Using Excel, run descriptive analysis for the first 12 months, the next 12 months, etc., on the dataset.Determine the 12 period moving average forecasts and the simple exponential smoothing forecasts (α = 0.05) based on the data. The mathematical formulas for these forecasting techniques are as follows. N period Moving Average Forecast (for next period) = (Sum of Actuals for N number of previous periods) / N. Note: For this assignment, assume N=12. Simple Exponential Smoothing Forecast (for next period) = Previous Period Forecast + (α * (Previous Period Actual - Previous Period Forecast)). Note: For this assignment, assume α = 0.05. For the Period 1, assume that the Forecast is the same as the Actual.value. Mean Absolute %Error = Average of: ((|Actual - Forecast|) / Actual )) for all periods where Actuals and Forecasts exist.Create a line chart for each forecast and ensure that each line chart also contains the historical dataset. Compute the mean absolute percentage error for each forecast, based on the available and computed data.Based on the line charts and the mean absolute percentage error calculations, indicate which forecasting technique should be usedUsing Excel, run descriptive analysis for the first 12 months, the next 12 months, etc., on the dataset.Determine the 12 period moving average forecasts and the simple exponential smoothing forecasts (α = 0.05) based on the data. The mathematical formulas for these forecasting techniques are as follows. N period Moving Average Forecast (for next period) = (Sum of Actuals for N number of previous periods) / N. Note: For this assignment, assume N=12. Simple Exponential Smoothing Forecast (for next period) = Previous Period Forecast + (α * (Previous Period Actual - Previous Period Forecast)). Note: For this assignment, assume α = 0.05. For the Period 1, assume that the Forecast is the same as the Actual.value. Mean Absolute %Error = Average of: ((|Actual - Forecast|) / Actual )) for all periods where Actuals and Forecasts exist.Create a line chart for each forecast and ensure that each line chart also contains the historical dataset. Compute the mean absolute percentage error for each forecast, based on the available and computed data.Based on the line charts and the mean absolute percentage error calculations, indicate which forecasting technique should be used***need steps in excel to solve*** Month1234567891011121314151617Customers10311312312514914415116517819220220011011411613413212 Period MovingAverage Forecast153.75154.33154.42153.83154.58Mean Absolute%Error39.77%35.38%33.12%14.80%17.11%a =0.05Simple ExponentialSmoothingForecast100.00100.15100.79101.90103.06105.35107.29109.47112.25115.54119.36123.49127.32126.45125.83125.34125.77Mean Absolute%Error2.91%11.37%18.05%18.48%30.83%26.84%28.95%33.65%36.94%39.82%40.91%38.25%15.74%10.92%8.47%6.46%4.72%Seasonal AverageForecast103.00113.00123.00125.00149.00

MonthsCostumers1103211331234125514961447151816591781019211202122001311014114151161613417132

Answered: Using Excel, run descriptive analysis…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Using Excel, run descriptive analysis for the first 12 months, the next 12 months, etc., on the dataset.
Determine the 12 period moving average forecasts and the simple exponential smoothing forecasts (α = 0.05) based on the data. The mathematical formulas for these forecasting techniques are as follows. N period Moving Average Forecast (for next period) = (Sum of Actuals for N number of previous periods) / N. Note: For this assignment, assume N=12. Simple Exponential Smoothing Forecast (for next period) = Previous Period Forecast + (α * (Previous Period Actual - Previous Period Forecast)). Note: For this assignment, assume α = 0.05. For the Period 1, assume that the Forecast is the same as the Actual.value. Mean Absolute %Error = Average of: ((|Actual - Forecast|) / Actual )) for all periods where Actuals and Forecasts exist.
Create a line chart for each forecast and ensure that each line chart also contains the historical dataset.
Compute the mean absolute percentage error for each forecast, based on the available and computed data.
Based on the line charts and the mean absolute percentage error calculations, indicate which forecasting technique should be used
Using Excel, run descriptive analysis for the first 12 months, the next 12 months, etc., on the dataset.
Determine the 12 period moving average forecasts and the simple exponential smoothing forecasts (α = 0.05) based on the data. The mathematical formulas for these forecasting techniques are as follows. N period Moving Average Forecast (for next period) = (Sum of Actuals for N number of previous periods) / N. Note: For this assignment, assume N=12. Simple Exponential Smoothing Forecast (for next period) = Previous Period Forecast + (α * (Previous Period Actual - Previous Period Forecast)). Note: For this assignment, assume α = 0.05. For the Period 1, assume that the Forecast is the same as the Actual.value. Mean Absolute %Error = Average of: ((|Actual - Forecast|) / Actual )) for all periods where Actuals and Forecasts exist.
Create a line chart for each forecast and ensure that each line chart also contains the historical dataset.
Compute the mean absolute percentage error for each forecast, based on the available and computed data.
Based on the line charts and the mean absolute percentage error calculations, indicate which forecasting technique should be used
***need steps in excel to solve***

Month
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Customers
103
113
123
125
149
144
151
165
178
192
202
200
110
114
116
134
132
12 Period Moving
Average Forecast
153.75
154.33
154.42
153.83
154.58
Mean Absolute
%Error
39.77%
35.38%
33.12%
14.80%
17.11%
a =
0.05
Simple Exponential
Smoothing
Forecast
100.00
100.15
100.79
101.90
103.06
105.35
107.29
109.47
112.25
115.54
119.36
123.49
127.32
126.45
125.83
125.34
125.77
Mean Absolute
%Error
2.91%
11.37%
18.05%
18.48%
30.83%
26.84%
28.95%
33.65%
36.94%
39.82%
40.91%
38.25%
15.74%
10.92%
8.47%
6.46%
4.72%
Seasonal Average
Forecast
103.00
113.00
123.00
125.00
149.00

Expert Solution

Step 1: Write the given information.

Months	Costumers
1	103
2	113
3	123
4	125
5	149
6	144
7	151
8	165
9	178
10	192
11	202
12	200
13	110
14	114
15	116
16	134
17	132

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