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Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareClassical Decomposition
Date of computationTue, 29 Nov 2011 16:17:55 -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/29/t1322601489szqkkwfj05z1r9b.htm/, Retrieved Fri, 29 Mar 2024 11:44:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148734, Retrieved Fri, 29 Mar 2024 11:44:52 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
F  M D  [Classical Decomposition] [] [2010-11-26 10:00:41] [8a9a6f7c332640af31ddca253a8ded58]
- RM        [Classical Decomposition] [] [2011-11-29 21:17:55] [4be1b05f688f7fa8db5b9e9e4d3a7e33] [Current]
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Dataseries X:
101,76
102,37
102,38
102,86
102,87
102,92
102,95
103,02
104,08
104,16
104,24
104,33
104,73
104,86
105,03
105,62
105,63
105,63
105,94
106,61
107,69
107,78
107,93
108,48
108,14
108,48
108,48
108,89
108,93
109,21
109,47
109,80
111,73
111,85
112,12
112,15
112,17
112,67
112,80
113,44
113,53
114,53
114,51
115,05
116,67
117,07
116,92
117,00
117,02
117,35
117,36
117,82
117,88
118,24
118,50
118,80
119,76
120,09




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=148734&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=148734&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148734&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
1101.76NANA-0.0784606481481403NA
2102.37NANA-0.0827662037036967NA
3102.38NANA-0.324710648148148NA
4102.86NANA-0.132210648148158NA
5102.87NANA-0.440960648148161NA
6102.92NANA-0.36637731481482NA
7102.95102.755706018519103.285416666667-0.5297106481481420.194293981481479
8103.02103.152372685185103.512916666667-0.360543981481478-0.132372685185203
9104.08104.565150462963103.7270833333330.838067129629633-0.485150462962977
10104.16104.653206018519103.95250.700706018518514-0.493206018518521
11104.24104.633622685185104.18250.45112268518519-0.393622685185179
12104.33104.736261574074104.4104166666670.325844907407408-0.406261574074037
13104.73104.569456018519104.647916666667-0.07846064814814030.160543981481496
14104.86104.83931712963104.922083333333-0.08276620370369670.0206828703703792
15105.03104.897372685185105.222083333333-0.3247106481481480.132627314814826
16105.62105.391122685185105.523333333333-0.1322106481481580.228877314814824
17105.63105.386956018519105.827916666667-0.4409606481481610.243043981481492
18105.63105.788206018518106.154583333333-0.36637731481482-0.158206018518499
19105.94105.939872685185106.469583333333-0.5297106481481420.000127314814832857
20106.61106.401956018519106.7625-0.3605439814814780.208043981481495
21107.69107.895150462963107.0570833333330.838067129629633-0.205150462962962
22107.78108.037789351852107.3370833333330.700706018518514-0.257789351851855
23107.93108.061956018519107.6108333333330.45112268518519-0.131956018518508
24108.48108.223344907407107.89750.3258449074074080.256655092592595
25108.14108.115289351852108.19375-0.07846064814814030.0247106481481438
26108.48108.390983796296108.47375-0.08276620370369670.0890162037037072
27108.48108.450289351852108.775-0.3247106481481480.0297106481481535
28108.89108.980706018519109.112916666667-0.132210648148158-0.0907060185185173
29108.93109.016122685185109.457083333333-0.440960648148161-0.0861226851851882
30109.21109.418206018519109.784583333333-0.36637731481482-0.208206018518524
31109.47109.575706018519110.105416666667-0.529710648148142-0.105706018518532
32109.8110.087372685185110.447916666667-0.360543981481478-0.287372685185176
33111.73111.64056712963110.80250.8380671296296330.0894328703703735
34111.85111.872789351852111.1720833333330.700706018518514-0.0227893518518414
35112.12112.004456018518111.5533333333330.451122685185190.115543981481508
36112.15112.292511574074111.9666666666670.325844907407408-0.142511574074049
37112.17112.319872685185112.398333333333-0.0784606481481403-0.149872685185187
38112.67112.74431712963112.827083333333-0.0827662037036967-0.0743171296296197
39112.8112.926956018518113.251666666667-0.324710648148148-0.126956018518499
40113.44113.542789351852113.675-0.132210648148158-0.10278935185184
41113.53113.651539351852114.0925-0.440960648148161-0.121539351851837
