Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select General Partial Least Squares to display the Partial Least Squares Models Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Partial Least Squares to display the Partial Least Squares Models Startup Panel.

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.

STATISTICA Partial Least Squares (PLS) implements the two most general algorithms for partial least squares analysis: SIMPLS and NIPALS. Like GLM and GLZ, PLS offers both the overparameterized and sigma restricted parameterization methods for categorical predictors in ANOVA/ANCOVA-like models. PLS will compute all the standard results for a partial least squares analysis, and also offers a large number of results options and in particular graphics options that are usually not available in other implementations; for example, graphs of parameter values as a function of the number of components, two-dimensional plots for all output statistics (parameters, factor loadings, etc.), two-dimensional plots for all residual statistics, etc. Also, like GLM, GRM, GDA, and GLZ, the Partial Least Squares module offers extensive residual analysis options, and predicted and residual statistics can be requested for observations that were used for fitting the model (the "training" sample), those that were not (i.e., the cross-validation or verification sample), and for cases without observed data on the dependent (response) variables (the prediction sample).

STATISTICA includes a large number of modules for fitting linear or nonlinear models, and for reducing the dimensionality of a variable space, e.g., General Linear Models (GLM), General Regression Models (GRM), Generalized Linear/Nonlinear Models (GLZ), Generalized Additive Models (GAM), and Factor Analysis, to name only a few.