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Modeling binary correlated responses...

Wilson, Jeffrey R.

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Modeling binary correlated responses = using SAS, SPSS, R and STATA /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling binary correlated responses/ by Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby.
其他題名:	using SAS, SPSS, R and STATA /
作者:	Wilson, Jeffrey R.
其他作者:	Lorenz, Kent A.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xxv, 282 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-62427-8
ISBN:	9783031624278

Modeling binary correlated responses = using 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

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

517247
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

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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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