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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 12 Dec 2013 05:14:07 -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/t1386843475r5zpb7e63sww2cm.htm/, Retrieved Tue, 07 Dec 2021 11:54:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232241, Retrieved Tue, 07 Dec 2021 11:54:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [] [2013-12-09 11:05:02] [520d9ebc87bc557903715fb12897748b]
- R PD    [Classical Decomposition] [] [2013-12-12 10:14:07] [17e53cb7c94beab0adf1165deaf51c6f] [Current]
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Dataseries X:
3.43
3.43
3.43
3.43
3.43
3.43
3.43
3.43
3.5
3.52
3.53
3.53
3.53
3.53
3.53
3.53
3.53
3.53
3.53
3.53
3.58
3.58
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.61
3.71
3.83
3.83
3.83
3.83
3.83
3.83
3.83
3.83
3.83
3.83
3.83
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.92
3.98
3.98
3.98
3.98
3.98
3.98
3.98
3.98
3.98
3.98
3.98
3.98
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.21
4.21
4.21
4.21




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232241&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232241&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13.43NANA0.0163079NA
23.43NANA0.0071412NA
33.43NANA-0.00237269NA
43.43NANA-0.0120949NA
53.43NANA-0.0216088NA
63.43NANA-0.0310532NA
73.433.425473.46417-0.03869210.00452546
83.433.427973.4725-0.04452550.00202546
93.53.507143.480830.0263079-0.0071412
103.523.529643.489170.0404745-0.0096412
113.533.532143.49750.0346412-0.0021412
123.533.531313.505830.0254745-0.00130787
133.533.530473.514170.0163079-0.000474537
143.533.529643.52250.00714120.000358796
153.533.527633.53-0.002372690.00237269
163.533.523743.53583-0.01209490.00626157
173.533.519223.54083-0.02160880.0107755
183.533.514783.54583-0.03105320.0152199
193.533.512143.55083-0.03869210.0178588
203.533.511313.55583-0.04452550.0186921
213.583.587143.560830.0263079-0.0071412
223.583.606313.565830.0404745-0.0263079
233.593.605473.570830.0346412-0.0154745
243.593.601313.575830.0254745-0.0113079
253.593.597143.580830.0163079-0.0071412
263.593.593813.586670.0071412-0.00380787
273.593.593043.59542-0.00237269-0.00304398
283.593.599163.61125-0.0120949-0.00915509
293.593.610063.63167-0.0216088-0.0200579
303.593.620613.65167-0.0310532-0.0306134
313.593.632973.67167-0.0386921-0.0429745
323.613.647143.69167-0.0445255-0.0371412
333.713.737973.711670.0263079-0.0279745
343.833.772143.731670.04047450.0578588
353.833.786313.751670.03464120.0436921
363.833.797143.771670.02547450.0328588
373.833.807973.791670.01630790.0220255
383.833.817973.810830.00714120.0120255
393.833.826383.82875-0.002372690.00362269
