In PCR instead of regressing the independent variables (the regressors) on the dependent variable directly, the principal components of the independent variables are used. One typically only uses a subset of the principal components in the regression, making a kind of regularized estimation. Often the principal components with the highest variance are selected. However, the low-variance principal components may also be important, — in some cases even more important.

See also

• Canonical correlation
• Partial least squares regression
• Total sum of squares

References