Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here): SUMMARY OUTPUT Regression Statistics Multiple R 0.991108748 R Square 0.982296551 Adjusted Square 0.977870689 Standard Error 67.58593536 Observations 6 ANOVA df Significance F 0.00011823 Regression Residual Total 1 1 4 5 SS MS F 1013811.8991013811.899 221.9446735 18271.43463 4567.858659 1032083333 Intercept X Variable 1 Coefficients 2945.94652 0.718590832 Standard Error Sta! P-value 194.105844 15.17701095 0.000109885 0.04823467 14.89780767 0.00011823 Lower 95% 2407.022299 0.584669919 What is the cost equation based upon the results of the regression analysis? COOTED O A. y = $2,945.95 + $0.72 OB. y = $072x Oc. y = $0.72x + $2,945.95 OD. y = $2,945.95x + $0.72