Principal component analysis (PCA) is an exploratory multivariate technique with two overall objectives. One objective is “dimension reduction”— to turn a collection of, for example, 100 variables into a collection of 10 variables that retain almost all the information that was contained in the original 100 variables. The other objective is to discover the structure in the relationships between the variables.
