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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 05 May 2012 13:28:02 -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/2012/May/05/t13362389144z805z1zrmignsu.htm/, Retrieved Wed, 01 May 2024 03:26:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166233, Retrieved Wed, 01 May 2024 03:26:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2012-05-05 17:28:02] [a24b2518071b11fdd7ee0fdaf33bbdcf] [Current]
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Dataseries X:
2,07
2,08
2,08
2,08
2,09
2,09
2,09
2,1
2,1
2,1
2,11
2,11
2,11
2,13
2,18
2,2
2,21
2,21
2,22
2,22
2,23
2,23
2,23
2,23
2,24
2,25
2,26
2,27
2,28
2,29
2,3
2,3
2,3
2,32
2,32
2,32
2,33
2,34
2,34
2,34
2,35
2,35
2,36
2,37
2,37
2,37
2,38
2,38
2,38
2,39
2,4
2,41
2,42
2,43
2,43
2,43
2,43
2,44
2,44
2,45




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'AstonUniversity' @ aston.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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166233&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166233&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166233&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'AstonUniversity' @ aston.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12.07NANA0.994249575483554NA
22.08NANA0.996724776755393NA
32.08NANA1.00162495129837NA
42.08NANA1.00298074724312NA
52.09NANA1.0042450743349NA
62.09NANA1.00330630074464NA
72.092.098459834791742.093333333333331.002448965664840.995968550528593
82.12.100878703114012.097083333333331.001809832599570.99958174495619
92.12.103328032949762.103333333333330.9999974800078090.998417729951
102.12.11066022465932.11250.9991291004304370.994949341189666
112.112.118873319575952.12250.9982913166435570.995812246303746
122.112.122246681527792.13250.9951918787938050.994229378877401
132.112.130593986129972.142916666666670.9942495754835540.990334157392713
142.132.146280685946612.153333333333330.9967247767553930.992414465613368
152.182.167265988371852.163751.001624951298371.00587561088324
162.22.181065216609112.174583333333331.002980747243121.0086814384305
172.212.194275487421752.1851.00424507433491.00716615241267
182.212.202257330134482.1951.003306300744641.00351578798698
192.222.210817656360012.205416666666671.002448965664841.00415336995956
202.222.219843620735212.215833333333331.001809832599571.00007044607256
212.232.224161061784032.224166666666670.9999974800078091.00262523174076
222.232.228474197751722.230416666666670.9991291004304371.00068468472725
232.232.232428956844152.236250.9982913166435570.998911966789937
242.232.231717788195112.24250.9951918787938050.99923028431095
252.242.236233003525092.249166666666670.9942495754835541.00168452771646
262.252.248444975564042.255833333333330.9967247767553931.00069159995146
272.262.265759108582862.262083333333331.001624951298370.997458199081693
282.272.275512570307842.268751.002980747243120.997577437989238
292.282.285912850454812.276251.00424507433490.997413352633441
302.292.291300764325572.283751.003306300744640.99943230310668
312.32.296861192579572.291251.002448965664841.00136656382657
322.32.302910352688262.298751.001809832599570.998736228405564
332.32.305827522651342.305833333333330.9999974800078090.997472697938552
342.322.310069740953542.312083333333330.9991291004304371.00429868365895
352.322.313956081036712.317916666666670.9982913166435571.00261194195206
362.322.312162465064272.323333333333330.9951918787938051.00338969906058
372.332.314944428250872.328333333333330.9942495754835541.00650364283712
