This document contains mathematical equations and statistical formulas relating to principal component analysis (PCA). PCA is used to reduce the dimensionality of large data sets while retaining most of the variation in the data. The equations define terms such as component scores, eigenvalues, variance explained by each component, and variable importance in projection (VIP) values.