Using the sample data from the accompanying table, complete parts (a) through (e). E Click the icon to view the data table. (a) Predict the mean test 2 score for entering freshmen who score a 23 on the test 1. O (Round to the nearest whole number as needed.) (b) Construct a 95% confidence interval for the mean test 2 score for entering freshmen who score a 23 on the test 1. Data Table The 95% confidence interval for the mean test 2 score for entering freshmen who score a 23 on the test 1 is lower bound: upper bound: (Round to the nearest whole number as needed.) Full data set D (c) Predict the test 2 score of a randomly selected freshman who scores a 23 on the test 1. Test 1, x 18 Test 2, y 1390 Test 1, x 19 Test 2, y 1470 (Round to the nearest whole number as needed.) 21 1340 17 1190 1770 2290 (d) Construct a 95% prediction interval for the test 2 score for a randomly selected freshman who scores a 23 on the test 1. 27 18 1910 1150 28 30 The 95% prediction interval for the test 2 score for a randomly selected freshman who scores a 23 on the test 1 is lower bound: upper bound: (Round to the nearest whole number as needed.) 20 1360 20 1660 25 1780 18 22 1480 1370 25 1590 (e) Explain why the predicted weights in parts (a) and (c) are the same, yet the intervals constructed in parts (b) and (d) are different. Choose the correct answer below. The least-squares regression equation is y = 63.1624x + 163.9988. O A. The intervals are different because there is no linear relationship between test 1 scores and test 2 scores. O B. The intervals are different because the distribution of the mean weights, part (a), has more variability than the distribution of individual weights, part (c) OC. The intervals are different because the distribution of the mean weights, part (a), has less variability than the distribution of individual weights, part (c). Print Done