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