Warning: Trying to access array offset on value of type bool in /home/topgsnkq/myessaydesk.com/wp-content/themes/enfold/framework/php/function-set-avia-frontend.php on line 637
Linear Regression
You are a consultant for the Excellent Consulting Group (ECG). You have completed the first assignment, developing and testing a forecasting method that uses Linear Regression (LR) techniques (Module 3 Case). However, the consulting manager at ECG wants to try a different forecasting method as well. Now you decide to try Single Exponential Smoothing (SES) to forecast sales.
Using this Excel template: Data chart for BUS520 Case 4, do the following:
Save your time - order a paper!
Get your paper written from scratch within the tight deadline. Our service is a reliable solution to all your troubles. Place an order on any task and we will take care of it. You won’t have to worry about the quality and deadlines
Order Paper Now- Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled “Year 2 Forecast – MAPE”).
- Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled “SES – MAPE”). Use 0.15 and 0.90 alphas.
- Compare the MAPE calculated for the LR forecast (#1 above) with the MAPEs calculated using SES.
Then
write a report to your boss in which you discuss the results obtained
above. Using calculated MAPE values, make a recommendation concerning
which method appears to be more accurate for the Year 2 data: SES or
Linear Regression.
Analysis
- Accurate and complete SES analysis in Excel.
Written Report
- Length requirements: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must submit 4–5 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.”
- Provide a brief introduction to/background of the problem.
- Complete a written analysis that supports your Excel analysis, discussing the assumptions, rationale, and logic used to complete your SES forecast.
- Give complete, meaningful, and accurate recommendation(s) relating to whether LR or SES is more accurate in predicting sales.