Fashion Bling operates a chain of 10 ratail department stores. Each department store makes its own purchasing decisions Summary results for these regressions are as follows (Cok the loon to view the summary resus) Read the res (Click the icon to view additional information) Baker decides to use simple regression analysis to examine whether one or more of three variables (the last three columne is the table) are coet drivers of purchasing department costs Requirement 1, Compare and evaluate the three simple regression models estimated by Daker. Graph each one We will begin by graphing each regression model Start with poting the regression model: POC XMP) 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 green and the fe tutton displayed beve so drew the graph) a of 40060 100 si Click to f enlarge graph PRABEN WE AROMA hande Punhet in dies) More info Bob Baker, assistant to the president of Fashion Bling, is interested in better understanding the drivers of purchasing department costs. For many years, Fashion Bling has alocated purchasing department costs to products on the basis of the dolar value of merchandise purchased. A $100 item is alocated 10 times as many overhead costs associated with the purchasing department as a $10 tom Baker recently attended a seminar titled "Cost Drivers in the Retail industry in a presentation at the seminar, Swank Fabrics, a leading compestor 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 Baker interiewed several members of the purchasing department at the Fashion Bling store in Miami. They believed that Swank Fabrics conclusions also applied to their purchasing department Baker collects the following data for the most recent year for Fashion Bling's 10 retail department stores A C D E Purchasing Dollar Value of Department Merchandise Costs (PDC) Purchased (MPS) Number of Purchase Orders (No. of Pos) Number of Suppliers (No. of Ss) 1 Department Store 2 Baltimore $ 65,315,000 33,450,000 4.357 2.550 3 Chicago 1,523,000 $ 1,100.000 547,000 2.049,000 121,160,000 4 Los Angeles 1.433 5 Miami 119,566,000 5,944 33,505,000 2,793 New York 1,056.000 529,000 1,327 7 Phoenis 29,854,000 anal A 3.000 132 222 11 190 23 33 amal Summary results for these regressions are as follows (Click the icon to view the summary t Data table Regression 1: PDC+(MPS) Variable Constant Independent vanable 1: MPS P-008 Dubin-Watson statistic2.41 Regression 2: PDC a*(* No. of PO) Variable Constant Independent variable 1: No. of POS 042: Durbin Watson static1.98 Regression 3: PDC a(bx No. of Ss) Variable Constant Independent variable 1 No. of Ss 7039, Durbin-Watson statistic-1.97 Print Coefficient Standant Error -Value $ 1.030.061 343,430 3.03 0.0031 0.0007 0.04 Coefficient Standard Error Value $ 730,716 265,419 2.75 64.00 243 $ 156.97 S Coefficient Standard Error -Value $ 247 321 329 814.662 S 3,875 1 $ 1,697 228 Done the seminar, Swank 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. Baker interviewed several members of the purchasing department at the Fashion Bling store in Miami. They believed that Swank Fabrics' conclusions also applied to their purchasing department. Baker collects the following data for the most recent year for Fashion Bling's 10 retail department stores: A B C D E Purchasing Department Costs (PDC) Dollar Value of Merchandise Purchased (MPS) Number of Purchase Orders Number of Suppliers 1 Department Store (No. of POS) (No. of Ss) 2 Baltimore $ 1,523,000 $ 68,315,000 4,357 132 F 3 Chicago 1,100,000 33,456,000 2,550 222 Los Angeles 547,000 121,160,000 1,433 11 2,049,000 119,566,000 5,944 190 1,056,000 33,505,000 2,793 23 529,000 29,854,000 1,327 33 1,538,000 102,875,000 7,586 104 1,754,000 38,674,000 3,617 119 1,612,000 208 139,312,000 1,707 201 1,257,000 4,731 130,944,000 4 5 Miami 6 New York 7 Phoenix 8 Seattle 9 St. Louis 10 Toronto 11 Vancouver Fashion Bling operates a chain of 10 ratail department stores. Each department store makes its own purchasing decisions Summary results for these regressions are as follows (Cok the loon to view the summary resus) Read the res (Click the icon to view additional information) Baker decides to use simple regression analysis to examine whether one or more of three variables (the last three columne is the table) are coet drivers of purchasing department costs Requirement 1, Compare and evaluate the three simple regression models estimated by Daker. Graph each one We will begin by graphing each regression model Start with poting the regression model: POC XMP) 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 green and the fe tutton displayed beve so drew the graph) a of 40060 100 si Click to f enlarge graph PRABEN WE AROMA hande Punhet in dies) More info Bob Baker, assistant to the president of Fashion Bling, is interested in better understanding the drivers of purchasing department costs. For many years, Fashion Bling has alocated purchasing department costs to products on the basis of the dolar value of merchandise purchased. A $100 item is alocated 10 times as many overhead costs associated with the purchasing department as a $10 tom Baker recently attended a seminar titled "Cost Drivers in the Retail industry in a presentation at the seminar, Swank Fabrics, a leading compestor 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 Baker interiewed several members of the purchasing department at the Fashion Bling store in Miami. They believed that Swank Fabrics conclusions also applied to their purchasing department Baker collects the following data for the most recent year for Fashion Bling's 10 retail department stores A C D E Purchasing Dollar Value of Department Merchandise Costs (PDC) Purchased (MPS) Number of Purchase Orders (No. of Pos) Number of Suppliers (No. of Ss) 1 Department Store 2 Baltimore $ 65,315,000 33,450,000 4.357 2.550 3 Chicago 1,523,000 $ 1,100.000 547,000 2.049,000 121,160,000 4 Los Angeles 1.433 5 Miami 119,566,000 5,944 33,505,000 2,793 New York 1,056.000 529,000 1,327 7 Phoenis 29,854,000 anal A 3.000 132 222 11 190 23 33 amal Summary results for these regressions are as follows (Click the icon to view the summary t Data table Regression 1: PDC+(MPS) Variable Constant Independent vanable 1: MPS P-008 Dubin-Watson statistic2.41 Regression 2: PDC a*(* No. of PO) Variable Constant Independent variable 1: No. of POS 042: Durbin Watson static1.98 Regression 3: PDC a(bx No. of Ss) Variable Constant Independent variable 1 No. of Ss 7039, Durbin-Watson statistic-1.97 Print Coefficient Standant Error -Value $ 1.030.061 343,430 3.03 0.0031 0.0007 0.04 Coefficient Standard Error Value $ 730,716 265,419 2.75 64.00 243 $ 156.97 S Coefficient Standard Error -Value $ 247 321 329 814.662 S 3,875 1 $ 1,697 228 Done the seminar, Swank 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. Baker interviewed several members of the purchasing department at the Fashion Bling store in Miami. They believed that Swank Fabrics' conclusions also applied to their purchasing department. Baker collects the following data for the most recent year for Fashion Bling's 10 retail department stores: A B C D E Purchasing Department Costs (PDC) Dollar Value of Merchandise Purchased (MPS) Number of Purchase Orders Number of Suppliers 1 Department Store (No. of POS) (No. of Ss) 2 Baltimore $ 1,523,000 $ 68,315,000 4,357 132 F 3 Chicago 1,100,000 33,456,000 2,550 222 Los Angeles 547,000 121,160,000 1,433 11 2,049,000 119,566,000 5,944 190 1,056,000 33,505,000 2,793 23 529,000 29,854,000 1,327 33 1,538,000 102,875,000 7,586 104 1,754,000 38,674,000 3,617 119 1,612,000 208 139,312,000 1,707 201 1,257,000 4,731 130,944,000 4 5 Miami 6 New York 7 Phoenix 8 Seattle 9 St. Louis 10 Toronto 11 Vancouver