Overview for CART® Regression

Use CART® Regression to create an decision tree for a continuous response with many categorical and continuous predictor variables. CART® Regression illustrates important patterns and relationships between a continuous response and important predictors within highly complicated data, without using parametric methods.

CART® Regression provides insights for a wide range of applications, including manufacturing quality control, drug discovery, fraud detection, credit scoring, and churn prediction. Use the results to identify important variables, to identify groups in the data with desirable characteristics, and to predict response values for new observations. For example, a bank manager wants to identify potential customers that have higher response rates to specific initiatives.

For a more complete introduction to the CART® methodology, see Breiman, Friedman, Olshen and Stone (1984)1.

Where to find this analysis

To create a regression tree, choose Stat > Predictive Analytics > CART® Regression.

When to use an alternate analysis

If you have a categorical response variable, use CART® Classification.

To try to improve the fit of the tree, Minitab offers TreeNet® Regression and Random Forests® Regression analyses with the Predictive Analytics Module. Click here for more information about how to activate the module.

1 Breiman, Friedman, Olshen & Stone. (1984). Classification and Regression Trees. Boca Raton, Florida: Chapman & Hall/CRC.