The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

PECO Energy, like many other companies, measures customer satisfaction, and has gone through a discussion of how best to model the results. PECO Energy utilizes a model of customer satisfaction based upon multiple regression, where both independent and dependent variables are responses to survey questions using a fully anchored five point scale. In addition to multiple regression, there are a number of other multivariate procedures that can be used to develop a quantitative model of customer satisfaction. This paper compares and contrasts results obtained from standard multiple regression, multiple regression with dummy coding, discriminant function analysis, and logistic regression procedures. Findings suggest that each of these methods can yield satisfactory information regarding customer perception.

Multiple regression models, clustering tree diagrams, regression trees (CHAID) and redundancy analysis (RDA) were applied to the study of the removal of organic matter and pharmaceuticals and personal care products (PPCPs) from urban wastewater by means of constructed wetlands (CWs). These four statistical analyses pointed out the importance of physico-chemical parameters, plant presence and chemical structure in the elimination of most pollutants. Temperature, pH values, dissolved oxygen concentration, redox potential and conductivity were related to the removal of the studied substances. Plant presence (Typha angustifolia and Phragmites australis) enhanced the removal of organic matter and some PPCPs. Multiple regression equations and CHAID trees provided numerical estimations of polluta...

The methodology of data envelopment analysis, (DEA) a linear programming-based method, is described. Other procedures often used for measuring relative productive efficiency are discussed in relation to DEA, including ratio analysis and multiple regression analysis. The DEA technique is graphically illustrated for only two inputs and one output. (LMO)

Cassava main stem barks from two experiments in which similar fertilizers were applied directly in a 2/sup 5/ confounded factorial design were analyzed and the bark nutrients used as a guide to cassava nutrition. The application of multiple regression analysis to the respective root yields and bark nutrient concentrations enable nutrient levels and optimum adjusted root yields to be derived. Differences in bark nutrient concentrations reflected soil fertility levels. Bark analysis and the application of multiple regression analysis to root yields and bark nutrients appear to be useful tools for predicting fertilizer recommendations for cassava production.

The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis?NIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA + SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbo...

This study evaluated a cumulative and syndromic relationship among commonly co-occurring vulnerabilites (homelessness, incarceration, low-income, residential transition) in association with HIV-related risk behaviors among 635 low-income women in Baltimore. Analysis included descriptive statistics, logistic regression, latent class analysis and latent class regression. Both methods of assessing multidimensional instability showed significant associations with risk indicators. Risk of multiple partners, sex exchange, and drug use decreased significantly with each additional domain. Higher stability class membership (77%) was associated with decreased likelihood of multiple partners, exchange partners, recent drug use, and recent STI. Multidimensional social vulnerabilities were cumulatively...

A major challenge for performing global query optimization in a multidatabase system (MDBS) is the lack of cost models for local database systems at the global level. In this paper we present a statistical procedure based on multiple regression analysis for building cost models for local database systems in an MDBS. Explanatory variables that can be included in a regression model are identified and a mixed forward and backward method for selecting significant explanatory variables is presented. Measures for developing useful regression cost models, such as removing outliers, eliminating multicollinearity, validating regression model assumptions, and checking significance of regression models, are discussed. Experimental results demonstrate that the presented statistical procedure can develop useful local cost models in an MDBS.

In our work we present an application of some biometrical methods useful in genotype stability evaluation, namely AMMI model, Joint Regression Analysis (JRA) and multiple comparison tests. A genotype stability analysis of oat (Avena Sativa L.) grain yield was carried out using data of the Portuguese Plant Breeding Board, sample of the 22 different genotypes during the years 2002, 2003 and 2004 in six locations. In Ferreira et al. (2006) the authors state the relevance of the regression models and of the Additive Main Effects and Multiplicative Interactions (AMMI) model, to study and to estimate phenotypic stability effects. As computational techniques we use the Zigzag algorithm to estimate the regression coefficients and the agricolae-package available in R software for AMMI model analysis.

Soft sensor application for properties estimation of splitter bottom product in a crude distillation unit (CDU) is investigated. Based on continuous temperature, pressure, and flow measurements, two soft sensors are developed as estimators of the initial boiling point and end boiling point of splitter product. Soft sensor models are developed using multiple regression techniques and neural networks. After performing multiple linear regression analysis, it was concluded that linear models are not sufficiently accurate for the implementation in the real plant. Within multilayer perceptron (MLP) and radial basis function (RBF) neural networks, different learning algorithms are used (back propagation with variations of learning rate and momentum, conjugate gradient descent, Levenberg-Marquardt...

The major objective of this study is to answer the questions, Is a recent mortality decline in the city of Chicago caused by a decrease in the amount of major air pollutants such as suspended particulate and sulfur dioxide. Based on multiple regression analysis for the cross-sect...

The LPCVD of silicon oxynitride has been characterized and optimized using the Design of Experiments method. The data of a half factorial matrix were converted with multiple linear regression analysis into an adequate empirical model : it appeared to be possible to predict the process output paramet...

The toxicity of a copper-cadmium-ferbam mixture has been studied using the protozoan Colpidium campylum bioassay. The assays were designed according to the factorial experiments method, associated with multiple regression analysis. The results show that, at the concentrations tested, a synergy occurs between cadmium and ferbam, whereas the copper is only oligodynamic. PMID:1282874

In this paper, the application of auto-regressive moving average vector models to system identification and damage detection is investigated. These parametric models have already been applied for the analysis of multiple input-output systems under ambient excitation. Their main advantage consists in...

Genetic association studies of schizophrenia typically utilize diagnostic status as the trait of interest. Among Indian schizophrenia (SZ) participants, we evaluated genetic associations (selected single nucleotide polymorphisms (SNPs) associated with SZ) with selected indices of severity and symptom pattern. Ordinal logistic regression enabled us to analyze variables with multiple categories as outcome variables, while incorporating key demographic variables; this form of analysis may be useful in future genetic association studies. No significant associations were detected following corrections for multiple comparisons.

Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods for fitting non-log-linear Cox and conditional logistic regression models. The authors use data from a study of lung cancer mortality among Colorado Plateau uranium miners (1950-1982) to illustrate these methods for fitting general relative risk models to matched case-control control data, countermatched data with weights, d:m matching, and full cohort Cox regression using the SAS statistical packag...

This page from the EPA's Statistical Primer lists, compares, and explains several multivariate statistical methods such as discriminant function analysis (DFA), multiple regression, multivariate ANOVA (MANOVA), principal components analysis (PCA), canonical correlation, and cluster analysis. Each concept includes a detailed description and either charts, graphs or illustrations for better understanding of the method. This is simply a portion of a larger set of statistical primers which address many other methods within statistics.

The purpose of this study was to determine if body composition and blood pressure (BP), two markers for Metabolic Syndrome (MetS), were correlated in college football players. Height, weight, BMI, systolic (SBP) and Diastolic (DBP) blood pressure and body composition (three measures) were assessed in a Division IAA football team (N = 55). Data analysis included a MANOVA, a Pearson Product Moment Correlation, and a Multiple Regression. Linemen were significantly different on all body composition measures from players in the other two positions. All body composition measures were correlated. The regression analysis revealed a single variable model to account for the variability between the four BMI categories. (Contains 5 tables and 3 figures.)

Background: The purpose of this study was to document the associations of stressors (combat exposure, retirement concerns, and late-life stressful events), personal resources (social support, sense of mastery, and positive appraisal of military experiences), and functional health (both physical and mental) with life satisfaction in older veterans. Methods: Participants were 562 male combat veterans (mean age-=-70). Self-report questionnaires were administered via mail survey. A hierarchical multiple regression analysis was performed. Results: Each step of the regression analysis demonstrated a significant contribution to variance in life satisfaction (48% in total). Although stressors were significant when entered as a set, their influence dissipated in the presence of personal resources a...

Informative variable (or wavelength) selection plays an important role in quantitative analysis by visible and near-infrared (Vis-NIR) spectroscopy. Four different variable selection methods, namely, stepwise multiple linear regression (SMLR), genetic algorithm-partial least squares regression (GA-PLS), interval PLS (iPLS), and successive projection algorithm-multiple linear regression combined with GA (GA-SPA-MLR), were studied to determine the sugar content of pears. The results derived by these techniques were then compared. The calibration model built using GA-SPA-MLR on 18 selected wavelengths (2% of the total number of variables) exhibited higher coefficient of determination (R2)=0.880 and root mean square error of prediction (RMSEP)=0.459degreeBrix for the validation set. Results sh...

Aim: We evaluated the impact of adherence to preferable behavior on serum lipid control assessed by a self-reported questionnaire in high-risk patients taking pravastatin for primary prevention of coronary artery disease.Methods: High-risk patients taking pravastatin were followed for 2 years. Questionnaire surveys comprising 21 questions, including 18 questions concerning awareness of health, and current status of diet, exercise, and drug therapy, were conducted at baseline and after 1 year. Potential domains were established by factor analysis from the results of questionnaires, and adherence scores were calculated in each domain. The relationship between adherence scores and lipid values during the 1-year treatment period was analyzed by each domain using multiple regression analysis.Results: A total of 5,792 patients taking pravastatin were included in the analysis. Multiple regression analysis showed a significant correlation in terms of “Intake of high fat/cholesterol/sugar foods” (regression coefficient ?0.58, p=0.0105) and “Adherence to pravastatin treatment” (regression coefficient .6.61, p<0.0001). Low-density lipoprotein cholesterol (LDL-C) values were significantly lower in patients who had an increase in the adherence score in the “Awareness of health” domain compared with those with a decreased score. There was a significant correlation between high-density lipoprotein (HDL-C) values and “Awareness of health” (regression coefficient 0.26; p= 0.0037), “Preferable dietary behaviors” (regression coefficient 0.75; p<0.0001), and “Exercise” (regression coefficient 0.73; p= 0.0002). Similar relations were seen with triglycerides.Conclusion: In patients who have a high awareness of their health, a positive attitude toward lipid-lowering treatment including diet, exercise, and high adherence to drug therapy, is related with favorable overall lipid control even in patients under treatment with pravastatin.   

Created by Neil S. Binnie of the Auckland University of Technology, this article describes a dataset containing information on bacterium culturing. Students can use graphical methods, one-way and two-way ANOVA, and multiple polynomial regression to estimate the optimal conditions for bacteria growth. The author breaks the site down in this way: an introduction, data source, description of data, pedagogical uses and references. The specific topics covered include: analysis of variance, exploratory data analysis, interactions, optimization, and outliers.

Abstract Composition of the essential oils of Rosmarinus officinalis of ten populations from the Balkan Peninsula were determined by GC/FID and GC/MS. The main constituents were 1,8-cineole, camphor, -pinene, and borneol. Multivariate statistical analysis (UPGMA cluster analysis and principal-component analysis (PCA)) revealed two major types of rosemary oil, i.e., 1,8-cineole and camphor-type, and two intermediate types, i.e., camphor/1,8-cineole/borneol type and 1,8-cineole/camphor type. The regression analyses (simple linear regression and stepwise multiple regression) have shown that, with respect to basic geographic, orographic, and 19 bioclimatic characteristics of each population, bioclimatic factor temperature of habitat represented the dominant abiogenetic factor, which, in chemic...

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegeta...

The contamination of hot water samples with Legionella spp. was studied in relation to temperature, total hardness, trace element concentrations (iron, zinc, manganese, and copper) and heterotrophic plate counts (HPC) at both 22 and 37 degreeC. Factor analysis and receiver operating characteristic (ROC) curves were used to establish the cut-off of water parameters as predictors for Legionella contamination. Legionella spp. was isolated in 194 out of 408 samples (47.5%), with Legionella pneumophila being the most common (92.8%). After multiple logistic regression analysis, the risk for legionellae colonisation was positively associated with Mn levels >6 mg l-1, HPC at 22 degreeC >27 CFU l-1, and negatively with temperature >55 degreeC and Cu levels >50 mg l-1. Multiple regression analysis r...

Electrical energy consumption data have been recorded for several hundred submetered residential structures in Middle Tennessee. All houses were constructed with a common ``energy package.`` Specifically, daily cooling usage data have been collected for 130 houses for the 1985 and 1986 cooling seasons, and monthly heating usage data for 186 houses have been recorded by occupant participation over a seven-year period. Cooling data have been analyzed using an SPSSx multiple regression analysis and results are compared to several cooling models. Heating, base, and total energy usage are also analyzed and regression correlation coefficients are determined as a function of several house parameters.

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

The aim of this study was to investigate the effects of formulation and process variables on the properties of niosomes formed from Span 40 as nonionic surfactant. A variety of formulations encapsulating Paclitaxel, a hydrophobic model drug, were prepared using different dicetyl phosphate (DCP) and Span 40-cholesterol (1:1) amounts. Formulations were optimized by multiple regression analysis to evaluate the changes on niosome characteristics such as entrapment efficiency, particle size, polydispersity index, zeta potential and in vitro drug release. Multiple regression analysis revealed that as Span 40-cholesterol amounts in the formulations were increased, zeta potential and percent of drug released at 24th hour were decreased. Besides, DCP was found to be effective on increasing niosome ...

Objective B-type natriuretic peptide (BNP) is a cardiac hormone. The results of previous in vitro studies suggest that neurohumoral factors, and not only hemodynamic factors, may cause BNP secretion. In this study, we examined the impact of serum C-reactive protein (CRP) levels on the relationship between echocardiographic parameters and plasma BNP levels in patients with cardiovascular diseases. Methods and Patients The study population comprised 417 patients who visited our cardiovascular unit with a problem. Both blood sampling and echocardiography were performed within one month. Results Multiple regression analysis showed that plasma BNP levels were negatively correlated with male gender, body mass index, and estimated glomerular filtration rate, and positively correlated with serum CRP levels and left ventricular end-systolic dimension (LVDs). The study population was divided into two groups based on the 75th percentile of the serum CRP levels. Single regression analysis showed that a regression line between LVDs and plasma BNP levels was steeper in the group of patients with CRP levels above the 75th percentile. Multiple regression analysis revealed that the interaction term (LVDs × CRP) was significant, which means LVDs had more impact on plasma BNP levels at higher CRP levels. Conclusion Plasma BNP levels increased with respect to the severity of cardiac dysfunction and serum CRP levels, and should therefore be considered a collective or total marker for life-threatening conditions including systemic inflammation, and not simply as a marker of cardiac dysfunction in patients with cardiovascular diseases.   

Background: Pollen information is indispensable for allergic individuals and clinicians. This study aimed to develop forecasting models for the total annual count of airborne pollen grains based on data monitored over the last 20 years at the Mie Chuo Medical Center, Tsu, Mie, Japan. Methods: Airborne pollen grains were collected using a Durham sampler. Total annual pollen count and pollen count from October to December (OD pollen count) of the previous year were transformed to logarithms. Regression analysis of the total pollen count was performed using variables such as the OD pollen count and the maximum temperature for mid-July of the previous year. Results: Time series analysis revealed an alternate rhythm of the series of total pollen count. The alternate rhythm consisted of a cyclic alternation of an "on" year (high pollen count) and an "off" year (low pollen count). This rhythm was used as a dummy variable in regression equations. Of the three models involving the OD pollen count, a multiple regression equation that included the alternate rhythm variable and the interaction of this rhythm with OD pollen count showed a high coefficient of determination (0.844). Of the three models involving the maximum temperature for mid-July, those including the alternate rhythm variable and the interaction of this rhythm with maximum temperature had the highest coefficient of determination (0.925). Conclusions: An alternate pollen dispersal rhythm represented by a dummy variable in the multiple regression analysis plays a key role in improving forecasting models for the total annual sugi pollen count.   