382.342.32610644775292.333750.9967247767553931.00597287895424
392.342.343385042308482.339583333333331.001624951298370.998555490349487
402.342.351571943640442.344583333333331.002980747243120.995079060340154
412.352.359139053791732.349166666666671.00424507433490.996126106353484
422.352.361950249669672.354166666666671.003306300744640.994940515926895
432.362.364526497761952.358751.002448965664840.998085664184252
442.372.367193150280062.362916666666671.001809832599571.00118572906465
452.372.367494033918492.36750.9999974800078091.00105848886866
462.372.370850094563062.372916666666670.9991291004304370.999641438923107
472.382.374685469465862.378750.9982913166435571.00223799345323
482.382.373532630923222.3850.9951918787938051.00272478625005
492.382.377499297375052.391250.9942495754835541.00105182055268
502.392.388817048290422.396666666666670.9967247767553931.0004952039799
512.42.405569258034922.401666666666671.001624951298370.997684848184553
522.412.414258240343142.407083333333331.002980747243120.998236211739084
532.422.422741241832942.41251.00424507433490.998868537099378
542.432.425911026342142.417916666666671.003306300744641.00168554147842
552.43NANA1.00244896566484NA
562.43NANA1.00180983259957NA
572.43NANA0.999997480007809NA
582.44NANA0.999129100430437NA
592.44NANA0.998291316643557NA
602.45NANA0.995191878793805NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2.07 & NA & NA & 0.994249575483554 & NA \tabularnewline
2 & 2.08 & NA & NA & 0.996724776755393 & NA \tabularnewline
3 & 2.08 & NA & NA & 1.00162495129837 & NA \tabularnewline
4 & 2.08 & NA & NA & 1.00298074724312 & NA \tabularnewline
5 & 2.09 & NA & NA & 1.0042450743349 & NA \tabularnewline
6 & 2.09 & NA & NA & 1.00330630074464 & NA \tabularnewline
7 & 2.09 & 2.09845983479174 & 2.09333333333333 & 1.00244896566484 & 0.995968550528593 \tabularnewline
8 & 2.1 & 2.10087870311401 & 2.09708333333333 & 1.00180983259957 & 0.99958174495619 \tabularnewline
9 & 2.1 & 2.10332803294976 & 2.10333333333333 & 0.999997480007809 & 0.998417729951 \tabularnewline
10 & 2.1 & 2.1106602246593 & 2.1125 & 0.999129100430437 & 0.994949341189666 \tabularnewline
11 & 2.11 & 2.11887331957595 & 2.1225 & 0.998291316643557 & 0.995812246303746 \tabularnewline
12 & 2.11 & 2.12224668152779 & 2.1325 & 0.995191878793805 & 0.994229378877401 \tabularnewline
13 & 2.11 & 2.13059398612997 & 2.14291666666667 & 0.994249575483554 & 0.990334157392713 \tabularnewline
14 & 2.13 & 2.14628068594661 & 2.15333333333333 & 0.996724776755393 & 0.992414465613368 \tabularnewline
15 & 2.18 & 2.16726598837185 & 2.16375 & 1.00162495129837 & 1.00587561088324 \tabularnewline
16 & 2.2 & 2.18106521660911 & 2.17458333333333 & 1.00298074724312 & 1.0086814384305 \tabularnewline
17 & 2.21 & 2.19427548742175 & 2.185 & 1.0042450743349 & 1.00716615241267 \tabularnewline
18 & 2.21 & 2.20225733013448 & 2.195 & 1.00330630074464 & 1.00351578798698 \tabularnewline
19 & 2.22 & 2.21081765636001 & 2.20541666666667 & 1.00244896566484 & 1.00415336995956 \tabularnewline
20 & 2.22 & 2.21984362073521 & 2.21583333333333 & 1.00180983259957 & 1.00007044607256 \tabularnewline
21 & 2.23 & 2.22416106178403 & 2.22416666666667 & 0.999997480007809 & 1.00262523174076 \tabularnewline
22 & 2.23 & 2.22847419775172 & 2.23041666666667 & 0.999129100430437 & 1.00068468472725 \tabularnewline
23 & 2.23 & 2.23242895684415 & 2.23625 & 0.998291316643557 & 0.998911966789937 \tabularnewline
24 & 2.23 & 2.23171778819511 & 2.2425 & 0.995191878793805 & 0.99923028431095 \tabularnewline
25 & 2.24 & 2.23623300352509 & 2.24916666666667 & 0.994249575483554 & 1.00168452771646 \tabularnewline