403.833.829163.84125-0.01209490.000844907
413.833.827143.84875-0.02160880.0028588
423.833.82523.85625-0.03105320.00480324
433.833.825063.86375-0.03869210.00494213
443.833.826723.87125-0.04452550.00327546
453.923.905063.878750.02630790.0149421
463.923.926723.886250.0404745-0.00672454
473.923.928393.893750.0346412-0.0083912
483.923.926723.901250.0254745-0.00672454
493.923.925063.908750.0163079-0.00505787
503.923.923393.916250.0071412-0.0033912
513.923.920133.9225-0.00237269-0.000127315
523.923.915413.9275-0.01209490.00459491
533.923.910893.9325-0.02160880.0091088
543.923.906453.9375-0.03105320.0135532
553.923.903813.9425-0.03869210.0161921
563.923.902973.9475-0.04452550.0170255
573.983.978813.95250.02630790.00119213
583.983.997973.95750.0404745-0.0179745
593.983.997143.96250.0346412-0.0171412
603.983.992973.96750.0254745-0.0129745
613.983.988813.97250.0163079-0.00880787
623.983.984643.97750.0071412-0.0046412
633.983.982213.98458-0.00237269-0.00221065
643.983.981663.99375-0.0120949-0.00165509
653.983.981314.00292-0.0216088-0.00130787
663.983.981034.01208-0.0310532-0.00103009
673.983.982564.02125-0.0386921-0.00255787
683.983.985894.03042-0.0445255-0.0058912
694.094.065894.039580.02630790.0241088
704.094.089224.048750.04047450.000775463
714.094.092564.057920.0346412-0.00255787
724.094.092564.067080.0254745-0.00255787
734.094.092564.076250.0163079-0.00255787
744.094.092564.085420.0071412-0.00255787
754.094.092634.095-0.00237269-0.00262731
764.094.092914.105-0.0120949-0.00290509
774.094.093394.115-0.0216088-0.0033912
784.094.093954.125-0.0310532-0.00394676
794.09NANA-0.0386921NA
804.09NANA-0.0445255NA
814.21NANA0.0263079NA
824.21NANA0.0404745NA
834.21NANA0.0346412NA
844.21NANA0.0254745NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 3.43 & NA & NA & 0.0163079 & NA \tabularnewline
2 & 3.43 & NA & NA & 0.0071412 & NA \tabularnewline
3 & 3.43 & NA & NA & -0.00237269 & NA \tabularnewline
4 & 3.43 & NA & NA & -0.0120949 & NA \tabularnewline
5 & 3.43 & NA & NA & -0.0216088 & NA \tabularnewline
6 & 3.43 & NA & NA & -0.0310532 & NA \tabularnewline
7 & 3.43 & 3.42547 & 3.46417 & -0.0386921 & 0.00452546 \tabularnewline
8 & 3.43 & 3.42797 & 3.4725 & -0.0445255 & 0.00202546 \tabularnewline
9 & 3.5 & 3.50714 & 3.48083 & 0.0263079 & -0.0071412 \tabularnewline
10 & 3.52 & 3.52964 & 3.48917 & 0.0404745 & -0.0096412 \tabularnewline
11 & 3.53 & 3.53214 & 3.4975 & 0.0346412 & -0.0021412 \tabularnewline
12 & 3.53 & 3.53131 & 3.50583 & 0.0254745 & -0.00130787 \tabularnewline
13 & 3.53 & 3.53047 & 3.51417 & 0.0163079 & -0.000474537 \tabularnewline
14 & 3.53 & 3.52964 & 3.5225 & 0.0071412 & 0.000358796 \tabularnewline
15 & 3.53 & 3.52763 & 3.53 & -0.00237269 & 0.00237269 \tabularnewline
