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You are still a consultant for the Excellent Consulting Group. You have complete

ID: 3773745 • Letter: Y

Question

You are still a consultant for the Excellent Consulting Group. You have completed the first assignment, developing and testing a forecasting method based on linear regression (Case 3). However, your consulting manager at ECG wants to go the next step and investigate another forecasting method. It is important to do a thorough job for the client, and you have the expertise to analyze different forecasting methods. You have decided to look at the sales data for client’s lottery app as a single data set and use a time series analysis, namely SES, single exponential smoothing. Using Excel, use the forecasted sales from Case 3 to compute the MAPE, by doing the following: Case 3 Data Following are the data for website hits and app sales (number of the Lottery apps.)

Month       Hits        Sales

Jan          1200   420

Feb         820    545

Mar          1151 301

Apr           1050   510

May           1180 485

Jun            1047   525

Jul             1102    460

Aug           1054 500

Sep           1254   402

Oct            1071    584

Nov           1120   422

Dec           1287   514

Jan            1164   441

Feb            1159    ----

Mar            1298     ----

April            ----       ----

IMPORTANT: Be sure to shift the monthly sales up by one month because the theory is that the hits predict the next month sales (e.g., the 1,200 hits in January are paired with February’s sales of 545). Therefore, your data will look like this:

Month       Hits      Sales

Jan          1200       545

Feb          820       301

Mar         1151      510

Apr          1050       485

May         1180       525

Jun          1047       460

Jul            1102      500

Aug          1054      402

Sep          1254     584

Oct           1071     422

Nov          1120     514

Dec          1287     441

Use the monthly hits for Jan through Mar to predict the sales for Feb through Apr. When you have done so, ask your Instructor to provide the data for the actual sales for Jan through Apr. 1. Calculate the MAPE for the first 12 months (assume the forecast for Month 1 – or January – is equal to January’s actual sales). Use 0.15 and 0.90 alphas. 2. Using the forecasted sales for Feb - April (taken from the Case 3 Linear Regression exercise), compute the MAPE by comparing actual sales for each month, or Y(t) to forecasted sales, or F(t). Compare this 3-month MAPE to the two MAPE values you calculated in your SES analysis above. Use the following table:

Month       Sales, Y(t)                                Sales F(t)                  Y(t) - F(t)                       PE                               APE

February       ?                                              ?                                ?                                 ?                                   ?

March            ?                                              ?                                 ?                                 ?                                   ?

April               ?                                              ?                                 ?                                 ?                                   ?

                                                                                                         ?                                  ?                                   ?

                                                                                                        ME                             MPE                             MAPE

Then write a report to your boss that briefly describes the results that you obtained. Using MAPE values, make a recommendation on which method appears to be more accurate -- SES or Linear Regression. Data: Use the data that you previously have generated from your analyses in Case 3

Explanation / Answer

In the every month the hits predict the next month sales. So, at some time this hits is return its actual sales.

Actual Forecast Jan 441 493.727 Feb 541 272.6822 Mar 529 462.0197 Apr 621 439.3717

ERRor 0.119562 0.495966 0.126617 0.292477

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