Applied Categorical Data Analysis
General
Prefix
STAT
Course Number
536
Course Level
Graduate
Department/Unit(s)
College/School
College of Science and Engineering
Description
Introduction to the analysis of discrete data; log-linear models for two-way and multi-way tables; linear logistics regression models; association models and models of symmetry; applications, use of statistical software.
Prerequisites
Credits
Min
3
Max
3
Repeatable
No
Goals and Diversity
MN Goal Course
No
Cultural Diversity
No
Learning Outcomes
Outcome
Use contingency tables to test hypotheses concerning two or more categorical variables.
Outcome
Use both Pearson chi-square and likelihood ratio chi-square test statistics.
Outcome
Differentiate between the application of binomial, Poisson, and hypergeometric probability models.
Outcome
Use exact tests for inference with small sample sizes.
Outcome
Apply generalized linear models to appropriate data, through the use of statistical software.
Outcome
Analyze the connections between the learning outcomes of this course and the larger picture of undergraduate statistics and the student's own experience, or explore a subset of course topics in greater depth than what is required for the typical undergraduate student, or both.