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Also known as empirical orthogonal function analysis, principal components analysis (PCA) is a multivariate data analysis technique that is employed to reduce the dimensionality of large data sets and simplify the representation of the data field under consideration. PCA is used to understand the interdependencies among variables and trim down the redundant (or significantly correlated) variables that are measuring the same construct. Data sets with a considerable proportion of interrelated variables are transformed into a set of new hypothetical variables known as principal components, which are uncorrelated or orthogonal to one another. These new variables are ordered so that the first few components retain most of the variation present in the original data matrix. The components reflect both common and unique variance of the variables ...

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