Partial Least Squares is a linear regression method that forms components (factors, or latent variables) as new independent variables (explanatory variables, or predictors) in a regression model. The components in partial least squares are determined by both the response variable(s) and the predictor variables. A regression model from partial least squares can be expected to have a smaller number of components without an appreciably smaller R-square value.

For an overview of partial least squares, see the Introductory Overview for the Partial Least Squares Model (PLS) method of analysis.