This book, "Logistic Regression: A Self-Learning Text" (Second Edition), is authored by David G. Kleinbaum and Mitchel Klein, with contributions from Erica Rihl Pryor. It is part of the "Statistics for Biology and Health" series, edited by K. Dietz, M. Gail, K. Krickeberg, A. Tsiatis, and J. Samet. The book is published by Springer and is available in multiple cities including New York, Berlin, Heidelberg, Hong Kong, London, Milan, Paris, and Tokyo. The second edition expands upon the first edition, originally published in 1994, by adding five new chapters and a new appendix. The new chapters cover polytomous logistic regression, ordinal logistic regression, logistic regression for correlated data using generalized estimating equations (GEE), GEE examples, and other approaches for analyzing correlated data. The appendix provides descriptions and examples of computer programs for performing logistic regression procedures using SAS, SPSS, and STATA. The book is designed for self-study and can also be used as a supplement in a classroom setting. It is structured in a "lecture-book" format, with each chapter containing objectives, an outline, key formulae, practice exercises, and a test. The authors recommend prior knowledge in quantitative methods in epidemiology and applied multiple regression. The book is dedicated to John Boring, Chair of the Department of Epidemiology at Emory University, for his support and guidance. The content covers the introduction to logistic regression, important special cases of the logistic model, computing odds ratios, maximum likelihood techniques, statistical inferences, modeling strategy guidelines, analysis of matched data, polytomous and ordinal logistic regression, and methods for analyzing correlated data. The book also includes datasets and test answers, along with a bibliography and index.This book, "Logistic Regression: A Self-Learning Text" (Second Edition), is authored by David G. Kleinbaum and Mitchel Klein, with contributions from Erica Rihl Pryor. It is part of the "Statistics for Biology and Health" series, edited by K. Dietz, M. Gail, K. Krickeberg, A. Tsiatis, and J. Samet. The book is published by Springer and is available in multiple cities including New York, Berlin, Heidelberg, Hong Kong, London, Milan, Paris, and Tokyo. The second edition expands upon the first edition, originally published in 1994, by adding five new chapters and a new appendix. The new chapters cover polytomous logistic regression, ordinal logistic regression, logistic regression for correlated data using generalized estimating equations (GEE), GEE examples, and other approaches for analyzing correlated data. The appendix provides descriptions and examples of computer programs for performing logistic regression procedures using SAS, SPSS, and STATA. The book is designed for self-study and can also be used as a supplement in a classroom setting. It is structured in a "lecture-book" format, with each chapter containing objectives, an outline, key formulae, practice exercises, and a test. The authors recommend prior knowledge in quantitative methods in epidemiology and applied multiple regression. The book is dedicated to John Boring, Chair of the Department of Epidemiology at Emory University, for his support and guidance. The content covers the introduction to logistic regression, important special cases of the logistic model, computing odds ratios, maximum likelihood techniques, statistical inferences, modeling strategy guidelines, analysis of matched data, polytomous and ordinal logistic regression, and methods for analyzing correlated data. The book also includes datasets and test answers, along with a bibliography and index.