We evaluated the relationship between the efficacy of low-dose azathioprine (AZA) therapy and the inosine triphosphate pyrophosphatase (ITPA) 94C>A (Pro32Thr) polymorphism in patients with systemic lupus erythematosus (SLE). We performed a multiple regression analysis to assess the influence of various factors on the reduction in SLE disease activity index (SLEDAI) scores. The ITPA 94C>A polymorphism had the highest correlation with the change in SLEDAI score (r = 0.354, P = 0.006). PMID:19129747

A classical HPLC method of measuring log P/sub o/w/ has been reevaluated and applied to 107 different mono- and disubstituted benzenes. The contribution of several functional groups has been estimated through multiple regression analysis, obtaining statistically significant mean ..pi../sup *//sub m/ values. Deviations of experimentally determined log P/sup */ values for a set of ortho-, meta-, and para- disubstituted isomers from the ''simple additive'' model have been evaluated, and interpretations are suggested.

NIR measurement was carried out for a starch dispersion circulating from a reactor to an NIR flow-through cell over a wavelength range of from 1000 to 2500 nm in 2 nm increments. Multiple regression analysis between NIR and HPLC shows a good correlation. It is shown that the NIR method can be used to determine glucose quantitatively in real time during enzymatic hydrolysis, and that NIR may be employed for controlling the reaction in a bioreactor.

In this study, using an indoor airflow analysis model established for housing units, a computer simulation was conducted to investigate the indoor-to-outdoor and room-to-room ventilation performance in multi-housing. In the computer simulation, actual measured ventilation performance data were used as the input conditions for the airflow analysis program COMIS, in which a network model for indoor airflow analysis was employed. A prediction model was developed to evaluate the ventilation performance of multi-family housing based on multiple regression analysis with the data obtained in the computer simulation.   

Purpose - This study aims to examine the relationships between consumer self-confidence in health information search and health-related outcomes (i.e. knowledge about cancer prevention, healthcare behavior, and use of the web as a primary source for health information). The associations between self-confidence in health information search and its predictors (i.e. health literacy and trust in health information sources) are explored as well. Design/methodology/approach - This study used the 2007 Health Information National Trends Survey data. Stepwise linear regression analyses, a logistic regression analysis, and stepwise multiple regression analyses were used to test the hypotheses. Findings - The results from this study revealed that consumer self-confidence in health information search ...

A two factor, three level (3^2) face centred, central composite design (CCD) was applied to investigate the main and interaction effects of tablet diameter and compression force (CF) on hardness, disintegration time (DT) and porosity of mannitol based orodispersible tablets (ODTs). Tablet diameters of 10, 13 and 15mm, and CF of 10, 15 and 20kN were studied. Results of multiple linear regression analysis show that both the tablet diameter and CF influence tablet characteristics. A negative value of regression coefficient for tablet diameter showed an inverse relationship with hardness and DT. A positive value of regression coefficient for CF indicated an increase in hardness and DT with increasing CF as a result of the decrease in tablet porosity. Interestingly, at the larger tablet diamete...

This activity is an advanced version of the "Keep your eyes on the ball" activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance. Key words: Linear regression, independent variable, dependent variables, analysis of variance

Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast analysis of superoxide dismutase (SOD) activity in barley (Hordeum vulgare L.) leaves. Seven different spectra preprocessing methods were compared. Four regression methods were used for comparison of prediction performance, including partial least squares (PLS), multiple linear regression (MLR), least squares-support vector machine (LS-SVM) and Gaussian process regress (GPR). Successive projections algorithm (SPA) and regression coefficients (RC) were applied to select effective wavelengths (EWs) to develop more parsimonious models. The results indicated that Savitzky-Golay smoothing (SG) and multiplicative scatter correction (MSC) should be selected as the optimum preprocessing methods. The best prediction performance was achieved by the LV-LS-SVM model on SG spectra, and the correlation coefficients (r) and root mean square error of prediction (RMSEP) were 0.9064 and 0.5336, respectively. The conclusion was that Vis/NIR spectroscopy combined with multivariate analysis could be successfully applied for the fast estimation of SOD activity in barley leaves. PMID:21806813

Menstrual disturbances generally affect the daily activities of young females. The aim of this study was to determine the effects of anthropometric measures and sociodemographic factors on menstrual disturbances among university students in Bangladesh. Data were collected from 995 students from Rajshahi University using a stratified sampling technique between July 2004 and May 2005. Factors influencing menstrual pain and irregular menstrual cycles were examined with multiple logistic regression analysis. Stepwise logistic regression analysis was also used to determine the most influential factors affecting menstrual disturbances. The prevalence of menstrual pain among the university students studied was very high (72.3%), while the experience of irregular menstrual cycles was very low (12.9%). Multiple logistic regressions demonstrated that menstrual pain was affected by early age at menarche, early birth order, longer length of menstrual flow and high family income. Students suffering from irregular menstrual cycles displayed a delay in menarche, longer duration of menstrual flow and larger body mass index (BMI) values. Moreover, stepwise logistic regression showed that age at menarche, birth order, duration of menstrual flow and family income were the most important factors influencing menstrual pain. Age at menarche, duration of menstrual flow and BMI were found to be the most important factors affecting irregular menstrual cycles. These results suggest that age at menarche and duration of menstrual flow are significant common factors which affect menstrual pain and irregular menstrual cycles. Consequently, menstrual disturbance can be considered as one of the major health problems of Bangladeshi university female students and requires attention.   

The aim of this study is to determine the patterns of nitrogen (N) concentrations in leaf litter of forest trees as functions of climatic factors, annual average temperature (Temp, °C) and annual precipitation (Precip, dm) and of forest type (coniferous vs. broadleaf, deciduous vs. evergreen, Pinus, etc.). Location: The review was conducted using data from studies across the Eurasian continent. Methods: Leaf litter N concentration was compiled from 204 sets of published data (81 sets from coniferous and 123 from broadleaf forests in Eurasia). We explored the relationships between leaf litter N concentration and Temp and Precip by means of regression analysis. Leaf litter data from N2-fixing species were excluded from the analysis. Results: Over the Eurasian continent, leaf litter N concentration increased with increasing Temp and Precip within functional groups such as conifers, broadleaf, deciduous, evergreen and the genus Pinus. There were highly significant linear relationships between ln(N) and Temp and Precip (P < 0.001) for all available data combined, as well as for coniferous trees, broadleaf trees, deciduous trees, evergreen trees and Pinus separately. With both Temp and Precip as independent variables in multiple regression equations, the adjusted coefficient of determination (R²adj) was evidently higher than in simple regressions with either Temp or Precip as independent variable. Standardized regression coefficients showed that Temp had a larger impact than Precip on litter N concentration for all groups except evergreens. The impact of temperature was particularly strong for Pinus. Conclusions: The relationship between leaf litter N concentration and temperature and precipitation can be well described with simple or multiple linear regression equations for forests over Eurasia. In the context of global warming, these regression equations are useful for a better understanding and modelling of the effects of geographical and climatic factors on leaf litter N at a regional and continental scale.

The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtain...

The analytical methodology for the in vivo study of selenium metabolism using two enriched selenium isotopes has been modified, allowing for the internal correction of spectral interferences and mass bias both for total selenium and speciation analysis. The method is based on the combination of an already described dual-isotope procedure with a new data treatment strategy based on multiple linear regression. A metabolic enriched isotope (77Se) is given orally to the test subject and a second isotope (74Se) is employed for quantification. In our approach, all possible polyatomic interferences occurring in the measurement of the isotope composition of selenium by collision cell quadrupole ICP-MS are taken into account and their relative contribution calculated by multiple linear regression a...

Ferritin levels have been associated with metabolic syndrome and insulin resistance. The aim of the present study was to evaluate the prediction of ferritin levels by variables related to cardiometabolic disease risk in a multivariate analysis. For this aim, 123 healthy women (72 premenopausal and 51 posmenopausal) were recruited. Data were collected through procedures of anthropometric measurements, questionnaires for personal/familial antecedents, and dietary intake (24-h recall), and biochemical determinations (ferritin, C reactive protein (CRP), glucose, insulin, and lipid profile) in blood serum samples obtained. Multiple linear regression analysis was used and variables with no normal distribution were log-transformed for this analysis. In premenopausal women, a model to explain log-...

To characterize factors affecting the number of bovine oocytes recovered transvaginally, a regression analysis was performed between the responsiveness to multiple-ovulation treatment and the number of oocytes recovered transvaginally. The number of embryos recovered following multiple-ovulation treatment and the number of oocytes recovered transvaginally increased when the number of follicles to be aspirated transvaginally increased (P<0.05, P<0.01). The number of cumulus-oocyte complexes recovered transvaginally also increased when the number of oocytes to be aspirated transvaginally increased (P<0.001). However, the number of viable embryos that recovered following multiple-ovulation treatment had no relation to the number of cumulus-oocyte complexes recovered transvaginally. These results suggested that more oocytes can be recovered from donors that have a high responsiveness to multiple-ovulation treatment.   

Constructing an indicator to measure the effectiveness of energy management is important for energy agencies and authorities. This paper uses multiple linear regression method and data envelopment analysis to examine the effectiveness of energy management. First, the regression method using environmental factors is used to calculate the predicted energy usage intensity of each evaluated building. Data envelopment analysis is then employed to calculate overall energy efficiency, using the predicted energy usage intensity as output and the observed energy usage intensity as input. Data envelopment analysis can be further applied to measure the overall energy efficiency in details by examining environmental factors and management factors. Finally, the energy consumption of buildings can be measured to evaluate the effectiveness of energy management. Buildings examined as a case study in this paper are government office buildings in Taiwan. The results show that most of the buildings evaluated to report a higher predicted energy usage intensity have successfully used efficient energy management methods in energy saving. (author)

Agricultural chemicals in surface water may constitute a human health risk or have adverse effects on aquatic life. Recent research on unregulated rivers in the midwestern USA documents that elevated concentrations of herbicides occur for 1-4 months following application in late spring and early summer. In contrast, nitrate concentrations in unregulated rivers are elevated during fall, winter, and spring months. Natural and anthropogenic variables of fiver drainage basins, such as soil permeability, amount of agricultural chemicals applied, or percentage of land planted in corn, affect agricultural chemical concentration and mass transport in rivers. Presented is an analysis of selected data on agricultural chemicals collected for three regional studies conducted by the US Geological Survey. Statistical techniques such as multiple linear and logistic regression were used to identify natural and anthropogenic variables of drainage basins that have strong relations to agricultural chemical concentrations and mass transport measured in rivers. A geographic information system (GIS) was used to manage and analyze spatial data. Statistical models were developed that estimated the concentration, annual transport, and annual mean concentration of selected agricultural chemicals in midwestern rivers. Multiple linear regression models were not very successful (R2 from 0.162 to 0.517) in explaining the variance in observed agricultural chemical concentrations during post-planting runoff. Logistic regression models were somewhat more successful, correctly matching the observed concentration category in 61-80% of observations. Linear and multiple linear regression models were moderately successful (R2 from 0.522 to 0.995) in explaining the variance in observed annual transport and annual mean concentration of agricultural chemicals. Explanatory variables that were commonly significant in the regression models include estimates of agricultural chemical use, crop acreage, soil characteristics, and basin topography.

This paper presents a multi-criteria evaluation methodology for determining the operating strategies for bio-chemical, wastewater treatment plants based on a model analysis under an uncertainty that can present multiple steady states. The method is based on Monte Carlo (MC) simulations and the expected utility theory in order to deal with the analysis of choices among risky operating strategies with multi-dimensional outcomes. The motivation is given by a case study using an anaerobic digestion model (ADM) adapted for multiple co-substrates. It is shown how the multi-criteria analyses' computational complexity can be reduced within an approximation based on Gaussian-process regression and how a reliability map can be built for a bio-process model under uncertainty and multiplicity. In our uncertainty-analyses case study, the reliability map shows the probability of a biogas-production collapse for a given set of substrates mixture input loads. PMID:23021337

ABSTRACT Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers. PMID:22835014

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

Due to the strict norm requirements of keeping products in crude refining units within specifications, laboratory testing and quality control of the products are necessary. Given this reason, virtual soft sensor for continuous quality estimation of light naphtha as the crude distillation unit (CDU) product was developed. Experimental data included available continuous measurements of CDU process streams (temperatures, pressures and flowrate) and laboratory analyses undertaken twice a day. The results are soft sensor models for light naphtha vapor pressure (RVP) estimation. Soft sensor models have been developed conducting multiple linear regression analysis and using neural network-based models such as LNN, MLP and RBF. Considering statistical and sensitivity analysis, the best results for...

Purpose - The main objective of this study is to understand the behavior of ecological consumers and their intention to purchase organic food. The study aims to determine the factors influencing consumer behavior towards organic food. Design/methodology/approach - The method used for the data collection was a face-to-face interview, using a structured questionnaire, with closed-ended questions. In total, 463 respondents participated in the survey. It was decided to use various multivariate analyses like multiple regressions, factor analysis and cluster analysis with large sample size. Findings - The results indicate that health, availability and education from demographic factors positively influence the consumer's attitude towards buying organic food. Overall satisfaction of consumers for...

Background: Little information is available on the analysis of medical costs of acute pancreatitis hospitalizations. Aim: This study aimed to determine the factors affecting medical costs of patients with acute pancreatitis during hospitalization using a Japanese administrative database. Methods: A total of 7193 patients with acute pancreatitis were referred to 776 hospitals. We defined ''patients with high medical costs'' as patients whose medical costs exceeded the 90th percentile in medical costs during hospitalization and identified the independent factors for patients with high medical costs with and without controlling for length of stay. Results: Multiple logistic regression analysis demonstrated that necrosectomy was the most significant factor for medical costs of acute pancreatit...

This study investigated whether intellectually gifted students in Australia, with different levels of giftedness and cultural orientations, had different experiences of the forced-choice dilemma—choosing between the needs for peer acceptance and academic achievement. A random sample of 231 intellectually gifted students attending secondary school completed and returned a self-report questionnaire. The data were analyzed using factor analysis and multiple regression analysis. The study found some support for the hypothesis that level of intellectual giftedness was positively related to the experience of the forced-choice dilemma. It was also shown that those with medium-level vertical allocentric orientations toward the family were likely to be the most vulnerable to the phenomenon. ...

Photovoltaic (PV) backsheets composed of polyvinylidene difluoride (PVDF) and polyethylene terephthalate (PET) films were evaluated via principal component analysis (PCA) of near infrared (NIR) spectral datasets obtained using a potable NIR spectrometer. The discrimination of material and the degree of deterioration of the backsheets following heating and humidifying or irradiating with UV light was examined. Multiple linear regression (MLR) analysis was also employed to evaluate the degree of deterioration. The reference values (peel and tensile strength) were obtained via the International Electrotechnical Commission (IEC) 61215 and Underwriters Laboratories (UL) 1703 standard methods. The values of the peel strength and tensile strength after heating/humidifying treatment correlate quit...