42114.53114.128206018518114.494583333333-0.366377314814820.401793981481504
43114.51114.369039351852114.89875-0.5297106481481420.140960648148152
44115.05114.935289351852115.295833333333-0.3605439814814780.114710648148161
45116.67116.518900462963115.6808333333330.8380671296296330.151099537037041
46117.07116.754039351852116.0533333333330.7007060185185140.315960648148149
47116.92116.868206018519116.4170833333330.451122685185190.051793981481481
48117117.078761574074116.7529166666670.325844907407408-0.0787615740740648
49117.02NA117.07375NANA
50117.35NA117.39625NANA
51117.36NA117.68125NANA
52117.82NA117.935833333333NANA
53117.88NANANANA
54118.24NANANANA
55118.5NANANANA
56118.8NANANANA
57119.76NANANANA
58120.09NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.76 & NA & NA & -0.0784606481481403 & NA \tabularnewline
2 & 102.37 & NA & NA & -0.0827662037036967 & NA \tabularnewline
3 & 102.38 & NA & NA & -0.324710648148148 & NA \tabularnewline
4 & 102.86 & NA & NA & -0.132210648148158 & NA \tabularnewline
5 & 102.87 & NA & NA & -0.440960648148161 & NA \tabularnewline
6 & 102.92 & NA & NA & -0.36637731481482 & NA \tabularnewline
7 & 102.95 & 102.755706018519 & 103.285416666667 & -0.529710648148142 & 0.194293981481479 \tabularnewline
8 & 103.02 & 103.152372685185 & 103.512916666667 & -0.360543981481478 & -0.132372685185203 \tabularnewline
9 & 104.08 & 104.565150462963 & 103.727083333333 & 0.838067129629633 & -0.485150462962977 \tabularnewline
10 & 104.16 & 104.653206018519 & 103.9525 & 0.700706018518514 & -0.493206018518521 \tabularnewline
11 & 104.24 & 104.633622685185 & 104.1825 & 0.45112268518519 & -0.393622685185179 \tabularnewline
12 & 104.33 & 104.736261574074 & 104.410416666667 & 0.325844907407408 & -0.406261574074037 \tabularnewline
13 & 104.73 & 104.569456018519 & 104.647916666667 & -0.0784606481481403 & 0.160543981481496 \tabularnewline
14 & 104.86 & 104.83931712963 & 104.922083333333 & -0.0827662037036967 & 0.0206828703703792 \tabularnewline
15 & 105.03 & 104.897372685185 & 105.222083333333 & -0.324710648148148 & 0.132627314814826 \tabularnewline
16 & 105.62 & 105.391122685185 & 105.523333333333 & -0.132210648148158 & 0.228877314814824 \tabularnewline
17 & 105.63 & 105.386956018519 & 105.827916666667 & -0.440960648148161 & 0.243043981481492 \tabularnewline
18 & 105.63 & 105.788206018518 & 106.154583333333 & -0.36637731481482 & -0.158206018518499 \tabularnewline
19 & 105.94 & 105.939872685185 & 106.469583333333 & -0.529710648148142 & 0.000127314814832857 \tabularnewline
20 & 106.61 & 106.401956018519 & 106.7625 & -0.360543981481478 & 0.208043981481495 \tabularnewline
21 & 107.69 & 107.895150462963 & 107.057083333333 & 0.838067129629633 & -0.205150462962962 \tabularnewline
22 & 107.78 & 108.037789351852 & 107.337083333333 & 0.700706018518514 & -0.257789351851855 \tabularnewline
23 & 107.93 & 108.061956018519 & 107.610833333333 & 0.45112268518519 & -0.131956018518508 \tabularnewline
24 & 108.48 & 108.223344907407 & 107.8975 & 0.325844907407408 & 0.256655092592595 \tabularnewline
25 & 108.14 & 108.115289351852 & 108.19375 & -0.0784606481481403 & 0.0247106481481438 \tabularnewline
26 & 108.48 & 108.390983796296 & 108.47375 & -0.0827662037036967 & 0.0890162037037072 \tabularnewline
27 & 108.48 & 108.450289351852 & 108.775 & -0.324710648148148 & 0.0297106481481535 \tabularnewline
28 & 108.89 & 108.980706018519 & 109.112916666667 & -0.132210648148158 & -0.0907060185185173 \tabularnewline
29 & 108.93 & 109.016122685185 & 109.457083333333 & -0.440960648148161 & -0.0861226851851882 \tabularnewline
30 & 109.21 & 109.418206018519 & 109.784583333333 & -0.36637731481482 & -0.208206018518524 \tabularnewline
31 & 109.47 & 109.575706018519 & 110.105416666667 & -0.529710648148142 & -0.105706018518532 \tabularnewline
32 & 109.8 & 110.087372685185 & 110.447916666667 & -0.360543981481478 & -0.287372685185176 \tabularnewline
33 & 111.73 & 111.64056712963 & 110.8025 & 0.838067129629633 & 0.0894328703703735 \tabularnewline
34 & 111.85 & 111.872789351852 & 111.172083333333 & 0.700706018518514 & -0.0227893518518414 \tabularnewline
