STAT 149 - Introduction to Categorical Data Analysis
Course Description
Topics to be discussed in this course include: categorical data; cross-
classification tables; analysis using loglinear, logistic and logit.
Course Learning Outcomes
After completion of the course, the student should be able to:
- Assess the strength of association between categorical variables through statistical measures;
- Construct models for categorical data analysis, assess fit of these models, and devise
conclusions from them;
- Demonstrate categorical data analysis techniques for data analysis in scientific papers;
- Develop the skill of performing analysis using statistical software.
Course Outline
UNIT 1. INTRODUCTION
- Definition of Terms and Notation
- Probability Distributions for Categorical Data
UNIT 2. MEASURES OF ASSOCIATION
- Contingency Tables
- Sampling Models
- Measures of Association for Nominal Variables
- Measures of Association for Ordinal Variables
- Measures of Association for Multidimensional Tables
UNIT 3. GENERALIZED LINEAR MODELS (GLMs)
- Components of GLMs
- Ordinary Linear Model: GLM with Normal Random Component
- GLMs for Binary Data
- GLMs for Count Data
- Statistical Inference and Model Checking
- GLM Using SAS
UNIT 4. LOGISTIC REGRESSION
- Introduction
- Interpretation of the Model
- Inference for Logistic Regression
- Model Building and Model Checking
UNIT 5. General Loglinear Model for Contingency Tables
- Loglinear Models for Two – way Tables
- Loglinear Models for Three – way Tables
- Inference for Loglinear Models
- Loglinear Models for Higher Dimensions
- Model Building