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Categorical Data Analysis

In this course, you learn how to model categorical data, when methods based on linear models are not appropriate.

The course covers analysis of discrete data, in particular count variables and proportions. This type of data is often represented by so called contingency tables. In such cases methods based on linear  models and normally distributed response variables are not appropriate. It is rather suitable to use generalized linear models (GLMs), which incorporate many other distributions of the response variable, such as binomial, multinomial and Poisson distributions. Depending on the link function of the GLM, this makes it possible to model various types of non-additive effects, for instance multiplicative effects. The two GLMs studied most extensively are logistic regression models for binary outcomes and loglinear models for Poisson distributed outcomes and contingency tables.

Course contents

The course covers models for categorical data, two way and multi way contingency tables, homogeinity and independence, generalized linear models for categorial data, logistic regression, log linear models for categorial data and diagnostics of models.