The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of measured variables that capture as much of the variability in the original variables as possible. Principal component analysis is a dimension-reduction technique. It can be used as an exploratory data analysis tool, but is also useful for constructing predictive models, as in principal components analysis regression (also known as PCA regression or PCR).