16 & 3.53 & 3.52374 & 3.53583 & -0.0120949 & 0.00626157 \tabularnewline
17 & 3.53 & 3.51922 & 3.54083 & -0.0216088 & 0.0107755 \tabularnewline
18 & 3.53 & 3.51478 & 3.54583 & -0.0310532 & 0.0152199 \tabularnewline
19 & 3.53 & 3.51214 & 3.55083 & -0.0386921 & 0.0178588 \tabularnewline
20 & 3.53 & 3.51131 & 3.55583 & -0.0445255 & 0.0186921 \tabularnewline
21 & 3.58 & 3.58714 & 3.56083 & 0.0263079 & -0.0071412 \tabularnewline
22 & 3.58 & 3.60631 & 3.56583 & 0.0404745 & -0.0263079 \tabularnewline
23 & 3.59 & 3.60547 & 3.57083 & 0.0346412 & -0.0154745 \tabularnewline
24 & 3.59 & 3.60131 & 3.57583 & 0.0254745 & -0.0113079 \tabularnewline
25 & 3.59 & 3.59714 & 3.58083 & 0.0163079 & -0.0071412 \tabularnewline
26 & 3.59 & 3.59381 & 3.58667 & 0.0071412 & -0.00380787 \tabularnewline
27 & 3.59 & 3.59304 & 3.59542 & -0.00237269 & -0.00304398 \tabularnewline
28 & 3.59 & 3.59916 & 3.61125 & -0.0120949 & -0.00915509 \tabularnewline
29 & 3.59 & 3.61006 & 3.63167 & -0.0216088 & -0.0200579 \tabularnewline
30 & 3.59 & 3.62061 & 3.65167 & -0.0310532 & -0.0306134 \tabularnewline
31 & 3.59 & 3.63297 & 3.67167 & -0.0386921 & -0.0429745 \tabularnewline
32 & 3.61 & 3.64714 & 3.69167 & -0.0445255 & -0.0371412 \tabularnewline
33 & 3.71 & 3.73797 & 3.71167 & 0.0263079 & -0.0279745 \tabularnewline
34 & 3.83 & 3.77214 & 3.73167 & 0.0404745 & 0.0578588 \tabularnewline
35 & 3.83 & 3.78631 & 3.75167 & 0.0346412 & 0.0436921 \tabularnewline
36 & 3.83 & 3.79714 & 3.77167 & 0.0254745 & 0.0328588 \tabularnewline
37 & 3.83 & 3.80797 & 3.79167 & 0.0163079 & 0.0220255 \tabularnewline
38 & 3.83 & 3.81797 & 3.81083 & 0.0071412 & 0.0120255 \tabularnewline
39 & 3.83 & 3.82638 & 3.82875 & -0.00237269 & 0.00362269 \tabularnewline
40 & 3.83 & 3.82916 & 3.84125 & -0.0120949 & 0.000844907 \tabularnewline
41 & 3.83 & 3.82714 & 3.84875 & -0.0216088 & 0.0028588 \tabularnewline
42 & 3.83 & 3.8252 & 3.85625 & -0.0310532 & 0.00480324 \tabularnewline
43 & 3.83 & 3.82506 & 3.86375 & -0.0386921 & 0.00494213 \tabularnewline
44 & 3.83 & 3.82672 & 3.87125 & -0.0445255 & 0.00327546 \tabularnewline
45 & 3.92 & 3.90506 & 3.87875 & 0.0263079 & 0.0149421 \tabularnewline
46 & 3.92 & 3.92672 & 3.88625 & 0.0404745 & -0.00672454 \tabularnewline
47 & 3.92 & 3.92839 & 3.89375 & 0.0346412 & -0.0083912 \tabularnewline
48 & 3.92 & 3.92672 & 3.90125 & 0.0254745 & -0.00672454 \tabularnewline
49 & 3.92 & 3.92506 & 3.90875 & 0.0163079 & -0.00505787 \tabularnewline
50 & 3.92 & 3.92339 & 3.91625 & 0.0071412 & -0.0033912 \tabularnewline
51 & 3.92 & 3.92013 & 3.9225 & -0.00237269 & -0.000127315 \tabularnewline
52 & 3.92 & 3.91541 & 3.9275 & -0.0120949 & 0.00459491 \tabularnewline
