Models for Discrete Data

Discrete or count data arise in experiments where the outcome variables are the numbers of individual classified into unique, non-overlapping categories. This revised edition describes the statistical models used in the analysis and summary of such data, and provides a sound introduction to
the subject for graduate students and practitioners needing a review of the methodology. With many numerical examples throughout, it includes topics not covered in depth elsewhere, such as the negative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free
models. A detailed treatment of sample size estimation and power are given in terms of both exact inference and asymptotic, non-central chi-squared methods. A new section covering Poisson regression has also been included. An important feature of this book, missing elsewhere, is the integration
of the software into the text.
Many more exercises are provided (including 84% more applied exercises) than in the previous edition, helping consolidate the reader's understanding of all subjects covered, and making the book highly suitable for use in a classroom setting. Several new datasets, mostly from the health and medical
sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.

1 - Introduction
2 - Sampling distributions
3 - Logistic regression
4 - Log-linear models
5 - Coordinate-free models
6 - Additional topics

Daniel Zelterman is Professor of Biostatistics in the Yale School of Public Health and Director of the Biostatistics Core of the Yale Comprehensive Cancer Center. He previously held academic positions at the University of Minnesota and at the State University of New York at Albany. He is an elected Fellow of the American Statistical Association. He is an Associate Editor of Biometrics and several other statistical journals.