Categorical Data Analysis
A valuable new edition of a standard reference
"A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis."
–Statistics in Medicine on Categorical Data Analysis, First Edition
The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.
Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second 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 regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:
- Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
- Stronger emphasis on logistic regression modeling of binary and multicategory data
- An appendix showing the use of SAS for conducting nearly all analyses in the book
- Prescriptions for how ordinal variables should be treated differently than nominal variables
- Discussion of exact small-sample procedures
- More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises
- An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
736 pages; ISBN 9780471458760
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Title: Categorical Data Analysis
Author: Alan Agresti
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