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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 26 May 2013 06:14:22 -0400
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/May/26/t1369563293bw95u8bmfi9pyir.htm/, Retrieved Thu, 31 Oct 2024 23:26:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=210570, Retrieved Thu, 31 Oct 2024 23:26:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-05-26 10:14:22] [fc1511b4e6b29b74e67a180b347b8f2c] [Current]
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Dataseries X:
41086
39690
43129
37863
35953
29133
24693
22205
21725
27192
21790
13253
37702
30364
32609
30212
29965
28352
25814
22414
20506
28806
22228
13971
36845
35338
35022
34777
26887
23970
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=210570&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=210570&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210570&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 time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
141086NANA1.32718577010684NA
239690NANA1.20084764006351NA
343129NANA1.32843407985928NA
437863NANA1.22345091771013NA
535953NANA1.01798223782367NA
629133NANA1.05339554978969NA
72469326076.929973288729668.33333333330.8789482604333040.94692895311272
82220522272.198147166529138.750.7643498141535420.996982868654342
92172523606.1062038528311.83333333330.8337893885542550.92031272808799
102719228306.413163973127554.70833333331.027280449553170.960630364662682
112179021346.144864911926986.41666666670.7909958972536871.02079322228426
121325314776.598720938226704.3750.5533399946989280.896891108047803
133770235460.468298006626718.54166666671.327185770106841.06321212915621
143036432151.444679742126773.95833333331.200847640063510.944405463034502
153260935511.533768538226731.8751.328434079859280.918265040663781
163021232725.27296388326748.33333333331.223450917710130.923200855599989
172996527316.365706054126833.83333333331.017982237823671.09696144510757
182835228317.3791694465268821.053395549789691.0012226000982
192581423622.796561626426876.20833333330.8789482604333041.0927580031711
202241420673.942685771527047.750.7643498141535421.08416668947361
212050622808.760359820527355.54166666670.8337893885542550.899040529888816
222880628400.494931811327646.29166666671.027280449553171.01427809864449
232222821917.112070079527708.250.7909958972536871.01418471233466
241397115160.086393097627397.41666666670.5533399946989280.921564669074773
253684535951.361134724827088.41666666671.327185770106841.02485688544382
263533832123.925254657326751.04166666671.200847640063511.100052366573
273502235305.239026220126576.58333333331.328434079859280.991977422217429
283477732343.403346192826436.20833333331.223450917710131.07524244210663
292688726527.132560254726058.54166666671.017982237823671.01356601354963
302397027130.597408164725755.3751.053395549789690.883504319473128
312278022352.423342546825430.8750.8789482604333041.01912887255671
321735119005.016714476124864.29166666670.7643498141535420.912969468044915
332138220251.215634103824288.16666666670.8337893885542551.05583785123456
342456124341.581465570623695.16666666671.027280449553171.00901414457149
351740918308.984284756823146.750.7909958972536870.950844663430834
