FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

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, 18 Dec 2010 16:38:38 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/18/t1292690224mdat3vshfvbeumb.htm/, Retrieved Thu, 14 Aug 2025 19:27:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112088, Retrieved Thu, 14 Aug 2025 19:27:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact306

Tree of Dependent Computations

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]
-    D      [Classical Decomposition] [classical decompo...] [2010-12-18 16:38:38] [e665313c9926a9f4bdf6ad1ee5aefad6] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
120,16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112088&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112088&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112088&T=0

As an alternative you can also use a QR Code:  
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'Gwilym Jenkins' @ 72.249.127.135






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1101.76NANA-0.0784606481481486NA
2102.37NANA-0.0827662037037097NA
3102.38NANA-0.324710648148157NA
4102.86NANA-0.132210648148147NA
5102.87NANA-0.440960648148146NA
6102.92NANA-0.366377314814809NA
7102.95102.755706018519103.285416666667-0.5297106481481360.194293981481493
8103.02103.152372685185103.512916666667-0.360543981481481-0.132372685185189
9104.08104.565150462963103.7270833333330.838067129629629-0.485150462962977
10104.16104.653206018519103.95250.700706018518515-0.493206018518507
11104.24104.633622685185104.18250.451122685185191-0.393622685185193
12104.33104.736261574074104.4104166666670.325844907407399-0.406261574074051
13104.73104.569456018518104.647916666667-0.07846064814814860.16054398148151
14104.86104.839317129630104.922083333333-0.08276620370370970.0206828703703934
15105.03104.897372685185105.222083333333-0.3247106481481570.132627314814826
16105.62105.391122685185105.523333333333-0.1322106481481470.228877314814838
17105.63105.386956018518105.827916666667-0.4409606481481460.24304398148152
18105.63105.788206018518106.154583333333-0.366377314814809-0.158206018518499
19105.94105.939872685185106.469583333333-0.5297106481481360.000127314814832857
20106.61106.401956018518106.7625-0.3605439814814810.208043981481509
21107.69107.895150462963107.0570833333330.838067129629629-0.205150462962948
22107.78108.037789351852107.3370833333330.700706018518515-0.257789351851841
23107.93108.061956018519107.6108333333330.451122685185191-0.131956018518508
24108.48108.223344907407107.89750.3258449074073990.256655092592609
25108.14108.115289351852108.19375-0.07846064814814860.0247106481481580
26108.48108.390983796296108.47375-0.08276620370370970.0890162037037072
27108.48108.450289351852108.775-0.3247106481481570.0297106481481535
28108.89108.980706018519109.112916666667-0.132210648148147-0.0907060185185173
29108.93109.016122685185109.457083333333-0.440960648148146-0.0861226851851882
30109.21109.418206018519109.784583333333-0.366377314814809-0.208206018518524
31109.47109.575706018519110.105416666667-0.529710648148136-0.105706018518518
32109.8110.087372685185110.447916666667-0.360543981481481-0.28737268518519
33111.73111.640567129630110.80250.8380671296296290.0894328703703735
34111.85111.872789351852111.1720833333330.700706018518515-0.0227893518518272
35112.12112.004456018519111.5533333333330.4511226851851910.115543981481494
36112.15112.292511574074111.9666666666670.325844907407399-0.142511574074078
37112.17112.319872685185112.398333333333-0.0784606481481486-0.149872685185187
38112.67112.744317129630112.827083333333-0.0827662037037097-0.0743171296296197
39112.8112.926956018519113.251666666667-0.324710648148157-0.126956018518513
40113.44113.542789351852113.675-0.132210648148147-0.102789351851854
41113.53113.651539351852114.0925-0.440960648148146-0.121539351851851
42114.53114.128206018518114.494583333333-0.3663773148148090.401793981481504
43114.51114.369039351852114.89875-0.5297106481481360.140960648148166