53 & 3.92 & 3.91089 & 3.9325 & -0.0216088 & 0.0091088 \tabularnewline
54 & 3.92 & 3.90645 & 3.9375 & -0.0310532 & 0.0135532 \tabularnewline
55 & 3.92 & 3.90381 & 3.9425 & -0.0386921 & 0.0161921 \tabularnewline
56 & 3.92 & 3.90297 & 3.9475 & -0.0445255 & 0.0170255 \tabularnewline
57 & 3.98 & 3.97881 & 3.9525 & 0.0263079 & 0.00119213 \tabularnewline
58 & 3.98 & 3.99797 & 3.9575 & 0.0404745 & -0.0179745 \tabularnewline
59 & 3.98 & 3.99714 & 3.9625 & 0.0346412 & -0.0171412 \tabularnewline
60 & 3.98 & 3.99297 & 3.9675 & 0.0254745 & -0.0129745 \tabularnewline
61 & 3.98 & 3.98881 & 3.9725 & 0.0163079 & -0.00880787 \tabularnewline
62 & 3.98 & 3.98464 & 3.9775 & 0.0071412 & -0.0046412 \tabularnewline
63 & 3.98 & 3.98221 & 3.98458 & -0.00237269 & -0.00221065 \tabularnewline
64 & 3.98 & 3.98166 & 3.99375 & -0.0120949 & -0.00165509 \tabularnewline
65 & 3.98 & 3.98131 & 4.00292 & -0.0216088 & -0.00130787 \tabularnewline
66 & 3.98 & 3.98103 & 4.01208 & -0.0310532 & -0.00103009 \tabularnewline
67 & 3.98 & 3.98256 & 4.02125 & -0.0386921 & -0.00255787 \tabularnewline
68 & 3.98 & 3.98589 & 4.03042 & -0.0445255 & -0.0058912 \tabularnewline
69 & 4.09 & 4.06589 & 4.03958 & 0.0263079 & 0.0241088 \tabularnewline
70 & 4.09 & 4.08922 & 4.04875 & 0.0404745 & 0.000775463 \tabularnewline
71 & 4.09 & 4.09256 & 4.05792 & 0.0346412 & -0.00255787 \tabularnewline
72 & 4.09 & 4.09256 & 4.06708 & 0.0254745 & -0.00255787 \tabularnewline
73 & 4.09 & 4.09256 & 4.07625 & 0.0163079 & -0.00255787 \tabularnewline
74 & 4.09 & 4.09256 & 4.08542 & 0.0071412 & -0.00255787 \tabularnewline
75 & 4.09 & 4.09263 & 4.095 & -0.00237269 & -0.00262731 \tabularnewline
76 & 4.09 & 4.09291 & 4.105 & -0.0120949 & -0.00290509 \tabularnewline
77 & 4.09 & 4.09339 & 4.115 & -0.0216088 & -0.0033912 \tabularnewline
78 & 4.09 & 4.09395 & 4.125 & -0.0310532 & -0.00394676 \tabularnewline
79 & 4.09 & NA & NA & -0.0386921 & NA \tabularnewline
80 & 4.09 & NA & NA & -0.0445255 & NA \tabularnewline
81 & 4.21 & NA & NA & 0.0263079 & NA \tabularnewline
82 & 4.21 & NA & NA & 0.0404745 & NA \tabularnewline
83 & 4.21 & NA & NA & 0.0346412 & NA \tabularnewline
84 & 4.21 & NA & NA & 0.0254745 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232241&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]3.43[/C][C]NA[/C][C]NA[/C][C]0.0163079[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]3.43[/C][C]NA[/C][C]NA[/C][C]0.0071412[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]3.43[/C][C]NA[/C][C]NA[/C][C]-0.00237269[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]3.43[/C][C]NA[/C][C]NA[/C][C]-0.0120949[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]3.43[/C][C]NA[/C][C]NA[/C][C]-0.0216088[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]3.43[/C][C]NA[/C][C]NA[/C][C]-0.0310532[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]3.43[/C][C]3.42547[/C][C]3.46417[/C][C]-0.0386921[/C][C]0.00452546[/C][/ROW]