361151412701.573740817222954.3750.5533399946989280.906501842602318
373151430398.249974376622904.29166666671.327185770106841.0367044164241
382707127487.702692963822890.251.200847640063510.984840395808325
392946230414.830366898122895.251.328434079859280.968672178821844
402610528018.19848935822900.95833333331.223450917710130.931715863527603
412239723357.305535378122944.70833333331.017982237823670.958886287892942
422384324359.859871849123125.08333333331.053395549789690.978782313421826
432170520345.857709665923147.95833333330.8789482604333041.06680191662249
441808917655.780052950523099.08333333330.7643498141535421.02453700407177
452076419582.447062034323486.08333333330.8337893885542551.06033734876049
462531624679.17150330524023.79166666671.027280449553171.02580428992966
471770419207.423791387424282.58333333330.7909958972536870.921726942263774
481554813454.000854442224314.16666666670.5533399946989281.15564137153049
492802932091.075424147824179.79166666671.327185770106840.873420402075675
502938328823.445551261124002.58333333331.200847640063511.01941317000925
513643831833.2658556679239631.328434079859281.1446516409975
523203429168.089420640823840.83333333331.223450917710131.0982549984001
532267924224.202242738123796.29166666671.017982237823670.936212461105861
542431925054.793694341623784.79166666671.053395549789690.970632618120188
551800420904.612443392223783.66666666670.8789482604333040.861245337542286
561753718112.861241814323697.08333333330.7643498141535420.968207052760668
572036619371.567829179123233.16666666670.8337893885542551.05133462503345
582278223315.071476354522695.91666666671.027280449553170.97713618519698
591916917760.362713686622453.16666666670.7909958972536871.07931354269178
601380712480.744971266122555.29166666670.5533399946989281.10626409174991
612974330083.872845082522667.41666666671.327185770106840.988669249905494
622559127278.104744454422715.70833333331.200847640063510.938151687580225
632909630204.6056737604227371.328434079859280.963296800304747
642648227630.058685714522583.70833333331.223450917710130.958448923370978
652240522833.426426238122430.08333333331.017982237823670.981236875349299
662704423461.006227509822271.79166666671.053395549789691.15272123189196
6717970NANA0.878948260433304NA
6818730NANA0.764349814153542NA
6919684NANA0.833789388554255NA
7019785NANA1.02728044955317NA
7118479NANA0.790995897253687NA
7210698NANA0.553339994698928NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 41086 & NA & NA & 1.32718577010684 & NA \tabularnewline
2 & 39690 & NA & NA & 1.20084764006351 & NA \tabularnewline
3 & 43129 & NA & NA & 1.32843407985928 & NA \tabularnewline
4 & 37863 & NA & NA & 1.22345091771013 & NA \tabularnewline
5 & 35953 & NA & NA & 1.01798223782367 & NA \tabularnewline
6 & 29133 & NA & NA & 1.05339554978969 & NA \tabularnewline
7 & 24693 & 26076.9299732887 & 29668.3333333333 & 0.878948260433304 & 0.94692895311272 \tabularnewline
8 & 22205 & 22272.1981471665 & 29138.75 & 0.764349814153542 & 0.996982868654342 \tabularnewline
9 & 21725 & 23606.10620385 & 28311.8333333333 & 0.833789388554255 & 0.92031272808799 \tabularnewline
10 & 27192 & 28306.4131639731 & 27554.7083333333 & 1.02728044955317 & 0.960630364662682 \tabularnewline
11 & 21790 & 21346.1448649119 & 26986.4166666667 & 0.790995897253687 & 1.02079322228426 \tabularnewline
