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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 06 May 2012 14:55:26 -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/06/t1336330585hxiy50me9hj3e41.htm/, Retrieved Sat, 27 Apr 2024 19:56:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166259, Retrieved Sat, 27 Apr 2024 19:56:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Decompositie Gemi...] [2012-05-06 18:55:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6,94
6,98
7,05
7,07
7,08
7,1
7,12
7,13
7,18
7,2
7,21
7,22
7,26
7,29
7,32
7,36
7,41
7,48
7,48
7,51
7,51
7,51
7,51
7,54
7,58
7,64
7,63
7,71
7,77
7,85
7,88
7,89
7,94
8,02
8,08
8,15
8,17
8,17
8,25
8,33
8,41
8,43
8,48
8,52
8,56
8,63
8,7
8,72
8,73
8,82
8,83
8,81
8,82
8,83
8,84
8,83
8,82
8,87
8,87
8,87

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'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=166259&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=166259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166259&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'AstonUniversity' @ aston.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
16.94NANA0.998188275950276NA
26.98NANA0.999280772344293NA
37.05NANA0.998332469646883NA
47.07NANA0.99982033951046NA
57.08NANA1.00179262117145NA
67.1NANA1.00328537329026NA
77.127.131405798571547.121.001601938001620.998400624099413
87.137.143620121396587.146250.9996319917994170.998093386663186
97.187.165798940584417.170416666666670.9993560031031781.00198178312472
107.27.193494478285497.193750.9999644800396861.00090436181388
117.217.216840105008717.219583333333330.9996200295504650.99905220222297
127.227.242828760787417.249166666666670.999125705592010.996848087737348
137.267.266810648918017.280.9981882759502760.99906277330633
147.297.305575179813737.310833333333330.9992807723442930.997868041950103
157.327.328176299070477.340416666666670.9983324696468830.998884265506616
167.367.365759759535187.367083333333330.999820339510460.999218035922537
177.417.405751952009947.39251.001792621171451.00057361467378
187.487.442705327524927.418333333333331.003285373290261.00501090273414
197.487.456926428422077.4451.001601938001621.00309424691251
207.517.470166572051067.472916666666670.9996319917994171.00533233463601
217.517.495586421608467.500416666666670.9993560031031781.00192294205961
227.517.527649275365427.527916666666670.9999644800396860.99765540679171
237.517.554628373327647.55750.9996200295504650.994092578599206
247.547.58128259355677.587916666666670.999125705592010.994554668943248
257.587.606194662741117.620.9981882759502760.99655614089534
267.647.64699611036477.65250.9992807723442930.999085116526316
277.637.673432944823357.686250.9983324696468830.994339828713476
287.717.724028714526437.725416666666670.999820339510460.998183756813326
297.777.784346080094317.770416666666671.001792621171450.998157060343065
307.857.845273583557647.819583333333331.003285373290261.00060245399883
317.887.882189917931927.869583333333331.001601938001620.999722168844607
327.897.913336755082147.916250.9996319917994170.99705095893118
337.947.959037768047567.964166666666670.9993560031031780.997608031447723
348.028.015548611251458.015833333333330.9999644800396861.00055534423961
358.088.065267605089678.068333333333330.9996200295504651.0018266467564
368.158.112068124652468.119166666666670.999125705592011.0046759808676
378.178.153534567387178.168333333333330.9981882759502761.00201942267819