[ROW][C]8[/C][C]3.43[/C][C]3.42797[/C][C]3.4725[/C][C]-0.0445255[/C][C]0.00202546[/C][/ROW]
[ROW][C]9[/C][C]3.5[/C][C]3.50714[/C][C]3.48083[/C][C]0.0263079[/C][C]-0.0071412[/C][/ROW]
[ROW][C]10[/C][C]3.52[/C][C]3.52964[/C][C]3.48917[/C][C]0.0404745[/C][C]-0.0096412[/C][/ROW]
[ROW][C]11[/C][C]3.53[/C][C]3.53214[/C][C]3.4975[/C][C]0.0346412[/C][C]-0.0021412[/C][/ROW]
[ROW][C]12[/C][C]3.53[/C][C]3.53131[/C][C]3.50583[/C][C]0.0254745[/C][C]-0.00130787[/C][/ROW]
[ROW][C]13[/C][C]3.53[/C][C]3.53047[/C][C]3.51417[/C][C]0.0163079[/C][C]-0.000474537[/C][/ROW]
[ROW][C]14[/C][C]3.53[/C][C]3.52964[/C][C]3.5225[/C][C]0.0071412[/C][C]0.000358796[/C][/ROW]
[ROW][C]15[/C][C]3.53[/C][C]3.52763[/C][C]3.53[/C][C]-0.00237269[/C][C]0.00237269[/C][/ROW]
[ROW][C]16[/C][C]3.53[/C][C]3.52374[/C][C]3.53583[/C][C]-0.0120949[/C][C]0.00626157[/C][/ROW]
[ROW][C]17[/C][C]3.53[/C][C]3.51922[/C][C]3.54083[/C][C]-0.0216088[/C][C]0.0107755[/C][/ROW]
[ROW][C]18[/C][C]3.53[/C][C]3.51478[/C][C]3.54583[/C][C]-0.0310532[/C][C]0.0152199[/C][/ROW]
[ROW][C]19[/C][C]3.53[/C][C]3.51214[/C][C]3.55083[/C][C]-0.0386921[/C][C]0.0178588[/C][/ROW]
[ROW][C]20[/C][C]3.53[/C][C]3.51131[/C][C]3.55583[/C][C]-0.0445255[/C][C]0.0186921[/C][/ROW]
[ROW][C]21[/C][C]3.58[/C][C]3.58714[/C][C]3.56083[/C][C]0.0263079[/C][C]-0.0071412[/C][/ROW]
[ROW][C]22[/C][C]3.58[/C][C]3.60631[/C][C]3.56583[/C][C]0.0404745[/C][C]-0.0263079[/C][/ROW]
[ROW][C]23[/C][C]3.59[/C][C]3.60547[/C][C]3.57083[/C][C]0.0346412[/C][C]-0.0154745[/C][/ROW]
[ROW][C]24[/C][C]3.59[/C][C]3.60131[/C][C]3.57583[/C][C]0.0254745[/C][C]-0.0113079[/C][/ROW]
[ROW][C]25[/C][C]3.59[/C][C]3.59714[/C][C]3.58083[/C][C]0.0163079[/C][C]-0.0071412[/C][/ROW]
[ROW][C]26[/C][C]3.59[/C][C]3.59381[/C][C]3.58667[/C][C]0.0071412[/C][C]-0.00380787[/C][/ROW]
[ROW][C]27[/C][C]3.59[/C][C]3.59304[/C][C]3.59542[/C][C]-0.00237269[/C][C]-0.00304398[/C][/ROW]
[ROW][C]28[/C][C]3.59[/C][C]3.59916[/C][C]3.61125[/C][C]-0.0120949[/C][C]-0.00915509[/C][/ROW]
[ROW][C]29[/C][C]3.59[/C][C]3.61006[/C][C]3.63167[/C][C]-0.0216088[/C][C]-0.0200579[/C][/ROW]
[ROW][C]30[/C][C]3.59[/C][C]3.62061[/C][C]3.65167[/C][C]-0.0310532[/C][C]-0.0306134[/C][/ROW]
[ROW][C]31[/C][C]3.59[/C][C]3.63297[/C][C]3.67167[/C][C]-0.0386921[/C][C]-0.0429745[/C][/ROW]
[ROW][C]32[/C][C]3.61[/C][C]3.64714[/C][C]3.69167[/C][C]-0.0445255[/C][C]-0.0371412[/C][/ROW]
[ROW][C]33[/C][C]3.71[/C][C]3.73797[/C][C]3.71167[/C][C]0.0263079[/C][C]-0.0279745[/C][/ROW]
[ROW][C]34[/C][C]3.83[/C][C]3.77214[/C][C]3.73167[/C][C]0.0404745[/C][C]0.0578588[/C][/ROW]
