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 régression of SALARY versus GMAT using 25 data points is shown below. FO= -92040 $1 = 228 s = 3213 r2 = .66r = .81 df = 23 t = 6.67 %3D Give a practical interpretation of r2 = .66. %3D O We expect to predict SALARY to within 2 N.66 of its true value using GMAT in a straight-line model. We can predict SALARY correctly 66% of the time using GMAT in a straight-line model. O 66% of the sample variation in SALARY can be explained by using GMAT in a straight-line model. We estimate SALARY to increase $.66 for every 1-point increase in GMAT.
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 régression of SALARY versus GMAT using 25 data points is shown below. FO= -92040 $1 = 228 s = 3213 r2 = .66r = .81 df = 23 t = 6.67 %3D Give a practical interpretation of r2 = .66. %3D O We expect to predict SALARY to within 2 N.66 of its true value using GMAT in a straight-line model. We can predict SALARY correctly 66% of the time using GMAT in a straight-line model. O 66% of the sample variation in SALARY can be explained by using GMAT in a straight-line model. We estimate SALARY to increase $.66 for every 1-point increase in GMAT.
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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![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.
20= -92040 51= 228 s = 3213 r2 = .66 r = .81 df = 23 t = 6.67
%3D
%3D
Give a practical interpretation of r2 = .66.
%3D
We expect to predict SALARY to within 2 W 66] of its true value using GMAT in a straight-line model.
O We can predict SALARY correctly 66% of the time using GMAT in a straight-line model.
5 66% of the sample variation in SALARY can be explained by using GMAT in a straight-line model.
We estimate SALARY to increase $.66 for every 1-point increase in GMAT.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcb3ddd84-7b8b-4010-bb81-d77c3d0228b1%2Fb8b96e51-755a-457f-b4ba-95342f31232e%2Fr96ke7v_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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.
20= -92040 51= 228 s = 3213 r2 = .66 r = .81 df = 23 t = 6.67
%3D
%3D
Give a practical interpretation of r2 = .66.
%3D
We expect to predict SALARY to within 2 W 66] of its true value using GMAT in a straight-line model.
O We can predict SALARY correctly 66% of the time using GMAT in a straight-line model.
5 66% of the sample variation in SALARY can be explained by using GMAT in a straight-line model.
We estimate SALARY to increase $.66 for every 1-point increase in GMAT.
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