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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 25 Apr 2016 15:36:08 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Apr/25/t1461595060ahajb2y3gn1re6d.htm/, Retrieved Mon, 06 May 2024 04:51:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294722, Retrieved Mon, 06 May 2024 04:51:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2016-04-25 14:36:08] [1af9caed13b550360754d0d82088541b] [Current]
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Dataseries X:
103,71
103,07
103,93
102,9
101,54
102,13
101,08
101,33
101,24
100,58
99,87
99,1
98,98
98,77
98,05
97,94
97,65
97,2
97,39
97,35
98,01
97,81
97,56
98,05
97,82
99,05
98,86
97,64
97,77
98,07
98,36
100
99,52
98,82
98,98
98,6
98,8
99,62
99,35
99,87
99,53
99,88
99,26
99,51
100,64
100,85
101,44
101,26
101,67
102,93
103,81
106,19
106,94
108,51
108,41
108,97
109,25
109,97
108,92
109,01
108,86
107,36
107,99
107,94
108,54
108,37
108,77
107,15
108,61
109,02
109,16
109,55




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294722&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294722&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294722&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1103.71NANA-0.37875NA
2103.07NANA-0.171333NA
3103.93NANA-0.21525NA
4102.9NANA-0.043NA
5101.54NANA-0.02075NA
6102.13NANA0.13475NA
7101.08101.356101.51-0.153917-0.275667
8101.33101.433101.1330.299417-0.10275
9101.24101.239100.7090.5298330.001
10100.58100.586100.2580.328-0.0055
1199.8799.864499.8887-0.02433330.00558333
1299.199.236699.5212-0.284667-0.136583
1398.9898.783399.1621-0.378750.196667
1498.7798.671298.8425-0.1713330.0988333
1598.0598.326898.5421-0.21525-0.276833
1697.9498.249198.2921-0.043-0.309083
1797.6598.059798.0804-0.02075-0.409667
1897.298.075297.94040.13475-0.875167
1997.3997.694497.8483-0.153917-0.304417
2097.3598.111197.81170.299417-0.761083
2198.0198.386997.85710.529833-0.376917
2297.8198.206397.87830.328-0.396333
2397.5697.846597.8708-0.0243333-0.2865
2498.0597.627497.9121-0.2846670.422583
2597.8297.6197.9888-0.378750.21
2699.0597.968298.1396-0.1713331.08175
2798.8698.097798.3129-0.215250.762333
2897.6498.374998.4179-0.043-0.734917
2997.7798.498498.5192-0.02075-0.728417
3098.0798.73698.60120.13475-0.666
3198.3698.511198.665-0.153917-0.151083
3210099.02998.72960.2994170.971
3399.5299.303698.77370.5298330.216417
3498.8299.215198.88710.328-0.395083
3598.9899.02999.0533-0.0243333-0.049
3698.698.917499.2021-0.284667-0.317417
3798.898.936299.315-0.37875-0.13625
3899.6299.160899.3321-0.1713330.45925
3999.3599.143199.3583-0.215250.206917
4099.8799.446699.4896-0.0430.423417
4199.5399.655999.6767-0.02075-0.125917
4299.88100.02599.890.13475-0.14475
4399.2699.9665100.12-0.153917-0.7065
4499.51100.677100.3780.299417-1.16733
45100.64101.232100.7020.529833-0.5915
46100.85101.479101.1510.328-0.628833
47101.44101.699101.723-0.0243333-0.258583
48101.26102.107102.391-0.284667-0.846583
49101.67102.753103.132-0.37875-1.08333
