Today's data sets often have dozens, hundreds, or even thousands of variables. Such high dimensionality poses many challenges. One of most popular approaches for analyzing such high dimensional data sets is dimensionality reduction, which represents the the original data set with a smaller number of variables or new variables. In turn, Principal Component Analysis (PCA) is one of the most well-estabilished techniques
for dimensionality reduction, and is often used for exploratory
data analysis. This tutorial will present the basic idea of PCA and demostrate how to use it in SAS. We will also briefly overview some other multivariate analysis techniques as time allows.