An academic advisor wants to predict the typical starting salary of a graduate a

Question

An academic advisor wants to predict the typical starting salary of a graduate at a top business school using the GMAT score of the school as a predictor variable. A simple linear regression of SALARY versus GMAT using 25 data points is shown below. beta_0 = -92040 beta_1 = 228 s = 3213 r^2 = .66 r = .81 df = 23 t = 6.67 For the situation above, write the equation of the least squares line. A) SALARY = 228 + 92040 (GMAT) B) GMAT = 228 - 92040 (SALARY) C) GMAT = -92040 + 228 (SALARY) D) SALARY = -92040 + 228 (GMAT) For the situation above, give a practical interpretation of r = .81 A) We can predict SALARY correctly 81% of the time using GMAT in a straight-line model. B) We estimate SALARY to increase 81% of the time using GMAT in a straight-line model. C) There appears to be a positive correlation between SALARY and GMAT. D) 81% of the sample variation in SALARY can be explained by using GMAT in a straight-line model. For the situation above, give a practical interpretation of s = 3213. A) We estimate SALARY to increase $3, 213 for every 1-point increase in GMAT. B) Our predicted value of SALARY will equal 2(3213) = $6, 426 for any value of GMAT. C) We expect to predict SALARY to within 2(3213) = $6, 426 of its true vale using GMAT in a straight-line model. D) We expect the predicted SALARY to deviate from actual SALARY by at least 2(3213) = $6, 426 using GMAT in a straight- line model.

Explanation / Answer

ANSWERS:

32) (D)

Here Salary is the dependent and GMAT is the independent variable

Salary = -92040 + 228 GMAT

33) (C)

r = 0.81. i.e. there is a significant positive correlation between SALARY and GMAT

34) (D)

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