388.178.213671581681618.219583333333330.9992807723442930.994683062105989
398.258.257873411429138.271666666666660.9983324696468830.999046557020572
408.338.321421367383938.322916666666670.999820339510461.00103090953304
418.418.389178375126578.374166666666671.001792621171451.00248196234987
428.438.451425163253848.423751.003285373290260.99746490528639
438.488.48440308315548.470833333333331.001601938001620.999481037957268
448.528.518114110120788.521250.9996319917994171.00022139758341
458.568.566979336601998.57250.9993560031031780.999185321181741
468.638.616360603008638.616666666666670.9999644800396861.0015829649686
478.78.650461830722348.653750.9996200295504651.00572665023522
488.728.679904567330588.68750.999125705592011.0046193402656
498.738.703369942723128.719166666666660.9981882759502761.00305974093393
508.828.740792189093228.747083333333330.9992807723442931.00906185723139
518.838.756207702527878.770833333333330.9983324696468831.00842742657313
528.818.790087151529468.791666666666670.999820339510461.00226537554489
538.828.8245407517448.808751.001792621171450.999485440447073
548.838.851067170281138.822083333333331.003285373290260.997619815794432
558.84NANA1.00160193800162NA
568.83NANA0.999631991799417NA
578.82NANA0.999356003103178NA
588.87NANA0.999964480039686NA
598.87NANA0.999620029550465NA
608.87NANA0.99912570559201NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 6.94 & NA & NA & 0.998188275950276 & NA \tabularnewline
2 & 6.98 & NA & NA & 0.999280772344293 & NA \tabularnewline
3 & 7.05 & NA & NA & 0.998332469646883 & NA \tabularnewline
4 & 7.07 & NA & NA & 0.99982033951046 & NA \tabularnewline
5 & 7.08 & NA & NA & 1.00179262117145 & NA \tabularnewline
6 & 7.1 & NA & NA & 1.00328537329026 & NA \tabularnewline
7 & 7.12 & 7.13140579857154 & 7.12 & 1.00160193800162 & 0.998400624099413 \tabularnewline
8 & 7.13 & 7.14362012139658 & 7.14625 & 0.999631991799417 & 0.998093386663186 \tabularnewline
9 & 7.18 & 7.16579894058441 & 7.17041666666667 & 0.999356003103178 & 1.00198178312472 \tabularnewline
10 & 7.2 & 7.19349447828549 & 7.19375 & 0.999964480039686 & 1.00090436181388 \tabularnewline
11 & 7.21 & 7.21684010500871 & 7.21958333333333 & 0.999620029550465 & 0.99905220222297 \tabularnewline
12 & 7.22 & 7.24282876078741 & 7.24916666666667 & 0.99912570559201 & 0.996848087737348 \tabularnewline
13 & 7.26 & 7.26681064891801 & 7.28 & 0.998188275950276 & 0.99906277330633 \tabularnewline
14 & 7.29 & 7.30557517981373 & 7.31083333333333 & 0.999280772344293 & 0.997868041950103 \tabularnewline
15 & 7.32 & 7.32817629907047 & 7.34041666666667 & 0.998332469646883 & 0.998884265506616 \tabularnewline
16 & 7.36 & 7.36575975953518 & 7.36708333333333 & 0.99982033951046 & 0.999218035922537 \tabularnewline
17 & 7.41 & 7.40575195200994 & 7.3925 & 1.00179262117145 & 1.00057361467378 \tabularnewline
18 & 7.48 & 7.44270532752492 & 7.41833333333333 & 1.00328537329026 & 1.00501090273414 \tabularnewline
19 & 7.48 & 7.45692642842207 & 7.445 & 1.00160193800162 & 1.00309424691251 \tabularnewline
20 & 7.51 & 7.47016657205106 & 7.47291666666667 & 0.999631991799417 & 1.00533233463601 \tabularnewline
21 & 7.51 & 7.49558642160846 & 7.50041666666667 & 0.999356003103178 & 1.00192294205961 \tabularnewline
22 & 7.51 & 7.52764927536542 & 7.52791666666667 & 0.999964480039686 & 0.99765540679171 \tabularnewline
23 & 7.51 & 7.55462837332764 & 7.5575 & 0.999620029550465 & 0.994092578599206 \tabularnewline
24 & 7.54 & 7.5812825935567 & 7.58791666666667 & 0.99912570559201 & 0.994554668943248 \tabularnewline