35 & 112.12 & 112.004456018518 & 111.553333333333 & 0.45112268518519 & 0.115543981481508 \tabularnewline
36 & 112.15 & 112.292511574074 & 111.966666666667 & 0.325844907407408 & -0.142511574074049 \tabularnewline
37 & 112.17 & 112.319872685185 & 112.398333333333 & -0.0784606481481403 & -0.149872685185187 \tabularnewline
38 & 112.67 & 112.74431712963 & 112.827083333333 & -0.0827662037036967 & -0.0743171296296197 \tabularnewline
39 & 112.8 & 112.926956018518 & 113.251666666667 & -0.324710648148148 & -0.126956018518499 \tabularnewline
40 & 113.44 & 113.542789351852 & 113.675 & -0.132210648148158 & -0.10278935185184 \tabularnewline
41 & 113.53 & 113.651539351852 & 114.0925 & -0.440960648148161 & -0.121539351851837 \tabularnewline
42 & 114.53 & 114.128206018518 & 114.494583333333 & -0.36637731481482 & 0.401793981481504 \tabularnewline
43 & 114.51 & 114.369039351852 & 114.89875 & -0.529710648148142 & 0.140960648148152 \tabularnewline
44 & 115.05 & 114.935289351852 & 115.295833333333 & -0.360543981481478 & 0.114710648148161 \tabularnewline
45 & 116.67 & 116.518900462963 & 115.680833333333 & 0.838067129629633 & 0.151099537037041 \tabularnewline
46 & 117.07 & 116.754039351852 & 116.053333333333 & 0.700706018518514 & 0.315960648148149 \tabularnewline
47 & 116.92 & 116.868206018519 & 116.417083333333 & 0.45112268518519 & 0.051793981481481 \tabularnewline
48 & 117 & 117.078761574074 & 116.752916666667 & 0.325844907407408 & -0.0787615740740648 \tabularnewline
49 & 117.02 & NA & 117.07375 & NA & NA \tabularnewline
50 & 117.35 & NA & 117.39625 & NA & NA \tabularnewline
51 & 117.36 & NA & 117.68125 & NA & NA \tabularnewline
52 & 117.82 & NA & 117.935833333333 & NA & NA \tabularnewline
53 & 117.88 & NA & NA & NA & NA \tabularnewline
54 & 118.24 & NA & NA & NA & NA \tabularnewline
55 & 118.5 & NA & NA & NA & NA \tabularnewline
56 & 118.8 & NA & NA & NA & NA \tabularnewline
57 & 119.76 & NA & NA & NA & NA \tabularnewline
58 & 120.09 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148734&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]101.76[/C][C]NA[/C][C]NA[/C][C]-0.0784606481481403[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]NA[/C][C]NA[/C][C]-0.0827662037036967[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]NA[/C][C]NA[/C][C]-0.324710648148148[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]NA[/C][C]NA[/C][C]-0.132210648148158[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]NA[/C][C]NA[/C][C]-0.440960648148161[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]NA[/C][C]NA[/C][C]-0.36637731481482[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.755706018519[/C][C]103.285416666667[/C][C]-0.529710648148142[/C][C]0.194293981481479[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.152372685185[/C][C]103.512916666667[/C][C]-0.360543981481478[/C][C]-0.132372685185203[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.565150462963[/C][C]103.727083333333[/C][C]0.838067129629633[/C][C]-0.485150462962977[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.653206018519[/C][C]103.9525[/C][C]0.700706018518514[/C][C]-0.493206018518521[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.633622685185[/C][C]104.1825[/C][C]0.45112268518519[/C][C]-0.393622685185179[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.736261574074[/C][C]104.410416666667[/C][C]0.325844907407408[/C][C]-0.406261574074037[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.569456018519[/C][C]104.647916666667[/C][C]-0.0784606481481403[/C][C]0.160543981481496[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]104.83931712963[/C][C]104.922083333333[/C][C]-0.0827662037036967[/C][C]0.0206828703703792[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]104.897372685185[/C][C]105.222083333333[/C][C]-0.324710648148148[/C][C]0.132627314814826[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.391122685185[/C][C]105.523333333333[/C][C]-0.132210648148158[/C][C]0.228877314814824[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.386956018519[/C][C]105.827916666667[/C][C]-0.440960648148161[/C][C]0.243043981481492[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]105.788206018518[/C][C]106.154