Interpreting Regression Results-Multiple Regression (Appendix): Malibu Products Corporation molds fiberglass into automobile bodies that are rep- licas of antique cars. A major component of the company's overhead is the costs of handling materials used in the molding process. It was suggested at a recent meeting of the controller and the production vice president that past data be reviewed to see if a relationship could be found between the materials-handling costs and some predic- tor variable. The production vice president suggested that the quantity of materials be used. The controller suggested that the dollar value of the materials be used since the dollar value would explicitly include the effects of price fluctuations. It was also noted in the discussion that some of the materials-handling costs seem to vary with the number of shipments received in a month.

Data were gathered on materials-handling costs, weight of materials received, dollar value of receipts, and number of shipments. The data were gathered for the past 18 months. Eighteen months ago, the semiautomated materials-handling equip- ment in use today was installed. Prior to that time, a manual system was in use.The data appear as follows:

Based on these data, the following regressions are obtained:

After reviewing the above regressions, it was decided that a multiple regression including the dollar value of materials and the weight of materials might be more useful. The results of that regression were:

Required:

a. Prepare the cost estimate for handling 2,600 units of weight with a value of $4.005,000, using each relevant regression.

b. Which regression would you recommend, if any? Why?

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Materials- Handling Costs Weight of Materials Dollar Value of Materials Number of Shipments $606,000 2,425 $3,031,000 491,000 1,790 2,238,000 621,000 2,613 3,266,000 602,000 2,419 3,084,000 561,000 2,110 2,701,000 684,000 2,732 3,688,000 630,000 2,504 3,305,000 681,000 2,915 3,717,000 599,000 2,004 2,725,000 518,000 1,610 2,222,000 539,000 1,824 2,517,000 612212265300 14 7 9 581,000 1,996 2,730,000 8 611,000 2,103 2,934,000 11 713,000 2,741 3,826,000 737,000 2,602 3,851,000 14 622,000 2,191 3,111,000 681,000 2,508 3,674,000 12 599,000 1,941 2,788,000 74927