Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 26 Nov 2011 05:20:06 -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/Nov/26/t1322302856u71es9hywqen4fs.htm/, Retrieved Sun, 05 Feb 2023 14:23:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147375, Retrieved Sun, 05 Feb 2023 14:23:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [WS8 Wine Sales Ti...] [2011-11-26 09:47:59] [9d4f280afcb4ecc352d7c6f913a0a151]
-    D  [Classical Decomposition] [WS8 Wine Sales Ti...] [2011-11-26 10:01:37] [9d4f280afcb4ecc352d7c6f913a0a151]
- R P       [Classical Decomposition] [WS8 Wine Sales Ti...] [2011-11-26 10:20:06] [2a6d487209befbc7c5ce02a41ecac161] [Current]
Feedback Forum

Post a new message
Dataseries X:
2564
2820
3508
3088
3299
2939
3320
3418
3604
3495
4163
4882
2211
3260
2992
2425
2707
3244
3965
3315
3333
3583
4021
4904
2252
2952
3573
3048
3059
2731
3563
3092
3478
3478
4308
5029
2075
3264
3308
3688
3136
2824
3644
4694
2914
3686
4358
5587
2265
3685
3754
3708
3210
3517
3905
3670
4221
4404
5086
5725




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147375&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147375&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147375&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'Herman Ole Andreas Wold' @ wold.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12564NANA0.634358889031499NA
22820NANA0.943282097489337NA
33508NANA0.975088275545786NA
43088NANA0.913016560064265NA
53299NANA0.859076422453378NA
62939NANA0.868544282340044NA
733203611.16299548913410.291666666671.058901510033830.919371405873174
834183595.168551397533413.916666666671.053092064753840.950720376843343
936043312.718102556773410.750.9712579645405751.08792836831435
1034953469.09989319483361.6251.03197111313571.00746594436673
1141634034.743648024353309.333333333331.219201344084711.03178798039337
1248824854.426722663353297.3751.472209476527041.00568002751137
1322112116.829181077743336.958333333330.6343588890314991.04448673504884
1432603168.995509936163359.541666666670.9432820974893371.02871714074018
1529923260.654564746963343.958333333330.9750882755457860.917607167698303
1624253046.127583227743336.333333333330.9130165600642650.796092722232737
1727072864.232382161433334.083333333330.8590764224533780.945104879359411
1832442891.45629460023329.083333333330.8685442823400441.12192600180683
1939653527.950985158953331.708333333331.058901510033831.12388182734953
2033153496.879958687183320.583333333331.053092064753840.947987931860418
2133333236.191068767343331.958333333330.9712579645405751.0299144670928
2235833490.255301014083382.1251.03197111313571.0265724684836
2340214173.021400465953422.751.219201344084710.963570423950143
2449045029.128913877893416.041666666671.472209476527040.975119167549553
2522522142.811463907653377.916666666670.6343588890314991.05095573639187
2629523161.763680522073351.8750.9432820974893370.933656116738163
2735733265.204976699513348.6250.9750882755457861.09426514583217
2830483058.871772711973350.291666666670.9130165600642650.996445822669339
2930592884.671242045643357.8750.8590764224533781.06043279920895
3027312931.373142242743375.041666666670.8685442823400440.931645296412369
3135633571.542430655343372.8751.058901510033830.997608195668623
3230923557.871540770843378.51.053092064753840.869058920359474
3334783283.297080047563380.458333333330.9712579645405751.05930103649031
3434783504.65989780163396.083333333331.03197111313570.992393014278413
3543084176.933004778233425.958333333331.219201344084711.03137876405292
3650295054.156474978853433.041666666671.472209476527040.995022616513083
