climate policy, a CO 2 tax is viewed as a cost-effective policy. The policy problem is that public support for CO 2 taxes is very low, leaving policy makers with high-cost options. Apart from traditional explanatory variables such as demographic factors, level of education, ideology and self-interest, what can explain this public opposition? Using individual level data, we analyse whether support for increases in the CO 2 tax on gasoline can be explained by citizens' generalized trust in other individuals (who they do not necessarily know) or by their trust in politicians. We find that only the latter measure gains support in a regression analysis. Moreover, when splitting the sample into high-trusting and low-trusting individuals, we find that high-trusting individuals who have access to a car (compared with those without access) are statistically no more likely to resist increases in CO 2 taxes than people without access to a car. Rather, it is individuals with access to a car and who do not trust their politicians who are likely to resist CO 2 taxes. (Author)

  14. Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection

    KAUST Repository

    Chen, Lisha

    2012-12-01

    The reduced-rank regression is an effective method in predicting multiple response variables from the same set of predictor variables. It reduces the number of model parameters and takes advantage of interrelations between the response variables and hence improves predictive accuracy. We propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty. We apply a group-lasso type penalty that treats each row of the matrix of the regression coefficients as a group and show that this penalty satisfies certain desirable invariance properties. We develop two numerical algorithms to solve the penalized regression problem and establish the asymptotic consistency of the proposed method. In particular, the manifold structure of the reduced-rank regression coefficient matrix is considered and studied in our theoretical analysis. In our simulation study and real data analysis, the new method is compared with several existing variable selection methods for multivariate regression and exhibits competitive performance in prediction and variable selection. © 2012 American Statistical Association.

  15. Implicit collinearity effect in linear regression: Application to basal ...

    African Journals Online (AJOL)

    Collinearity of predictor variables is a severe problem in the least square regression analysis. It contributes to the instability of regression coefficients and leads to a wrong prediction accuracy. Despite these problems, studies are conducted with a large number of observed and derived variables linked with a response ...

  16. Within-session analysis of the extinction of pavlovian fear-conditioning using robust regression

    Directory of Open Access Journals (Sweden)

    Vargas-Irwin, Cristina

    2010-06-01

    Full Text Available Traditionally , the analysis of extinction data in fear conditioning experiments has involved the use of standard linear models, mostly ANOVA of between-group differences of subjects that have undergone different extinction protocols, pharmacological manipulations or some other treatment. Although some studies report individual differences in quantities such as suppression rates or freezing percentages, these differences are not included in the statistical modeling. Withinsubject response patterns are then averaged using coarse-grain time windows which can overlook these individual performance dynamics. Here we illustrate an alternative analytical procedure consisting of 2 steps: the estimation of a trend for within-session data and analysis of group differences in trend as main outcome. This procedure is tested on real fear-conditioning extinction data, comparing trend estimates via Ordinary Least Squares (OLS and robust Least Median of Squares (LMS regression estimates, as well as comparing between-group differences and analyzing mean freezing percentage versus LMS slopes as outcomes

  17. Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

    Science.gov (United States)

    Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

    2017-08-01

    Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

  18. Notes on power of normality tests of error terms in regression models

    International Nuclear Information System (INIS)

    Střelec, Luboš

    2015-01-01

    Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models

  19. Notes on power of normality tests of error terms in regression models

    Energy Technology Data Exchange (ETDEWEB)

    Střelec, Luboš [Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, Brno, 61300 (Czech Republic)

    2015-03-10

    Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models.

  20. Spontaneous regression in an ulcerated CK7 positive Merkel cell carcinoma

    Directory of Open Access Journals (Sweden)

    Anza Khader

    2015-01-01

    Full Text Available Merkel cell carcinoma is an aggressive and frequently lethal tumor of the elderly, associated with sun exposure and immunosuppression which is less common in the dark-skinned. We report the case of a 40-year-old woman who presented with multiple slowly progressive, mildly itchy ulcerated plaques of size ranging from 2 × 3 cm to 5 × 7 cm on the left knee of 1 year duration. Skin biopsy showed diffuse dermal infiltration by small round cells with molding of cells and lymphocyte infiltration. The cells stained positive for cytokeratin (CK 20, CK7, neuron-specific enolase, and chromogranin. The skin lesions underwent spontaneous regression within 1 month of skin biopsy and have not recurred during the past 2 years. The immune mechanisms triggered by biopsy possibly explain the spontaneous regression.