Categorical Data Analysis
#
Website for CATEGORICAL DATA ANALYSIS, 3rd edition

For the third edition of Categorical Data Analysis by Alan Agresti
(Wiley, 2013), this site contains (1) information on the use of other
software (SAS, R and S-plus, Stata, SPSS, and others), (2) data sets
for examples and many exercises (for many of which, only excerpts were
shown in the text itself), (3) short answers for some of the
exercises, (4) extra exercises that did not fit in the text itself,
and (5) corrections of errors in early printings of the book. Also,
there's
(6) a seminar on the history of CDA, and (7) a survey paper on
Bayesian inference for CDA.
Here is a link to the webpage for
the *Website for
2nd edition (2002) of Categorical Data Analysis*, which is no
longer being updated.

## 1. Software Appendix

In this appendix we provide details about how to use R, SAS, Stata,
and SPSS statistical software for categorical data analysis, with
examples in many cases showing how to perform analyses discussed in
the text. This supplements the brief description found in Appendix A
of the "Categorical Data Analysis" text, 3rd edition, Wiley (2013).
For each package, the material is organized by chapter of presentation
and refers to datasets analyzed in those chapters. The full data sets
are available at
**datasets**.
### SAS

Go to ** SAS ** for a pdf file
containing details about the use of SAS for CDA, with illustrations
for data sets in the CDA text.
### R and S-Plus

Go to ** R ** for a pdf file containing
details about the use of R for CDA, and illustrations for data sets in
the CDA text. Here is a **
manual** that Dr. Laura Thompson prepared on
the use of R and S-Plus to conduct all the analyses in the 2nd edition
of the CDA text.
### Stata

Go to ** Stata ** for discussion
of using Stata for CDA.
### SPSS

Go to ** SPSS ** for discussion
of using SPSS for CDA.
### Other software

Go to ** other software **
for discussion of other software useful for CDA, such as StatXact and
LogXact.
## 2. Primary datasets:

Here are
**datasets** for many of the main examples
in the text, and for some of the exercises. 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
upcoming book "Foundations of Linear and Generalized Linear Models"
(to be published by Wiley, December 2014).

## 3. Selected short solutions to exercises:

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.

## 4. Additional exercises:

Here is a pdf file containing ** Extra exercises**, mainly
taken from the first two editions of the book.

## 5. Corrections:

Here is a pdf file
showing ** corrections ** of
typos/errors in the third edition.

## 6. History of CDA:

The final chapter gives a historical
tour of CDA. Here is
a **
history of CDA seminar ** that I presented in September, 2009,
to the Boston chapter of the American Statistical Association, with
some discussion at the end of the talk on advances having a Boston
connection. To watch this, enter your email address and click on
Playback. (Scroll below toward the right and you'll see a highly
discretized copy of my presentation.)

## 7. Bayes:

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.

*Copyright © 2013, Alan Agresti, Department of Statistics,
University of Florida.*