The present study investigated the associations between scores on paranormal beliefs, locus of control, and certain psychological processes such as affect and cognitions as measured by the Linguistic Inquiry and Word Count. Analysis yielded significant correlations between scores on Locus of Control and two subscales of Tobacyk's (1988) Revised Paranormal Beliefs Scale, New Age Philosophy and Traditional Paranormal Beliefs. A step-wise multiple regression analysis indicated that Locus of Control was significantly related to New Age Philosophy. Other correlations were found between Tobacyk's subscales, Locus of Control, and three processes measured by the Linguistic Inquiry and Word Count. PMID:15362401

This study focuses on the relationship between the concepts of cyberbullying/cybervictimization and loneliness. The subjects of the study were 389 secondary school students, of whom 159 were boys and 230 were girls, from various schools in the city of Trabzon. The study was carried out in the fall semester of the 2009-2010 academic year. The Loneliness Scale (UCLA) and the Cyberbullying Scale were used in the study. Correlation technique, multiple regression analysis and independent t test, were employed for statistical analysis. The study concluded that there was a significant correlation between becoming a cybervictim and loneliness among adolescents. Loneliness predicted by cybervictimization. These results were discussed and some conclusions were made.

This paper shows the potential of excitation-emission fluorescence spectroscopy (EEFS) and three-way methods of analysis [parallel factor analysis (PARAFAC) and multiway partial least-squares (N-PLS) regression] as a complementary technique for olive oil characterization. The fluorescence excitation-emission matrices of a set of Spanish extra virgin, virgin, pure, and olive pomace oils were measured, and the relationship between them and some of the quality parameters of olive oils (peroxide value, K232, and K270) was studied. N-PLS was found to be more suitable than PARAFAC combined with multiple linear regression for correlating fluorescence and quality parameters, yielding better fits and lower prediction errors. The best results were obtained for predicting K270. EEFS allowed detection of extra virgin olive oils highly degraded at early stages (with high peroxide value) and little oxidized pure olive oils (with low K270). The proposed methodology may be used as an aid to analyze doubtful samples. PMID:16302742

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimat...

Indoor residual spraying (IRS) has become an increasingly popular method of insecticide use for malaria control, and many recent studies have reported on its effectiveness in reducing malaria burden in a single community or region. There is a need for systematic review and integration of the published literature on IRS and the contextual determining factors of its success in controlling malaria. This study reports the findings of a meta-regression analysis based on 13 published studies, which were chosen from more than 400 articles through a systematic search and selection process. The summary relative risk for reducing malaria prevalence was 0.38 (95% confidence interval = 0.31-0.46), which indicated a risk reduction of 62%. However, an excessive degree of heterogeneity was found between the studies. The meta-regression analysis indicates that IRS is more effective with high initial prevalence, multiple rounds of spraying, use of DDT, and in regions with a combination of Plasmodium falciparum and P. vivax malaria. PMID:22764301

The aim of this research is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of 6061 aluminum alloy with HSS and carbide tools under dry and wet conditions. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of cutting parameters on the surface roughness. The second-order mathematical models in terms of machining parameters have been developed for each of these conditions on the basis of experimental results. Genetic algorithm (GA) supported with the regression equation is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. The value obtained ...

A relation to determine local magnitude (M L) based on the original Richter definition is empirically derived from synthetic Wood-Anderson seismograms recorded by the South African National Seismograph Network. In total, 263 earthquakes in the distance range 10 to 1,000 km, representing 1,681 trace amplitudes measured in nanometers from synthesized Wood-Anderson records on the vertical channel were considered to derive an attenuation relation appropriate for South Africa through multiple regression analysis. Additionally, station corrections were determined for 26 stations during the regression analysis resulting in values ranging between -0.31 and 0.50. The most appropriate M L scale for South Africa from this study satisfies the equation: {M_{{{L}}}} = {{lo}}{{{g}}_{{10}}}(A) + 1.149{{lo}}{{{g}}_{{10}}}(R) + 0.00063R + 2.04 - S The anelastic attenuation term derived from this study indicates that ground motion attenuation is significantly different from Southern California but comparable with stable continental regions.

Semisolid extrusion experiments have been carried out on SiC particle reinforced aluminum composite. Different die diameters and ram velocities have been used in order to obtain a range of extrusion conditions at a constant temperature of 580 C. The experimental results were fitted to a multiple linear regression to obtain a constitutive equation describing the behavior of the material in these conditions. From the regression equation, it was possible to model the material by finite element analysis. The predicted values of the extrusion force resulting from the numerical analysis are consistent with the experimental values for the different conditions. The flow of primary phase particles through the die observed on micrographs has been compared with the predicted pattern and shows good agreement. These results also justify the use of a frictional stress factor corresponding to a sticking condition at the interface between the billet and the die components.

Summary Quantifying relationships between stream water quality and catchment land uses is a major goal of many water quality monitoring programs. This is a challenging task that is rarely achieved through simple analysis of raw data alone. Multiple regression analysis provides one approach, which despite significant limitations, can be successful when very large data sets are available and only annual estimates are required. However, regression techniques have limited application to sub-annual data sets. We present a new method for isolating the water quality responses of different land uses from monitoring data through hydrological model calibration, using a process of simultaneous calibration at several monitoring sites. In addition to model parameters, model algorithm complexity and the...

Principal component analysis was applied to effectively optimize the operational conditions of a fluidized bed granulator for preparing granules with excellent compaction and tablet physical properties. The crucial variables that affect the properties of the granules, their compactability and the resulting tablet properties were determined through analysis of a series of granulation and tabletting experiments. Granulation was performed while the flow rate and concentration of the binder were changed as independent operational variables, according to a two-factor central composite design. Thirteen physicochemical properties of granules and tablets were examined: powder properties (particle size, size distribution width, Carr's index, Hausner ratio and aspect ratio), compactability properties (pressure transmission ratio, die wall force and ejection force) and tablet properties (tensile strength, friability, disintegration time, weight variation and drug content uniformity). Principal component analysis showed that the pressure transmission ratio, die wall force and Carr's index were the most important variables in granule preparation. Multiple regression analysis also confirmed these results. Furthermore, optimized operational conditions obtained from the multiple regression analysis enabled the production of granules with desirable properties for tabletting. This study presents the first use of principle component analysis for identifying and successfully predicting the most important variables in the process of granulation and tabletting. PMID:21371547

The purpose of this study was to examine factors that influence the quality of life among Tianjin Chinese living with type 2 diabetes. In this study, the quality of life was assessed in 174 participants. The dependent variables included demographic and clinical data, depressive symptoms and lifestyle behavioral factors. Chi-square tests and logistic regression analysis were conducted to identify significant factors. Using multiple regression analyses, the odds ratios (ORs) of having low quality of life were 4.53 (95% confidence interval (CI)?=?1.89-10.87), 2.83 (95% CI?=?1.21-6.63), and 2.48 (95% CI?=?1.03-5.96) for patients with microvascular complications, diabetic neuropathy, and peripheral vascular disease, respectively. Coronary heart disease, depression, and unhealthy eating habits were also found to have significant negative effects on quality of life. In addition, multiple regression analysis showed that regular exercise (OR?=?0.29, 95% CI?=?0.12-0.71) was a protective factor for health-related quality of life. The identification of these influencing factors will assist nurses to provide continuous care to people living with diabetes, thus to postpone or avoid complications as well as improve their quality of life. PMID:22957839

This study examined the potential for utilizing Landsat spectral signatures, instead of in-situ measurements, to determine values for various physical, chemical, and biotic properties of coal mine surface features. The ten properties selected for evaluation were those that are most significantly altered by the mining activity, and which might ultimately inhibit soil-plant relationships. In-situ and spectral measurements for each of the properties were collected at 33 diffrent mine locations. Three statistical techniques were employed to identify relationships between the two data sets. Canonical correlation analysis was used to determine the degree to which the two data sets were sensitive to the semicharacteristics of the mine feature properties. Multiple regression analysis was used to test the potential of the spectral data set for predicting values of the mine feature properties. Multiple discrimanant analysis was also used for prediction of several of the properties which were evaluated nonmetrically.

More than 400 students from four universities in America and Britain completed measures of learning style preference, general knowledge (as a proxy for intelligence), and preference for examination method. Learning style was consistently associated with preferences: surface learners preferred multiple choice and group work options, and viewed essay-type and dissertation options less favourably. Deep learners, on the other hand, favoured essay-type and oral exams as well as final dissertations. Males favoured oral (viva voce) exams and females coursework assessment. Extraverts preferred multiple choice, oral, and group work assessment, while openness was positively associated with essays and oral exams but negatively associated with multiple choice and group work. Regression analysis showed that personality, learning style, general knowledge, and demographic factors accounted for 5-10% of the variance in preferred examination technique. Results in part replicate earlier studies and are discussed in terms of changes in examination methods. (Contains 5 tables.)

Laser-induced breakdown spectroscopy (LIBS) will be employed by the ChemCam instrument on the Mars Science Laboratory rover Curiosity to obtain UV, VIS, and VNIR atomic emission spectra of surface rocks and soils. LIBS quantitative analysis is complicated by chemical matrix effects related to abundances of neutral and ionized species in the resultant plasma, collisional interactions within plasma, laser-to-sample coupling efficiency, and self-absorption. Atmospheric composition and pressure also influence the intensity of LIBS plasma. These chemical matrix effects influence the ratio of intensity or area of a given emission line to the abundance of the element producing that line. To compensate for these complications, multivariate techniques, specifically partial least-squares regression (PLS), have been utilized to predict major element compositions (>1 wt.% oxide) of rocks, PLS methods regress one or multiple response variables (elemental concentrations) against multiple explanatory variables (intensity at each pixel of the spectrometers). Because PLS utilizes all available explanatory variable and eliminates multicollinearity, it generally performs better than univariate methods for prediction of major elements. However, peaks arising from emissions from trace elements may be masked by peaks of higher intensities from major elements. Thus in PLS regression, wherein a correlation coefficient is determined for each elemental concentration at each spectrometer pixel, trace elements may show high correlation with more intense lines resulting from optical emissions of other elements. This could result in error in predictions of trace element concentrations. Here, results of simple linear regression (SLR) and multivariate PLS-2 regression for determination of trace Rb, Sr, Cr, Ba, and V in igneous rock samples are compared. This study focuses on comparisons using only line intensities rather than peak areas to highlight differences between SLR and PLS.

Assessment of parametric uncertainty for groundwater reactive transport models is challenging, because the models are highly nonlinear with respect to their parameters due to nonlinear reaction equations and process coupling. The nonlinearity may yield parameter distributions that are non-Gaussian and have multiple modes. For such parameter distributions, the widely used nonlinear regression methods may not be able to accurately quantify predictive uncertainty. One solution to this problem is to use Markov Chain Monte Carlo (MCMC) techniques. Both the nonlinear regression and MCMC methods are used in this study for quantification of parametric uncertainty of a surface complexation model (SCM), developed to simulate hexavalent uranium [U(VI)] transport in column experiments. Firstly, a brute force Monte Carlo (MC) simulation with hundreds of thousands of model executions is conducted to understand the surface of objective function and predictive uncertainty of uranium concentration. Subsequently, the Gauss-Marquardt-Levenberg method is applied to calibrate the model. It shows that, even with multiple initial guesses, the local optimization method has difficulty of finding the global optimum because of the rough surface of the objective function and local optima/minima due to model nonlinearity. Another problem of the nonlinear regression is the underestimation of predictive uncertainty, as both the linear and nonlinear confidence intervals are narrower than that obtained from the native MC simulation. Since the naïve MC simulation is computationally expensive, the above challenges for parameter estimation and predictive uncertainty analysis are addressed using a computationally efficient MCMC technique, the DiffeRential Evolution Adaptive Metropolis algorithm (DREAM) algorithm. The results obtained from running DREAM compared with those from brute force Monte Carlo simulations shown that MCMC not only successfully infers the multi-modals posterior probability distribution, but also can provide good estimates of predictive uncertainty. The reason for the poor performance of the nonlinear regression methods is that Gaussian marginal distributions assumed in the nonlinear regression deviate significantly from the marginal posterior probability distributions estimated by DREAM and the brute force MC simulations.

    The purpose of this study was to examine the relationship between perceived motivational climate and goal orientations among Japanese high school ice hockey players. One hundred eighty-five male ice hockey players completed questionnaires in their athletic season (mid-October) . Among the research items, the dependent variable was perceived motivational climate. The independent variables included goal orientations (task orientation and ego orientation) . Multiple regression analysis was utilized for examining the predictor of contemplation. Exploratory factor analysis suggested that the perceived motivational climate contained two higher-order scales (Task-Involving and Ego-Involving) , each with two subscales (Task : Cooperative and Important Role, Effort/Improvement; Ego: Unequal Recognition, Punishment for Mistakes) . Results of confirmatory factor analysis to test the factor structure of the motivational climate supported the validity of the oblique four-factor model and oblique hierarchical model. Multiple regression analysis revealed that Task-Involving climate alone positively predicted task orientation, while ego-involving climate didn’ t emerge as significant. In addition, the analysis indicated that the Task-Involving climate and the Ego-Involving climate were significant predictors of ego orientation. These results indicate that a perceived motivational climate may influence goal orientations. Therefore, it would be important for youth sport coaches to understand their athletes’ perceptions of the motivational climate   

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13{+-}8.85 and 16.60{+-}9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification. (orig.)

The pH history of lakes can be inferred from diatom remains in dated sediment cores. To derive transfer functions for pH inference in acidic lakes, the authors counted diatoms in surface-sediment from 31 soft-water lakes in n. New England (NE) and 36 in Norway (N), covering pH 4.4-7.1. Cluster analysis of each data set indicates that pH 6 is an upper limit for a group of similar diatom assemblages. For each set multiple linear regressions were developed to relate three versions of the diatom data to pH of surface-waters: relative frequencies of selected diatom taxa, the first principal component (1PC) of these frequencies, and the frequencies of Hustedt pH groups. Also, simple linear regressions were developed for two versions: Index B and Index Alpha, both based on pH groups. Regressions were run separately for lakes with pH < or = 6; these are most relevant for pH inference in acidic lakes. The best regressions (N: taxa and 1PC taxa) have r squared 0.69-0.91 and S sub e 0.24-0.31 pH units, the worst (NE: log alpha) have r squared 0.27-0.57 and S sub e 0.51. In all cases, errors for NE are greater than N, partly due to greater diversity of NE lakes. Regressions based on pH groups (directly and by indices) have smaller r squared and larger S sub e than those based on taxa and 1PC taxa. Four regressions based on pH < or = 6 lakes were used for inferring pH in a core from Nedre Malmesvatn. (now pH 4.6). There is good agreement among three of the four that there has been a decrease of ca. 0.6 pH units starting in the late 1800's. 50 references, 9 figures, 5 tables.

