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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 18 Dec 2011 07:43:39 -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/18/t13242123383kismyfteto4vu6.htm/, Retrieved Sun, 05 May 2024 11:08:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156785, Retrieved Sun, 05 May 2024 11:08:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W92
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2011-12-18 12:43:39] [76c30f62b7052b57088120e90a652e05] [Current]
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Dataseries X:
4,23
4,38
4,43
4,44
4,44
4,44
4,44
4,44
4,45
4,45
4,45
4,45
4,45
4,45
4,45
4,45
4,46
4,46
4,46
4,48
4,58
4,67
4,68
4,68
4,69
4,69
4,69
4,69
4,69
4,69
4,69
4,73
4,78
4,79
4,79
4,8
4,8
4,81
5,16
5,26
5,29
5,29
5,29
5,3
5,3
5,3
5,3
5,3
5,3
5,3
5,3
5,35
5,44
5,47
5,47
5,48
5,48
5,48
5,48
5,48
5,48
5,48
5,5
5,55
5,55
5,57
5,58
5,58
5,58
5,59
5,59
5,59
5,61
5,61
5,61
5,63
5,69
5,7
5,7
5,7
5,7
5,7
5,7
5,7




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=156785&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=156785&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156785&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
14.23NANA-0.0309317129629629NA
24.38NANA-0.0467650462962962NA
34.43NANA-0.00252893518518538NA
44.44NANA0.0167766203703699NA
54.44NANA0.0310821759259258NA
64.44NANA0.0237210648148146NA
74.444.434762731481484.429166666666670.005596064814815080.0052372685185178
84.444.442054398148154.441250.000804398148148626-0.00205439814814934
94.454.45573495370374.4450.0107349537037039-0.00573495370370392
104.454.45885995370374.446250.0126099537037038-0.00885995370370374
114.454.444832175925934.4475-0.002667824074074290.00516782407407401
124.454.43073495370374.44916666666667-0.01843171296296290.0192650462962956
134.454.419901620370374.45083333333333-0.03093171296296290.0300983796296288
144.454.406568287037044.45333333333333-0.04676504629629620.0434317129629624
154.454.457887731481484.46041666666667-0.00252893518518538-0.0078877314814827
164.454.491776620370374.4750.0167766203703699-0.0417766203703716
174.464.524832175925934.493750.0310821759259258-0.0648321759259272
184.464.536637731481484.512916666666670.0237210648148146-0.0766377314814823
194.464.538096064814824.53250.00559606481481508-0.0780960648148161
204.484.553304398148154.55250.000804398148148626-0.0733043981481485
214.584.58323495370374.57250.0107349537037039-0.00323495370370441
224.674.605109953703714.59250.01260995370370380.0648900462962949
234.684.609415509259264.61208333333333-0.002667824074074290.0705844907407398
244.684.612818287037044.63125-0.01843171296296290.0671817129629622
254.694.61948495370374.65041666666667-0.03093171296296290.0705150462962969
264.694.623651620370374.67041666666667-0.04676504629629620.0663483796296305
274.694.686637731481484.68916666666667-0.002528935185185380.00336226851851951
284.694.719276620370374.70250.0167766203703699-0.0292766203703687
294.694.743165509259264.712083333333330.0310821759259258-0.0531655092592578
304.694.745387731481484.721666666666670.0237210648148146-0.0553877314814804
314.694.736846064814814.731250.00559606481481508-0.0468460648148143
324.734.741637731481484.740833333333330.000804398148148626-0.0116377314814802
334.784.776151620370374.765416666666670.01073495370370390.00384837962963047
344.794.82135995370374.808750.0126099537037038-0.0313599537037037
354.794.854832175925934.8575-0.00266782407407429-0.0648321759259254
364.84.889068287037044.9075-0.0184317129629629-0.089068287037037
374.84.926568287037044.9575-0.0309317129629629-0.126568287037037
384.814.95948495370375.00625-0.0467650462962962-0.149484953703705
395.165.049137731481485.05166666666667-0.002528935185185380.110862268518518
405.265.11135995370375.094583333333330.01677662037036990.148640046296295
415.295.168165509259265.137083333333330.03108217592592580.12183449074074
