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.
College Algebra (MindTap Course List)
12th Edition
ISBN:9781305652231
Author:R. David Gustafson, Jeff Hughes
Publisher:R. David Gustafson, Jeff Hughes
Chapter6: Linear Systems
Section6.8: Linear Programming
Problem 5SC: If during the following year it is predicted that each comedy skit will generate 30 thousand and...
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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 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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Step 1: Write the given information.
VIEWStep 2: Determine the cost equation using the high-low method.
VIEWStep 3: Determine the future expected purchase costs for each of the last 3 months of the year.
VIEWStep 4: Determine relationship between the quantity purchased and purchase costs using regression analysis.
VIEWStep 5: Evaluate regression line using criteria of economic plausibility, goodness of fit an significance.
VIEWStep 6: Determine expected purchase costs for October, November, and December using regression results.
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