Partial Least Squares regression model equations The algorithms used by XLSTAT are such that the PLS 1 is only a particular case of PLS 2. PLS 2 corresponds to the case where there are several dependent variables. PLS 1 corresponds to the case where there is only one dependent variable. Some programs differentiate PLS 1 from PLS 2. The idea behind the PLS regression is to create, starting from a table with n observations described by p variables, a set of h components with the PLS 1 and PLS 2 algorithms These predictors are then used to perfom a regression. The Partial Least Squares regression (PLS) is a method which reduces the variables, used to predict, to a smaller set of predictors. What is Partial Least Squares regression? Choose between the fast algorithm and the more precise one.

Automatically choose the number of components to be kept using one of multiple criteria or choose this number manually.

Use the leave one out (LOO) cross validation option.

Choose several response variables in one analysis.

XLSTAT proposes several standard and advanced options that will let you gain a deep insight on your data:

XLSTAT provides a complete PLS regression method to model and predict your data in excel. that the explanatory variables are correlated. It is recommended in cases of regression where the number of explanatory variables is high, and where it is likely that there is multicollinearity among the variables, i.e. Partial Least Squares regression (PLS) is a quick, efficient and optimal regression method based on covariance.

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