50102.93103.736103.908-0.171333-0.806167
51103.81104.445104.66-0.21525-0.635167
52106.19105.356105.399-0.0430.833833
53106.94106.07106.091-0.020750.869917
54108.51106.86106.7250.134751.64983
55108.41107.194107.348-0.1539171.216
56108.97108.131107.8320.2994170.8385
57109.25108.721108.1910.5298330.529333
58109.97108.766108.4380.3281.20408
59108.92108.553108.578-0.02433330.366833
60109.01108.354108.638-0.2846670.656333
61108.86108.269108.648-0.378750.59125
62107.36108.415108.587-0.171333-1.05533
63107.99108.269108.484-0.21525-0.278917
64107.94108.375108.418-0.043-0.434917
65108.54108.368108.388-0.020750.172417
66108.37108.556108.4210.13475-0.185583
67108.77NANA-0.153917NA
68107.15NANA0.299417NA
69108.61NANA0.529833NA
70109.02NANA0.328NA
71109.16NANA-0.0243333NA
72109.55NANA-0.284667NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 103.71 & NA & NA & -0.37875 & NA \tabularnewline
2 & 103.07 & NA & NA & -0.171333 & NA \tabularnewline
3 & 103.93 & NA & NA & -0.21525 & NA \tabularnewline
4 & 102.9 & NA & NA & -0.043 & NA \tabularnewline
5 & 101.54 & NA & NA & -0.02075 & NA \tabularnewline
6 & 102.13 & NA & NA & 0.13475 & NA \tabularnewline
7 & 101.08 & 101.356 & 101.51 & -0.153917 & -0.275667 \tabularnewline
8 & 101.33 & 101.433 & 101.133 & 0.299417 & -0.10275 \tabularnewline
9 & 101.24 & 101.239 & 100.709 & 0.529833 & 0.001 \tabularnewline
10 & 100.58 & 100.586 & 100.258 & 0.328 & -0.0055 \tabularnewline
11 & 99.87 & 99.8644 & 99.8887 & -0.0243333 & 0.00558333 \tabularnewline
12 & 99.1 & 99.2366 & 99.5212 & -0.284667 & -0.136583 \tabularnewline
13 & 98.98 & 98.7833 & 99.1621 & -0.37875 & 0.196667 \tabularnewline
14 & 98.77 & 98.6712 & 98.8425 & -0.171333 & 0.0988333 \tabularnewline
15 & 98.05 & 98.3268 & 98.5421 & -0.21525 & -0.276833 \tabularnewline
16 & 97.94 & 98.2491 & 98.2921 & -0.043 & -0.309083 \tabularnewline
17 & 97.65 & 98.0597 & 98.0804 & -0.02075 & -0.409667 \tabularnewline
18 & 97.2 & 98.0752 & 97.9404 & 0.13475 & -0.875167 \tabularnewline
19 & 97.39 & 97.6944 & 97.8483 & -0.153917 & -0.304417 \tabularnewline
20 & 97.35 & 98.1111 & 97.8117 & 0.299417 & -0.761083 \tabularnewline
21 & 98.01 & 98.3869 & 97.8571 & 0.529833 & -0.376917 \tabularnewline
22 & 97.81 & 98.2063 & 97.8783 & 0.328 & -0.396333 \tabularnewline
23 & 97.56 & 97.8465 & 97.8708 & -0.0243333 & -0.2865 \tabularnewline
24 & 98.05 & 97.6274 & 97.9121 & -0.284667 & 0.422583 \tabularnewline
25 & 97.82 & 97.61 & 97.9888 & -0.37875 & 0.21 \tabularnewline
26 & 99.05 & 97.9682 & 98.1396 & -0.171333 & 1.08175 \tabularnewline
27 & 98.86 & 98.0977 & 98.3129 & -0.21525 & 0.762333 \tabularnewline
28 & 97.64 & 98.3749 & 98.4179 & -0.043 & -0.734917 \tabularnewline
29 & 97.77 & 98.4984 & 98.5192 & -0.02075 & -0.728417 \tabularnewline
30 & 98.07 & 98.736 & 98.6012 & 0.13475 & -0.666 \tabularnewline
31 & 98.36 & 98.5111 & 98.665 & -0.153917 & -0.151083 \tabularnewline
32 & 100 & 99.029 & 98.7296 & 0.299417 & 0.971 \tabularnewline
33 & 99.52 & 99.3036 & 98.7737 & 0.529833 & 0.216417 \tabularnewline
