Use Partial Least Squares Regression (PLS) to describe the relationship between a set of predictors and one or more continuous responses. Use PLS when your predictors are highly collinear, or when you have more predictors than observations. PLS is also appropriate to use when the predictors are not fixed and are measured with error. PLS reduces the predictors to a smaller set of uncorrelated components and performs least squares regression on these components, instead of on the original data. For more information, go to What is partial least squares regression?.
If you perform the analysis with correlated response variables, PLS can detect multivariate response patterns and weaker relationships than are possible with a separate analysis for each response.
For example, a chemical spectrography company uses PLS to model the relationship between spectral measurements (NIR, IR, UV), because these models include many variables that are correlated with one another.
To perform partial least squares regression, choose .
If the predictors are fixed and do not have considerable measurement error, or the predictors are not highly collinear and your data include more observations than the number of terms, use Fit Regression Model.