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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 14 Dec 2011 10:30:32 -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/2011/Dec/14/t1323876674btjfexwaotiscx7.htm/, Retrieved Wed, 01 May 2024 22:19:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155064, Retrieved Wed, 01 May 2024 22:19:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W92
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Gemiddelde consum...] [2011-12-14 15:30:32] [c0f35385ddfb9717cc41b82f2b64d087] [Current]
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Dataseries X:
49,98
50,12
50,37
50,39
50,34
50,32
50,32
50,32
50,67
50,86
50,95
51,02
51,02
51,06
50,9
51,23
51,29
51,3
51,3
51,3
51,46
51,47
51,77
51,82
51,82
51,84
51,9
51,94
52,22
52,27
52,27
52,28
52,53
52,73
52,72
52,67
52,67
52,65
52,69
52,73
52,84
52,83
52,83
52,84
52,82
53,09
53,4
53,43
53,43
53,42
53,6
53,69
54,05
54,04
54,04
54,08
54,05
54,39
54,38
54,46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155064&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155064&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155064&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
149.98NANA1.00050675083072NA
250.12NANA0.999167148698138NA
350.37NANA0.99828377070355NA
450.39NANA0.999328838370632NA
550.34NANA1.00177908483889NA
650.32NANA1.00061097657055NA
750.3250.421858950316350.5150.9981561704506830.997979865232326
850.3250.440403580695550.59750.996895174281250.997612953661187
950.6750.617770624403350.658750.9991910701389861.00103183871894
1050.8650.771278019304150.71583333333331.001093242136171.0017474836986
1150.9550.942473424191350.79041666666671.002993808035141.00014774657183
1251.0250.972267991738350.87083333333331.00199396494531.00093643092886
1351.0250.978320221702150.95251.000506750830721.00081759811066
1451.0650.991662794518951.03416666666660.9991671486981381.00134016428836
1550.951.020203762802851.10791666666670.998283770703550.99764399681033
1651.2351.131909176281451.166250.9993288383706321.0019183876624
1751.2951.31696843677651.22583333333331.001779084838890.999474473305858
1851.351.324672358225551.29333333333331.000610976570550.999519288539178
1951.351.265300914347151.360.9981561704506831.00067685325228
2051.351.266165083391851.42583333333330.996895174281251.00065998532469
2151.4651.458340112157851.50.9991910701389861.00003225692548
2251.4751.627629863514851.571251.001093242136170.996946792561822
2351.7751.794182332847851.63958333333331.002993808035140.999533107160716
2451.8251.821875374514851.718751.00199396494530.999963811141507
2551.8251.825832815218351.79958333333331.000506750830720.999887453516877
2651.8451.8376243137551.88083333333330.9991671486981381.00004582938129
2751.951.877063999323451.966250.998283770703551.00044212218095
2851.9452.02839042170352.06333333333330.9993288383706320.998301111739445
2952.2252.248205577724152.15541666666671.001779084838890.99946016179097
3052.2752.262328227520252.23041666666671.000610976570551.00014679354594
3152.2752.204815409783852.301250.9981561704506831.00124863175369