34 & 98.82 & 99.2151 & 98.8871 & 0.328 & -0.395083 \tabularnewline
35 & 98.98 & 99.029 & 99.0533 & -0.0243333 & -0.049 \tabularnewline
36 & 98.6 & 98.9174 & 99.2021 & -0.284667 & -0.317417 \tabularnewline
37 & 98.8 & 98.9362 & 99.315 & -0.37875 & -0.13625 \tabularnewline
38 & 99.62 & 99.1608 & 99.3321 & -0.171333 & 0.45925 \tabularnewline
39 & 99.35 & 99.1431 & 99.3583 & -0.21525 & 0.206917 \tabularnewline
40 & 99.87 & 99.4466 & 99.4896 & -0.043 & 0.423417 \tabularnewline
41 & 99.53 & 99.6559 & 99.6767 & -0.02075 & -0.125917 \tabularnewline
42 & 99.88 & 100.025 & 99.89 & 0.13475 & -0.14475 \tabularnewline
43 & 99.26 & 99.9665 & 100.12 & -0.153917 & -0.7065 \tabularnewline
44 & 99.51 & 100.677 & 100.378 & 0.299417 & -1.16733 \tabularnewline
45 & 100.64 & 101.232 & 100.702 & 0.529833 & -0.5915 \tabularnewline
46 & 100.85 & 101.479 & 101.151 & 0.328 & -0.628833 \tabularnewline
47 & 101.44 & 101.699 & 101.723 & -0.0243333 & -0.258583 \tabularnewline
48 & 101.26 & 102.107 & 102.391 & -0.284667 & -0.846583 \tabularnewline
49 & 101.67 & 102.753 & 103.132 & -0.37875 & -1.08333 \tabularnewline
50 & 102.93 & 103.736 & 103.908 & -0.171333 & -0.806167 \tabularnewline
51 & 103.81 & 104.445 & 104.66 & -0.21525 & -0.635167 \tabularnewline
52 & 106.19 & 105.356 & 105.399 & -0.043 & 0.833833 \tabularnewline
53 & 106.94 & 106.07 & 106.091 & -0.02075 & 0.869917 \tabularnewline
54 & 108.51 & 106.86 & 106.725 & 0.13475 & 1.64983 \tabularnewline
55 & 108.41 & 107.194 & 107.348 & -0.153917 & 1.216 \tabularnewline
56 & 108.97 & 108.131 & 107.832 & 0.299417 & 0.8385 \tabularnewline
57 & 109.25 & 108.721 & 108.191 & 0.529833 & 0.529333 \tabularnewline
58 & 109.97 & 108.766 & 108.438 & 0.328 & 1.20408 \tabularnewline
59 & 108.92 & 108.553 & 108.578 & -0.0243333 & 0.366833 \tabularnewline
60 & 109.01 & 108.354 & 108.638 & -0.284667 & 0.656333 \tabularnewline
61 & 108.86 & 108.269 & 108.648 & -0.37875 & 0.59125 \tabularnewline
62 & 107.36 & 108.415 & 108.587 & -0.171333 & -1.05533 \tabularnewline
63 & 107.99 & 108.269 & 108.484 & -0.21525 & -0.278917 \tabularnewline
64 & 107.94 & 108.375 & 108.418 & -0.043 & -0.434917 \tabularnewline
65 & 108.54 & 108.368 & 108.388 & -0.02075 & 0.172417 \tabularnewline
66 & 108.37 & 108.556 & 108.421 & 0.13475 & -0.185583 \tabularnewline
67 & 108.77 & NA & NA & -0.153917 & NA \tabularnewline
68 & 107.15 & NA & NA & 0.299417 & NA \tabularnewline
69 & 108.61 & NA & NA & 0.529833 & NA \tabularnewline
70 & 109.02 & NA & NA & 0.328 & NA \tabularnewline
71 & 109.16 & NA & NA & -0.0243333 & NA \tabularnewline
72 & 109.55 & NA & NA & -0.284667 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294722&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]103.71[/C][C]NA[/C][C]NA[/C][C]-0.37875[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]103.07[/C][C]NA[/C][C]NA[/C][C]-0.171333[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]103.93[/C][C]NA[/C][C]NA[/C][C]-0.21525[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]NA[/C][C]NA[/C][C]-0.043[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]101.54[/C][C]NA[/C][C]NA[/C][C]-0.02075[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]102.13[/C][C]NA[/C][C]NA[/C][C]0.13475[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]101.08[/C][C]101.356[/C][C]101.51[/C][C]-0.153917[/C][C]-0.275667[/C][/ROW]
