Linear Regression Options
The following options are available:
Stepping Method Criteria. These options apply when either the forward, backward, or stepwise variable selection method is specified. Variables can be entered or removed from the model depends on either the significance (probability) of the F value or the F value itself.
- Use Probability of F. A variable is entered into the model if the significance level of its F value is less than the Entry value and is removed if the significance level is greater than the Removal value. Entry must be less than Removal, and both values must be positive. To enter more variables into the model, increase the Entry value. To remove more variables from the model, lower the Removal value.
- Use F Value. A variable is entered into the model if its F value is greater than the Entry value and is removed if the F value is less than the Removal value. Entry must be greater than Removal, and both values must be positive. To enter more variables into the model, lower the Entry value. To remove more variables from the model, increase the Removal value.
Tolerance. By default, the value is .0001. Tolerance is the proportion of the variance of a variable in the equation that is not accounted for by other independent variables in the equation. The minimum tolerance for any variable in the equation if the variable under consideration was included in the analysis is the minimum tolerance of a variable that is not included in the equation. Variables must pass tolerance and minimum tolerance tests to enter and remain in a regression equation. If a variable passes the tolerance criteria, it is eligible for inclusion based on the method in effect.
Include constant in equation. By default, the regression model includes a constant term. Deselecting this option forces regression through the origin, which is rarely done. Some results of regression through the origin are not comparable to results of regression that do include a constant. For example, R ^{2} cannot be interpreted in the usual way.
Missing Values. You can choose one of the following:
- Exclude cases listwise. Only cases with valid values for all variables are included in the analyses.
- Exclude cases pairwise. Cases with complete data for the pair of variables being correlated are used to compute the correlation coefficient on which the regression analysis is based. Degrees of freedom are based on the minimum pairwise N.
- Replace with mean. All cases are used for computations, with the mean of the variable substituted for missing observations.
Specifying Options for a Linear Regression
This feature requires the Statistics Base option.
- From the menus choose:
- In the Linear Regression dialog box, click Options.
- Select the options that you want.