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Q
uantitative
R
easoning &
P
roblem
S
olving
348
© 2014 Pacific Crest
3. Given the data set that follows (and which is also available online in Excel format),
a. Find the correlation coefficient.
b. Determine if a predictive model would make sense for someone to use as a professional betting
scheme. Explain why or why not.
This data set includes the total number of passing yards and the total number of wins for NFL teams
in the 2013 season. Note: There was one tie during the season and each team was awarded 0.5 wins
for that game.
passing yards
wins
Arizona
4002
10
Atlanta
4243
4
Baltimore
3590
8
Buffalo
3103
6
Carolina
3043
12
Chicago
4281
8
Cincinnati
4136
11
Cleveland
4047
4
Dallas
3954
8
Denver
5444
13
Detroit
4482
7
Green Bay
4268
8.5
Houston
3813
2
Indianapolis
3725
11
Jacksonville
3441
4
Kansas City
3340
11
passing yards
wins
Miami
3567
8
Minnesota
3427
5.5
New England
4087
12
New Orleans
4918
11
NY Giants
3588
7
NY Jets
2932
8
Oakland
3340
4
Philadelphia
4110
10
Pittsburgh
4017
8
San Diego
4328
9
San Francisco
2979
12
Seattle
3236
13
St. Louis
3125
7
Tampa Bay
2820
4
Tennessee
3496
7
Washington
3751
3
4. Using the data at
right, predict the
number of wins for
a team with a WHIP
of 1.35.
Team
WHIP Wins
Baltimore
1.32 85
Boston
1.30 97
Chicago Sox
1.33 63
Cleveland
1.33 92
Detroit
1.25 93
Houston
1.49 51
Kansas City
1.27 86
LA Angels
1.38 78
Team
WHIP Wins
Minnesota
1.41 66
NY Yankees
1.31 85
Oakland
1.22 96
Seattle
1.33 71
Tampa Bay
1.23 92
Texas
1.28 91
Toronto
1.34 74
5. Identify situations where correlation does not imply causation. (Hint: think about common
superstitions and how they might have started.)