1 Basic Elements

Principal Component Analysis (PCA) is a statistical technique with a strong descriptive flavor that can be used to get an approximate visualization (optimal in a certain sense) of the information contained in a data table. Simply put, PCA allows us to simultaneously describe the association between variables, as well as the ressemblance among individuals. PCA can also be regarded to as a dimension reduction technique of quantitative variables, often employed as an intermediate step towards a subsequent model building phase.

In this chapter, we describe PCA as an exploratory tool that will allows us to visualize and gain insight into the structure of a data set.