Specify the default methods for regression trees. The changes you make to the defaults remain until you change them again, even after you exit Minitab.

- Node splitting method
- Choose the splitting method to generate your decision tree. You can compare the results from both splitting methods to determine the best choice for your application.
- Least squared error: The least squared error method is the default method that works well across many applications. The least squared error method minimizes the sum of the squared errors.
- Least absolute deviation: The least absolute deviation method minimizes the sum of absolute values of errors.

- Criterion for selecting optimal tree
- When Least squared error is the criterion for the node splitting method, choose between these criteria to produce the tree in the results. You can compare results from different trees to determine the best choice for your application.
- Maximum R-squared
- Select this option to display results for the tree with the maximum R-squared value.
- Within K standard errors of maximum R-squared; K=
- Select this option to have Minitab choose the smallest tree with an R
^{2}value that falls within K standard errors of the tree with the maximum R^{2}value. By default, K=1, so the tree in the results is the smallest classification tree with an R^{2}value within 1 standard error of the maximum R^{2}value.

- Minimum number of cases to split an internal node
- Enter a value to represent the minimum number of cases an internal node to be split. The default is 10. With larger sample sizes, you may want to increase this minimum. For example, if an internal node has 10 or more cases, Minitab will try to perform a split. If the internal node has 9 cases or less, Minitab will not try to perform a split.
- Minimum number of cases allowed for a terminal node
- Enter a value to represent the minimum number of cases that may be separated into a terminal node. The default is 3. With larger sample sizes, you may want to increase this minimum. For example, if a split would create a node with less than 3 cases, Minitab will not perform a split.
- Missing value penalty
- Enter a penalty value for a predictor with missing values. Because it is easier to be a good splitter with less data, predictors with missing data have an advantage over predictors without missing data. Use this option to penalize predictors with missing data.
- High level category penalty
- Enter a penalty value for categorical predictors that have many values. Because categorical predictors with many levels can distort a tree due to their increased splitting power, they have an advantage over predictors with less levels. Use this option to penalize predictors with many levels.
- Display of graphs and tables
- Residuals for plots
- Specify the type of residuals to display on the boxplot of residual plots.
- Regular: By default, the boxplot displays regular residuals.
- Percent: Specify to display the percentage residuals on the boxplot.

- Terminal node type
- Choose whether to display the best nodes, the worst nodes, or both for the Fits and error statistics table and the Criteria for classifying subjects table.
- Best: By default, Minitab displays the best terminal nodes. The best nodes have the lowest MSE or MAD values.
- Worst: Select to display the worst terminal nodes. The worst nodes have the highest MSE or MAD values.
- Best and Worst: Select to display the best and the worst terminal nodes.