THE TASK: In the Excel Sheet and from the Case study un the files section answer
THE TASK: In the Excel Sheet and from the Case study un the files section answer the following:
1. Identify and build the best time series models in Excel, focusing on the SMA, WMA, and ES models, to forecast ‘Flu_lntensity’ and for 9/1/2023. For the ES model, when inputting Ft-1, consider the observed demand of the previous month as reasonable. When optimizing each model, use RMSE as your evaluation criterion for performance.
2. Develop a robust multiple regression model to predict the demand for 9/1/2023. Utilize your best time series model to obtain the input values for ‘Flu_lntensity’ and Take into account the statements made by the executives, and make assumptions where necessary.
3. Based on your model, which factor emerges as the main driver behind demand? Support your conclusion with qualitative and quantitative reasoning.
DELIVERABLES:
1) Submit your Excel file. Ensure your workbook includes all your work, but specifically ensure the following sheets are present for grading:
• TS_analytics sheet: This should contain only a table with (a) the predictions for 9/1 /2023 for ‘Flu_lntensityl and using your best SMA, WMA, and ES. Make sure to include the (b) model parameter (e.g., SMA with t=6, WMA with t=4 and w=[0.4,0.3,0.2,0.1], ES with Alpha=0.3) and the (c) RMSE for each model.
• Reg_analytics sheet: This sheet should include the (a) ANOVA table of your final regression model, your (b) demand forecast for 9/1/2023 based on the regression model, and the associated value of your x-variables. Finally, provide a (c) comprehensive text answer to the question regarding the primary driver behind sales, supporting your conclusion with both qualitative and quantitative reasoning.
