13 Generalized linear models
This chapter covers
- Formulating a generalized linear model
- Predicting categorical outcomes
- Modeling count data
In chapters 8 (regression) and 9 (ANOVA), we explored linear models that can be used to predict a normally distributed response variable from a set of continuous and/or categorical predictor variables. But in many situations, it’s unreasonable to assume that the dependent variable is normally distributed (or even continuous). For example:
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The outcome variable may be categorical. Binary variables (for example, yes/no, passed/failed, lived/died) and polytomous variables (for example, poor/good/excellent, Republican/Democrat/independent) clearly aren’t normally distributed.
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The outcome variable may be ...
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