Maureen Stone is the new manager of the materials storeroom for Burfield Manufacturing. Maureen has been asked to estimate future monthly purchas as follows: (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 follows: (Click the icon to view the expected demand for the rest of the year.) Month January February Cost of Purchase Quantity Purchased $ 12,687 2,650 parts 12,220 2,800 Required March 17,673 4,116 April 16,115 3,761 May 13,000 2,934 equation does she get? June 14,038 3,332 Complete the equation below. July 15,316 3,600 Purchase costs (y) = $ 1209 + ($4 x Quantity purchased (X) ) Requirement 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last three months of the year. August September 10,073 2,216 14,970 3,512 Month October November Purchase Quantity Expected High-Low Method Expected Cost 3,320 parts 14,489 Print Done 3,740 parts 3,060 parts 16,169 13,449 December Requirement 3. After a few hours, Maureen's computer is fixed. Maureen 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 li follows: y $1,996.6 +3.74X. Run a regression on the nine months of data using Microsoft Excel to determine the R Square and t Stat for the X Variable 1 statistics. (Enter the R Square Regression Statistics ItS R Square 0.9757 t Stat X Variable 1 16.7527 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 Expected demand for rest of the year Month October November Purchase Quantity Expected 3,320 parts 3,740 December 3,060 Print Done