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, models for matching pairs, and modeling correlated and clustered responses; use of computer software such as SAS and R.

Prerequisites: STAT 201 or STAT 216, or equivalent, or permission of department chair.

3 Credits

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