Use this information for Questions 24-26. The following is output from a Linear Regression Model used to examine a student's expected exam score (maximum 100%) based on hours studied. SUMMARY OUTPUT Regression Statistics Multiple R 0.928848801 R Square 0.862760095 Adjusted R Square 0.828450119 Standard Error 7.147155821 Observations ANOVA of SS MS Significance F Regression 1284 505988 1284.505988 25.14604173 0.007413638 Residual 4 204.3273453 51.08183633 Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95%% Lower 95.0% Upper 95.09% Intercept 57.59481038 5.198012561 11.08015991 0.00037735 43.16281385 72.02680691 43.16281385 72.02680691 Hours 7.844311377 1564299868 5.014582907 0.007413638 3.501118667 12 18750409 3.501118667 12.18750409 24) Use the above regression output to predict a student's score if he/she studies 4.25 hours for the exam. A. No score can be computed with a decimal input. B. 90.93% C. 92.88% D. 97.50% 25) Using standard measures of significance, how could one determine if the number of hours " studied is a significant predictor of exam scores? A. An intercept coefficient result of greater than 50. B. A t-statistic > .05 C. A t-critical value of > .05 D. A p-value of <.05 what is the sst of this model a. b. c. d.>