3252.2852.207815650098352.37041666666670.996895174281251.00138263493699
3352.5352.394665410800552.43708333333330.9991910701389861.00258298412898
3452.7352.560315067438352.50291666666671.001093242136171.00322838499625
3552.7252.719026206673552.56166666666671.002993808035141.00001847138304
3652.6752.715737490743152.61083333333331.00199396494530.999132375018919
3752.6752.684184231868552.65751.000506750830720.999730768691301
3852.6552.660271932844852.70416666666670.9991671486981380.999804939616379
3952.6952.649070115334152.73958333333330.998283770703551.00077740945047
4052.7352.731251704690452.76666666666670.9993288383706320.99997626256442
4152.8452.903953470341652.811.001779084838890.998791140053882
4252.8352.902302331285152.871.000610976570550.99863328573429
4352.8352.835733289189552.93333333333330.9981561704506830.999891488414514
4452.8452.832536625981352.99708333333330.996895174281251.00014126472994
4552.8253.02415578498853.06708333333330.9991910701389860.996149758879408
4653.0953.203100353326653.1451.001093242136170.99787417739614
4753.453.394793284837253.23541666666671.002993808035141.00009751353723
4853.4353.442600612813853.336251.00199396494530.999764221563522
4953.4353.464162619703653.43708333333331.000506750830720.99936101833397
5053.4253.494576502007853.53916666666670.9991671486981380.998605905366781
5153.653.550021218394153.64208333333330.998283770703551.00093331021107
5253.6953.711426740325653.74750.9993288383706320.999601076686546
5354.0553.938290375437753.84251.001779084838891.0020710635021
5454.0453.959197675287753.926251.000610976570551.0014974708334
5554.04NANA0.998156170450683NA
5654.08NANA0.99689517428125NA
5754.05NANA0.999191070138986NA
5854.39NANA1.00109324213617NA
5954.38NANA1.00299380803514NA
6054.46NANA1.0019939649453NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 49.98 & NA & NA & 1.00050675083072 & NA \tabularnewline
2 & 50.12 & NA & NA & 0.999167148698138 & NA \tabularnewline
3 & 50.37 & NA & NA & 0.99828377070355 & NA \tabularnewline
4 & 50.39 & NA & NA & 0.999328838370632 & NA \tabularnewline
5 & 50.34 & NA & NA & 1.00177908483889 & NA \tabularnewline
6 & 50.32 & NA & NA & 1.00061097657055 & NA \tabularnewline
7 & 50.32 & 50.4218589503163 & 50.515 & 0.998156170450683 & 0.997979865232326 \tabularnewline
8 & 50.32 & 50.4404035806955 & 50.5975 & 0.99689517428125 & 0.997612953661187 \tabularnewline
9 & 50.67 & 50.6177706244033 & 50.65875 & 0.999191070138986 & 1.00103183871894 \tabularnewline
10 & 50.86 & 50.7712780193041 & 50.7158333333333 & 1.00109324213617 & 1.0017474836986 \tabularnewline
11 & 50.95 & 50.9424734241913 & 50.7904166666667 & 1.00299380803514 & 1.00014774657183 \tabularnewline
12 & 51.02 & 50.9722679917383 & 50.8708333333333 & 1.0019939649453 & 1.00093643092886 \tabularnewline
13 & 51.02 & 50.9783202217021 & 50.9525 & 1.00050675083072 & 1.00081759811066 \tabularnewline
14 & 51.06 & 50.9916627945189 & 51.0341666666666 & 0.999167148698138 & 1.00134016428836 \tabularnewline
15 & 50.9 & 51.0202037628028 & 51.1079166666667 & 0.99828377070355 & 0.99764399681033 \tabularnewline