25 & 7.58 & 7.60619466274111 & 7.62 & 0.998188275950276 & 0.99655614089534 \tabularnewline
26 & 7.64 & 7.6469961103647 & 7.6525 & 0.999280772344293 & 0.999085116526316 \tabularnewline
27 & 7.63 & 7.67343294482335 & 7.68625 & 0.998332469646883 & 0.994339828713476 \tabularnewline
28 & 7.71 & 7.72402871452643 & 7.72541666666667 & 0.99982033951046 & 0.998183756813326 \tabularnewline
29 & 7.77 & 7.78434608009431 & 7.77041666666667 & 1.00179262117145 & 0.998157060343065 \tabularnewline
30 & 7.85 & 7.84527358355764 & 7.81958333333333 & 1.00328537329026 & 1.00060245399883 \tabularnewline
31 & 7.88 & 7.88218991793192 & 7.86958333333333 & 1.00160193800162 & 0.999722168844607 \tabularnewline
32 & 7.89 & 7.91333675508214 & 7.91625 & 0.999631991799417 & 0.99705095893118 \tabularnewline
33 & 7.94 & 7.95903776804756 & 7.96416666666667 & 0.999356003103178 & 0.997608031447723 \tabularnewline
34 & 8.02 & 8.01554861125145 & 8.01583333333333 & 0.999964480039686 & 1.00055534423961 \tabularnewline
35 & 8.08 & 8.06526760508967 & 8.06833333333333 & 0.999620029550465 & 1.0018266467564 \tabularnewline
36 & 8.15 & 8.11206812465246 & 8.11916666666667 & 0.99912570559201 & 1.0046759808676 \tabularnewline
37 & 8.17 & 8.15353456738717 & 8.16833333333333 & 0.998188275950276 & 1.00201942267819 \tabularnewline
38 & 8.17 & 8.21367158168161 & 8.21958333333333 & 0.999280772344293 & 0.994683062105989 \tabularnewline
39 & 8.25 & 8.25787341142913 & 8.27166666666666 & 0.998332469646883 & 0.999046557020572 \tabularnewline
40 & 8.33 & 8.32142136738393 & 8.32291666666667 & 0.99982033951046 & 1.00103090953304 \tabularnewline
41 & 8.41 & 8.38917837512657 & 8.37416666666667 & 1.00179262117145 & 1.00248196234987 \tabularnewline
42 & 8.43 & 8.45142516325384 & 8.42375 & 1.00328537329026 & 0.99746490528639 \tabularnewline
43 & 8.48 & 8.4844030831554 & 8.47083333333333 & 1.00160193800162 & 0.999481037957268 \tabularnewline
44 & 8.52 & 8.51811411012078 & 8.52125 & 0.999631991799417 & 1.00022139758341 \tabularnewline
45 & 8.56 & 8.56697933660199 & 8.5725 & 0.999356003103178 & 0.999185321181741 \tabularnewline
46 & 8.63 & 8.61636060300863 & 8.61666666666667 & 0.999964480039686 & 1.0015829649686 \tabularnewline
47 & 8.7 & 8.65046183072234 & 8.65375 & 0.999620029550465 & 1.00572665023522 \tabularnewline
48 & 8.72 & 8.67990456733058 & 8.6875 & 0.99912570559201 & 1.0046193402656 \tabularnewline
49 & 8.73 & 8.70336994272312 & 8.71916666666666 & 0.998188275950276 & 1.00305974093393 \tabularnewline
50 & 8.82 & 8.74079218909322 & 8.74708333333333 & 0.999280772344293 & 1.00906185723139 \tabularnewline
51 & 8.83 & 8.75620770252787 & 8.77083333333333 & 0.998332469646883 & 1.00842742657313 \tabularnewline
52 & 8.81 & 8.79008715152946 & 8.79166666666667 & 0.99982033951046 & 1.00226537554489 \tabularnewline
53 & 8.82 & 8.824540751744 & 8.80875 & 1.00179262117145 & 0.999485440447073 \tabularnewline
54 & 8.83 & 8.85106717028113 & 8.82208333333333 & 1.00328537329026 & 0.997619815794432 \tabularnewline
55 & 8.84 & NA & NA & 1.00160193800162 & NA \tabularnewline
56 & 8.83 & NA & NA & 0.999631991799417 & NA \tabularnewline
57 & 8.82 & NA & NA & 0.999356003103178 & NA \tabularnewline
58 & 8.87 & NA & NA & 0.999964480039686 & NA \tabularnewline
59 & 8.87 & NA & NA & 0.999620029550465 & NA \tabularnewline