ABSTRACT: INTRODUCTION: Literature data suggest that sleep disturbances are prevalent among patients with ankylosing spondylitis (AS) and have a close correlation with pain. Other studies indicate that sleep disturbances are constantly accompanied by depression and anxiety in AS, but their interrelations are poorly understood. This study was designed to evaluate sleep disturbances and their association with demographic variables, pain, disease-specific variables, functional status, covering depression and anxiety in AS patients. METHODS: The 314 patients with AS and age- and sex-matched controls took part in the study, completed a battery of questionnaires, and participated in long-term follow-up. Blood samples were taken to measure C-reactive protein (CRP) and the erythrocyte sedimentation rate (ESR). The association among sleep, pain, disease activity, functional status, depression, and anxiety were assessed by using Pearson/Spearman correlations and multiple regression analysis. RESULTS: The Pittsburgh Sleep Quality Index (PSQI) score of the Chinese version was significantly higher in the AS group than in the control group (P = 0.020). Of the 314 patients with AS, 184 (58.6%) had a high risk for sleep disturbances. The PSQI score was associated with age, years of education, ESR, CRP, overall assessment of health, pain, morning stiffness, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), depression, and anxiety (all P 0.05). In hierarchic multiple regression analysis, the medical and psychological variables contributed significantly to the variance in sleep-disturbances scores, adding an additional 23.9% to the overall R2 beyond that accounted for by demographic variables (R-square, 8.5%), resulting in a final R2of 42.6%. Multiple stepwise regression analysis revealed that anxiety was the maximal statistical contribution in predicting sleep disturbances (standardized coefficients, 0.287). CONCLUSIONS: The prevalence of sleep disturbances in AS patients is higher than it is generally thought to be. Depression, anxiety, nocturnal pain, and total back pain are the major contributors of sleep disturbances in AS. PMID:23058191

Although lung age calculated backward from regression formulas constructed for FEV1 estimation is widely used, it possesses a couple of faults. We developed novel equations predicting lung age from varied spirometric parameters (spirometry-derived lung age (SDL-age)). Applying multiple regression analysis, equations predicting SDL-age were invented using data from 8015 never-smokers with normal spirometry (group I). Validation was made based on data from 6398 never-smokers with normal spirometry (group II). Equations were further applied for 446 subjects with airflow limitation. FEV1, FEV1/FVC, FEF50, and PEF were selected as explanatory variables for reference value of SDL-age. Normal limits of difference between SDL-age and chronological-age were +/-13.4 years in the male and +/-15.0 yea...

Flank wear of an alumina-based ceramic cutting tool was determined in hard turning two workpieces (AISI 4340 and 52100 hardened steels) at three cutting speeds (142, 181, and 264?m/min) to devise a real-time monitoring system. Results of the six turning tests were assessed using Kruskal?Wallis test, regression models, and linear trend analysis. Multiple non-linear regression models that explained variation in flank wear as a function of time (second) had a range of Formula Not Shown values of 27.7% for the test 4340-142 to 95% for the test 52100-181. Linear trend models revealed that the highest flank wear rate of the ceramic cutting tool belonged to the test 52100-181. Interaction effect of the three cutting speeds and the two workpiece types was determined to account for 82.2% of variati...

The Igenity genotyping panel with a total of 233 SNP markers was used to genotype 2749 animals of the Beefbooster breeding stock population. A total of 144 SNP makers were used in conducting the association of the SNP markers and the growth traits based on multiple markers regression using stepwise method. The numbers of SNP markers that significantly (p<0.05) associated with birth weight (BWT), direct genetic effect of weaning weight (WWT), maternal genetic effect of weaning weight (Milk), yearling weight (YWT), mature weight (MWT), and scrotal circumference (SC) were 139, 135, 12, 89, 129 and 105, respectively. Marker score of each individual was calculated as the linear regression on the number of copies of specific allele of all significant (p<0.05) SNP markers. Bi-variable analysis of...

The response surface methodology was employed to study the extraction of polysaccharides from Scutellaria barbata D. Don. The quantitative effects of extraction temperature, time, number and ratio of water to raw material on yield of polysaccharides were investigated with Box-Behnken design. The experimental data were fitted to a second-order polynomial equation using multiple regression analysis and also analyzed using the appropriate statistical methods. By solving the regression equation and analyzing 3D plots, the optimum condition was at extraction temperature 70^oC, time 3h, numbers 3 and ratio of water to raw material 18.5mL/g. Under these conditions, the experimental polysaccharides yield was 2.43+/-0.11%, which was in good agreement with the predicted value. The antioxidant activi...

Abstract Enzymatic transesterification of waste cooking oil, comprising fats, oil and grease (FOG), to produce fatty acid methyl esters (FAME) i.e. biodiesel, was investigated using a novel strain of the fungus Aspergillus niger, immobilized as a whole-cell biocatalyst. Response surface methodology (RSM), with a five-level-three-factor central composite rotatable design, was used to optimize the reaction and analyze the relationship of reaction variables and their coinfluent on the response i.e. FAME yield. Independent variables that affect the transesterification reaction include temperature, feedstock water content and enzyme amount. Using RSM, a second-order polynomial equation was derived for FAME yield using multiple regression analysis. The second-order polynomial regression model wa...

The response surface methodology was employed to study the extraction of acidic polysaccharides from the stem of Ephedra sinica Stapf. The quantitative effects of extraction temperature, time, number and ratio of water to raw material on yield of ephedra acidic polysaccharides were investigated with Box?Behnken design. The experimental data were fitted to three second-order polynomial equations using multiple regression analysis and also analyzed using the appropriate statistical methods. By solving the regression equation and analyzing 3-D plots, the optimum condition was at extraction temperature 100 ?C, time 3.5 h, numbers 4 and ratio of water to raw material 13.6. Under these conditions, the experimental extraction yield, polysaccharide yield and uronic acid yield were 49.47 mg/g, 33.2...

In a study of habitat exploitation in a forest-floor small mammal community, we have collected habitat and population data for Peromyscus leucopus, Ochrotomys nuttalli, and Tamias striatus. Using multiple regression analysis, researchers estimate the effects of habitat selection and competition on the local distributions of these species during three seasons. Each of the partial regression coefficients relating the density of an independent species to the density of the dependent species is negative. This result indicates that competition is pervasive among these species. Competitive ability and habitat selectivity both increase in the order Peromyscus-Tamias-Ochrotomys. Peromyscus is a poorly competitive habitat generalist, Ochrotomys is a strongly competitive habitat specialist, and Tamias is intermediate in both respects. The competitive hierarchy is stable between seasons. These results both confirm the conclusions reached in previous studies of this small mammal community and suggest the design of experiments to further clarify the mode and consequences of interaction between these species.

Abstract in english The relationship between total phosphorus (TP) and molybdate-reactive phosphorus (MRP) concentrations in subsurface drainage waters in the hydrological conditions prevailing during autumn and spring flow events was statistically analysed using multiple linear regression analysis. Data on hydrological conditions in three drainage experimental plots in a loamy sand in south-east Sweden complemented with DRAINMOD-predicted data were used as independent variables. Regression (more) models explained at least 80% of the variation in TP and MRP concentrations in drain outflow, based on adjusted coefficient of determination (R²adj) calculations. DRAINMOD-predicted cumulative infiltration (INFILcum) was identified as the most important hydrological condition controlling TP and MRP concentrations in drain outflow in three autumn events and in two out of three spring events. This suggests that the first infiltrating water found more soluble P forms available for transport, after which TP and MRP concentration in drainage outflows gradually decreased during the flow events.

AbstractBACKGROUND: Single nucleotide polymorphisms (SNPs) in nucleotide excision repair (NER) pathway genes may modulate DNA repair capacity and increase susceptibility to breast cancer (BC). A case-control study was conducted by evaluating genes involved in DNA repair to identify polymorphisms associated with BC. METHODS: The 384 SNPs of 38 candidate genes were genotyped using the Illumina GoldenGate method. Genotypes were determined in a case-control study that consisted of 346 BC patients and 361 controls. Odds ratios and 95% confidence intervals were computed using logistic regression models. Multiple logistic regression models adjusted for age, family history of BC, and body mass index were used. RESULTS: Gene-gene interaction analysis among the DNA repair pathway genes showed signif...

Plasma-sprayed yttria-stabilized zirconia coatings have a complex microstructure consisting of a variety of pores and cracks. These microstructure features which are determined by the spray process are known to influence the thermal conductivity of coatings. In this article, the microstructure features such as total porosity, large pores, and small pores were quantified by means of scanning electron microscopy (SEM) and image analysis, and for each spray process, the particle velocity and particle temperature were measured prior to impact onto the substrate using the online monitoring system (Spray Watch 2i). Multiple linear regression was used to find the relationship between the particle state and the spray gun parameters. The linear regression models were also investigated between the p...

Oil spills occur across large landscapes in a variety of soils. Visible and near-infrared (VisNIR, 350-2500nm) diffuse reflectance spectroscopy (DRS) is a rapid, cost-effective sensing method that has shown potential for characterizing petroleum contaminated soils. This study used DRS to measure reflectance patterns of 68 samples made by mixing samples from two soils with different clay content, three levels of organic carbon, three petroleum types and three or more levels of contamination per type. Both first derivative of reflectance and discrete wavelet transformations were used to preprocess the spectra. Three clustering analyses (linear discriminant analysis, support vector machines, and random forest) and three multivariate regression methods (stepwise multiple linear regression, MLR...

Background Short sleep duration has been reported to associate with increased insulin resistance. However, no studies have investigated whether such association exists in vegetarians. The aim of this study was to investigate the association between sleep duration and insulin resistance in Taiwanese vegetarians. Methods A total of 1290 individuals were recruited from a regional hospital in south Taiwan during their regular routine physical examination. Only individuals who described themselves as Buddhist vegetarians were included in the study. Demographic information and clinical characteristics were collected and multiple logistic regression analysis was used to evaluate the association between sleep duration and insulin resistance. Results A total of 433 vegetarians were included in the study. Results from univariate logistic regression indicated that insulin resistance was significantly associated with male sex, greater waist circumference, higher triglyceride levels, lower high-density lipoprotein cholesterol levels, higher plasma creatinine levels, higher alanine transaminase levels, greater energy expenditure, and sleep duration of more than 8 hours per night. Multiple logistic regression revealed that insulin resistance was significantly and independently associated with sleep duration of more than 8 hours per night (odd ratios?=?2.27, 95% confidence interval?=?1.24, 4.11) after adjusting for waist circumference and levels of alanine transaminase. Conclusions Sleep duration of more than 8 hours per night is an independent risk factor associated with increased insulin resistance in vegetarians.

The ChemCam instrument on the Mars Science Laboratory (MSL) will include a laser-induced breakdown spectrometer (LIBS) to quantify major and minor elemental compositions. The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. This approach contrasts with a new multivariate method in which elemental concentrations are predicted by step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element. The method is tested on LIBS data from igneous and metamorphosed rocks. Between 4 and 13 partial regression coefficients are needed to describe each elemental abundance accurately (i.e., with a regression line of R{sup 2} > 0.9995 for the relationship between predicted and measured elemental concentration) for all major and minor elements studied. Validation plots suggest that the method is limited at present by the small data set, and will work best for prediction of concentration when a wide variety of compositions and rock types has been analyzed.

Nutrients in animal manure are valuable inputs in agronomic crop production. Timely and reliable information on animal manure nutrient content will facilitate the utilization of manure as organic fertilizer and reduce any associated potential environmental problems. The objective of this study was to investigate the feasibility of using multiple linear regression (MLR), polynomial regression, and artificial neural network (ANN) models to determine nutrient content in dairy manure. Fresh manure samples (n = 86) from Holstein dairy cattle were collected from 34 dairy farms located in Beijing city, China. All samples were analyzed for nutrient content (ammonium nitrogen, total potassium, total nitrogen, and total phosphorus) by standard laboratory methods. The physicochemical properties (specific gravity, electrical conductivity, and pH) of dairy manure samples were measured. Relationships between nutrient content and physicochemical properties were explored by MLR, polynomial regression, and ANN models. Several parameters (R(2), modeling efficiency statistic, mean squared error of prediction, mean bias, linear bias, and maximum bias) were calculated to evaluate model performance. The residual analysis results indicated that all MLR models for the testing data set had significant mean and linear bias. When compared with MLR and polynomial regression models, the ANN model for all nutrient contents had better performance with higher R(2) and modeling efficiency statistics and lower mean squared error of prediction, mean bias, linear bias, and maximum bias. These findings demonstrated that the ANN model may be an appropriate tool to predict dairy manure nutrient content. PMID:19038957

Although pharmacist counseling assumes an important role in the clinical setting, oncology pharmacy practitioners worldwide currently lack adequate guidance. This study aimed to identify the determinants and causal relationships that affect quality of life (QOL) in breast cancer patients before adjuvant systemic therapy for improving pharmacist counseling and guidance. This study analyzed 93 postoperative patients with breast cancer before pharmacist counseling for adjuvant systemic therapy. Patients were asked to complete questionnaires to assess QOL (the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 [EORTC QLQ-C30] and its breast cancer module [EORTC QLQ-BR23]) before pharmacist counseling. We analyzed factors affecting QOL by stepwise multiple linear regression analysis and evaluated causal association using path analysis. In the multiple linear regression model using variables selected by stepwise analysis, the factors affecting global health status (GHS)/QOL included fatigue, emotional functioning, systemic therapy side effects, future perspectives, and appetite loss. In the path analysis model, GHS/QOL were strongly influenced by fatigue directly; and emotional functioning, directly and indirectly via other factors. Our results indicated that fatigue and emotional functioning are strong factors affecting QOL. These factors may be able to predict poor QOL before initiating adjuvant systemic therapy. Thus, our findings suggest that these factors may be potentially useful for pharmacist counseling at the beginning of adjuvant systemic therapy.   

An increased incidence of sudden death has been observed among patients treated with antidepressants. A prolonged QTc interval is a known prognostic factor for fatal arrhythmia, and several studies have shown that the use of antidepressants can cause a prolonged QTc interval. However, few studies, especially in Japan, have compared the effects of multiple drugs on QTc interval or examined dose relationships in a clinical setting.We compared the effects of antidepressants on QT interval, corrected to QTc by Bazett's formula, in 729 Japanese patients who were diagnosed with mood disorder.Using stepwise multiple linear regression analysis, we found that the use of tricyclic antidepressants (Pinterval; both Pinterval. Analysis of individual antidepressants also revealed that the use of clomipramine (Pinterval.Our results reveal that tricyclic antidepressants, especially clomipramine and amitriptyline, confer a risk of prolonged QTc interval in a dose-dependent manner. The selective serotonin reuptake inhibitors investigated (fluvoxamine, paroxetine, sertraline) were not indicated as risk factors for QTc prolongation. PMID:22592502

This study examined relationships between students' personal variables (gender, prior achievements, age and academic major) and their success in the first year undergraduate calculus course. The study sample consisted of 59 first year undergraduate students taking Math 154 Calculus II course. A written test about integral, sequence and series including demographic survey items was used to gather data. The test was administered prior to and upon the completion of the calculus course. Multiple regression analysis result indicated that there is relationship between students' personal variables (gender and prior achievements) and their success. Gender differences favouring males typically occurred on Riemann sum and Riemann integral.

Both solar irradiance and primary production have been proposed as independent controls on seawater dimethyl sulphide (DMS) and dimethylsulphoniopropionate (DMSP) concentrations. However, irradiance also drives photosynthesis, and thus influences a complex set of inter-related processes that modulate marine DMS. We investigate the potential inter-relationships between the rate of primary production (carbon assimilation), water-attenuated irradiance and DMS/DMSP dynamics by applying correlation analysis to a high resolution, concurrently sampled in situ data set from a range of latitudes covering multiple biogeochemical provinces from 3 of the 4 Longhurst biogeochemical domains. The combination of primary production (PP) and underwater irradiance (Iz) within a multivariate regression model ...

Cognitive theory conceptualizes worry as influenced by metacognitive beliefs about worry, intolerance of uncertainty, and perceptions of control over events and reactions. This study tests the hypothesis that the effect of intolerance of uncertainty would interact with meta-cognitive beliefs on worry and perceived control. One hundred eighteen individuals with generalized anxiety disorder and 54 controls completed the Meta-Cognition Questionnaire, the Intolerance of Uncertainty Scale, the Anxiety Control Scale, and the Penn State Worry Questionnaire. Models were tested measuring interactive effects in multiple regression linear analysis. The interaction model was confirmed. The effect of intolerance of uncertainty on worry was increased by its interaction with metacognitive and control bel...

