Mindy O'Malley is the new manager of the materials storeroom for Keller Manufacturing. Mindy has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Mindy 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: (Click the icon to view the expected demand for the rest of the year.) X Data Table i Requirements Data Table Month Cost of Purchase Quantity Purchased Purchase Quantity Expected Month January $ 12,675 2,680 parts 3,310 parts February 12,330 2,890 October November 3,710 March 17,303 4,095 December 3,070 April 3,777 15,825 13,025 May 2,923 Print Done June 13,900 3,210 1. The computer in Mindy's office is down, and Mindy has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Mindy 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 Mindy's computer is fixed. Mindy 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 Mindy obtains is as follows: y = $1,994.3 + 3.71X 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 Comment on your results. Screenshot July 15,237 3,600 ntity purchased and purchase costs of part #696. The August 10,115 2,298 September 14,980 3,512 unts to six decimal places, O.XXXXXX.) Print Done ? Save for Later Requirement 1. The computer in Mindy's office is down, and Mindy has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Mindy grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? + ( Requirement 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. High-Low Method Expected Cost Month October Purchase Quantity Expected 3,310 parts 3,710 parts November December 3,070 parts Requirement 3. After a few hours Mindy's computer is fixed. Mindy 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 Mindy obtains is as follows: y = $1,994.3 + 3.71X Run a regression on the 9 months of data using Microsoft Excel to determine the R Square and t Stat for the X Variable 1 statistics. (Enter the R Square and t Stat amounts to six decimal places, O.XXXXXX.) Regression Statistics R Square t Stat X Variable 1 Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Economic plausibility Nonplausible Goodness of fit Poor fit Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Economic plausibility Nonplausible Goodness of fit Poor fit Significance of independent variable Insignificant Compare the regression equation to the equation based on the high-low method. Which is a better fit? Why? The 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. Begin by using the regression results to calculate the expected purchase costs for October, November, and December. (Use amounts as given 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.) 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 given 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 Expected Cost Month October Purchase Quantity Expected 3,310 parts 3,710 parts 3,070 parts November December Compare the expected purchase costs to the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results. within the relevant range they give Although the two equations are expected costs. This implies that the high and low points of the data are noints within the relevant ranne representation of the total set of Mindy O'Malley is the new manager of the materials storeroom for Keller Manufacturing. Mindy has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Mindy 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: (Click the icon to view the expected demand for the rest of the year.) X Data Table i Requirements Data Table Month Cost of Purchase Quantity Purchased Purchase Quantity Expected Month January $ 12,675 2,680 parts 3,310 parts February 12,330 2,890 October November 3,710 March 17,303 4,095 December 3,070 April 3,777 15,825 13,025 May 2,923 Print Done June 13,900 3,210 1. The computer in Mindy's office is down, and Mindy has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Mindy 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 Mindy's computer is fixed. Mindy 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 Mindy obtains is as follows: y = $1,994.3 + 3.71X 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 Comment on your results. Screenshot July 15,237 3,600 ntity purchased and purchase costs of part #696. The August 10,115 2,298 September 14,980 3,512 unts to six decimal places, O.XXXXXX.) Print Done ? Save for Later Requirement 1. The computer in Mindy's office is down, and Mindy has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Mindy grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? + ( Requirement 2. Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. High-Low Method Expected Cost Month October Purchase Quantity Expected 3,310 parts 3,710 parts November December 3,070 parts Requirement 3. After a few hours Mindy's computer is fixed. Mindy 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 Mindy obtains is as follows: y = $1,994.3 + 3.71X Run a regression on the 9 months of data using Microsoft Excel to determine the R Square and t Stat for the X Variable 1 statistics. (Enter the R Square and t Stat amounts to six decimal places, O.XXXXXX.) Regression Statistics R Square t Stat X Variable 1 Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Economic plausibility Nonplausible Goodness of fit Poor fit Evaluate the regression line using the criteria of economic plausibility, goodness of fit, and significance of the independent variable. Economic plausibility Nonplausible Goodness of fit Poor fit Significance of independent variable Insignificant Compare the regression equation to the equation based on the high-low method. Which is a better fit? Why? The 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. Begin by using the regression results to calculate the expected purchase costs for October, November, and December. (Use amounts as given 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.) 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 given 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 Expected Cost Month October Purchase Quantity Expected 3,310 parts 3,710 parts 3,070 parts November December Compare the expected purchase costs to the expected purchase costs calculated using the high-low method in requirement 2. Comment on your results. within the relevant range they give Although the two equations are expected costs. This implies that the high and low points of the data are noints within the relevant ranne representation of the total set of