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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 24 May 2010 09:14:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/May/24/t12746926980dwsc7bqbv2dsuf.htm/, Retrieved Sun, 05 May 2024 07:27:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76296, Retrieved Sun, 05 May 2024 07:27:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W52
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Classical Decompo...] [2010-05-24 09:14:43] [2aa5bad7942f7e33426bcac9d4e6ffec] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3592,21
5955,74
4652,25
4211,65
4787,85
3599,73
4174,27
5106,33
5325,75
6604,61
5711,90
6919,30
7048,76
8655,98
6658,53
7247,03
8779,57
6602,49
9832,48
9369,49
8582,76
8206,94
6515,83
8618,10
8505,39
9881,64
9375,29
15642,50
12232,73
6288,93
12473,94
11142,82
10236,32
10581,51
8763,71
10819,04
11636,25
14650,13
10671,38
17468,63
13873,19
13077,58
16866,81
14186,64
19919,87
17681,78
9984,28
17423,09
13514,45
12334,57
12274,56
11752,23
13054,00
12460,98
8626,68
13722,62
12066,22
6798,83
6593,82
6606,19
6315,28
7232,95
6747,44
7803,61
6700,31
5369,53
8081,19
10718,39
9447,21
6815,10
5497,80
6805,31

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76296&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76296&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76296&T=0

As an alternative you can also use a QR Code:  
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13592.21NANA0.939141670558009NA
25955.74NANA1.05581132590604NA
34652.25NANA0.904809104827505NA
44211.65NANA1.16452206257041NA
54787.85NANA1.07340443509932NA
63599.73NANA0.844802307861316NA
74174.275409.886531263025197.488751.040865462433760.771600286970428
85106.335980.420904406695454.021666666671.096515795116330.853841239876174
95325.756269.533563581635650.126666666671.109627081560700.849465107091241
106604.615851.674212891825860.195833333330.9985458471553051.12867014801497
115711.94776.586960795266152.991666666670.7763031740595441.19581199858424
126919.36416.406237322296444.428333333330.9956517328517571.07837623493234
137048.766391.142767193566805.302083333330.9391416705580091.10289509353195
148655.987621.577299732997218.69251.055811325906041.13572029247847
156658.536815.051960860477532.032916666670.9048091048275050.97703290279232
167247.039007.002200778667734.505416666671.164522062570410.804599559148935
178779.578409.872840716497834.766251.073404435099321.04395989883386
186602.496706.924946218697939.046666666670.8448023078613160.984428788594456
199832.488400.328567738518070.522916666671.040865462433761.17048754947059
209369.498972.004742643428182.2851.096515795116331.04430283629559
218582.769261.560691668188346.55251.109627081560700.92670774243494
228206.948796.751633569338809.562083333330.9985458471553050.93295120083662
236515.837222.146385585339303.2550.7763031740595440.902201319680384
248618.19393.049802764339434.071666666670.9956517328517570.917497530723593
258505.398951.022654151149531.06750.9391416705580090.950214330655897
269881.6410257.2250679549715.017083333331.055811325906040.963383364851044
279375.298919.430963606529857.804166666670.9048091048275051.05110853352120
2815642.511675.082367911110025.64291666671.164522062570411.33981924127519
2912232.7310968.309501931510218.2451.073404435099321.11527943279189
306288.938788.9958500948410403.61250.8448023078613160.715545906183599