26 & 2.25 & 2.24844497556404 & 2.25583333333333 & 0.996724776755393 & 1.00069159995146 \tabularnewline
27 & 2.26 & 2.26575910858286 & 2.26208333333333 & 1.00162495129837 & 0.997458199081693 \tabularnewline
28 & 2.27 & 2.27551257030784 & 2.26875 & 1.00298074724312 & 0.997577437989238 \tabularnewline
29 & 2.28 & 2.28591285045481 & 2.27625 & 1.0042450743349 & 0.997413352633441 \tabularnewline
30 & 2.29 & 2.29130076432557 & 2.28375 & 1.00330630074464 & 0.99943230310668 \tabularnewline
31 & 2.3 & 2.29686119257957 & 2.29125 & 1.00244896566484 & 1.00136656382657 \tabularnewline
32 & 2.3 & 2.30291035268826 & 2.29875 & 1.00180983259957 & 0.998736228405564 \tabularnewline
33 & 2.3 & 2.30582752265134 & 2.30583333333333 & 0.999997480007809 & 0.997472697938552 \tabularnewline
34 & 2.32 & 2.31006974095354 & 2.31208333333333 & 0.999129100430437 & 1.00429868365895 \tabularnewline
35 & 2.32 & 2.31395608103671 & 2.31791666666667 & 0.998291316643557 & 1.00261194195206 \tabularnewline
36 & 2.32 & 2.31216246506427 & 2.32333333333333 & 0.995191878793805 & 1.00338969906058 \tabularnewline
37 & 2.33 & 2.31494442825087 & 2.32833333333333 & 0.994249575483554 & 1.00650364283712 \tabularnewline
38 & 2.34 & 2.3261064477529 & 2.33375 & 0.996724776755393 & 1.00597287895424 \tabularnewline
39 & 2.34 & 2.34338504230848 & 2.33958333333333 & 1.00162495129837 & 0.998555490349487 \tabularnewline
40 & 2.34 & 2.35157194364044 & 2.34458333333333 & 1.00298074724312 & 0.995079060340154 \tabularnewline
41 & 2.35 & 2.35913905379173 & 2.34916666666667 & 1.0042450743349 & 0.996126106353484 \tabularnewline
42 & 2.35 & 2.36195024966967 & 2.35416666666667 & 1.00330630074464 & 0.994940515926895 \tabularnewline
43 & 2.36 & 2.36452649776195 & 2.35875 & 1.00244896566484 & 0.998085664184252 \tabularnewline
44 & 2.37 & 2.36719315028006 & 2.36291666666667 & 1.00180983259957 & 1.00118572906465 \tabularnewline
45 & 2.37 & 2.36749403391849 & 2.3675 & 0.999997480007809 & 1.00105848886866 \tabularnewline
46 & 2.37 & 2.37085009456306 & 2.37291666666667 & 0.999129100430437 & 0.999641438923107 \tabularnewline
47 & 2.38 & 2.37468546946586 & 2.37875 & 0.998291316643557 & 1.00223799345323 \tabularnewline
48 & 2.38 & 2.37353263092322 & 2.385 & 0.995191878793805 & 1.00272478625005 \tabularnewline
49 & 2.38 & 2.37749929737505 & 2.39125 & 0.994249575483554 & 1.00105182055268 \tabularnewline
50 & 2.39 & 2.38881704829042 & 2.39666666666667 & 0.996724776755393 & 1.0004952039799 \tabularnewline
51 & 2.4 & 2.40556925803492 & 2.40166666666667 & 1.00162495129837 & 0.997684848184553 \tabularnewline
52 & 2.41 & 2.41425824034314 & 2.40708333333333 & 1.00298074724312 & 0.998236211739084 \tabularnewline
53 & 2.42 & 2.42274124183294 & 2.4125 & 1.0042450743349 & 0.998868537099378 \tabularnewline
54 & 2.43 & 2.42591102634214 & 2.41791666666667 & 1.00330630074464 & 1.00168554147842 \tabularnewline
55 & 2.43 & NA & NA & 1.00244896566484 & NA \tabularnewline
56 & 2.43 & NA & NA & 1.00180983259957 & NA \tabularnewline
57 & 2.43 & NA & NA & 0.999997480007809 & NA \tabularnewline
58 & 2.44 & NA & NA & 0.999129100430437 & NA \tabularnewline
59 & 2.44 & NA & NA & 0.998291316643557 & NA \tabularnewline