425.295.202887731481485.179166666666670.02372106481481460.087112268518518
435.295.226429398148155.220833333333330.005596064814815080.0635706018518523
445.35.262887731481485.262083333333330.0008043981481486260.037112268518519
455.35.299068287037045.288333333333330.01073495370370390.000931712962963793
465.35.310526620370375.297916666666660.0126099537037038-0.0105266203703689
475.35.305248842592595.30791666666667-0.00266782407407429-0.00524884259259117
485.35.30323495370375.32166666666667-0.0184317129629629-0.00323495370370264
495.35.30573495370375.33666666666667-0.0309317129629629-0.00573495370370214
505.35.304901620370375.35166666666666-0.0467650462962962-0.00490162037036868
515.35.364137731481485.36666666666667-0.00252893518518538-0.0641377314814804
525.355.398443287037045.381666666666670.0167766203703699-0.0484432870370366
535.445.427748842592595.396666666666670.03108217592592580.0122511574074089
545.475.435387731481485.411666666666670.02372106481481460.0346122685185186
555.475.432262731481485.426666666666670.005596064814815080.0377372685185184
565.485.442471064814825.441666666666670.0008043981481486260.0375289351851853
575.485.46823495370375.45750.01073495370370390.011765046296297
585.485.486776620370375.474166666666670.0126099537037038-0.00677662037036963
595.485.484415509259265.48708333333333-0.00266782407407429-0.0044155092592586
605.485.477401620370375.49583333333333-0.01843171296296290.00259837962963072
615.485.473651620370375.50458333333333-0.03093171296296290.00634837962962997
625.485.466568287037045.51333333333333-0.04676504629629620.0134317129629631
635.55.519137731481485.52166666666667-0.00252893518518538-0.0191377314814822
645.555.547193287037045.530416666666670.01677662037036990.00280671296296209
655.555.570665509259265.539583333333330.0310821759259258-0.0206655092592598
665.575.572471064814825.548750.0237210648148146-0.00247106481481474
675.585.564346064814825.558750.005596064814815080.0156539351851848
685.585.570387731481485.569583333333330.0008043981481486260.00961226851851826
695.585.590318287037045.579583333333330.0107349537037039-0.0103182870370375
705.595.60010995370375.58750.0126099537037038-0.0101099537037044
715.595.593998842592595.59666666666667-0.00266782407407429-0.00399884259259231
725.595.58948495370375.60791666666667-0.01843171296296290.000515046296296617
735.615.587401620370375.61833333333333-0.03093171296296290.0225983796296303
745.615.581568287037045.62833333333333-0.04676504629629620.0284317129629628
755.615.635804398148155.63833333333333-0.00252893518518538-0.0258043981481482
765.635.664693287037045.647916666666670.0167766203703699-0.0346932870370376
775.695.688165509259265.657083333333330.03108217592592580.00183449074074016
785.75.689971064814825.666250.02372106481481460.0100289351851837
795.7NANA0.00559606481481508NA
805.7NANA0.000804398148148626NA
815.7NANA0.0107349537037039NA
825.7NANA0.0126099537037038NA
835.7NANA-0.00266782407407429NA
845.7NANA-0.0184317129629629NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 4.23 & NA & NA & -0.0309317129629629 & NA \tabularnewline
2 & 4.38 & NA & NA & -0.0467650462962962 & NA \tabularnewline
3 & 4.43 & NA & NA & -0.00252893518518538 & NA \tabularnewline
4 & 4.44 & NA & NA & 0.0167766203703699 & NA \tabularnewline
5 & 4.44 & NA & NA & 0.0310821759259258 & NA \tabularnewline
6 & 4.44 & NA & NA & 0.0237210648148146 & NA \tabularnewline
7 & 4.44 & 4.43476273148148 & 4.42916666666667 & 0.00559606481481508 & 0.0052372685185178 \tabularnewline
8 & 4.44 & 4.44205439814815 & 4.44125 & 0.000804398148148626 & -0.00205439814814934 \tabularnewline
9 & 4.45 & 4.4557349537037 & 4.445 & 0.0107349537037039 & -0.00573495370370392 \tabularnewline
10 & 4.45 & 4.4588599537037 & 4.44625 & 0.0126099537037038 & -0.00885995370370374 \tabularnewline
11 & 4.45 & 4.44483217592593 & 4.4475 & -0.00266782407407429 & 0.00516782407407401 \tabularnewline
