Question: Problem 6-42 Continuation of Preceding Problem; Computing Least-Squares Regression Estimates; Comparing Multiple Methods (Appendix) (LO 6-1, 6-2, 6-5, 6-6, 6-8) [The following information applies to

Problem 6-42 Continuation of Preceding Problem; Computing Least-Squares Regression Estimates; Comparing Multiple Methods (Appendix) (LO 6-1, 6-2, 6-5, 6-6, 6-8)

[The following information applies to the questions displayed below.]

Marthas Vineyard Marine Supply is a wholesaler for a large variety of boating and fishing equipment. The companys controller, Mathew Knight, has recently completed a cost study of the firms material-handling department in which he used work measurement to quantify the departments activity. The control factor unit used in the work-measurement study was hundreds of pounds of equipment unloaded or loaded at the companys loading dock. Knight compiled the following data.

Units of Activity
Month (hundreds of pounds of equipment loaded or unloaded) Material-Handling Department Costs
January 1,900 $ 11,800
February 1,700 11,400
March 1,400 11,300
April 1,100 10,300
May 2,300 11,200
June 2,500 12,700
July 2,100 12,100
August 1,900 11,500
September 2,700 12,240
October 1,200 11,100
November 1,300 11,450
December 1,500 11,450

Problem 6-42 Part 1

Required:

  1. Compute the least-squares regression estimate of the variable- and fixed-cost components in the companys material-handling department costs. (Do not round intermediate calculations. Round "Variable-cost component" to 2 decimal places and other answer to the nearest whole dollar amount.)

  1. Complete the least-squares regression equation for the departments costs. (Do not round intermediate calculations. Round variable cost per unit to 2 decimal places and other answer to the nearest whole dollar amount.)

  1. Predict the firms material-handling departments costs for a month when 2,300 units of activity are recorded. (Do not round intermediate calculations. Round variable cost per unit to 2 decimal places and final answer to the nearest whole dollar amount.)

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