[ROW][C]8[/C][C]101.33[/C][C]101.433[/C][C]101.133[/C][C]0.299417[/C][C]-0.10275[/C][/ROW]
[ROW][C]9[/C][C]101.24[/C][C]101.239[/C][C]100.709[/C][C]0.529833[/C][C]0.001[/C][/ROW]
[ROW][C]10[/C][C]100.58[/C][C]100.586[/C][C]100.258[/C][C]0.328[/C][C]-0.0055[/C][/ROW]
[ROW][C]11[/C][C]99.87[/C][C]99.8644[/C][C]99.8887[/C][C]-0.0243333[/C][C]0.00558333[/C][/ROW]
[ROW][C]12[/C][C]99.1[/C][C]99.2366[/C][C]99.5212[/C][C]-0.284667[/C][C]-0.136583[/C][/ROW]
[ROW][C]13[/C][C]98.98[/C][C]98.7833[/C][C]99.1621[/C][C]-0.37875[/C][C]0.196667[/C][/ROW]
[ROW][C]14[/C][C]98.77[/C][C]98.6712[/C][C]98.8425[/C][C]-0.171333[/C][C]0.0988333[/C][/ROW]
[ROW][C]15[/C][C]98.05[/C][C]98.3268[/C][C]98.5421[/C][C]-0.21525[/C][C]-0.276833[/C][/ROW]
[ROW][C]16[/C][C]97.94[/C][C]98.2491[/C][C]98.2921[/C][C]-0.043[/C][C]-0.309083[/C][/ROW]
[ROW][C]17[/C][C]97.65[/C][C]98.0597[/C][C]98.0804[/C][C]-0.02075[/C][C]-0.409667[/C][/ROW]
[ROW][C]18[/C][C]97.2[/C][C]98.0752[/C][C]97.9404[/C][C]0.13475[/C][C]-0.875167[/C][/ROW]
[ROW][C]19[/C][C]97.39[/C][C]97.6944[/C][C]97.8483[/C][C]-0.153917[/C][C]-0.304417[/C][/ROW]
[ROW][C]20[/C][C]97.35[/C][C]98.1111[/C][C]97.8117[/C][C]0.299417[/C][C]-0.761083[/C][/ROW]
[ROW][C]21[/C][C]98.01[/C][C]98.3869[/C][C]97.8571[/C][C]0.529833[/C][C]-0.376917[/C][/ROW]
[ROW][C]22[/C][C]97.81[/C][C]98.2063[/C][C]97.8783[/C][C]0.328[/C][C]-0.396333[/C][/ROW]
[ROW][C]23[/C][C]97.56[/C][C]97.8465[/C][C]97.8708[/C][C]-0.0243333[/C][C]-0.2865[/C][/ROW]
[ROW][C]24[/C][C]98.05[/C][C]97.6274[/C][C]97.9121[/C][C]-0.284667[/C][C]0.422583[/C][/ROW]
[ROW][C]25[/C][C]97.82[/C][C]97.61[/C][C]97.9888[/C][C]-0.37875[/C][C]0.21[/C][/ROW]
[ROW][C]26[/C][C]99.05[/C][C]97.9682[/C][C]98.1396[/C][C]-0.171333[/C][C]1.08175[/C][/ROW]
[ROW][C]27[/C][C]98.86[/C][C]98.0977[/C][C]98.3129[/C][C]-0.21525[/C][C]0.762333[/C][/ROW]
[ROW][C]28[/C][C]97.64[/C][C]98.3749[/C][C]98.4179[/C][C]-0.043[/C][C]-0.734917[/C][/ROW]
[ROW][C]29[/C][C]97.77[/C][C]98.4984[/C][C]98.5192[/C][C]-0.02075[/C][C]-0.728417[/C][/ROW]
[ROW][C]30[/C][C]98.07[/C][C]98.736[/C][C]98.6012[/C][C]0.13475[/C][C]-0.666[/C][/ROW]
[ROW][C]31[/C][C]98.36[/C][C]98.5111[/C][C]98.665[/C][C]-0.153917[/C][C]-0.151083[/C][/ROW]
[ROW][C]32[/C][C]100[/C][C]99.029[/C][C]98.7296[/C][C]0.299417[/C][C]0.971[/C][/ROW]
[ROW][C]33[/C][C]99.52[/C][C]99.3036[/C][C]98.7737[/C][C]0.529833[/C][C]0.216417[/C][/ROW]
[ROW][C]34[/C][C]98.82[/C][C]99.2151[/C][C]98.8871[/C][C]0.328[/C][C]-0.395083[/C][/ROW]
[ROW][C]35[/C][C]98.98[/C][C]99.029[/C][C]99.0533[/C][C]-0.0243333[/C][C]-0.049[/C][/ROW]
[ROW][C]36[/C][C]98.6[/C][C]98.9174[/C][C]99.2021[/C][C]-0.284667[/C][C]-0.317417[/C][/ROW]