12 & 4.45 & 4.4307349537037 & 4.44916666666667 & -0.0184317129629629 & 0.0192650462962956 \tabularnewline
13 & 4.45 & 4.41990162037037 & 4.45083333333333 & -0.0309317129629629 & 0.0300983796296288 \tabularnewline
14 & 4.45 & 4.40656828703704 & 4.45333333333333 & -0.0467650462962962 & 0.0434317129629624 \tabularnewline
15 & 4.45 & 4.45788773148148 & 4.46041666666667 & -0.00252893518518538 & -0.0078877314814827 \tabularnewline
16 & 4.45 & 4.49177662037037 & 4.475 & 0.0167766203703699 & -0.0417766203703716 \tabularnewline
17 & 4.46 & 4.52483217592593 & 4.49375 & 0.0310821759259258 & -0.0648321759259272 \tabularnewline
18 & 4.46 & 4.53663773148148 & 4.51291666666667 & 0.0237210648148146 & -0.0766377314814823 \tabularnewline
19 & 4.46 & 4.53809606481482 & 4.5325 & 0.00559606481481508 & -0.0780960648148161 \tabularnewline
20 & 4.48 & 4.55330439814815 & 4.5525 & 0.000804398148148626 & -0.0733043981481485 \tabularnewline
21 & 4.58 & 4.5832349537037 & 4.5725 & 0.0107349537037039 & -0.00323495370370441 \tabularnewline
22 & 4.67 & 4.60510995370371 & 4.5925 & 0.0126099537037038 & 0.0648900462962949 \tabularnewline
23 & 4.68 & 4.60941550925926 & 4.61208333333333 & -0.00266782407407429 & 0.0705844907407398 \tabularnewline
24 & 4.68 & 4.61281828703704 & 4.63125 & -0.0184317129629629 & 0.0671817129629622 \tabularnewline
25 & 4.69 & 4.6194849537037 & 4.65041666666667 & -0.0309317129629629 & 0.0705150462962969 \tabularnewline
26 & 4.69 & 4.62365162037037 & 4.67041666666667 & -0.0467650462962962 & 0.0663483796296305 \tabularnewline
27 & 4.69 & 4.68663773148148 & 4.68916666666667 & -0.00252893518518538 & 0.00336226851851951 \tabularnewline
28 & 4.69 & 4.71927662037037 & 4.7025 & 0.0167766203703699 & -0.0292766203703687 \tabularnewline
29 & 4.69 & 4.74316550925926 & 4.71208333333333 & 0.0310821759259258 & -0.0531655092592578 \tabularnewline
30 & 4.69 & 4.74538773148148 & 4.72166666666667 & 0.0237210648148146 & -0.0553877314814804 \tabularnewline
31 & 4.69 & 4.73684606481481 & 4.73125 & 0.00559606481481508 & -0.0468460648148143 \tabularnewline
32 & 4.73 & 4.74163773148148 & 4.74083333333333 & 0.000804398148148626 & -0.0116377314814802 \tabularnewline
33 & 4.78 & 4.77615162037037 & 4.76541666666667 & 0.0107349537037039 & 0.00384837962963047 \tabularnewline
34 & 4.79 & 4.8213599537037 & 4.80875 & 0.0126099537037038 & -0.0313599537037037 \tabularnewline
35 & 4.79 & 4.85483217592593 & 4.8575 & -0.00266782407407429 & -0.0648321759259254 \tabularnewline
36 & 4.8 & 4.88906828703704 & 4.9075 & -0.0184317129629629 & -0.089068287037037 \tabularnewline
37 & 4.8 & 4.92656828703704 & 4.9575 & -0.0309317129629629 & -0.126568287037037 \tabularnewline
38 & 4.81 & 4.9594849537037 & 5.00625 & -0.0467650462962962 & -0.149484953703705 \tabularnewline
39 & 5.16 & 5.04913773148148 & 5.05166666666667 & -0.00252893518518538 & 0.110862268518518 \tabularnewline
40 & 5.26 & 5.1113599537037 & 5.09458333333333 & 0.0167766203703699 & 0.148640046296295 \tabularnewline
41 & 5.29 & 5.16816550925926 & 5.13708333333333 & 0.0310821759259258 & 0.12183449074074 \tabularnewline
42 & 5.29 & 5.20288773148148 & 5.17916666666667 & 0.0237210648148146 & 0.087112268518518 \tabularnewline
43 & 5.29 & 5.22642939814815 & 5.22083333333333 & 0.00559606481481508 & 0.0635706018518523 \tabularnewline
44 & 5.3 & 5.26288773148148 & 5.26208333333333 & 0.000804398148148626 & 0.037112268518519 \tabularnewline
45 & 5.3 & 5.29906828703704 & 5.28833333333333 & 0.0107349537037039 & 0.000931712962963793 \tabularnewline
46 & 5.3 & 5.31052662037037 & 5.29791666666666 & 0.0126099537037038 & -0.0105266203703689 \tabularnewline
47 & 5.3 & 5.30524884259259 & 5.30791666666667 & -0.00266782407407429 & -0.00524884259259117 \tabularnewline
48 & 5.3 & 5.3032349537037 & 5.32166666666667 & -0.0184317129629629 & -0.00323495370370264 \tabularnewline
49 & 5.3 & 5.3057349537037 & 5.33666666666667 & -0.0309317129629629 & -0.00573495370370214 \tabularnewline
