Kidstuff, Inc., was started 10 years ago by selling children’s clothing through catalogs. Jan Switzer, Kidstuff’s controller, had determined that the cost of filling and shipping orders was fairly consistently related to the number of orders. She had been using the following formula to describe monthly order filling costs: Order filling cost = $7,800+$7.50× orders Lately, however, Jan noticed that order filling costs varied widely and did not seem to follow the above relationship. After a number of discussions with order pickers and fillers, Jan determined that Kidstuff’s expansion into children’s toys had made order filling a more complex operation. Number of orders was still an important variable, but so were the number of categories included in an order (an order for just clothing was quicker to pick, fill, and pack than an order with both clothing and toys) and whether or not any items needed to be gift wrapped. Jan ran a multiple regression on the past 24 months of data for Kidstuff for three variables: the cost of filling orders (picking and packing the order for shipment), the number of orders, and the number of complex orders (orders with both clothing and toys) and gifts (the number of gift-wrapped items). The following printout was obtained:
1. Write out the cost equation for Kidstuff’s monthly order filling cost.
2. If Kidstuff expects to have 300 orders next month (65 with both clothing and toys) and expects that 100 items must be gift wrapped, what are the anticipated order filling costs?
3. Calculate a 99 percent confidence interval for the prediction made in Requirement 2.
4. What does R2 mean in this equation? Overall, what is your evaluation of the cost equation that was developed for the cost of order filling? Suppose that Kidstuff charges an extra $2.50 to gift wrap an item. How might Jan use the results of the regression equation to see whether or not the $2.50 charge is appropriate?
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