Categorical Data Analysis

Learning Outcomes

On successful completion of this course, students will be able to: Recognise data as being categorical data and summarise data as categorical data where appropriate; Explain the need for, the structure, and the usefulness of generalized linear model; Explain the need for, the structure, and the usefulness of logistic regression; Explain the need for, the structure, and the usefulness of contingency tables; Apply the method which are appropriate with data; Interpret the results of the method for categorical data.

Topics

1. Introduction
2. Contingency Tables
3. Generalized Linear Model
4. Logistic Regression
5. Building and Applying Logistic Regression Model
6. Multi-Category Logit Models
7. Log-Linear Models for Contingency Tables
8. Model for Matched Pairs
9. Modelling Correlated
10. Random Effects: Generalized Linear Mixed Models