3720752182.379599610993440.291666666670.6343588890314990.950797010918663
3832643311.313196394863510.416666666670.9432820974893370.985711651665456
3933083465.138701864543553.666666666670.9750882755457860.954651540563156
4036883231.013436640763538.833333333330.9130165600642651.14143753107829
4131363049.363351200143549.583333333330.8590764224533781.02841138913989
4228243104.973430675463574.916666666670.8685442823400440.909508587770963
4336443818.487086974483606.083333333331.058901510033830.954304654435082
4446943824.347711989593631.541666666671.053092064753841.22739885426317
4529143562.250461279983667.666666666670.9712579645405750.818022211428936
4636863804.963491724093687.083333333331.03197111313570.968734656197665
4743584500.0721610166836911.219201344084710.968428914929982
4855875480.974539048653722.958333333331.472209476527041.0193442717524
4922652386.90747808293762.708333333330.6343588890314990.948926600967034
5036853519.306898891263730.916666666670.9432820974893371.0470811741826
5137543649.471014620843742.708333333330.9750882755457861.02864222923278
5237083494.190460079283827.083333333330.9130165600642651.06119000734604
5332103339.516412883773887.333333333330.8590764224533780.961217015618161
5435173407.661113070973923.416666666670.8685442823400441.03208619733624
553905NANA1.05890151003383NA
563670NANA1.05309206475384NA
574221NANA0.971257964540575NA
584404NANA1.0319711131357NA
595086NANA1.21920134408471NA
605725NANA1.47220947652704NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2564 & NA & NA & 0.634358889031499 & NA \tabularnewline
2 & 2820 & NA & NA & 0.943282097489337 & NA \tabularnewline
3 & 3508 & NA & NA & 0.975088275545786 & NA \tabularnewline
4 & 3088 & NA & NA & 0.913016560064265 & NA \tabularnewline
5 & 3299 & NA & NA & 0.859076422453378 & NA \tabularnewline
6 & 2939 & NA & NA & 0.868544282340044 & NA \tabularnewline
7 & 3320 & 3611.1629954891 & 3410.29166666667 & 1.05890151003383 & 0.919371405873174 \tabularnewline
8 & 3418 & 3595.16855139753 & 3413.91666666667 & 1.05309206475384 & 0.950720376843343 \tabularnewline
9 & 3604 & 3312.71810255677 & 3410.75 & 0.971257964540575 & 1.08792836831435 \tabularnewline
10 & 3495 & 3469.0998931948 & 3361.625 & 1.0319711131357 & 1.00746594436673 \tabularnewline
11 & 4163 & 4034.74364802435 & 3309.33333333333 & 1.21920134408471 & 1.03178798039337 \tabularnewline
12 & 4882 & 4854.42672266335 & 3297.375 & 1.47220947652704 & 1.00568002751137 \tabularnewline
13 & 2211 & 2116.82918107774 & 3336.95833333333 & 0.634358889031499 & 1.04448673504884 \tabularnewline
14 & 3260 & 3168.99550993616 & 3359.54166666667 & 0.943282097489337 & 1.02871714074018 \tabularnewline
15 & 2992 & 3260.65456474696 & 3343.95833333333 & 0.975088275545786 & 0.917607167698303 \tabularnewline
16 & 2425 & 3046.12758322774 & 3336.33333333333 & 0.913016560064265 & 0.796092722232737 \tabularnewline
17 & 2707 & 2864.23238216143 & 3334.08333333333 & 0.859076422453378 & 0.945104879359411 \tabularnewline
18 & 3244 & 2891.4562946002 & 3329.08333333333 & 0.868544282340044 & 1.12192600180683 \tabularnewline
19 & 3965 & 3527.95098515895 & 3331.70833333333 & 1.05890151003383 & 1.12388182734953 \tabularnewline
20 & 3315 & 3496.87995868718 & 3320.58333333333 & 1.05309206475384 & 0.947987931860418 \tabularnewline
21 & 3333 & 3236.19106876734 & 3331.95833333333 & 0.971257964540575 & 1.0299144670928 \tabularnewline
22 & 3583 & 3490.25530101408 & 3382.125 & 1.0319711131357 & 1.0265724684836 \tabularnewline
23 & 4021 & 4173.02140046595 & 3422.75 & 1.21920134408471 & 0.963570423950143 \tabularnewline
24 & 4904 & 5029.12891387789 & 3416.04166666667 & 1.47220947652704 & 0.975119167549553 \tabularnewline