3112473.9411059.997872152710625.77083333331.040865462433761.12784289329815
3211142.8212012.232305959410954.91041666671.096515795116330.927622752889312
3310236.3212436.257866333611207.601251.109627081560700.823102906840725
3410581.5111321.207010381211337.693750.9985458471553050.934662707809964
358763.718913.6178454801811482.1350.7763031740595440.983182154757039
3610819.0411781.893358666911833.34791666670.9956517328517570.918276856753367
3711636.2511550.733104593212299.24458333330.9391416705580091.00740359028578
3814650.1313312.837628224012609.10666666671.055811325906041.10045133946055
3910671.3811888.661193095713139.413750.9048091048275050.897609901289588
4017468.6316115.517562958113838.73958333331.164522062570411.08396332489824
4113873.1915226.715551590914185.441251.073404435099320.911108502223944
4213077.5812259.320882453514511.46708333330.8448023078613161.06674587649611
4316866.8115472.354940816414864.89416666671.040865462433761.09012558621603
4414186.6416279.609073642914846.67083333331.096515795116330.871436158928934
4519919.8716441.331521835614816.98833333331.109627081560701.21157279588606
4617681.7814624.307219705414645.60416666670.9985458471553051.20906787134332
479984.2811158.029031380014373.28791666670.7763031740595440.894806777426456
4817423.0914251.224570943414313.46333333330.9956517328517571.22256792132261
4913514.4513095.798024342413944.43291666670.9391416705580091.03196842031920
5012334.5714339.776033737613581.761.055811325906040.860164759266818
5112274.5611975.320793125713235.19041666670.9048091048275051.02498799088923
5211752.2314503.538572630612454.498751.164522062570410.810300875275877
531305412730.333295272711859.77333333331.073404435099321.02542484137847
5412460.989519.0634445197411267.80.8448023078613161.30905525240237
558626.6810946.917814619910517.13041666671.040865462433760.788046475372166
5613722.6210970.199182531310004.59751.096515795116331.25089980333735
5712066.2210609.95825332839561.733333333331.109627081560701.13725423907439
586798.839153.580743867979166.910833333330.9985458471553050.742750863322484
596593.826783.06381152318737.647916666670.7763031740595440.97210054087924
606606.198141.876083467948177.433750.9956517328517570.811384247595447
616315.287380.928434954477859.227916666660.9391416705580090.855621356534526
627232.958141.702073135477711.322916666671.055811325906040.888383035270474
636747.446765.276903988747477.021250.9048091048275050.997363462835022
647803.618580.866701552177368.573751.164522062570410.909419790729114
656700.317861.167725323187323.584166666671.073404435099320.852330116099202
665369.536155.40983956997286.213333333330.8448023078613160.872326967650816
678081.19NANA1.04086546243376NA
6810718.39NANA1.09651579511633NA
699447.21NANA1.10962708156070NA
706815.1NANA0.998545847155305NA
715497.8NANA0.776303174059544NA
726805.31NANA0.995651732851757NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 3592.21 & NA & NA & 0.939141670558009 & NA \tabularnewline
2 & 5955.74 & NA & NA & 1.05581132590604 & NA \tabularnewline
3 & 4652.25 & NA & NA & 0.904809104827505 & NA \tabularnewline
4 & 4211.65 & NA & NA & 1.16452206257041 & NA \tabularnewline
5 & 4787.85 & NA & NA & 1.07340443509932 & NA \tabularnewline
6 & 3599.73 & NA & NA & 0.844802307861316 & NA \tabularnewline
7 & 4174.27 & 5409.88653126302 & 5197.48875 & 1.04086546243376 & 0.771600286970428 \tabularnewline
