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. $$\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 s = 3213 r ^ { 2 } = .66 r = .81 \mathrm { df } = 23 \quad t = 6.67$$ Give a practical interpretation of r = .81.
A) There appears to be a positive correlation between SALARY and GMAT.
B) We estimate SALARY to increase 81% for every 1-point increase in GMAT.
C) 81% of the sample variation in SALARY can be explained by using GMAT in a straight -line model.
D) We can predict SALARY correctly 81% of the time using GMAT in a straight-line model.

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The Answer of 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. $$\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 s = 3213 r ^ { 2 } = .66 r = .81 \mathrm { df } = 23 \quad t = 6.67$$ Give a practical interpretation of r = .81.
A) There appears to be a positive correlation between SALARY and GMAT.
B) We estimate SALARY to increase 81% for every 1-point increase in GMAT.
C) 81% of the sample variation in SALARY can be explained by using GMAT in a straight -line model.
D) We can predict SALARY correctly 81% of the time using GMAT in a straight-line model.

An Academic Advisor Wants to Predict the Typical Starting Salary β^0=−92040β^1=228s=3213r2=.66r=.81df=23t=6.67\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 s = 3213 r ^ { 2 } = .66 r = .81 \mathrm { df } = 23 \quad t = 6.67β^​0​=−92040β^​1=228s=3213r2=.66r=.81df=23t=6.67

An Academic Advisor Wants to Predict the Typical Starting Salary $\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 s = 3213 r ^ { 2 } = .66 r = .81 \mathrm { df } = 23 \quad t = 6.67$