Categorical Data Analysis,

Categorical Data Analysis describes the most important methods, offering a unified presentation of modeling using generalized linear models and emphasizing loglinear and logit modeling techniques. Contributions of noted statisticians (Pearson, Yule, Fisher, Neyman, Cochran), whose pioneering efforts set the pace for the evolution of modern methods, are examined as well.

Special features of the book include:

• Coverage of methods for repeated measurement data, which have become increasingly important in biomedical applications
• Prescriptions for how ordinal variables should be treated differently than nominal variables
• Derivations of basic asymptotic and fixed-sample-size inferential methods
• Discussion of exact small sample procedures
• More than 40 examples of analyses of "real" data sets, including: aspirin use and heart disease; job satisfaction and income; seat belt use and injuries in auto accidents; and predicting outcomes of baseball games
• More than 400 exercises to facilitate interpretation and application of methods

Categorical Data Analysis also contains an appendix that describes the use of computer software currently available for performing the analyses presented in the book. A comprehensive bibliography and notes and the end of each chapter round out the work, making it a complete, invaluable reference for statisticians, biostatisticians and professional researchers.

About the author ALAN AGRESTI is a professor in the Department of Statistics at the University of Florida. Since receiving his PhD at the University of Wisconsin, he has published in many professional journals, including Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrics, Statistics in Medicine, Psychometrika, and Sociological Methodology. He is also the author of the texts Statistical Methods for the Social Sciences and Analysis of Ordinal Categorical Data.