Designer Wear operates a chain of 10 retail department stores. Each department store makes its own purchasing decisions. Barry Lee, assistant to the president of Designer Wear, is interested in better understanding the drivers of purchasing department costs. For many years, Designer Wear 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.

Lee recently attended a seminar titled “ Cost Drivers in the Retail Industry.” In a presentation at the seminar, Couture 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 purchas-ing department costs. The dollar value of merchandise purchased in each purchase order was not found to be a significant cost driver. Lee interviewed several members of the purchasing department at the Designer Wear store in Miami. They believed that Couture Fabrics’ conclusions also applied to their purchasing department.

Lee collects the following data for the most recent year for Designer Wear’s 10 retail department stores:

Lee 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 Lee. Graph each one. Also, use the format employed in Exhibit to evaluate the information.
2. Do the regression results support the Couture Fabrics’ presentation about the purchasing depart-ment’s cost drivers? Which of these cost drivers would you recommend in designing an ABC system?
3. How might Lee gain additional evidence on drivers of purchasing department costs at each of Designer Wear’sstores?

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Purchasing Dollar Vale Number of 0 Store (PDC) Department MerchandisePcasNumber of Costs Purchased Orders Suppliers Departmen Baltimore (MPS)(No. of POs)(No. of Ss) $1,525,000 68,325,000 4.350 130 1.120,000 33,450,000 2555 535,000 121,100,000 119,550,000 33,520,000 847000 102,886,000 143812 5,940 2795 1,315 7,592 3,610 1,710 4,725 Los Angeles 2,042,000 193 20 39 112 124 215 1 New York Phoenix Seattle St Louis Toronto Vancouver 1,050,000 522 00029 1,533,000 1,748,00038,665,000 1,618,000 1,251,000 139,315,000 130,940,000 Regression : PDC abx MPS) Variable Constant Independent variable 1: MPS 2008 Durbin-Watson statistic Coefficient Standard Err t-Value 3.02 0.83 $1,040594 $344,830 0.0031 0.0037 242 Regression 2 PDC alb No. of POs) Variable Constant Independent variable 1: No. of POs Coefficient Standard Error t-Value S731 687 S 156.18 $267,395 $ 65.19 2.74 2.40 042; Durbin-Watson statistic 1.99 Regression 3, PDC + (b No. of Ss) Variable Constant Independent variable 1: No. of Ss 20.40 Durbin-Watson statistic Coefficient Standard Error Value %248,566 1,660 $802,629 3.23 2.32 3,848 2.00