44115.05114.935289351852115.295833333333-0.3605439814814810.114710648148161
45116.67116.518900462963115.6808333333330.8380671296296290.151099537037027
46117.07116.754039351852116.0533333333330.7007060185185150.315960648148135
47116.92116.868206018519116.4170833333330.4511226851851910.0517939814814667
48117117.078761574074116.7529166666670.325844907407399-0.0787615740740648
49117.02NA117.07375NANA
50117.35NA117.39625NANA
51117.36NA117.68125NANA
52117.82NA117.935833333333NANA
53117.88NA118.196666666667NANA
54118.24NANANANA
55118.5NANANANA
56118.8NANANANA
57119.76NANANANA
58120.09NANANANA
59120.16NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.76 & NA & NA & -0.0784606481481486 & NA \tabularnewline
2 & 102.37 & NA & NA & -0.0827662037037097 & NA \tabularnewline
3 & 102.38 & NA & NA & -0.324710648148157 & NA \tabularnewline
4 & 102.86 & NA & NA & -0.132210648148147 & NA \tabularnewline
5 & 102.87 & NA & NA & -0.440960648148146 & NA \tabularnewline
6 & 102.92 & NA & NA & -0.366377314814809 & NA \tabularnewline
7 & 102.95 & 102.755706018519 & 103.285416666667 & -0.529710648148136 & 0.194293981481493 \tabularnewline
8 & 103.02 & 103.152372685185 & 103.512916666667 & -0.360543981481481 & -0.132372685185189 \tabularnewline
9 & 104.08 & 104.565150462963 & 103.727083333333 & 0.838067129629629 & -0.485150462962977 \tabularnewline
10 & 104.16 & 104.653206018519 & 103.9525 & 0.700706018518515 & -0.493206018518507 \tabularnewline
11 & 104.24 & 104.633622685185 & 104.1825 & 0.451122685185191 & -0.393622685185193 \tabularnewline
12 & 104.33 & 104.736261574074 & 104.410416666667 & 0.325844907407399 & -0.406261574074051 \tabularnewline
13 & 104.73 & 104.569456018518 & 104.647916666667 & -0.0784606481481486 & 0.16054398148151 \tabularnewline
14 & 104.86 & 104.839317129630 & 104.922083333333 & -0.0827662037037097 & 0.0206828703703934 \tabularnewline
15 & 105.03 & 104.897372685185 & 105.222083333333 & -0.324710648148157 & 0.132627314814826 \tabularnewline
16 & 105.62 & 105.391122685185 & 105.523333333333 & -0.132210648148147 & 0.228877314814838 \tabularnewline
17 & 105.63 & 105.386956018518 & 105.827916666667 & -0.440960648148146 & 0.24304398148152 \tabularnewline
18 & 105.63 & 105.788206018518 & 106.154583333333 & -0.366377314814809 & -0.158206018518499 \tabularnewline
19 & 105.94 & 105.939872685185 & 106.469583333333 & -0.529710648148136 & 0.000127314814832857 \tabularnewline
20 & 106.61 & 106.401956018518 & 106.7625 & -0.360543981481481 & 0.208043981481509 \tabularnewline
21 & 107.69 & 107.895150462963 & 107.057083333333 & 0.838067129629629 & -0.205150462962948 \tabularnewline
22 & 107.78 & 108.037789351852 & 107.337083333333 & 0.700706018518515 & -0.257789351851841 \tabularnewline
23 & 107.93 & 108.061956018519 & 107.610833333333 & 0.451122685185191 & -0.131956018518508 \tabularnewline
24 & 108.48 & 108.223344907407 & 107.8975 & 0.325844907407399 & 0.256655092592609 \tabularnewline
25 & 108.14 & 108.115289351852 & 108.19375 & -0.0784606481481486 & 0.0247106481481580 \tabularnewline
26 & 108.48 & 108.390983796296 & 108.47375 & -0.0827662037037097 & 0.0890162037037072 \tabularnewline
27 & 108.48 & 108.450289351852 & 108.775 & -0.324710648148157 & 0.0297106481481535 \tabularnewline
28 & 108.89 & 108.980706018519 & 109.112916666667 & -0.132210648148147 & -0.0907060185185173 \tabularnewline
29 & 108.93 & 109.016122685185 & 109.457083333333 & -0.440960648148146 & -0.0861226851851882 \tabularnewline
30 & 109.21 & 109.418206018519 & 109.784583333333 & -0.366377314814809 & -0.208206018518524 \tabularnewline
31 & 109.47 & 109.575706018519 & 110.105416666667 & -0.529710648148136 & -0.105706018518518 \tabularnewline
32 & 109.8 & 110.087372685185 & 110.447916666667 & -0.360543981481481 & -0.28737268518519 \tabularnewline
33 & 111.73 & 111.640567129630 & 110.8025 & 0.838067129629629 & 0.0894328703703735 \tabularnewline
34 & 111.85 & 111.872789351852 & 111.172083333333 & 0.700706018518515 & -0.0227893518518272 \tabularnewline