16 & 51.23 & 51.1319091762814 & 51.16625 & 0.999328838370632 & 1.0019183876624 \tabularnewline
17 & 51.29 & 51.316968436776 & 51.2258333333333 & 1.00177908483889 & 0.999474473305858 \tabularnewline
18 & 51.3 & 51.3246723582255 & 51.2933333333333 & 1.00061097657055 & 0.999519288539178 \tabularnewline
19 & 51.3 & 51.2653009143471 & 51.36 & 0.998156170450683 & 1.00067685325228 \tabularnewline
20 & 51.3 & 51.2661650833918 & 51.4258333333333 & 0.99689517428125 & 1.00065998532469 \tabularnewline
21 & 51.46 & 51.4583401121578 & 51.5 & 0.999191070138986 & 1.00003225692548 \tabularnewline
22 & 51.47 & 51.6276298635148 & 51.57125 & 1.00109324213617 & 0.996946792561822 \tabularnewline
23 & 51.77 & 51.7941823328478 & 51.6395833333333 & 1.00299380803514 & 0.999533107160716 \tabularnewline
24 & 51.82 & 51.8218753745148 & 51.71875 & 1.0019939649453 & 0.999963811141507 \tabularnewline
25 & 51.82 & 51.8258328152183 & 51.7995833333333 & 1.00050675083072 & 0.999887453516877 \tabularnewline
26 & 51.84 & 51.83762431375 & 51.8808333333333 & 0.999167148698138 & 1.00004582938129 \tabularnewline
27 & 51.9 & 51.8770639993234 & 51.96625 & 0.99828377070355 & 1.00044212218095 \tabularnewline
28 & 51.94 & 52.028390421703 & 52.0633333333333 & 0.999328838370632 & 0.998301111739445 \tabularnewline
29 & 52.22 & 52.2482055777241 & 52.1554166666667 & 1.00177908483889 & 0.99946016179097 \tabularnewline
30 & 52.27 & 52.2623282275202 & 52.2304166666667 & 1.00061097657055 & 1.00014679354594 \tabularnewline
31 & 52.27 & 52.2048154097838 & 52.30125 & 0.998156170450683 & 1.00124863175369 \tabularnewline
32 & 52.28 & 52.2078156500983 & 52.3704166666667 & 0.99689517428125 & 1.00138263493699 \tabularnewline
33 & 52.53 & 52.3946654108005 & 52.4370833333333 & 0.999191070138986 & 1.00258298412898 \tabularnewline
34 & 52.73 & 52.5603150674383 & 52.5029166666667 & 1.00109324213617 & 1.00322838499625 \tabularnewline
35 & 52.72 & 52.7190262066735 & 52.5616666666667 & 1.00299380803514 & 1.00001847138304 \tabularnewline
36 & 52.67 & 52.7157374907431 & 52.6108333333333 & 1.0019939649453 & 0.999132375018919 \tabularnewline
37 & 52.67 & 52.6841842318685 & 52.6575 & 1.00050675083072 & 0.999730768691301 \tabularnewline
38 & 52.65 & 52.6602719328448 & 52.7041666666667 & 0.999167148698138 & 0.999804939616379 \tabularnewline
39 & 52.69 & 52.6490701153341 & 52.7395833333333 & 0.99828377070355 & 1.00077740945047 \tabularnewline
40 & 52.73 & 52.7312517046904 & 52.7666666666667 & 0.999328838370632 & 0.99997626256442 \tabularnewline
41 & 52.84 & 52.9039534703416 & 52.81 & 1.00177908483889 & 0.998791140053882 \tabularnewline
42 & 52.83 & 52.9023023312851 & 52.87 & 1.00061097657055 & 0.99863328573429 \tabularnewline
43 & 52.83 & 52.8357332891895 & 52.9333333333333 & 0.998156170450683 & 0.999891488414514 \tabularnewline
44 & 52.84 & 52.8325366259813 & 52.9970833333333 & 0.99689517428125 & 1.00014126472994 \tabularnewline
45 & 52.82 & 53.024155784988 & 53.0670833333333 & 0.999191070138986 & 0.996149758879408 \tabularnewline
