Short Description

Linear models for a quantitative response variable, with multiple categorical and/or quantitative predictors. Matrix formulation of linear regression. Model assessment and prediction.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Fit and interpret a linear regression model.
  2. Identify whether a linear regression model is appropriate for a given dataset.
  3. Critique a specific regression model applied to a given dataset on the basis of both diagnostic plots and hypothesis tests.
  4. Specify and interpret interaction terms and nonlinear terms.

Reference Material

  • Faraway, Julian J. Linear Models with R, 2nd Edition. Chapman and Hall, 2014.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.