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. $0 = -92040 $1 = 228 s = 3213 r2 = .66 r= 81 df = 23 t= 6.67 Give a practical interpretation of r= 81. O There appears to be a positive correlation between SALARY and GMAT. O We estimate SALARY to increase 81% for every 1-point increase in GMAT. O We can predict SALARY correctly 81% of the time using GMAT in a straight-line model. O 81% of the sample variation in SALARY can be explained by using GMAT in a straight-line model.

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. $0 = -92040 $1 = 228 s = 3213 r2 = .66 r= 81 df = 23 t= 6.67 Give a practical interpretation of r= 81. O There appears to be a positive correlation between SALARY and GMAT. O We estimate SALARY to increase 81% for every 1-point increase in GMAT. O We can predict SALARY correctly 81% of the time using GMAT in a straight-line model. O 81% of the sample variation in SALARY can be explained by using GMAT in a straight-line model.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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