50 & 5.3 & 5.30490162037037 & 5.35166666666666 & -0.0467650462962962 & -0.00490162037036868 \tabularnewline
51 & 5.3 & 5.36413773148148 & 5.36666666666667 & -0.00252893518518538 & -0.0641377314814804 \tabularnewline
52 & 5.35 & 5.39844328703704 & 5.38166666666667 & 0.0167766203703699 & -0.0484432870370366 \tabularnewline
53 & 5.44 & 5.42774884259259 & 5.39666666666667 & 0.0310821759259258 & 0.0122511574074089 \tabularnewline
54 & 5.47 & 5.43538773148148 & 5.41166666666667 & 0.0237210648148146 & 0.0346122685185186 \tabularnewline
55 & 5.47 & 5.43226273148148 & 5.42666666666667 & 0.00559606481481508 & 0.0377372685185184 \tabularnewline
56 & 5.48 & 5.44247106481482 & 5.44166666666667 & 0.000804398148148626 & 0.0375289351851853 \tabularnewline
57 & 5.48 & 5.4682349537037 & 5.4575 & 0.0107349537037039 & 0.011765046296297 \tabularnewline
58 & 5.48 & 5.48677662037037 & 5.47416666666667 & 0.0126099537037038 & -0.00677662037036963 \tabularnewline
59 & 5.48 & 5.48441550925926 & 5.48708333333333 & -0.00266782407407429 & -0.0044155092592586 \tabularnewline
60 & 5.48 & 5.47740162037037 & 5.49583333333333 & -0.0184317129629629 & 0.00259837962963072 \tabularnewline
61 & 5.48 & 5.47365162037037 & 5.50458333333333 & -0.0309317129629629 & 0.00634837962962997 \tabularnewline
62 & 5.48 & 5.46656828703704 & 5.51333333333333 & -0.0467650462962962 & 0.0134317129629631 \tabularnewline
63 & 5.5 & 5.51913773148148 & 5.52166666666667 & -0.00252893518518538 & -0.0191377314814822 \tabularnewline
64 & 5.55 & 5.54719328703704 & 5.53041666666667 & 0.0167766203703699 & 0.00280671296296209 \tabularnewline
65 & 5.55 & 5.57066550925926 & 5.53958333333333 & 0.0310821759259258 & -0.0206655092592598 \tabularnewline
66 & 5.57 & 5.57247106481482 & 5.54875 & 0.0237210648148146 & -0.00247106481481474 \tabularnewline
67 & 5.58 & 5.56434606481482 & 5.55875 & 0.00559606481481508 & 0.0156539351851848 \tabularnewline
68 & 5.58 & 5.57038773148148 & 5.56958333333333 & 0.000804398148148626 & 0.00961226851851826 \tabularnewline
69 & 5.58 & 5.59031828703704 & 5.57958333333333 & 0.0107349537037039 & -0.0103182870370375 \tabularnewline
70 & 5.59 & 5.6001099537037 & 5.5875 & 0.0126099537037038 & -0.0101099537037044 \tabularnewline
71 & 5.59 & 5.59399884259259 & 5.59666666666667 & -0.00266782407407429 & -0.00399884259259231 \tabularnewline
72 & 5.59 & 5.5894849537037 & 5.60791666666667 & -0.0184317129629629 & 0.000515046296296617 \tabularnewline
73 & 5.61 & 5.58740162037037 & 5.61833333333333 & -0.0309317129629629 & 0.0225983796296303 \tabularnewline
74 & 5.61 & 5.58156828703704 & 5.62833333333333 & -0.0467650462962962 & 0.0284317129629628 \tabularnewline
75 & 5.61 & 5.63580439814815 & 5.63833333333333 & -0.00252893518518538 & -0.0258043981481482 \tabularnewline
76 & 5.63 & 5.66469328703704 & 5.64791666666667 & 0.0167766203703699 & -0.0346932870370376 \tabularnewline
77 & 5.69 & 5.68816550925926 & 5.65708333333333 & 0.0310821759259258 & 0.00183449074074016 \tabularnewline
78 & 5.7 & 5.68997106481482 & 5.66625 & 0.0237210648148146 & 0.0100289351851837 \tabularnewline
79 & 5.7 & NA & NA & 0.00559606481481508 & NA \tabularnewline
80 & 5.7 & NA & NA & 0.000804398148148626 & NA \tabularnewline
81 & 5.7 & NA & NA & 0.0107349537037039 & NA \tabularnewline
82 & 5.7 & NA & NA & 0.0126099537037038 & NA \tabularnewline
83 & 5.7 & NA & NA & -0.00266782407407429 & NA \tabularnewline
84 & 5.7 & NA & NA & -0.0184317129629629 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156785&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]4.23[/C][C]NA[/C][C]NA[/C][C]-0.0309317129629629[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]4.38[/C][C]NA[/C][C]NA[/C][C]-0.0467650462962962[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]4.43[/C][C]NA[/C][C]NA[/C][C]-0.00252893518518538[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]4.44[/C][C]NA[/C][C]NA[/C][C]0.0167766203703699[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]4.44[/C][C]NA[/C][C]NA[/C][C]0.0310821759259258[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]4.44[/C][C]NA[/C][C]NA[/C][C]0.0237210648148146[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]4.44[/C][C]4.43476273148148[/C][C]4.42916666666667[/C][C]0.00559606481481508[/C][C]0.0052372685185178[/C][/ROW]
