calculate Mean absolute deviation for 3 months moving average3 months weightedmovingexponential smoothing with constant 0,2 and the other with 0.5 12301/12/202201/01/202301/02/202301/03/202301/04/20236 01/05/2023701/06/202301/07/202301/08/202301/09/202301/10/202301/11/202301/12/2023458observationdate9012Total Vehicle Sales,Millions of Units,Monthly13.8816.4615.4415.4016.5915.6316.1916.2515.8116.1815.9115.86b.) 3 month C.)d.)a.) 3 month weightedmovi exponential exponentialmovingaverage15.2615.7715.8115.8716.1316.0216.0816.0815.9715.99ng average (3- smoothing smoothing 3MONTHS2-1)with constant withMAD15.5215.5916.0015.9116.0716.1316.0216.0715.9915.931413.9814.4714.6614.8115.1715.2615.4515.6115.6515.7615.7915.801413.9415.2015.3215.3615.9715.8015.9916.1215.9716.0815.9915.93

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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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calculate Mean absolute deviation for

3 months moving average

3 months weightedmoving

exponential smoothing with constant 0,2 and the other with 0.5

1
2
3
01/12/2022
01/01/2023
01/02/2023
01/03/2023
01/04/2023
6 01/05/2023
7
01/06/2023
01/07/2023
01/08/2023
01/09/2023
01/10/2023
01/11/2023
01/12/2023
4
5
8
observation
date
9012
Total Vehicle Sales,
Millions of Units,
Monthly
13.88
16.46
15.44
15.40
16.59
15.63
16.19
16.25
15.81
16.18
15.91
15.86
b.) 3 month C.)
d.)
a.) 3 month weightedmovi exponential exponential
moving
average
15.26
15.77
15.81
15.87
16.13
16.02
16.08
16.08
15.97
15.99
ng average (3- smoothing smoothing 3MONTHS
2-1)
with constant with
MAD
15.52
15.59
16.00
15.91
16.07
16.13
16.02
16.07
15.99
15.93
14
13.98
14.47
14.66
14.81
15.17
15.26
15.45
15.61
15.65
15.76
15.79
15.80
14
13.94
15.20
15.32
15.36
15.97
15.80
15.99
16.12
15.97
16.08
15.99
15.93

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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