12 & 13253 & 14776.5987209382 & 26704.375 & 0.553339994698928 & 0.896891108047803 \tabularnewline
13 & 37702 & 35460.4682980066 & 26718.5416666667 & 1.32718577010684 & 1.06321212915621 \tabularnewline
14 & 30364 & 32151.4446797421 & 26773.9583333333 & 1.20084764006351 & 0.944405463034502 \tabularnewline
15 & 32609 & 35511.5337685382 & 26731.875 & 1.32843407985928 & 0.918265040663781 \tabularnewline
16 & 30212 & 32725.272963883 & 26748.3333333333 & 1.22345091771013 & 0.923200855599989 \tabularnewline
17 & 29965 & 27316.3657060541 & 26833.8333333333 & 1.01798223782367 & 1.09696144510757 \tabularnewline
18 & 28352 & 28317.3791694465 & 26882 & 1.05339554978969 & 1.0012226000982 \tabularnewline
19 & 25814 & 23622.7965616264 & 26876.2083333333 & 0.878948260433304 & 1.0927580031711 \tabularnewline
20 & 22414 & 20673.9426857715 & 27047.75 & 0.764349814153542 & 1.08416668947361 \tabularnewline
21 & 20506 & 22808.7603598205 & 27355.5416666667 & 0.833789388554255 & 0.899040529888816 \tabularnewline
22 & 28806 & 28400.4949318113 & 27646.2916666667 & 1.02728044955317 & 1.01427809864449 \tabularnewline
23 & 22228 & 21917.1120700795 & 27708.25 & 0.790995897253687 & 1.01418471233466 \tabularnewline
24 & 13971 & 15160.0863930976 & 27397.4166666667 & 0.553339994698928 & 0.921564669074773 \tabularnewline
25 & 36845 & 35951.3611347248 & 27088.4166666667 & 1.32718577010684 & 1.02485688544382 \tabularnewline
26 & 35338 & 32123.9252546573 & 26751.0416666667 & 1.20084764006351 & 1.100052366573 \tabularnewline
27 & 35022 & 35305.2390262201 & 26576.5833333333 & 1.32843407985928 & 0.991977422217429 \tabularnewline
28 & 34777 & 32343.4033461928 & 26436.2083333333 & 1.22345091771013 & 1.07524244210663 \tabularnewline
29 & 26887 & 26527.1325602547 & 26058.5416666667 & 1.01798223782367 & 1.01356601354963 \tabularnewline
30 & 23970 & 27130.5974081647 & 25755.375 & 1.05339554978969 & 0.883504319473128 \tabularnewline
31 & 22780 & 22352.4233425468 & 25430.875 & 0.878948260433304 & 1.01912887255671 \tabularnewline
32 & 17351 & 19005.0167144761 & 24864.2916666667 & 0.764349814153542 & 0.912969468044915 \tabularnewline
33 & 21382 & 20251.2156341038 & 24288.1666666667 & 0.833789388554255 & 1.05583785123456 \tabularnewline
34 & 24561 & 24341.5814655706 & 23695.1666666667 & 1.02728044955317 & 1.00901414457149 \tabularnewline
35 & 17409 & 18308.9842847568 & 23146.75 & 0.790995897253687 & 0.950844663430834 \tabularnewline
36 & 11514 & 12701.5737408172 & 22954.375 & 0.553339994698928 & 0.906501842602318 \tabularnewline
37 & 31514 & 30398.2499743766 & 22904.2916666667 & 1.32718577010684 & 1.0367044164241 \tabularnewline
38 & 27071 & 27487.7026929638 & 22890.25 & 1.20084764006351 & 0.984840395808325 \tabularnewline
39 & 29462 & 30414.8303668981 & 22895.25 & 1.32843407985928 & 0.968672178821844 \tabularnewline
40 & 26105 & 28018.198489358 & 22900.9583333333 & 1.22345091771013 & 0.931715863527603 \tabularnewline
41 & 22397 & 23357.3055353781 & 22944.7083333333 & 1.01798223782367 & 0.958886287892942 \tabularnewline
42 & 23843 & 24359.8598718491 & 23125.0833333333 & 1.05339554978969 & 0.978782313421826 \tabularnewline
43 & 21705 & 20345.8577096659 & 23147.9583333333 & 0.878948260433304 & 1.06680191662249 \tabularnewline
