Cover by Robert Kabacoff

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

O'Reilly logo

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. ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required