Module:  Principal Component Analysis                                                                       Print

Lesson Objective Learn how to use PCA to reduce data dimensionality to verify clustering analysis
Section Description
Principal Component Analysis A discussion of the theory of Principal Component Analysis and reduction of data dimensionality.
Principal Component Analysis Capability How to launch the Principal Component Analysis tool.
Principal Component Analysis Dialog Box Determining the columns and rows to be used for the calculations and methods for dealing with empty values. Input options for the number of principal components and the variety of output options for results.
Results of Principal Component Analysis Examining and evaluating the results of PCA, including:  Variability Preserved, Eigenvalues, Eigenvectors, Scree Plot, and Loadings Plot.