44 & 18089 & 17655.7800529505 & 23099.0833333333 & 0.764349814153542 & 1.02453700407177 \tabularnewline
45 & 20764 & 19582.4470620343 & 23486.0833333333 & 0.833789388554255 & 1.06033734876049 \tabularnewline
46 & 25316 & 24679.171503305 & 24023.7916666667 & 1.02728044955317 & 1.02580428992966 \tabularnewline
47 & 17704 & 19207.4237913874 & 24282.5833333333 & 0.790995897253687 & 0.921726942263774 \tabularnewline
48 & 15548 & 13454.0008544422 & 24314.1666666667 & 0.553339994698928 & 1.15564137153049 \tabularnewline
49 & 28029 & 32091.0754241478 & 24179.7916666667 & 1.32718577010684 & 0.873420402075675 \tabularnewline
50 & 29383 & 28823.4455512611 & 24002.5833333333 & 1.20084764006351 & 1.01941317000925 \tabularnewline
51 & 36438 & 31833.2658556679 & 23963 & 1.32843407985928 & 1.1446516409975 \tabularnewline
52 & 32034 & 29168.0894206408 & 23840.8333333333 & 1.22345091771013 & 1.0982549984001 \tabularnewline
53 & 22679 & 24224.2022427381 & 23796.2916666667 & 1.01798223782367 & 0.936212461105861 \tabularnewline
54 & 24319 & 25054.7936943416 & 23784.7916666667 & 1.05339554978969 & 0.970632618120188 \tabularnewline
55 & 18004 & 20904.6124433922 & 23783.6666666667 & 0.878948260433304 & 0.861245337542286 \tabularnewline
56 & 17537 & 18112.8612418143 & 23697.0833333333 & 0.764349814153542 & 0.968207052760668 \tabularnewline
57 & 20366 & 19371.5678291791 & 23233.1666666667 & 0.833789388554255 & 1.05133462503345 \tabularnewline
58 & 22782 & 23315.0714763545 & 22695.9166666667 & 1.02728044955317 & 0.97713618519698 \tabularnewline
59 & 19169 & 17760.3627136866 & 22453.1666666667 & 0.790995897253687 & 1.07931354269178 \tabularnewline
60 & 13807 & 12480.7449712661 & 22555.2916666667 & 0.553339994698928 & 1.10626409174991 \tabularnewline
61 & 29743 & 30083.8728450825 & 22667.4166666667 & 1.32718577010684 & 0.988669249905494 \tabularnewline
62 & 25591 & 27278.1047444544 & 22715.7083333333 & 1.20084764006351 & 0.938151687580225 \tabularnewline
63 & 29096 & 30204.6056737604 & 22737 & 1.32843407985928 & 0.963296800304747 \tabularnewline
64 & 26482 & 27630.0586857145 & 22583.7083333333 & 1.22345091771013 & 0.958448923370978 \tabularnewline
65 & 22405 & 22833.4264262381 & 22430.0833333333 & 1.01798223782367 & 0.981236875349299 \tabularnewline
66 & 27044 & 23461.0062275098 & 22271.7916666667 & 1.05339554978969 & 1.15272123189196 \tabularnewline
67 & 17970 & NA & NA & 0.878948260433304 & NA \tabularnewline
68 & 18730 & NA & NA & 0.764349814153542 & NA \tabularnewline
69 & 19684 & NA & NA & 0.833789388554255 & NA \tabularnewline
70 & 19785 & NA & NA & 1.02728044955317 & NA \tabularnewline
71 & 18479 & NA & NA & 0.790995897253687 & NA \tabularnewline
72 & 10698 & NA & NA & 0.553339994698928 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210570&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]41086[/C][C]NA[/C][C]NA[/C][C]1.32718577010684[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]39690[/C][C]NA[/C][C]NA[/C][C]1.20084764006351[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]43129[/C][C]NA[/C][C]NA[/C][C]1.32843407985928[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]37863[/C][C]NA[/C][C]NA[/C][C]1.22345091771013[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]35953[/C][C]NA[/C][C]NA[/C][C]1.01798223782367[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]29133[/C][C]NA[/C][C]NA[/C][C]1.05339554978969[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]24693[/C][C]26076.9299732887[/C][C]29668.3333333333[/C][C]0.878948260433304[/C][C]0.94692895311272[/C][/ROW]
