Alan Agresti www.stat.ufl.edu/~aa.
Date:
Event location: Seminar Room 1st Floor, Department of Statistical Science, Alma Mater Studiorum - University of Bologna
Type: Cycle 32 - Mandatory Courses
4-day course, with about 3 hours lecturing each day, following the book "Categorical Data Analysis," 3rd ed., by Alan Agresti
This short course surveys the most common methods for analyzing categorical data. The first part of the course focuses on contingency table analysis, logistic regression modeling of binary data, and loglinear models. The second part introduces logistic models for multi-category ordinal and nominal responses and for clustered data using generalized estimating equations (GEE) and random effects. The presentation emphasizes interpretation rather than technical details, with examples including social surveys and randomized clinical trials. Examples show the use of R, with SAS and Stata also for some examples.
Outline timetable (tentative)
Day 1
Contingency tables, odds ratios, chi-squared tests, Fisher's exact test, logistic regression as a generalized linear model, interpretations, inference using logistic regression
Day 2
Logistic regression goodness of fit, model building, detecting infinite logistic estimates (with remedies), loglinear models for contingency tables
Day 3
Loglinear models for count data, baseline-category logit model for nominal responses, cumulative logit model for ordinal responses
Day 4
Analyzing correlated categorical data, GEE for marginal models, random effects models, multilevel models