A dean of a business school has fit a regression model to predict college GPA based on a student's SAT score (SAT_Score), the percentile at which the student graduated high school (HS_Percentile) (for instance, graduating 4th in a class of 500 implies that 496 other students are at or below that student, so the percentile is 496/500 x 100 = 99), and the total college hours the student has accumulated (Total_Hours). The regression results are shown below. SUMMARY OUTPUT --....-. . . .. . . ." Regression Statistics Multiple R 0.53292259 R Square 0.284006487 Adjusted R Square 0.283486772 ...... Standard Error 0.557515239 Observations 4137 ANOVA di SS MS Significance F Regression 3 509.5632077| 169.8544 546.4662 -.-. I..-. ..- -. 4.0431E-299 Residual 4133 1284.632456 0.310823 1794. 195664 .... 1. . . . Total 4136 Coefficients + Standard Error If Stat : P-value Lower 95% Upper 95% Intercept -0.0426780491 0.070175203 -0.60816: 0.543112 -0. 18025921 0.094903113 SAT Score 0.001491364 6.48677E-05 22.99086 3.6E-110 0.001364189 0.00161854 HS_Percentile Q.013087778 0.000548313 23.86919 4.5E-118 to 0.01201279 ??? Total Hours 0.001926045 0.000246629 7.809486 7.23E-15 0.001442519 0.00240967 Which of the following correctly specifies the hypothesis test for testing whether Total_Hours has a significant relationship with GPA, over and above the effects of the other variables? Read carefully