Pat Wilson is the new manager of the materials storeroom for Perth Manufacturing. Pat has been asked to estimate future monthly purchase costs for part no. 4599, used in two of Perth Manufacturing's products. Pat has cost of purchase and quantity data for the past nine months as follows: Month Cost of purchase Quantity purchased January $12675 2710 parts February 13000 2810 March 17653 4153 April 15825 3756 May 13125 2912 June 13814 3387 July 15300 3622 August 10233 2298 September 14950 3562 Estimated monthly purchases for this part, based on expected demand of the two products for the rest of the year, are: Month Purchase quantity expected October 3340 parts November 3710 December 3040 Required 1. The computer in Pat's office is down and Pat has been asked to provide immediately an equation to estimate the future purchase cost for part no. 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 the purchase costs of part no. 4599. The regression line Pat obtains is: y = $2582.60+ 3.54x 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 with the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results. Pat Wilson is the new manager of the materials storeroom for Perth Manufacturing. Pat has been asked to estimate future monthly purchase costs for part no. 4599, used in two of Perth Manufacturing's products. Pat has cost of purchase and quantity data for the past nine months as follows: Month Cost of purchase Quantity purchased January $12675 2710 parts February 13000 2810 March 17653 4153 April 15825 3756 May 13125 2912 June 13814 3387 July 15300 3622 August 10233 2298 September 14950 3562 Estimated monthly purchases for this part, based on expected demand of the two products for the rest of the year, are: Month Purchase quantity expected October 3340 parts November 3710 December 3040 Required 1. The computer in Pat's office is down and Pat has been asked to provide immediately an equation to estimate the future purchase cost for part no. 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 the purchase costs of part no. 4599. The regression line Pat obtains is: y = $2582.60+ 3.54x 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 with the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results