Instructions: 1. Students should review Appendices 14.4 & 15.3 (Anderson et al.,

Instructions: 1. Students should review Appendices 14.4 & 15.3 (Anderson et al., 9e) and Chap 14&15 Supplemental Note (Excel Lab Solutions) posted in GaView for guidance and examples. 2. Each student must submit his/her current original work on this assignment. Students may contact the instructor for assistance on the assignment. However, as a test, the instructor will not review Hw before submission. 3. Any form of cheating, including copied work or work allowed to be copied or any form of plagiarism, will result in the cancellation of the Hw assignment, and a zero score will be assigned. Late assignments or assignments not submitted/uploaded in GaView Assignment Submission Folder will not be graded but assigned zero. 4. Provide Microsoft Excel solutions to all questions as applicable. Excel solutions must contain verifiable Excel formulas/commands; credit will not be given for typing manual solutions into Excel Spreadsheets. 5. Submit/upload your Excel Spreadsheets (one file) with clearly shown answers to each question. (47 points) 1. An important application of regression analysis in economics and business is the estimation of cost functions. Suppose you are tasked with the estimation of the cost functional form for a manufacturing process and you postulate that the cost functional form to be estimated is TC(Q) = o + 1Q, where TC(Q) is the total cost for a given level of production volume (Q). The sample data of production volume (Q) and total cost for the manufacturing operation are: Production Total Volume (Units) Cost ($) 400 4000 450 5000 550 5400 600 5900 700 6400 750 7000 a. With the ordinary least squares (OLS) method, develop an estimated regression equation that could be used to predict the total cost of operation for a given production volume. Provide an Excel printout of the regression analysis. b. With the Excel printout in (a): i. Write the estimated regression equation and provide an economic interpretation of each estimated parameter. ii. Determine the proportion of total cost variation explained by the production volume.