Maeve Curie is the new manager of the materials storeroom for Keller Manufacturing. Maeve has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Maeve has purchase cost and quantity data for the past 9 months as follows: (Click the icon to view the data for the past 9 months.) Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as follows: E: (Click the icon to view the expected demand for the rest of the year.) Read the fequirements Requirement 1. The computer in Maeve's office is down, and Maeve has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Maeve grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? 1. The computer in Maeve's office is down, and Maeve has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Maeve grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. 3. After a few hours Maeve's computer is fixed. Maeve uses the first 9 months of data and regression analysis to estimate the relationship between the quantity purchased and purchase costs of part #696. The regression line Maeve obtains is as follows: y = $1,780.1 + 3.76x 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. i - Data Table Month October November December Purchase Quantity Expected 3,370 parts 3,760 3,040 Print Done i Data Table Month Cost of Purchase Quantity Purchased $ January February March April May 12,410 12,330 17,494 16,023 13,150 14,055 15,316 10,094 14,990 2,670 parts 2,830 4,137 3,724 2,978 3,365 3,622 2,287 3,592 June July August September Maeve Curie is the new manager of the materials storeroom for Keller Manufacturing. Maeve has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Maeve has purchase cost and quantity data for the past 9 months as follows: (Click the icon to view the data for the past 9 months.) Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as follows: E: (Click the icon to view the expected demand for the rest of the year.) Read the fequirements Requirement 1. The computer in Maeve's office is down, and Maeve has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Maeve grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? 1. The computer in Maeve's office is down, and Maeve has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Maeve grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. 3. After a few hours Maeve's computer is fixed. Maeve uses the first 9 months of data and regression analysis to estimate the relationship between the quantity purchased and purchase costs of part #696. The regression line Maeve obtains is as follows: y = $1,780.1 + 3.76x 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. i - Data Table Month October November December Purchase Quantity Expected 3,370 parts 3,760 3,040 Print Done i Data Table Month Cost of Purchase Quantity Purchased $ January February March April May 12,410 12,330 17,494 16,023 13,150 14,055 15,316 10,094 14,990 2,670 parts 2,830 4,137 3,724 2,978 3,365 3,622 2,287 3,592 June July August September