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Modeling Binary Correlated Responses de Jeffrey R. Wilson

Modeling Binary Correlated Responses: ICSA Book Series in Statistics

Autor Jeffrey R. Wilson, Kent A. Lorenz, Lori P. Selby
en Limba Engleză Hardback – 15 sep 2024
 
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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Specificații

ISBN-13: 9783031624261
ISBN-10: 3031624262
Pagini: 308
Ilustrații: Approx. 300 p.
Dimensiuni: 160 x 241 x 23 mm
Greutate: 0.58 kg
Ediția:Second Edition 2024
Editura: Springer
Colecția ICSA Book Series in Statistics
Seria ICSA Book Series in Statistics

Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

Jeffrey R. Wilson is a Fellow of the American Statistical Association and Professor of Statistics and Biostatistics at Arizona State University, where he serves as Associate Dean for Research and Associate Dean of Inclusive Excellence. An award-winning educator, researcher, consultant, and author, he has published more than 100 scholarly articles and several books spanning statistics, data science, business, and health research. His work has been supported by the National Institutes of Health (NIH), National Science Foundation (NSF), and U.S. Department of Agriculture (USDA). Throughout his career, Dr. Wilson has been passionate about helping people think more critically, ask better questions, and make informed decisions based on evidence rather than assumption. In Everyone Says..., he brings the principles of statistical thinking to everyday language, showing readers how a few simple questions can lead to clearer thinking and better conversations. Chiu-I (Aki) Chen is an educator, translator, interpreter, and international communications professional with more than 30 years of experience working across Taiwan, Japan, Hong Kong, Singapore, China, and the United States. Fluent in Mandarin, Japanese, and English, she has built a career helping people navigate cultural, linguistic, and organizational differences. A graduate of the University of Chicago Booth School of Business and Arizona State University, Aki has worked in education, hospitality, corporate strategy, marketing, and international relations. Her lifelong fascination with how people interact, interpret meaning, and form shared assumptions inspired her contribution to Everyone Says..., where she brings a unique cross-cultural perspective to the ways language shapes understanding and decision-making.

Textul de pe ultima copertă

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.

Caracteristici

Includes Timely compilation of methods for correlated binary data analysis Progression of chapters from an introduction to the analysis of binary data, to the realm of correlated binary data Showcases both traditional and new methods for application to health-related research