MBA Data Program Per-Year Mean Starting Salary Tuition ($) Upon Graduation ($) 62,295 154,498 66,530 152,229 66,419 148,947 66,548 143,356 65,017 139,661 66,544 151,847 65,461 150,567 69,447 150,681 64,583 138,468 63,422 141,815 67,541 148, 176 61,654 144,678 60,439 141,716 58,343 140,693 55,827 121,685 55,052 117,706 56,838 124,516 51,644 127,360 52,642 134,542 49,666 120,614 47,189 111,606 47,714 106,586 49,046 110,515 45,613 105,907A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The results are displayed in the accompanying table, and the regression equation and other values are provided. Use this information to complete parts (a) through (c) below. Y = - 12,155.4 + 2.4125X; n 1= [ Y; = 4,225,636, _ Y2 = 519,669,467,892, _ X;Y, = 233,292,055,995 i = 1 i = 1 i = 1 Click the icon to view the data table. a. Determine the coefficient of determination, r, and interpret its meaning. 2 =(Round to four decimal places as needed.)What is the meaning of the coefficient of determination? O A. It is the proportion of the variation in starting salary that is explained by the variability in per-year tuition. O B. It measures the variability in the actual per-year tuition from the predicted number of starting salary. O C. It measures the variability in the actual starting salary from the predicted per-year tuition. O D. It is the proportion of the variation in per-year tuition that is explained by the variability in starting salary.b. Determine the standard error of the estimate. SYx (Round to two decimal places as needed.)c. How useful do you think this regression model is for predicting starting salary? Since the value of r is (1) the regression model is (2) for predicting starting salary.37,695 83,074 46,351 82,494 48,142 100,076 49,271 77,651 39,252 88,983 32,035 75,345 42,506 77,740 40,321 54.368 50, 193 62,192 32,844 102,424 24,376 53,487 40,660 82,625 38,866 56,808