60 & 8.87 & NA & NA & 0.99912570559201 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166259&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]6.94[/C][C]NA[/C][C]NA[/C][C]0.998188275950276[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]6.98[/C][C]NA[/C][C]NA[/C][C]0.999280772344293[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]7.05[/C][C]NA[/C][C]NA[/C][C]0.998332469646883[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]7.07[/C][C]NA[/C][C]NA[/C][C]0.99982033951046[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]7.08[/C][C]NA[/C][C]NA[/C][C]1.00179262117145[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]7.1[/C][C]NA[/C][C]NA[/C][C]1.00328537329026[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]7.12[/C][C]7.13140579857154[/C][C]7.12[/C][C]1.00160193800162[/C][C]0.998400624099413[/C][/ROW]
[ROW][C]8[/C][C]7.13[/C][C]7.14362012139658[/C][C]7.14625[/C][C]0.999631991799417[/C][C]0.998093386663186[/C][/ROW]
[ROW][C]9[/C][C]7.18[/C][C]7.16579894058441[/C][C]7.17041666666667[/C][C]0.999356003103178[/C][C]1.00198178312472[/C][/ROW]
[ROW][C]10[/C][C]7.2[/C][C]7.19349447828549[/C][C]7.19375[/C][C]0.999964480039686[/C][C]1.00090436181388[/C][/ROW]
[ROW][C]11[/C][C]7.21[/C][C]7.21684010500871[/C][C]7.21958333333333[/C][C]0.999620029550465[/C][C]0.99905220222297[/C][/ROW]
[ROW][C]12[/C][C]7.22[/C][C]7.24282876078741[/C][C]7.24916666666667[/C][C]0.99912570559201[/C][C]0.996848087737348[/C][/ROW]
[ROW][C]13[/C][C]7.26[/C][C]7.26681064891801[/C][C]7.28[/C][C]0.998188275950276[/C][C]0.99906277330633[/C][/ROW]
[ROW][C]14[/C][C]7.29[/C][C]7.30557517981373[/C][C]7.31083333333333[/C][C]0.999280772344293[/C][C]0.997868041950103[/C][/ROW]
[ROW][C]15[/C][C]7.32[/C][C]7.32817629907047[/C][C]7.34041666666667[/C][C]0.998332469646883[/C][C]0.998884265506616[/C][/ROW]
[ROW][C]16[/C][C]7.36[/C][C]7.36575975953518[/C][C]7.36708333333333[/C][C]0.99982033951046[/C][C]0.999218035922537[/C][/ROW]
[ROW][C]17[/C][C]7.41[/C][C]7.40575195200994[/C][C]7.3925[/C][C]1.00179262117145[/C][C]1.00057361467378[/C][/ROW]
[ROW][C]18[/C][C]7.48[/C][C]7.44270532752492[/C][C]7.41833333333333[/C][C]1.00328537329026[/C][C]1.00501090273414[/C][/ROW]
[ROW][C]19[/C][C]7.48[/C][C]7.45692642842207[/C][C]7.445[/C][C]1.00160193800162[/C][C]1.00309424691251[/C][/ROW]
[ROW][C]20[/C][C]7.51[/C][C]7.47016657205106[/C][C]7.47291666666667[/C][C]0.999631991799417[/C][C]1.00533233463601[/C][/ROW]
[ROW][C]21[/C][C]7.51[/C][C]7.49558642160846[/C][C]7.50041666666667[/C][C]0.999356003103178[/C][C]1.00192294205961[/C][/ROW]
[ROW][C]22[/C][C]7.51[/C][C]7.52764927536542[/C][C]7.52791666666667[/C][C]0.999964480039686[/C][C]0.99765540679171[/C][/ROW]
[ROW][C]23[/C][C]7.51[/C][C]7.55462837332764[/C][C]7.5575[/C][C]0.999620029550465[/C][C]0.994092578599206[/C][/ROW]
[ROW][C]24[/C][C]7.54[/C][C]7.5812825935567[/C][C]7.58791666666667[/C][C]0.99912570559201[/C][C]0.994554668943248[/C][/ROW]
[ROW][C]25[/C][C]7.58[/C][C]7.60619466274111[/C][C]7.62[/C][C]0.998188275950276[/C][C]0.99655614089534[/C][/ROW]
[ROW][C]26[/C][C]7.64[/C][C]7.6469961103647[/C][C]7.6525[/C][C]0.999280772344293[/C][C]0.999085116526316[/C][/ROW]
[ROW][C]27[/C][C]7.63[/C][C]7.67343294482335[/C][C]7.68625[/C][C]0.998332469646883[/C][C]0.994339828713476[/C][/ROW]
[ROW][C]28[/C][C]7.71[/C][C]7.72402871452643[/C][C]7.72541666666667[/C][C]0.99982033951046[/C][C]0.998183756813326[/C][/ROW]
[ROW][C]29[/C][C]7.77[/C][C]7.78434608009431[/C][C]7.77041666666667[/C][C]1.00179262117145[/C][C]0.998157060343065[/C][/ROW]
[ROW][C]30[/C][C]7.85[/C][C]7.84527358355764[/C][C]7.81958333333333[/C][C]1.00328537329026[/C][C]1.00060245399883[/C][/ROW]
