. Run a regression tree (Analyze>Predictive Modelling>Partition) with the output variable Price and input variables, Age, Mileage, Fuel Type, Horse Power, Metallic, Automatic, CC, Doors, QuartTax, and Weight. Cast the column Validation to Validation. Use the split button repeatedly (16 times) to create a tree (hint, use the red triangle options to hide the tree and the graph). As you split, keep an eye on RMSE and RSquare for the training and validation sets, describe what happens to the RSquare and RMSE for the training and validation sets as you continue to split the tree. For the first several splits RSquare Values [ Select ] and RMSE Values |[ Select ] . Based on this tree, which are the most important car specifications for predicting the car's price? (You may use Column contributions (red triangle option)) The most important variable, by far, is Age . Refit this model (Relaunch the Partition Platform with the same output/inputs), and use the Go button to automatically split and prune the tree based on the validation RSquare. Save the prediction formula for this (second) model to the data table. (Save your script to data file with a different name) ure.com/courses/23704/quizzes/149064