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A professor investigated some of the factors that affect an individual student's final grade in his course. He proposed the multiple regression models Y=B + BX2 + B3X3 + B4X4 + U. where Y is the final mark (out of 100), X2 is the number of lectures skipped, X3 is the number of late assignments, X4 is the mid-term test mark (out of 100). The professor recorded the data for 50 randomly selected students. The computer output is shown below. The regression equation is = 41.6-3.18x2 - 1.17x3 + 0.63x4- Predictor Coefficient StDev t Constant 41.6 -3.18 X2 X3 X4 -1.17 0.339 17.8 2.337 1.66 -1.916 1.13 -1.035 0.13 2.607 S = 13.74 R-Sq = 30.0% ANALYSIS OF VARIANCE Source of Variation df SS Regression 3 3,716 Error Total MS F 1,238.667 6.558 46 8,688 188.870 49 12,404 Does the data provide enough evidence to conclude that, at the 5% significance level, the final mark and the number of late assignments are negatively linearly related? (PLEASE SHOW ME THE STEPS) A. No, the critical value t 46,0.025 = -2.013 B. No, the critical value t 46,0.05 = -1.679 C. Yes, the critical value t 46,0.05 = 1.679 D. Yes, the critical value t 46,0.025 = 2.013