San Diego Mesa College Week 5 Predicting Winnings for NASCAR Drivers Discussion

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Cnyv01

Mathematics

San Diego Mesa College

Description

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 from Chapter 15 of the ebook.
  • Step 2: Do

    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.

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Explanation & Answer

View attached explanation and answer. Let me know if you have any questions.

Driver
Points
Tony Stewart
Carl Edwards
Kevin Harvick
Matt Kenseth
Brad Keselowski
Jimmie Johnson
Dale Earnhardt Jr.
Jeff Gordon
Denny Hamlin
Ryan Newman
Kurt Busch
Kyle Busch
Clint Bowyer
Kasey Kahne
A.J. Allmendinger
Greg Biffle
Paul Menard
Martin Truex Jr.
Marcos Ambrose
Jeff Burton
Juan Montoya
Mark Martin
David Ragan
Joey Logano
Brian Vickers
Regan Smith
Jamie McMurray
David Reutimann
Bobby Labonte
David Gilliland
Casey Mears
Dave Blaney
Andy Lally*
Robby Gordon
J.J. Yeley

Poles
2403
2403
2345
2330
2319
2304
2290
2287
2284
2284
2262
2246
1047
1041
1013
997
947
937
936
935
932
930
906
902
846
820
795
757
670
572
541
508
398
268
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

19
26
19
20
14
21
12
18
14
17
16
18
16
15
10
10
8
12
12
5
8
10
8
6
7
5
4
3
2
2
0
1
0
0
0

Correlation
Poles
Poles
Wins
Top 5
Top 10

1
0.133135266
0.43731218
0.457811773

Wins

Top 5

1
0.725170253
1
0.697178094 0.901745

Top 10

1

Winnings ($)

0.406087062

0.661561743 0.861168 0.897756

Regression
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.905808159
R Square
0.820488422
Adjusted R Square 0.796553544
Standard Error
581382.1968
Observations
35
ANOVA
df
Regression
Residual
Total

Intercept
Poles
Wins
Top 5
Top 10

SS
MS
F
4 4.63473E+13 1.16E+13 34.28003
30 1.01402E+13 3.38E+11
34 5.64875E+13

Coefficients Standard Error t Stat
P-value
3140367.087 184229.0243
17.046 5.59E-17
-12938.9208 107205.0751 -0.12069 0.904739
13544.81269 111226.2163 0.121777 0.903888
71629.39328 50666.86771 1.413732 0.167734
117070.5768 33432.88382 3.50166 0.00147

Regression of Winnings ($) an Top 10 only
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.897755883
R Square
0.805965625
Adjusted R Square 0.800085795
Standard Error
576313.0996
Observations
35
ANOVA
df
Regression
Residual
Total

SS
MS
1 4.5527E+13 4.55E+13
33 1.09605E+13 3.32E+11
34 5.64875E+13

F
137.073

Intercept
Top 10

Coefficients Standard Error t Stat
P-value
3049156.661 171768.9286 17.7515 1.89E-18
161934.0136 13831.27412 11.70782 2.71E-13

Winnings ($)
6,529,870
8,485,990
6,197,140
6,183,580
5,087,740
6,296,360
4,163,690
5,912,830
5,401,190
5,303,020
5,936,470
6,161,020
5,633,950
4,775,160
4,825,560
4,318,050
3,853,690
3,955,560
4,750,390
3,807,780
5,020,780
3,830,910
4,203,660
3,856,010
4,301,880
4,579,860
4,794,770
4,374,770
4,505,650
3,878,390
2,838,320
3,229,210
2,868,220
2,271,890
2,559,500

Winnings ($)

1

Significance F
8.6194E-11

Lower 95%
2764121.22
-231880.893
-213609.425
-31846.1551
48791.5191

Significance F
2.712E-13

Upper 95% Lower 95.0%
3516612.95 2764121.225
206003.051 -231880.8929
240699.051 -213609.4253
175104.942 -31846.15509
185349.635 48791.51907

Upper 95.0%
3516612.949
206003.0513
240699.0507
175104.9416
185349.6346

Lower 95% Upper 95% Lower 95.0% Upper 95.0%
2699690.15 3398623.17 2699690.148 3398623.174
133794.075 190073.952 133794.0748 190073.9524


Predicting Winners for NASCAR Drivers
I.

Case Scenario
A. Matt Kenseth was a winner of Daytona 500 in 2012
B. It was due to his historical improvements that NASCAR system recorded data
was used to develop a prediction model (multiple regression equation) to predict
the Winnings ($) and assess...


Anonymous
Really useful study material!

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