[ROW][C]31[/C][C]7.88[/C][C]7.88218991793192[/C][C]7.86958333333333[/C][C]1.00160193800162[/C][C]0.999722168844607[/C][/ROW]
[ROW][C]32[/C][C]7.89[/C][C]7.91333675508214[/C][C]7.91625[/C][C]0.999631991799417[/C][C]0.99705095893118[/C][/ROW]
[ROW][C]33[/C][C]7.94[/C][C]7.95903776804756[/C][C]7.96416666666667[/C][C]0.999356003103178[/C][C]0.997608031447723[/C][/ROW]
[ROW][C]34[/C][C]8.02[/C][C]8.01554861125145[/C][C]8.01583333333333[/C][C]0.999964480039686[/C][C]1.00055534423961[/C][/ROW]
[ROW][C]35[/C][C]8.08[/C][C]8.06526760508967[/C][C]8.06833333333333[/C][C]0.999620029550465[/C][C]1.0018266467564[/C][/ROW]
[ROW][C]36[/C][C]8.15[/C][C]8.11206812465246[/C][C]8.11916666666667[/C][C]0.99912570559201[/C][C]1.0046759808676[/C][/ROW]
[ROW][C]37[/C][C]8.17[/C][C]8.15353456738717[/C][C]8.16833333333333[/C][C]0.998188275950276[/C][C]1.00201942267819[/C][/ROW]
[ROW][C]38[/C][C]8.17[/C][C]8.21367158168161[/C][C]8.21958333333333[/C][C]0.999280772344293[/C][C]0.994683062105989[/C][/ROW]
[ROW][C]39[/C][C]8.25[/C][C]8.25787341142913[/C][C]8.27166666666666[/C][C]0.998332469646883[/C][C]0.999046557020572[/C][/ROW]
[ROW][C]40[/C][C]8.33[/C][C]8.32142136738393[/C][C]8.32291666666667[/C][C]0.99982033951046[/C][C]1.00103090953304[/C][/ROW]
[ROW][C]41[/C][C]8.41[/C][C]8.38917837512657[/C][C]8.37416666666667[/C][C]1.00179262117145[/C][C]1.00248196234987[/C][/ROW]
[ROW][C]42[/C][C]8.43[/C][C]8.45142516325384[/C][C]8.42375[/C][C]1.00328537329026[/C][C]0.99746490528639[/C][/ROW]
[ROW][C]43[/C][C]8.48[/C][C]8.4844030831554[/C][C]8.47083333333333[/C][C]1.00160193800162[/C][C]0.999481037957268[/C][/ROW]
[ROW][C]44[/C][C]8.52[/C][C]8.51811411012078[/C][C]8.52125[/C][C]0.999631991799417[/C][C]1.00022139758341[/C][/ROW]
[ROW][C]45[/C][C]8.56[/C][C]8.56697933660199[/C][C]8.5725[/C][C]0.999356003103178[/C][C]0.999185321181741[/C][/ROW]
[ROW][C]46[/C][C]8.63[/C][C]8.61636060300863[/C][C]8.61666666666667[/C][C]0.999964480039686[/C][C]1.0015829649686[/C][/ROW]
[ROW][C]47[/C][C]8.7[/C][C]8.65046183072234[/C][C]8.65375[/C][C]0.999620029550465[/C][C]1.00572665023522[/C][/ROW]
[ROW][C]48[/C][C]8.72[/C][C]8.67990456733058[/C][C]8.6875[/C][C]0.99912570559201[/C][C]1.0046193402656[/C][/ROW]
[ROW][C]49[/C][C]8.73[/C][C]8.70336994272312[/C][C]8.71916666666666[/C][C]0.998188275950276[/C][C]1.00305974093393[/C][/ROW]
[ROW][C]50[/C][C]8.82[/C][C]8.74079218909322[/C][C]8.74708333333333[/C][C]0.999280772344293[/C][C]1.00906185723139[/C][/ROW]
[ROW][C]51[/C][C]8.83[/C][C]8.75620770252787[/C][C]8.77083333333333[/C][C]0.998332469646883[/C][C]1.00842742657313[/C][/ROW]
[ROW][C]52[/C][C]8.81[/C][C]8.79008715152946[/C][C]8.79166666666667[/C][C]0.99982033951046[/C][C]1.00226537554489[/C][/ROW]
[ROW][C]53[/C][C]8.82[/C][C]8.824540751744[/C][C]8.80875[/C][C]1.00179262117145[/C][C]0.999485440447073[/C][/ROW]
[ROW][C]54[/C][C]8.83[/C][C]8.85106717028113[/C][C]8.82208333333333[/C][C]1.00328537329026[/C][C]0.997619815794432[/C][/ROW]
[ROW][C]55[/C][C]8.84[/C][C]NA[/C][C]NA[/C][C]1.00160193800162[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]8.83[/C][C]NA[/C][C]NA[/C][C]0.999631991799417[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]8.82[/C][C]NA[/C][C]NA[/C][C]0.999356003103178[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]8.87[/C][C]NA[/C][C]NA[/C][C]0.999964480039686[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]8.87[/C][C]NA[/C][C]NA[/C][C]0.999620029550465[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]8.87[/C][C]NA[/C][C]NA[/C][C]0.99912570559201[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166259&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
