Best-subset and stepwise multiple regression with categorical factor effects; builds a linear model for continuous and categorical predictor variables, for one or more continuous dependent variables. By default, only main effects will be evaluated for categorical predictors; you can also construct factorial designs up to a certain degree (e.g., to degree 3, to include all 2-way and 3-way interactions of categorical predictors). Note that the algorithm for stepwise and best subset selection of categorical factor effects ensures that complete (possibly multiple-degrees-of-freedom) effects are moved into and out of the model.

**General**

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

**Detail of
computed results reported**. Specifies the detail of computed
results reported in the results. If All results is requested, the program
will also report all univariate results (for multivariate designs), descriptive
statistics, details about the design terms, and the whole-model R. Residual
and predicted statistics (for observations) can be requested as options.

**Construct
factorial to degree**. Specifies the factorial degree of the
design to be tested; the program will construct a factorial design for
all categorical predictors up to the specified degree (i.e., by default
up to degree 1, so that the final model will include only main effects
for categorical predictors; if you set this parameter to 2, then all two-way
interactions will also be included, and so on).

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

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

**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.

**Generate
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.

**Selected
Results**

**Least square
means**. Computes 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.

**Test 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).

**Plot 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**. Specifies the normal probability plot of residuals

**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
univariate analysis problems (single continuous variable), and for designs
that do not include categorical factor effects (which may have more than
one degree of freedom).

**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
for single continuous dependent variables only).

**F to remove**.
Specifies F-to-remove, for stepwise selection of predictors (available
for single continuous dependent variables only).

**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; the program 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. The program will force the first
k predictors in the list of continuous predictors into the model, with
k as specified here by the user.

**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.

**Generates SVB code**. Generates Statistica Visual
Basic code for deployment of predictive model.

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