Then, we add another regressor Hours_workedi:

Gordon_GPAi= b1+ b2HS_GPAi+ b3Hours_workedi+ ei (Model II)

1. Interpret the partial regression coefficients in Model II, each separately (i.e.: , ). [4 points]
2. With the result from Model I and Model II, is the model with hours worked variable (Model II) better than the one without the hours worked Variable (Model I)? Explain. [5 points]
3. Present the results in the Tabular Format [5 points]

4. Table: Regression Results for Multiple Results

   	Model I	Model II
   	(1)	(2)
   High School Grade
   Hours Worked
   Constant
   N
   Adj. R2

   Standard errors in parentheses *p p p

SUMMARY OUTPUT T Regression Statistics Multiple Re R Square Adjusted R Square Standard Error Observations 0.490 ? ? 0.266 284 T. TT ANOVA T. 1. df sse Fe Significance F 0.032 2 0.560 Mse 0.280 0.071 Regression Residuale Totale 3.946 25 1.773 T. T. T. 274 2.332 e e Coefficientse Standard Error 2.582 0.343 0.282 ? -0.008 0.004 Intercepte HS_GPA Hours worked a t Stat 7.524 2.656 P-value 0.000 0.014 3.28944 Lower 95% Upper 95%46 1.875 0.063 0.501 -0.017 0.001 -1.855 0.075 SUMMARY OUTPUT T Regression Statistics Multiple Re R Square Adjusted R Square Standard Error Observations 0.490 ? ? 0.266 284 T. TT ANOVA T. 1. df sse Fe Significance F 0.032 2 0.560 Mse 0.280 0.071 Regression Residuale Totale 3.946 25 1.773 T. T. T. 274 2.332 e e Coefficientse Standard Error 2.582 0.343 0.282 ? -0.008 0.004 Intercepte HS_GPA Hours worked a t Stat 7.524 2.656 P-value 0.000 0.014 3.28944 Lower 95% Upper 95%46 1.875 0.063 0.501 -0.017 0.001 -1.855 0.075