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 v.".. . .. .. . . Regression Statistics Multiple R 0.53292259 R Square 0.284006487 Adjusted R Square 0.283486772 Standard Error 0.567515239 Observations 4137 ANOVA df SS MS Significance F Regression 509.5632077 169.8544: 546.4662 4,0431E-299 Residual 4133 1284.632456 0.310823 Total 4136 1794.195664 Coefficients : Standard Error at Stat P-value Lower 95% Upper 95% Intercept 1 ISVIV -0.042678049 0.070175203 -0.60816 0.543112 -0. 18025921 0.094903113 0.001491364 SAT Score 6.48677E-05 22.99086. 3.6E-110 0.001364189 0.00161854 HS_Percentile 0.013087778 0.000548313 23.86919 4.5E-118 0.01201279 ??? Total Hours 0.001926045 0.000246629 7.809486 7.23E-15 0.001442519 0.00240957 For testing the hypothesis that Total_Hours has a significant relationship with GPA (refer to an earlier problem), what would be the conclusion at the 0.05 level of significance, in the context of the problem? Read carefully