[ROW][C]8[/C][C]4.44[/C][C]4.44205439814815[/C][C]4.44125[/C][C]0.000804398148148626[/C][C]-0.00205439814814934[/C][/ROW]
[ROW][C]9[/C][C]4.45[/C][C]4.4557349537037[/C][C]4.445[/C][C]0.0107349537037039[/C][C]-0.00573495370370392[/C][/ROW]
[ROW][C]10[/C][C]4.45[/C][C]4.4588599537037[/C][C]4.44625[/C][C]0.0126099537037038[/C][C]-0.00885995370370374[/C][/ROW]
[ROW][C]11[/C][C]4.45[/C][C]4.44483217592593[/C][C]4.4475[/C][C]-0.00266782407407429[/C][C]0.00516782407407401[/C][/ROW]
[ROW][C]12[/C][C]4.45[/C][C]4.4307349537037[/C][C]4.44916666666667[/C][C]-0.0184317129629629[/C][C]0.0192650462962956[/C][/ROW]
[ROW][C]13[/C][C]4.45[/C][C]4.41990162037037[/C][C]4.45083333333333[/C][C]-0.0309317129629629[/C][C]0.0300983796296288[/C][/ROW]
[ROW][C]14[/C][C]4.45[/C][C]4.40656828703704[/C][C]4.45333333333333[/C][C]-0.0467650462962962[/C][C]0.0434317129629624[/C][/ROW]
[ROW][C]15[/C][C]4.45[/C][C]4.45788773148148[/C][C]4.46041666666667[/C][C]-0.00252893518518538[/C][C]-0.0078877314814827[/C][/ROW]
[ROW][C]16[/C][C]4.45[/C][C]4.49177662037037[/C][C]4.475[/C][C]0.0167766203703699[/C][C]-0.0417766203703716[/C][/ROW]
[ROW][C]17[/C][C]4.46[/C][C]4.52483217592593[/C][C]4.49375[/C][C]0.0310821759259258[/C][C]-0.0648321759259272[/C][/ROW]
[ROW][C]18[/C][C]4.46[/C][C]4.53663773148148[/C][C]4.51291666666667[/C][C]0.0237210648148146[/C][C]-0.0766377314814823[/C][/ROW]
[ROW][C]19[/C][C]4.46[/C][C]4.53809606481482[/C][C]4.5325[/C][C]0.00559606481481508[/C][C]-0.0780960648148161[/C][/ROW]
[ROW][C]20[/C][C]4.48[/C][C]4.55330439814815[/C][C]4.5525[/C][C]0.000804398148148626[/C][C]-0.0733043981481485[/C][/ROW]
[ROW][C]21[/C][C]4.58[/C][C]4.5832349537037[/C][C]4.5725[/C][C]0.0107349537037039[/C][C]-0.00323495370370441[/C][/ROW]
[ROW][C]22[/C][C]4.67[/C][C]4.60510995370371[/C][C]4.5925[/C][C]0.0126099537037038[/C][C]0.0648900462962949[/C][/ROW]
[ROW][C]23[/C][C]4.68[/C][C]4.60941550925926[/C][C]4.61208333333333[/C][C]-0.00266782407407429[/C][C]0.0705844907407398[/C][/ROW]
[ROW][C]24[/C][C]4.68[/C][C]4.61281828703704[/C][C]4.63125[/C][C]-0.0184317129629629[/C][C]0.0671817129629622[/C][/ROW]
[ROW][C]25[/C][C]4.69[/C][C]4.6194849537037[/C][C]4.65041666666667[/C][C]-0.0309317129629629[/C][C]0.0705150462962969[/C][/ROW]
[ROW][C]26[/C][C]4.69[/C][C]4.62365162037037[/C][C]4.67041666666667[/C][C]-0.0467650462962962[/C][C]0.0663483796296305[/C][/ROW]
[ROW][C]27[/C][C]4.69[/C][C]4.68663773148148[/C][C]4.68916666666667[/C][C]-0.00252893518518538[/C][C]0.00336226851851951[/C][/ROW]
[ROW][C]28[/C][C]4.69[/C][C]4.71927662037037[/C][C]4.7025[/C][C]0.0167766203703699[/C][C]-0.0292766203703687[/C][/ROW]
[ROW][C]29[/C][C]4.69[/C][C]4.74316550925926[/C][C]4.71208333333333[/C][C]0.0310821759259258[/C][C]-0.0531655092592578[/C][/ROW]
[ROW][C]30[/C][C]4.69[/C][C]4.74538773148148[/C][C]4.72166666666667[/C][C]0.0237210648148146[/C][C]-0.0553877314814804[/C][/ROW]
[ROW][C]31[/C][C]4.69[/C][C]4.73684606481481[/C][C]4.73125[/C][C]0.00559606481481508[/C][C]-0.0468460648148143[/C][/ROW]
[ROW][C]32[/C][C]4.73[/C][C]4.74163773148148[/C][C]4.74083333333333[/C][C]0.000804398148148626[/C][C]-0.0116377314814802[/C][/ROW]
[ROW][C]33[/C][C]4.78[/C][C]4.77615162037037[/C][C]4.76541666666667[/C][C]0.0107349537037039[/C][C]0.00384837962963047[/C][/ROW]
[ROW][C]34[/C][C]4.79[/C][C]4.8213599537037[/C][C]4.80875[/C][C]0.0126099537037038[/C][C]-0.0313599537037037[/C][/ROW]
[ROW][C]35[/C][C]4.79[/C][C]4.85483217592593[/C][C]4.8575[/C][C]-0.00266782407407429[/C][C]-0.0648321759259254[/C][/ROW]
[ROW][C]36[/C][C]4.8[/C][C]4.88906828703704[/C][C]4.9075[/C][C]-0.0184317129629629[/C][C]-0.089068287037037[/C][/ROW]