[ROW][C]35[/C][C]3.83[/C][C]3.78631[/C][C]3.75167[/C][C]0.0346412[/C][C]0.0436921[/C][/ROW]
[ROW][C]36[/C][C]3.83[/C][C]3.79714[/C][C]3.77167[/C][C]0.0254745[/C][C]0.0328588[/C][/ROW]
[ROW][C]37[/C][C]3.83[/C][C]3.80797[/C][C]3.79167[/C][C]0.0163079[/C][C]0.0220255[/C][/ROW]
[ROW][C]38[/C][C]3.83[/C][C]3.81797[/C][C]3.81083[/C][C]0.0071412[/C][C]0.0120255[/C][/ROW]
[ROW][C]39[/C][C]3.83[/C][C]3.82638[/C][C]3.82875[/C][C]-0.00237269[/C][C]0.00362269[/C][/ROW]
[ROW][C]40[/C][C]3.83[/C][C]3.82916[/C][C]3.84125[/C][C]-0.0120949[/C][C]0.000844907[/C][/ROW]
[ROW][C]41[/C][C]3.83[/C][C]3.82714[/C][C]3.84875[/C][C]-0.0216088[/C][C]0.0028588[/C][/ROW]
[ROW][C]42[/C][C]3.83[/C][C]3.8252[/C][C]3.85625[/C][C]-0.0310532[/C][C]0.00480324[/C][/ROW]
[ROW][C]43[/C][C]3.83[/C][C]3.82506[/C][C]3.86375[/C][C]-0.0386921[/C][C]0.00494213[/C][/ROW]
[ROW][C]44[/C][C]3.83[/C][C]3.82672[/C][C]3.87125[/C][C]-0.0445255[/C][C]0.00327546[/C][/ROW]
[ROW][C]45[/C][C]3.92[/C][C]3.90506[/C][C]3.87875[/C][C]0.0263079[/C][C]0.0149421[/C][/ROW]
[ROW][C]46[/C][C]3.92[/C][C]3.92672[/C][C]3.88625[/C][C]0.0404745[/C][C]-0.00672454[/C][/ROW]
[ROW][C]47[/C][C]3.92[/C][C]3.92839[/C][C]3.89375[/C][C]0.0346412[/C][C]-0.0083912[/C][/ROW]
[ROW][C]48[/C][C]3.92[/C][C]3.92672[/C][C]3.90125[/C][C]0.0254745[/C][C]-0.00672454[/C][/ROW]
[ROW][C]49[/C][C]3.92[/C][C]3.92506[/C][C]3.90875[/C][C]0.0163079[/C][C]-0.00505787[/C][/ROW]
[ROW][C]50[/C][C]3.92[/C][C]3.92339[/C][C]3.91625[/C][C]0.0071412[/C][C]-0.0033912[/C][/ROW]
[ROW][C]51[/C][C]3.92[/C][C]3.92013[/C][C]3.9225[/C][C]-0.00237269[/C][C]-0.000127315[/C][/ROW]
[ROW][C]52[/C][C]3.92[/C][C]3.91541[/C][C]3.9275[/C][C]-0.0120949[/C][C]0.00459491[/C][/ROW]
[ROW][C]53[/C][C]3.92[/C][C]3.91089[/C][C]3.9325[/C][C]-0.0216088[/C][C]0.0091088[/C][/ROW]
[ROW][C]54[/C][C]3.92[/C][C]3.90645[/C][C]3.9375[/C][C]-0.0310532[/C][C]0.0135532[/C][/ROW]
[ROW][C]55[/C][C]3.92[/C][C]3.90381[/C][C]3.9425[/C][C]-0.0386921[/C][C]0.0161921[/C][/ROW]
[ROW][C]56[/C][C]3.92[/C][C]3.90297[/C][C]3.9475[/C][C]-0.0445255[/C][C]0.0170255[/C][/ROW]
[ROW][C]57[/C][C]3.98[/C][C]3.97881[/C][C]3.9525[/C][C]0.0263079[/C][C]0.00119213[/C][/ROW]
[ROW][C]58[/C][C]3.98[/C][C]3.99797[/C][C]3.9575[/C][C]0.0404745[/C][C]-0.0179745[/C][/ROW]
[ROW][C]59[/C][C]3.98[/C][C]3.99714[/C][C]3.9625[/C][C]0.0346412[/C][C]-0.0171412[/C][/ROW]
[ROW][C]60[/C][C]3.98[/C][C]3.99297[/C][C]3.9675[/C][C]0.0254745[/C][C]-0.0129745[/C][/ROW]
[ROW][C]61[/C][C]3.98[/C][C]3.98881[/C][C]3.9725[/C][C]0.0163079[/C][C]-0.00880787[/C][/ROW]
[ROW][C]62[/C][C]3.98[/C][C]3.98464[/C][C]3.9775[/C][C]0.0071412[/C][C]-0.0046412[/C][/ROW]
[ROW][C]63[/C][C]3.98[/C][C]3.98221[/C][C]3.98458[/C][C]-0.00237269[/C][C]-0.00221065[/C][/ROW]
