1.a.

May Blackwell in the new manager of the materials storeroom for Clayton Manufacturing. May has been asked to estimate future monthly purchase costs for part 686, une two of Clayton's products. May han purchase cont and quantity data for the past nine months as follows HE (Click the icon to view the data for the past nine months.) Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as foll as follows: Click the icon to view the expected domand for the rest of the year) Data for last 9 months Kd to part #8967 br. w Expected demand for rest of the year Month Month January February March April May June July August September pected Cost of Purchase $ 12,675 13,000 17,653 15,825 13,125 13,814 15,300 10.233 14,950 Quantity Purchased 2,710 parts 2,810 4,153 3,756 2.912 3,387 October November December Purchase Quantity Expected 3,340 parts 3,710 3,040 Print Done 3.622 2.298 3,562 inue to the next question Requirement 1. The computer in May's office is down, and May has been asked to immediately provide an equation to estimate the future purchas cost for part #696. May grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? Complete the equation below. Requirement 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last three months of the year, High-Low Method Month Purchase Quantity Expected Expected Cost October 3,340 parts November 3,710 parts December 3,040 parts Requirement 3. After a few hours, May's computer is fixed. May uses the first nine months of data and regression analysis to estimate the relationship between the quantity purchased and purchase costs of part #696. The regression line May obtains is as follows: y = $2,582.6 +3.54X Run a regression on the nine months of data using Microsoft Excel to determine the RSquare and t Stut for the X Variable 1 statistics. (Enter the R Square and Stat amounts to four decimal places, XXXXX.) Regression Statistics R Square Star X Variable 1 Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Economic plausibility Goodness of fit Significance of independent variable Compare the regression equation to the equation based on the high-low method. Which is a better fit? Why? is a better fit and more accurate estimate because it uses while the other method only relies on data points and may therefore miss some important information contained in the other data Requirement 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. The Requirement 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 Begin by using the regression results to calculate the expected purchase costs for October, November, and December. (Use amounts as provided in the information to two decimal places. Do not round interim calculations, but then do round your final answers (expected cost amounts) to the nearest whole dollar) Regression Equation Month Purchase Quantity Expected Expected Cost October 3,340 parts November 3,710 parts December 3,040 parts Compare the expected purchase costs to the expected purchase costs calculated using the high low method in requirement 2. Comment on your Regression Equation Month Purchase Quantity Expected Expected Cost October 3,340 parts November 3,710 parts December 3,040 parts Compare the expected purchase costs to the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results. Although the two equations are expected costs. This implies that the high and low points of the data are points within the relevant rango within the relevant range they give representation of the total set of