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Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 12 Dec 2013 04:55:00 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/12/t13868421229ui0f3epg0lpvyv.htm/, Retrieved Tue, 07 Dec 2021 11:08:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232240, Retrieved Tue, 07 Dec 2021 11:08:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-12 09:55:00] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1,38
1,96
1,36
1,24
1,35
1,23
1,09
1,08
1,33
1,35
1,38
1,5
1,47
2,09
1,52
1,29
1,52
1,27
1,35
1,29
1,41
1,39
1,45
1,53
1,45
2,11
1,53
1,38
1,54
1,35
1,29
1,33
1,47
1,47
1,54
1,59
1,5
2
1,51
1,4
1,62
1,44
1,29
1,28
1,4
1,39
1,46
1,49
1,45
2,05
1,59
1,42
1,73
1,39
1,23
1,37
1,51
1,47
1,5
1,54
1,54
2,15
1,62
1,4
1,65
1,49
1,45
1,45
1,51
1,48
1,56
1,57




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232240&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]6 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=232240&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232240&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11.38NANA-0.00756944NA
21.96NANA0.584347NA
31.36NANA0.0537639NA
41.24NANA-0.124819NA
51.35NANA0.106597NA
61.23NANA-0.119486NA
71.091.142181.35792-0.215736-0.0521806
81.081.168431.36708-0.198653-0.0884306
91.331.330761.37917-0.0484028-0.000763889
101.351.326011.38792-0.06190280.0239861
111.381.383351.39708-0.0137361-0.00334722
121.51.451431.405830.04559720.0485694
131.471.410761.41833-0.007569440.0592361
142.092.022261.437920.5843470.0677361
151.521.503761.450.05376390.0162361
161.291.330181.455-0.124819-0.0401806
171.521.566181.459580.106597-0.0461806
181.271.344261.46375-0.119486-0.0742639
191.351.248431.46417-0.2157360.101569
201.291.265511.46417-0.1986530.0244861
211.411.417011.46542-0.0484028-0.00701389
221.391.407681.46958-0.0619028-0.0176806
231.451.460431.47417-0.0137361-0.0104306
241.531.523931.478330.04559720.00606944
251.451.47161.47917-0.00756944-0.0215972
262.112.062681.478330.5843470.0473194
271.531.536261.48250.0537639-0.00626389
281.381.363511.48833-0.1248190.0164861
291.541.602011.495420.106597-0.0620139
301.351.382181.50167-0.119486-0.0321806
311.291.290511.50625-0.215736-0.000513889
321.331.30511.50375-0.1986530.0249028
331.471.449931.49833-0.04840280.0200694
341.471.436431.49833-0.06190280.0335694
351.541.488761.5025-0.01373610.0512361
361.591.555181.509580.04559720.0348194
371.51.505761.51333-0.00756944-0.00576389
3822.09561.511250.584347-0.0955972
391.511.560011.506250.0537639-0.0500139
401.41.375181.5-0.1248190.0248194
411.621.599931.493330.1065970.0200694
421.441.366351.48583-0.1194860.0736528
431.291.263851.47958-0.2157360.0261528
441.281.280931.47958-0.198653-0.000930556
451.41.43661.485-0.0484028-0.0365972
461.391.427261.48917-0.0619028-0.0372639
471.461.480851.49458-0.0137361-0.0208472
481.491.542681.497080.0455972-0.0526806
491.451.484931.4925-0.00756944-0.0349306
502.052.07811.493750.584347-0.0280972
511.591.555851.502080.05376390.0341528
521.421.385181.51-0.1248190.0348194