8 & 5106.33 & 5980.42090440669 & 5454.02166666667 & 1.09651579511633 & 0.853841239876174 \tabularnewline
9 & 5325.75 & 6269.53356358163 & 5650.12666666667 & 1.10962708156070 & 0.849465107091241 \tabularnewline
10 & 6604.61 & 5851.67421289182 & 5860.19583333333 & 0.998545847155305 & 1.12867014801497 \tabularnewline
11 & 5711.9 & 4776.58696079526 & 6152.99166666667 & 0.776303174059544 & 1.19581199858424 \tabularnewline
12 & 6919.3 & 6416.40623732229 & 6444.42833333333 & 0.995651732851757 & 1.07837623493234 \tabularnewline
13 & 7048.76 & 6391.14276719356 & 6805.30208333333 & 0.939141670558009 & 1.10289509353195 \tabularnewline
14 & 8655.98 & 7621.57729973299 & 7218.6925 & 1.05581132590604 & 1.13572029247847 \tabularnewline
15 & 6658.53 & 6815.05196086047 & 7532.03291666667 & 0.904809104827505 & 0.97703290279232 \tabularnewline
16 & 7247.03 & 9007.00220077866 & 7734.50541666667 & 1.16452206257041 & 0.804599559148935 \tabularnewline
17 & 8779.57 & 8409.87284071649 & 7834.76625 & 1.07340443509932 & 1.04395989883386 \tabularnewline
18 & 6602.49 & 6706.92494621869 & 7939.04666666667 & 0.844802307861316 & 0.984428788594456 \tabularnewline
19 & 9832.48 & 8400.32856773851 & 8070.52291666667 & 1.04086546243376 & 1.17048754947059 \tabularnewline
20 & 9369.49 & 8972.00474264342 & 8182.285 & 1.09651579511633 & 1.04430283629559 \tabularnewline
21 & 8582.76 & 9261.56069166818 & 8346.5525 & 1.10962708156070 & 0.92670774243494 \tabularnewline
22 & 8206.94 & 8796.75163356933 & 8809.56208333333 & 0.998545847155305 & 0.93295120083662 \tabularnewline
23 & 6515.83 & 7222.14638558533 & 9303.255 & 0.776303174059544 & 0.902201319680384 \tabularnewline
24 & 8618.1 & 9393.04980276433 & 9434.07166666667 & 0.995651732851757 & 0.917497530723593 \tabularnewline
25 & 8505.39 & 8951.02265415114 & 9531.0675 & 0.939141670558009 & 0.950214330655897 \tabularnewline
26 & 9881.64 & 10257.225067954 & 9715.01708333333 & 1.05581132590604 & 0.963383364851044 \tabularnewline
27 & 9375.29 & 8919.43096360652 & 9857.80416666667 & 0.904809104827505 & 1.05110853352120 \tabularnewline
28 & 15642.5 & 11675.0823679111 & 10025.6429166667 & 1.16452206257041 & 1.33981924127519 \tabularnewline
29 & 12232.73 & 10968.3095019315 & 10218.245 & 1.07340443509932 & 1.11527943279189 \tabularnewline
30 & 6288.93 & 8788.99585009484 & 10403.6125 & 0.844802307861316 & 0.715545906183599 \tabularnewline
31 & 12473.94 & 11059.9978721527 & 10625.7708333333 & 1.04086546243376 & 1.12784289329815 \tabularnewline
32 & 11142.82 & 12012.2323059594 & 10954.9104166667 & 1.09651579511633 & 0.927622752889312 \tabularnewline
33 & 10236.32 & 12436.2578663336 & 11207.60125 & 1.10962708156070 & 0.823102906840725 \tabularnewline
34 & 10581.51 & 11321.2070103812 & 11337.69375 & 0.998545847155305 & 0.934662707809964 \tabularnewline
35 & 8763.71 & 8913.61784548018 & 11482.135 & 0.776303174059544 & 0.983182154757039 \tabularnewline
36 & 10819.04 & 11781.8933586669 & 11833.3479166667 & 0.995651732851757 & 0.918276856753367 \tabularnewline
37 & 11636.25 & 11550.7331045932 & 12299.2445833333 & 0.939141670558009 & 1.00740359028578 \tabularnewline
38 & 14650.13 & 13312.8376282240 & 12609.1066666667 & 1.05581132590604 & 1.10045133946055 \tabularnewline
39 & 10671.38 & 11888.6611930957 & 13139.41375 & 0.904809104827505 & 0.897609901289588 \tabularnewline
40 & 17468.63 & 16115.5175629581 & 13838.7395833333 & 1.16452206257041 & 1.08396332489824 \tabularnewline
