13. Linear Regression II

This is the second of three sessions that explore linear regression modelling. These are models where the outcome of interest is a continuous variable.

Intended learning outcomes

By the end of this session, you will be able to:

  • explain the difference between a univariable and multivariable linear regression model

  • fit and interpret a multivariable linear regression

  • describe the principles of centering

  • interpret categorical variables, quadratic terms and interaction terms included in a linear regression model

Acknowledgements: Thank you to Jennifer Nicholas and Chris Frost whose notes on linear regression were particularly useful in the development of the current lesson.