Statistics Assignment

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Statistics Assignment

Data:
For this assignment, please download the Homework data file.
Requirements:
1. Create a line chart and identify the time series components in the time series. Then, compute
the correlation between the time series variable and time using either the =CORREL() function or
the correlation tool in the date analysis toolpack. Justify your answer. (Hint: it should be some
combination of average or base, cycle, trend, and random variation.)
2. Create as many forecast as possible on the historical data using each of the methods below.
a. 3-period moving average.
c. Exponential smoothing forecast with alpha = 0.8.
d. Trend forecast (whether or not there is a trend). Use the =TREND() function in Excel.

IMPORTANT NOTES: When computing your moving average forecasts, do not use the Moving
Average Data Analysis tool. This tool will not give you a valid forecast because it uses the
current period in the computation. Instead, use the AVERAGE() function. Also, for both the ES
and the MA forecasts, do not include the period you are forecasting in the history you are using
to compute the forecast.
3. Starting with the fourth period, compute the MAE for each forecasting model, and choose the
best model based on this analysis.
4. Using the best model, make a new forecast for the next period.
Deliverables
Please place all of your analysis on a single spreadsheet. Clearly label your answers. When you
have completed the assignment, post your Excel file on the HW 4 assignment dropbox.
Hints: In this assignment, you are using your entire history to build good models and to test the
forecasting skill of the models. Once you have computed the forecasts and calculated the MAEs
for each model, you will choose the most accurate model on historical data to make a future
forecast. The moving average forecasts will begin at period 4, the ES forecasts will begin at
period 2 (with the naïve starting value), and the trend forecasts will begin at period 1. For
consistency, you should compute the MAEs for periods 3-98 for each. the first draft is by
Wednesday

Date Total Instances of Fraud
10/29/2019 428
10/30/2019 314
10/31/2019 429
11/1/2019 474
11/2/2019 443
11/3/2019 462
11/4/2019 361
11/5/2019 458
11/6/2019 410
11/7/2019 595
11/8/2019 396
11/9/2019 511
11/10/2019 508
11/11/2019 447
11/12/2019 463
11/13/2019 321
11/14/2019 628
11/15/2019 340
11/16/2019 363
11/17/2019 438
11/18/2019 369
11/19/2019 430
11/20/2019 338
11/21/2019 637
11/22/2019 352
11/23/2019 468
11/24/2019 366
11/25/2019 440
11/26/2019 343
11/27/2019 504
11/28/2019 657
11/29/2019 514
11/30/2019 343
12/1/2019 458
12/2/2019 484
12/3/2019 428
12/4/2019 456
12/5/2019 609
12/6/2019 493
12/7/2019 477
12/8/2019 442
12/9/2019 457
12/10/2019 369
12/11/2019 459
12/12/2019 674
12/13/2019 378
12/14/2019 394
12/15/2019 408
12/16/2019 385
12/17/2019 511
12/18/2019 353
12/19/2019 619
12/20/2019 480
12/21/2019 529
12/22/2019 509
12/23/2019 388
12/24/2019 359
12/25/2019 430
12/26/2019 610
12/27/2019 439
12/28/2019 480
12/29/2019 378
12/30/2019 446
12/31/2019 438
1/1/2020 484
1/2/2020 625
1/3/2020 446
1/4/2020 533
1/5/2020 413
1/6/2020 469
1/7/2020 534
1/8/2020 516
1/9/2020 577
1/10/2020 493
1/11/2020 525
1/12/2020 397
1/13/2020 533
1/14/2020 420
1/15/2020 426
1/16/2020 569
1/17/2020 417
1/18/2020 453
1/19/2020 427
1/20/2020 458
1/21/2020 455
1/22/2020 559
1/23/2020 652
1/24/2020 414
1/25/2020 426
1/26/2020 426
1/27/2020 582
1/28/2020 471
1/29/2020 569
1/30/2020 631
1/31/2020 484
2/1/2020 549
2/2/2020 408

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