PROC PLS fits models by using any of a number of linear predictive methods, including partial least squares (PLS).
Ordinary least squares regression, as implemented in the GLM or REG procedure, has the single goal of minimizing sample response
prediction error by seeking linear functions of the predictors that explain as much variation in each response as possible.
The techniques that are implemented in PROC PLS have the additional goal of accounting for variation in the predictors under
the assumption that directions in the predictor space that are well sampled should provide better prediction for *new* observations when the predictors are highly correlated. All the techniques that are implemented in PROC PLS work by extracting
successive linear combinations of the predictors, called *factors* (also called *components* or *latent vectors*), which optimally address one or both of these two goalsâ€”explaining response variation and explaining predictor variation.
In particular, the method of partial least squares balances the two objectives, seeking factors that explain both response
variation and predictor variation.