Regression 56% i 7:46 AM Eco 4000 Exam Fall 2016 Version Eta with Standard Error
ID: 3324883 • Letter: R
Question
Regression
56% i 7:46 AM Eco 4000 Exam Fall 2016 Version Eta with Standard Errors (1) Saved SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.989894408 0.97989094 0.946375839 0.093997207 ANOVA df MS Significance F Regression Residual Total 5 1.291627011 0.258325402 29.23729686 0.009507436 3 0.026506425 0.008835475 8 1.318133436 t Stat P-value Lower 95% Upper 95% Intercept GPA Fin Major Gender NYC MajorXGender Coefficients Standard Error 2.000000 1.000000 1.000000 0.250000 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 0.041472992 0.034889694 0.924235104 0.5000000 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 earnings in the first year out of school. Considering the Y variable and the continuous variable GPA, would this equation come under the "Diminishing Returns", "Explosive Growth", or "Elasticity" characterization discussed in class.Explanation / Answer
The multiple linear regression equation being represented is Y = 2 + (1.0 * GPA) - (1.0* Fin Major) -(0.25*Gender )-(0.5*NYC) +( 0.5 * MajorXGender) where Y is the logof earnings in the first year out of school. This can be found in the Coeeficints column of the regression output given. This equation can be used to predict Y
Considering Y and the continuous variable GPA the quation would come uder the "Elasticity" Criterion an the coeff of the GPA variable is 1 which means that for every unit increase or decrease in GPA, there will be unit increase or decrease in the Y variable predicted.
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