From before:

A & A Industrial Products budgets for both scheduled maintenance and unscheduled repair costs for its plants' equipment, mostly large industrial machines. Budgets for scheduled maintenance activities are easy to estimate and are based on the equipment manufacturer's recommendations. The unscheduled repair costs, however, are harder to determine. Historically, A & A has estimated unscheduled maintenance using a formula based on the average number of hours of operation between major equipment failures at a plant. Specifically, plants were given a budget of $65.00 per hour of operation between major failures. This amount came from dividing aggregate historical repair costs by the total number of hours between failures. Then plant averages were used to estimate unscheduled repair cost. For example, if a plant averaged 450 hours of run time before a major repair occurred, the plant was allocated a repair budget of 450 X $65 = $29,250 per repair. If the plant was expected to be in operation 3150 hours per year, the company would anticipate seven unscheduled repairs (3150/450) annually and budget $205,750 for annual unscheduled repair costs.

Management is becoming more and more convinced this approach is not working. Not only are they upset about the difference between predicted and actual costs of repair, but plant managers believe the model does not account for potential differences among the company's three plants when allocating dollars for unscheduled repairs. The data included in the Excel file is from 64 randomly selected unscheduled repairs. Use the instructions below to guide your analysis of the data.

Repair Costs hours of Operation Plant $ 34,771.52 528 3 $ 20,930.79 419 2 $ 47,934.56 685 1 $ 45,513.51 661 3 $ 44,994.35 644 3 $ 46,024.74 646 3 $ 31,368.16 524 3 $ 35,137.30 534 3 $ 25,865.20 495 3 $ 35,316.75 554 3 $ 44,624.50 641 3 $ 17,676.72 367 2 $ 19,802.38 394 2 $ 5,526.64 190 2 $ 19,227.53 432 2 $ 39,433.56 606 3 $ 35,039.73 547 3 $ 24,962.49 478 2 $ 16,228.46 354 2 $ 35,800.40 568 3 $ 21,298.56 450 2 $ 60,686.70 760 1 $ 48,078.79 674 1 $ 23,190.44 465 2 $ 23,625.64 443 2 $ 36,181.38 548 3 $ 58,862.21 771 1 $ 23,329.88 464 2 $ 45,175.04 640 3 $ 21,941.18 467 2 $ 20,324.06 433 2 $ 40,832.13 607 3 $ 1,507.60 70 2 $ 61,650.91 783 1 $ 30,041.39 506 3 $ 36,458.70 579 3 $ 46,191.67 655 3 $ 47,420.04 662 3 $ 64,142.73 791 1 $ 32,662.09 536 3 $ 40,711.63 589 3 $ 31,347.16 525 3 $ 19,160.13 410 2 $ 14,591.34 325 2 $ 47,684.44 652 3 $ 18,962.25 405 2 $ 39,026.16 587 3 $ 51,414.33 699 1 $ 18,513.72 405 2 $ 16,155.88 360 2 $ 41,027.82 595 3 $ 35,593.64 558 3 $ 32,055.21 520 3 $ 11,697.91 309 2 $ 26,002.04 488 3 $ 11,823.13 291 2 $ 41,093.23 593 3 $ 15,119.35 312 2 $ 49,139.47 683 3 $ 31,062.16 522 3 $ 13,972.69 307 2 $ 43,652.54 644 3 $ 20,567.51 403 2 $ 41,423.70 591 3

Write report worthy of the office of the president of A & A Industrial Products.

NOTE: You are expected to create written report using your work. You will submit similarly to the Business Application weekly projects. Follow instructions carefully. Remember to add any supporting graphics and calculations into your report.

Part 3 - Due in Module 08

As always, you may use tools such as Excel or Excel Calculators, at need and as appropriate.

1. Create scatterplot of the costs and hours of operation.
   • Which would be the dependent, y, variable? Be sure to state that in your writing
   • Label the axes and give your scatterplot an appropriate business title (i.e. NOT "scatterplot")
   • Does there appear to be a positive, negative, or no linear relationship from the scatterplot?
2. Find and report the linear correlation between these variables. TIP: For the last time, you are reminded that there is an Excel calculator you can use.
   • Is this consistent with your scatterplot?
   • Test the linear correlation coefficient for significance. Use alpha = 0.01
3. Use the Excel Data Analysis Toolpak to perform simple linear regression analysis.
   • Copy the output into your report.
   • State the linear regression equation in your report.
4. Test the model for significance using p-values at the alpha = .05 level of significance. TIP: Since this is simple linear regression, you can test the slope with its t-statistic.
5. Test the slope of the model:. remembering that you can use your regression analysis from earlier
   • Interpret the slope using the units of measure for your variables.
   • Find the 95% confidence interval for the slope of the model.
6. Find the coefficient of determination, R², and interpret it.
7. Use the regression to predict the value of the cost of a repair if the hours of operation since the last unscheduled repair are 800.
8. Find the 95% confidence interval estimate of the average cost of repair when the hours of operation are 800 hours.
   • Be sure to interpret your interval..
9. Find the 95% prediction interval estimate of the average cost of repair when the hours are 800.
   • Be sure to interpret your interval.
   • How is a "prediction interval estimate" different from a "confidence interval estimate"?
10. Use your findings here to comment on the company's policy of budgeting $65 per hour between unscheduled repairs.