[ROW][C]64[/C][C]3.98[/C][C]3.98166[/C][C]3.99375[/C][C]-0.0120949[/C][C]-0.00165509[/C][/ROW]
[ROW][C]65[/C][C]3.98[/C][C]3.98131[/C][C]4.00292[/C][C]-0.0216088[/C][C]-0.00130787[/C][/ROW]
[ROW][C]66[/C][C]3.98[/C][C]3.98103[/C][C]4.01208[/C][C]-0.0310532[/C][C]-0.00103009[/C][/ROW]
[ROW][C]67[/C][C]3.98[/C][C]3.98256[/C][C]4.02125[/C][C]-0.0386921[/C][C]-0.00255787[/C][/ROW]
[ROW][C]68[/C][C]3.98[/C][C]3.98589[/C][C]4.03042[/C][C]-0.0445255[/C][C]-0.0058912[/C][/ROW]
[ROW][C]69[/C][C]4.09[/C][C]4.06589[/C][C]4.03958[/C][C]0.0263079[/C][C]0.0241088[/C][/ROW]
[ROW][C]70[/C][C]4.09[/C][C]4.08922[/C][C]4.04875[/C][C]0.0404745[/C][C]0.000775463[/C][/ROW]
[ROW][C]71[/C][C]4.09[/C][C]4.09256[/C][C]4.05792[/C][C]0.0346412[/C][C]-0.00255787[/C][/ROW]
[ROW][C]72[/C][C]4.09[/C][C]4.09256[/C][C]4.06708[/C][C]0.0254745[/C][C]-0.00255787[/C][/ROW]
[ROW][C]73[/C][C]4.09[/C][C]4.09256[/C][C]4.07625[/C][C]0.0163079[/C][C]-0.00255787[/C][/ROW]
[ROW][C]74[/C][C]4.09[/C][C]4.09256[/C][C]4.08542[/C][C]0.0071412[/C][C]-0.00255787[/C][/ROW]
[ROW][C]75[/C][C]4.09[/C][C]4.09263[/C][C]4.095[/C][C]-0.00237269[/C][C]-0.00262731[/C][/ROW]
[ROW][C]76[/C][C]4.09[/C][C]4.09291[/C][C]4.105[/C][C]-0.0120949[/C][C]-0.00290509[/C][/ROW]
[ROW][C]77[/C][C]4.09[/C][C]4.09339[/C][C]4.115[/C][C]-0.0216088[/C][C]-0.0033912[/C][/ROW]
[ROW][C]78[/C][C]4.09[/C][C]4.09395[/C][C]4.125[/C][C]-0.0310532[/C][C]-0.00394676[/C][/ROW]
[ROW][C]79[/C][C]4.09[/C][C]NA[/C][C]NA[/C][C]-0.0386921[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]4.09[/C][C]NA[/C][C]NA[/C][C]-0.0445255[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]4.21[/C][C]NA[/C][C]NA[/C][C]0.0263079[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]4.21[/C][C]NA[/C][C]NA[/C][C]0.0404745[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]4.21[/C][C]NA[/C][C]NA[/C][C]0.0346412[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]4.21[/C][C]NA[/C][C]NA[/C][C]0.0254745[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232241&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
13.43NANA0.0163079NA
23.43NANA0.0071412NA
33.43NANA-0.00237269NA
43.43NANA-0.0120949NA
53.43NANA-0.0216088NA
63.43NANA-0.0310532NA
73.433.425473.46417-0.03869210.00452546
83.433.427973.4725-0.04452550.00202546
93.53.507143.480830.0263079-0.0071412
103.523.529643.489170.0404745-0.0096412
113.533.532143.49750.0346412-0.0021412
123.533.531313.505830.0254745-0.00130787
133.533.530473.514170.0163079-0.000474537
143.533.529643.52250.00714120.000358796
153.533.527633.53-0.002372690.00237269
163.533.523743.53583-0.01209490.00626157
173.533.519223.54083-0.02160880.0107755
183.533.514783.54583-0.03105320.0152199
