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Flowers Microbrewery is in the process of analyzing its manufacturing overhead costs. The managers are not sure if the number of processing hours or the number of cases is the best driver of the manufacturing overhead costs. To help the managers decide which driver is better, the controller used Excel's regression function to analyze the following data: Month Overhead ProcHrs NoCases January $29,500 680 8,000 February 27,800 575 6,750 March 24,500 500 5,500 April 29,000 600 7,250 May 28,000 650 7,800 June 29,750 710 5,600 Partial results from the Excel analysis are as follows (ANOVA tables are omitted to save space): Regression 1: Processing hours is the independent variable Regression Statistics Multiple R 0.8892 R Square Adjusted R Square 0.7384 Standard Error 985.1906 Observations 0.7907 6 t Stat P-value Coefficients 14,265.8618 22.3297 Standard Error 3,578.9872 5.7437 Intercept ProcHrs 3.9860 0.0163 Lower 95% Upper 95% 4,329.0002 24,202.7234 6.3826 38.2768 3.8877 0.0177 Regression 2: Number of cases is the independent variable Regression Statistics Multiple R 0.4542 R Square 0.2063 Adjusted R Square 0.0079 Standard Error 1,918.6267 Observations 6 t Stat P-value Intercept NoCases Coefficients 22,541.3178 0.8142 Standard Error 5,499.2051 0.7985 4.0990 1.0197 0.0149 0.3655 Lower 95% Upper 95% 7,273.0767 37,809.5590 -1.4028 3.0312 a. Which of the variables, processing hours or number of cases, is the best driver of the overhead costs? b. Using the driver you selected in part (a), estimate overhead costs for a month when there are 620 processing hours and 6,800 cases. (round intercept to whole dollar and coefficient(s) to two decimal places - nearest cent) C. Place a 95 percent confidence interval around the cost you estimated in part (b). (round Standard Error to whole dollar and final answers to two decimal places)