41 & 13873.19 & 15226.7155515909 & 14185.44125 & 1.07340443509932 & 0.911108502223944 \tabularnewline
42 & 13077.58 & 12259.3208824535 & 14511.4670833333 & 0.844802307861316 & 1.06674587649611 \tabularnewline
43 & 16866.81 & 15472.3549408164 & 14864.8941666667 & 1.04086546243376 & 1.09012558621603 \tabularnewline
44 & 14186.64 & 16279.6090736429 & 14846.6708333333 & 1.09651579511633 & 0.871436158928934 \tabularnewline
45 & 19919.87 & 16441.3315218356 & 14816.9883333333 & 1.10962708156070 & 1.21157279588606 \tabularnewline
46 & 17681.78 & 14624.3072197054 & 14645.6041666667 & 0.998545847155305 & 1.20906787134332 \tabularnewline
47 & 9984.28 & 11158.0290313800 & 14373.2879166667 & 0.776303174059544 & 0.894806777426456 \tabularnewline
48 & 17423.09 & 14251.2245709434 & 14313.4633333333 & 0.995651732851757 & 1.22256792132261 \tabularnewline
49 & 13514.45 & 13095.7980243424 & 13944.4329166667 & 0.939141670558009 & 1.03196842031920 \tabularnewline
50 & 12334.57 & 14339.7760337376 & 13581.76 & 1.05581132590604 & 0.860164759266818 \tabularnewline
51 & 12274.56 & 11975.3207931257 & 13235.1904166667 & 0.904809104827505 & 1.02498799088923 \tabularnewline
52 & 11752.23 & 14503.5385726306 & 12454.49875 & 1.16452206257041 & 0.810300875275877 \tabularnewline
53 & 13054 & 12730.3332952727 & 11859.7733333333 & 1.07340443509932 & 1.02542484137847 \tabularnewline
54 & 12460.98 & 9519.06344451974 & 11267.8 & 0.844802307861316 & 1.30905525240237 \tabularnewline
55 & 8626.68 & 10946.9178146199 & 10517.1304166667 & 1.04086546243376 & 0.788046475372166 \tabularnewline
56 & 13722.62 & 10970.1991825313 & 10004.5975 & 1.09651579511633 & 1.25089980333735 \tabularnewline
57 & 12066.22 & 10609.9582533283 & 9561.73333333333 & 1.10962708156070 & 1.13725423907439 \tabularnewline
58 & 6798.83 & 9153.58074386797 & 9166.91083333333 & 0.998545847155305 & 0.742750863322484 \tabularnewline
59 & 6593.82 & 6783.0638115231 & 8737.64791666667 & 0.776303174059544 & 0.97210054087924 \tabularnewline
60 & 6606.19 & 8141.87608346794 & 8177.43375 & 0.995651732851757 & 0.811384247595447 \tabularnewline
61 & 6315.28 & 7380.92843495447 & 7859.22791666666 & 0.939141670558009 & 0.855621356534526 \tabularnewline
62 & 7232.95 & 8141.70207313547 & 7711.32291666667 & 1.05581132590604 & 0.888383035270474 \tabularnewline
63 & 6747.44 & 6765.27690398874 & 7477.02125 & 0.904809104827505 & 0.997363462835022 \tabularnewline
64 & 7803.61 & 8580.86670155217 & 7368.57375 & 1.16452206257041 & 0.909419790729114 \tabularnewline
65 & 6700.31 & 7861.16772532318 & 7323.58416666667 & 1.07340443509932 & 0.852330116099202 \tabularnewline
66 & 5369.53 & 6155.4098395699 & 7286.21333333333 & 0.844802307861316 & 0.872326967650816 \tabularnewline
67 & 8081.19 & NA & NA & 1.04086546243376 & NA \tabularnewline
68 & 10718.39 & NA & NA & 1.09651579511633 & NA \tabularnewline
69 & 9447.21 & NA & NA & 1.10962708156070 & NA \tabularnewline
70 & 6815.1 & NA & NA & 0.998545847155305 & NA \tabularnewline
71 & 5497.8 & NA & NA & 0.776303174059544 & NA \tabularnewline
