Chapter 9 Multiple Linear Regression

Hopefully by now you have some motivation for why we need to have a robust model that can incorporate information from multiple variables at the same time. Multiple linear regression is our tool to expand our MODEL to better fit the DATA.

  • Extends simple linear regression.
  • Describes a linear relationship between a single continuous \(Y\) variable, and several \(X\) variables.
  • Predicts \(Y\) from \(X_{1}, X_{2}, \ldots , X_{P}\).
  • X’s can be continuous or discrete (categorical)
  • X’s can be transformations of other X’s, e.g., \(log(x), x^{2}\).

Now it’s no longer a 2D regression line, but a \(p\) dimensional regression plane.

This section uses functions from the dotwhisker and gtsummary visualize results from multiple regression models.