We evaluated the relationship between the efficacy of low-dose azathioprine (AZA) therapy and the inosine triphosphate pyrophosphatase (ITPA) 94C>A (Pro32Thr) polymorphism in patients with systemic lupus erythematosus (SLE). We performed a multiple regression analysis to assess the influence of various factors on the reduction in SLE disease activity index (SLEDAI) scores. The ITPA 94C>A polymorphism had the highest correlation with the change in SLEDAI score (r = 0.354, P = 0.006).Clinical Pharmacology & Therapeutics (2009); 85, 5, 527–530 doi:10.1038/clpt.2008.261

Objective: To assess the clinical usefulness of an automated analysis of event-related potentials (ERPs). Methods: Nociceptive laser-evoked potentials (LEPs) and non-nociceptive somatosensory electrically-evoked potentials (SEPs) were recorded in 37 patients with syringomyelia and 21 controls. LEP and SEP peak amplitudes and latencies were estimated using a single-trial automated approach based on time-frequency wavelet filtering and multiple linear regression, as well as a conventional approach based on visual inspection. Results: The amplitudes and latencies of normal and abnormal LEP and SEP peaks were identified reliably using both approaches, with similar sensitivity and specificity. Because the automated approach provided an unbiased solution to account for average waveforms where no...

This quantitative study aimed to determine the differences between rural and urban residencies related to home-based appraisal (including care burden and positive appraisal) for Japanese primary family caregivers of the elderly with extensive care needs. The study examined a sample of 196 caregivers (106 rural, 90 urban), and stepwise multiple regression analysis was performed. Resident location was significantly associated with care burden, and each factor associated with the appraisal, especially care burden, differed between rural and urban areas. The social and physical environment is thus a necessary consideration to support family caregivers and the elderly requiring home-based care.

This study examined factors influencing whether acquired employees identify with their acquirer and the complex role that the different types of continuity play in moderating relationships between procedural justice and post-acquisition identification. Data were obtained (n-=-156) from a three-country sample of employees working for a recently acquired multinational. Multiple regressions showed that expected job continuity moderated the relationship between acquirer procedural justice and post-acquisition identification. Analysis examined combinations of two organisational identity-related continuity forms (organisational similarity and acquirer organisational identity); results showed complex interactional effects of organisational similarity and acquirer organisational identity on the re...

  A new method evaluating the fibril width and length of disintegrated bacterial cellulose was developed using optical and rheological analysis.   During the early stages of the disintegration process, the bacterial cellulose particles formed loose fibrous aggregates, followed by cutting of the disintegrated fibrils that produced short fibrils. On the other hand, the fibril width decreased steadily throughout disintegration.   The relationships between fibril structure and suspension properties were analyzed by a multiple regression method. The thinner and longer the disintegrated bacterial cellulose fibrils were, the higher the viscosity and water-holding capacity became.   

Abstract Depressive symptoms often coexist with memory deficits in older adults and also are associated with incident cognitive decline in the elderly. However, little is known about the neural correlates of the association between depressive symptoms and memory deficits in nondemented elderly. Fifteen amnestic mild cognitive impairment (aMCI) and 20 cognitively normal (CN) subjects completed resting-state functional magnetic resonance imaging (R-fMRI) scans. Multiple linear regression analysis was performed to test the main effects of the Geriatric Depression Scale (GDS) and Rey Auditory Verbal Learning Test delayed recall (RAVLT-DR) scores, and their interaction on the intrinsic amygdala functional connectivity (AFC) network activity. Severer depressive symptoms and memory deficits were ...

Biological wastewater treatment plants (WWTP) are complex systems to assess. Many parameters are recorded daily in WWTP to monitor and control the treatment process, providing huge amounts of registered data. A combined approach of extracting information from the WWTP databases by statistical methods and from the sludge physico-chemical characterization was used here for a better understanding of the WWTP operation. The monitored parameters were analysed by multivariate statistical methods: Principal Components Analysis and multiple partial linear regression. The WWTP operational conditions determine the sludge characteristics. The bacterial activity of the sludge in terms of extra-cellular polymeric substances (EPS) production was assessed using size exclusion chromatography and the inter...

The present research work was undertaken to optimize and formulate Promethazine Theoclate as a fast dissolving tablet using pore forming technology that disintegrates or dissolves rapidly and offer a suitable approach for the treatment of nausea and vomiting. Fast dissolving tablets of Promethazine Theoclate was prepared by increasing the solubility i.e. using ?-cyclodextrin, crospovidone, and menthol. A 33 full factorial design was employed to investigate the combined influence of these three independent variables, i.e., amount of menthol, crospovidone and ?-cyclodextrin on disintegration time, percentage friability and percentage drug release after 5 min. In the optimization study, multiple regression analysis has revealed that an optimum amount of menthol, crospovidone and ?-cyclodextri...

Gross calorific value (GCV) is an important characteristic of coal and organic shale; the determination of GCV, however, is difficult, time-consuming, and expensive and is also a destructive analysis. In this article, the use of some soft computing techniques such as ANNs (artificial neural networks) and ANFIS (adaptive neuro-fuzzy inference system) for predicting GCV (gross calorific value) of coals is described and compared with the traditional statistical model of MR (multiple regression). This article shows that the constructed ANFIS models exhibit high performance for predicting GCV. The use of soft computing techniques will provide new approaches and methodologies in prediction of some parameters in investigations about the fuel.

Phytoplankton biomass in the northern Humboldt Current system is known to fluctuate over intra- and interannual time scales in response to environmental variability. General changes in the phytoplankton community are known, but a quantitative description of the link between environmental signals and observed changes is lacking. The present study examines these links through an analysis of long-term phytoplankton community changes in Ancon Bay, Peru (11degreeS) from 1992 to 2004. The correlation of several environmental signals with phytoplankton biovolume was explored using stepwise multiple regression and community analyses. Results indicate that environmental signals of interannual periodicity, e.g. those most associated with the El Nino Southern Oscillation, were responsible for overall...

Prenatal depression is a significant predictor of postpartum depression and is detrimental to fetal development. Locus of control was examined in this study as a potential predictor of prenatal depression. One hundred and thirty-three rural pregnant women recruited from obstetricians' offices completed the Levenson Scale on Locus of Control and the Edinburgh Postnatal Depression Scale. Multiple regression analysis indicated that after controlling for previous or current mental health problems, the External Locus of Control-Chance Scale accounted for 17% of the variance in depression scores.

Background. Hepcidin is a central regulator of iron homeostasis. Increased hepcidin concentrations could cause iron-restricted erythropoiesis in chronic kidney disease (CKD)-associated anemia. This cross-sectional observational study was conducted to evaluate the association between hepcidin and CKD-associated anemia in non-dialysis CKD patients. Methods. A total of 505 non-dialysis CKD patients not treated with parenteral iron were recruited, and serum hepcidin-25 levels were measured by liquid chromatography tandem mass spectrometry. Multiple linear regression analysis was used to examine the relationship between hepcidin and glomerular filtration rate (GFR) and the relationship between hemoglobin concentration and predictors including the hepcidin level. Results. The median hepcidin lev...

In this program, a Detroit Diesel 4-53T was heavily insulated and cooled using a chilled coolant circulation system and cooled combustion air was provided. An external cranking motor was used to turn the engine at a constant rpm. Twenty-one test fuels were blended, and a minimum unaided starting temperature was obtained for each fuel. Multiple linear-regression analysis was then performed in order to relate fuel properties to minimum starting temperature. Fuel properties examined were: viscosity; ASTM D 86 and D 2887 boiling-point temperatures, cetane number, autoignition temperature, and flash point. Cetane number, viscosity, 50% boiling temperature, and autoignition temperature had statistically significant impact on minimum starting temperature.

Although researchers have examined the efficacy of acceptance- and mindfulness-based interventions in the treatment of eating disorders, few studies have explored the association between trait mindfulness and eating pathology. Therefore, the purpose of the current investigation was to examine the unique associations between multiple facets of mindfulness (acting with awareness, nonreactivity, nonjudgment, describing, and observing) and eating pathology. Undergraduate women (N = 276) completed the Eating Attitudes Test-26, the Five Facet Mindfulness Questionnaire, and the 21-item version of the Depression Anxiety Stress Scales. A hierarchical regression analysis revealed that four mindfulness facets (awareness, nonreactivity, nonjudgment, and describing) were uniquely associated w...

Surveillance systems are indispensable for injury prevention; yet, detailed electronic records are rarely available. The "Student's Health Card" is a self-reporting electronic tool addressing health issues of University students, while aiming to actively involve them in preventive practices and health promotion. Utilizing data from the injury prevention related section, this study sought to investigate the impact of risk-taking behavior on road crash involvement among University students residing in two Mediterranean countries. A total of 978 University students, 451 Greek and 527 Italian, provided information on prior road crash involvement, as well as on eight behavioral variables, comprising a risky behavior score. Multiple logistic regression analysis was performed. The already known t...

"That's so gay," a popular expression on campuses, is a sexual orientation microaggression that can contribute to a hostile environment for lesbian, gay, and bisexual (LGB) students. Using data from a campus climate survey conducted at a large urban university, we investigated use of the phrase among heterosexual male undergraduates who are emerging adults (18-25 years). Multiple regression analysis suggested that saying the phrase is positively associated with hearing peers say it and with holding negative perceptions of feminine men, whereas having LGB acquaintances was negatively associated with use of this expression. We offer practice and policy recommendations for curbing its use, thereby enhancing campus climate. PMID:22929342

Purpose - This paper aims to explore the roles different ownership structures, the joint effect of related and unrelated diversification strategies, and previous performance levels have on the restructuring strategies of such firms. Design/methodology/approach - Annual reports of publicly traded firms in the two Chinese stock exchanges are used to collect data. Multiple regression and ANOVA analysis are used to examine the impact of ownership structure types, match between diversification strategies, and previous performance on the change of business scopes of the sample business groups. Findings - Compared to other ownership types, government owned business groups tend to increase their business scope during asset restructuring, while private business groups tend to decrease their scopes ...

ABSTRACT The DFC Intelligence predicts worldwide online game revenues will reach $9.8 billion by 2009, making online gaming a mainstream recreational activity. Understanding online game player personality traits is therefore important. This study researches the relationship between personality traits and life satisfaction in online game players. Taipei, Taiwan, is the study location, with questionnaire surveys conducted in cyber cafe shops. Multiple regression analysis studies the causal relationship between personality traits and life satisfaction in online game players. The result shows that neuroticism has significant negative influence on life satisfaction. Both openness and conscientiousness have significant positive influence on life satisfaction. Finally, implications for leisure pr...

Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) is an emerging risk factor for cardiovascular disease. In the present study, plasma levels of Lp-PLA(2) were measured in patients (n=301) admitted to elective coronary angiography because of suspected coronary artery disease (CAD). In a multiple linear regression analysis, the degree of CAD (0-, 1-, 2- or 3-vessel disease) and plasma LDL cholesterol significantly correlated to Lp-PLA(2) levels. Also the content of the marine n-3 fatty acid, eicosapentaenoic acid (EPA) in adipose tissue, a measure of long-term intake of seafood independently and inversely (r=-0.18, p

Statistical analysis has been performed on the gross alpha- and beta-activity measurements of surface waters collected at the Atomic Weapons Establishment at Aldermaston (UK) during the period January 2002?September 2005. The results have been found to follow a lognormal distribution and this has important applications when considering gross activity exemption thresholds. This implies that the gross activity is the multiplicative product of many small independent factors, such as meteorology, flow conditions and site operations. The influence of meteorological parameters has been investigated using linear regression, and some correlation has been identified between gross beta-activity and parameters indicative of fine weather. Variations in gross activity have been considered on monthly, w...

Landiolol hydrochloride, an ultrashort-acting ?1-selective blocker, is a highly regulated drug. This study evaluated the safety and efficacy of this drug for cases of coronary artery bypass grafting (CABG) with left ventricular dysfunction. Between September 2006 and August 2009, 32 patients with a left ventricular ejection fraction of pressure. No change was observed in other parameters; the hemodynamics were stable. The occurrence of atrial fibrillation during the intensive care unit stay (during landiolol hydrochloride administration) was significantly lower in the administration group. The difference remained significant after multiple logistic regression analysis; landiolol hydrochloride was the sole inhibitory factor. PMID:22917822

The DFC Intelligence predicts worldwide online game revenues will reach $9.8 billion by 2009, making online gaming a mainstream recreational activity. Understanding online game player personality traits is therefore important. This study researches the relationship between personality traits and life satisfaction in online game players. Taipei, Taiwan, is the study location, with questionnaire surveys conducted in cyber cafe shops. Multiple regression analysis studies the causal relationship between personality traits and life satisfaction in online game players. The result shows that neuroticism has significant negative influence on life satisfaction. Both openness and conscientiousness have significant positive influence on life satisfaction. Finally, implications for leisure practice and further research are discussed. PMID:18422405

Aim: This study evaluates the correlation between alcohol consumption in adolescence and parenting styles of socialization among Brazilian adolescents. The sample was composed of 273 adolescents, 58% whom were males. Instruments were: 1) Sociodemographic Questionnaire; 2) Demand and Responsiveness Scales; 3) Drug Use Screening Inventory (DUSI). Study analyses employed multiple correspondence analysis and logistic regression. Results: Maternal, but not paternal, authoritative and authoritarian parenting styles were directly related to adolescent alcohol intake. Conclusions: The style that mothers use to interact with their children may influence uptake of high-risk behaviors. PMID:22253326

The present study was designed to establish quantitative relationships between lake air-equilibrated pH, alkalinity, and diatoms occurring in the surface sediments in high-elevation Sierra Nevada Lakes. These relationships provided the necessary information to develop predictive equations relating lake pH to the composition of surface-sediment diatom assemblages in 27 study lakes. Using the Hustedt diatom pH classification system, Index B of Renberg and Hellberg, and multiple linear regression analysis, two equations were developed which predict lake pH from the relative abundance of sediment diatoms occurring in each of four diatom pH groupings.

We build a method for new feature descriptions of 3D models. This method is based on independent or combined descriptors with shape and specular reflection to retrieve 3D models with light. Light plays an important role in form and appearance of 3D objects. We obtain a good relationship between shape and specular reflection and high-grade of 3D chair models. We compare independent descriptors and combined descriptors for shape and specular reflection by multiple regression analysis. As a result, we find that form and the position of specular reflection play an important role in high-grade response to 3D chair models using two kinds of light.   

