High-low, regression Pat Flip is the new manager of the materials storeroom for Serth manufacturing. Pat has been asked to estimate future monthly purchase costs for part #4599, used in two of Serth’s products. Pat has purchase cost and quantity data for the past nine months as follows:

Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are:

1. The computer in Pat’s office is down and Pat has been asked to immediately provide an equation to estimate the future purchase cost for part #4599. Pat grabs a calculator and uses the high-low method to estimate a cost equation. What equation does Pat get?

2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last three months of the year.

3. After a few hours Pat’s computer is fixed. Pat uses the first nine months of data and regression analysis to estimate the relationship between the quantity purchased and purchase costs of part #4599. The regression line Pat obtains is:

y = $501.54 + $5.84X

Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Compare the regression equation to the equation based on the high-low method. Which is a better fit? Why?

4. Use the regression results to calculate the expected purchase costs for October, November, and December. Compare the expected purchase costs to the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results.

Transcribed Image Text:

Cost of purchase $13,900 Quantity purchased 2,350 parts Month January February 14,650 2,450 March 11,560 1,910 3,220 2,590 April May 20,420 16,640 16,880 June 2,780 July August September 16,810 3,130 3,400 20,500 18,460 3,060