Example of prediction with MARS^® Regression

Note

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

A team of researchers collects data from the sale of individual residential properties in Ames, Iowa. The researchers want to identify the variables that affect the sale price. Variables include the lot size and various features of the residential property. The researchers want to make predictions with a MARS^® model.

Complete Example of MARS® Regression.
Open the sample data AmesHousingPredictions.mtw.
Ensure that the worksheet that contains the prediction data is active and click the Predict button at the bottom of the results.
From the drop-down list, select Enter columns of values.
In each row, enter the column with the data for the predictor. In this data set, the columns have the same names as the predictors. For example, in the row for Lot Frontage, enter 'Lot Frontage'.
Click OK.

Interpret the results

Minitab uses the model in the results to estimate the fit for the set of prediction values. The researchers find the predicted sales prices for various settings of the predictors.

Fit
249282
80080
431480
447929
232095
108761
138793
182891
231014
211210
384041
180677
246418
281047

Example of prediction with MARS® Regression

Note

Interpret the results

Example of prediction with MARS^® Regression