[ROW][C]37[/C][C]4.8[/C][C]4.92656828703704[/C][C]4.9575[/C][C]-0.0309317129629629[/C][C]-0.126568287037037[/C][/ROW]
[ROW][C]38[/C][C]4.81[/C][C]4.9594849537037[/C][C]5.00625[/C][C]-0.0467650462962962[/C][C]-0.149484953703705[/C][/ROW]
[ROW][C]39[/C][C]5.16[/C][C]5.04913773148148[/C][C]5.05166666666667[/C][C]-0.00252893518518538[/C][C]0.110862268518518[/C][/ROW]
[ROW][C]40[/C][C]5.26[/C][C]5.1113599537037[/C][C]5.09458333333333[/C][C]0.0167766203703699[/C][C]0.148640046296295[/C][/ROW]
[ROW][C]41[/C][C]5.29[/C][C]5.16816550925926[/C][C]5.13708333333333[/C][C]0.0310821759259258[/C][C]0.12183449074074[/C][/ROW]
[ROW][C]42[/C][C]5.29[/C][C]5.20288773148148[/C][C]5.17916666666667[/C][C]0.0237210648148146[/C][C]0.087112268518518[/C][/ROW]
[ROW][C]43[/C][C]5.29[/C][C]5.22642939814815[/C][C]5.22083333333333[/C][C]0.00559606481481508[/C][C]0.0635706018518523[/C][/ROW]
[ROW][C]44[/C][C]5.3[/C][C]5.26288773148148[/C][C]5.26208333333333[/C][C]0.000804398148148626[/C][C]0.037112268518519[/C][/ROW]
[ROW][C]45[/C][C]5.3[/C][C]5.29906828703704[/C][C]5.28833333333333[/C][C]0.0107349537037039[/C][C]0.000931712962963793[/C][/ROW]
[ROW][C]46[/C][C]5.3[/C][C]5.31052662037037[/C][C]5.29791666666666[/C][C]0.0126099537037038[/C][C]-0.0105266203703689[/C][/ROW]
[ROW][C]47[/C][C]5.3[/C][C]5.30524884259259[/C][C]5.30791666666667[/C][C]-0.00266782407407429[/C][C]-0.00524884259259117[/C][/ROW]
[ROW][C]48[/C][C]5.3[/C][C]5.3032349537037[/C][C]5.32166666666667[/C][C]-0.0184317129629629[/C][C]-0.00323495370370264[/C][/ROW]
[ROW][C]49[/C][C]5.3[/C][C]5.3057349537037[/C][C]5.33666666666667[/C][C]-0.0309317129629629[/C][C]-0.00573495370370214[/C][/ROW]
[ROW][C]50[/C][C]5.3[/C][C]5.30490162037037[/C][C]5.35166666666666[/C][C]-0.0467650462962962[/C][C]-0.00490162037036868[/C][/ROW]
[ROW][C]51[/C][C]5.3[/C][C]5.36413773148148[/C][C]5.36666666666667[/C][C]-0.00252893518518538[/C][C]-0.0641377314814804[/C][/ROW]
[ROW][C]52[/C][C]5.35[/C][C]5.39844328703704[/C][C]5.38166666666667[/C][C]0.0167766203703699[/C][C]-0.0484432870370366[/C][/ROW]
[ROW][C]53[/C][C]5.44[/C][C]5.42774884259259[/C][C]5.39666666666667[/C][C]0.0310821759259258[/C][C]0.0122511574074089[/C][/ROW]
[ROW][C]54[/C][C]5.47[/C][C]5.43538773148148[/C][C]5.41166666666667[/C][C]0.0237210648148146[/C][C]0.0346122685185186[/C][/ROW]
[ROW][C]55[/C][C]5.47[/C][C]5.43226273148148[/C][C]5.42666666666667[/C][C]0.00559606481481508[/C][C]0.0377372685185184[/C][/ROW]
[ROW][C]56[/C][C]5.48[/C][C]5.44247106481482[/C][C]5.44166666666667[/C][C]0.000804398148148626[/C][C]0.0375289351851853[/C][/ROW]
[ROW][C]57[/C][C]5.48[/C][C]5.4682349537037[/C][C]5.4575[/C][C]0.0107349537037039[/C][C]0.011765046296297[/C][/ROW]
[ROW][C]58[/C][C]5.48[/C][C]5.48677662037037[/C][C]5.47416666666667[/C][C]0.0126099537037038[/C][C]-0.00677662037036963[/C][/ROW]
[ROW][C]59[/C][C]5.48[/C][C]5.48441550925926[/C][C]5.48708333333333[/C][C]-0.00266782407407429[/C][C]-0.0044155092592586[/C][/ROW]
[ROW][C]60[/C][C]5.48[/C][C]5.47740162037037[/C][C]5.49583333333333[/C][C]-0.0184317129629629[/C][C]0.00259837962963072[/C][/ROW]
[ROW][C]61[/C][C]5.48[/C][C]5.47365162037037[/C][C]5.50458333333333[/C][C]-0.0309317129629629[/C][C]0.00634837962962997[/C][/ROW]
[ROW][C]62[/C][C]5.48[/C][C]5.46656828703704[/C][C]5.51333333333333[/C][C]-0.0467650462962962[/C][C]0.0134317129629631[/C][/ROW]
[ROW][C]63[/C][C]5.5[/C][C]5.51913773148148[/C][C]5.52166666666667[/C][C]-0.00252893518518538[/C][C]-0.0191377314814822[/C][/ROW]
[ROW][C]64[/C][C]5.55[/C][C]5.54719328703704[/C][C]5.53041666666667[/C][C]0.0167766203703699[/C][C]0.00280671296296209[/C][/ROW]
[ROW][C]65[/C][C]5.55[/C][C]5.57066550925926[/C][C]5.53958333333333[/C][C]0.0310821759259258[/C][C]-0.0206655092592598[/C][/ROW]
[ROW][C]66[/C][C]5.57[/C][C]5.57247106481482[/C][C]5.54875[/C][C]0.0237210648148146[/C][C]-0.00247106481481474[/C][/ROW]
[ROW][C]67[/C][C]5.58[/C][C]5.56434606481482[/C][C]5.55875[/C][C]0.00559606481481508[/C][C]0.0156539351851848[/C][/ROW]
