**Prerequisites & Bulletin Description**

## Course Objectives

Specialized statistical methods for categorical data are widely used today for applications in different areas of science such as biomedical and social sciences. This course will cover most important methods for analyzing categorical data. It will include standard descriptive and inferential methods for contingency tables and matched pair data, modeling categorical data using generalized liner models, logistic regression models for binomial and multinomial data. The course will involve the practical application of the ideas and their implementation through statistical software (SAS and/or R).

## Course Objectives

This course is designed to provide modern statistical methods for analyzing categorical data. The course objectives are:

- Understand inference for proportions and odds ratios
- Understand probability structure and inference for contingency tables
- Perform logistic regression analyses with multiple predictors
- Understand inference for matched pair designs
- Understand when to utilize hypothesis testing versus modeling

## Evaluation of Students

Student will be evaluated based on Homework, projects, midterm, and final examinations.

## Course Outline

Probability Distributions for Categorical Data

Statistical Inference for Proportion and Discrete Data

Inferential methods for contingency tables

Generalized Linear Models for Binary and Count Data

Logistic Regression Models for Binomial Data

Logistic Regression Models for Multinomial Responses

Log-Linear Models for Poisson Data

Methods for Matched-Pair Data

## Textbooks & Software

Agresti, Alan (2007). *An Introduction to Categorical Data Analysis*. John Wiley & Sons, Inc.: New York.

Statistical software: SAS and/or R.

Submitted by: Mohammad Kafai

Date: October 31, 2016