Problem:
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.