72 & 6805.31 & NA & NA & 0.995651732851757 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76296&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]3592.21[/C][C]NA[/C][C]NA[/C][C]0.939141670558009[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]5955.74[/C][C]NA[/C][C]NA[/C][C]1.05581132590604[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]4652.25[/C][C]NA[/C][C]NA[/C][C]0.904809104827505[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]4211.65[/C][C]NA[/C][C]NA[/C][C]1.16452206257041[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]4787.85[/C][C]NA[/C][C]NA[/C][C]1.07340443509932[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]3599.73[/C][C]NA[/C][C]NA[/C][C]0.844802307861316[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]4174.27[/C][C]5409.88653126302[/C][C]5197.48875[/C][C]1.04086546243376[/C][C]0.771600286970428[/C][/ROW]
[ROW][C]8[/C][C]5106.33[/C][C]5980.42090440669[/C][C]5454.02166666667[/C][C]1.09651579511633[/C][C]0.853841239876174[/C][/ROW]
[ROW][C]9[/C][C]5325.75[/C][C]6269.53356358163[/C][C]5650.12666666667[/C][C]1.10962708156070[/C][C]0.849465107091241[/C][/ROW]
[ROW][C]10[/C][C]6604.61[/C][C]5851.67421289182[/C][C]5860.19583333333[/C][C]0.998545847155305[/C][C]1.12867014801497[/C][/ROW]
[ROW][C]11[/C][C]5711.9[/C][C]4776.58696079526[/C][C]6152.99166666667[/C][C]0.776303174059544[/C][C]1.19581199858424[/C][/ROW]
[ROW][C]12[/C][C]6919.3[/C][C]6416.40623732229[/C][C]6444.42833333333[/C][C]0.995651732851757[/C][C]1.07837623493234[/C][/ROW]
[ROW][C]13[/C][C]7048.76[/C][C]6391.14276719356[/C][C]6805.30208333333[/C][C]0.939141670558009[/C][C]1.10289509353195[/C][/ROW]
[ROW][C]14[/C][C]8655.98[/C][C]7621.57729973299[/C][C]7218.6925[/C][C]1.05581132590604[/C][C]1.13572029247847[/C][/ROW]
[ROW][C]15[/C][C]6658.53[/C][C]6815.05196086047[/C][C]7532.03291666667[/C][C]0.904809104827505[/C][C]0.97703290279232[/C][/ROW]
[ROW][C]16[/C][C]7247.03[/C][C]9007.00220077866[/C][C]7734.50541666667[/C][C]1.16452206257041[/C][C]0.804599559148935[/C][/ROW]
[ROW][C]17[/C][C]8779.57[/C][C]8409.87284071649[/C][C]7834.76625[/C][C]1.07340443509932[/C][C]1.04395989883386[/C][/ROW]
[ROW][C]18[/C][C]6602.49[/C][C]6706.92494621869[/C][C]7939.04666666667[/C][C]0.844802307861316[/C][C]0.984428788594456[/C][/ROW]
[ROW][C]19[/C][C]9832.48[/C][C]8400.32856773851[/C][C]8070.52291666667[/C][C]1.04086546243376[/C][C]1.17048754947059[/C][/ROW]
[ROW][C]20[/C][C]9369.49[/C][C]8972.00474264342[/C][C]8182.285[/C][C]1.09651579511633[/C][C]1.04430283629559[/C][/ROW]
[ROW][C]21[/C][C]8582.76[/C][C]9261.56069166818[/C][C]8346.5525[/C][C]1.10962708156070[/C][C]0.92670774243494[/C][/ROW]
[ROW][C]22[/C][C]8206.94[/C][C]8796.75163356933[/C][C]8809.56208333333[/C][C]0.998545847155305[/C][C]0.93295120083662[/C][/ROW]
[ROW][C]23[/C][C]6515.83[/C][C]7222.14638558533[/C][C]9303.255[/C][C]0.776303174059544[/C][C]0.902201319680384[/C][/ROW]
[ROW][C]24[/C][C]8618.1[/C][C]9393.04980276433[/C][C]9434.07166666667[/C][C]0.995651732851757[/C][C]0.917497530723593[/C][/ROW]
[ROW][C]25[/C][C]8505.39[/C][C]8951.02265415114[/C][C]9531.0675[/C][C]0.939141670558009[/C][C]0.950214330655897[/C][/ROW]
[ROW][C]26[/C][C]9881.64[/C][C]10257.225067954[/C][C]9715.01708333333[/C][C]1.05581132590604[/C][C]0.963383364851044[/C][/ROW]
[ROW][C]27[/C][C]9375.29[/C][C]8919.43096360652[/C][C]9857.80416666667[/C][C]0.904809104827505[/C][C]1.05110853352120[/C][/ROW]
[ROW][C]28[/C][C]15642.5[/C][C]11675.0823679111[/C][C]10025.6429166667[/C][C]1.16452206257041[/C][C]1.33981924127519[/C][/ROW]
[ROW][C]29[/C][C]12232.73[/C][C]10968.3095019315[/C][C]10218.245[/C][C]1.07340443509932[/C][C]1.11527943279189[/C][/ROW]
[ROW][C]30[/C][C]6288.93[/C][C]8788.99585009484[/C][C]10403.6125[/C][C]0.844802307861316[/C][C]0.715545906183599[/C][/ROW]
