Standard Multiple Regression with Deployment

Standard Multiple Regression performs standard regression analysis for the continuous dependent variable on the continuous independent variables. Both standard regression and regression through the origin (intercept=0) can be computed. Predicted and residual values can be computed as an option.

For deployed data sets, the previously estimated regression equation will be applied to the data.


Detail of computed results reported. Specifies the detail of computed results reported in the results. If Comprehensive is selected, partial correlations and redundancy statistics are reported; if All results are 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. Specifies the missing data can be deleted Casewise, Pairwise, or missing data can be substituted by the means for the respective variables.

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.

Computes residual statistics. Computes predicted and residual statistics for each case. If All results are 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. Specifies the default p-value for highlighting is .05; significant predictors with p less than or equal this value will be shown in the highlight color in the results spreadsheets.

Generate datasource, 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.

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.