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Overview: HPPRINCOMP Procedure

The HPPRINCOMP procedure is a high-performance procedure that performs principal component analysis. It is a high-performance
version of the PRINCOMP procedure in SAS/STAT software. PROC HPPRINCOMP accepts raw data as input and can create output data
sets that contain eigenvalues, eigenvectors, and standardized or unstandardized principal component scores.

Principal component analysis is a multivariate technique for examining relationships among several quantitative variables.
The choice between using factor analysis and using principal component analysis depends in part on your research objectives.
You should use the HPPRINCOMP procedure if you are interested in summarizing data and detecting linear relationships. You
can use principal component analysis to reduce the number of variables in regression, clustering, and so on.

Principal component analysis was originated by Pearson (1901) and later developed by Hotelling (1933). The application of principal components is discussed by Rao (1964); Cooley and Lohnes (1971); Gnanadesikan (1977). Excellent statistical treatments of principal components are found in Kshirsagar (1972); Morrison (1976); Mardia, Kent, and Bibby (1979).

If you have a data set that contains p numeric variables, you can compute p principal components. Each principal component is a linear combination of the original variables, with coefficients equal
to the eigenvectors of the correlation or covariance matrix. The eigenvectors are usually taken with unit length. The principal
components are sorted by descending order of the eigenvalues, which are equal to the variances of the components.

PROC HPPRINCOMP runs in either single-machine mode or distributed mode.

**Note**: Distributed mode requires SAS High-Performance Statistics .