583333333[/C][C]-0.36637731481482[/C][C]-0.158206018518499[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]105.939872685185[/C][C]106.469583333333[/C][C]-0.529710648148142[/C][C]0.000127314814832857[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.401956018519[/C][C]106.7625[/C][C]-0.360543981481478[/C][C]0.208043981481495[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.895150462963[/C][C]107.057083333333[/C][C]0.838067129629633[/C][C]-0.205150462962962[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.037789351852[/C][C]107.337083333333[/C][C]0.700706018518514[/C][C]-0.257789351851855[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.061956018519[/C][C]107.610833333333[/C][C]0.45112268518519[/C][C]-0.131956018518508[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.223344907407[/C][C]107.8975[/C][C]0.325844907407408[/C][C]0.256655092592595[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]108.115289351852[/C][C]108.19375[/C][C]-0.0784606481481403[/C][C]0.0247106481481438[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.390983796296[/C][C]108.47375[/C][C]-0.0827662037036967[/C][C]0.0890162037037072[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.450289351852[/C][C]108.775[/C][C]-0.324710648148148[/C][C]0.0297106481481535[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.980706018519[/C][C]109.112916666667[/C][C]-0.132210648148158[/C][C]-0.0907060185185173[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.016122685185[/C][C]109.457083333333[/C][C]-0.440960648148161[/C][C]-0.0861226851851882[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.418206018519[/C][C]109.784583333333[/C][C]-0.36637731481482[/C][C]-0.208206018518524[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.575706018519[/C][C]110.105416666667[/C][C]-0.529710648148142[/C][C]-0.105706018518532[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]110.087372685185[/C][C]110.447916666667[/C][C]-0.360543981481478[/C][C]-0.287372685185176[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.64056712963[/C][C]110.8025[/C][C]0.838067129629633[/C][C]0.0894328703703735[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.872789351852[/C][C]111.172083333333[/C][C]0.700706018518514[/C][C]-0.0227893518518414[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]112.004456018518[/C][C]111.553333333333[/C][C]0.45112268518519[/C][C]0.115543981481508[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.292511574074[/C][C]111.966666666667[/C][C]0.325844907407408[/C][C]-0.142511574074049[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.319872685185[/C][C]112.398333333333[/C][C]-0.0784606481481403[/C][C]-0.149872685185187[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.74431712963[/C][C]112.827083333333[/C][C]-0.0827662037036967[/C][C]-0.0743171296296197[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.926956018518[/C][C]113.251666666667[/C][C]-0.324710648148148[/C][C]-0.126956018518499[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.542789351852[/C][C]113.675[/C][C]-0.132210648148158[/C][C]-0.10278935185184[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.651539351852[/C][C]114.0925[/C][C]-0.440960648148161[/C][C]-0.121539351851837[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.128206018518[/C][C]114.494583333333[/C][C]-0.36637731481482[/C][C]0.401793981481504[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.369039351852[/C][C]114.89875[/C][C]-0.529710648148142[/C][C]0.140960648148152[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.935289351852[/C][C]115.295833333333[/C][C]-0.360543981481478[/C][C]0.114710648148161[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.518900462963[/C][C]115.680833333333[/C][C]0.838067129629633[/C][C]0.151099537037041[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.754039351852[/C][C]116.053333333333[/C][C]0.700706018518514[/C][C]0.315960648148149[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.868206018519[/C][C]116.417083333333[/C][C]0.45112268518519[/C][C]0.051793981481481[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.078761574074[/C][C]116.752916666667[/C][C]0.325844907407408[/C][C]-0.0787615740740648[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]NA[/C][C]117.07375[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]NA[/C][C]117.39625[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]NA[/C][C]117.68125[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]NA[/C][C]117.935833333333[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148734&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148734&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