[ROW][C]37[/C][C]98.8[/C][C]98.9362[/C][C]99.315[/C][C]-0.37875[/C][C]-0.13625[/C][/ROW]
[ROW][C]38[/C][C]99.62[/C][C]99.1608[/C][C]99.3321[/C][C]-0.171333[/C][C]0.45925[/C][/ROW]
[ROW][C]39[/C][C]99.35[/C][C]99.1431[/C][C]99.3583[/C][C]-0.21525[/C][C]0.206917[/C][/ROW]
[ROW][C]40[/C][C]99.87[/C][C]99.4466[/C][C]99.4896[/C][C]-0.043[/C][C]0.423417[/C][/ROW]
[ROW][C]41[/C][C]99.53[/C][C]99.6559[/C][C]99.6767[/C][C]-0.02075[/C][C]-0.125917[/C][/ROW]
[ROW][C]42[/C][C]99.88[/C][C]100.025[/C][C]99.89[/C][C]0.13475[/C][C]-0.14475[/C][/ROW]
[ROW][C]43[/C][C]99.26[/C][C]99.9665[/C][C]100.12[/C][C]-0.153917[/C][C]-0.7065[/C][/ROW]
[ROW][C]44[/C][C]99.51[/C][C]100.677[/C][C]100.378[/C][C]0.299417[/C][C]-1.16733[/C][/ROW]
[ROW][C]45[/C][C]100.64[/C][C]101.232[/C][C]100.702[/C][C]0.529833[/C][C]-0.5915[/C][/ROW]
[ROW][C]46[/C][C]100.85[/C][C]101.479[/C][C]101.151[/C][C]0.328[/C][C]-0.628833[/C][/ROW]
[ROW][C]47[/C][C]101.44[/C][C]101.699[/C][C]101.723[/C][C]-0.0243333[/C][C]-0.258583[/C][/ROW]
[ROW][C]48[/C][C]101.26[/C][C]102.107[/C][C]102.391[/C][C]-0.284667[/C][C]-0.846583[/C][/ROW]
[ROW][C]49[/C][C]101.67[/C][C]102.753[/C][C]103.132[/C][C]-0.37875[/C][C]-1.08333[/C][/ROW]
[ROW][C]50[/C][C]102.93[/C][C]103.736[/C][C]103.908[/C][C]-0.171333[/C][C]-0.806167[/C][/ROW]
[ROW][C]51[/C][C]103.81[/C][C]104.445[/C][C]104.66[/C][C]-0.21525[/C][C]-0.635167[/C][/ROW]
[ROW][C]52[/C][C]106.19[/C][C]105.356[/C][C]105.399[/C][C]-0.043[/C][C]0.833833[/C][/ROW]
[ROW][C]53[/C][C]106.94[/C][C]106.07[/C][C]106.091[/C][C]-0.02075[/C][C]0.869917[/C][/ROW]
[ROW][C]54[/C][C]108.51[/C][C]106.86[/C][C]106.725[/C][C]0.13475[/C][C]1.64983[/C][/ROW]
[ROW][C]55[/C][C]108.41[/C][C]107.194[/C][C]107.348[/C][C]-0.153917[/C][C]1.216[/C][/ROW]
[ROW][C]56[/C][C]108.97[/C][C]108.131[/C][C]107.832[/C][C]0.299417[/C][C]0.8385[/C][/ROW]
[ROW][C]57[/C][C]109.25[/C][C]108.721[/C][C]108.191[/C][C]0.529833[/C][C]0.529333[/C][/ROW]
[ROW][C]58[/C][C]109.97[/C][C]108.766[/C][C]108.438[/C][C]0.328[/C][C]1.20408[/C][/ROW]
[ROW][C]59[/C][C]108.92[/C][C]108.553[/C][C]108.578[/C][C]-0.0243333[/C][C]0.366833[/C][/ROW]
[ROW][C]60[/C][C]109.01[/C][C]108.354[/C][C]108.638[/C][C]-0.284667[/C][C]0.656333[/C][/ROW]
[ROW][C]61[/C][C]108.86[/C][C]108.269[/C][C]108.648[/C][C]-0.37875[/C][C]0.59125[/C][/ROW]
[ROW][C]62[/C][C]107.36[/C][C]108.415[/C][C]108.587[/C][C]-0.171333[/C][C]-1.05533[/C][/ROW]
[ROW][C]63[/C][C]107.99[/C][C]108.269[/C][C]108.484[/C][C]-0.21525[/C][C]-0.278917[/C][/ROW]
[ROW][C]64[/C][C]107.94[/C][C]108.375[/C][C]108.418[/C][C]-0.043[/C][C]-0.434917[/C][/ROW]
[ROW][C]65[/C][C]108.54[/C][C]108.368[/C][C]108.388[/C][C]-0.02075[/C][C]0.172417[/C][/ROW]
[ROW][C]66[/C][C]108.37[/C][C]108.556[/C][C]108.421[/C][C]0.13475[/C][C]-0.185583[/C][/ROW]
