This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.

Specify how to determine the terms in the regression model. Usually, an analysis that considers linear terms and terms of order 2 in combination with stepwise model selection provides a model with good predictive capability. You can select Forward selection with validation to determine whether the method produces a model with higher prediction accuracy.

If you have a large number of predictors, the selection of the final model can take a long time to consider linear terms and terms of order 2 with stepwise model selection. If the number of predictors is greater than 15, the default selection is to consider only linear terms. To evaluate some higher order terms in addition to linear terms, select to specify the terms in the model.

Select whether to use the default terms or to specify your own set of terms.

- Linear terms and terms of order 2
- The analysis uses all of the linear terms and terms of order 2. Terms of order 2 include all of the interactions between 2 linear terms and square terms for the continuous predictors.
- Linear terms
- The analysis uses all the linear terms.
- Specify terms
- You can add interaction terms and polynomial terms to your model. The initial model depends on the number of predictors that you enter in the main dialog box. If the number of predictors is 15 or less, the model contains the linear terms and terms of order 2 for the predictors. If the number of predictors is greater than 15, then the model contains the linear terms. Click Default to return to the initial model.

Specify whether to use a model selection method. The selections that Minitab presents depend on the size of the data set. The selections combine with selections on the Validation subdialog to provide an analysis that balances rigor and calculation speed:

- N < 1,500
- The validation method on the Validation subdialog is K-fold cross-validation. The number of folds is 5. The Regression model selection method on the Terms subdialog is Stepwise.
- 1,500 ≤ N < 2,000
- The validation method on the Validation subdialog is K-fold cross-validation. The number of folds is 5. The Regression model selection method on the Terms subdialog is Forward selection with validation.
- 2,000 ≤ N
- The validation method on the Validation subdialog is Validation with a test set. The proportion of data in the test set is 0.3. The Regression model selection method on the Terms subdialog is Forward selection with validation.

- Stepwise: This method starts with an empty model. Then, Minitab adds or removes a term for each step. Minitab stops when all variables that are not in the model have p-values that are greater than 0.15 and when all variables that are in the model have p-values that are less than or equal to 0.15.
- Forward selection with
validation:
When you select
Forward selection with
validation,
choose the validation method to test your model. Usually, with smaller samples,
the K-fold cross-validation method is appropriate. With larger samples, you can
divide the data into a training data set and a test data set. The procedure is
similar to forward selection. At the end of each step, Minitab calculates the
test R
^{2}statistic. At the end of the forward selection procedure, the model with the greatest test R^{2}value is the final model.The procedure continues until one of the following conditions occurs:- The procedure does not find an improvement of the criterion for 8 consecutive steps.
- The procedure fits the full model.
- The procedure fits a model that leaves 1 degree of freedom for error.

- None: Fit the model with all the terms for the regression model.