60 & 2.45 & NA & NA & 0.995191878793805 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166233&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]2.07[/C][C]NA[/C][C]NA[/C][C]0.994249575483554[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2.08[/C][C]NA[/C][C]NA[/C][C]0.996724776755393[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]2.08[/C][C]NA[/C][C]NA[/C][C]1.00162495129837[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]2.08[/C][C]NA[/C][C]NA[/C][C]1.00298074724312[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]2.09[/C][C]NA[/C][C]NA[/C][C]1.0042450743349[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2.09[/C][C]NA[/C][C]NA[/C][C]1.00330630074464[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]2.09[/C][C]2.09845983479174[/C][C]2.09333333333333[/C][C]1.00244896566484[/C][C]0.995968550528593[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]2.10087870311401[/C][C]2.09708333333333[/C][C]1.00180983259957[/C][C]0.99958174495619[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.10332803294976[/C][C]2.10333333333333[/C][C]0.999997480007809[/C][C]0.998417729951[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.1106602246593[/C][C]2.1125[/C][C]0.999129100430437[/C][C]0.994949341189666[/C][/ROW]
[ROW][C]11[/C][C]2.11[/C][C]2.11887331957595[/C][C]2.1225[/C][C]0.998291316643557[/C][C]0.995812246303746[/C][/ROW]
[ROW][C]12[/C][C]2.11[/C][C]2.12224668152779[/C][C]2.1325[/C][C]0.995191878793805[/C][C]0.994229378877401[/C][/ROW]
[ROW][C]13[/C][C]2.11[/C][C]2.13059398612997[/C][C]2.14291666666667[/C][C]0.994249575483554[/C][C]0.990334157392713[/C][/ROW]
[ROW][C]14[/C][C]2.13[/C][C]2.14628068594661[/C][C]2.15333333333333[/C][C]0.996724776755393[/C][C]0.992414465613368[/C][/ROW]
[ROW][C]15[/C][C]2.18[/C][C]2.16726598837185[/C][C]2.16375[/C][C]1.00162495129837[/C][C]1.00587561088324[/C][/ROW]
[ROW][C]16[/C][C]2.2[/C][C]2.18106521660911[/C][C]2.17458333333333[/C][C]1.00298074724312[/C][C]1.0086814384305[/C][/ROW]
[ROW][C]17[/C][C]2.21[/C][C]2.19427548742175[/C][C]2.185[/C][C]1.0042450743349[/C][C]1.00716615241267[/C][/ROW]
[ROW][C]18[/C][C]2.21[/C][C]2.20225733013448[/C][C]2.195[/C][C]1.00330630074464[/C][C]1.00351578798698[/C][/ROW]
[ROW][C]19[/C][C]2.22[/C][C]2.21081765636001[/C][C]2.20541666666667[/C][C]1.00244896566484[/C][C]1.00415336995956[/C][/ROW]
[ROW][C]20[/C][C]2.22[/C][C]2.21984362073521[/C][C]2.21583333333333[/C][C]1.00180983259957[/C][C]1.00007044607256[/C][/ROW]
[ROW][C]21[/C][C]2.23[/C][C]2.22416106178403[/C][C]2.22416666666667[/C][C]0.999997480007809[/C][C]1.00262523174076[/C][/ROW]
[ROW][C]22[/C][C]2.23[/C][C]2.22847419775172[/C][C]2.23041666666667[/C][C]0.999129100430437[/C][C]1.00068468472725[/C][/ROW]
[ROW][C]23[/C][C]2.23[/C][C]2.23242895684415[/C][C]2.23625[/C][C]0.998291316643557[/C][C]0.998911966789937[/C][/ROW]
[ROW][C]24[/C][C]2.23[/C][C]2.23171778819511[/C][C]2.2425[/C][C]0.995191878793805[/C][C]0.99923028431095[/C][/ROW]
[ROW][C]25[/C][C]2.24[/C][C]2.23623300352509[/C][C]2.24916666666667[/C][C]0.994249575483554[/C][C]1.00168452771646[/C][/ROW]
[ROW][C]26[/C][C]2.25[/C][C]2.24844497556404[/C][C]2.25583333333333[/C][C]0.996724776755393[/C][C]1.00069159995146[/C][/ROW]
[ROW][C]27[/C][C]2.26[/C][C]2.26575910858286[/C][C]2.26208333333333[/C][C]1.00162495129837[/C][C]0.997458199081693[/C][/ROW]
[ROW][C]28[/C][C]2.27[/C][C]2.27551257030784[/C][C]2.26875[/C][C]1.00298074724312[/C][C]0.997577437989238[/C][/ROW]
[ROW][C]29[/C][C]2.28[/C][C]2.28591285045481[/C][C]2.27625[/C][C]1.0042450743349[/C][C]0.997413352633441[/C][/ROW]
[ROW][C]30[/C][C]2.29[/C][C]2.29130076432557[/C][C]2.28375[/C][C]1.00330630074464[/C][C]0.99943230310668[/C][/ROW]
[ROW][C]31[/C][C]2.3[/C][C]2.29686119257957[/C][C]2.29125[/C][C]1.00244896566484[/C][C]1.00136656382657[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.30291035268826[/C][C]2.29875[/C][C]1.00180983259957[/C][C]0.998736228405564[/C][/ROW]