PURPOSE: This study investigated whether there is a correlation between time-trial performance and competitive performance capacity of male and female junior cross-country skiers and sought to explain gender-specific competitive performance capacity through multiple regression modeling. METHODS: The International Ski Federation's (FIS) junior ranking points for distance (FISdist) and sprint (FISsprint) competitions were used as performance parameters. A total of 38 elite junior (18.5 ± 1.0 years old) cross-country skiers (24 men and 14 women) completed three time-trial tests: a 3 km level running time trial (TTRun), a 2 km moderate uphill (1.2º slope) roller skiing time trial using the double poling technique (TTDP), and a 2 km uphill (2.8º slope) roller skiing time trial using the diagonal stride technique (TTDiag). The correlations were investigated using Pearson correlation analysis, and regression models were created using multiple linear regression analysis. RESULTS: For men, FISsprint and FISdist were correlated with the times for TTRun, TTDP, and TTDiag (all P < 0.001). For women, FISsprint was correlated with the times for TTRun (P < 0.05), TTDP (P < 0.01), and TTDiag (P < 0.01), whereas FISdist was correlated with the times for TTDP (P < 0.01) and TTDiag (P < 0.05). The models developed for FISdist and FISsprint explained 73.9-82.3% of the variance in the performance capacity of male junior cross-country skiers. No statistically valid regression model was found for the women. CONCLUSIONS: The running and roller skiing time trials are useful for the accurate prediction of the performance capacity of junior cross-country skiers. PMID:23038700

Abstract in spanish El análisis de regresión múltiple es un método estadístico empleado en muchas áreas del conocimiento. En este estudio, dicho análisis se aplicó a los datos de concentraciones de cuatro contaminantes atmosféricos (SO2, NO2, H2S y PM10), monitoreados en tres estaciones que se ubican en la Zona Norte del estado de Chiapas. El periodo que abarcó el estudio fue de enero 2001 a febrero 2005. El objetivo fue proponer funciones de regresión para describir la concentrac (more) ión en función del tiempo y/o las variables meteorológicas. Se empleó un análisis de regresión lineal múltiple paso a paso en la selección de variables regresoras. Las más importantes fueron la temperatura, la humedad relativa y la dirección del viento. Se obtuvieron funciones de regresión de la concentración anual, mensual y diaria de estos contaminantes. Se obtuvo una regresión lineal simple para explicar la concentración anual de SO2, alcanzando un coeficiente de determinación de 0.927. Los modelos de la concentración mensual alcanzaron un coeficiente de determinación entre 0.417 y 0.846; mientras que para los de la concentración diaria, este coeficiente varió de 0.285 a 0.581. De este estudio paramétrico, se concluyó que las variables meteorológicas describieron adecuadamente la concentración anual y mensual, pero no la concentración diaria. Abstract in english The multiple regression analysis is a statistical method used in many academic areas. In this study, this analysis was applied to the concentration data of four atmospheric pollutants (SO2, NO2, H2S y PM10), monitored in three stations located in northern Chiapas. The study was carried out from January 2001 to February 2005. The purpose of the study was to propose regression functions to describe the concentration as a function of time and/or meteorological variables. A s (more) tepwise multiple linear regression analysis was used in the selection of regressive variables, the most important of which included the temperature, relative humidity and wind direction. Regression functions of the annual, monthly and daily concentrations of these pollutants were obtained. A simple linear regression was obtained to explain the annual concentration of SO2, with a determination coefficient of 0.927. The models for the monthly concentration obtained determination coefficients of 0.417 to 0.846,whereas the coefficients for the daily concentration model varied from 0.285 to 0.581. This parametric study made it possible to conclude that the meteorological variables described the annual and monthly concentrations adequately, but not the daily concentrations.

In this paper, a user-friendly software, CASAC-ANA, for material data analysis and property prediction is presented. In CASAC-ANA, there are seven methods: Nonlinear Mapping (NLM), Principal Component Analysis (PCA), Stepwise Discriminant Analysis (SDA), Discriminant Analysis with Constellation Graph (DACG), Hierarchical Clustering Analysis (HCA), Stepwise Multiple Linear Regression (SMLR), and Artificial Neural Networks (ANN). The software has some noteworthy features: (1) only one input file is needed and multipath output is produced; (2) both quantitative and qualitative data of dependent variables are accepted; and (3) it is easy to link with materials property databases. As a generalized modeling tool, CASAC-ANA can be used to treat material data concerning composition, technological processes, properties, and to predict properties of materials. The validity of the CASAC-ANA software has been tested successfully with three typical case studies concerning structural alloy steels, nickel-base superalloys, and continuously cast copper alloys. These CASAC-ANA methods have been compared and discussed.

Abstract in english Diabetic retinopathy (DR) is a sight-threatening chronic complication of diabetes mellitus and is the leading cause of acquired blindness in adults. In this cross-sectional study, we investigated the prevalence of and the factors associated with DR in an analysis of 210 consecutive and unrelated Brazilian Caucasians with type 2 diabetes mellitus. Retinopathy was evaluated by ophthalmoscopy and/or biomicroscopy through dilated pupils. The relationship between clinical and (more) metabolic variables and the presence of DR was assessed by logistic regression analysis. DR was detected in 99 of the 210 patients (47%). In the univariate logistic regression analyses, male sex, duration of diabetes, body mass index, glycated hemoglobin, C-peptide, LDL cholesterol, smoking, and albumin excretion rate were found to be associated with the presence of DR. However, the multiple logistic regression analysis showed that only duration of diabetes (odds ratio (OR) = 1.15, 95% CI = 1.09-1.22; P 100 µg/min (OR = 12.72, 95% CI = 3.89-41.56; P

Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the Clinical Antipsychotic Trails of Intervention Effectiveness antibody study. PMID:22899176

The paper examines whether variables of HIV/AIDS related knowledge, attitude and beliefs of Dodoma University students (UDOM) can predict practices which risk HIV infection. A pre-designed questionnaire was administered in a sample of 547 (539 students and 8 staff members). Since variables involved were many and in many cases highly correlated and unobservable, data were analyzed using factor analysis. Multiple regression analysis were used to analyze factor scores obtained from factor analysis. Based on responses of students to practice questions, two factors emerged and were labeled as sex with high risk persons and common risk sexual behavior. The common predictors in the Multiple Linear Regression (MLR) for sex with high risk persons score (R2 = 5.1%, p knowledge on transmission through sexual contact, beliefs about HIV/AIDS and attitudes toward VCT testing. The MLR for common risk sexual behavior score (R2 = 5.4%, p attitudes toward condom use. Knowledge on transmission through sexual contact, beliefs about HIV/AIDS and attitudes toward VCT testing and condom use appeared to be common determinants of HIV/AIDS risk behaviors among students. PMID:23136707

AIM: To identify plain radiographic findings that predict segmental lumbar spine instability as shown by functional flexion-extension radiography. MATERIALS AND METHODS: Plain radiographs and flexion-extension radiographs of 215 patients with clinically suspected lumbar spine instability were analysed. Instability was classified into anterior or posterior sliding instability. The registered plain radiographic findings were traction spur, spondylarthrosis, arthrosis of facet joints, disc degeneration, retrolisthesis, degenerative spondylolisthesis, spondylolytic spondylolisthesis and vacuum phenomena. Factors reaching statistical significance in univariate analyses (P < 0.05) were included in stepwise multiple logistic regression analysis. RESULTS: Degenerative spondylolisthesis (P = 0.004 at L3-4 level and P = 0.017 at L4-5 level in univariate analysis and odds ratio 16.92 at L4-5 level in multiple logistic regression analyses) and spondylolytic spondylolisthesis (P = 0.003 at L5-S1 level in univariate analyses) were the strongest independent determinants of anterior sliding instability. Retrolisthesis (odds ratio 10.97), traction spur (odds ratio 4.45) and spondylarthrosis (odds ratio 3.20) at L3-4 level were statistically significant determinants of posterior sliding instability in multivariate analysis. CONCLUSION: Sliding instability is strongly associated with various plain radiographic findings. In mechanical back pain, functional flexion-extension radiographs should be limited to situations when symptoms are not explained by findings of plain radiographs and/or when they are likely to alter therapy. Pitkaenen, M.T. et al. (2002)

classifying voice quality are compared: regression analysis and hidden Markov models (HMMs). The findings of this research show that HMMs can be used to classify voice quality. The HMMs performed better than the regression models in classifying breathines...

BACKGROUND: There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. OBJECTIVE: To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. METHODS: We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. RESULTS: The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. CONCLUSION: The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. PMID:22871397

This study tested whether the plasma level of total homocysteine (tHcy) was predictive of obstructive coronary artery disease (CAD) and clinical outcome in patients undergoing coronary angiographic (CAG) study. From September 2002 to October 2004, 1,305 consecutive patients with angina pectoris undergoing CAG study were consecutively enrolled. Blood samples were prospectively collected to assess the plasma level of tHcy from each patient before catheterization. Of these 1305 patients, 676 (51.8%) had multivessel disease (group 1), 367 (28.1%) had single-vessel disease (group 2), and 262 (20.1%) had normal coronary artery or insignificant coronary artery disease (group 3). The plasma level of tHcy was notably higher in group 1 than in groups 2 and 3 (11.6 ± 4.4 versus 10.9 ± 4.0 versus 10.4 ± 3.8, P < 0.001). Univariate binary logistic regression analysis demonstrated that the plasma tHcy level was strongly associated with multiple-vessel disease (MVD) (defined as ? 2 vessel disease) (P < 0.001). Multivariate binary logistic regression analysis showed that tHcy level, fasting blood sugar, diabetes mellitus, and age were significantly and independently predictive of MVD (all P < 0.03). Univariate Cox regression analysis demonstrated that tHcy level was predictive of long-term mortality (P = 0.042). However, the tHcy level was not an independent predictor of long-term mortality on multivariate Cox regression analysis (P > 0.05). The results of our study support the hypothesis that tHcy level is an independent predictor of MVD in patients with chest pain undergoing CAG study. Conversely, our study did not support the tHcy level as an independent predictor of long-term mortality in this clinical setting.   

Colorectal cancer (CRC), one of the most frequent neoplasias worldwide, has both genetic and environmental causes. As yet, however, gene-environment (G × E) interactions in CRC have been studied mostly for a small number of candidate genes only. Therefore, we investigated the possible interaction, in CRC etiology, between single-nucleotide polymorphisms (SNPs) on the one hand, and overweight, smoking and alcohol consumption on the other, at a genome-wide level. To this end, we adopted a two-tiered approach comprising a case-only screening stage I (314 cases) and a case-control validation stage II (259 cases, 1,002 controls). Interactions with the smallest p value in stage I were verified in stage II using multiple logistic regression analysis adjusted for sex and age. In addition, we specifically studied known CRC-associated SNPs for possible G × E interactions. Upon adjustment for sex and age, and after allowing for multiple testing, however, only a single SNP (rs1944511) was found to be involved in a statistically significant interaction, namely with overweight (multiplicity-corrected p = 0.042 in stage II). Several other G × E interactions were nominally significant but failed correction for multiple testing, including a previously reported interaction between rs9929218 and alcohol consumption that also emerged in our candidate SNP study (nominal p = 0.008). Notably, none of the interactions identified in our genome-wide analysis was with a previously reported CRC-associated SNP. Our study therefore highlights the potential of an "agnostic" genome-wide approach to G × E analysis. PMID:23114982

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net. PMID:21949842

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, a...

Objectives The Iowa Healthy and Active Minds Study is a four-arm randomised controlled trial of a visual processing speed training programme (Road Tour). This article presents the preplanned interim results immediately after training (6-8 weeks post-randomisation) for the primary outcome. Design Within two age strata (50-64 vs ?65), 681 men and women attending general internal and family medicine clinics were randomised to four training groups: (1) supervised, on-site standard (10 h) dose of Road Tour training; (2) supervised, on-site standard dose of Road Tour training with 4 h of subsequent booster training scheduled to occur at 11 months post-randomisation (ie, no booster training had occurred at the time of this interim analysis); (3) supervised, on-site standard dose of attention control (crossword puzzles) training and (4) self-administered, at-home standard dose of Road Tour training. The primary outcome was the Useful Field of View (UFOV) test. Three intent-to-treat interim analyses were conducted, including (1) multiple linear regression models of composite UFOV scores using Blom rank transformations, (2) general linear mixed effects models and (3) multiple logistic regression models among the 620 participants (91%) with complete data. Results In the linear regression analyses of both age strata, random assignment to any Road Tour training group versus the attention control group was significant (p<0.001), with an effect size of -0.558 (adjusted for the Blom rank transformed UFOV score at randomisation). Similar results were obtained for each Road Tour group and within each age stratum and from the general linear and logistic regression models. Conclusions Assignment to a standard dose of Road Tour training yielded medium-sized post-training improvements in visual processing speed. Road Tour was equally effective whether administered under laboratory supervision or self-administered in the patient's home and for participants in both age strata (50-64 vs ?65). Clinical trial registration number NCT01165463. PMID:22106377

Objective Both brain natriuretic peptide (BNP) and cardiac sympathetic activity are useful surrogate markers of congestive heart failure. BNP is known to be secreted in response to sympathetic tone. This study examined the relationship between the cardiac sympathetic system and BNP. Patients and Methods Sixty patients with chronic ischemic heart failure (mean age,72 years-old; 46 males and 14 females) who had undergone cardiac catheterization and were classified as NYHA II underwent resting 99mTc-sestamibi quantitative gated imaging (MIBI) and 123I-metaiodobenzylguanidine imaging (MIBG). MIBI was used to obtain left ventricular (LV) dimension. MIBG was used to obtain the washout rate and the H/M ratios as well as the extent of LV washout rate abnormality (RSNA), which was defined as the number of regions with a regional washout rate of more than mean+2SD of 15 normal subjects on a two-dimensional polar map divided into 20 regions. Blood samples were obtained to measure neurohormones such as BNP, renin activity, noradrenaline, and angiotensin II. Results Simple linear regression analysis showed that BNP had significant correlations to age, LVEF, LV end diastolic volume, LV end systolic volume, RSNA, global washout rate, myocardial ischemia, and LV end diastolic pressure. Among them, multiple linear regression analysis showed that only RSNA (partial regression coefficient =0.618, p<0.002) had a significant positive correlation with BNP. Conclusion This study suggested that the extent of cardiac sympathetic overactivity in the LV could enhance BNP release.   

Water-extracted mycelial polysaccharide (WPS) from the endophytic fungus Fusarium oxysporum Dzf17 isolated from Dioscorea zingiberensis was found to be an efficient elicitor to enhance diosgenin accumulation in D. zingigerensis cultures, and also demonstrated antioxidant activity. In this study, response surface methodology (RSM) was employed to optimize the extraction process of WPS from F. oxysporum Dzf17 using Box-Behnken design (BBD). The ranges of the factors investigated were 1-3 h for extraction time (X(1)), 80-100 °C for extraction temperature (X(2)), and 20-40 (v/w) for ratio of water volume (mL) to raw material weight (g) (X(3)). The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis. Statistical analysis showed that the polynomial regression model was in good agreement with the experimental results with the determination coefficient (R(2)) of 0.9978. By solving the regression equation and analyzing the response surface contour plots, the extraction parameters were optimized as 1.7 h for extraction time, 95 °C for extraction temperature, 39 (v/w) for ratio of water volume (mL) to raw material weight (g), and with 2 extractions. The maximum value (10.862%) of WPS yield was obtained when the WPS extraction process was conducted under the optimal conditions. PMID:22754306

Abstract in english The aim of this study was to develop a method for estimating the chronological age of Indian adults based on the relationship between age and various morphological variables of canine teeth, obtained using orthopantomographs. Orthopantomographs of 120 selected patients were digitized, and radiographic images of the right maxillary canine in each case were processed using a computer aided drafting program. Pulp/tooth area ratio, pulp/root length ratio, pulp/tooth length ra (more) tio, pulp/root width ratio at the cemento-enamel junction level, pulp/root width ratio at midroot level, and pulp/root width ratio at the midpoint between the cemento-enamel junction and the midroot of the canine were calculated by measuring various features on the images. Pearson's correlation, multiple linear regression, one way analysis of variance (ANOVA), and Student's t-test were used for statistical analysis. Regression equations were developed to estimate age from morphological variables. The observed minus the estimated age in the total study sample ranged from -2.2 to +1.5 years, in males from -0.9 to +0.8 years, while in females it was from -1 to +0.8 years. Differences between observed and estimated ages of subjects were not statistically significant. In conclusion there is a linear relationship of pulp/root width ratio at mid-root level and pulp/tooth area ratio of the right maxillary canine with chronological age in the Indian population. Age of subjects can therefore be estimated with a good degree of accuracy using regression equations.

