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ecn 102 hw 4 #2

# ecn 102 hw 4 #2 - \$0.05499 α is the salary if games make...

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2) A. I added another column in the excel sheet that reported the profits (revenues minus expenses) and imported it into GRETL. Using the Ordinary Least Squares Model with a dependent variable of salary and independent variable of profits, I received the following results: Model 1: OLS, using observations 1-45 Dependent variable: Salary coefficient std. error t-ratio p-value -------------------------------------------------------- const 0.582436 0.0853854 6.821 2.34e-08 *** Profits 0.0549997 0.0179942 3.057 0.0038 *** Mean dependent var 0.767333 S.D. dependent var 0.440900 Sum squared resid 7.026641 S.E. of regression 0.404240 R-squared 0.178486 Adjusted R-squared 0.159381 F(1, 43) 9.342372 P-value(F) 0.003840 Log-likelihood -22.07077 Akaike criterion 48.14155 Schwarz criterion 51.75487 Hannan-Quinn 49.48856 Salary = α + β *profits + ε ; α = 0.582436; β = 0.0549997 B. β shows that for every \$1 the games make in profit, the coaches’ salary increase by

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Unformatted text preview: \$0.05499 α is the salary if games make no profit. C. : β = 0 : β ≠ 0 Formula: = 3.057 P-value: .0038, which is less than the p-value of 0.005 for a 1% level of significance, therefore we can reject the null of : β = 0 so profit from a basketball program does influence the coaches’ salary. D. E. The histogram does not look evenly distributed. The data is positively skewed because some of the coaches are making more than the model predicts(perhaps due to the fact some teams are more popular than others and have more fans attending their games); this pulls the average higher than the median (what a bulk of college basketball coaches make). A higher average than median makes the data positively skewed. This also means that other factors besides the profit influences the coaches’ salary....
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