531.731.62161.5150.1065970.108403
541.391.399261.51875-0.119486-0.00926389
551.231.308851.52458-0.215736-0.0788472
561.371.333851.5325-0.1986530.0361528
571.511.489511.53792-0.04840280.0204861
581.471.476431.53833-0.0619028-0.00643056
591.51.520431.53417-0.0137361-0.0204306
601.541.58061.5350.0455972-0.0405972
611.541.540761.54833-0.00756944-0.000763889
622.152.145181.560830.5843470.00481944
631.621.617931.564170.05376390.00206944
641.41.439761.56458-0.124819-0.0397639
651.651.67411.56750.106597-0.0240972
661.491.451761.57125-0.1194860.0382361
671.45NANA-0.215736NA
681.45NANA-0.198653NA
691.51NANA-0.0484028NA
701.48NANA-0.0619028NA
711.56NANA-0.0137361NA
721.57NANA0.0455972NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.38 & NA & NA & -0.00756944 & NA \tabularnewline
2 & 1.96 & NA & NA & 0.584347 & NA \tabularnewline
3 & 1.36 & NA & NA & 0.0537639 & NA \tabularnewline
4 & 1.24 & NA & NA & -0.124819 & NA \tabularnewline
5 & 1.35 & NA & NA & 0.106597 & NA \tabularnewline
6 & 1.23 & NA & NA & -0.119486 & NA \tabularnewline
7 & 1.09 & 1.14218 & 1.35792 & -0.215736 & -0.0521806 \tabularnewline
8 & 1.08 & 1.16843 & 1.36708 & -0.198653 & -0.0884306 \tabularnewline
9 & 1.33 & 1.33076 & 1.37917 & -0.0484028 & -0.000763889 \tabularnewline
10 & 1.35 & 1.32601 & 1.38792 & -0.0619028 & 0.0239861 \tabularnewline
11 & 1.38 & 1.38335 & 1.39708 & -0.0137361 & -0.00334722 \tabularnewline
12 & 1.5 & 1.45143 & 1.40583 & 0.0455972 & 0.0485694 \tabularnewline
13 & 1.47 & 1.41076 & 1.41833 & -0.00756944 & 0.0592361 \tabularnewline
14 & 2.09 & 2.02226 & 1.43792 & 0.584347 & 0.0677361 \tabularnewline
15 & 1.52 & 1.50376 & 1.45 & 0.0537639 & 0.0162361 \tabularnewline
16 & 1.29 & 1.33018 & 1.455 & -0.124819 & -0.0401806 \tabularnewline
17 & 1.52 & 1.56618 & 1.45958 & 0.106597 & -0.0461806 \tabularnewline
18 & 1.27 & 1.34426 & 1.46375 & -0.119486 & -0.0742639 \tabularnewline
19 & 1.35 & 1.24843 & 1.46417 & -0.215736 & 0.101569 \tabularnewline
20 & 1.29 & 1.26551 & 1.46417 & -0.198653 & 0.0244861 \tabularnewline
21 & 1.41 & 1.41701 & 1.46542 & -0.0484028 & -0.00701389 \tabularnewline
22 & 1.39 & 1.40768 & 1.46958 & -0.0619028 & -0.0176806 \tabularnewline
23 & 1.45 & 1.46043 & 1.47417 & -0.0137361 & -0.0104306 \tabularnewline
24 & 1.53 & 1.52393 & 1.47833 & 0.0455972 & 0.00606944 \tabularnewline
25 & 1.45 & 1.4716 & 1.47917 & -0.00756944 & -0.0215972 \tabularnewline
26 & 2.11 & 2.06268 & 1.47833 & 0.584347 & 0.0473194 \tabularnewline
27 & 1.53 & 1.53626 & 1.4825 & 0.0537639 & -0.00626389 \tabularnewline
28 & 1.38 & 1.36351 & 1.48833 & -0.124819 & 0.0164861 \tabularnewline
29 & 1.54 & 1.60201 & 1.49542 & 0.106597 & -0.0620139 \tabularnewline
30 & 1.35 & 1.38218 & 1.50167 & -0.119486 & -0.0321806 \tabularnewline
31 & 1.29 & 1.29051 & 1.50625 & -0.215736 & -0.000513889 \tabularnewline
32 & 1.33 & 1.3051 & 1.50375 & -0.198653 & 0.0249028 \tabularnewline
33 & 1.47 & 1.44993 & 1.49833 & -0.0484028 & 0.0200694 \tabularnewline
34 & 1.47 & 1.43643 & 1.49833 & -0.0619028 & 0.0335694 \tabularnewline
