(Situation P) Below Are the Results of a Survey of America's
Question 106
Question 106
Short Answer
(Situation P) Below are the results of a survey of America's best graduate and professional schools. The top 25 business schools, as determined by reputation, student selectivity, placement success, and graduation rate, are listed in the table. For each school, three variables were measured: (1) GMAT score for the typical incoming student; (2) student acceptance rate (percentage accepted of all students who applied); and (3) starting salary of the typical graduating student. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. School Harvard Stanford Penn Northwestern MIT Chicago Duke Dartmouth Virginia Michigan Columbia Cornell CMU UNC Cal-Berkeley UCLA Texas Indiana NYU Purdue USC Pittsburgh Georgetown Maryland Rochester GMAT 644665644640650632630649630620635648630625634640612600610595610605617593605 Acc. Rate 15.0%10.219.422.621.330.018.213.423.032.437.114.931.215.424.720.728.129.035.026.831.933.031.728.135.9 Salary $63,00060,00055,00054,00057,00055,26953,30052,00055,26953.30052,00050,70052,05050,80050,00051,49443,98544,11953,16143,50049,08043,50045,15642,92544,499 The academic advisor wants to predict the typical starting salary of a graduate at a top business school using GMAT score of the school as a predictor variable. A simple linear regression of SALARY versus GMAT using the 25 data points in the table are shown below. β0=−92040β^1=228s=3213r2=.66r=.81df=23t=6.67 -For the situation above, give a practical interpretation of r2=.66 . A) We expect to predict SALARY to within 2(.66) of its true value using GMAT in a straight-line model. B) We estimate SALARY to increase $.66 for every 1 -point increase in GMAT. C) 66% of the sample variation in SALARY can be explained by using GMAT in a straight -line model. D) We can predict SALARY correctly 66% of the time using GMAT in a straight-line model.
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