[ROW][C]33[/C][C]2.3[/C][C]2.30582752265134[/C][C]2.30583333333333[/C][C]0.999997480007809[/C][C]0.997472697938552[/C][/ROW]
[ROW][C]34[/C][C]2.32[/C][C]2.31006974095354[/C][C]2.31208333333333[/C][C]0.999129100430437[/C][C]1.00429868365895[/C][/ROW]
[ROW][C]35[/C][C]2.32[/C][C]2.31395608103671[/C][C]2.31791666666667[/C][C]0.998291316643557[/C][C]1.00261194195206[/C][/ROW]
[ROW][C]36[/C][C]2.32[/C][C]2.31216246506427[/C][C]2.32333333333333[/C][C]0.995191878793805[/C][C]1.00338969906058[/C][/ROW]
[ROW][C]37[/C][C]2.33[/C][C]2.31494442825087[/C][C]2.32833333333333[/C][C]0.994249575483554[/C][C]1.00650364283712[/C][/ROW]
[ROW][C]38[/C][C]2.34[/C][C]2.3261064477529[/C][C]2.33375[/C][C]0.996724776755393[/C][C]1.00597287895424[/C][/ROW]
[ROW][C]39[/C][C]2.34[/C][C]2.34338504230848[/C][C]2.33958333333333[/C][C]1.00162495129837[/C][C]0.998555490349487[/C][/ROW]
[ROW][C]40[/C][C]2.34[/C][C]2.35157194364044[/C][C]2.34458333333333[/C][C]1.00298074724312[/C][C]0.995079060340154[/C][/ROW]
[ROW][C]41[/C][C]2.35[/C][C]2.35913905379173[/C][C]2.34916666666667[/C][C]1.0042450743349[/C][C]0.996126106353484[/C][/ROW]
[ROW][C]42[/C][C]2.35[/C][C]2.36195024966967[/C][C]2.35416666666667[/C][C]1.00330630074464[/C][C]0.994940515926895[/C][/ROW]
[ROW][C]43[/C][C]2.36[/C][C]2.36452649776195[/C][C]2.35875[/C][C]1.00244896566484[/C][C]0.998085664184252[/C][/ROW]
[ROW][C]44[/C][C]2.37[/C][C]2.36719315028006[/C][C]2.36291666666667[/C][C]1.00180983259957[/C][C]1.00118572906465[/C][/ROW]
[ROW][C]45[/C][C]2.37[/C][C]2.36749403391849[/C][C]2.3675[/C][C]0.999997480007809[/C][C]1.00105848886866[/C][/ROW]
[ROW][C]46[/C][C]2.37[/C][C]2.37085009456306[/C][C]2.37291666666667[/C][C]0.999129100430437[/C][C]0.999641438923107[/C][/ROW]
[ROW][C]47[/C][C]2.38[/C][C]2.37468546946586[/C][C]2.37875[/C][C]0.998291316643557[/C][C]1.00223799345323[/C][/ROW]
[ROW][C]48[/C][C]2.38[/C][C]2.37353263092322[/C][C]2.385[/C][C]0.995191878793805[/C][C]1.00272478625005[/C][/ROW]
[ROW][C]49[/C][C]2.38[/C][C]2.37749929737505[/C][C]2.39125[/C][C]0.994249575483554[/C][C]1.00105182055268[/C][/ROW]
[ROW][C]50[/C][C]2.39[/C][C]2.38881704829042[/C][C]2.39666666666667[/C][C]0.996724776755393[/C][C]1.0004952039799[/C][/ROW]
[ROW][C]51[/C][C]2.4[/C][C]2.40556925803492[/C][C]2.40166666666667[/C][C]1.00162495129837[/C][C]0.997684848184553[/C][/ROW]
[ROW][C]52[/C][C]2.41[/C][C]2.41425824034314[/C][C]2.40708333333333[/C][C]1.00298074724312[/C][C]0.998236211739084[/C][/ROW]
[ROW][C]53[/C][C]2.42[/C][C]2.42274124183294[/C][C]2.4125[/C][C]1.0042450743349[/C][C]0.998868537099378[/C][/ROW]
[ROW][C]54[/C][C]2.43[/C][C]2.42591102634214[/C][C]2.41791666666667[/C][C]1.00330630074464[/C][C]1.00168554147842[/C][/ROW]
[ROW][C]55[/C][C]2.43[/C][C]NA[/C][C]NA[/C][C]1.00244896566484[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]2.43[/C][C]NA[/C][C]NA[/C][C]1.00180983259957[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]2.43[/C][C]NA[/C][C]NA[/C][C]0.999997480007809[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]2.44[/C][C]NA[/C][C]NA[/C][C]0.999129100430437[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]2.44[/C][C]NA[/C][C]NA[/C][C]0.998291316643557[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]2.45[/C][C]NA[/C][C]NA[/C][C]0.995191878793805[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166233&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166233&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
12.07NANA0.994249575483554NA
22.08NANA0.996724776755393NA
32.08NANA1.00162495129837NA
