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Traditionally, principal component analysis is performed on the Covariance matrix or on the Correlation matrix.

The TableOfReal is therefore interpreted as numberOfRows data vectors, each data vector has numberofColumns elements. We assume that the multi-dimensional data have been collected in a TableOfReal data matrix, in which the rows are associated with the cases and the columns with the variables. To identify new meaningful underlying variables. To discover or to reduce the dimensionality of the data set. Objectives of principal component analysis The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.ġ. Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. This tutorial describes how you can perform principal component analysis with P RAAT.