25 & 2252 & 2142.81146390765 & 3377.91666666667 & 0.634358889031499 & 1.05095573639187 \tabularnewline
26 & 2952 & 3161.76368052207 & 3351.875 & 0.943282097489337 & 0.933656116738163 \tabularnewline
27 & 3573 & 3265.20497669951 & 3348.625 & 0.975088275545786 & 1.09426514583217 \tabularnewline
28 & 3048 & 3058.87177271197 & 3350.29166666667 & 0.913016560064265 & 0.996445822669339 \tabularnewline
29 & 3059 & 2884.67124204564 & 3357.875 & 0.859076422453378 & 1.06043279920895 \tabularnewline
30 & 2731 & 2931.37314224274 & 3375.04166666667 & 0.868544282340044 & 0.931645296412369 \tabularnewline
31 & 3563 & 3571.54243065534 & 3372.875 & 1.05890151003383 & 0.997608195668623 \tabularnewline
32 & 3092 & 3557.87154077084 & 3378.5 & 1.05309206475384 & 0.869058920359474 \tabularnewline
33 & 3478 & 3283.29708004756 & 3380.45833333333 & 0.971257964540575 & 1.05930103649031 \tabularnewline
34 & 3478 & 3504.6598978016 & 3396.08333333333 & 1.0319711131357 & 0.992393014278413 \tabularnewline
35 & 4308 & 4176.93300477823 & 3425.95833333333 & 1.21920134408471 & 1.03137876405292 \tabularnewline
36 & 5029 & 5054.15647497885 & 3433.04166666667 & 1.47220947652704 & 0.995022616513083 \tabularnewline
37 & 2075 & 2182.37959961099 & 3440.29166666667 & 0.634358889031499 & 0.950797010918663 \tabularnewline
38 & 3264 & 3311.31319639486 & 3510.41666666667 & 0.943282097489337 & 0.985711651665456 \tabularnewline
39 & 3308 & 3465.13870186454 & 3553.66666666667 & 0.975088275545786 & 0.954651540563156 \tabularnewline
40 & 3688 & 3231.01343664076 & 3538.83333333333 & 0.913016560064265 & 1.14143753107829 \tabularnewline
41 & 3136 & 3049.36335120014 & 3549.58333333333 & 0.859076422453378 & 1.02841138913989 \tabularnewline
42 & 2824 & 3104.97343067546 & 3574.91666666667 & 0.868544282340044 & 0.909508587770963 \tabularnewline
43 & 3644 & 3818.48708697448 & 3606.08333333333 & 1.05890151003383 & 0.954304654435082 \tabularnewline
44 & 4694 & 3824.34771198959 & 3631.54166666667 & 1.05309206475384 & 1.22739885426317 \tabularnewline
45 & 2914 & 3562.25046127998 & 3667.66666666667 & 0.971257964540575 & 0.818022211428936 \tabularnewline
46 & 3686 & 3804.96349172409 & 3687.08333333333 & 1.0319711131357 & 0.968734656197665 \tabularnewline
47 & 4358 & 4500.07216101668 & 3691 & 1.21920134408471 & 0.968428914929982 \tabularnewline
48 & 5587 & 5480.97453904865 & 3722.95833333333 & 1.47220947652704 & 1.0193442717524 \tabularnewline
49 & 2265 & 2386.9074780829 & 3762.70833333333 & 0.634358889031499 & 0.948926600967034 \tabularnewline
50 & 3685 & 3519.30689889126 & 3730.91666666667 & 0.943282097489337 & 1.0470811741826 \tabularnewline
51 & 3754 & 3649.47101462084 & 3742.70833333333 & 0.975088275545786 & 1.02864222923278 \tabularnewline
52 & 3708 & 3494.19046007928 & 3827.08333333333 & 0.913016560064265 & 1.06119000734604 \tabularnewline
53 & 3210 & 3339.51641288377 & 3887.33333333333 & 0.859076422453378 & 0.961217015618161 \tabularnewline
54 & 3517 & 3407.66111307097 & 3923.41666666667 & 0.868544282340044 & 1.03208619733624 \tabularnewline
55 & 3905 & NA & NA & 1.05890151003383 & NA \tabularnewline
56 & 3670 & NA & NA & 1.05309206475384 & NA \tabularnewline
57 & 4221 & NA & NA & 0.971257964540575 & NA \tabularnewline
58 & 4404 & NA & NA & 1.0319711131357 & NA \tabularnewline
59 & 5086 & NA & NA & 1.21920134408471 & NA \tabularnewline
