Interpreting Regression Output Rikki Bake, the controller for XYZ Incorporated, suspects that factory overhead costs are driven by the number of machine hours used. Using Excel, Ms. Bake input twelve months of historical data for total factory overhead costs and machine hours used. The summary output information is provided below. Use the output information provided to develop a cost formula for overhead cost. SUMMARY OUTPUT 4 Regression Statistics Multiple R 0.963809718 R Square 0.928929173 Adjusted R Square 0.92182209 Standard Error 56.42709871 Observations ANOVA SS F 130.7047086 MS 416166.0753 3184.017469 Significance F 4.6008E-07 Regression Residual Total 416166.0753 31840. 17469 448006.25 10 11 17 Intercept 18 Machine Hours Coofficients $970.12 36.138 Standard Error 415.4592475 3.160962926 Stat 2.335045169 1 1.432616 P-value 0.0416902 4.6008E-07 Lower 95% 44.41521808 29.09501101 Upper 95% 1895.816999 43.18113962 Lower 95% 44.41521808 29.09501101 Upper 95% 1895.816999 43.18113962 Complete the formula below. Round answers to two decimal places. Overhead Cost = $ x machine hours)