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QUESTION 3 The management of Wheeler Company has decided to develop cost formulas for its major overhead activities. Wheeler uses a highly automated manufacturing process, and power costs are a significant manufacturing cost. Cost analysts have decided that power costs are mixed; thus, they must be broken into their fixed and variable elements so that the cost behavior of the power usage activity can be properly described. Machine hours have been selected as the activity driver for power costs. The following data for the past eight quarters have been collected: Quarter Machine Hours Power Cost 1 20,000 $ 26,000 2 25,000 $ 38,000 3 30,000 $ 42,500 22,000 $ 35,000 5 21,000 $ 34,000 6 18,000 $ 31,400 7 24,000 $ 36,000 8 28,000 42,000 4 Wheeler Company conducted simple regression to determine the cost equation. The first regression assumed that the power cost was a mixed cost. The second regression assumed that the power cost was a variable cost. The results of the regression analyses are provided below: Regression Model #1 (Mixed Cost) SUMMARY OUTPUT Regression Statistics 0.896887463 Multiple R Click Save and Submit to save and submit. Click Save All Answers to save all answers. Wheeler Company conducted simple regression to determine the cost equation. The first regression assumed that the power cost was a mixed cost. The second regression assumed that the power cost was a variable cost. The results of the regression analyses are provided below: Regression Model #1 (Mixed Cost) SUMMARY OUTPUT Regression Statistics Multiple R 0.896887463 R Square 0.804407121 Adjusted R Square 0.771808307 Standard Error 2598.991985 Observations ANOVA df Regression Residual Total SS MS Significance F 1 166680194 166680194 24.67596336 0.002533196 6 40528556.03 6754759.339 7 207208750 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 7442.887931 5744.757622 1.295596511 0.24272306-6614.027576 21499.80344 -6614.027576 21499.80344 X Variable 1 1.198706897 0.241310348 4.96749065 0.002533196 0.608241747 1.789172046 0.608241747 1.789172046 Regression Model #2 (Variable Cost) SUMMARY OUTPUT Click Save and Submit to save and submit. Click Save All Answers to save all answers, SOVE A Anshers Regression Statistics Multiple R 0.997491985 R Square 0.99499026 Adjusted R Square 0.852133117 Standard Error 2722.05003 Observations 8 ANOVA df SS MS F Significance Regression 1 10301343105 10301343105 1390.277986 0.000000024836 Residual 7 51866894.57 7409556.368 Total 8 10353210000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.09 Upper 95.0% #N/A #N/A #N/A #N/A Intercept 0 #N/A #N/A #N/A X Variable 1 1.507322453 0.040425495 37.28643166 0.0000000025950 Required: 1. Use the high-low method to determine the cost equation. 1.411731347 1.602913558 1.411731347 1.602913558 2. Using the equation from part 1, estimate the power cost for 22,500 machine hours. 3. Do the repression models ordure sionificant results? Fxnlain the significance of the two models Click Save and Submit to save and submit. Click Save All Answers to save all answers. Sovet quired: 1. Use the high-low method to determine the cost equation. 2. Using the equation from part 1, estimate the power cost for 22,500 machine hours. 3. Do the regression models produce significant results? Explain the significance of the two models. 4. Which regression model is the better model? Explain. 5. Using the better regression model equation, estimate the power cost for 22,500 machine hours. For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac). BI V S Paragraph Arial 14px Zick Save and Submit to save and submit. Click Save All Answers to save all answers