Chapter 1 Learning objectives

A significant limitation of general linear models is that they cannot accommodate response variables that do not have a normal error distribution - a situation that is very common when analyzing biological data.

In this workshop, you will learn how to use generalized linear models, which are powerful tools to overcome some of the distributional assumptions of linear models. Specifically, we will:

1. Distinguish generalized linear models from general linear models (including many of their equations!).

2. Identify situations for when the use of generalized linear models is appropriate.

3. Test assumptions for generalized linear models.

4. Implement and execute generalized linear models in binary, proportion and count data.

5. Validate, interpret and visualise results of generalized linear models.

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