Question

ECON 483 Case Study Chapter 4 Bowie State University athletic department wants to develop its budget for the coming year, using a f attendance. Football attendance accounts for the largest portion of the University revenues. The new President of the university who is also a football fan has asked the athletic director to come up with strategies in promoting the university football team. The athletic director believes that attendance is directly related to the number of wins by the team. Instead of attempting to predict attendance based on only one variable (wins), the athletic department has included a second variable for advertising and promotional expenditures as well. The university president is anxious to know the result of this forecast to determine strategies that improve attendance and boost revenues for the University. The business manager of the BSU football team has accumulated total annual attendance figures for the past 8 years: orecast for foo bal Wins Promotion Attendanoe 4 $14500 21300 6 40700 25100 6 56300 26200 8 72000 38000 6 60000 29000 7 57000 30600 5 40300 24000 7 66600 32500 Given the number of returning starters and the strength of the schedule, the athletic director believes the team will win at least seven games next year. He wants to develop a multiple regression equation for these data to forecast attendance for this level of success. Discussion 1. Given that Attendance as the dependent variable and Wins questionS and Promotion as independent variables, use excel to estimate the relationship between attendance and promotion and wins. Copy and paste results from excel output). Also write down the estimated equation. 2. What is the strength of this relationship? Use the coefficient
Wins Promotion Attendance 4 $14500 21300 6 40700 25100 6 56300 26200 8 72000 38000 6 60000 29000 7 57000 30600 5 40300 24000 7 66600 32500 Given the number of returning starters and the strength of the schedule, the athletic director believes the team will win at least seven games next year. He wants to develop a multiple regression equation for these data to forecast attendance ft level of success. Discussion questions 1. Given that Attendance as the dependent variable and Wins and Promotion as independent variables, use excel to estimate the relationship between attendance and promotion and wins. (Copy and paste results from excel output). Also write down the estimated equation 2. What is the strength of this relationship? Use the coefficient of determination R squared from the excel output to describe this relationship. 3. Is the relationship significant? Use the Fisher significance from the excel output to determine this. Begin by stating the null and the alternate hypotheses.
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1) Using Excel output: SUMMARY OUTPUT Regression Statistics Multiple R 0.949280683 R Square0.901133815 Adjusted R 0.861587341 Square Standard1986.59925 Observations 8 Error ANOVA df MS SignificanceRegression 2 Residual Total 179858948 89929473.99 22.78670442 0.003073403 19732882.9 3946576.579 199591830.9 P-value Coefficients Standard t Stat Error Lower 9596 | Upper 95% Intercept4656.401319 |4965.428663 0.9377642160.39141762-8107.639405 17420.44 3558.133271 1498.406214 2.374611929 0.063588076293.642524 7409.909 76396-0.223153615 0.297338 Wins Promotion 0.037091947 0.101239944 0.366376603 0.72907Regression equation: Y 4656.401319 +3558.133271 Wins +0.037091947*Promotion 2) Coefficient of determination (R-squared value) - 0.901133815 The R-squared value defines, the relationship is strong because it is greater than 0.8. The percentage estimated variance explained by regression equation is 90.1 13381 596. 3) Using ANOVA table: The test statistic: F- MSReg/ MS Res 22.7867 P-value 0.00307 The test statistic is significant and rejects H0 at 0.05 significant level. There is sufficient evidence to support the claim that there is significant relationship between variables.

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