# Purchasing department cost drivers, multiple regression analysis (continuation of 10-47). Carl…

Purchasing department cost drivers, multiple regression analysis (continuation of 10-47). Carl Hart decides that the simple regression analysis used in Problem 10-47 could be extended to a multiple regression analysis. He finds the following results for two multiple regression analyses:

Required

1. Evaluate regression 4 using the criteria of economic plausibility, goodness of fit, significance of independent variables, and specification analysis. Compare regression 4 with regressions 2 and 3 in Problem 10-47. Which one of these models would you recommend that Hart use? Why?

2. Compare regression 5 with regression 4. Which one of these models would you recommend that Hart use? Why?

3. Hart estimates the following data for the Baltimore store for next year: dollar value of merchandise purchased, \$78,500,000; number of purchase orders, 4,100; number of suppliers, 110. How much should Hart budget for purchasing department costs for the Baltimore store for next year?

4. What difficulties do not arise in simple regression analysis that may arise in multiple regression analysis? Is there evidence of such difficulties in either of the multiple regressions presented in this problem? Explain.

5. Give two examples of decisions in which the regression results reported here (and in Problem 10-47) could be informative.

Problem 10-47

Purchasing department cost drivers, activity-based costing, simple regression analysis. Perfect Fit operates a chain of 10 retail department stores. Each department store makes its own purchasing decisions. Carl Hart, assistant to the president of Perfect Fit, is interested in better understanding the drivers of purchasing department costs. For many years, Perfect Fit has allocated purchasing department costs to products on the basis of the dollar value of merchandise purchased. A \$100 item is allocated 10 times as many overhead costs associated with the purchasing department as a \$10 item.
Hart recently attended a seminar titled “Cost Drivers in the Retail Industry.” In a presentation at the seminar, Kaliko Fabrics, a leading competitor that has implemented activity-based costing, reported number of purchase orders and number of suppliers to be the two most important cost drivers of purchasing department costs. The dollar value of merchandise purchased in each purchase order was not found to be a significant cost driver. Hart interviewed several members of the purchasing department at the Perfect Fit store in Miami. They believed that Kaliko Fabrics’ conclusions also applied to their purchasing department.
Hart collects the following data for the most recent year for Perfect Fit’s 10 retail department stores:

Hart decides to use simple regression analysis to examine whether one or more of three variables (the last three columns in the table) are cost drivers of purchasing department costs. Summary results for these regressions are as follows:

1. Compare and evaluate the three simple regression models estimated by Hart. Graph each one. Also, use the format employed in Exhibit 10-18 (page 406) to evaluate the information.

2. Do the regression results support the Kaliko Fabrics’ presentation about the purchasing department’s cost drivers? Which of these cost drivers would you recommend in designing an ABC system?

3. How might Hart gain additional evidence on drivers of purchasing department costs at each of Perfect Fit’s stores?

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