Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing the proportions of variance in the data. The goal of the method is to decrease the dimensionality of the data. The principal components, new variables, are defined as the linear functions of the usual variables. If the first few principal components account for the large percentage of the variance of the observations (say it above 70%) they can be used both to simplify subsequent analyses and to display and summarize the data in a parsimonious way.