Builds a linear model to predict continuous dependent variables. The parameters in Statistica allow full access to the GLM syntax for specifying models. Default results include the ANOVA/ANCOVA (MANOVA/MANCOVA) table; set the Level of detail parameter to All results to request tables of means and other statistics. Residual and predicted values can be computed on request.

**General**

**Detail of computed results reported**. Specifies the detail of
computed results reported. If All results is requested, Statistica
will report 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 linear models.
You can specify here the complete syntax, as, for example, copied from
a Statistica analysis. Set this string to empty, or just
GLM; to create the syntax from the specific options specified below.

**Design**.
Specifies 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;

**Parameterization**.
Specifies either the sigma-restricted model (keyword SIGMA), or the overparameterized
model (keyword OVERP); SIGMA is the default parameterization, except for
nested designs or mixed-model ANOVA and ANCOVA designs.; see the Electronic
Manual topic: The Sigma-Restricted vs. Overparameterized Model for additional
details.

**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.

**Type of
sums of squares**. Specifies how to construct the hypotheses
for the tests of main effects and interactions; for the sigma-restricted
model (PARAM=SIGMA) the default value is 6 (unique or effective hypothesis
decomposition; see Hocking, 1985) and option 4 is not valid; for the overparameterized
model (PARAM=OVERP) the default value is 3 (orthogonal; see Goodnight,
1980), and option 6 is not valid. For a description and discussion of
the different options for constructing main effect and interaction hypotheses
in unbalanced and incomplete designs, see also the Six Types of Sums of
Squares topic in the Electronic Manual.

**Random effects**.
Optional; specify the names of the between-group factors (categorical
predictors); all effects (main effects and interactions) involving random
factor effects will also be treated as random effects in the analysis.

**SDelta**.
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.

**IDelta**.
specify 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.

**Repeated
Measures**

**Repeated
measures**. Specifies the names and number of levels for the
repeated measures factors; specify the within-subject (repeated measures)
design via the Within subject design edit field; if no Within subject
design is specified, the analysis will be performed on the within-model
intercept, i.e., effectively on the overall mean for the variables specified
in the dependent variable list.

Syntax:

Repeated measures:

{ NONE }{ Name Value Name Value ... Name Value}

Example.

Repeated measures:

TIME 3 DIALS 3

**Within subject
design**. Specifies the design for the within-subject (repeated
measures) factors specified in the Repeated measures edit field; if no
Within subject design is specified, the analysis will be performed on
the within-model intercept, i.e., effectively on the overall mean for
the variables specified in the dependent variable list.

Syntax:

Within subject design:

{ NONE }{ Design specs }

Example.

Within subject design:

TIME | DIALS

**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;
this option is not applicable to within-subjects (repeated measures) designs.

**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.

**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.

**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.