Best-Subset and Stepwise ANCOVA with Deployment

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.


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.