Using the customer traffic data and matching sales for each month of Year 1, create a Linear Regression (LR) equation in Excel, assuming all assumptions for linear regression have been met. Use the Excel template provided (see “Module 2 Case – LR –Year 1” spreadsheet tab), and be sure to include your LR chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart.
After you have developed the LR equation above, you will use the LR equation to forecast sales for Year 2 (see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has collected customer traffic data for Year 2. Your role is to complete the sales forecast using the LR equation from Step 1 above.
After you have forecast Year 2 sales, your Professor will provide you with 12 months of actual sales data for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and average (total) variances from forecast to actual sales.
(4 pages)Written Report
write a report for the client that describes the process you used above, and that analyzes the results for Year 2.
(What is the difference between forecast vs. actual sales for Year 2—by month and for the year as a whole?)
Make a recommendation concerning how the LR equation might be used by New Star Grocery Company to forecast future sales.
Chase, C. W., (2013). Demand-driven forecasting: A structured approach to forecasting. John Wiley & Sons. Somerset, NJ. Retrieved from Ebrary in the Trident Online Library.
Regression Line Example: http://www.khanacademy.org/video/regression-line-example?topic=statistics
Second Regression Example: http://www.khanacademy.org/video/second-regression-example?topic=statistics
Emerald Group Publishing. (n.d.). Developing Critical Thinking. Retrieved from http://www.emeraldinsight.com/learning/study_skills/skills/critical_thinking.htm
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