[ROW][C]67[/C][C]108.77[/C][C]NA[/C][C]NA[/C][C]-0.153917[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]107.15[/C][C]NA[/C][C]NA[/C][C]0.299417[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]108.61[/C][C]NA[/C][C]NA[/C][C]0.529833[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]109.02[/C][C]NA[/C][C]NA[/C][C]0.328[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]109.16[/C][C]NA[/C][C]NA[/C][C]-0.0243333[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]109.55[/C][C]NA[/C][C]NA[/C][C]-0.284667[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294722&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294722&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1103.71NANA-0.37875NA
2103.07NANA-0.171333NA
3103.93NANA-0.21525NA
4102.9NANA-0.043NA
5101.54NANA-0.02075NA
6102.13NANA0.13475NA
7101.08101.356101.51-0.153917-0.275667
8101.33101.433101.1330.299417-0.10275
9101.24101.239100.7090.5298330.001
10100.58100.586100.2580.328-0.0055
1199.8799.864499.8887-0.02433330.00558333
1299.199.236699.5212-0.284667-0.136583
1398.9898.783399.1621-0.378750.196667
1498.7798.671298.8425-0.1713330.0988333
1598.0598.326898.5421-0.21525-0.276833
1697.9498.249198.2921-0.043-0.309083
1797.6598.059798.0804-0.02075-0.409667
1897.298.075297.94040.13475-0.875167
1997.3997.694497.8483-0.153917-0.304417
2097.3598.111197.81170.299417-0.761083
2198.0198.386997.85710.529833-0.376917
2297.8198.206397.87830.328-0.396333
2397.5697.846597.8708-0.0243333-0.2865
2498.0597.627497.9121-0.2846670.422583
2597.8297.6197.9888-0.378750.21
2699.0597.968298.1396-0.1713331.08175
2798.8698.097798.3129-0.215250.762333
2897.6498.374998.4179-0.043-0.734917
2997.7798.498498.5192-0.02075-0.728417
3098.0798.73698.60120.13475-0.666
3198.3698.511198.665-0.153917-0.151083
3210099.02998.72960.2994170.971
3399.5299.303698.77370.5298330.216417
3498.8299.215198.88710.328-0.395083
3598.9899.02999.0533-0.0243333-0.049
3698.698.917499.2021-0.284667-0.317417
3798.898.936299.315-0.37875-0.13625
3899.6299.160899.3321-0.1713330.45925
3999.3599.143199.3583-0.215250.206917
4099.8799.446699.4896-0.0430.423417
4199.5399.655999.6767-0.02075-0.125917
4299.88100.02599.890.13475-0.14475
4399.2699.9665100.12-0.153917-0.7065
4499.51100.677100.3780.299417-1.16733
45100.64101.232100.7020.529833-0.5915
46100.85101.479101.1510.328-0.628833
47101.44101.699101.723-0.0243333-0.258583
48101.26102.107102.391-0.284667-0.846583
49101.67102.753103.132-0.37875-1.08333
50102.93103.736103.908-0.171333-0.806167
51103.81104.445104.66-0.21525-0.635167
52106.19105.356105.399-0.0430.833833
53106.94106.07106.091-0.020750.869917
54108.51106.86106.7250.134751.64983
55108.41107.194107.348-0.1539171.216
56108.97108.131107.8320.2994170.8385
57109.25108.721108.1910.5298330.529333
58109.97108.766108.4380.3281.20408
59108.92108.553108.578-0.02433330.366833
60109.01108.354108.638-0.2846670.656333
61108.86108.269108.648-0.378750.59125
62107.36108.415108.587-0.171333-1.05533
63107.99108.269108.484-0.21525-0.278917
64107.94108.375108.418-0.043-0.434917
65108.54108.368108.388-0.020750.172417
66108.37108.556108.4210.13475-0.185583
67108.77NANA-0.153917NA
68107.15NANA0.299417NA
69108.61NANA0.529833NA
70109.02NANA0.328NA
71109.16NANA-0.0243333NA
72109.55NANA-0.284667NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')