Agresti Categorical Data Analysis Solutions

Working with Agresti Categorical Data Analysis Solutions in Practice

I've spent years running logistic regression models, working with contingency tables, and dealing with sparse categorical data where standard asymptotics completely break down. The textbook by Alan Agresti is still the reference most people point to, but the actual solutions manual and accompanying code are where things get useful. Here's how I approach it. The solutions manual walks through worked examples for most of the exercises in the main text. What you need to understand first is that the book covers a wide range of methods — log-linear models, generalized linear models for binary and ordinal responses, multinomial logistic regression, random effects for clustered categorical data, and missing data approaches. The solutions aren't just answers; they show the model specification, the convergence diagnostics, and often the code in SAS or R. I used to spend hours re-deriving results on paper before realizing I could just pull up the solution and trace my steps against it. That saved me maybe three hours a week over a semester. More importantly, it exposed where I was making assumptions about independent observations when my data had clustering.

One specific problem I ran into was with a 7x7 contingency table from an epidemiology study where over 40% of the cells had expected counts below five. The standard Pearson chi-square test was completely unreliable there. The Agresti solutions walk through exact conditional inference for these kinds of tables, but what they don't emphasize enough is that Monte Carlo simulation gives you essentially the same answer in about 30 seconds on any modern machine. I ended up writing a quick R script that resampled the margins 10,000 times and compared the observed statistic to the simulated null distribution. It took me from a two-hour manual calculation to about ten minutes, and the result was more trustworthy. The trick most people miss with these solutions is that the exercise numbers in different editions don't always line up. The third edition has a different numbering scheme than the second, and the solutions manual sometimes references appendix tables that were reorganized. If you're working through problems and the reference doesn't match, check the edition date on your book — I've seen people waste an afternoon looking for a table that was moved to a different chapter in a later printing. When you're actually using the methods in the solutions for real research, the common pitfall is treating the conditional logistic regression results for matched case-control studies as if they apply to unmatched designs. The partial likelihood estimation is different. I once submitted a paper where I'd incorrectly specified a conditional model on unmatched data and the reviewer caught it in ten minutes. The fix was straightforward — re-specify using the unconditional partial likelihood — but it cost me three weeks of revisions.

Another thing that isn't obvious from the solutions: the book assumes a decent working knowledge of matrix algebra and maximum likelihood theory. If you're struggling with the derivations in Chapter 3, you're not alone. I found that going through a few chapters of a computational statistics text on the side helped significantly. It wasn't about learning new material — it was about seeing the same likelihood derivations from a different angle. The solutions do have gaps. Some of the more advanced exercises on frailty models and latent class analysis only have brief sketches rather than full worked solutions. For those, I ended up consulting the original papers Agresti references, which are usually available through university libraries. The 2012 third edition updated a lot of the computational approaches, but a few of the older exercises still reference SAS PROC CATMOD syntax that has since been deprecated in favor of PROC GENMOD. If you're following the solutions exactly, make sure you're using current software versions. For people working with ordinal data specifically, the proportional odds model solutions are solid, but the assumption checking section could be more thorough. I ran into a dataset where the proportional odds assumption was violated for one of the predictor variables, and the solutions manual doesn't give a detailed walkthrough of what to do next. I ended up fitting a partial proportional odds model instead, which Agresti touches on later in the book but doesn't fully work through in the exercises.

If you're looking to download or access the solutions, they're typically available through the publisher's companion website or academic institutions. Make sure you're matching the edition of your textbook to the solution set you use. Using mismatched editions will cost you time and lead to frustration when the problem numbers don't correspond.