Best-Subset and Stepwise Regression

Best-subset and stepwise multiple regression; builds a linear model for continuous predictor variables, for one or more continuous dependent variable; use Best-Subset and Stepwise ANOCVA to include categorical predictors.

General

Model building method. Specifies a model building method.

Detail of computed results reported. Specifies the level of computed results reported. If All results is requested, Statistica 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.

Residual analysis. Creates and reports 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.

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.

Normal probability plot. Normal probability plot of residuals

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 univariate analysis problems (single continuous 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 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; 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.

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.