This is the second edition of "Logistic Regression: A Self-Learning Text," originally published in 1994. The book is designed for self-study and includes a "lecture-book" format with objectives, outlines, key formulae, practice exercises, and tests. Each chapter presents the topic with illustrations and formulae on the left and text on the right, allowing readers to follow the material visually. The second edition has expanded the first edition 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 GEE, GEE examples, and other approaches for analyzing correlated data. The appendix provides descriptions and examples of computer programs for logistic regression using SAS, SPSS, and STATA. Chapter 8 has been expanded to include discussions on assessing interaction, pooling exchangeable matched sets, and analyzing matched follow-up data. The book is suitable for self-study or as a supplement to a course on quantitative methods in epidemiology or applied multiple regression. It assumes familiarity with natural logarithms, exponentials, and mathematical notation. The authors thank Erica Pryor for her contributions to the second edition and dedicate the book to John Boring. The book includes a detailed table of contents with chapters covering logistic regression basics, special cases, odds ratios, maximum likelihood techniques, statistical inference, modeling strategies, interaction and confounding, matched data analysis, and advanced topics. Each chapter includes practice exercises and tests with answers. The book also includes datasets, software examples, test answers, bibliography, and index.This is the second edition of "Logistic Regression: A Self-Learning Text," originally published in 1994. The book is designed for self-study and includes a "lecture-book" format with objectives, outlines, key formulae, practice exercises, and tests. Each chapter presents the topic with illustrations and formulae on the left and text on the right, allowing readers to follow the material visually. The second edition has expanded the first edition 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 GEE, GEE examples, and other approaches for analyzing correlated data. The appendix provides descriptions and examples of computer programs for logistic regression using SAS, SPSS, and STATA. Chapter 8 has been expanded to include discussions on assessing interaction, pooling exchangeable matched sets, and analyzing matched follow-up data. The book is suitable for self-study or as a supplement to a course on quantitative methods in epidemiology or applied multiple regression. It assumes familiarity with natural logarithms, exponentials, and mathematical notation. The authors thank Erica Pryor for her contributions to the second edition and dedicate the book to John Boring. The book includes a detailed table of contents with chapters covering logistic regression basics, special cases, odds ratios, maximum likelihood techniques, statistical inference, modeling strategies, interaction and confounding, matched data analysis, and advanced topics. Each chapter includes practice exercises and tests with answers. The book also includes datasets, software examples, test answers, bibliography, and index.