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Multiple Regression: Case Problem Predicting Winnings for NASCAR Drivers [WLOs: 1, 2, 3] [CLOs: 1, 2, 3, 4, 5, 6, 7] Prior to beginning work
Multiple Regression: Case Problem Predicting Winnings for NASCAR Drivers [WLOs: 1, 2, 3] [CLOs: 1, 2, 3, 4, 5, 6, 7]
Prior to beginning work on this discussion forum, watch the Week 5 Introduction(Links to an external site.) video, and read Chapter 15 in the MindTap ebook by clicking on the Getting Ready link for each perspective chapter.
Step 1: Read
- Review Case Problem 2: Predicting Winnings for NASCAR Drivers Download Case Problem 2: Predicting Winnings for NASCAR Drivers from Chapter 15 of the ebook.
Step 2: Do
- Run a Regression for the Data File NASCAR (Chapter 15) using the video How to Add Excel's Data Analysis ToolPak(Links to an external site.) for assistance.
In a managerial report,
- Suppose you wanted to predict Winnings ($) using only the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), or the number of top ten finishes (Top 10). Which of these four variables provides the best single predictor of winnings?
- Develop an estimated regression equation that can be used to predict Winnings ($) given the number of poles won (Poles), the number of wins (Wins), the number of top five finishes (Top 5), and the number of top ten (Top 10) finishes. Test for individual significance, and then discuss your findings and conclusions.
Step 3: Discuss:
- What did you find in your analysis of the data? Were there any surprising results? What recommendations would you make based on your findings? Include details from your managerial report to support your recommendations.
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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