46 & 53.09 & 53.2031003533266 & 53.145 & 1.00109324213617 & 0.99787417739614 \tabularnewline
47 & 53.4 & 53.3947932848372 & 53.2354166666667 & 1.00299380803514 & 1.00009751353723 \tabularnewline
48 & 53.43 & 53.4426006128138 & 53.33625 & 1.0019939649453 & 0.999764221563522 \tabularnewline
49 & 53.43 & 53.4641626197036 & 53.4370833333333 & 1.00050675083072 & 0.99936101833397 \tabularnewline
50 & 53.42 & 53.4945765020078 & 53.5391666666667 & 0.999167148698138 & 0.998605905366781 \tabularnewline
51 & 53.6 & 53.5500212183941 & 53.6420833333333 & 0.99828377070355 & 1.00093331021107 \tabularnewline
52 & 53.69 & 53.7114267403256 & 53.7475 & 0.999328838370632 & 0.999601076686546 \tabularnewline
53 & 54.05 & 53.9382903754377 & 53.8425 & 1.00177908483889 & 1.0020710635021 \tabularnewline
54 & 54.04 & 53.9591976752877 & 53.92625 & 1.00061097657055 & 1.0014974708334 \tabularnewline
55 & 54.04 & NA & NA & 0.998156170450683 & NA \tabularnewline
56 & 54.08 & NA & NA & 0.99689517428125 & NA \tabularnewline
57 & 54.05 & NA & NA & 0.999191070138986 & NA \tabularnewline
58 & 54.39 & NA & NA & 1.00109324213617 & NA \tabularnewline
59 & 54.38 & NA & NA & 1.00299380803514 & NA \tabularnewline
60 & 54.46 & NA & NA & 1.0019939649453 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155064&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]49.98[/C][C]NA[/C][C]NA[/C][C]1.00050675083072[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]50.12[/C][C]NA[/C][C]NA[/C][C]0.999167148698138[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]50.37[/C][C]NA[/C][C]NA[/C][C]0.99828377070355[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]50.39[/C][C]NA[/C][C]NA[/C][C]0.999328838370632[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]50.34[/C][C]NA[/C][C]NA[/C][C]1.00177908483889[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]50.32[/C][C]NA[/C][C]NA[/C][C]1.00061097657055[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]50.32[/C][C]50.4218589503163[/C][C]50.515[/C][C]0.998156170450683[/C][C]0.997979865232326[/C][/ROW]
[ROW][C]8[/C][C]50.32[/C][C]50.4404035806955[/C][C]50.5975[/C][C]0.99689517428125[/C][C]0.997612953661187[/C][/ROW]
[ROW][C]9[/C][C]50.67[/C][C]50.6177706244033[/C][C]50.65875[/C][C]0.999191070138986[/C][C]1.00103183871894[/C][/ROW]
[ROW][C]10[/C][C]50.86[/C][C]50.7712780193041[/C][C]50.7158333333333[/C][C]1.00109324213617[/C][C]1.0017474836986[/C][/ROW]
[ROW][C]11[/C][C]50.95[/C][C]50.9424734241913[/C][C]50.7904166666667[/C][C]1.00299380803514[/C][C]1.00014774657183[/C][/ROW]
[ROW][C]12[/C][C]51.02[/C][C]50.9722679917383[/C][C]50.8708333333333[/C][C]1.0019939649453[/C][C]1.00093643092886[/C][/ROW]
[ROW][C]13[/C][C]51.02[/C][C]50.9783202217021[/C][C]50.9525[/C][C]1.00050675083072[/C][C]1.00081759811066[/C][/ROW]
[ROW][C]14[/C][C]51.06[/C][C]50.9916627945189[/C][C]51.0341666666666[/C][C]0.999167148698138[/C][C]1.00134016428836[/C][/ROW]
[ROW][C]15[/C][C]50.9[/C][C]51.0202037628028[/C][C]51.1079166666667[/C][C]0.99828377070355[/C][C]0.99764399681033[/C][/ROW]
