Use the sample data in the accompanying table. The table includes the results for the top performers in the 1996 Molson Indy Toronto. Note that the data in the Final Position column are ordinal and thus should not be used in the regressions. The distance between the first and second final positions, for example, may be quite different from the distance between the second and third final positions. Intervals between starting positions, however, are assumed to correspond to consistent track distances. Times are given in seconds and speeds in miles per hour.

Let y = points, x, = starting position, and = qualifying time. Use software such as Excel or STATDISK to find the multiple regression equation of the form = b0 + bxxx + b2x2. Also identify the value of the multiple coefficient of determination R2, the adjusted R2, and the P-value representing the overall significance of the multiple regression equation. Based on the results, should the multiple regression equation be used for making predictions? Why or why not?

Final Position Starting Position Qualifying Time Qualifying Speed Points 109.838 110.455 108.825 109.748 108.507 108.533 108.450 108.634 107.615 109.803 20 17 14 12 10 58.471 58.145 59.016 58.519 59.189 59.175 59.220 59.120 59.680 58.490 4 13 18 4 4 10 Based on data from Molson Indy Toronto.