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Principal Components Analysis
Principal components analysis
Introduction

Principal components analysis (PCA) attempts to analyse the structure in a data set in order to define uncorrelated components that capture the variation in the data. The identification of components is often desirable as it is usually easier to consider a relatively small number of unrelated components which have been derived from the data than a larger group of related variables. PCA is particularly useful in management research, as it is often used as a first step in assigning meanings to the structure in the data (by attaching descriptions to the components) through the technique of factor analysis. PCA can also help to alleviate some of the problems with variable selection in regression models that are associated with multicollinearity, which is ...

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