Davy Company is developing a cost function to estimate the manufacturing overhead rate for next year. You are the management accountant in charge of this project. You have discussed possible cost drivers with several engineers and managers familiar with operations of the company. Based on these discussions, you have collected data on overhead costs (in constant dollars), machine hours, and setups for the last 60 months operations. (To save you some time, I have put the data in an Excel file. Access the file from Canvas Additional information: The factory was completely renovated in month 27, including the replacement of all of its machinery. The residuals were normally distributed, had a Durbin Watson statistic of 1.95, and showed a constant variance. Machine hours and setups may be significantly correlated. One of your co-workers is very familiar with multiple regression analysis and suggested using correlation analysis in Excel to explore the issue. Requirements: a. Determine a cost function of the form y= a + bx + cx2, where x is machine hours and x2 is number of setups over the appropriate period. Should the appropriate time period be all 60 months, or just the months after the renovation? Why? b. Evaluate the cost function for multi-collinearity by completing a correlation analysis on the two independent variables over the appropriate time period. c. Do two more regression analyses over the appropriate time period. On one, use setups. On the other, use hours as the independent variable. d. Which equation is most appropriate? Why? Using the most appropriate cost equation identified in part d, estimate the amount of overhead for January. Estimated machine hours and setups in January are 180 and 80, respectively. Limit the precision of the estimated cost to two decimal places.