## Y637 : Categorical Data Analysis

### Class Description

This course will introduce students to the theory and practice of categorical data analysis including measures of association, logistic regression, Poisson regression, loglinear modeling and other selected topics. Students will learn to develop, implement, interpret, and report research involving categorical analyses. Further, students are expected to gain proficiency in SAS as it pertains to categorical data analysis.

### Class Information

**Website: **Oncourse

**Capacity: ** 20

### Other Details

**Sequence: ** No

**Prerequisites: ** Two semesters of graduate-level statistics

**Algebra Required?: ** For notation, some proofs

**Calculus Required?: ** used for some concepts and derivations

**Day(s) per week offered: ** 2 days per week, usually Tue/Thu

**Recommended follow-up classes: ** Depends on research interests.

**Substantive Orientation: ** Social sciences; however, links to the natural/physical sciences are plentiful

**Statistical Orientation: ** Frequentist

**Books used: ** Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken, NJ: Wiley.

**Applied/Theoretical: ** A mix of theory and application

**Formal Computing Lab?: ** Yes

**Software Used: **
SAS

**How the software is used: ** Used for computation, some programming, and data analysis

**Problem Sets: ** Yes, with a mix of theoretical/conceptual/analytic problems and applied, data analysis.

**Data Analysis: ** Yes, for problem sets and final project

**Presentations: ** Yes, students will present their final projects.

**Exams: ** A final project is required for the course. There is no formal exam at the moment; however, that could change.

**Comments: **