[ROW][C]16[/C][C]51.23[/C][C]51.1319091762814[/C][C]51.16625[/C][C]0.999328838370632[/C][C]1.0019183876624[/C][/ROW]
[ROW][C]17[/C][C]51.29[/C][C]51.316968436776[/C][C]51.2258333333333[/C][C]1.00177908483889[/C][C]0.999474473305858[/C][/ROW]
[ROW][C]18[/C][C]51.3[/C][C]51.3246723582255[/C][C]51.2933333333333[/C][C]1.00061097657055[/C][C]0.999519288539178[/C][/ROW]
[ROW][C]19[/C][C]51.3[/C][C]51.2653009143471[/C][C]51.36[/C][C]0.998156170450683[/C][C]1.00067685325228[/C][/ROW]
[ROW][C]20[/C][C]51.3[/C][C]51.2661650833918[/C][C]51.4258333333333[/C][C]0.99689517428125[/C][C]1.00065998532469[/C][/ROW]
[ROW][C]21[/C][C]51.46[/C][C]51.4583401121578[/C][C]51.5[/C][C]0.999191070138986[/C][C]1.00003225692548[/C][/ROW]
[ROW][C]22[/C][C]51.47[/C][C]51.6276298635148[/C][C]51.57125[/C][C]1.00109324213617[/C][C]0.996946792561822[/C][/ROW]
[ROW][C]23[/C][C]51.77[/C][C]51.7941823328478[/C][C]51.6395833333333[/C][C]1.00299380803514[/C][C]0.999533107160716[/C][/ROW]
[ROW][C]24[/C][C]51.82[/C][C]51.8218753745148[/C][C]51.71875[/C][C]1.0019939649453[/C][C]0.999963811141507[/C][/ROW]
[ROW][C]25[/C][C]51.82[/C][C]51.8258328152183[/C][C]51.7995833333333[/C][C]1.00050675083072[/C][C]0.999887453516877[/C][/ROW]
[ROW][C]26[/C][C]51.84[/C][C]51.83762431375[/C][C]51.8808333333333[/C][C]0.999167148698138[/C][C]1.00004582938129[/C][/ROW]
[ROW][C]27[/C][C]51.9[/C][C]51.8770639993234[/C][C]51.96625[/C][C]0.99828377070355[/C][C]1.00044212218095[/C][/ROW]
[ROW][C]28[/C][C]51.94[/C][C]52.028390421703[/C][C]52.0633333333333[/C][C]0.999328838370632[/C][C]0.998301111739445[/C][/ROW]
[ROW][C]29[/C][C]52.22[/C][C]52.2482055777241[/C][C]52.1554166666667[/C][C]1.00177908483889[/C][C]0.99946016179097[/C][/ROW]
[ROW][C]30[/C][C]52.27[/C][C]52.2623282275202[/C][C]52.2304166666667[/C][C]1.00061097657055[/C][C]1.00014679354594[/C][/ROW]
[ROW][C]31[/C][C]52.27[/C][C]52.2048154097838[/C][C]52.30125[/C][C]0.998156170450683[/C][C]1.00124863175369[/C][/ROW]
[ROW][C]32[/C][C]52.28[/C][C]52.2078156500983[/C][C]52.3704166666667[/C][C]0.99689517428125[/C][C]1.00138263493699[/C][/ROW]
[ROW][C]33[/C][C]52.53[/C][C]52.3946654108005[/C][C]52.4370833333333[/C][C]0.999191070138986[/C][C]1.00258298412898[/C][/ROW]
[ROW][C]34[/C][C]52.73[/C][C]52.5603150674383[/C][C]52.5029166666667[/C][C]1.00109324213617[/C][C]1.00322838499625[/C][/ROW]
[ROW][C]35[/C][C]52.72[/C][C]52.7190262066735[/C][C]52.5616666666667[/C][C]1.00299380803514[/C][C]1.00001847138304[/C][/ROW]
[ROW][C]36[/C][C]52.67[/C][C]52.7157374907431[/C][C]52.6108333333333[/C][C]1.0019939649453[/C][C]0.999132375018919[/C][/ROW]
[ROW][C]37[/C][C]52.67[/C][C]52.6841842318685[/C][C]52.6575[/C][C]1.00050675083072[/C][C]0.999730768691301[/C][/ROW]
[ROW][C]38[/C][C]52.65[/C][C]52.6602719328448[/C][C]52.7041666666667[/C][C]0.999167148698138[/C][C]0.999804939616379[/C][/ROW]
[ROW][C]39[/C][C]52.69[/C][C]52.6490701153341[/C][C]52.7395833333333[/C][C]0.99828377070355[/C][C]1.00077740945047[/C][/ROW]
[ROW][C]40[/C][C]52.73[/C][C]52.7312517046904[/C][C]52.7666666666667[/C][C]0.999328838370632[/C][C]0.99997626256442[/C][/ROW]
