PLS Regression; performs partial least squares analyses with a list of continuous dependent variables, and a list of continuous predictor variables.

**General**

**Detail of computed results reported**. Specifies the level of computed
results reported. If Minimal results is requested, Statistica
will report summary statistics for the components and the regression coefficients;
if All results is requested, summary plots, weights, and other statistics.
Residual (observational) statistics can be requested as a separate option.

**PLS method**.
Select the algorithm (method) that is to be used to compute the PLS components;
the default is PLS. If PLS (the default) is specified, then a standard
PLS analysis using the NIPALS algorithm (Rannar, Lindgren, Geladi, and
Wold, 1994) will be performed; if you specify SIMPLS the factor scores
will be computed via the SIMPLS algorithm (de Jong, 1993).

**Max number
of components**. Specifies the maximum number of components to
be extracted; the default value is 120.

**Delta for
R-square; 1.E-**. Specifies the negative exponent for a base-10
constant delta (delta = 10^-RDelta); the default value is 12. Delta is
used by *STATISTICA* as a criterion for determining whether to stop
extracting additional PLS components.

**Intercept**.
Specifies whether the intercept (constant) is to be included in the model.

**Auto scaling**.
Each column in the predictor design matrix X and matrix of dependent (response)
variables Y will be divided by its respective standard deviation, and
all computations will be performed on these scaled matrices X and Y. Note
that the coefficients that are computed when the

**Delta for
eigenvalues; 1.E-**. Specifies the negative exponent for a base-10
constant delta (delta = 10^-Edelta); the default value is 12. Delta is
used for checking the convergence of the iterative computation of eigenvectors
for each PLS component.

**Maximum
number of iterations**. Specifies the maximum number of iterations
for the iterative computation of eigenvectors for each PLS component.
The default value is 200. PLS uses an iterative power method (see Golub
and van Loan, 1996) to compute the eigenvector of

**Residuals
and Observational Statistics**

**Residual
analysis**. Creates predicted and residual values, and factor
scores for each observation.

**Normal probability
plot**. Normal probability plot of residuals.

**Generates
data source, if N for input less than**. Generates a data source
for further analyses with other Data Miner nodes if the input data source
has fewer than k observations, as specified in this edit field; note that
parameter k (number of observations) will be evaluated against the number
of observations in the input data source, not the number of valid or selected
observations.