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    Overhead Costing High-Low Method

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    Managers in the Stamping Department have been studying overhead cost and its relationship with machine hours. Data from the most recent 12 months follows.
    Month Overhead Machine Hours
    January $5,030 2,730
    February 1,600 600
    March 7,210 3,403
    April 4,560 2,200
    May 6,880 3,411
    June 6,520 2,586
    July 6,230 3,364
    August 5,570 2,411
    September 7,728 3,960
    October 5,810 2,897
    November 4,580 2,207
    December 6,010 2,864

    The manager of the department has requested a regression analysis of these two variables(labeled no.1 below) However, the staff person performing the analysis decided to run another regression that excluded February (labeled No. 2) She observed that the volume of activity was very low for that month because of two factors: a severe flu outbreak and an electrical fire that disrupted operations for about 10 working days.

    Regression No. 1
    Constant 428.00
    R2 0.79
    b coefficient 1.86

    Regression No. 2
    Constant 550.00
    R2 0.74
    b coefficient 1.90

    A. Prepare an overhead cost breakdown by using the high-low method. The analysis should be useful in helping predict variable and fixed costs under normal operating conditions.

    B. Prepare an estimate of overhead cost for a volume of 3,000 machine hours by using Regression No. 1.

    C. You now have the ability to analyze three cost estimates from the high-low data in part(A) and the two regression equations. Which one do you feel would provide the best estimate? Explain the factors that support your choice. NOTE: DO NOT calculate an overhead cost estimate with regression No. 2.

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    Solution Preview

    Month Overhead Machine Hrs
    Jan $5,030.00 2730
    Feb $1,600.00 600
    Mar $7,210.00 3403
    Apr $4,560.00 2200
    May $6,880.00 3411
    Jun $6,520.00 2586
    Jul $6,230.00 3364
    Aug $5,570.00 2411
    Sep ...

    Solution Summary

    The solution is presented on an excel spreadsheet and prepares an overhead cost breakdown and analyzes the two regressions.