35 & 112.12 & 112.004456018519 & 111.553333333333 & 0.451122685185191 & 0.115543981481494 \tabularnewline
36 & 112.15 & 112.292511574074 & 111.966666666667 & 0.325844907407399 & -0.142511574074078 \tabularnewline
37 & 112.17 & 112.319872685185 & 112.398333333333 & -0.0784606481481486 & -0.149872685185187 \tabularnewline
38 & 112.67 & 112.744317129630 & 112.827083333333 & -0.0827662037037097 & -0.0743171296296197 \tabularnewline
39 & 112.8 & 112.926956018519 & 113.251666666667 & -0.324710648148157 & -0.126956018518513 \tabularnewline
40 & 113.44 & 113.542789351852 & 113.675 & -0.132210648148147 & -0.102789351851854 \tabularnewline
41 & 113.53 & 113.651539351852 & 114.0925 & -0.440960648148146 & -0.121539351851851 \tabularnewline
42 & 114.53 & 114.128206018518 & 114.494583333333 & -0.366377314814809 & 0.401793981481504 \tabularnewline
43 & 114.51 & 114.369039351852 & 114.89875 & -0.529710648148136 & 0.140960648148166 \tabularnewline
44 & 115.05 & 114.935289351852 & 115.295833333333 & -0.360543981481481 & 0.114710648148161 \tabularnewline
45 & 116.67 & 116.518900462963 & 115.680833333333 & 0.838067129629629 & 0.151099537037027 \tabularnewline
46 & 117.07 & 116.754039351852 & 116.053333333333 & 0.700706018518515 & 0.315960648148135 \tabularnewline
47 & 116.92 & 116.868206018519 & 116.417083333333 & 0.451122685185191 & 0.0517939814814667 \tabularnewline
48 & 117 & 117.078761574074 & 116.752916666667 & 0.325844907407399 & -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 & 118.196666666667 & 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
59 & 120.16 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112088&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]-0.0784606481481486[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]NA[/C][C]NA[/C][C]-0.0827662037037097[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]NA[/C][C]NA[/C][C]-0.324710648148157[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]NA[/C][C]NA[/C][C]-0.132210648148147[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]NA[/C][C]NA[/C][C]-0.440960648148146[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]NA[/C][C]NA[/C][C]-0.366377314814809[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.755706018519[/C][C]103.285416666667[/C][C]-0.529710648148136[/C][C]0.194293981481493[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.152372685185[/C][C]103.512916666667[/C][C]-0.360543981481481[/C][C]-0.132372685185189[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.565150462963[/C][C]103.727083333333[/C][C]0.838067129629629[/C][C]-0.485150462962977[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.653206018519[/C][C]103.9525[/C][C]0.700706018518515[/C][C]-0.493206018518507[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.633622685185[/C][C]104.1825[/C][C]0.451122685185191[/C][C]-0.393622685185193[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.736261574074[/C][C]104.410416666667[/C][C]0.325844907407399[/C][C]-0.406261574074051[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.569456018518[/C][C]104.647916666667[/C][C]-0.0784606481481486[/C][C]0.16054398148151[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]104.839317129630[/C][C]104.922083333333[/C][C]-0.0827662037037097[/C][C]0.0206828703703934[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]104.897372685185[/C][C]105.222083333333[/C][C]-0.324710648148157[/C][C]0.132627314814826[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.391122685185[/C][C]105.523333333333[/C][C]-0.132210648148147[/C][C]0.228877314814838[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]105.386956018518[/C][C]105.827916666667[/C][C]-0.440960648148146[/C][C]0.24304398148152[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]105.788206018518[/C][C]106.154583333333[/C][C]-0.366377314814809[/C][C]-0.158206018518499[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]105.939872685185[/C][C]106.469583333333[/C][C]-0.529710648148136[/C][C]0.000127314814832857[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.