[ROW][C]8[/C][C]22205[/C][C]22272.1981471665[/C][C]29138.75[/C][C]0.764349814153542[/C][C]0.996982868654342[/C][/ROW]
[ROW][C]9[/C][C]21725[/C][C]23606.10620385[/C][C]28311.8333333333[/C][C]0.833789388554255[/C][C]0.92031272808799[/C][/ROW]
[ROW][C]10[/C][C]27192[/C][C]28306.4131639731[/C][C]27554.7083333333[/C][C]1.02728044955317[/C][C]0.960630364662682[/C][/ROW]
[ROW][C]11[/C][C]21790[/C][C]21346.1448649119[/C][C]26986.4166666667[/C][C]0.790995897253687[/C][C]1.02079322228426[/C][/ROW]
[ROW][C]12[/C][C]13253[/C][C]14776.5987209382[/C][C]26704.375[/C][C]0.553339994698928[/C][C]0.896891108047803[/C][/ROW]
[ROW][C]13[/C][C]37702[/C][C]35460.4682980066[/C][C]26718.5416666667[/C][C]1.32718577010684[/C][C]1.06321212915621[/C][/ROW]
[ROW][C]14[/C][C]30364[/C][C]32151.4446797421[/C][C]26773.9583333333[/C][C]1.20084764006351[/C][C]0.944405463034502[/C][/ROW]
[ROW][C]15[/C][C]32609[/C][C]35511.5337685382[/C][C]26731.875[/C][C]1.32843407985928[/C][C]0.918265040663781[/C][/ROW]
[ROW][C]16[/C][C]30212[/C][C]32725.272963883[/C][C]26748.3333333333[/C][C]1.22345091771013[/C][C]0.923200855599989[/C][/ROW]
[ROW][C]17[/C][C]29965[/C][C]27316.3657060541[/C][C]26833.8333333333[/C][C]1.01798223782367[/C][C]1.09696144510757[/C][/ROW]
[ROW][C]18[/C][C]28352[/C][C]28317.3791694465[/C][C]26882[/C][C]1.05339554978969[/C][C]1.0012226000982[/C][/ROW]
[ROW][C]19[/C][C]25814[/C][C]23622.7965616264[/C][C]26876.2083333333[/C][C]0.878948260433304[/C][C]1.0927580031711[/C][/ROW]
[ROW][C]20[/C][C]22414[/C][C]20673.9426857715[/C][C]27047.75[/C][C]0.764349814153542[/C][C]1.08416668947361[/C][/ROW]
[ROW][C]21[/C][C]20506[/C][C]22808.7603598205[/C][C]27355.5416666667[/C][C]0.833789388554255[/C][C]0.899040529888816[/C][/ROW]
[ROW][C]22[/C][C]28806[/C][C]28400.4949318113[/C][C]27646.2916666667[/C][C]1.02728044955317[/C][C]1.01427809864449[/C][/ROW]
[ROW][C]23[/C][C]22228[/C][C]21917.1120700795[/C][C]27708.25[/C][C]0.790995897253687[/C][C]1.01418471233466[/C][/ROW]
[ROW][C]24[/C][C]13971[/C][C]15160.0863930976[/C][C]27397.4166666667[/C][C]0.553339994698928[/C][C]0.921564669074773[/C][/ROW]
[ROW][C]25[/C][C]36845[/C][C]35951.3611347248[/C][C]27088.4166666667[/C][C]1.32718577010684[/C][C]1.02485688544382[/C][/ROW]
[ROW][C]26[/C][C]35338[/C][C]32123.9252546573[/C][C]26751.0416666667[/C][C]1.20084764006351[/C][C]1.100052366573[/C][/ROW]
[ROW][C]27[/C][C]35022[/C][C]35305.2390262201[/C][C]26576.5833333333[/C][C]1.32843407985928[/C][C]0.991977422217429[/C][/ROW]
[ROW][C]28[/C][C]34777[/C][C]32343.4033461928[/C][C]26436.2083333333[/C][C]1.22345091771013[/C][C]1.07524244210663[/C][/ROW]
[ROW][C]29[/C][C]26887[/C][C]26527.1325602547[/C][C]26058.5416666667[/C][C]1.01798223782367[/C][C]1.01356601354963[/C][/ROW]
[ROW][C]30[/C][C]23970[/C][C]27130.5974081647[/C][C]25755.375[/C][C]1.05339554978969[/C][C]0.883504319473128[/C][/ROW]
[ROW][C]31[/C][C]22780[/C][C]22352.4233425468[/C][C]25430.875[/C][C]0.878948260433304[/C][C]1.01912887255671[/C][/ROW]
