1

The National Football League (NFL) records a variety of performance data for individuals and team...

Question

The National Football League (NFL) records a variety of performance data for individuals and team...

The National Football League (NFL) records a variety of performance data for individuals and teams. To investigate the importc. Develop the estimated regression equation that could be used to predict the percentage of games won given the average numb

The National Football League (NFL) records a variety of performance data for individuals and teams. To investigate the importance of passing on the percentage of games won by a team, the following data show the conference (Conf), average number of passing yards per attempt (Yds/Att), the number of interceptions thrown per attempt (Int/Att), and the percentage of games won (Win96) for a random sample of 16 NFL teams for a season Conf NFC NFC NFC AFC NFC NFC AFC AFC AFC NFC AFC NFC AFC NFC AFC NFC Yds/Att Team Arizona Cardinals Atlanta Falcons Carolina Panthers Cincinnati Bengals Detroit Lions Green Bay Packers Houstan Texans Indianapolis Colts Jacksonville Jaguars Minnesota Vikings New England Patriots New Orleans Saints Oakland Raiders San Francisco 49ers Tennessee Titans Washington Redskins Int/Att 0.044 0.021 0.032 0.027 0.023 0.015 0.018 0.025 0.033 0.032 0.021 0.021 0.043 0.010 0.023 0.041 Win% 49.9 62.7 37.7 56.2 62.3 93.8 62.7 12.5 31.0 18.7 81.0 81.4 49.9 81.4 56.1 31.2 7.3 7.5 6.3 7.7 4.7 8.4 7.4 6.7 6.6 6.6 a. Develop the estimated regression equation that could be used to predict the percentage of games won given the average number of passing yards per attempt (to 1 decimal) Enter negative value as negative number 1 Yds/Att b. Develop the estimated regression equation that could be used to predict the percentage of games won given the number of interceptions thrown per attempt (to 1 decimal) Enter negative value as negative number 1 Int/Att
c. Develop the estimated regression equation that could be used to predict the percentage of games won given the average number of passing yards per attempt and the number of interceptions thrown per attempt (to 1 decimal). Enter negative value as negative number Win% d. The average number of passing yards per attempt for the Kansas City Chiefs was 6.2 and the number of interceptions thrown per attempt was 0.036. Use the estimated regression equation developed in part (c) to predict the percentage of games won by the Kansas City Chiefs. (Note: For a season the Kansas City Chiefs' record was 7 wins and 9 losses.) Compare your prediction to the actual percentage of games won by the Kansas City Chiefs (to whole number) Predicted percentagee .Yds/Att Int/Att Actual percentage Select your answer

Answers

Answer:

a).

win% = -60.6+16.5*yds/att

b).

win%= 94.2 +(-1487.8)*Int/Att

c). win% = -14.4+13.3*yds/Att +(-889.9)*int/Att

d).

predicted percentage 36 < actual percentage 44

Excel Addon Megastat used.

Menu used: correlation/Regression ---- Regression Analysis.

Regression Analysis

0.603

n

16

r

0.776

k

1

Std. Error of Estimate

15.398

Dep. Var.

winn%

Regression output

confidence interval

variables

coefficients

std. error

   t (df=14)

p-value

95% lower

95% upper

Intercept

a =

-60.597

ydsatt

b =

16.514

3.582

4.610

.0004

8.832

24.197

ANOVA table

Source

SS

df

MS

F

p-value

Regression

5,039.831

1  

5,039.831

21.26

.0004

Residual

3,319.473

14  

237.105

Total

8,359.304

15  

Regression Analysis

0.393

n

16

r

-0.627

k

1

Std. Error of Estimate

19.036

Dep. Var.

winn%

Regression output

confidence interval

variables

coefficients

std. error

   t (df=14)

p-value

95% lower

95% upper

Intercept

a =

94.173

intatt

b =

-1,487.807

494.088

-3.011

.0093

-2,547.521

-428.093

ANOVA table

Source

SS

df

MS

F

p-value

Regression

3,285.905

1  

3,285.905

9.07

.0093

Residual

5,073.399

14  

362.386

Total

8,359.304

15  

Regression Analysis

0.721

Adjusted R²

0.678

n

16

R

0.849

k

2

Std. Error of Estimate

13.402

Dep. Var.

winn%

Regression output

confidence interval

variables

coefficients

std. error

   t (df=13)

p-value

95% lower

95% upper

Intercept

a =

-14.384

ydsatt

b1 =

13.301

3.406

3.905

.0018

5.942

20.660

intatt

b2 =

-889.874

380.047

-2.341

.0358

-1,710.916

-68.833

ANOVA table

Source

SS

df

MS

F

p-value

Regression

6,024.500

2  

3,012.250

16.77

.0003

Residual

2,334.805

13  

179.600

Total

8,359.304

15  

Predicted values for: winn%

95% Confidence Interval

95% Prediction Interval

ydsatt

intatt

Predicted

lower

upper

lower

upper

Leverage

6.2

0.036

36.047

25.730

46.363

5.311

66.782

0.127


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