60 & 5725 & NA & NA & 1.47220947652704 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147375&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]2564[/C][C]NA[/C][C]NA[/C][C]0.634358889031499[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2820[/C][C]NA[/C][C]NA[/C][C]0.943282097489337[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]3508[/C][C]NA[/C][C]NA[/C][C]0.975088275545786[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]3088[/C][C]NA[/C][C]NA[/C][C]0.913016560064265[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]3299[/C][C]NA[/C][C]NA[/C][C]0.859076422453378[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2939[/C][C]NA[/C][C]NA[/C][C]0.868544282340044[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]3320[/C][C]3611.1629954891[/C][C]3410.29166666667[/C][C]1.05890151003383[/C][C]0.919371405873174[/C][/ROW]
[ROW][C]8[/C][C]3418[/C][C]3595.16855139753[/C][C]3413.91666666667[/C][C]1.05309206475384[/C][C]0.950720376843343[/C][/ROW]
[ROW][C]9[/C][C]3604[/C][C]3312.71810255677[/C][C]3410.75[/C][C]0.971257964540575[/C][C]1.08792836831435[/C][/ROW]
[ROW][C]10[/C][C]3495[/C][C]3469.0998931948[/C][C]3361.625[/C][C]1.0319711131357[/C][C]1.00746594436673[/C][/ROW]
[ROW][C]11[/C][C]4163[/C][C]4034.74364802435[/C][C]3309.33333333333[/C][C]1.21920134408471[/C][C]1.03178798039337[/C][/ROW]
[ROW][C]12[/C][C]4882[/C][C]4854.42672266335[/C][C]3297.375[/C][C]1.47220947652704[/C][C]1.00568002751137[/C][/ROW]
[ROW][C]13[/C][C]2211[/C][C]2116.82918107774[/C][C]3336.95833333333[/C][C]0.634358889031499[/C][C]1.04448673504884[/C][/ROW]
[ROW][C]14[/C][C]3260[/C][C]3168.99550993616[/C][C]3359.54166666667[/C][C]0.943282097489337[/C][C]1.02871714074018[/C][/ROW]
[ROW][C]15[/C][C]2992[/C][C]3260.65456474696[/C][C]3343.95833333333[/C][C]0.975088275545786[/C][C]0.917607167698303[/C][/ROW]
[ROW][C]16[/C][C]2425[/C][C]3046.12758322774[/C][C]3336.33333333333[/C][C]0.913016560064265[/C][C]0.796092722232737[/C][/ROW]
[ROW][C]17[/C][C]2707[/C][C]2864.23238216143[/C][C]3334.08333333333[/C][C]0.859076422453378[/C][C]0.945104879359411[/C][/ROW]
[ROW][C]18[/C][C]3244[/C][C]2891.4562946002[/C][C]3329.08333333333[/C][C]0.868544282340044[/C][C]1.12192600180683[/C][/ROW]
[ROW][C]19[/C][C]3965[/C][C]3527.95098515895[/C][C]3331.70833333333[/C][C]1.05890151003383[/C][C]1.12388182734953[/C][/ROW]
[ROW][C]20[/C][C]3315[/C][C]3496.87995868718[/C][C]3320.58333333333[/C][C]1.05309206475384[/C][C]0.947987931860418[/C][/ROW]
[ROW][C]21[/C][C]3333[/C][C]3236.19106876734[/C][C]3331.95833333333[/C][C]0.971257964540575[/C][C]1.0299144670928[/C][/ROW]
[ROW][C]22[/C][C]3583[/C][C]3490.25530101408[/C][C]3382.125[/C][C]1.0319711131357[/C][C]1.0265724684836[/C][/ROW]
[ROW][C]23[/C][C]4021[/C][C]4173.02140046595[/C][C]3422.75[/C][C]1.21920134408471[/C][C]0.963570423950143[/C][/ROW]
[ROW][C]24[/C][C]4904[/C][C]5029.12891387789[/C][C]3416.04166666667[/C][C]1.47220947652704[/C][C]0.975119167549553[/C][/ROW]
[ROW][C]25[/C][C]2252[/C][C]2142.81146390765[/C][C]3377.91666666667[/C][C]0.634358889031499[/C][C]1.05095573639187[/C][/ROW]
[ROW][C]26[/C][C]2952[/C][C]3161.76368052207[/C][C]3351.875[/C][C]0.943282097489337[/C][C]0.933656116738163[/C][/ROW]
[ROW][C]27[/C][C]3573[/C][C]3265.20497669951[/C][C]3348.625[/C][C]0.975088275545786[/C][C]1.09426514583217[/C][/ROW]
[ROW][C]28[/C][C]3048[/C][C]3058.87177271197[/C][C]3350.29166666667[/C][C]0.913016560064265[/C][C]0.996445822669339[/C][/ROW]
[ROW][C]29[/C][C]3059[/C][C]2884.67124204564[/C][C]3357.875[/C][C]0.859076422453378[/C][C]1.06043279920895[/C][/ROW]
[ROW][C]30[/C][C]2731[/C][C]2931.37314224274[/C][C]3375.04166666667[/C][C]0.868544282340044[/C][C]0.931645296412369[/C][/ROW]
[ROW][C]31[/C][C]3563[/C][C]3571.54243065534[/C][C]3372.875[/C][C]1.05890151003383[/C][C]0.997608195668623[/C][/ROW]
[ROW][C]32[/C][C]3092[/C][C]3557.87154077084[/C][C]3378.5[/C][C]1.05309206475384[/C][C]0.869058920359474[/C][/ROW]
