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A legal tiem wanted to determine the relationship between its monthly operating costs and a potential cost driver, professional hours. An excerpt from the output of a regression analysis performed using Excel showed the following information Click the icon to the regression data) a. Given this output wile the legal arm's monthly cost equation b. Should management this equation to predict monthly operating costs? Explain your answer 6. Given this output will the legal's monthly contaquation (Enter all amounts to the nearest cont XXX) X The legal's monthly cost equations 6. Should management this equation to predict monthly operating costs? Explain your answer This equation to predict Com The R-square statistic can range from a high of to a low or In this case, the Risquare indicates that the cost uwon indicating there M relationship between the potential cost driver and the advertising agency's operating cost A B D TI E F G 1 0.92 0.84 SUMMARY OUTPUT 2 Regression Statistics 3 Multiple R 4 R Square 5 Adjusted R Square 6 Standard Error 7 Observations 8 ANOVA 0.81 148.38 12 9 d of SS MS Significance F 1 26.03 0.00 10 Regression 11 Residual 573 116.90 110,083 10 573,116.90 22,016.62 10 Print Done A legal tiem wanted to determine the relationship between its monthly operating costs and a potential cost driver, professional hours. An excerpt from the output of a regression analysis performed using Excel showed the following information Click the icon to the regression data) a. Given this output wile the legal arm's monthly cost equation b. Should management this equation to predict monthly operating costs? Explain your answer 6. Given this output will the legal's monthly contaquation (Enter all amounts to the nearest cont XXX) X The legal's monthly cost equations 6. Should management this equation to predict monthly operating costs? Explain your answer This equation to predict Com The R-square statistic can range from a high of to a low or In this case, the Risquare indicates that the cost uwon indicating there M relationship between the potential cost driver and the advertising agency's operating cost A B D TI E F G 1 0.92 0.84 SUMMARY OUTPUT 2 Regression Statistics 3 Multiple R 4 R Square 5 Adjusted R Square 6 Standard Error 7 Observations 8 ANOVA 0.81 148.38 12 9 d of SS MS Significance F 1 26.03 0.00 10 Regression 11 Residual 573 116.90 110,083 10 573,116.90 22,016.62 10 Print Done

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