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    Scenario: 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 clientAc?cs 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:
    Find the MAPE for the first 12 months (assume the forecast for Month 1 Ac?o or January Ac?o is equal to JanuaryAc?cs actual sales). To find the MAPE you will need to compare actual sales for each month or Y(t) to forecasted sales or F(t).
    Next forecast the sales for the next three months (Feb – Apr) and compute the MAPE for this 3-month period. Compare this 3-month MAPE to the MAPE you calculated for the SES analysis (Case 4).
    Then write a report to your boss that briefly describes the results that you obtained. Make a final recommendation on which method to use SES or Linear Regression.
    Data: Use the data that you previously have and generated from your analyses in Case 3.
    Analysis
    Accurate and complete SES analysis in Excel.
    Written Report
    Length requirements = 4Ac?o5 pages minimum (not including Cover and Reference pages)
    Provide a brief introduction/ background of the problem.
    Complete and accurate Excel analysis.
    Written analysis that supports Excel analysis and provides thorough discussion of assumptions rationale and logic used.
    Complete meaningful and accurate recommendation(s).
    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 1200 hits in January are paired with FebruaryAc?cs 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.

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