Oakland Athletic club (Oakland A) plays baseball in major leagues in the US. Mark Nobel is currently a star player for Oakland A. Oakland and Nobel’s agent is in the midst of a negotiation. Nobel’s agent claims that Nobel’s contribution was not only on the pitch but also substantially helped pull in audience to especially to see Nobel pitch. Demanding an appropriate compensation for increased ticket revenues, Nobel’s agent put forth a figure of $105,650 measuring the value of Nobel to the team.
A key factor attributing to Oakland’s success is the quality of the pitching staff led by Nobel. Nobel had won several games for Oakland, was the second best pitcher in the league and also received several awards in 1980 in …show more content…
Dependent Variable: TIX
This model is a linear regression between ticket sales, Nobel, Yankees, Boston, double header, promotion and Kansas City. The inclusion of the variable explaining the games against Kansas City has increased the R square value to 0.770. This suggests that the ticket sales increase when there is a match played against Kansas City apart from the other variables considered in the previous models.
Best Model
DependantVariable : TIX Independent Variable: YANKS, O9, DH, O13, PROMO
Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics Durbin-Watson R Square Change F Change df1 df2 Sig. F Change
1 .877a .768 .751 4831.1744 .768 44.438 5 67 .000 1.953
a. Predictors: (Constant), O13, YANKS, O9, DH, PROMO
b. Dependent Variable: TIX
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations B Std. Error Beta Zero-order Partial Part
1 (Constant) 6656.653 744.482 8.941 .000 YANKS 30754.969 2301.348 .808 13.364 .000 .832 .853 .786 O9 6366.681 2108.150 .182 3.020 .004 .065 .346 .178 DH 6224.686 2123.511 .178 2.931 .005 .220 .337 .172 PROMO 4471.103 1601.337 .172 2.792 .007 .245 .323 …show more content…
the independent variables (Xi). The scatter plot is for the variable OD – Opening Day is shown in figure IV-(a) below.
Fig IV-(a): The scatter plot for the Partial Regression of Residuals (Ticket Sales) vs. OD – Opening Day.
The requirement for validation of the linearity assumption is that the residuals must be randomly distributed around their mean. As can be seen from figure IV (a) above, the residuals are fairly random in their distribution along the mean (0) both above and below zero. Therefore we can conclude that the linearity assumption is reasonable in this case.
Let us look at another variable for further confirmation of our assumption. This variable is O13 – the categorical variable that is the Match against Kansas City Royals, and it is used in the plot in figure IV-(b) below. Figure IV-(b): The scatter plot of Residuals vs. O13 (Kansas City Royals) categorical variable.
We can see from Figure IV-(b) above that the points are fairly randomly distributed on either side of the mean (0). Therefore, we can conclude that the linearity assumption is fairly valid and