[ROW][C]41[/C][C]52.84[/C][C]52.9039534703416[/C][C]52.81[/C][C]1.00177908483889[/C][C]0.998791140053882[/C][/ROW]
[ROW][C]42[/C][C]52.83[/C][C]52.9023023312851[/C][C]52.87[/C][C]1.00061097657055[/C][C]0.99863328573429[/C][/ROW]
[ROW][C]43[/C][C]52.83[/C][C]52.8357332891895[/C][C]52.9333333333333[/C][C]0.998156170450683[/C][C]0.999891488414514[/C][/ROW]
[ROW][C]44[/C][C]52.84[/C][C]52.8325366259813[/C][C]52.9970833333333[/C][C]0.99689517428125[/C][C]1.00014126472994[/C][/ROW]
[ROW][C]45[/C][C]52.82[/C][C]53.024155784988[/C][C]53.0670833333333[/C][C]0.999191070138986[/C][C]0.996149758879408[/C][/ROW]
[ROW][C]46[/C][C]53.09[/C][C]53.2031003533266[/C][C]53.145[/C][C]1.00109324213617[/C][C]0.99787417739614[/C][/ROW]
[ROW][C]47[/C][C]53.4[/C][C]53.3947932848372[/C][C]53.2354166666667[/C][C]1.00299380803514[/C][C]1.00009751353723[/C][/ROW]
[ROW][C]48[/C][C]53.43[/C][C]53.4426006128138[/C][C]53.33625[/C][C]1.0019939649453[/C][C]0.999764221563522[/C][/ROW]
[ROW][C]49[/C][C]53.43[/C][C]53.4641626197036[/C][C]53.4370833333333[/C][C]1.00050675083072[/C][C]0.99936101833397[/C][/ROW]
[ROW][C]50[/C][C]53.42[/C][C]53.4945765020078[/C][C]53.5391666666667[/C][C]0.999167148698138[/C][C]0.998605905366781[/C][/ROW]
[ROW][C]51[/C][C]53.6[/C][C]53.5500212183941[/C][C]53.6420833333333[/C][C]0.99828377070355[/C][C]1.00093331021107[/C][/ROW]
[ROW][C]52[/C][C]53.69[/C][C]53.7114267403256[/C][C]53.7475[/C][C]0.999328838370632[/C][C]0.999601076686546[/C][/ROW]
[ROW][C]53[/C][C]54.05[/C][C]53.9382903754377[/C][C]53.8425[/C][C]1.00177908483889[/C][C]1.0020710635021[/C][/ROW]
[ROW][C]54[/C][C]54.04[/C][C]53.9591976752877[/C][C]53.92625[/C][C]1.00061097657055[/C][C]1.0014974708334[/C][/ROW]
[ROW][C]55[/C][C]54.04[/C][C]NA[/C][C]NA[/C][C]0.998156170450683[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]54.08[/C][C]NA[/C][C]NA[/C][C]0.99689517428125[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]54.05[/C][C]NA[/C][C]NA[/C][C]0.999191070138986[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]54.39[/C][C]NA[/C][C]NA[/C][C]1.00109324213617[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]54.38[/C][C]NA[/C][C]NA[/C][C]1.00299380803514[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]54.46[/C][C]NA[/C][C]NA[/C][C]1.0019939649453[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155064&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155064&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
149.98NANA1.00050675083072NA
250.12NANA0.999167148698138NA
350.37NANA0.99828377070355NA
450.39NANA0.999328838370632NA
550.34NANA1.00177908483889NA
650.32NANA1.00061097657055NA
750.3250.421858950316350.5150.9981561704506830.997979865232326
850.3250.440403580695550.59750.996895174281250.997612953661187
950.6750.617770624403350.658750.9991910701389861.00103183871894
1050.8650.771278019304150.71583333333331.001093242136171.0017474836986
1150.9550.942473424191350.79041666666671.002993808035141.00014774657183
1251.0250.972267991738350.87083333333331.00199396494531.00093643092886
1351.0250.978320221702150.95251.000506750830721.00081759811066