42.08NANA1.00298074724312NA
52.09NANA1.0042450743349NA
62.09NANA1.00330630074464NA
72.092.098459834791742.093333333333331.002448965664840.995968550528593
82.12.100878703114012.097083333333331.001809832599570.99958174495619
92.12.103328032949762.103333333333330.9999974800078090.998417729951
102.12.11066022465932.11250.9991291004304370.994949341189666
112.112.118873319575952.12250.9982913166435570.995812246303746
122.112.122246681527792.13250.9951918787938050.994229378877401
132.112.130593986129972.142916666666670.9942495754835540.990334157392713
142.132.146280685946612.153333333333330.9967247767553930.992414465613368
152.182.167265988371852.163751.001624951298371.00587561088324
162.22.181065216609112.174583333333331.002980747243121.0086814384305
172.212.194275487421752.1851.00424507433491.00716615241267
182.212.202257330134482.1951.003306300744641.00351578798698
192.222.210817656360012.205416666666671.002448965664841.00415336995956
202.222.219843620735212.215833333333331.001809832599571.00007044607256
212.232.224161061784032.224166666666670.9999974800078091.00262523174076
222.232.228474197751722.230416666666670.9991291004304371.00068468472725
232.232.232428956844152.236250.9982913166435570.998911966789937
242.232.231717788195112.24250.9951918787938050.99923028431095
252.242.236233003525092.249166666666670.9942495754835541.00168452771646
262.252.248444975564042.255833333333330.9967247767553931.00069159995146
272.262.265759108582862.262083333333331.001624951298370.997458199081693
282.272.275512570307842.268751.002980747243120.997577437989238
292.282.285912850454812.276251.00424507433490.997413352633441
302.292.291300764325572.283751.003306300744640.99943230310668
312.32.296861192579572.291251.002448965664841.00136656382657
322.32.302910352688262.298751.001809832599570.998736228405564
332.32.305827522651342.305833333333330.9999974800078090.997472697938552
342.322.310069740953542.312083333333330.9991291004304371.00429868365895
352.322.313956081036712.317916666666670.9982913166435571.00261194195206
362.322.312162465064272.323333333333330.9951918787938051.00338969906058
372.332.314944428250872.328333333333330.9942495754835541.00650364283712
382.342.32610644775292.333750.9967247767553931.00597287895424
392.342.343385042308482.339583333333331.001624951298370.998555490349487
402.342.351571943640442.344583333333331.002980747243120.995079060340154
412.352.359139053791732.349166666666671.00424507433490.996126106353484
422.352.361950249669672.354166666666671.003306300744640.994940515926895
432.362.364526497761952.358751.002448965664840.998085664184252
442.372.367193150280062.362916666666671.001809832599571.00118572906465
452.372.367494033918492.36750.9999974800078091.00105848886866
462.372.370850094563062.372916666666670.9991291004304370.999641438923107
472.382.374685469465862.378750.9982913166435571.00223799345323
482.382.373532630923222.3850.9951918787938051.00272478625005
492.382.377499297375052.391250.9942495754835541.00105182055268
502.392.388817048290422.396666666666670.9967247767553931.0004952039799
512.42.405569258034922.401666666666671.001624951298370.997684848184553
522.412.414258240343142.407083333333331.002980747243120.998236211739084
532.422.422741241832942.41251.00424507433490.998868537099378
542.432.425911026342142.417916666666671.003306300744641.00168554147842
552.43NANA1.00244896566484NA
562.43NANA1.00180983259957NA
572.43NANA0.999997480007809NA
582.44NANA0.999129100430437NA
592.44NANA0.998291316643557NA
602.45NANA0.995191878793805NA



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')