Stepwise Multiple Regression

Performs stepwise multiple regression analysis for the continuous dependent variable on the continuous predictors. You can specify tolerance for matrix inversion, F to enter/remove for stepwise analysis, number of steps for stepwise analysis, p-value for highlighting, etc. If comprehensive (or greater) level of detail for results is requested, partial correlations and redundancy statistics are also reported. Predicted and residual statistics can be computed as an option.

General

Element Name Description
Detail of computed results reported Specifies the level of computed results reported. If Comprehensive is selected, partial correlations and redundancy statistics are reported; if All results is requested the current sweep matrix and the covariances of regression coefficients are computed. This parameter also determines the detail of residual statistics that are computed (see the Residual analysis parameter).
MD Deletion Missing data can be deleted Casewise, Pairwise, or by Means Substitution depending on the option selection under MD deletion.
Stepwise method Select either forward selection or backward elimination of continuous predictors.
F To Enter The F to enter value determines how significant the contribution of a variable in the regression equation has to be in order for it to be added to the equation.
F To Remove The F to remove value determines how 'insignificant' the contribution of a variable in the regression equation has to be in order for it to be removed from the equation.
Maximum number of steps Maximum number of steps for stepwise regression analysis.
Intercept You can include the intercept in the regression analysis (select Intercept included) or force the regression line through the origin (intercept forced to zero, regression through the origin; select Set to zero).
Tolerance (for matrix inversion) Specifies the minimum tolerance value for matrix inversion (for detecting matrix singularity). The tolerance of a variable is defined as 1 minus the squared multiple correlation of the variable with all other independent (predictor) variables in the regression equation. The smaller the tolerance of a variable, the more redundant is its contribution to the regression.

Residual analysis

Element Name Description
Creates residual statistics Creates predicted and residual statistics for each case. If All results is requested (for the Level of detail) the Durbin-Watson statistic for the residuals, histograms, normal probability plots, and other plots are also reported.
p for highlighting results The default p-value for highlighting is .05; significant predictors with p less than or equal to this value will be shown in the highlight color in the results spreadsheets.
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.

Deployment

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

Element Name Description
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.
Saves C/C++ code Save C/C++ code for deployment of predictive model.
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.
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. 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.