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

Question 42

Multiple Choice

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. β^0=92040β^1=228s=3213df=23t=6.67\hat { \beta } _ { 0 } = - 92040 \hat { \beta } 1 = 228 \quad s = 3213 \quad \mathrm { df } = 23 \quad t = 6.67
Set up the null and alternative hypotheses for testing whether a linear relationship exists between SALARY and GMAT.


A) H0:β1>0H _ { 0 } : \beta _ { 1 } > 0 vs. Ha:β1<0\mathrm { H } _ { \mathrm { a } } : \beta _ { 1 } < 0
B) H0:β1=0H _ { 0 } : \beta _ { 1 } = 0 vs. Ha:β10H _ { \mathrm { a } } : \beta _ { 1 } \neq 0
C) H0:β1=0H _ { 0 } : \beta _ { 1 } = 0 vs. Ha:β1>0H _ { \mathrm { a } } : \beta _ { 1 } > 0
D) H0:β^1=228H _ { 0 } : \hat { \beta } 1 = 228 vs. Ha:β^1>228H _ { \mathrm { a } } : \hat { \beta } _ { 1 } > 228

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