Managers in the Priming Department Have Been Studying Overhead Cost

Managers in the Priming Department have been studying overhead cost and the relationship with machine hours. Data from the most recent 12 months follow. $\begin{array}{lcr}\underline{\text { Month }} & \underline{\text { Overhead } }& \text { Machine } \\&&\underline{\text { Hours }}\\\text { January } & \$ 5,130 & 2,830 \\\text { February } & 1,700 & 700 \\\text { March } & 7,310 & 3,503 \\\text { April } & 4,660 & 2,300 \\\text { May } & 6,980 & 3,511 \\\text { June } & 6,620 & 2,686 \\\text { July } & 6,330 & 3,464 \\\text { August } & 5,670 & 2,511 \\\text { September } & 7,828 & 4,060 \\\text { October } & 5,910 & 2,997 \\\text { November } & 4,680 & 2,307 \\\text { December } & 6,110 & 2,964\end{array}$ 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. $\quad$$\quad$$\text { Regression No. } 1$ $\quad$$\quad$$\quad$$\quad$ $\text { Regression No. } 2$
$\begin{array} { l r l l r } \text { Constant } & 428.00 & & \text { Constant } & 550.00 \\\mathrm { R } ^ { 2 } & 0.79 & & \mathrm { R } ^ { 2 } & 0.74 \\\mathrm {~b} \text { coefficient } & 1.86 & & \mathrm {~b} \text { coefficient } & 1.90\end{array}$ Required:
A. 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.
B. Prepare an estimate of overhead cost for a volume of 3,100 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.