Dirt bikes 2014, revisited. Off-road motorcycles (often called “dirt bikes”) are a segment (about 18%) of the growing motorcycle market. Because dirt bikes offer great variation in features, they are a good market segment to study to learn about which features account for the cost (manufacturer’s suggested retail price, MSRP) of a bike. Researchers collected data on 2005-model dirt bikes (lib.stat.cmu.edu/

datasets/dirtbike_aug.csv). Their original goal was to study market differentiation among brands (Jiang Lu, Joseph B.

Kadane, and Peter Boatwright, The Dirt on Bikes: An Illustration of CART Models for Brand Differentiation). We’ve updated their data to 2014-model bikes. In Chapter 18, Exercises 41, 42, and 43 dealt with these data.

Here’s a regression model and some associated graphs.

Response variable is: MSRP R squared = 95.9% R squared (adjusted) = 95.6%

s = 550.0 with 57 - 5 = 52 degrees of freedom Source Sum of Squares df Mean Square F-ratio Regression 365430120 4 91357530 302 Residual 15730703 52 302514 Variable Coefficient SE(Coeff) t-ratio P-value Intercept -10417.0 681.6 -15.3 60.0001 Weight -22.1163 5.939 -3.72 0.0005 Wheel Base 265.191 28.18 9.41 60.0001 Rake 28.0126 4.813 5.82 60.0001 Bore 85.2723 7.742 11.0 60.0001

a) List aspects of this regression model that lead to the conclusion that it is likely to be a useful model.

b) What aspects of the displays indicate that the model is a good one?