This post is primarily to give the basic overview of principal components analysis (PCA) for dimensionality reduction and regression. I wanted to create it as a guide for my regression students who may find it useful for their projects. First, let’s note the two main times that you may want to use PCA - dimensionality reduction (reducing variables in a dataset) and removing colinearity issues. These are not exclusive problems, often you want to do both.