1451.0650.991662794518951.03416666666660.9991671486981381.00134016428836
1550.951.020203762802851.10791666666670.998283770703550.99764399681033
1651.2351.131909176281451.166250.9993288383706321.0019183876624
1751.2951.31696843677651.22583333333331.001779084838890.999474473305858
1851.351.324672358225551.29333333333331.000610976570550.999519288539178
1951.351.265300914347151.360.9981561704506831.00067685325228
2051.351.266165083391851.42583333333330.996895174281251.00065998532469
2151.4651.458340112157851.50.9991910701389861.00003225692548
2251.4751.627629863514851.571251.001093242136170.996946792561822
2351.7751.794182332847851.63958333333331.002993808035140.999533107160716
2451.8251.821875374514851.718751.00199396494530.999963811141507
2551.8251.825832815218351.79958333333331.000506750830720.999887453516877
2651.8451.8376243137551.88083333333330.9991671486981381.00004582938129
2751.951.877063999323451.966250.998283770703551.00044212218095
2851.9452.02839042170352.06333333333330.9993288383706320.998301111739445
2952.2252.248205577724152.15541666666671.001779084838890.99946016179097
3052.2752.262328227520252.23041666666671.000610976570551.00014679354594
3152.2752.204815409783852.301250.9981561704506831.00124863175369
3252.2852.207815650098352.37041666666670.996895174281251.00138263493699
3352.5352.394665410800552.43708333333330.9991910701389861.00258298412898
3452.7352.560315067438352.50291666666671.001093242136171.00322838499625
3552.7252.719026206673552.56166666666671.002993808035141.00001847138304
3652.6752.715737490743152.61083333333331.00199396494530.999132375018919
3752.6752.684184231868552.65751.000506750830720.999730768691301
3852.6552.660271932844852.70416666666670.9991671486981380.999804939616379
3952.6952.649070115334152.73958333333330.998283770703551.00077740945047
4052.7352.731251704690452.76666666666670.9993288383706320.99997626256442
4152.8452.903953470341652.811.001779084838890.998791140053882
4252.8352.902302331285152.871.000610976570550.99863328573429
4352.8352.835733289189552.93333333333330.9981561704506830.999891488414514
4452.8452.832536625981352.99708333333330.996895174281251.00014126472994
4552.8253.02415578498853.06708333333330.9991910701389860.996149758879408
4653.0953.203100353326653.1451.001093242136170.99787417739614
4753.453.394793284837253.23541666666671.002993808035141.00009751353723
4853.4353.442600612813853.336251.00199396494530.999764221563522
4953.4353.464162619703653.43708333333331.000506750830720.99936101833397
5053.4253.494576502007853.53916666666670.9991671486981380.998605905366781
5153.653.550021218394153.64208333333330.998283770703551.00093331021107
5253.6953.711426740325653.74750.9993288383706320.999601076686546
5354.0553.938290375437753.84251.001779084838891.0020710635021
5454.0453.959197675287753.926251.000610976570551.0014974708334
5554.04NANA0.998156170450683NA
5654.08NANA0.99689517428125NA
5754.05NANA0.999191070138986NA
5854.39NANA1.00109324213617NA
5954.38NANA1.00299380803514NA
6054.46NANA1.0019939649453NA



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')