401956018518[/C][C]106.7625[/C][C]-0.360543981481481[/C][C]0.208043981481509[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.895150462963[/C][C]107.057083333333[/C][C]0.838067129629629[/C][C]-0.205150462962948[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.037789351852[/C][C]107.337083333333[/C][C]0.700706018518515[/C][C]-0.257789351851841[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.061956018519[/C][C]107.610833333333[/C][C]0.451122685185191[/C][C]-0.131956018518508[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.223344907407[/C][C]107.8975[/C][C]0.325844907407399[/C][C]0.256655092592609[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]108.115289351852[/C][C]108.19375[/C][C]-0.0784606481481486[/C][C]0.0247106481481580[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.390983796296[/C][C]108.47375[/C][C]-0.0827662037037097[/C][C]0.0890162037037072[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.450289351852[/C][C]108.775[/C][C]-0.324710648148157[/C][C]0.0297106481481535[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.980706018519[/C][C]109.112916666667[/C][C]-0.132210648148147[/C][C]-0.0907060185185173[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.016122685185[/C][C]109.457083333333[/C][C]-0.440960648148146[/C][C]-0.0861226851851882[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.418206018519[/C][C]109.784583333333[/C][C]-0.366377314814809[/C][C]-0.208206018518524[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.575706018519[/C][C]110.105416666667[/C][C]-0.529710648148136[/C][C]-0.105706018518518[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]110.087372685185[/C][C]110.447916666667[/C][C]-0.360543981481481[/C][C]-0.28737268518519[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.640567129630[/C][C]110.8025[/C][C]0.838067129629629[/C][C]0.0894328703703735[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.872789351852[/C][C]111.172083333333[/C][C]0.700706018518515[/C][C]-0.0227893518518272[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]112.004456018519[/C][C]111.553333333333[/C][C]0.451122685185191[/C][C]0.115543981481494[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.292511574074[/C][C]111.966666666667[/C][C]0.325844907407399[/C][C]-0.142511574074078[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.319872685185[/C][C]112.398333333333[/C][C]-0.0784606481481486[/C][C]-0.149872685185187[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.744317129630[/C][C]112.827083333333[/C][C]-0.0827662037037097[/C][C]-0.0743171296296197[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.926956018519[/C][C]113.251666666667[/C][C]-0.324710648148157[/C][C]-0.126956018518513[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.542789351852[/C][C]113.675[/C][C]-0.132210648148147[/C][C]-0.102789351851854[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.651539351852[/C][C]114.0925[/C][C]-0.440960648148146[/C][C]-0.121539351851851[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.128206018518[/C][C]114.494583333333[/C][C]-0.366377314814809[/C][C]0.401793981481504[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.369039351852[/C][C]114.89875[/C][C]-0.529710648148136[/C][C]0.140960648148166[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.935289351852[/C][C]115.295833333333[/C][C]-0.360543981481481[/C][C]0.114710648148161[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.518900462963[/C][C]115.680833333333[/C][C]0.838067129629629[/C][C]0.151099537037027[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.754039351852[/C][C]116.053333333333[/C][C]0.700706018518515[/C][C]0.315960648148135[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.868206018519[/C][C]116.417083333333[/C][C]0.451122685185191[/C][C]0.0517939814814667[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.078761574074[/C][C]116.752916666667[/C][C]0.325844907407399[/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]118.196666666667[/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]
