General regression models; builds a linear model to predict continuous dependent variables. The parameters in Statistica allow full access to the GRM syntax for specifying models and for controlling the parameters for stepwise and best-subset selection of predictor effects (for categorical and continuous predictor variables). Default results include the ANOVA/ANCOVA (MANOVA/MANCOVA) table; set the Level of detail parameter to All results to produce tables of means and other statistics. Residual and predicted values can be computed on request.

**General**

**Detail of computed results reported**. Specifies the level of computed
results reported. If All results is requested, Statistica
will produce all univariate results (for multivariate designs), descriptive
statistics, details about the design terms, the whole-model R, regression
coefficients, and the least-squares means for all effects. Residual and
predicted statistics (for observations) can be requested as options.

**Analysis syntax**. Analysis syntax string for general regression
models. You can specify here the complete syntax, as, for example, copied
from a Statistica analysis. Set this string to empty, or
just GRM; to create the syntax from the specific options specified below.

**Design**.
Required; specify the design for the between group design (categorical
and continuous predictors); by default (if no design is specified) a full
factorial design will be constructed for categorical predictors, and continuous
predictor main effects are evaluated.

Use the syntax:

DESIGN = Design specifications

Example 1.

DESIGN = GROUP | GENDER | TIME | PAID; {makes a full factorial design}

Example 2.

DESIGN = SEQUENCE + PERSON(SEQUENCE) + TREATMNT + SEQUENCE*TREATMNT;

Example 3.

DESIGN = MULLET | SHEEPSHD | CROAKER @2; {Makes factorial design to degree
2}

Example 4.

DESIGN = TEMPERAT | MULLET | SHEEPSHD | CROAKER - TEMPERAT; {Removes main
effect for TEMPERAT from factorial design}

Example 5.

DESIGN = BLOCK + DEGREES + DEGREES*DEGREES + TIME + TIME*TIME + TIME*DEGREES;

**Intercept**.
Specifies whether the intercept (constant) is to be included in the model
(i.e., a parameter is to be estimated for the intercept); the default
is INTERCEPT=INCLUDE.

**Model building
method**. Specifies a model building method.

**Contrast
coefficients**. Specifies contrasts for least squares means;
consult the Electronic Manual for syntax details.

**Estimate
(custom hypotheses)**. Optional; specify the coefficients that
are to be used in the linear combination of parameter estimates for the
custom hypothesis; multiple ESTIMATE specifications can appear in the
same analysis. Note that tests of linear combinations of parameter estimates
can also be requested from the Results dialog, where a convenient and
efficient user interface is provided for specifying the coefficients.

**Sweep delta
1.E-**. Specifies the negative exponent for a base-10 constant
Delta (delta = 10^-sdelta); the default value is 7. Delta is used (1)
in sweeping, to detect redundant columns in the design matrix, and (2)
for evaluating the estimability of hypotheses; specifically a value of
2*delta is used for the estimability check.

**Inverse
delta 1.E-**. Specifies the negative exponent for a base-10 constant
Delta (delta = 10^-idelta); the default value is 12. Delta is used to
check for matrix singularity in matrix inversion calculations.

**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.

**Parameters
for Stepwise Selection**

**Stepwise
selection criterion**. Specifies the criterion to use for stepwise
selection of predictors. Note that the F statistic is only available for
analysis problems with continuous (single degree of freedom) predictor
variables and a single dependent variable.

**p to enter**.
Specifies p-to-enter for stepwise selection of predictors.

**p to remove**.
Specifies p-to-remove for stepwise selection of predictors.

**F to enter**.
Specifies F-to-enter for stepwise selection of predictors (available only
for models with continuous predictor variables and a single dependent
variable).

**F to remove**.
Specifies F-to-remove, for stepwise selection of predictors (available
only for models with continuous predictor variables and a single dependent
variable).

**Maximum
number of steps**. Specifies maximum number of steps for stepwise
selection of variables.

**Parameters
for Best-Subset Selection**

**Best subsets
measure**. Specifies the selection criterion for best subset
selection of predictors. These options are only available (meaningful)
for analysis problems with a single dependent variable. In designs with
multiple dependent variables, the selection of the best subset is based
on the p for the multivariate test (Wilks' Lambda).

**Start for
best subsets**. Specifies the smallest number of predictors to
be included in the model chosen via best subset selection, i.e., the start
of the search for the best subset of predictors.

**Stop for
best subsets**. Specifies the maximum number of predictors to
be included in the model chosen via best subset selection.

**Number of subsets to display**. Specifies the number of subsets
to display in the results; Statistica will keep a log of
the best k predictor models of any given size, using k as specified by
this parameter.

**Number of variables to force**. Specifies the number of predictors
to force into the model, i.e., to select into all models considered during
the best-subset selection of predictors. Statistica will
force the first k predictors in the list of continuous predictors into
the model, with k as specified here by you.

**Selected
Results**

**Least square
means**. Creates the expected marginal means, given the current
model; either all marginal means tables can be computed, or only the means
for the highest-order effect of the factorial design.

**Post Hoc
Tests**. Performs post-hoc comparisons between the means in the
design.

**Lack of
fit**. Requests the computation of pure error for testing the
lack-of-fit hypothesis.

**Tests homogeneity
of variances**. Tests the homogeneity of variances/covariances
assumption. One of the assumptions of univariate ANOVA is that the variances
are equal (homogeneous) across the cells of the between-groups design.
In the multivariate case (MANOVA), this assumption applies to the variance/covariance
matrix of dependent variables (and covariates).

**Plots of
means vs. std. dev**. Plots the (unweighted) marginal means (see
also the Means tab) for the selected Variables against the standard deviations.

**Residual analysis**. In addition to the predicted, observed, and
residual values, Statistica will compute the (default) 95%
Prediction intervals and 95% Confidence limits, the Standardized predicted
and Standardized residual score, the Leverage values, the Deleted residual
and Studentized deleted residual scores, Mahalanobis and Cook distance
scores, the DFFITS statistic, and the Standardized DFFITS statistic.

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

**Deployment. **Deployment is
available if the Statistica installation is licensed for this feature.

**Generates
C/C++ code**. Generates C/C++ code for deployment of predictive
model (for a single dependent variable only).

**Generates SVB code**. Generates Statistica Visual
Basic code for deployment of predictive model (for a single dependent
variable only).

**Generates
PMML code**. Generates PMML (Predictive Models Markup Language)
code for deployment of predictive model (for a single dependent variable
only). This code can be used via the Rapid Deployment options to efficiently
compute predictions for (score) large data sets.

**Saves C/C++
code**. Save C/C++ code for deployment of predictive model (for
a single dependent variable only).

**File name
for C/C code**. Specify the name and location of the file where
to save the (C/C++) deployment code information.

**Saves SVB code**. Save Statistica Visual Basic code
for deployment of predictive model (for a single dependent variable only).

**File name
for SVB code**. Specify the name and location of the file where
to save the (SVB/VB) deployment code information.

**Saves PMML
code**. Saves PMML (Predictive Models Markup Language) code for
deployment of predictive model (for a single dependent variable only).
This code can be used via the Rapid Deployment options to efficiently
compute predictions for (score) large data sets.

**File name
for PMML (XML) code**. Specify the name and location of the file
where to save the (PMML/XML) deployment code information.