35 & 1.54 & 1.48876 & 1.5025 & -0.0137361 & 0.0512361 \tabularnewline
36 & 1.59 & 1.55518 & 1.50958 & 0.0455972 & 0.0348194 \tabularnewline
37 & 1.5 & 1.50576 & 1.51333 & -0.00756944 & -0.00576389 \tabularnewline
38 & 2 & 2.0956 & 1.51125 & 0.584347 & -0.0955972 \tabularnewline
39 & 1.51 & 1.56001 & 1.50625 & 0.0537639 & -0.0500139 \tabularnewline
40 & 1.4 & 1.37518 & 1.5 & -0.124819 & 0.0248194 \tabularnewline
41 & 1.62 & 1.59993 & 1.49333 & 0.106597 & 0.0200694 \tabularnewline
42 & 1.44 & 1.36635 & 1.48583 & -0.119486 & 0.0736528 \tabularnewline
43 & 1.29 & 1.26385 & 1.47958 & -0.215736 & 0.0261528 \tabularnewline
44 & 1.28 & 1.28093 & 1.47958 & -0.198653 & -0.000930556 \tabularnewline
45 & 1.4 & 1.4366 & 1.485 & -0.0484028 & -0.0365972 \tabularnewline
46 & 1.39 & 1.42726 & 1.48917 & -0.0619028 & -0.0372639 \tabularnewline
47 & 1.46 & 1.48085 & 1.49458 & -0.0137361 & -0.0208472 \tabularnewline
48 & 1.49 & 1.54268 & 1.49708 & 0.0455972 & -0.0526806 \tabularnewline
49 & 1.45 & 1.48493 & 1.4925 & -0.00756944 & -0.0349306 \tabularnewline
50 & 2.05 & 2.0781 & 1.49375 & 0.584347 & -0.0280972 \tabularnewline
51 & 1.59 & 1.55585 & 1.50208 & 0.0537639 & 0.0341528 \tabularnewline
52 & 1.42 & 1.38518 & 1.51 & -0.124819 & 0.0348194 \tabularnewline
53 & 1.73 & 1.6216 & 1.515 & 0.106597 & 0.108403 \tabularnewline
54 & 1.39 & 1.39926 & 1.51875 & -0.119486 & -0.00926389 \tabularnewline
55 & 1.23 & 1.30885 & 1.52458 & -0.215736 & -0.0788472 \tabularnewline
56 & 1.37 & 1.33385 & 1.5325 & -0.198653 & 0.0361528 \tabularnewline
57 & 1.51 & 1.48951 & 1.53792 & -0.0484028 & 0.0204861 \tabularnewline
58 & 1.47 & 1.47643 & 1.53833 & -0.0619028 & -0.00643056 \tabularnewline
59 & 1.5 & 1.52043 & 1.53417 & -0.0137361 & -0.0204306 \tabularnewline
60 & 1.54 & 1.5806 & 1.535 & 0.0455972 & -0.0405972 \tabularnewline
61 & 1.54 & 1.54076 & 1.54833 & -0.00756944 & -0.000763889 \tabularnewline
62 & 2.15 & 2.14518 & 1.56083 & 0.584347 & 0.00481944 \tabularnewline
63 & 1.62 & 1.61793 & 1.56417 & 0.0537639 & 0.00206944 \tabularnewline
64 & 1.4 & 1.43976 & 1.56458 & -0.124819 & -0.0397639 \tabularnewline
65 & 1.65 & 1.6741 & 1.5675 & 0.106597 & -0.0240972 \tabularnewline
66 & 1.49 & 1.45176 & 1.57125 & -0.119486 & 0.0382361 \tabularnewline
67 & 1.45 & NA & NA & -0.215736 & NA \tabularnewline
68 & 1.45 & NA & NA & -0.198653 & NA \tabularnewline
69 & 1.51 & NA & NA & -0.0484028 & NA \tabularnewline
70 & 1.48 & NA & NA & -0.0619028 & NA \tabularnewline
71 & 1.56 & NA & NA & -0.0137361 & NA \tabularnewline
72 & 1.57 & NA & NA & 0.0455972 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232240&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]1.38[/C][C]NA[/C][C]NA[/C][C]-0.00756944[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.96[/C][C]NA[/C][C]NA[/C][C]0.584347[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.36[/C][C]NA[/C][C]NA[/C][C]0.0537639[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.24[/C][C]NA[/C][C]NA[/C][C]-0.124819[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.35[/C][C]NA[/C][C]NA[/C][C]0.106597[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.23[/C][C]NA[/C][C]NA[/C][C]-0.119486[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.09[/C][C]1.14218[/C][C]1.35792[/C][C]-0.215736[/C][C]-0.0521806[/C][/ROW]
