Based upon the results of your analysis, what estimated regression equation would you recommend using to predict Winnings ($)? Yes, ◦P-values from the regression table: ◦Poles = 0.9047 > 0.01 (not significant) ◦Wins = 0.9039 > 0.01 (not significant) ◦Top 5 = 0.1677 > 0.01 (not significant)
Based upon the results of your analysis, what estimated regression equation would you recommend using to predict Winnings ($)? Yes, ◦P-values from the regression table: ◦Poles = 0.9047 > 0.01 (not significant) ◦Wins = 0.9039 > 0.01 (not significant) ◦Top 5 = 0.1677 > 0.01 (not significant)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Based upon the results of your analysis, what estimated regression equation would you recommend using to predict Winnings ($)? Yes, ◦P-values from the regression table: ◦Poles = 0.9047 > 0.01 (not significant) ◦Wins = 0.9039 > 0.01 (not significant) ◦Top 5 = 0.1677 > 0.01 (not significant) ◦Top 10 = 0.0015 < 0.01 (significant) ◦Conclusion: Top 10 provides the best single predictor of winnings Provide an interpretation of the estimated regression coefficients for this equation. |
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 3140367.09 | 184229.02 | 17.05 | 0.0000 | 2764121.22 | 3516612.95 | 2764121.22 | 3516612.95 |
| Poles | -12938.92 | 107205.08 | -0.12 | 0.9047 | -231880.89 | 206003.05 | -231880.89 | 206003.05 |
| Wins | 13544.81 | 111226.22 | 0.12 | 0.9039 | -213609.43 | 240699.05 | -213609.43 | 240699.05 |
| Top 5 | 71629.39 | 50666.87 | 1.41 | 0.1677 | -31846.16 | 175104.94 | -31846.16 | 175104.94 |
| Top 10 | 117070.58 | 33432.88 | 3.50 | 0.0015 | 48791.52 | 185349.63 | 48791.52 |
185349.63
|
| Driver | Points | Poles | Wins | Top 5 | Top 10 | Winnings ($) |
| Tony Stewart | 2403 | 1 | 5 | 9 | 19 | 6,529,870 |
| Carl Edwards | 2403 | 3 | 1 | 19 | 26 | 8,485,990 |
| Kevin Harvick | 2345 | 0 | 4 | 9 | 19 | 6,197,140 |
| Matt Kenseth | 2330 | 3 | 3 | 12 | 20 | 6,183,580 |
| Brad Keselowski | 2319 | 1 | 3 | 10 | 14 | 5,087,740 |
| Jimmie Johnson | 2304 | 0 | 2 | 14 | 21 | 6,296,360 |
| Dale Earnhardt Jr. | 2290 | 1 | 0 | 4 | 12 | 4,163,690 |
| Jeff Gordon | 2287 | 1 | 3 | 13 | 18 | 5,912,830 |
| Denny Hamlin | 2284 | 0 | 1 | 5 | 14 | 5,401,190 |
| Ryan Newman | 2284 | 3 | 1 | 9 | 17 | 5,303,020 |
| Kurt Busch | 2262 | 3 | 2 | 8 | 16 | 5,936,470 |
| Kyle Busch | 2246 | 1 | 4 | 14 | 18 | 6,161,020 |
| Clint Bowyer | 1047 | 0 | 1 | 4 | 16 | 5,633,950 |
| Kasey Kahne | 1041 | 2 | 1 | 8 | 15 | 4,775,160 |
| A.J. Allmendinger | 1013 | 0 | 0 | 1 | 10 | 4,825,560 |
| Greg Biffle | 997 | 3 | 0 | 3 | 10 | 4,318,050 |
| Paul Menard | 947 | 0 | 1 | 4 | 8 | 3,853,690 |
| Martin Truex Jr. | 937 | 1 | 0 | 3 | 12 | 3,955,560 |
| Marcos Ambrose | 936 | 0 | 1 | 5 | 12 | 4,750,390 |
| Jeff Burton | 935 | 0 | 0 | 2 | 5 | 3,807,780 |
| Juan Montoya | 932 | 2 | 0 | 2 | 8 | 5,020,780 |
| Mark Martin | 930 | 2 | 0 | 2 | 10 | 3,830,910 |
| David Ragan | 906 | 2 | 1 | 4 | 8 | 4,203,660 |
| Joey Logano | 902 | 2 | 0 | 4 | 6 | 3,856,010 |
| Brian Vickers | 846 | 0 | 0 | 3 | 7 | 4,301,880 |
| Regan Smith | 820 | 0 | 1 | 2 | 5 | 4,579,860 |
| Jamie McMurray | 795 | 1 | 0 | 2 | 4 | 4,794,770 |
| David Reutimann | 757 | 1 | 0 | 1 | 3 | 4,374,770 |
| Bobby Labonte | 670 | 0 | 0 | 1 | 2 | 4,505,650 |
| David Gilliland | 572 | 0 | 0 | 1 | 2 | 3,878,390 |
| Casey Mears | 541 | 0 | 0 | 0 | 0 | 2,838,320 |
| Dave Blaney | 508 | 0 | 0 | 1 | 1 | 3,229,210 |
| Andy Lally* | 398 | 0 | 0 | 0 | 0 | 2,868,220 |
| Robby Gordon | 268 | 0 | 0 | 0 | 0 | 2,271,890 |
| J.J. Yeley | 192 | 0 | 0 | 0 | 0 | 2,559,500 |
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