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Modeling binary correlated responsesusing SAS, SPSS, R and STATA /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling binary correlated responsesby Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby.
Reminder of title:	using SAS, SPSS, R and STATA /
Author:	Wilson, Jeffrey R.
other author:	Lorenz, Kent A.
Published:	Cham :Springer International Publishing :2024.
Description:	xxv, 282 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Statistics.
Online resource:	https://doi.org/10.1007/978-3-031-62427-8
ISBN:	9783031624278$q(electronic bk.)

Modeling binary correlated responsesusing SAS, SPSS, R and STATA /
Wilson, Jeffrey R.

Modeling binary correlated responsesusing SAS, SPSS, R and STATA /[electronic resource] :by Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby. - Second edition. - Cham :Springer International Publishing :2024. - xxv, 282 p. :ill. (chiefly color), digital ;24 cm. - ICSA book series in statistics,2199-0999. - ICSA book series in statistics..

Introduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model.

This book is an updated edition of Modeling Binary Correlated Responses Using SAS, SPSS and R, and now it includes the use of STATA. It uses these Statistical tools to analyze correlated binary data, accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages, as well as showcase both traditional and new methods for application to health-related research. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Short tutorials are in the appendix, for readers interested in learning more about the languages. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, SPSS and STATA, allows for easy implementation by readers. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.

ISBN: 9783031624278$q(electronic bk.)

Standard No.: 10.1007/978-3-031-62427-8doiSubjects--Topical Terms:

182057
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Modeling binary correlated responsesusing SAS, SPSS, R and STATA /
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245 1 0 $a Modeling binary correlated responses $h [electronic resource] : $b using SAS, SPSS, R and STATA / $c by Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby.
250 $a Second edition.
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300 $a xxv, 282 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a ICSA book series in statistics, $x 2199-0999
505 0 $a Introduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model.
520 $a This book is an updated edition of Modeling Binary Correlated Responses Using SAS, SPSS and R, and now it includes the use of STATA. It uses these Statistical tools to analyze correlated binary data, accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages, as well as showcase both traditional and new methods for application to health-related research. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Short tutorials are in the appendix, for readers interested in learning more about the languages. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, SPSS and STATA, allows for easy implementation by readers. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
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