Multiple Regression: Case Problem Predicting Winnings for NASCAR Drivers [WLOs: 1, 2, 3] [CLOs: 1, 2, 3, 4, 5, 6, 7]

Case Problem 2Predicting Winnings for NASCAR Drivers
Matt Kenseth won the 2012 Daytona 500, the most important race of the NASCAR season. His
win was no surprise because for the 2011 season he finished fourth in the point standings with
2330 points, behind Tony Stewart (2403 points), Carl Edwards (2403 points), and Kevin Harvick
(2345 points). In 2011 he earned $6,183,580 by winning three Poles (fastest driver in qualifying),
winning three races, finishing in the top five 12 times, and finishing in the top ten 20 times.
NASCAR’s point system in 2011 allocated 43 points to the driver who finished first, 42 points to
the driver who finished second, and so on down to 1 point for the driver who finished in the 43rd
position. In addition any driver who led a lap received 1 bonus point, the driver who led the most
laps received an additional bonus point, and the race winner was awarded 3 bonus points. But the
maximum number of points a driver could earn in any race was 48. Table 15.8 shows data for the
2011 season for the top 35 drivers (NASCAR website).
•
Review Case Problem 2: Predicting Winnings for NASCAR Drivers Download Case
Problem 2: Predicting Winnings for NASCAR Driversfrom Chapter 15 of the ebook.
Step 2: Do
•
Run a Regression for the Data File NASCAR (Chapter 15) using the video How to
Add Excel’s Data Analysis ToolPak Links to an external site.for assistance.
In a managerial report,
•
•
Suppose you wanted to predict Winnings ($) 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 ($)
given the number of poles won (Poles), the number of wins (Wins), the number of top
five finishes (Top 5), and the number of top ten (Top 10) finishes. Test for individual
significance, and then discuss your findings and conclusions.
Step 3: Discuss:
•
What did you find in your analysis of the data? Were there any surprising results?
What recommendations would you make based on your findings? Include details
from your managerial report to support your recommendations.
Driver
Points
Poles
Tony Stewart
2403
Carl Edwards
2403
Kevin Harvick
2345
Matt Kenseth
2330
Brad Keselowski
2319
Jimmie Johnson
2304
Dale Earnhardt Jr.
2290
Jeff Gordon
2287
Denny Hamlin
2284
Ryan Newman
2284
Kurt Busch
2262
Kyle Busch
2246
Clint Bowyer
1047
Kasey Kahne
1041
A.J. Allmendinger
1013
Greg Biffle
997
Paul Menard
947
Martin Truex Jr.
937
Marcos Ambrose
936
Jeff Burton
935
Juan Montoya
932
Mark Martin
930
David Ragan
906
Joey Logano
902
Brian Vickers
846
Regan Smith
820
Jamie McMurray
795
David Reutimann
757
Bobby Labonte
670
David Gilliland
572
Casey Mears
541
Dave Blaney
508
Andy Lally*
398
Robby Gordon
268
J.J. Yeley
192
Wins
1
3
0
3
1
0
1
1
0
3
3
1
0
2
0
3
0
1
0
0
2
2
2
2
0
0
1
1
0
0
0
0
0
0
0
Top 5
5
1
4
3
3
2
0
3
1
1
2
4
1
1
0
0
1
0
1
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
Top 10
9
19
9
12
10
14
4
13
5
9
8
14
4
8
1
3
4
3
5
2
2
2
4
4
3
2
2
1
1
1
0
1
0
0
0
Winnings ($)
19
6,529,870
26
8,485,990
19
6,197,140
20
6,183,580
14
5,087,740
21
6,296,360
12
4,163,690
18
5,912,830
14
5,401,190
17
5,303,020
16
5,936,470
18
6,161,020
16
5,633,950
15
4,775,160
10
4,825,560
10
4,318,050
8
3,853,690
12
3,955,560
12
4,750,390
5
3,807,780
8
5,020,780
10
3,830,910
8
4,203,660
6
3,856,010
7
4,301,880
5
4,579,860
4
4,794,770
3
4,374,770
2
4,505,650
2
3,878,390
0
2,838,320
1
3,229,210
0
2,868,220
0
2,271,890
0
2,559,500