[ROW][C]31[/C][C]12473.94[/C][C]11059.9978721527[/C][C]10625.7708333333[/C][C]1.04086546243376[/C][C]1.12784289329815[/C][/ROW]
[ROW][C]32[/C][C]11142.82[/C][C]12012.2323059594[/C][C]10954.9104166667[/C][C]1.09651579511633[/C][C]0.927622752889312[/C][/ROW]
[ROW][C]33[/C][C]10236.32[/C][C]12436.2578663336[/C][C]11207.60125[/C][C]1.10962708156070[/C][C]0.823102906840725[/C][/ROW]
[ROW][C]34[/C][C]10581.51[/C][C]11321.2070103812[/C][C]11337.69375[/C][C]0.998545847155305[/C][C]0.934662707809964[/C][/ROW]
[ROW][C]35[/C][C]8763.71[/C][C]8913.61784548018[/C][C]11482.135[/C][C]0.776303174059544[/C][C]0.983182154757039[/C][/ROW]
[ROW][C]36[/C][C]10819.04[/C][C]11781.8933586669[/C][C]11833.3479166667[/C][C]0.995651732851757[/C][C]0.918276856753367[/C][/ROW]
[ROW][C]37[/C][C]11636.25[/C][C]11550.7331045932[/C][C]12299.2445833333[/C][C]0.939141670558009[/C][C]1.00740359028578[/C][/ROW]
[ROW][C]38[/C][C]14650.13[/C][C]13312.8376282240[/C][C]12609.1066666667[/C][C]1.05581132590604[/C][C]1.10045133946055[/C][/ROW]
[ROW][C]39[/C][C]10671.38[/C][C]11888.6611930957[/C][C]13139.41375[/C][C]0.904809104827505[/C][C]0.897609901289588[/C][/ROW]
[ROW][C]40[/C][C]17468.63[/C][C]16115.5175629581[/C][C]13838.7395833333[/C][C]1.16452206257041[/C][C]1.08396332489824[/C][/ROW]
[ROW][C]41[/C][C]13873.19[/C][C]15226.7155515909[/C][C]14185.44125[/C][C]1.07340443509932[/C][C]0.911108502223944[/C][/ROW]
[ROW][C]42[/C][C]13077.58[/C][C]12259.3208824535[/C][C]14511.4670833333[/C][C]0.844802307861316[/C][C]1.06674587649611[/C][/ROW]
[ROW][C]43[/C][C]16866.81[/C][C]15472.3549408164[/C][C]14864.8941666667[/C][C]1.04086546243376[/C][C]1.09012558621603[/C][/ROW]
[ROW][C]44[/C][C]14186.64[/C][C]16279.6090736429[/C][C]14846.6708333333[/C][C]1.09651579511633[/C][C]0.871436158928934[/C][/ROW]
[ROW][C]45[/C][C]19919.87[/C][C]16441.3315218356[/C][C]14816.9883333333[/C][C]1.10962708156070[/C][C]1.21157279588606[/C][/ROW]
[ROW][C]46[/C][C]17681.78[/C][C]14624.3072197054[/C][C]14645.6041666667[/C][C]0.998545847155305[/C][C]1.20906787134332[/C][/ROW]
[ROW][C]47[/C][C]9984.28[/C][C]11158.0290313800[/C][C]14373.2879166667[/C][C]0.776303174059544[/C][C]0.894806777426456[/C][/ROW]
[ROW][C]48[/C][C]17423.09[/C][C]14251.2245709434[/C][C]14313.4633333333[/C][C]0.995651732851757[/C][C]1.22256792132261[/C][/ROW]
[ROW][C]49[/C][C]13514.45[/C][C]13095.7980243424[/C][C]13944.4329166667[/C][C]0.939141670558009[/C][C]1.03196842031920[/C][/ROW]
[ROW][C]50[/C][C]12334.57[/C][C]14339.7760337376[/C][C]13581.76[/C][C]1.05581132590604[/C][C]0.860164759266818[/C][/ROW]
[ROW][C]51[/C][C]12274.56[/C][C]11975.3207931257[/C][C]13235.1904166667[/C][C]0.904809104827505[/C][C]1.02498799088923[/C][/ROW]
[ROW][C]52[/C][C]11752.23[/C][C]14503.5385726306[/C][C]12454.49875[/C][C]1.16452206257041[/C][C]0.810300875275877[/C][/ROW]
[ROW][C]53[/C][C]13054[/C][C]12730.3332952727[/C][C]11859.7733333333[/C][C]1.07340443509932[/C][C]1.02542484137847[/C][/ROW]
[ROW][C]54[/C][C]12460.98[/C][C]9519.06344451974[/C][C]11267.8[/C][C]0.844802307861316[/C][C]1.30905525240237[/C][/ROW]
[ROW][C]55[/C][C]8626.68[/C][C]10946.9178146199[/C][C]10517.1304166667[/C][C]1.04086546243376[/C][C]0.788046475372166[/C][/ROW]
[ROW][C]56[/C][C]13722.62[/C][C]10970.1991825313[/C][C]10004.5975[/C][C]1.09651579511633[/C][C]1.25089980333735[/C][/ROW]
[ROW][C]57[/C][C]12066.22[/C][C]10609.9582533283[/C][C]9561.73333333333[/C][C]1.10962708156070[/C][C]1.13725423907439[/C][/ROW]