[ROW][C]59[/C][C]120.16[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112088&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112088&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1101.76NANA-0.0784606481481486NA
2102.37NANA-0.0827662037037097NA
3102.38NANA-0.324710648148157NA
4102.86NANA-0.132210648148147NA
5102.87NANA-0.440960648148146NA
6102.92NANA-0.366377314814809NA
7102.95102.755706018519103.285416666667-0.5297106481481360.194293981481493
8103.02103.152372685185103.512916666667-0.360543981481481-0.132372685185189
9104.08104.565150462963103.7270833333330.838067129629629-0.485150462962977
10104.16104.653206018519103.95250.700706018518515-0.493206018518507
11104.24104.633622685185104.18250.451122685185191-0.393622685185193
12104.33104.736261574074104.4104166666670.325844907407399-0.406261574074051
13104.73104.569456018518104.647916666667-0.07846064814814860.16054398148151
14104.86104.839317129630104.922083333333-0.08276620370370970.0206828703703934
15105.03104.897372685185105.222083333333-0.3247106481481570.132627314814826
16105.62105.391122685185105.523333333333-0.1322106481481470.228877314814838
17105.63105.386956018518105.827916666667-0.4409606481481460.24304398148152
18105.63105.788206018518106.154583333333-0.366377314814809-0.158206018518499
19105.94105.939872685185106.469583333333-0.5297106481481360.000127314814832857
20106.61106.401956018518106.7625-0.3605439814814810.208043981481509
21107.69107.895150462963107.0570833333330.838067129629629-0.205150462962948
22107.78108.037789351852107.3370833333330.700706018518515-0.257789351851841
23107.93108.061956018519107.6108333333330.451122685185191-0.131956018518508
24108.48108.223344907407107.89750.3258449074073990.256655092592609
25108.14108.115289351852108.19375-0.07846064814814860.0247106481481580
26108.48108.390983796296108.47375-0.08276620370370970.0890162037037072
27108.48108.450289351852108.775-0.3247106481481570.0297106481481535
28108.89108.980706018519109.112916666667-0.132210648148147-0.0907060185185173
29108.93109.016122685185109.457083333333-0.440960648148146-0.0861226851851882
30109.21109.418206018519109.784583333333-0.366377314814809-0.208206018518524
31109.47109.575706018519110.105416666667-0.529710648148136-0.105706018518518
32109.8110.087372685185110.447916666667-0.360543981481481-0.28737268518519
33111.73111.640567129630110.80250.8380671296296290.0894328703703735
34111.85111.872789351852111.1720833333330.700706018518515-0.0227893518518272
35112.12112.004456018519111.5533333333330.4511226851851910.115543981481494
36112.15112.292511574074111.9666666666670.325844907407399-0.142511574074078
37112.17112.319872685185112.398333333333-0.0784606481481486-0.149872685185187
38112.67112.744317129630112.827083333333-0.0827662037037097-0.0743171296296197
39112.8112.926956018519113.251666666667-0.324710648148157-0.126956018518513
40113.44113.542789351852113.675-0.132210648148147-0.102789351851854
41113.53113.651539351852114.0925-0.440960648148146-0.121539351851851
42114.53114.128206018518114.494583333333-0.3663773148148090.401793981481504
43114.51114.369039351852114.89875-0.5297106481481360.140960648148166
44115.05114.935289351852115.295833333333-0.3605439814814810.114710648148161
45116.67116.518900462963115.6808333333330.8380671296296290.151099537037027
46117.07116.754039351852116.0533333333330.7007060185185150.315960648148135
47116.92116.868206018519116.4170833333330.4511226851851910.0517939814814667
48117117.078761574074116.7529166666670.325844907407399-0.0787615740740648
49117.02NA117.07375NANA
50117.35NA117.39625NANA
51117.36NA117.68125NANA
52117.82NA117.935833333333NANA
53117.88NA118.196666666667NANA
54118.24NANANANA
55118.5NANANANA
56118.8NANANANA
57119.76NANANANA
58120.09NANANANA
59120.16NANANANA


Figures (Output of Computation)

Input Parameters & R Code

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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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')