Lesson 15: Logistic, Poisson & Nonlinear Regression

Overview Section

Multiple linear regression can be generalized to handle a response variable that is categorical or a count variable. This lesson covers the basics of such models, specifically logistic and Poisson regression, including model fitting and inference.

Multiple linear regression, logistic regression, and Poisson regression are examples of generalized linear models, which this lesson introduces briefly.

The lesson concludes with some examples of nonlinear regression, specifically exponential regression and population growth models.

Objectives

Upon completion of this lesson, you should be able to:

  • Apply logistic regression techniques to datasets with a binary response variable.
  • Apply Poisson regression techniques to datasets with a count response variable.
  • Understand the basics of fitting and inference for nonlinear regression methods when the regression function acting on the predictors is not linear in the parameters.