The stratosphere is thought to play a central role in the atmospheric response to solar irradiance variability. Recent observations suggest that the spectral solar irradiance (SSI) variability involves significant time-dependent spectral variations, with variable degrees of correlation between wavelengths, and new reconstructions are being developed. In this paper, we propose a simplified modelling framework to characterise the effect of short term SSI variability on stratospheric ozone. We focus on the pure photochemical effect, for it is the best constrained one. The photochemical effect is characterised using an ensemble simulation approach with multiple linear regression analysis. A photochemical column model is used with interactive photolysis for this purpose. Regression models and their coefficients provide a characterisation of the stratospheric ozone response to SSI variability and will allow future inter-comparisons between different SSI reconstructions. As a first step in this study, and to allow comparison with past studies, we take the representation of SSI variability from the Lean (1997) solar minimum and maximum spectra. First, solar maximum-minimum response is analysed for all chemical families and partitioning ratios, and is compared with past studies. The ozone response peaks at 0.18 ppmv (approximately 3%) at 37 km altitude. Second, ensemble simulations are regressed following two linear models. In the simplest case, an adjusted coefficient of determination R2 larger than 0.97 is found throughout the stratosphere using two predictors, namely the previous day's ozone perturbation and the current day's solar irradiance perturbation. A better accuracy (R2 larger than 0.9992) is achieved with an additional predictor, the previous day's solar irradiance perturbation. The regression models also provide simple parameterisations of the ozone perturbation due to SSI variability. Their skills as proxy models are evaluated independently against the photochemistry column model. The bias and RMS error of the best regression model are found smaller than 1% and 15% of the ozone response, respectively. Sensitivities to initial conditions and to magnitude of the SSI variability are also discussed.

The blood flow velocity of the right brachial artery was measured noninvasively by pulsed Doppler flowmetry in 50 patients with angina pectoris. Reactive hyperemia was induced by a 2-minute occlusion of the artery by a tourniquet. We assessed the peak velocity ratio (PVR) and 50% recovery time (RT) which were defined as the ratio of maximal to baseline systolic peak velocity and as the interval from the resumption of arterial flow to 50% decline of the increased systolic peak velocity, respectively. Multiple regression analysis for determinants of PVR and 50% RT was performed with 7 variables which were age, sex, hypertension, diabetes mellitus, smoking, total cholesterol level, and the number of diseased coronary arteries. Multiple R was 0.649 (p brachial artery will provide a clue for noninvasive estimation of the extent of coronary artery disease. PMID:9057678

This study reveals national factors that are context bound by contrasting survey data on sexual risk behaviour among young men in Finland and Estonia (including Russian speaking Estonians). The study will highlight the same problems in two countries that are culturally and geographically close to each other, increasingly share cross-traffic and migration, but yet are in a different public health stage.Two representative probability survey samples were used, including 1765 men/Finland and 748/Estonia, aged 18-25 in 2005, with response rates of 95% and 43% respectively. Multiple regression analysis was used to study the associations between different risk factors.Multiple (> 5) lifetime partners were reported by 31% of Finns, 22% of Estonians and33% of Russian speaking Estonians. Finns and E...

The purpose of this study will be to determine if there is a relationship between Hispanic student achievement and the percentage of Hispanic teachers per district in the state of Texas. Specifically, this study will investigate the relationship between the percentage of Hispanic students per district who pass the Texas Assessment of Knowledge and Skills (TAKS) test in eleventh grade and the percentage of Hispanic teachers in their corresponding districts. This correlational investigation will include data from the Texas Education Agency (TEA). This data will consist of the 2007-2008 academic school year. TEA database data collected will include district type and district percentages of eleventh grade Hispanic students passing TAKS, economically disadvantaged students, at risk students, Hispanic teachers, teachers by years of experience, and teacher turnover rates. The main predictor variable identified was the percentage of Hispanic teachers per district. To determine if district community type is a significant factor in student achievement, an Analysis of Variance (ANOVA) will be employed. If significance is found by district type, then a minimum of six key informant interviews will be conducted to investigate this difference. These key informants would include two Hispanic teachers from urban, suburban, and rural districts. A one-way ANOVA was conducted to explore Hispanic student achievement based on district type and evidence of statistical significance was not found due to a lack of homogeneity. A multiple regression analysis was conducted on Hispanic students in 11th grade who passed all portions of the TAKS test based on district percentages of Hispanic teachers, at-risk students, economically disadvantaged students, teacher turnover, and teacher experience. A statistically significant relationship was observed, and district percentage of at-risk students and teacher turnover was a statistically significant predictor of Hispanic student achievement on all portions of the TAKS test in 11th grade. A multiple regression analysis was conducted on Hispanic students in 11th grade who passed the English Language Arts (ELA) TAKS test based on district percentages of Hispanic teachers, at-risk students, economically disadvantaged students, teacher turnover, and teacher experience. A statistically significant relationship was observed, and district percentage of at-risk students was a statistically significant predictor of Hispanic student achievement on the ELA TAKS test in 11th grade. A multiple regression analysis was conducted on Hispanic students in 11th grade who passed the Mathematics TAKS test based on district percentages of Hispanic teachers, at-risk students, economically disadvantaged students, teacher turnover, and teacher experience. A statistically significant relationship was observed, and district percentage of at-risk students, economically disadvantaged students, and teacher turnover was a statistically significant predictor of Hispanic student achievement on the Mathematics TAKS test in 11th grade. A multiple regression analysis was conducted on Hispanic students in 11th grade who passed the Science TAKS test based on district percentages of Hispanic teachers, at-risk students, economically disadvantaged students, teacher turnover, and teacher experience. A statistically significant relationship was observed, and district percentage of at-risk students and teacher turnover was a statistically significant predictor of Hispanic student achievement on the Science TAKS test in 11th grade. A multiple regression analysis was conducted on Hispanic students in 11th grade who passed the Social Studies TAKS test based on district percentages of Hispanic teachers, at-risk students, economically disadvantaged students, teacher turnover, and teacher experience. A statistically significant relationship was observed, and district percentage of at-risk students and economically disadvantaged students were a statistically significant predictor of Hispanic student achievement on the Social Studies TAKS test in 11th grade

The objective of the study was to analyze the influence of antioxidant components on antioxidant activity of dehydrated green leafy vegetables (GLV), namely, Amaranthus sp., Centella asiatica, Murraya koenigii and Trigonella feonum graecum. The edible portion of the GLV was chemically treated, steam blanched, oven dried and analyzed for antioxidant components and antioxidant activity. Multiple regression analysis was done to test for the relationship between antioxidant activity and the antioxidant components. The ascorbic acid, total carotene, ?-carotene and total polyphenol content (tannic acid equivalents) of the GLV ranged between 1.94 – 8.63, 12.13 – 29.8, 2.63 – 4.24 and 637.50 – 1170.83 mg/100g respectively. The total antioxidant activity was highest in Trigonella feonum graecum and the least in Amaranthus sp. The GLV exhibited varied levels of antioxidant activity as analyzed by 1,1-diphenyl-2-picryl hydrazyl (free radical scavenger), reducing power and ferrous ion chelating activity at different concentrations of the extract, which were significantly different. Multiple regression analysis showed that the relationship of (i) total antioxidant activity, (ii) free radical scavenging activity and (iii) reducing power, each with antioxidant components in dehydrated GLV, was statistically significant.   

In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

OBJECTIVES: Proton pump inhibitors (PPIs) are associated with an increased risk of bone fractures. This study sought to evaluate the effect of PPIs on biochemical markers of calcium and bone metabolism. METHODS: Prospective matched controlled study involving healthy adult males (age 18-50years) suffering from frequent heartburn. Patients received standard-dose PPI for 12weeks and were matched by age with healthy controls. Blood studies were taken at 0, 1 and 3months for biochemical markers of mineral and bone metabolism. Two-way (time and PPI treatment) repeated measures analysis of variance (RM-ANOVA) and multiple linear regression were used for analysis. RESULTS: A total of 58 participants (29 per group) completed the study. Mean age of participants was 33.2±7.5years. Baseline characteristics and biomarkers were similar for both groups except for higher BMI (28.6 vs. 25.6kg/m(2), p=0.008) and serum C-terminal cross linked telopeptides of type I collagen [CrossLaps, (300 vs. 228pg/ml, p=0.028)] in the PPI group. There was no difference in parathormone (PTH), ionized calcium, vitamin D, osteocalcin and CrossLaps between the PPI and control subjects (all non-significant; 2-way RM-ANOVA). Multiple linear regression modeling showed no effect of PPIs on any of the studied calcium or bone metabolism biomarkers. CONCLUSION: PPI intake for 12weeks has no measurable effect on calcium or bone metabolism in healthy young males. PMID:23102518

We describe a new method for the evaluation of the bitterness of medicines by semi-continuous measurement of adsorption using a multichannel taste sensor or ‘electric tongue’. The bitterness of 10 basic medicines was evaluated by both the taste sensor and in human gustatory sensation tests with 11 volunteers. The sensor part of the taste sensor consists of eight electrodes made of lipid/polymer membranes. Three variables were obtained from the taste sensor data: sensor output (S), the change of membrane potential caused by adsorption, corresponding to aftertaste (C), and the ratio C/S. These variables were used to predict an estimated bitterness score in multiple regression analysis. Semi-continuous measurement of C (every 30 s up to 150 s) was adopted as an additional explanatory variable, and the attenuation rate of C was defined as C'. These data were also subjected to multiple regression analysis. The correlation coefficient (r) estimated for the bitterness score predicted by the taste sensor, using C' for channel 2 and C/S for channel 4, and the score obtained by human gustatory sensation, was 0.824. This value was greater than that obtained using C/S for both channels 2 and 4 (0.734). The method described in the present study seems to offer good predictability for the evaluation of bitterness.   

Sources and concentrations of indoor nitrogen dioxide (NO2) were examined in Barcelona, Spain, during 1996-1999. A total of 340 dwellings of infants participating in a hospital-based cohort study were selected from different areas of the city. Passive filter badges were used for indoor NO2 measurement over 7-30 days. Dwelling inhabitants completed a questionnaire on housing characteristics and smoking habits. Data on outdoor NO2 concentrations were available for the entire period of the study in the areas of the city where indoor concentrations were determined. Bivariate analysis was performed to investigate relationships between indoor NO2 concentrations on one hand and outdoor NO2 concentrations, housing, and occupant characteristics on the other. Stepwise multiple linear regression was performed with variables that were found to have a significant bivariate relationship. Indoor NO2 mean values ranged between 23.57 ppb in 1996 and 27.02 ppb in 1999, with the highest yearly value of 27.82 ppb in 1997. In the same time period, mean outdoor NO2 concentration ranged between 25.26 and 25.78 ppb with a peak of 30.5 ppb in 1998. Multiple regression analysis showed that principal sources of indoor NO2 concentrations were the use of a gas cooker, the absence of an extractor fan when cooking, and cigarette smoking. The absence of central heating was also associated with higher NO2 concentrations. Finally, each ppb increase in outdoor NO2 was associated with a 1% increase in indoor concentrations. PMID:14649750

To evaluate factors associated with initiation and duration of breastfeeding in Italy, 1601 (73%) respondents among 2192 randomly selected mothers were interviewed within 1 mo of delivery. Mothers who started breastfeeding (85%) were followed-up for 12 mo. A compliance rate of 100% was obtained. At multiple logistic regression analysis, mother having been breastfed herself (p < 0.01), nursing guidance in the maternity ward (p = 0.01) and higher social class (p = 0.03) were positively associated with initiation of breastfeeding. We found that 42%, 19%, 10% and 4% mothers were still breastfeeding at 3, 6, 9 and 12 mo after delivery, respectively. Cox multiple regression analysis showed a negative association between duration of breastfeeding and pacifier use (p < 0.01), and a positive association with a higher level of maternal education (p = 0.04). Formula supplementation in the maternity ward (given to 30% of infants) was associated with a shorter duration of exclusive breastfeeding (p = 0.03). Mothers need support with breastfeeding, particularly those from lower social backgrounds and with lower levels of education. Early use of the pacifier should be discouraged. PMID:10342540

In a cross-sectional study, the primary objective was to assess the plasma concentration of 25-hydroxyvitamin D (25(OH) D) in healthy 9-month-old infants (n = 255). The secondary objective was to evaluate nutritional variables and season in relation to 25(OH) D. The concentration of 25(OH) D was 77.2 +/- 22.7 nmol/l (mean +/- s.d.), ranging from 12 to 151 nmol/l. During the first 9 months, 97% received vitamin D supplementation (10 mu g/day) and 89% had sufficient levels of 25(OH) D (50-250 nmol/l). In multiple regression analysis, controlled for body mass index (BMI) and intake of infant formula, a longer period of exclusive breastfeeding (P = 0.026) and breastfeeding at 9 months (P = 0.001) were both associated with lower levels. Dietary vitamin D intake was 4.4 +/- 3.1 mu g/day and in multiple regression analysis, controlled for BMI, intake of infant formula and mean energy intake, it was positively associated with 25(OH) D (P = 0.001). There was a significant seasonal difference in 25(OH) D, with higher levels during summer-autumn compared with winter-spring (P = 0.021) after control for BMI. European Journal of Clinical Nutrition (2011) 65, 657-660; doi: 10.1038/ejcn.2010.285; published online 19 January 2011

Predictors of gastric emptying (GE) in patients with idiopathic Parkinson's disease (PD) of a solid and liquid meal are not well defined. For measurement of GE 80 patients with PD were randomly assigned to receive either a solid meal (250 kcal) containing 13C-octanoate (n = 40) or a liquid meal (315 kcal) with 13C-acetate (n = 40). All patient groups were off medication affecting motility and were matched for age, gender, body mass index, disease duration and severity, using Unified Parkinson's Disease Rating Scale (UPDRS). Gastric emptying was compared with a healthy control group (n = 40). Multiple regression analysis was used to determine predictors of gastric emptying. Exactly 88% and 38% of PD patients had delayed GE of solids and liquids respectively. Solid and liquid emptying was similar in women and men. There were no differences in GE in PD patients or = 65 years. Multiple regression analysis showed that motor handicaps such as rigour and action tremor are independent predictors of solid GE (r = 0.68, P gastroparesis in PD and solid emptying is more likely to be delayed. PMID:16629864

Background: Atrial fibrillation (AF) is the most common supraventricular arrhythmia, often occurring during hemodialysis (HD). Prolongation of the total filtered P-wave duration (PWD) and reduction of the root mean square voltages for the last 20ms of the P wave (RMS20) on a P-wave signal-averaged electrocardiogram (P-SAECG) are predictors of AF. We investigated whether HD induces alterations of P-SAECG, and determined the influential factors. Methods and Results: Thirty-three end-stage kidney disease patients (66.7±12.6 years, 23 males) undergoing maintenance HD were enrolled in this study. Digital ambulatory P-SAECG monitoring and laboratory examination of serum proteins and ions were carried out before, during, and after the HD sessions. Data were analyzed by multiple regression analysis. PWD was significantly prolonged, and RMS20 significantly reduced, during HD. These values recovered after completion of HD. Multiple regression analysis showed that prolongation of PWD significantly correlated with HD duration and the rate of removal of body fluid. On the other hand, RMS20 significantly correlated with HD duration and blood urea nitrogen variation. Conclusions: HD resulted in prolongation of PWD and reduction of RMS20, indicating the vulnerability of HD patients to AF. These P-SAECG changes correlated with HD duration and the rate of removal of the body fluid. These findings underline the importance of the control of dialysis variables in the prevention of atrial arrhythmias following HD. (Circ J 2012; 76: 612-617)   

This study examined the potential of plant cell wall fractions as predictors of indigestible neutral detergent fibre (INDF) in forages with respect to species within plant type, cut number and stage of maturity (harvest time) within primary growth, and for concentrates with respect to species within plant type, where INDF is defined as the portion of plant cell walls not digested after 288 h rumen incubation in Dacron bags with 12 ?m pore size. INDF is one of the more important parameters determining the net energy (NE) value of a diet in some recently developed ruminant feed evaluation systems. Effects of maturity and cut number on INDF in three legumes and 18 grasses were determined based on an experiment in which each forage was cut at three times of primary growth and once in each of the following three regrowths. These data were supplemented with data from earlier experiments to develop regression equations for INDF intended for use in practice based on a total of 321 samples. Plant type and species within plant type affected (P<0.001) all cell wall fractions. The INDF/lignin(sa) ratio varied substantially from the 2.4 factor used in the Cornell Net Carbohydrate and Protein System (CNCPS) to predict INDF, averaging 2.6 for legumes, grains and grain byproducts, 2.7 for grasses and 1.0 for oilseeds including byproducts. The INDF/IOM ratio varied less among plant species within plant type than among plant types. Multiple linear regression analysis revealed higher INDF prediction accuracy compared to simple regression equations. Estimation of INDF in DM was more accurate than estimation of INDF in ash-free neutral detergent fibre (aNDFom). Results indicate that universal regression equations among plant types are not possible, and, within plant types, prediction equations are not promising for grasses and legumes if only lignin(sa) is included. Comparing plant types, INDF prediction was best for grains and byproducts in simple linear regression and best for grass/clover in multiple linear regression equations. Variations within and among plant type were related to stage of maturity and cut number.

