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

Select the graphs that you want to display for the analysis.

- R-squared vs number of basis functions plot
- Select to show the plot when R-squared is the criterion for the selection of the optimal model. The plot shows the relationship between R-squared value and the number of basis functions in the model. The maximum on the plot shows the number of basis functions in the optimal model for the other results.
- Mean absolute deviation vs number of basis functions plot
- Select to show the plot when the mean absolute deviation is the criterion for the selection of the optimal model. The plot shows the relationship between the mean absolute deviation value and the number of basis functions in the model. The minimum on the plot shows the number of basis functions in the optimal model for the other results.
- Variable importance chart
- The variable importance chart shows the relative importance of the predictors.
You can choose whether to display all or some of the important variables.
Variables are important when the model has a basis function for that variable.
- Display all important variables: By default, this chart displays all important variables.
- Display a percentage of important variables: Specify the percentage of important variables to display. Enter a value between 0 and 100.
- Display all predictor variables: Display all predictors whether or not they are important variables.

- Fitted vs. actual response value plot
- The Fitted vs. actual response value plot shows the fitted Y (response) values versus the actual Y (response) values for both the training and test data sets.
- Boxplot of residuals
- The Boxplot of residuals shows the residual values or the percent residuals for both the training and test data sets.
- One predictor partial dependence plot for additive models
- The one predictor partial dependence plots display fits for the important variables in the model. Use the plots to gain insight into how the variables affect the predicted response.