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Home Multivariate Data Modeling PCA Principal Component Regression  
See also: PCA, MLR  
Principal Component Regression
PCR, or principal component regression, is a simple extension of MLR and PCA. In the first step, the principal components are calculated. The scores of the most important principal components are used as the basis for the multiple linear regression with the target data y. The most important point in PCR is the proper selection of the eigenvectors to be included. A plot of the eigenvalues usually indicates to the "best" number of eigenvectors. Advantages of PCR over MLR:


Home Multivariate Data Modeling PCA Principal Component Regression 
Last Update: 20121008