Marcia Baker is the new manager of the materials storeroom for Keller Manufacturing. Marcia has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Marciahas purchase cost and quantity data for the past 9 months as follows: Month Cost of Purchase Quantity Purchased January $12,410. 2,730 parts February. 12,880 2,880 March 17,505 4,148 April 15,869 3,761 May 13,170 2,912 June 13,867 3,332 July 15,237 3,644 August 10,061 2,287 September 14,960 3,582 Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as follows: Month Purchase Quantity Expected October 3,330 parts November 3,730 December 3,060 The computer in Marcia's office is down, and Marcia has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Marcia grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. After a few hours Marcia's computer is fixed. Marcia 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 Marcia obtains is as follows: y = $2,082 + 3.66X 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? 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.

Marcia Baker is the new manager of the materials storeroom for Keller Manufacturing. Marcia has been asked to estimate future monthly purchase costs for part #696, used in two of Keller's products. Marciahas purchase cost and quantity data for the past 9 months as follows: Month Cost of Purchase Quantity Purchased January $12,410. 2,730 parts February. 12,880 2,880 March 17,505 4,148 April 15,869 3,761 May 13,170 2,912 June 13,867 3,332 July 15,237 3,644 August 10,061 2,287 September 14,960 3,582 Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as follows: Month Purchase Quantity Expected October 3,330 parts November 3,730 December 3,060 The computer in Marcia's office is down, and Marcia has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Marcia grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get? Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year. After a few hours Marcia's computer is fixed. Marcia 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 Marcia obtains is as follows: y = $2,082 + 3.66X 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? 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.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Month Cost of Purchase Quantity Purchased

January $12,410. 2,730 parts

February. 12,880 2,880

March 17,505 4,148

April 15,869 3,761

May 13,170 2,912

June 13,867 3,332

July 15,237 3,644

August 10,061 2,287

September 14,960 3,582

Estimated monthly purchases for this part based on expected demand of the two products for the rest of the year are as follows:

Month	Purchase Quantity Expected
October	3,330 parts
November	3,730
December	3,060

The computer in Marcia's office is down, and Marcia has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Marcia grabs a calculator and uses the high-low method to estimate a cost equation. What equation does she get?
	Using the equation from requirement 1, calculate the future expected purchase costs for each of the last 3 months of the year.
	After a few hours Marcia's computer is fixed. Marcia 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 Marcia obtains is as follows: y = $2,082 + 3.66X 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?
	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.

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