Reference no: EM13578553
Predicting Winning Percentage for the NFL
The National Football League (NFL) records a variety of performance data for individuals and teams (http://www.nfl.com). Some of the year-end performance data for the 2005 season appear on the data disk in the file named NFLStats. Each row of the data set corresponds to an NFL wain, and the teams are ranked by winning percentage. Descriptions for the data follow:
WinPct Percentage of games won
DefYds/G Average number of yards per game given up on defense
RushYds/G Average number of rushing yards per game
PassYds/6 Average number of passing yards per game
FGPct Percentage of field goals
TakeInt Takeaway interceptions; the total number of interceptions made by the team's defense
TakeFum Takeaway fumbles; the total number of fumbles recovered by the team's defense
GiveInt Giveaway interceptions; the total number of interceptions thrown by the team's offense
GiveFum Giveaway fumbles; the total number of fumbles lost by the team's offense
| Team |
Division |
WinPct |
TakeInt |
TakeFum |
GiveInt |
GiveFum |
DefYds/G |
RushYds/G |
PassYds/G |
FGPct |
| Indianapolis |
AFC SOUTH |
0.875 |
18 |
13 |
11 |
8 |
307.1 |
106.4 |
256 |
88.5 |
| Denver |
AFC WEST |
0.813 |
20 |
16 |
7 |
9 |
312.9 |
158.7 |
201.7 |
75 |
| Seattle |
NFC WEST |
0.813 |
16 |
11 |
10 |
7 |
316.8 |
153.6 |
216.1 |
72 |
| Jacksonville |
AFC SOUTH |
0.75 |
19 |
9 |
6 |
11 |
290.9 |
122.4 |
199.4 |
76.7 |
| Carolina |
NFC SOUTH |
0.688 |
23 |
19 |
16 |
10 |
282.6 |
104.9 |
204.4 |
76.5 |
| Chicago |
NFC NORTH |
0.688 |
24 |
10 |
15 |
13 |
281.8 |
131.2 |
125.1 |
71 |
| Cincinnati |
AFC NORTH |
0.688 |
31 |
13 |
14 |
6 |
338.7 |
119.4 |
238.8 |
87.5 |
| New York (A) |
NFC EAST |
0.688 |
21 |
7 |
15 |
19 |
308.8 |
83 |
165.1 |
78.6 |
| Pittsburgh |
AFC NORTH |
0.688 |
15 |
15 |
14 |
9 |
284 |
138.9 |
182.9 |
82.8 |
| Tampa Bay |
NFC SOUTH |
0.688 |
17 |
13 |
14 |
9 |
277.8 |
114.1 |
180.6 |
81.5 |
| Kansas City |
AFC WEST |
0.625 |
16 |
15 |
10 |
13 |
328.1 |
148.9 |
238.1 |
81.8 |
| New England |
AFC EAST |
0.625 |
10 |
8 |
15 |
9 |
330.2 |
94.5 |
257.5 |
80 |
| Washington |
NFC EAST |
0.625 |
16 |
12 |
11 |
16 |
297.9 |
136.4 |
194.1 |
81 |
| Dallas |
NFC EAST |
0.563 |
15 |
11 |
17 |
14 |
300.9 |
116.3 |
208.8 |
71.4 |
| Miami |
AFC EAST |
0.563 |
14 |
17 |
16 |
14 |
317.4 |
118.6 |
206.2 |
83.3 |
| Minnesota |
NFC NORTH |
0.563 |
24 |
11 |
16 |
14 |
323.3 |
91.7 |
196.6 |
73.5 |
| San Diego |
AFC WEST |
0.563 |
10 |
10 |
16 |
12 |
309.2 |
129.5 |
218.4 |
87.5 |
| Atlanta |
NFC SOUTH |
0.5 |
16 |
13 |
13 |
16 |
325 |
159.1 |
167.4 |
88.9 |
| Baltimore |
AFC NORTH |
0.375 |
12 |
14 |
21 |
15 |
284.7 |
100.3 |
193 |
85.7 |
| Cleveland |
AFC NORTH |
0.375 |
15 |
8 |
18 |
12 |
316.8 |
93.9 |
190.8 |
93.1 |
| Philadelphia |
NFC EAST |
0.375 |
17 |
10 |
20 |
14 |
325.4 |
89.5 |
229.8 |
75.9 |
| St. Louis |
NFC WEST |
0.375 |
13 |
14 |
24 |
13 |
350.1 |
95.9 |
252.2 |
87.1 |
| Arizona |
NFC WEST |
0.313 |
15 |
11 |
21 |
16 |
295.6 |
71.1 |
277.3 |
95.6 |
| Buffalo |
AFC EAST |
0.313 |
17 |
13 |
16 |
10 |
343.5 |
100.4 |
157.2 |
82.9 |
| Detroit |
NFC NORTH |
0.313 |
19 |
12 |
18 |
12 |
322.4 |
91.9 |
178 |
79.2 |
| Green Bay |
NFC NORTH |
0.25 |
10 |
11 |
30 |
15 |
293.1 |
84.5 |
235.4 |
74.1 |
| New York (N) |
AFC EAST |
0.25 |
17 |
19 |
17 |
8 |
327.5 |
138.1 |
223.6 |
83.3 |
| Oakland |
AFC WEST |
0.25 |
5 |
14 |
14 |
9 |
330.8 |
85.6 |
223.9 |
66.7 |
| San Francisco |
NFC WEST |
0.25 |
16 |
10 |
21 |
14 |
391.2 |
105.6 |
118.6 |
89.7 |
| Tennessee |
AFC SOUTH |
0.25 |
9 |
11 |
14 |
12 |
319.4 |
95.3 |
224.8 |
79.3 |
| New Orleans |
NFC SOUTH |
0.188 |
10 |
9 |
24 |
19 |
312.1 |
105.5 |
208.9 |
78.1 |
| Houston |
AFC SOUTH |
0.125 |
7 |
9 |
13 |
11 |
364 |
113.5 |
139.8 |
76.5 |
Use the data file provided, answers following questions: (hint: Y Variable :WinPct )
1. Develop an estimated regression equation that can be used to estimate WinPct using the following independent variables DefYds/G, RushYds/G, PassYds/G, and FGPct.
2. Explain the adjusted R2aof regression results from question 1.
3. Examine the whether the overall regression results from question 1 is useful or not.
4. Explain the coefficients of the X variables in regression results from question 1.
5. Examine the significance of each variable in regression results from question 1.
6. Starting with the estimated regression equation developed in question 1, delete any independent variables that are not useful (i.e., the variable with p_value bigger than 0.05). Use the variables left, run the regression (Y variable is the same).
7. Examine the total significance of the regression results from question 6, the significance of each X variable, and the adjusted R2a .
8. Compare regression result in Question 6 and the regression result in question 1.
(hint: compare R2a, a regression result with bigger R2a is better; compare significance F of the two regression results, the regression result with smaller significance F is better.)