We demonstrate a Bayesian classification and regression tree (CART) approach to link multiple environmental stressors to biological responses and quantify uncertainty in model predictions. Such an approach can: (1) report prediction uncertainty, (2) be consistent with the amou...

SUMMARY. Recent introduction of the raised lung volume rapid thoraco-abdominal compression (RVRTC) technique for measuring forced expiratory maneuvers in infants provides the potential opportunity to assess respiratory mechanics simultaneously by using multiple linear regression (MLR) of the relaxed...

Spectral analysis of Nd(III) complexes with some amino acids viz.; glycine, l-alanine, l-phenylalanine and l-aspartic acid in the presence and absence of Ca^2^+ was carried out in some organic solvents; CH3OH, CH3CN, DMF and dioxane using comparative absorption spectra of 4f-4f transitions. The study was carried out by evaluating various energy interaction parameters like Slator-Condon (Fk), Lande factor (4f), nephelauxetic ratio (b), bonding parameter (b^1^/^2), percent-covalency (d) by applying partial and multiple regression analysis. The values of oscillator strength (Pobs) and Judd-Ofelt electric dipole intensity parameter Tl (l=2, 4, 6) for different 4f-4f transitions have been calculated. On analysis of the variation of the various energy interaction parameters as well as the change...

This paper proposes the load survey system to determine the load characteristics of various customer classes in an utility company. The questionnaires are adopted to find the power consumption of key electric appliances. 500 meters are installed to collect the actual power consumption of customers. The sampling theory has been applied to find the proper sample size so that the customer load characteristics will be derived with sufficient confidence level. The power consumption model of each customer class is derived by statistic analysis including multiple linear regression analysis and time series analysis. The temperature effect to the power consumption of each customer class is also investigated by differentiating the proposed power consumption model with respect to the ambient temperature. 8 refs, 13 figs

Aim To identify variables that predict in-hospital length of stay (LOS) after laparoscopic ventral hernia repair (LVHR). Methods Univariate analysis of patient and intraoperative variables was conducted on an operating room database of LVHRs performed from April 2001 to April 2009. Analysis was performed using either chi-square or linear trend analysis, as appropriate. A multivariate logistic regression model was created manually, to determine independent variables that predict LOS. p Value <0.05 was considered significant. Results A total of 221 patients, with mean age of 56?years (range 25?88?years) underwent LVHR, for a total of 121 incisional and 100 primary ventral hernias. Of patients, 40% had incarcerated hernias and 25% had complex hernias (defined as multiple points of weakness on...

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. Finn and Rock [1] link the academic and social integration of students to increased rates of retention. We utilize social network analysis to quantify interactions in Florida International University's Physics Learning Center (PLC) that support the development of academic and social integration,. The tools of social network analysis allow us to visualize and quantify student interactions, and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors which contribute to participation in the learning community. ...

This study investigates and analyzes the factors affecting customer relationship management (CRM) practices in Thai academic libraries. The research conceptual framework focuses on factors affecting CRM practices was developed using Combe (2004)'s study on assessing CRM strategies. Mixed methods, qualitative, and quantitative approaches were used as a research methodology. Data was collected by using the interview and survey techniques with the administrators, staff and customers of six selected academic libraries in Thailand. Analysis of the data was done by using Pearson's correlation coefficient, factor analysis, and multiple regression analysis. The results of the study show that factors that have statistically significant impact on CRM practices in Thai academic libraries at 0.05 leve...

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and...

Student evaluations of instruction have been conducted through questionnaires in a freshman class for fundamental physics at Kanazawa Institute of Technology. The data from the evaluations are analyzed by principal-component analysis, which reveals three components with an eigenvalue greater than 1. The first and second components are interpreted as the quality of the instruction and the basic scholastic ability of the students evaluating the instruction, respectively. We find that the first component can be controlled by the instructor, but the second one cannot be. Correlation between the data from the student evaluations and students' exam scores is analyzed by multiple-regression analysis, showing that the first two components in the data are significant predictors of the exam scores. The analysis shows that the instructor's effort of improving the quality of the teaching (first component) would be able to significantly raise the students' exam scores.   

Soil CO2 efflux was measured with a closed chamber system along a 180m transect on a bare soil field characterized by a gentle slope and a gradient in soil properties at 28days within a year. Principal component analysis (PCA) was used to extract the most important patterns (empirical orthogonal functions, EOFs) of the underlying spatiotemporal variability in CO2 efflux. These patterns were analyzed with respect to their geostatistical properties, their relation to soil parameters obtained from laboratory analysis, and the relation of their loading time series to temporal variability of soil temperature and moisture. A particular focus was set on the analysis of the overfitting behaviour of two statistical models describing the spatiotemporal efflux variability: i) a multiple regression mo...

Metal loss from in-bed heat exchangers has been a persistent problem in FBC systems. As part of its program in FBC technology development, the US Department of Energy/Morgantown Energy Technology Center (DOE/METC) supports a number of projects directed toward providing both theoretical and experimental results which will guide solutions to the metal loss problem. As a part of this effort, METC and The MITRE Corporation began a project in 1984 to collect and analyze metal loss data from various experimental, pilot-scale, and full-scale coal-fired FBC systems worldwide. The specific objective of this effort is to investigate the effects of unit design parameters and operating conditions on metal loss through the use of regression and analysis of variance techniques. To date, forty-one FBC systems worldwide have been identified and most of the data sets which characterize the metal loss occurrences in those units have been developed. The results of MITRE's effort to date were reported earlier (Interim Report No. DOE/MC/21351-1930, August 1985). This report describes the statistical procedures that MITRE will follow to analyze FBC metal loss data. The data will be analyzed using several regression techniques to find variables related to metal loss. Correlation and single variable regressions will be used to indicate important relationships. The joint relationships between the explanatory variables and metal loss will be examined by building multiple regression models. In order to prevent erroneous conclusions, diagnostics will be performed based on partial residual plots, residual analysis, and multicollinearity statistics. 7 refs.

Although lung age calculated backward from regression formulas constructed for FEV(1) estimation is widely used, it possesses a couple of faults. We developed novel equations predicting lung age from varied spirometric parameters (spirometry-derived lung age (SDL-age)). Applying multiple regression analysis, equations predicting SDL-age were invented using data from 8015 never-smokers with normal spirometry (group I). Validation was made based on data from 6398 never-smokers with normal spirometry (group II). Equations were further applied for 446 subjects with airflow limitation. FEV(1), FEV(1)/FVC, FEF(50), and PEF were selected as explanatory variables for reference value of SDL-age. Normal limits of difference between SDL-age and chronological-age were ± 13.4 years in the male and ± 15.0 years in the female. Established equations predicted SDL-age of group II. SDL-age was older than chronological-age only in subjects with severe airflow limitation. Novel regression equations allowing prediction of reference value of SDL-age and normal limits of difference between SDL-age and chronological-age were elaborated in both genders. PMID:22750572

Purpose: Several previous reports have described factors that affect masticatory function. However, there are no known predictors that affect the food mixing ability of the masticatory function, and it has been impossible to predict masticatory function in mandibulectomy and/or glossectomy patients. The purpose of the present study was to develop a numerical formula that could predict the food mixing ability of the masticatory function among mandibulectomy and/or glossectomy patients. The null hypothesis of the study was that five predictors, namely mandibulectomy, mandibular continuity, number of residual mandibular teeth, occlusal units and tongue movement score, were unable to account for the mixing ability index (MAI) in mandibulectomy and/or glossectomy patients. Methods: The subjects were 20 patients who had undergone mandibulectomy and/or glossectomy. The above-described five predictors were assessed. Tongue movement was evaluated with a tongue movement test and the MAI was evaluated with a mixing ability test. Multiple regression analysis was used to examine whether the five predictors affected the MAI after prosthetic treatment. Results: A regression equation was determined for the five predictors (R² = 0.83; adjusted R² = 0.77; p < 0.001). Conclusion: The obtained regression equation could successfully account for the MAI in mandibulectomy and/or glossectomy patients.   

The Space Environment Services Center (SESC) of the National Oceanic and Atmospheric Administration provides probability forecasts of regional solar flare disturbances. This report describes a statistical method useful to obtain 24 hour solar flare forecasts which, historically, have been subjectively formulated. In Section 1 of this report flare classifications of the SESC and the particular probability forecasts to be considered are defined. In Section 2 we describe the solar flare data base and outline general principles for effective data management. Three statistical techniques for solar flare probability forecasting are discussed in Section 3, viz, discriminant analysis, logistic regression, and multiple linearregression. We also review two scoring measures and suggest the logistic regression approach for obtaining 24 hour forecasts. In Section 4 a heuristic procedure is used to select nine basic predictors from the many available explanatory variables. Using these nine variables logistic regression is demonstrated by example in Section 5. We conclude in Section 6 with band broad suggestions regarding continued development of objective methods for solar flare probability forecasting.

The purpose of this study was to investigate the relationship between 30-m sprint running time and muscle cross-sectional areas of the psoas major and lower limb muscles. In sixteen male college short and middle distance runners, the muscle anatomical cross-sectional areas (CSAs) of the psoas major, the quadriceps femoris, the hip adductors (ADD), the hamstrings, the triceps surae, and the tibialis anterior and extensor digtrum longus complex (DF) were measured using a magnetic resonance imaging system. In addition, the relative values of CSA to the two-third power of body weight (CSA-to-BW2/3) were calculated. A stepwise multiple regression analysis produced a prediction equation (R2 = 0.605) of 30-m sprint running time with explanatory variables of ADD CSA-to-BW2/3 and DF CSA. The ADD CSA-to-BW2/3 had a negative partial regression coefficient (r = ?0.768, p < 0.01) and the DF CSA had a positive partial regression coefficient (r = 0.526, p < 0.05). From the present results, it is concluded that to have greater hip adductor muscles relative to the body size and smaller dorsiflexors is advantageous for achieving higher performance in 30-m sprint running.   

A two factor, three level (3(2)) face centred, central composite design (CCD) was applied to investigate the main and interaction effects of tablet diameter and compression force (CF) on hardness, disintegration time (DT) and porosity of mannitol based orodispersible tablets (ODTs). Tablet diameters of 10, 13 and 15 mm, and CF of 10, 15 and 20 kN were studied. Results of multiple linear regression analysis show that both the tablet diameter and CF influence tablet characteristics. A negative value of regression coefficient for tablet diameter showed an inverse relationship with hardness and DT. A positive value of regression coefficient for CF indicated an increase in hardness and DT with increasing CF as a result of the decrease in tablet porosity. Interestingly, at the larger tablet diameter of 15 mm, while hardness increased and porosity decreased with an increase in CF, the DT was resistant to change. The optimised combination was a tablet of 15 mm diameter compressed at 15 kN showing a rapid DT of 37.7s and high hardness of 71.4N. Using these parameters, ODTs containing ibuprofen showed no significant change in DT (ANOVA; p>0.05) irrespective of the hydrophobicity of the ibuprofen. PMID:22465631

Objective: To estimate the prevalence of abnormal eating attitudes among Greek adolescents and identify possible risk factors associated with these attitudes. Design: Cross-sectional, school-based study. Setting: Six randomly selected schools in Patras, southern Greece. Participants: The study population consisted of 540 Greek students aged 13-18 years, and the response rate was 97%. Main Outcome Measure: The dependent variable was scores on the Eating Attitudes Test-26, with scores greater than or equal to 20 indicating abnormal eating attitudes. Analysis: Bivariate analysis included independent Student "t" test, chi-square test, and Fisher's exact test. Multivariate logistic regression analysis was applied for the identification of the predictive factors, which were associated independently with abnormal eating attitudes. A 2-sided "P" value of less than 0.05 was considered statistically significant. Results: The prevalence of abnormal eating attitudes was 16.7%. Multivariate logistic regression analysis demonstrated that females, urban residents, and those with a body mass index outside normal range, a perception of being overweight, body dissatisfaction, and a family member on a diet were independently related to abnormal eating attitudes. Conclusions and Implications: The results indicate that a proportion of Greek adolescents report abnormal eating attitudes and suggest that multiple factors contribute to the development of these attitudes. These findings are useful for further research into this topic and would be valuable in designing preventive interventions. (Contains 3 tables.)

School teaching is regarded as a stressful occupation. The present study aimed to compare the likelihood of having minor psychiatric disorders (MPD) among school teachers with that among civil servants, and to investigate what factors were specifically associated with MPD in teachers. We conducted a questionnaire-based survey of 403 teachers employed at state schools and 611 civil servants as a comparison group in a medium-sized city in Japan. The response rate was 59.6% for teachers and 62.0% for civil servants. Mental health was assessed using the 28-item General Health Questionnaire (GHQ-28), according to which those with a score of six or higher were considered to have MPD. Logistic regression analysis was used to identify the factors associated with MPD. Although the proportion of subjects with MPD among teachers was greater than that among civil servants, the difference in the proportion was not statistically significant in the multiple logistic regression analysis adjusted for potential confounders. In a separate analysis of the teachers, reduced job satisfaction and shorter time spent of leisure were significantly associated with an increased likelihood of having MPD. In the group of civil servants, longer working hours, reduced life satisfaction, a history of sick leave, and physical illness were associated with an increased likelihood of having MPD. When this analysis was conducted separately for male and female teachers, job dissatisfaction alone was associated with MPD only in female teachers. Poor mental health of Japanese school teachers, female teachers in particular, was found to be associated with job dissatisfaction.