However, your consulting manager at ECG wants to go the next step and investigat
ID: 3398311 • Letter: H
Question
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
421
Mar
1298
355
April 1298 512
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.
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
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
421
Mar
1298
355
Explanation / Answer
Using 0.15 alpha
using 0.9 alpha
Sales Forecast Error Absolute error Square of absolute error Absolute error/Sales 420 420 0 0 0 0 545 420 125 125 15625 0.229358 301 526.25 -225.25 225.25 50737.56 0.748339 510 334.7875 175.2125 175.2125 30699.42 0.343554 485 483.7181 1.281875 1.281875 1.643204 0.002643 525 484.8077 40.19228 40.19228 1615.419 0.076557 460 518.9712 -58.9712 58.97116 3477.597 0.128198 500 468.8457 31.15433 31.15433 970.592 0.062309 402 495.3269 -93.3269 93.32685 8709.901 0.232156 584 415.999 168.001 168.001 28224.33 0.287673 422 558.7999 -136.8 136.7999 18714.2 0.32417 514 442.52 71.48002 71.48002 5109.394 0.139066 441 503.278 -62.278 62.278 3878.549 0.14122 421 450.3417 -29.3417 29.3417 860.9353 0.069695 355 425.4013 -70.4013 70.40125 4956.337 0.198313 512 365.5602 146.4398 146.4398 21444.62 0.286015 ME MAD MSE MAPE 5.14954406 89.6956666 12189.09 0.213305Related Questions
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