[ROW][C]8[/C][C]1.08[/C][C]1.16843[/C][C]1.36708[/C][C]-0.198653[/C][C]-0.0884306[/C][/ROW]
[ROW][C]9[/C][C]1.33[/C][C]1.33076[/C][C]1.37917[/C][C]-0.0484028[/C][C]-0.000763889[/C][/ROW]
[ROW][C]10[/C][C]1.35[/C][C]1.32601[/C][C]1.38792[/C][C]-0.0619028[/C][C]0.0239861[/C][/ROW]
[ROW][C]11[/C][C]1.38[/C][C]1.38335[/C][C]1.39708[/C][C]-0.0137361[/C][C]-0.00334722[/C][/ROW]
[ROW][C]12[/C][C]1.5[/C][C]1.45143[/C][C]1.40583[/C][C]0.0455972[/C][C]0.0485694[/C][/ROW]
[ROW][C]13[/C][C]1.47[/C][C]1.41076[/C][C]1.41833[/C][C]-0.00756944[/C][C]0.0592361[/C][/ROW]
[ROW][C]14[/C][C]2.09[/C][C]2.02226[/C][C]1.43792[/C][C]0.584347[/C][C]0.0677361[/C][/ROW]
[ROW][C]15[/C][C]1.52[/C][C]1.50376[/C][C]1.45[/C][C]0.0537639[/C][C]0.0162361[/C][/ROW]
[ROW][C]16[/C][C]1.29[/C][C]1.33018[/C][C]1.455[/C][C]-0.124819[/C][C]-0.0401806[/C][/ROW]
[ROW][C]17[/C][C]1.52[/C][C]1.56618[/C][C]1.45958[/C][C]0.106597[/C][C]-0.0461806[/C][/ROW]
[ROW][C]18[/C][C]1.27[/C][C]1.34426[/C][C]1.46375[/C][C]-0.119486[/C][C]-0.0742639[/C][/ROW]
[ROW][C]19[/C][C]1.35[/C][C]1.24843[/C][C]1.46417[/C][C]-0.215736[/C][C]0.101569[/C][/ROW]
[ROW][C]20[/C][C]1.29[/C][C]1.26551[/C][C]1.46417[/C][C]-0.198653[/C][C]0.0244861[/C][/ROW]
[ROW][C]21[/C][C]1.41[/C][C]1.41701[/C][C]1.46542[/C][C]-0.0484028[/C][C]-0.00701389[/C][/ROW]
[ROW][C]22[/C][C]1.39[/C][C]1.40768[/C][C]1.46958[/C][C]-0.0619028[/C][C]-0.0176806[/C][/ROW]
[ROW][C]23[/C][C]1.45[/C][C]1.46043[/C][C]1.47417[/C][C]-0.0137361[/C][C]-0.0104306[/C][/ROW]
[ROW][C]24[/C][C]1.53[/C][C]1.52393[/C][C]1.47833[/C][C]0.0455972[/C][C]0.00606944[/C][/ROW]
[ROW][C]25[/C][C]1.45[/C][C]1.4716[/C][C]1.47917[/C][C]-0.00756944[/C][C]-0.0215972[/C][/ROW]
[ROW][C]26[/C][C]2.11[/C][C]2.06268[/C][C]1.47833[/C][C]0.584347[/C][C]0.0473194[/C][/ROW]
[ROW][C]27[/C][C]1.53[/C][C]1.53626[/C][C]1.4825[/C][C]0.0537639[/C][C]-0.00626389[/C][/ROW]
[ROW][C]28[/C][C]1.38[/C][C]1.36351[/C][C]1.48833[/C][C]-0.124819[/C][C]0.0164861[/C][/ROW]
[ROW][C]29[/C][C]1.54[/C][C]1.60201[/C][C]1.49542[/C][C]0.106597[/C][C]-0.0620139[/C][/ROW]
[ROW][C]30[/C][C]1.35[/C][C]1.38218[/C][C]1.50167[/C][C]-0.119486[/C][C]-0.0321806[/C][/ROW]
[ROW][C]31[/C][C]1.29[/C][C]1.29051[/C][C]1.50625[/C][C]-0.215736[/C][C]-0.000513889[/C][/ROW]
[ROW][C]32[/C][C]1.33[/C][C]1.3051[/C][C]1.50375[/C][C]-0.198653[/C][C]0.0249028[/C][/ROW]
[ROW][C]33[/C][C]1.47[/C][C]1.44993[/C][C]1.49833[/C][C]-0.0484028[/C][C]0.0200694[/C][/ROW]
[ROW][C]34[/C][C]1.47[/C][C]1.43643[/C][C]1.49833[/C][C]-0.0619028[/C][C]0.0335694[/C][/ROW]
[ROW][C]35[/C][C]1.54[/C][C]1.48876[/C][C]1.5025[/C][C]-0.0137361[/C][C]0.0512361[/C][/ROW]
[ROW][C]36[/C][C]1.59[/C][C]1.55518[/C][C]1.50958[/C][C]0.0455972[/C][C]0.0348194[/C][/ROW]
