Whether you followed the blog series on variable selection in multiple regression as it developed or you learned about it later, this page contains links to all of the pages in the series. There is also a Github repository with source code for all of the R notebooks.

- Collecting my thoughts about multiple regression
- What is multiple regression doing?
- Challenges of multiple regression (or why we might want to select variables)
- Trying out a couple of simple strategies for reducing the number of covariates
- Principal components regression
- An update on principal components regression
- Using the Lasso for variable selection
- A Bayesian approach to variable selection using horseshoe priors
- Using projection prediction for variable selection in a Bayesian regression
- Some parting thoughts on variable selection in multiple regression