STAT 534 Applied Categorical Data Analysis
Introduction to analysis and interpretation of categorical data using analysis of variance or regression analogs. Topics may include contingency tables, generalized linear models, logistic regression, log-linear models. Advanced topics may include multicategory logit models, models for matching pairs, and generalized linear mixed models. Computations require the use of statistical software such as R or SAS.
Credits
4
Cross Listed Courses
Linked with
STAT 402. No credit given for students with
STAT 402
Prerequisite
STAT 201 or
STAT 216, or equivalent, or permission of department chair.