[ROW][C]37[/C][C]1.5[/C][C]1.50576[/C][C]1.51333[/C][C]-0.00756944[/C][C]-0.00576389[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]2.0956[/C][C]1.51125[/C][C]0.584347[/C][C]-0.0955972[/C][/ROW]
[ROW][C]39[/C][C]1.51[/C][C]1.56001[/C][C]1.50625[/C][C]0.0537639[/C][C]-0.0500139[/C][/ROW]
[ROW][C]40[/C][C]1.4[/C][C]1.37518[/C][C]1.5[/C][C]-0.124819[/C][C]0.0248194[/C][/ROW]
[ROW][C]41[/C][C]1.62[/C][C]1.59993[/C][C]1.49333[/C][C]0.106597[/C][C]0.0200694[/C][/ROW]
[ROW][C]42[/C][C]1.44[/C][C]1.36635[/C][C]1.48583[/C][C]-0.119486[/C][C]0.0736528[/C][/ROW]
[ROW][C]43[/C][C]1.29[/C][C]1.26385[/C][C]1.47958[/C][C]-0.215736[/C][C]0.0261528[/C][/ROW]
[ROW][C]44[/C][C]1.28[/C][C]1.28093[/C][C]1.47958[/C][C]-0.198653[/C][C]-0.000930556[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]1.4366[/C][C]1.485[/C][C]-0.0484028[/C][C]-0.0365972[/C][/ROW]
[ROW][C]46[/C][C]1.39[/C][C]1.42726[/C][C]1.48917[/C][C]-0.0619028[/C][C]-0.0372639[/C][/ROW]
[ROW][C]47[/C][C]1.46[/C][C]1.48085[/C][C]1.49458[/C][C]-0.0137361[/C][C]-0.0208472[/C][/ROW]
[ROW][C]48[/C][C]1.49[/C][C]1.54268[/C][C]1.49708[/C][C]0.0455972[/C][C]-0.0526806[/C][/ROW]
[ROW][C]49[/C][C]1.45[/C][C]1.48493[/C][C]1.4925[/C][C]-0.00756944[/C][C]-0.0349306[/C][/ROW]
[ROW][C]50[/C][C]2.05[/C][C]2.0781[/C][C]1.49375[/C][C]0.584347[/C][C]-0.0280972[/C][/ROW]
[ROW][C]51[/C][C]1.59[/C][C]1.55585[/C][C]1.50208[/C][C]0.0537639[/C][C]0.0341528[/C][/ROW]
[ROW][C]52[/C][C]1.42[/C][C]1.38518[/C][C]1.51[/C][C]-0.124819[/C][C]0.0348194[/C][/ROW]
[ROW][C]53[/C][C]1.73[/C][C]1.6216[/C][C]1.515[/C][C]0.106597[/C][C]0.108403[/C][/ROW]
[ROW][C]54[/C][C]1.39[/C][C]1.39926[/C][C]1.51875[/C][C]-0.119486[/C][C]-0.00926389[/C][/ROW]
[ROW][C]55[/C][C]1.23[/C][C]1.30885[/C][C]1.52458[/C][C]-0.215736[/C][C]-0.0788472[/C][/ROW]
[ROW][C]56[/C][C]1.37[/C][C]1.33385[/C][C]1.5325[/C][C]-0.198653[/C][C]0.0361528[/C][/ROW]
[ROW][C]57[/C][C]1.51[/C][C]1.48951[/C][C]1.53792[/C][C]-0.0484028[/C][C]0.0204861[/C][/ROW]
[ROW][C]58[/C][C]1.47[/C][C]1.47643[/C][C]1.53833[/C][C]-0.0619028[/C][C]-0.00643056[/C][/ROW]
[ROW][C]59[/C][C]1.5[/C][C]1.52043[/C][C]1.53417[/C][C]-0.0137361[/C][C]-0.0204306[/C][/ROW]
[ROW][C]60[/C][C]1.54[/C][C]1.5806[/C][C]1.535[/C][C]0.0455972[/C][C]-0.0405972[/C][/ROW]
[ROW][C]61[/C][C]1.54[/C][C]1.54076[/C][C]1.54833[/C][C]-0.00756944[/C][C]-0.000763889[/C][/ROW]
[ROW][C]62[/C][C]2.15[/C][C]2.14518[/C][C]1.56083[/C][C]0.584347[/C][C]0.00481944[/C][/ROW]
[ROW][C]63[/C][C]1.62[/C][C]1.61793[/C][C]1.56417[/C][C]0.0537639[/C][C]0.00206944[/C][/ROW]
[ROW][C]64[/C][C]1.4[/C][C]1.43976[/C][C]1.56458[/C][C]-0.124819[/C][C]-0.0397639[/C][/ROW]
[ROW][C]65[/C][C]1.65[/C][C]1.6741[/C][C]1.5675[/C][C]0.106597[/C][C]-0.0240972[/C][/ROW]
[ROW][C]66[/C][C]1.49[/C][C]1.45176[/C][C]1.57125[/C][C]-0.119486[/C][C]0.0382361[/C][/ROW]
