Here are datasets for many of the main examples in the text, and for some of the exercises. The separate directory data files. contains some individual files (Crabs for Table 4.3, Teratology for Table 4.7, Credit for Exercise 5.22, Endometrial for Table 7.2, Infection for Table 6.9, SoreThroat for Table 6.15, Substance use for Table 9.3, MBTI for Table 9.17, Substance2 for Table 10.1, Insomnia for Table 12.3, Abortion for Table 13.3). The horseshoe crab data are used to illustrate logistic regression (modeling whether a female crab has at least one satellite) and models for count data (e.g., negative binomial modeling of the number of satellites). For the count data, better models allow zero-inflation. See crab zero-inflation for an excerpt about this, taken from my new book "Foundations of Linear and Generalized Linear Models" (published by Wiley, 2015).
Here is a pdf file of short solutions for some of the exercises at the ends of the chapters. These are mainly the solutions that were provided for some of the odd-numbered exercises from the 2nd edition of the book. Please report errors to AA@STAT.UFL.EDU, so they can be corrected in future revisions of this site. The author regrets that he cannot provide solutions of exercises not in this file.
Here is a pdf file containing Extra exercises, mainly taken from the first two editions of the book.
Here is a pdf file showing corrections of typos/errors in the third edition.
The final chapter gives a historical tour of CDA.
Here is a seminar (in mp4 format) on the
Here is the video for a half-day course I taught in 2020 for the Harvard School of Public Health on Modeling Ordinal Categorical Data.
David Hitchcock (Statistics Dept., Univ. of South Carolina) and I wrote a survey paper about Bayesian inference for categorical data analysis that appeared in Statistical Methods and Applications, the Journal of the Italian Statistical Society, in 2005 (volume 14, pages 297-330). It was partly a by-product of a very nice summer that I spent in Florence, Italy. A somewhat longer version of this paper is a UF technical report in the Statistics Department at UF.
Professor Harry Khamis has kindly provided powerpoint slides from a course in which he used this textbook. If you would like to use these or adapt them for your own purposes, please request his permission at harry.khamis@wright.edu.