[ROW][C]33[/C][C]3478[/C][C]3283.29708004756[/C][C]3380.45833333333[/C][C]0.971257964540575[/C][C]1.05930103649031[/C][/ROW]
[ROW][C]34[/C][C]3478[/C][C]3504.6598978016[/C][C]3396.08333333333[/C][C]1.0319711131357[/C][C]0.992393014278413[/C][/ROW]
[ROW][C]35[/C][C]4308[/C][C]4176.93300477823[/C][C]3425.95833333333[/C][C]1.21920134408471[/C][C]1.03137876405292[/C][/ROW]
[ROW][C]36[/C][C]5029[/C][C]5054.15647497885[/C][C]3433.04166666667[/C][C]1.47220947652704[/C][C]0.995022616513083[/C][/ROW]
[ROW][C]37[/C][C]2075[/C][C]2182.37959961099[/C][C]3440.29166666667[/C][C]0.634358889031499[/C][C]0.950797010918663[/C][/ROW]
[ROW][C]38[/C][C]3264[/C][C]3311.31319639486[/C][C]3510.41666666667[/C][C]0.943282097489337[/C][C]0.985711651665456[/C][/ROW]
[ROW][C]39[/C][C]3308[/C][C]3465.13870186454[/C][C]3553.66666666667[/C][C]0.975088275545786[/C][C]0.954651540563156[/C][/ROW]
[ROW][C]40[/C][C]3688[/C][C]3231.01343664076[/C][C]3538.83333333333[/C][C]0.913016560064265[/C][C]1.14143753107829[/C][/ROW]
[ROW][C]41[/C][C]3136[/C][C]3049.36335120014[/C][C]3549.58333333333[/C][C]0.859076422453378[/C][C]1.02841138913989[/C][/ROW]
[ROW][C]42[/C][C]2824[/C][C]3104.97343067546[/C][C]3574.91666666667[/C][C]0.868544282340044[/C][C]0.909508587770963[/C][/ROW]
[ROW][C]43[/C][C]3644[/C][C]3818.48708697448[/C][C]3606.08333333333[/C][C]1.05890151003383[/C][C]0.954304654435082[/C][/ROW]
[ROW][C]44[/C][C]4694[/C][C]3824.34771198959[/C][C]3631.54166666667[/C][C]1.05309206475384[/C][C]1.22739885426317[/C][/ROW]
[ROW][C]45[/C][C]2914[/C][C]3562.25046127998[/C][C]3667.66666666667[/C][C]0.971257964540575[/C][C]0.818022211428936[/C][/ROW]
[ROW][C]46[/C][C]3686[/C][C]3804.96349172409[/C][C]3687.08333333333[/C][C]1.0319711131357[/C][C]0.968734656197665[/C][/ROW]
[ROW][C]47[/C][C]4358[/C][C]4500.07216101668[/C][C]3691[/C][C]1.21920134408471[/C][C]0.968428914929982[/C][/ROW]
[ROW][C]48[/C][C]5587[/C][C]5480.97453904865[/C][C]3722.95833333333[/C][C]1.47220947652704[/C][C]1.0193442717524[/C][/ROW]
[ROW][C]49[/C][C]2265[/C][C]2386.9074780829[/C][C]3762.70833333333[/C][C]0.634358889031499[/C][C]0.948926600967034[/C][/ROW]
[ROW][C]50[/C][C]3685[/C][C]3519.30689889126[/C][C]3730.91666666667[/C][C]0.943282097489337[/C][C]1.0470811741826[/C][/ROW]
[ROW][C]51[/C][C]3754[/C][C]3649.47101462084[/C][C]3742.70833333333[/C][C]0.975088275545786[/C][C]1.02864222923278[/C][/ROW]
[ROW][C]52[/C][C]3708[/C][C]3494.19046007928[/C][C]3827.08333333333[/C][C]0.913016560064265[/C][C]1.06119000734604[/C][/ROW]
[ROW][C]53[/C][C]3210[/C][C]3339.51641288377[/C][C]3887.33333333333[/C][C]0.859076422453378[/C][C]0.961217015618161[/C][/ROW]
[ROW][C]54[/C][C]3517[/C][C]3407.66111307097[/C][C]3923.41666666667[/C][C]0.868544282340044[/C][C]1.03208619733624[/C][/ROW]
[ROW][C]55[/C][C]3905[/C][C]NA[/C][C]NA[/C][C]1.05890151003383[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]3670[/C][C]NA[/C][C]NA[/C][C]1.05309206475384[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]4221[/C][C]NA[/C][C]NA[/C][C]0.971257964540575[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]4404[/C][C]NA[/C][C]NA[/C][C]1.0319711131357[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]5086[/C][C]NA[/C][C]NA[/C][C]1.21920134408471[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]5725[/C][C]NA[/C][C]NA[/C][C]1.47220947652704[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147375&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147375&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
12564NANA0.634358889031499NA
22820NANA0.943282097489337NA
33508NANA0.975088275545786NA
43088NANA0.913016560064265NA
53299NANA0.859076422453378NA
