Case Study: Maintenance Contract Business decision making is something that mana
ID: 3235880 • Letter: C
Question
Case Study: Maintenance Contract Business decision making is something that management must to do on a daily basis. Jensen Tire & Auto is a family owned auto service company. In recent months, business has been slow so in an effort to reduce cost, the company is deciding on whether to purchase a maintenance contract for its new computer wheel alignment and balancing machine, which costs $5,000 per year, instead of maintaining their own machine. The owner feels that maintenance expense should be related to usage so he collected a random sample of 30 weeks of weekly u (hours) and annual sage maintenance expense (in hundreds of dollars). For each week, the following variables were recorded Weekly usage (hours) Annual Maintenance Expense (S100s) However, he is unsure how to do the analysis. Knowing you are about to finish the course in business analytics, your manager recommended you to the owner and you are now appointed to do the analysis for the above sample information using the appropriate statistical method to determine whether Jensen Tire & Auto should purchase the maintenance contract by answering the following questions a. How many hours the company will have to use the machine per week if they were to buy the contract? b. Comparing the usage from part a, how does this usage compare to the average usage per week? Use the weekly usage average to determine whether Jensen Tire & Auto should purchase the maintenance contract.Explanation / Answer
Enter data in excel.
Click on data->Data Analysis and select Regression from list.
The regression output is shown below:
From the above regression output, we have:
Regression equation is:
Annual maintenance = 22.4673 + 0.497*weekly usage
a) 5000 = 50 ($100s)
50 = 22.4673 + 0.497*weekly usage
weekly usage=(50-22.4673)/0.497=55.4 hours
b) Since p-value for F test is less than 0.05, the given model is significant of purchase the maintenance contract.
SUMMARY OUTPUT Regression Statistics Multiple R 0.455131277 R Square 0.20714448 Adjusted R Square 0.178828211 Standard Error 8.195399628 Observations 30 ANOVA df SS MS F Significance F Regression 1 491.3348984 491.3349 7.315388 0.011499555 Residual 28 1880.608102 67.16458 Total 29 2371.943 Coefficients Standard Error t Stat P-value Lower 95% Intercept 22.46730718 4.216686521 5.32819 1.13E-05 13.82981651 Weekly Usage 0.496709293 0.183646906 2.704697 0.0115 0.120525665