[ROW][C]32[/C][C]17351[/C][C]19005.0167144761[/C][C]24864.2916666667[/C][C]0.764349814153542[/C][C]0.912969468044915[/C][/ROW]
[ROW][C]33[/C][C]21382[/C][C]20251.2156341038[/C][C]24288.1666666667[/C][C]0.833789388554255[/C][C]1.05583785123456[/C][/ROW]
[ROW][C]34[/C][C]24561[/C][C]24341.5814655706[/C][C]23695.1666666667[/C][C]1.02728044955317[/C][C]1.00901414457149[/C][/ROW]
[ROW][C]35[/C][C]17409[/C][C]18308.9842847568[/C][C]23146.75[/C][C]0.790995897253687[/C][C]0.950844663430834[/C][/ROW]
[ROW][C]36[/C][C]11514[/C][C]12701.5737408172[/C][C]22954.375[/C][C]0.553339994698928[/C][C]0.906501842602318[/C][/ROW]
[ROW][C]37[/C][C]31514[/C][C]30398.2499743766[/C][C]22904.2916666667[/C][C]1.32718577010684[/C][C]1.0367044164241[/C][/ROW]
[ROW][C]38[/C][C]27071[/C][C]27487.7026929638[/C][C]22890.25[/C][C]1.20084764006351[/C][C]0.984840395808325[/C][/ROW]
[ROW][C]39[/C][C]29462[/C][C]30414.8303668981[/C][C]22895.25[/C][C]1.32843407985928[/C][C]0.968672178821844[/C][/ROW]
[ROW][C]40[/C][C]26105[/C][C]28018.198489358[/C][C]22900.9583333333[/C][C]1.22345091771013[/C][C]0.931715863527603[/C][/ROW]
[ROW][C]41[/C][C]22397[/C][C]23357.3055353781[/C][C]22944.7083333333[/C][C]1.01798223782367[/C][C]0.958886287892942[/C][/ROW]
[ROW][C]42[/C][C]23843[/C][C]24359.8598718491[/C][C]23125.0833333333[/C][C]1.05339554978969[/C][C]0.978782313421826[/C][/ROW]
[ROW][C]43[/C][C]21705[/C][C]20345.8577096659[/C][C]23147.9583333333[/C][C]0.878948260433304[/C][C]1.06680191662249[/C][/ROW]
[ROW][C]44[/C][C]18089[/C][C]17655.7800529505[/C][C]23099.0833333333[/C][C]0.764349814153542[/C][C]1.02453700407177[/C][/ROW]
[ROW][C]45[/C][C]20764[/C][C]19582.4470620343[/C][C]23486.0833333333[/C][C]0.833789388554255[/C][C]1.06033734876049[/C][/ROW]
[ROW][C]46[/C][C]25316[/C][C]24679.171503305[/C][C]24023.7916666667[/C][C]1.02728044955317[/C][C]1.02580428992966[/C][/ROW]
[ROW][C]47[/C][C]17704[/C][C]19207.4237913874[/C][C]24282.5833333333[/C][C]0.790995897253687[/C][C]0.921726942263774[/C][/ROW]
[ROW][C]48[/C][C]15548[/C][C]13454.0008544422[/C][C]24314.1666666667[/C][C]0.553339994698928[/C][C]1.15564137153049[/C][/ROW]
[ROW][C]49[/C][C]28029[/C][C]32091.0754241478[/C][C]24179.7916666667[/C][C]1.32718577010684[/C][C]0.873420402075675[/C][/ROW]
[ROW][C]50[/C][C]29383[/C][C]28823.4455512611[/C][C]24002.5833333333[/C][C]1.20084764006351[/C][C]1.01941317000925[/C][/ROW]
[ROW][C]51[/C][C]36438[/C][C]31833.2658556679[/C][C]23963[/C][C]1.32843407985928[/C][C]1.1446516409975[/C][/ROW]
[ROW][C]52[/C][C]32034[/C][C]29168.0894206408[/C][C]23840.8333333333[/C][C]1.22345091771013[/C][C]1.0982549984001[/C][/ROW]
[ROW][C]53[/C][C]22679[/C][C]24224.2022427381[/C][C]23796.2916666667[/C][C]1.01798223782367[/C][C]0.936212461105861[/C][/ROW]
[ROW][C]54[/C][C]24319[/C][C]25054.7936943416[/C][C]23784.7916666667[/C][C]1.05339554978969[/C][C]0.970632618120188[/C][/ROW]
[ROW][C]55[/C][C]18004[/C][C]20904.6124433922[/C][C]23783.6666666667[/C][C]0.878948260433304[/C][C]0.861245337542286[/C][/ROW]
[ROW][C]56[/C][C]17537[/C][C]18112.8612418143[/C][C]23697.0833333333[/C][C]0.764349814153542[/C][C]0.968207052760668[/C][/ROW]
[ROW][C]57[/C][C]20366[/C][C]19371.5678291791[/C][C]23233.1666666667[/C][C]0.833789388554255[/C][C]1.05133462503345[/C][/ROW]
