Drop downs for requirement one: number of months, purchase costs, quantity purchased.

Req 3- plausible or non, excellent or poor fit, significant or not

high low or regression

all available data, four data points, two data points

all, two, zero

req 4

different in both fixed and variable, same for fixed diff for variable, same for variable different for fixed, same in both fixed and variable

somewhat different, drastically different

a reasonable, not a reasonable

These are all the details provided.

Marla Barton is the new manager of the materials storeroom for Tudor Manufacturing. Marla has been asked to estimate future monthly purchase costs for part #696, used in two of Tudor's products. Marla has purchase cost and quantity data for the past 9 months as follows: E (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.) Read the requirements. i X Data Table ked i Data Table X fost - a Month Cost of Purchase Quantity Purchased ec January $ 12,745 Month 2,670 parts Purchase Quantity Expected 3,370 parts February 12,110 2,800 October March 17,465 4,072 November 3,770 April 15,869 3,712 December 3,060 May 13,155 2,945 June 14,022 3,210 July 15,322 3,622 Print Done 9 n line Marla obtains is as follows: y = $2,015.7 +3.74X 10,061 2,221 August September R Square and t Stat for the X Variable 1 statistics. (Enter the R Square and 14,970 3,512 Requirement 1. The computer in Marla's office is down, and Marla has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Marla 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 Purchase Quantity Expected October 3,370 parts 3,770 parts November December 3,060 parts Requirement 3. After a few hours Marla's computer is fixed. Marla 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 Marla obtains is as follows: y = $2,015.7 + 3.74X 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 Goodness of fit Significance of independent variable 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.) Regression Equation Expected Cost Month October Purchase Quantity Expected 3,370 parts 3,770 parts 3,060 parts November December Compare the expected purchase costs to the expected purchase costs calculated using the high-low method 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 representation of the total set of points within the relevant range. Marla Barton is the new manager of the materials storeroom for Tudor Manufacturing. Marla has been asked to estimate future monthly purchase costs for part #696, used in two of Tudor's products. Marla has purchase cost and quantity data for the past 9 months as follows: E (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.) Read the requirements. i X Data Table ked i Data Table X fost - a Month Cost of Purchase Quantity Purchased ec January $ 12,745 Month 2,670 parts Purchase Quantity Expected 3,370 parts February 12,110 2,800 October March 17,465 4,072 November 3,770 April 15,869 3,712 December 3,060 May 13,155 2,945 June 14,022 3,210 July 15,322 3,622 Print Done 9 n line Marla obtains is as follows: y = $2,015.7 +3.74X 10,061 2,221 August September R Square and t Stat for the X Variable 1 statistics. (Enter the R Square and 14,970 3,512 Requirement 1. The computer in Marla's office is down, and Marla has been asked to immediately provide an equation to estimate the future purchase cost for part #696. Marla 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 Purchase Quantity Expected October 3,370 parts 3,770 parts November December 3,060 parts Requirement 3. After a few hours Marla's computer is fixed. Marla 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 Marla obtains is as follows: y = $2,015.7 + 3.74X 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 Goodness of fit Significance of independent variable 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.) Regression Equation Expected Cost Month October Purchase Quantity Expected 3,370 parts 3,770 parts 3,060 parts November December Compare the expected purchase costs to the expected purchase costs calculated using the high-low method 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 representation of the total set of points within the relevant range