[ROW][C]67[/C][C]1.45[/C][C]NA[/C][C]NA[/C][C]-0.215736[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]1.45[/C][C]NA[/C][C]NA[/C][C]-0.198653[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]1.51[/C][C]NA[/C][C]NA[/C][C]-0.0484028[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]-0.0619028[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]1.56[/C][C]NA[/C][C]NA[/C][C]-0.0137361[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]1.57[/C][C]NA[/C][C]NA[/C][C]0.0455972[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232240&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232240&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
11.38NANA-0.00756944NA
21.96NANA0.584347NA
31.36NANA0.0537639NA
41.24NANA-0.124819NA
51.35NANA0.106597NA
61.23NANA-0.119486NA
71.091.142181.35792-0.215736-0.0521806
81.081.168431.36708-0.198653-0.0884306
91.331.330761.37917-0.0484028-0.000763889
101.351.326011.38792-0.06190280.0239861
111.381.383351.39708-0.0137361-0.00334722
121.51.451431.405830.04559720.0485694
131.471.410761.41833-0.007569440.0592361
142.092.022261.437920.5843470.0677361
151.521.503761.450.05376390.0162361
161.291.330181.455-0.124819-0.0401806
171.521.566181.459580.106597-0.0461806
181.271.344261.46375-0.119486-0.0742639
191.351.248431.46417-0.2157360.101569
201.291.265511.46417-0.1986530.0244861
211.411.417011.46542-0.0484028-0.00701389
221.391.407681.46958-0.0619028-0.0176806
231.451.460431.47417-0.0137361-0.0104306
241.531.523931.478330.04559720.00606944
251.451.47161.47917-0.00756944-0.0215972
262.112.062681.478330.5843470.0473194
271.531.536261.48250.0537639-0.00626389
281.381.363511.48833-0.1248190.0164861
291.541.602011.495420.106597-0.0620139
301.351.382181.50167-0.119486-0.0321806
311.291.290511.50625-0.215736-0.000513889
321.331.30511.50375-0.1986530.0249028
331.471.449931.49833-0.04840280.0200694
341.471.436431.49833-0.06190280.0335694
351.541.488761.5025-0.01373610.0512361
361.591.555181.509580.04559720.0348194
371.51.505761.51333-0.00756944-0.00576389
3822.09561.511250.584347-0.0955972
391.511.560011.506250.0537639-0.0500139
401.41.375181.5-0.1248190.0248194
411.621.599931.493330.1065970.0200694
421.441.366351.48583-0.1194860.0736528
431.291.263851.47958-0.2157360.0261528
441.281.280931.47958-0.198653-0.000930556
451.41.43661.485-0.0484028-0.0365972
461.391.427261.48917-0.0619028-0.0372639
471.461.480851.49458-0.0137361-0.0208472
481.491.542681.497080.0455972-0.0526806
491.451.484931.4925-0.00756944-0.0349306
502.052.07811.493750.584347-0.0280972
511.591.555851.502080.05376390.0341528
521.421.385181.51-0.1248190.0348194
531.731.62161.5150.1065970.108403
541.391.399261.51875-0.119486-0.00926389
551.231.308851.52458-0.215736-0.0788472
561.371.333851.5325-0.1986530.0361528
571.511.489511.53792-0.04840280.0204861
581.471.476431.53833-0.0619028-0.00643056
591.51.520431.53417-0.0137361-0.0204306
601.541.58061.5350.0455972-0.0405972
611.541.540761.54833-0.00756944-0.000763889
622.152.145181.560830.5843470.00481944
631.621.617931.564170.05376390.00206944
641.41.439761.56458-0.124819-0.0397639
651.651.67411.56750.106597-0.0240972
661.491.451761.57125-0.1194860.0382361
671.45NANA-0.215736NA
681.45NANA-0.198653NA
691.51NANA-0.0484028NA
701.48NANA-0.0619028NA
711.56NANA-0.0137361NA
721.57NANA0.0455972NA



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')