[ROW][C]58[/C][C]22782[/C][C]23315.0714763545[/C][C]22695.9166666667[/C][C]1.02728044955317[/C][C]0.97713618519698[/C][/ROW]
[ROW][C]59[/C][C]19169[/C][C]17760.3627136866[/C][C]22453.1666666667[/C][C]0.790995897253687[/C][C]1.07931354269178[/C][/ROW]
[ROW][C]60[/C][C]13807[/C][C]12480.7449712661[/C][C]22555.2916666667[/C][C]0.553339994698928[/C][C]1.10626409174991[/C][/ROW]
[ROW][C]61[/C][C]29743[/C][C]30083.8728450825[/C][C]22667.4166666667[/C][C]1.32718577010684[/C][C]0.988669249905494[/C][/ROW]
[ROW][C]62[/C][C]25591[/C][C]27278.1047444544[/C][C]22715.7083333333[/C][C]1.20084764006351[/C][C]0.938151687580225[/C][/ROW]
[ROW][C]63[/C][C]29096[/C][C]30204.6056737604[/C][C]22737[/C][C]1.32843407985928[/C][C]0.963296800304747[/C][/ROW]
[ROW][C]64[/C][C]26482[/C][C]27630.0586857145[/C][C]22583.7083333333[/C][C]1.22345091771013[/C][C]0.958448923370978[/C][/ROW]
[ROW][C]65[/C][C]22405[/C][C]22833.4264262381[/C][C]22430.0833333333[/C][C]1.01798223782367[/C][C]0.981236875349299[/C][/ROW]
[ROW][C]66[/C][C]27044[/C][C]23461.0062275098[/C][C]22271.7916666667[/C][C]1.05339554978969[/C][C]1.15272123189196[/C][/ROW]
[ROW][C]67[/C][C]17970[/C][C]NA[/C][C]NA[/C][C]0.878948260433304[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]18730[/C][C]NA[/C][C]NA[/C][C]0.764349814153542[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]19684[/C][C]NA[/C][C]NA[/C][C]0.833789388554255[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]19785[/C][C]NA[/C][C]NA[/C][C]1.02728044955317[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]18479[/C][C]NA[/C][C]NA[/C][C]0.790995897253687[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]10698[/C][C]NA[/C][C]NA[/C][C]0.553339994698928[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210570&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210570&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
141086NANA1.32718577010684NA
239690NANA1.20084764006351NA
343129NANA1.32843407985928NA
437863NANA1.22345091771013NA
535953NANA1.01798223782367NA
629133NANA1.05339554978969NA
72469326076.929973288729668.33333333330.8789482604333040.94692895311272
82220522272.198147166529138.750.7643498141535420.996982868654342
92172523606.1062038528311.83333333330.8337893885542550.92031272808799
102719228306.413163973127554.70833333331.027280449553170.960630364662682
112179021346.144864911926986.41666666670.7909958972536871.02079322228426
121325314776.598720938226704.3750.5533399946989280.896891108047803
133770235460.468298006626718.54166666671.327185770106841.06321212915621
143036432151.444679742126773.95833333331.200847640063510.944405463034502
153260935511.533768538226731.8751.328434079859280.918265040663781
163021232725.27296388326748.33333333331.223450917710130.923200855599989
172996527316.365706054126833.83333333331.017982237823671.09696144510757
182835228317.3791694465268821.053395549789691.0012226000982
192581423622.796561626426876.20833333330.8789482604333041.0927580031711
202241420673.942685771527047.750.7643498141535421.08416668947361
212050622808.760359820527355.54166666670.8337893885542550.899040529888816
222880628400.494931811327646.29166666671.027280449553171.01427809864449
232222821917.112070079527708.250.7909958972536871.01418471233466
241397115160.086393097627397.41666666670.5533399946989280.921564669074773
