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1. Jensen Tire & Auto is in the process of deciding whether to purchase a mainte

ID: 3356504 • Letter: 1

Question

1.      Jensen Tire & Auto is in the process of deciding whether to purchase a maintenance contract for its new computer wheel alignment and balancing machine. Managers feel that maintenance expense should be related to usage, and they collected the information on weekly usage (hours) and annual maintenance expense (in thousands of dollars).

Note: You would not need sample data to answer the questions.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.9272

R Square

0.8597

Adjusted R Square

0.8422

Standard Error

4.1466

Observations

10

ANOVA

df

SS

MS

F

Significance F

Regression

1

843.1737

843.1737

49.0391

0.00011

Residual

8

137.5513

17.1939

Total

9

980.7250

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

10.3350

3.8186

2.7065

0.0268

1.5293

19.1408

Weekly Usage (in Hrs)

0.9586

0.1369

7.0028

0.0001

0.6429

1.2742

a)      Comment on how good the relationship fit is. How is that determined?

b)       What is the value of the correlation coefficient in this problem?

c)      Jensen expects to use the new machine 30 hours per week. What is the 95% confidence interval for the company’s annual maintenance expense (Use the correct units) based on the above report for a given “x bar” = 30 hours per week.

d)      If maintenance contract costs $30,000 per year for a machine, would you recommend purchasing it for the new machine in part (g)? Why or why not?

Please tell me exactly what to type into Excel for this problem and if any formulas could be used to get the answers. Thanks so much.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.9272

R Square

0.8597

Adjusted R Square

0.8422

Standard Error

4.1466

Observations

10

ANOVA

df

SS

MS

F

Significance F

Regression

1

843.1737

843.1737

49.0391

0.00011

Residual

8

137.5513

17.1939

Total

9

980.7250

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

10.3350

3.8186

2.7065

0.0268

1.5293

19.1408

Weekly Usage (in Hrs)

0.9586

0.1369

7.0028

0.0001

0.6429

1.2742

Explanation / Answer

a) P-value of Regression = 0.00011 < alpha 0.05, so we reject H0
Thus we conclude that the regression equation is best fit to the given data
Alsor R^2 = 0.8597 which means that 85.97% of variation in the annual maintence expense is explained by weekly usage

b) Correlation Coeffiecient is 0.9272

c) If X = 30 then the predict value of Y is
Y = 10.3350 + 0.9586(30) = 39.093

d) Yes, we recommend that to purchase new machine becuase our machine maintaing cost $39, 930 for 30 hours, but new machine cost only $30,000.