[ROW][C]68[/C][C]5.58[/C][C]5.57038773148148[/C][C]5.56958333333333[/C][C]0.000804398148148626[/C][C]0.00961226851851826[/C][/ROW]
[ROW][C]69[/C][C]5.58[/C][C]5.59031828703704[/C][C]5.57958333333333[/C][C]0.0107349537037039[/C][C]-0.0103182870370375[/C][/ROW]
[ROW][C]70[/C][C]5.59[/C][C]5.6001099537037[/C][C]5.5875[/C][C]0.0126099537037038[/C][C]-0.0101099537037044[/C][/ROW]
[ROW][C]71[/C][C]5.59[/C][C]5.59399884259259[/C][C]5.59666666666667[/C][C]-0.00266782407407429[/C][C]-0.00399884259259231[/C][/ROW]
[ROW][C]72[/C][C]5.59[/C][C]5.5894849537037[/C][C]5.60791666666667[/C][C]-0.0184317129629629[/C][C]0.000515046296296617[/C][/ROW]
[ROW][C]73[/C][C]5.61[/C][C]5.58740162037037[/C][C]5.61833333333333[/C][C]-0.0309317129629629[/C][C]0.0225983796296303[/C][/ROW]
[ROW][C]74[/C][C]5.61[/C][C]5.58156828703704[/C][C]5.62833333333333[/C][C]-0.0467650462962962[/C][C]0.0284317129629628[/C][/ROW]
[ROW][C]75[/C][C]5.61[/C][C]5.63580439814815[/C][C]5.63833333333333[/C][C]-0.00252893518518538[/C][C]-0.0258043981481482[/C][/ROW]
[ROW][C]76[/C][C]5.63[/C][C]5.66469328703704[/C][C]5.64791666666667[/C][C]0.0167766203703699[/C][C]-0.0346932870370376[/C][/ROW]
[ROW][C]77[/C][C]5.69[/C][C]5.68816550925926[/C][C]5.65708333333333[/C][C]0.0310821759259258[/C][C]0.00183449074074016[/C][/ROW]
[ROW][C]78[/C][C]5.7[/C][C]5.68997106481482[/C][C]5.66625[/C][C]0.0237210648148146[/C][C]0.0100289351851837[/C][/ROW]
[ROW][C]79[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]0.00559606481481508[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]0.000804398148148626[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]0.0107349537037039[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]0.0126099537037038[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]-0.00266782407407429[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]-0.0184317129629629[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156785&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156785&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
14.23NANA-0.0309317129629629NA
24.38NANA-0.0467650462962962NA
34.43NANA-0.00252893518518538NA
44.44NANA0.0167766203703699NA
54.44NANA0.0310821759259258NA
64.44NANA0.0237210648148146NA
74.444.434762731481484.429166666666670.005596064814815080.0052372685185178
84.444.442054398148154.441250.000804398148148626-0.00205439814814934
94.454.45573495370374.4450.0107349537037039-0.00573495370370392
104.454.45885995370374.446250.0126099537037038-0.00885995370370374
114.454.444832175925934.4475-0.002667824074074290.00516782407407401
124.454.43073495370374.44916666666667-0.01843171296296290.0192650462962956
134.454.419901620370374.45083333333333-0.03093171296296290.0300983796296288
144.454.406568287037044.45333333333333-0.04676504629629620.0434317129629624
154.454.457887731481484.46041666666667-0.00252893518518538-0.0078877314814827
164.454.491776620370374.4750.0167766203703699-0.0417766203703716
174.464.524832175925934.493750.0310821759259258-0.0648321759259272
184.464.536637731481484.512916666666670.0237210648148146-0.0766377314814823
194.464.538096064814824.53250.00559606481481508-0.0780960648148161
204.484.553304398148154.55250.000804398148148626-0.0733043981481485
214.584.58323495370374.57250.0107349537037039-0.00323495370370441
224.674.605109953703714.59250.01260995370370380.0648900462962949
234.684.609415509259264.61208333333333-0.002667824074074290.0705844907407398
244.684.612818287037044.63125-0.01843171296296290.0671817129629622
254.694.61948495370374.65041666666667-0.03093171296296290.0705150462962969
264.694.623651620370374.67041666666667-0.04676504629629620.0663483796296305
274.694.686637731481484.68916666666667-0.002528935185185380.00336226851851951
284.694.719276620370374.70250.0167766203703699-0.0292766203703687
294.694.743165509259264.712083333333330.0310821759259258-0.0531655092592578