253684535951.361134724827088.41666666671.327185770106841.02485688544382
263533832123.925254657326751.04166666671.200847640063511.100052366573
273502235305.239026220126576.58333333331.328434079859280.991977422217429
283477732343.403346192826436.20833333331.223450917710131.07524244210663
292688726527.132560254726058.54166666671.017982237823671.01356601354963
302397027130.597408164725755.3751.053395549789690.883504319473128
312278022352.423342546825430.8750.8789482604333041.01912887255671
321735119005.016714476124864.29166666670.7643498141535420.912969468044915
332138220251.215634103824288.16666666670.8337893885542551.05583785123456
342456124341.581465570623695.16666666671.027280449553171.00901414457149
351740918308.984284756823146.750.7909958972536870.950844663430834
361151412701.573740817222954.3750.5533399946989280.906501842602318
373151430398.249974376622904.29166666671.327185770106841.0367044164241
382707127487.702692963822890.251.200847640063510.984840395808325
392946230414.830366898122895.251.328434079859280.968672178821844
402610528018.19848935822900.95833333331.223450917710130.931715863527603
412239723357.305535378122944.70833333331.017982237823670.958886287892942
422384324359.859871849123125.08333333331.053395549789690.978782313421826
432170520345.857709665923147.95833333330.8789482604333041.06680191662249
441808917655.780052950523099.08333333330.7643498141535421.02453700407177
452076419582.447062034323486.08333333330.8337893885542551.06033734876049
462531624679.17150330524023.79166666671.027280449553171.02580428992966
471770419207.423791387424282.58333333330.7909958972536870.921726942263774
481554813454.000854442224314.16666666670.5533399946989281.15564137153049
492802932091.075424147824179.79166666671.327185770106840.873420402075675
502938328823.445551261124002.58333333331.200847640063511.01941317000925
513643831833.2658556679239631.328434079859281.1446516409975
523203429168.089420640823840.83333333331.223450917710131.0982549984001
532267924224.202242738123796.29166666671.017982237823670.936212461105861
542431925054.793694341623784.79166666671.053395549789690.970632618120188
551800420904.612443392223783.66666666670.8789482604333040.861245337542286
561753718112.861241814323697.08333333330.7643498141535420.968207052760668
572036619371.567829179123233.16666666670.8337893885542551.05133462503345
582278223315.071476354522695.91666666671.027280449553170.97713618519698
591916917760.362713686622453.16666666670.7909958972536871.07931354269178
601380712480.744971266122555.29166666670.5533399946989281.10626409174991
612974330083.872845082522667.41666666671.327185770106840.988669249905494
622559127278.104744454422715.70833333331.200847640063510.938151687580225
632909630204.6056737604227371.328434079859280.963296800304747
642648227630.058685714522583.70833333331.223450917710130.958448923370978
652240522833.426426238122430.08333333331.017982237823670.981236875349299
662704423461.006227509822271.79166666671.053395549789691.15272123189196
6717970NANA0.878948260433304NA
6818730NANA0.764349814153542NA
6919684NANA0.833789388554255NA
7019785NANA1.02728044955317NA
7118479NANA0.790995897253687NA
7210698NANA0.553339994698928NA



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')