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Determining Factors Causing Child Labor By Using Multiple Linear And Logistic Regression Analysis

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Logistic Regression Using SAS

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul;

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Logistic Regression Using SAS

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul;

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Data Analysis Using Stata

on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. The book begins with an;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Interaction Effects in Logistic Regression

This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the;

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Data Analysis Using Regression & Multi

researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety;

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An Introduction to Generalized Linear Models

multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian;

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Regression and Analysis of Variance

This book provides an in-depth overview of simple and multiple linear regression analysis, one-way and multi-way analysis of variance, and;

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Statistical Data Mining Using SAS Applications

(FACTOR2) Multiple linear regressions (REGDIAG2) Logistic regression (LOGIST2) CHAID analysis (CHAID2) Requiring no experience with SAS;

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Data Analysis Using Regression and Multilevel/Hierarchical Models

perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the;

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Data Analysis

analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression;

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Data Analysis

analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression;

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Logistic Regression Inside and Out

, probability, and statistics. Here I will take you on a journey into the art and science of predictive modeling using logistic regression, inside-and;

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Modeling in Mathematics

with fuzzy entropy under linear restrictions, a hybrid approach of stepwise regression, logistic regression, support vector machine, and;

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Introductory Statistics with R

introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.;

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An Introduction to Generalized Linear Models

this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival;

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An Introduction to Generalized Linear Models

this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival;

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Applied Regression Modeling

linear regression using matrices. The methods described in the text are clearly illustrated with multi-format datasets available on the;

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Logistic Regression

regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression;

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Regression Analysis with R

Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven;

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Handbook for Applied Modeling

explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling;

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SAS Statistics by Example

, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and;

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SAS Statistics by Example

, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and;

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