193.533.512143.55083-0.03869210.0178588
203.533.511313.55583-0.04452550.0186921
213.583.587143.560830.0263079-0.0071412
223.583.606313.565830.0404745-0.0263079
233.593.605473.570830.0346412-0.0154745
243.593.601313.575830.0254745-0.0113079
253.593.597143.580830.0163079-0.0071412
263.593.593813.586670.0071412-0.00380787
273.593.593043.59542-0.00237269-0.00304398
283.593.599163.61125-0.0120949-0.00915509
293.593.610063.63167-0.0216088-0.0200579
303.593.620613.65167-0.0310532-0.0306134
313.593.632973.67167-0.0386921-0.0429745
323.613.647143.69167-0.0445255-0.0371412
333.713.737973.711670.0263079-0.0279745
343.833.772143.731670.04047450.0578588
353.833.786313.751670.03464120.0436921
363.833.797143.771670.02547450.0328588
373.833.807973.791670.01630790.0220255
383.833.817973.810830.00714120.0120255
393.833.826383.82875-0.002372690.00362269
403.833.829163.84125-0.01209490.000844907
413.833.827143.84875-0.02160880.0028588
423.833.82523.85625-0.03105320.00480324
433.833.825063.86375-0.03869210.00494213
443.833.826723.87125-0.04452550.00327546
453.923.905063.878750.02630790.0149421
463.923.926723.886250.0404745-0.00672454
473.923.928393.893750.0346412-0.0083912
483.923.926723.901250.0254745-0.00672454
493.923.925063.908750.0163079-0.00505787
503.923.923393.916250.0071412-0.0033912
513.923.920133.9225-0.00237269-0.000127315
523.923.915413.9275-0.01209490.00459491
533.923.910893.9325-0.02160880.0091088
543.923.906453.9375-0.03105320.0135532
553.923.903813.9425-0.03869210.0161921
563.923.902973.9475-0.04452550.0170255
573.983.978813.95250.02630790.00119213
583.983.997973.95750.0404745-0.0179745
593.983.997143.96250.0346412-0.0171412
603.983.992973.96750.0254745-0.0129745
613.983.988813.97250.0163079-0.00880787
623.983.984643.97750.0071412-0.0046412
633.983.982213.98458-0.00237269-0.00221065
643.983.981663.99375-0.0120949-0.00165509
653.983.981314.00292-0.0216088-0.00130787
663.983.981034.01208-0.0310532-0.00103009
673.983.982564.02125-0.0386921-0.00255787
683.983.985894.03042-0.0445255-0.0058912
694.094.065894.039580.02630790.0241088
704.094.089224.048750.04047450.000775463
714.094.092564.057920.0346412-0.00255787
724.094.092564.067080.0254745-0.00255787
734.094.092564.076250.0163079-0.00255787
744.094.092564.085420.0071412-0.00255787
754.094.092634.095-0.00237269-0.00262731
764.094.092914.105-0.0120949-0.00290509
774.094.093394.115-0.0216088-0.0033912
784.094.093954.125-0.0310532-0.00394676
794.09NANA-0.0386921NA
804.09NANA-0.0445255NA
814.21NANA0.0263079NA
824.21NANA0.0404745NA
834.21NANA0.0346412NA
844.21NANA0.0254745NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- '12'
par1 <- 'additive'
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')