Agresti A. Categorical Data Analysis

3rd ed. — John Wiley & Sons., 2012. — 742 p. — (Wiley Series in Probability and Statistics 792). — ISBN: 0470463635, 9780470463635.The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis.
Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features:
An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models
Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis
New sections introducing the Bayesian approach for methods in that chapter
More than 100 analyses of data sets and over 600 exercises
Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources
A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions
Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.Introduction: Distributions and Inference for Categorical Data
Describing Contingency Tables
Inference for Two-Way Contingency Tables
Introduction to Generalized Linear Models
Logistic Regression
Building, Checking, and Applying Logistic Regression Models
Alternative Modeling of Binary Response Data
Models for Multinomial Responses
Loglinear Models for Contingency Tables
Building and Intending Loglincar Models
Models for Matched Pairs
Clustered Categorical Data: Marginal and Transitional Models
Clustered Categorical Data: Random Effects Models
Other Mixture Models for Discrete Data
Non-Model-Based Classification and Clustering
Large- and Small-Sampie Theory for Multinomial Models
Historical Tour of Categorical Data Analysis
Appendix A Statistical Software for Categorical Data Analysis
Appendix B Chi-Squared Distribution Values