Second-Hand Vehicle Asking Prices Year Mfgr Model Price Age Car Truck SUV Van 2002 GM Astro Gulf Stream Conversion 12,988 3 0 0 0 1 1996 GM Astro LS 4.3L V6 5,950 9 0 0 0 1 2001 GM Astro LS V6 19,995 4 0 0 0 1 1999 GM Astro V6 7 Passenger 5,763 6 0 0 0 1 2002 GM Avalanche 4x4 20,988 3 0 1 0 0 2002 GM Avalanche 5.3L V8 4x4 22,700 3 0 1 0 0 2003 GM Avalanche V8 4 DR 23,671 2 0 1 0 0 2003 GM Avalanche V8 4x4 24,995 2 0 1 0 0 2002 GM Avalanche V8 4x4 & 2x2's 19,990 3 0 1 0 0 2002 GM Avalanche Z71 20,995 3 0 1 0 0 2002 GM Avalanche Z71 4x4 19,995 3 0 1 0 0 2003 GM Avalanche Z71 5.3L 4x4 24,495 2 0 1 0 0 2002 GM Avalanche Z71 5.3L 4x4 22,900 3 0 1 0 0 2002 GM Avalanche Z71 V8 4x4 23,995 3 0 1 0 0 2002 GM Avalanche Z71 V8 4x4 22,995 3 0 1 0 0 1

Your manager helps you acquire data that allows you to analyze the factors that potentially impact the value of the second-hand vehicle. The following are the factors (i.e. variables). Price = Asking price in $ Age = Vehicle age in Years If the vehicle is a Passenger Car = 1; otherwise = 0 If the vehicle is a Truck = 1; otherwise = 0 If the vehicle is a Sports Utility Vehicle (SUV) = 1; otherwise = 0 If the vehicle is a Van = 1; otherwise = 0 The Excel data file (Project #1 Second-Hand Vehicles Prices) is provided. Multiple Regression 1) State your a priori hypotheses about the sign (+ or -) of each predictor and your reasoning about cause and effect. In other words, just simply reviewing the variables, do any or all of these variables make sense in considering a second-hand vehicle price? Would the intercept have meaning in this problem? Explain. 2) Does your sample size fulfill Evans Rule or at least Doanes Rule? 3) Perform the regression and write the estimated regression equation (round to nearest three or four decimals). Do the coefficient signs agree with youre a priori expectations? 4) Does the 95% confidence interval for reach predictor coefficient include zero? What conclusion can you draw? 5) Do a two-tailed t-test for zero slope for each predictor coefficient at alpha = 0.05. State the degrees of freedom, and look up the critical value in Appendix D (or from Excel). 6) Which p-values indicate predictor significance at alpha = 0.05? Do the p-values support the conclusions you reached from the t-tests? Do you prefer the t-test or the p-value approach? Why? 7) Based on the R^2 and ANOVA table for our model, how would you describe the fit? 8) Use the standard error to construct an approximate prediction interval for Y. Based on the width of this prediction interval, would you say the predictions are good enough to have practical value? 9) Generate a correlation matrix (chart) for your predictors. Round the results to three decimal places. Based on the correlation matrix (chart), is collinearity a problem? 10) Re-run the regression requesting variance inflation factors (VIFs) for your predictors. Do the VIFs suggest that multicollinearity is a problem? Explain.