35% 2:47 PM Eco 4000 Exam Fall 2016 Version Eta with Standard Errors Saved SUMMA

Question

35% 2:47 PM Eco 4000 Exam Fall 2016 Version Eta with Standard Errors Saved SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.989894408 0.97989094 0.946375839 0.093997207 ANOVA MS SignificanceF Regression Residual Total 5 1.291627011 0.258325402 29.23729686 0.009507436 3 0.026506425 0.008835475 8 1.318133436 P-volue Coefficients Standard Error tS 2.000000 tat Lower 95% Upper 95% Intercept GPA Fin Major Gender NYC MajorXGender 0.500000 0.199040522 12.23566447 0.001175553 1.801957267 3.068828814 0.116964675 7.144529794 0.005645963 0.46342381 1.207891408 0.221410557 -4.794091072 0.017265888-1.766089586 -0.356835166 0.115122597 -2.041317871 0.133876794-0.601373298 0.131369669 0.130770483 -4.049701664 0.027116334 -0.945751481 -0.113411402 0.139726695 3.432145865 0.0417292 0.034889694 0.924235104 1.000000 1.000000 0.250000 0.500000 10. For the regression results above, use the variable names in the output and write the regression equation being represented. The Y variable is the log of eamings in the first year out of school. Considering the Y variable and the continuous variable GPA, would this equation come under the"Diminishing Retums Explosive Growth" or "Elasticity" characterization discussed in class.

Explanation / Answer

1.

MajorxGender variable will basically measure the interaction

of Major and Gender. i.e. what would happen if its a male and a

major in Finance? on log(earnings)

2. Lets check this: test statistic
= (B-truemean)/SE
= (-0.50 - (-1))/.13077
= 3.823

Which is more than the critical t-value, i.e. indicating the true mean is not equal to -1 or it is statistically different from -1

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