[ROW][C]58[/C][C]6798.83[/C][C]9153.58074386797[/C][C]9166.91083333333[/C][C]0.998545847155305[/C][C]0.742750863322484[/C][/ROW]
[ROW][C]59[/C][C]6593.82[/C][C]6783.0638115231[/C][C]8737.64791666667[/C][C]0.776303174059544[/C][C]0.97210054087924[/C][/ROW]
[ROW][C]60[/C][C]6606.19[/C][C]8141.87608346794[/C][C]8177.43375[/C][C]0.995651732851757[/C][C]0.811384247595447[/C][/ROW]
[ROW][C]61[/C][C]6315.28[/C][C]7380.92843495447[/C][C]7859.22791666666[/C][C]0.939141670558009[/C][C]0.855621356534526[/C][/ROW]
[ROW][C]62[/C][C]7232.95[/C][C]8141.70207313547[/C][C]7711.32291666667[/C][C]1.05581132590604[/C][C]0.888383035270474[/C][/ROW]
[ROW][C]63[/C][C]6747.44[/C][C]6765.27690398874[/C][C]7477.02125[/C][C]0.904809104827505[/C][C]0.997363462835022[/C][/ROW]
[ROW][C]64[/C][C]7803.61[/C][C]8580.86670155217[/C][C]7368.57375[/C][C]1.16452206257041[/C][C]0.909419790729114[/C][/ROW]
[ROW][C]65[/C][C]6700.31[/C][C]7861.16772532318[/C][C]7323.58416666667[/C][C]1.07340443509932[/C][C]0.852330116099202[/C][/ROW]
[ROW][C]66[/C][C]5369.53[/C][C]6155.4098395699[/C][C]7286.21333333333[/C][C]0.844802307861316[/C][C]0.872326967650816[/C][/ROW]
[ROW][C]67[/C][C]8081.19[/C][C]NA[/C][C]NA[/C][C]1.04086546243376[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]10718.39[/C][C]NA[/C][C]NA[/C][C]1.09651579511633[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]9447.21[/C][C]NA[/C][C]NA[/C][C]1.10962708156070[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]6815.1[/C][C]NA[/C][C]NA[/C][C]0.998545847155305[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]5497.8[/C][C]NA[/C][C]NA[/C][C]0.776303174059544[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]6805.31[/C][C]NA[/C][C]NA[/C][C]0.995651732851757[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76296&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76296&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13592.21NANA0.939141670558009NA
25955.74NANA1.05581132590604NA
34652.25NANA0.904809104827505NA
44211.65NANA1.16452206257041NA
54787.85NANA1.07340443509932NA
63599.73NANA0.844802307861316NA
74174.275409.886531263025197.488751.040865462433760.771600286970428
85106.335980.420904406695454.021666666671.096515795116330.853841239876174
95325.756269.533563581635650.126666666671.109627081560700.849465107091241
106604.615851.674212891825860.195833333330.9985458471553051.12867014801497
115711.94776.586960795266152.991666666670.7763031740595441.19581199858424
126919.36416.406237322296444.428333333330.9956517328517571.07837623493234
137048.766391.142767193566805.302083333330.9391416705580091.10289509353195
148655.987621.577299732997218.69251.055811325906041.13572029247847
156658.536815.051960860477532.032916666670.9048091048275050.97703290279232
167247.039007.002200778667734.505416666671.164522062570410.804599559148935
178779.578409.872840716497834.766251.073404435099321.04395989883386
186602.496706.924946218697939.046666666670.8448023078613160.984428788594456
199832.488400.328567738518070.522916666671.040865462433761.17048754947059
209369.498972.004742643428182.2851.096515795116331.04430283629559
218582.769261.560691668188346.55251.109627081560700.92670774243494
228206.948796.751633569338809.562083333330.9985458471553050.93295120083662
236515.837222.146385585339303.2550.7763031740595440.902201319680384
248618.19393.049802764339434.071666666670.9956517328517570.917497530723593
