Question 4: It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students.
Note: Some of the numbers in the output are purposely erased.
Regression Statistics 




 
Multiple R  0.8857 




 
R Square  0.7845 




 
Adjusted R Square  0.7801 




 
Standard Error  1.3704 




 
Observations  51 




 






 
ANOVA 





 
 df  SS  MS  F  Significance F 
 
Regression  1  335.0472  335.0473  178.3859 

 
Residual 

 1.8782 


 
Total  50  427.0798 



 






 
 Coefficients  Standard Error  t Stat  Pvalue  Lower 95%  Upper 95%  
Intercept  1.8940  0.4018  4.7134  2.051E05  2.7015  1.0865  
Hours  0.9795  0.0733  13.3561  5.944E18  0.8321  1.1269 
a) What is the estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying?
b) What is the value of the measured ttest statistic to test whether average SALARY depends linearly on HOURS?
c) What is the pvalue of the measured Ftest statistic to test whether HOURS affects SALARY?
d) What are the degrees of freedom for testing whether HOURS affects SALARY?
e) What is the error sum of squares (SSE) of the above regression? Show how you obtain your answer.
f) The 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per day studying is
A) wider than [2.70159, 1.08654].
B) narrower than [2.70159, 1.08654].
C) wider than [0.8321927, 1.12697].
D) narrower than [0.8321927, 1.12697].
Explain your reasoning.
g) To test the claim that average SALARY depends positively on HOURS against the null hypothesis that average SALARY does not depend linearly on HOURS, what is the pvalue of the test statistic? What are the results of the test? Explain your answer.
Question 5: The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results:
Customers  Sales (Thousands of Dollars) 
907  11.20 
926  11.05 
713  8.21 
741  9.21 
780  9.42 
898  10.08 
510  6.73 
529  7.02 
460  6.12 
872  9.52 
650  7.53 
603  7.25 
a) Estimate a linear regression. What are the values of the estimated intercept and slope? Show how you obtain your answer.
b) What is the value of the coefficient of determination?
c) What is the value of the coefficient of correlation?
d) What is the value of the standard error of the estimate?
e) Which of the following is the correct null hypothesis for testing whether the number of customers who make purchases affects weekly sales?
A) H0 : β0 = 0
B) H0 : β1 = 0
C) H0 : μ = 0
D) H0 : ρ = 0
f) What is the value of the t test statistic when testing whether the number of customers who make purchases affects weekly sales?
g) What are the degrees of freedom of the t test statistic when testing whether the number of customers who make purchases affects weekly sales?
h) Construct a 95% confidence interval for the change in average weekly sales when the number of customers who make purchases increases by one. Show how you obtain your answer.
i) Construct a 95% confidence interval for the average weekly sales when the number of customers who make purchases is 600. Show how you obtain your answer.
j) Construct a 95% prediction interval for the weekly sales of a store that has 600 purchasing customers. Show how you obtain your answer.
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