=+Here’s a regression model and some associated graphs.

Dependent variable is: MSRP R-squared = 91.0% R-squared (adjusted) = 90.5%

s = 606.4 with 100 - 6 = 94 degrees of freedom Source Sum of Squares df Mean Square F-ratio Regression 349911096 5 69982219 190 Residual 34566886 94 367733 Variable Coeff SE(Coeff) t-ratio P-value Intercept -5514.66 826.2 -6.67 60.0001 Bore 83.7950 6.145 13.6 60.0001 Clearance 152.617 52.02 2.93 0.0042 Engine Strokes -315.812 89.83 -3.52 0.0007 Total Weight -13.8502 3.017 -4.59 60.0001 Wheelbase 119.138 34.26 3.48 0.0008 13Well, in honesty, we’ve removed one luxury handmade bike whose MSRP was $19,500 as a clearly identified outlier.

M18_SHAR8696_03_SE_C18.indd 668 14/07/14 7:38 AM Exercises 669 Residuals 2000 4000 6000 Predicted 750 0

–750

–1.25 0.00 1.25 Nscores Residuals750 0

–750 Leverages 0.00 0.08 0.16 50 40 30 20 10