1. Jensen Tire & Auto is in the process of deciding whether to purchase a mainte
ID: 3355931 • 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) Write down what the estimated regression equation is that relates annual maintenance expense to weekly usage.
b) Test the significance of regression at .05 level of significance using p-value approach
c) Test the significance of regression at .05 level of significance using critical-value approach
d) Comment on how good the relationship fit is. How is that determined?
e) What is R-Sq? How is that determined?
f) What is the value of the correlation coefficient in this problem?
g) 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.
h) 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 asnwers. 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
We are allowed to do 4 subparts question at a time. Post again for more subparts of question.
a) Equation:
Expense = 10.335 + 0.9586 * usage
b) For F = 49.0391, p value = 0.00011 < alpha (0.05)
Thus, null is rejected and the model is significant.
c) F critical value at 95%:
df1 = 1, df2 = 8
F critical = 5.32
Reject null as F > F critical
d) R2 is a statistic that will give some information about the goodness of fit of a model. In regression, the R2coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R2 of 1 indicates that the regression line perfectly fits the data.
Here, R^2 = 85.97% which says 85.97% of the variation in expenses is explained by the usage.
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