Case Study 2-26 Hi-Lo Estimate

1. Using the Hi-Lo Method, estimate a manufacturing overhead cost formula.

ANSWER: Because there is a fixed monthly fee up to 19,500 hours, two calculating the formula at least two times is necessary. Additionally, a third formula can be made to encompass all of the data.

ALL DATA:

Formula for data >19,500 hours:

Formula for data <19,500 hours:

2. Prepare a scattergraph using all of the data for the two-year period. Fit a straight line or lines to the plotted points using a ruler. Describe the cost behavior pattern revealed by your scattergraph plot.

Below the threshold of 19,500 hours, the slope is more flat. Above the 19,500 hours threshold, the slope becomes more steep. The scattergraph reveals that the hi-lo approach is a good estimate but does not match up with the line of best fit for this particular data set. Obviously, the scatterplot also reveals that for every increase in the number of hours the machines are running, there is a corresponding increase in the overhead cost.

3. Assume a least-squares regression analysis using all of the given data points estimated the total fixed costs to be $40,102 and the variable costs to be $2.13 per machine-hour. Do you have any concerns about the accuracy of the hi-lo estimates that you have computed or the least-squares regression estimates that have been provided?

ANSWER: The Hi-Lo estimate OVER-estimated the least-squares regression analysis of the fixed cost by $1,398 and of the per-machine-hour cost by $.17. The hi-lo estimate line would cause the slope to be artificially steep due to the high value of the month with the highest overall cost. Again, the hi-lo estimate is a quick and easy estimate as to the least squares regression line HOWEVER it is inaccurate and considering the low cost and time required for excel, the least squares regression method should be favored.

4. Assume that the company consumes 22,500 machine-hours...