Aims
This session introduces binary logistic regression models. These models are the simplest form of a broader class of models called generalised linear models, which are applicable when the outcome (“dependent”, “response”, “explained”, etc.) variable cannot be assumed to follow a Gaussian (i.e. “normal”) distribution, but it instead a bounded or discrete measurement (e.g. think of variables whose values cannot be negative - i.e. have a lower limit of 0 - or fall into discrete categories such as “yes”/“no”, “disagree”/“neither agree nor disagree”/“agree”, or “blue”/“green”/“black”/“brown”/“other”). Binary logistic regression is the simplest case, where the outcome can take only two values (therefore “binary”). However, the logic that underpins it is similar to that of other generalised linear models.
By the end of the session you will learn how to:
- Fit and summarise logistic regression models in
R - Interpret results from logistic regression models
- Manipulate the regression output to ease interpretation
- Plot and visualise results from logistic regression models to aid interpretation