258505.398951.022654151149531.06750.9391416705580090.950214330655897
269881.6410257.2250679549715.017083333331.055811325906040.963383364851044
279375.298919.430963606529857.804166666670.9048091048275051.05110853352120
2815642.511675.082367911110025.64291666671.164522062570411.33981924127519
2912232.7310968.309501931510218.2451.073404435099321.11527943279189
306288.938788.9958500948410403.61250.8448023078613160.715545906183599
3112473.9411059.997872152710625.77083333331.040865462433761.12784289329815
3211142.8212012.232305959410954.91041666671.096515795116330.927622752889312
3310236.3212436.257866333611207.601251.109627081560700.823102906840725
3410581.5111321.207010381211337.693750.9985458471553050.934662707809964
358763.718913.6178454801811482.1350.7763031740595440.983182154757039
3610819.0411781.893358666911833.34791666670.9956517328517570.918276856753367
3711636.2511550.733104593212299.24458333330.9391416705580091.00740359028578
3814650.1313312.837628224012609.10666666671.055811325906041.10045133946055
3910671.3811888.661193095713139.413750.9048091048275050.897609901289588
4017468.6316115.517562958113838.73958333331.164522062570411.08396332489824
4113873.1915226.715551590914185.441251.073404435099320.911108502223944
4213077.5812259.320882453514511.46708333330.8448023078613161.06674587649611
4316866.8115472.354940816414864.89416666671.040865462433761.09012558621603
4414186.6416279.609073642914846.67083333331.096515795116330.871436158928934
4519919.8716441.331521835614816.98833333331.109627081560701.21157279588606
4617681.7814624.307219705414645.60416666670.9985458471553051.20906787134332
479984.2811158.029031380014373.28791666670.7763031740595440.894806777426456
4817423.0914251.224570943414313.46333333330.9956517328517571.22256792132261
4913514.4513095.798024342413944.43291666670.9391416705580091.03196842031920
5012334.5714339.776033737613581.761.055811325906040.860164759266818
5112274.5611975.320793125713235.19041666670.9048091048275051.02498799088923
5211752.2314503.538572630612454.498751.164522062570410.810300875275877
531305412730.333295272711859.77333333331.073404435099321.02542484137847
5412460.989519.0634445197411267.80.8448023078613161.30905525240237
558626.6810946.917814619910517.13041666671.040865462433760.788046475372166
5613722.6210970.199182531310004.59751.096515795116331.25089980333735
5712066.2210609.95825332839561.733333333331.109627081560701.13725423907439
586798.839153.580743867979166.910833333330.9985458471553050.742750863322484
596593.826783.06381152318737.647916666670.7763031740595440.97210054087924
606606.198141.876083467948177.433750.9956517328517570.811384247595447
616315.287380.928434954477859.227916666660.9391416705580090.855621356534526
627232.958141.702073135477711.322916666671.055811325906040.888383035270474
636747.446765.276903988747477.021250.9048091048275050.997363462835022
647803.618580.866701552177368.573751.164522062570410.909419790729114
656700.317861.167725323187323.584166666671.073404435099320.852330116099202
665369.536155.40983956997286.213333333330.8448023078613160.872326967650816
678081.19NANA1.04086546243376NA
6810718.39NANA1.09651579511633NA
699447.21NANA1.10962708156070NA
706815.1NANA0.998545847155305NA
715497.8NANA0.776303174059544NA
726805.31NANA0.995651732851757NA


Figures (Output of Computation)

Input Parameters & R Code

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')