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Requirements Adam Haulton, assistant to the president of True Fit, is interested in better understanding the drivers of purchasing department costs. For many years, True Fit has allocated purchasing department 1. Compare and evaluate the three simple regression models estimated by products on the basis of the dollar value of merchandise purchased. A $100 item is allocated 10 times as Haulton. Graph each one. many overhead costs associated with the purchasing department as a $10 item. 2. Do the regression results support the Sunshine Fabrics' pres purchasing department's cost drivers? Which of these a Presentation about the Haulton recently attended a seminar titled "Cost Drivers in the Retail Industry." In a presentation at the recommend in designing an ABC system? seminar, Sunshine 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. Haulton interviewed several members of the purchasing department at True Fit operates a chain of 10 retail department stores. Each department store Summary results for these regressions are as follows: the True Fit store in Miami. They believed that Sunshine Fabrics' conclusions also applied to their makes its own purchasing decisions. (Click the icon to view the summary results.) i (Click the icon to view additional information.) purchasing department. Read the Cequirement Haulton decides to use simple regression analysis to examine whether one or daulton collects the following data for the most recent year for True Fit's 10 retail department stores: more of three variables (the last three columns in the table) are cost drivers of purchasing department costs. Purchasing Dollar Value of Number of Number of Requirement 1. Compare and evaluate the three simple regress on models estimated by Haulton. Graph each one. Department Merchandise urchase Orders Suppliers (No. of POs) We will begin by graphing each regression model. Start with plotting the regression model: PDC = a + (b xMPS 1 Department Store Costs (PDC) Purchased (MPS) (No. of Ss) 2 Baltimore 1,536,000 s 68,320,000 4,360 When drawing the regression line, use the highest and lowest values given for "MPS", then round the purchasing department costs to the nearest increment of $10,000 and merchandise purchased to the nearest million. (Enlarge the graph and use the line button displayed below to draw the graph.) 3 Chicago 1,105,000 83,452,000 2,540 4 Los Angeles 545,000 21,500,000 1,435 14 5 Miami .052,000 119,200,000 5,960 185 6 New York .054,000 33,485,000 2,785 22 7 Phoenix 525,000 29,850,000 1,325 30 8 Seattle 1,540,000 02,850,000 7,580 9 St . Louis 1,750,000 8,670,000 3,620 10 Toronto 1,610,00 139,306,000 1,705 40 60 80 100 120 14050 212 11 Vancouver 1,260,000 130,955,000 4,740 205 Next plot the regression model: PDC = a + (b xNo. of POs). 1 Data Table When drawing the regression line, use the highest and lowest values given for "POs", then round the purchasing department costs to the nearest increment of $10,000 and purchase orders to the nearest increment of $50. (Enlarge the graph and use the line button displayed below to draw the graph.) Regression 1: PDC = a + (b x MPS) Variable Coefficient Standard Error t-Value Constant $ 1,041,906 $ 344,815 3.02 Independent variable 1: MPS 0.0031 0.0037 0.84 2 = 0.08; Durbin-Watson statistic = 2.42 Regression 2: PDC = a + (b x No. of POs) 1,000,008,004, 009,006,007,008,000 Click to Variable Coefficient Standard Error t-Value Number of Purchase Orders enlarge graph Constant $ 727,863 $ 265,080 2.75 Independent variable 1: No. of POs 158.07 $ 64.58 2.45 12 = 0.43; Durbin-Watson statistic = 1.98 Regression 3: PDC = a + (b x No. of Ss) Variable Coefficient Standard Error t-Value Constant $ 826,102 $ 248,935 3.32 Independent variable 1: No. of Ss $ 3,847 $ 1,724 2.23 2 = 0.38; Durbin-Watson statistic = 1.99