1101.76NANA-0.0784606481481403NA
2102.37NANA-0.0827662037036967NA
3102.38NANA-0.324710648148148NA
4102.86NANA-0.132210648148158NA
5102.87NANA-0.440960648148161NA
6102.92NANA-0.36637731481482NA
7102.95102.755706018519103.285416666667-0.5297106481481420.194293981481479
8103.02103.152372685185103.512916666667-0.360543981481478-0.132372685185203
9104.08104.565150462963103.7270833333330.838067129629633-0.485150462962977
10104.16104.653206018519103.95250.700706018518514-0.493206018518521
11104.24104.633622685185104.18250.45112268518519-0.393622685185179
12104.33104.736261574074104.4104166666670.325844907407408-0.406261574074037
13104.73104.569456018519104.647916666667-0.07846064814814030.160543981481496
14104.86104.83931712963104.922083333333-0.08276620370369670.0206828703703792
15105.03104.897372685185105.222083333333-0.3247106481481480.132627314814826
16105.62105.391122685185105.523333333333-0.1322106481481580.228877314814824
17105.63105.386956018519105.827916666667-0.4409606481481610.243043981481492
18105.63105.788206018518106.154583333333-0.36637731481482-0.158206018518499
19105.94105.939872685185106.469583333333-0.5297106481481420.000127314814832857
20106.61106.401956018519106.7625-0.3605439814814780.208043981481495
21107.69107.895150462963107.0570833333330.838067129629633-0.205150462962962
22107.78108.037789351852107.3370833333330.700706018518514-0.257789351851855
23107.93108.061956018519107.6108333333330.45112268518519-0.131956018518508
24108.48108.223344907407107.89750.3258449074074080.256655092592595
25108.14108.115289351852108.19375-0.07846064814814030.0247106481481438
26108.48108.390983796296108.47375-0.08276620370369670.0890162037037072
27108.48108.450289351852108.775-0.3247106481481480.0297106481481535
28108.89108.980706018519109.112916666667-0.132210648148158-0.0907060185185173
29108.93109.016122685185109.457083333333-0.440960648148161-0.0861226851851882
30109.21109.418206018519109.784583333333-0.36637731481482-0.208206018518524
31109.47109.575706018519110.105416666667-0.529710648148142-0.105706018518532
32109.8110.087372685185110.447916666667-0.360543981481478-0.287372685185176
33111.73111.64056712963110.80250.8380671296296330.0894328703703735
34111.85111.872789351852111.1720833333330.700706018518514-0.0227893518518414
35112.12112.004456018518111.5533333333330.451122685185190.115543981481508
36112.15112.292511574074111.9666666666670.325844907407408-0.142511574074049
37112.17112.319872685185112.398333333333-0.0784606481481403-0.149872685185187
38112.67112.74431712963112.827083333333-0.0827662037036967-0.0743171296296197
39112.8112.926956018518113.251666666667-0.324710648148148-0.126956018518499
40113.44113.542789351852113.675-0.132210648148158-0.10278935185184
41113.53113.651539351852114.0925-0.440960648148161-0.121539351851837
42114.53114.128206018518114.494583333333-0.366377314814820.401793981481504
43114.51114.369039351852114.89875-0.5297106481481420.140960648148152
44115.05114.935289351852115.295833333333-0.3605439814814780.114710648148161
45116.67116.518900462963115.6808333333330.8380671296296330.151099537037041
46117.07116.754039351852116.0533333333330.7007060185185140.315960648148149
47116.92116.868206018519116.4170833333330.451122685185190.051793981481481
48117117.078761574074116.7529166666670.325844907407408-0.0787615740740648
49117.02NA117.07375NANA
50117.35NA117.39625NANA
51117.36NA117.68125NANA
52117.82NA117.935833333333NANA
53117.88NANANANA
54118.24NANANANA
55118.5NANANANA
56118.8NANANANA
57119.76NANANANA
58120.09NANANANA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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')