Lesson 15: Logistic, Poisson & Nonlinear Regression

Printer-friendly versionPrinter-friendly version


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.

Learning objectives and outcomes

  • 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.