Principal component regression  From Wikipedia, the free
encyclopedia Jump to: navigation, search  In statistics,
principal component regression (PCR) is a regression analysis
that uses principal component analysis when estimating
regression coefficients. It is a procedure used to overcome
problems which arise when the exploratory variables are close to
being collinear.[1]  In PCR instead of regressing the dependent
variable on the independent variables 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.[2]  The
principle[edit]  PCR (principal components regression) is a
regression method that can be divided into three steps:[citation
needed]    1. The first step is to run a principal componen