304.694.745387731481484.721666666666670.0237210648148146-0.0553877314814804
314.694.736846064814814.731250.00559606481481508-0.0468460648148143
324.734.741637731481484.740833333333330.000804398148148626-0.0116377314814802
334.784.776151620370374.765416666666670.01073495370370390.00384837962963047
344.794.82135995370374.808750.0126099537037038-0.0313599537037037
354.794.854832175925934.8575-0.00266782407407429-0.0648321759259254
364.84.889068287037044.9075-0.0184317129629629-0.089068287037037
374.84.926568287037044.9575-0.0309317129629629-0.126568287037037
384.814.95948495370375.00625-0.0467650462962962-0.149484953703705
395.165.049137731481485.05166666666667-0.002528935185185380.110862268518518
405.265.11135995370375.094583333333330.01677662037036990.148640046296295
415.295.168165509259265.137083333333330.03108217592592580.12183449074074
425.295.202887731481485.179166666666670.02372106481481460.087112268518518
435.295.226429398148155.220833333333330.005596064814815080.0635706018518523
445.35.262887731481485.262083333333330.0008043981481486260.037112268518519
455.35.299068287037045.288333333333330.01073495370370390.000931712962963793
465.35.310526620370375.297916666666660.0126099537037038-0.0105266203703689
475.35.305248842592595.30791666666667-0.00266782407407429-0.00524884259259117
485.35.30323495370375.32166666666667-0.0184317129629629-0.00323495370370264
495.35.30573495370375.33666666666667-0.0309317129629629-0.00573495370370214
505.35.304901620370375.35166666666666-0.0467650462962962-0.00490162037036868
515.35.364137731481485.36666666666667-0.00252893518518538-0.0641377314814804
525.355.398443287037045.381666666666670.0167766203703699-0.0484432870370366
535.445.427748842592595.396666666666670.03108217592592580.0122511574074089
545.475.435387731481485.411666666666670.02372106481481460.0346122685185186
555.475.432262731481485.426666666666670.005596064814815080.0377372685185184
565.485.442471064814825.441666666666670.0008043981481486260.0375289351851853
575.485.46823495370375.45750.01073495370370390.011765046296297
585.485.486776620370375.474166666666670.0126099537037038-0.00677662037036963
595.485.484415509259265.48708333333333-0.00266782407407429-0.0044155092592586
605.485.477401620370375.49583333333333-0.01843171296296290.00259837962963072
615.485.473651620370375.50458333333333-0.03093171296296290.00634837962962997
625.485.466568287037045.51333333333333-0.04676504629629620.0134317129629631
635.55.519137731481485.52166666666667-0.00252893518518538-0.0191377314814822
645.555.547193287037045.530416666666670.01677662037036990.00280671296296209
655.555.570665509259265.539583333333330.0310821759259258-0.0206655092592598
665.575.572471064814825.548750.0237210648148146-0.00247106481481474
675.585.564346064814825.558750.005596064814815080.0156539351851848
685.585.570387731481485.569583333333330.0008043981481486260.00961226851851826
695.585.590318287037045.579583333333330.0107349537037039-0.0103182870370375
705.595.60010995370375.58750.0126099537037038-0.0101099537037044
715.595.593998842592595.59666666666667-0.00266782407407429-0.00399884259259231
725.595.58948495370375.60791666666667-0.01843171296296290.000515046296296617
735.615.587401620370375.61833333333333-0.03093171296296290.0225983796296303
745.615.581568287037045.62833333333333-0.04676504629629620.0284317129629628
755.615.635804398148155.63833333333333-0.00252893518518538-0.0258043981481482
765.635.664693287037045.647916666666670.0167766203703699-0.0346932870370376
775.695.688165509259265.657083333333330.03108217592592580.00183449074074016
785.75.689971064814825.666250.02372106481481460.0100289351851837
795.7NANA0.00559606481481508NA
805.7NANA0.000804398148148626NA
815.7NANA0.0107349537037039NA
825.7NANA0.0126099537037038NA
835.7NANA-0.00266782407407429NA
845.7NANA-0.0184317129629629NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; 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')