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Managers in the Stamping Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. Overhead (RM) Machine Hours 5,030 1,600 7,210 4,560 6,880 6,520 6,230 5,570 7,728 5,810 4,580 6,010 Month January February March 2,730 600 3,403 2,200 3,411 2,586 3,364 2,411 3,960 2,897 2,207 2,864 April May June July August September October November December 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 Regression No. 2 Constant 428.00 Constant 550.00 R? 0.79 R? 0.74 b coefficient 1.86 b coefficient 1.90 Required: i. Prepare an overhead cost breakdown by using the high-low method. The analysis should be useful in helping to predict variable and fixed costs under normal operating conditions. ii. Prepare an estimate of overhead cost for a volume of 3,000 machine hours by using regression no. 1. You now have the ability to analyze three cost estimates from the high-low data in part (i) and the two regression equations. (a) Which one do you feel would provide the best estimate? (b) Explain the factors that support your choice. iii.

Managers in the Stamping Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. Overhead (RM) Machine Hours 5,030 1,600 7,210 4,560 6,880 6,520 6,230 5,570 7,728 5,810 4,580 6,010 Month January February March 2,730 600 3,403 2,200 3,411 2,586 3,364 2,411 3,960 2,897 2,207 2,864 April May June July August September October November December 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 Regression No. 2 Constant 428.00 Constant 550.00 R? 0.79 R? 0.74 b coefficient 1.86 b coefficient 1.90 Required: i. Prepare an overhead cost breakdown by using the high-low method. The analysis should be useful in helping to predict variable and fixed costs under normal operating conditions. ii. Prepare an estimate of overhead cost for a volume of 3,000 machine hours by using regression no. 1. You now have the ability to analyze three cost estimates from the high-low data in part (i) and the two regression equations. (a) Which one do you feel would provide the best estimate? (b) Explain the factors that support your choice. iii.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Managers in the Stamping Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. Overhead (RM) Machine Hours 5,030 1,600 7,210 4,560 6,880 6,520 6,230 5,570 7,728 5,810 4,580 6,010 Month January February March 2,730 600 3,403 2,200 3,411 2,586 3,364 2,411 3,960 2,897 2,207 2,864 April Мay June July August September October November December 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 Regression No. 2 428.00 Constant 550.00 R? 0.79 0.74 b coefficient 1.90 R? b coefficient 1.86 Required: i. Prepare an overhead cost breakdown by using the high-low method. The analysis should be useful in helping to predict variable and fixed costs under normal operating conditions. ii. Prepare an estimate of overhead cost for a volume of 3,000 machine hours by using regression no. 1. iii. You now have the ability to analyze three cost estimates from the high-low data in part (i) and the two regression equations. (a) Which one do you feel would provide the best estimate? (b) Explain the factors that support your choice.
Transcribed Image Text:Managers in the Stamping Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. Overhead (RM) Machine Hours 5,030 1,600 7,210 4,560 6,880 6,520 6,230 5,570 7,728 5,810 4,580 6,010 Month January February March 2,730 600 3,403 2,200 3,411 2,586 3,364 2,411 3,960 2,897 2,207 2,864 April Мay June July August September October November December 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 Regression No. 2 428.00 Constant 550.00 R? 0.79 0.74 b coefficient 1.90 R? b coefficient 1.86 Required: i. Prepare an overhead cost breakdown by using the high-low method. The analysis should be useful in helping to predict variable and fixed costs under normal operating conditions. ii. Prepare an estimate of overhead cost for a volume of 3,000 machine hours by using regression no. 1. iii. You now have the ability to analyze three cost estimates from the high-low data in part (i) and the two regression equations. (a) Which one do you feel would provide the best estimate? (b) Explain the factors that support your choice.
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