62939NANA0.868544282340044NA
733203611.16299548913410.291666666671.058901510033830.919371405873174
834183595.168551397533413.916666666671.053092064753840.950720376843343
936043312.718102556773410.750.9712579645405751.08792836831435
1034953469.09989319483361.6251.03197111313571.00746594436673
1141634034.743648024353309.333333333331.219201344084711.03178798039337
1248824854.426722663353297.3751.472209476527041.00568002751137
1322112116.829181077743336.958333333330.6343588890314991.04448673504884
1432603168.995509936163359.541666666670.9432820974893371.02871714074018
1529923260.654564746963343.958333333330.9750882755457860.917607167698303
1624253046.127583227743336.333333333330.9130165600642650.796092722232737
1727072864.232382161433334.083333333330.8590764224533780.945104879359411
1832442891.45629460023329.083333333330.8685442823400441.12192600180683
1939653527.950985158953331.708333333331.058901510033831.12388182734953
2033153496.879958687183320.583333333331.053092064753840.947987931860418
2133333236.191068767343331.958333333330.9712579645405751.0299144670928
2235833490.255301014083382.1251.03197111313571.0265724684836
2340214173.021400465953422.751.219201344084710.963570423950143
2449045029.128913877893416.041666666671.472209476527040.975119167549553
2522522142.811463907653377.916666666670.6343588890314991.05095573639187
2629523161.763680522073351.8750.9432820974893370.933656116738163
2735733265.204976699513348.6250.9750882755457861.09426514583217
2830483058.871772711973350.291666666670.9130165600642650.996445822669339
2930592884.671242045643357.8750.8590764224533781.06043279920895
3027312931.373142242743375.041666666670.8685442823400440.931645296412369
3135633571.542430655343372.8751.058901510033830.997608195668623
3230923557.871540770843378.51.053092064753840.869058920359474
3334783283.297080047563380.458333333330.9712579645405751.05930103649031
3434783504.65989780163396.083333333331.03197111313570.992393014278413
3543084176.933004778233425.958333333331.219201344084711.03137876405292
3650295054.156474978853433.041666666671.472209476527040.995022616513083
3720752182.379599610993440.291666666670.6343588890314990.950797010918663
3832643311.313196394863510.416666666670.9432820974893370.985711651665456
3933083465.138701864543553.666666666670.9750882755457860.954651540563156
4036883231.013436640763538.833333333330.9130165600642651.14143753107829
4131363049.363351200143549.583333333330.8590764224533781.02841138913989
4228243104.973430675463574.916666666670.8685442823400440.909508587770963
4336443818.487086974483606.083333333331.058901510033830.954304654435082
4446943824.347711989593631.541666666671.053092064753841.22739885426317
4529143562.250461279983667.666666666670.9712579645405750.818022211428936
4636863804.963491724093687.083333333331.03197111313570.968734656197665
4743584500.0721610166836911.219201344084710.968428914929982
4855875480.974539048653722.958333333331.472209476527041.0193442717524
4922652386.90747808293762.708333333330.6343588890314990.948926600967034
5036853519.306898891263730.916666666670.9432820974893371.0470811741826
5137543649.471014620843742.708333333330.9750882755457861.02864222923278
5237083494.190460079283827.083333333330.9130165600642651.06119000734604
5332103339.516412883773887.333333333330.8590764224533780.961217015618161
5435173407.661113070973923.416666666670.8685442823400441.03208619733624
553905NANA1.05890151003383NA
563670NANA1.05309206475384NA
574221NANA0.971257964540575NA
584404NANA1.0319711131357NA
595086NANA1.21920134408471NA
605725NANA1.47220947652704NA



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')