The data below represent the number of days absent, x, and the final grade, y, for a sample of college students at a large university. Complete parts (a) through (e) below. No. of absences, x 0 1 2 3 4 5 6 7 8 9 2 Final grade, y 88.7 85.9 82.9 80.5 77.5 73.2 63.7 68.1 65.2 62.3 (a) Find the least-squares regression line treating the number of absences, x, as the explanatory variable and the final grade, y, as the response variable. y = [x + 0 (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Interpret the slope. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. (Round to three decimal places as needed.) O A. For a final score of zero, the number of days absent is predicted to be days. O B. For every day absent, the final grade falls by , on average. O C. For every unit change in the final grade, the number of days absent falls by days, on average. O D. For zero days absent, the final score is predicted to be O E. It is not appropriate to interpret the slope. Interpret the y-intercept. Select the correct choice below and, if necessary, fill in the answer box to complete your choice. (Round to three decimal places as needed.) O A. For a final score of zero, the number of days absent is predicted to be days. O B. For zero days absent, the final score is predicted to be. O C. For every day absent, the final grade falls by , on average. O D. For every unit change in the final grade, the number of days absent falls by days, on average. O E. It is not appropriate to interpret the y-intercept. (c) Predict the final grade for a student who misses five class periods and compute the residual. Is the observed final grade above or below average for this number of absences? The predicted final grade is . This observation has a residual of , which indicates that the final grade is | |average