BSTA 613 Categorical Data Analysis

Credits: 4
This course is designed for students from PhD programs on campus. This course covers topics in categorical data analysis such as statistics for contingency tables, statistics for matched samples, and methods to assess confounding and interaction via stratified tables. We will explore logistic regression in detail, and relate results back to those found with stratified analyses. Similar to linear regression, topics for logistic regression will include: parameter interpretation, statistical adjustment, variable selection techniques and model fit assessment. We will discuss Poisson regression, the model for count data, which is another type of commonly encountered categorical data. All homework assignments have at least a portion to be completed using statistical software. Extra homework problems and reading materials will be assigned.

Graded: A-F
May be taken only once for credit
Also offered as: BSTA 513