Chapter 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 there are many situations in which it’s unreasonable to assume that the dependent variable is normally distributed (or even continuous). For example:

  • 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) are clearly not normally distributed. ...

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