Chapter 8 M8: GAMs

This module looks at some options for dealing with non-linear relationships between your predictors and the response. One approach is to apply transformations (functions) to the various predictors before fitting the model. We look at a few common types of functions people use for this, including polynomials, step functions, and splines.

This module’s reading is all in the textbook! Relevant sections include:

  • Intro to chapter 7, “Moving Beyond Linearity”
  • 7.7 “Generalized Additive Models”
  • 7.1 “Polynomial Regression”
  • 7.2 “Step Functions”
  • 7.3 “Basis Functions”
  • 7.4 “Regression Splines”
  • 7.6 “Local Regression”
  • Optional: 7.5 “Smoothing Splines.”