Categorical Data Analysis / Wiley Series in Probability and Statistics (PDF)

Praise for the Second Edition

"A must-have book for anyone expecting to do research and/or
applications in categorical data analysis."

--Statistics in Medicine

"It is a total delight reading this book."

--Pharmaceutical Research

"If you do any analysis of categorical data, this is an
essential desktop reference."

--Technometrics

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.