The following table gives a football teams seasontickets sal

The following table gives a football team’s season-tickets sales, percentage of games won, and number of active alumni for the years from 1998 to 2011.

Year; Season ticket sales; % games won; number of active alumni

1998 4995 40 NA

1999 8599 54 3450

2000 8479 55 3801

2001 8419 58 4000

2002 10253 63 1098

2003 12457 75 6315

2004 13285 36 6860

2005 14177 27 8423

2006 15730 63 9000

2007 15805 70 9500

2008 15575 72 9530

2009 15900 75 9550

2010 14010 80 9560

2011 12500 82 9575

Estimate a regression model for sales = Percentage of games won. Estimate a regression model for sales = Number of active alumni. If sales is the dependent variable, which of the two independent variables do you think explains sales better? Explain. Use Excel or StatCruntch for your work and please show me your work and simply not the answers.

Solution

Regression Analysis: Season ticket sales versus % games won

The regression equation is
Season ticket sales = 7511 + 76.5 % games won

Coefficient of determination=14%

Regression Analysis: Season ticket sales versus Number of sctive alumni

The regression equation is
Season ticket sales = 6157 + 0.926 Number of sctive alumni

Coefficient of determination=83%

If sales is the dependent variable, the two independent variable Number of active alumni explains sales better.

Work:

Regression Analysis: Season ticket sales versus % games won

The regression equation is
Season ticket sales = 7511 + 76.5 % games won

Predictor Coef SE Coef T P
Constant 7511 3446 2.18 0.050
% games won 76.50 54.81 1.40 0.188

S = 3342.62 R-Sq = 14.0% R-Sq(adj) = 6.8%

Analysis of Variance

Source DF SS MS F P
Regression 1 21762676 21762676 1.95 0.188
Residual Error 12 134077510 11173126
Total 13 155840186

Regression Analysis: Season ticket sales versus Number of sctive alumni

The regression equation is
Season ticket sales = 6157 + 0.926 Number of sctive alumni

Predictor Coef SE Coef T P
Constant 6157.4 879.8 7.00 0.000
Number of sctive alumni 0.9263 0.1212 7.64 0.000

S = 1487.58 R-Sq = 83.0% R-Sq(adj) = 81.5%

Analysis of Variance

Source DF SS MS F P
Regression 1 129285425 129285425 58.42 0.000
Residual Error 12 26554761 2212897
Total 13 155840186

Unusual Observations

Number of Season
sctive ticket
Obs alumni sales Fit SE Fit Residual St Resid
5 1098 10253 7174 763 3079 2.41R

R denotes an observation with a large standardized residual.