16.94NANA0.998188275950276NA
26.98NANA0.999280772344293NA
37.05NANA0.998332469646883NA
47.07NANA0.99982033951046NA
57.08NANA1.00179262117145NA
67.1NANA1.00328537329026NA
77.127.131405798571547.121.001601938001620.998400624099413
87.137.143620121396587.146250.9996319917994170.998093386663186
97.187.165798940584417.170416666666670.9993560031031781.00198178312472
107.27.193494478285497.193750.9999644800396861.00090436181388
117.217.216840105008717.219583333333330.9996200295504650.99905220222297
127.227.242828760787417.249166666666670.999125705592010.996848087737348
137.267.266810648918017.280.9981882759502760.99906277330633
147.297.305575179813737.310833333333330.9992807723442930.997868041950103
157.327.328176299070477.340416666666670.9983324696468830.998884265506616
167.367.365759759535187.367083333333330.999820339510460.999218035922537
177.417.405751952009947.39251.001792621171451.00057361467378
187.487.442705327524927.418333333333331.003285373290261.00501090273414
197.487.456926428422077.4451.001601938001621.00309424691251
207.517.470166572051067.472916666666670.9996319917994171.00533233463601
217.517.495586421608467.500416666666670.9993560031031781.00192294205961
227.517.527649275365427.527916666666670.9999644800396860.99765540679171
237.517.554628373327647.55750.9996200295504650.994092578599206
247.547.58128259355677.587916666666670.999125705592010.994554668943248
257.587.606194662741117.620.9981882759502760.99655614089534
267.647.64699611036477.65250.9992807723442930.999085116526316
277.637.673432944823357.686250.9983324696468830.994339828713476
287.717.724028714526437.725416666666670.999820339510460.998183756813326
297.777.784346080094317.770416666666671.001792621171450.998157060343065
307.857.845273583557647.819583333333331.003285373290261.00060245399883
317.887.882189917931927.869583333333331.001601938001620.999722168844607
327.897.913336755082147.916250.9996319917994170.99705095893118
337.947.959037768047567.964166666666670.9993560031031780.997608031447723
348.028.015548611251458.015833333333330.9999644800396861.00055534423961
358.088.065267605089678.068333333333330.9996200295504651.0018266467564
368.158.112068124652468.119166666666670.999125705592011.0046759808676
378.178.153534567387178.168333333333330.9981882759502761.00201942267819
388.178.213671581681618.219583333333330.9992807723442930.994683062105989
398.258.257873411429138.271666666666660.9983324696468830.999046557020572
408.338.321421367383938.322916666666670.999820339510461.00103090953304
418.418.389178375126578.374166666666671.001792621171451.00248196234987
428.438.451425163253848.423751.003285373290260.99746490528639
438.488.48440308315548.470833333333331.001601938001620.999481037957268
448.528.518114110120788.521250.9996319917994171.00022139758341
458.568.566979336601998.57250.9993560031031780.999185321181741
468.638.616360603008638.616666666666670.9999644800396861.0015829649686
478.78.650461830722348.653750.9996200295504651.00572665023522
488.728.679904567330588.68750.999125705592011.0046193402656
498.738.703369942723128.719166666666660.9981882759502761.00305974093393
508.828.740792189093228.747083333333330.9992807723442931.00906185723139
518.838.756207702527878.770833333333330.9983324696468831.00842742657313
528.818.790087151529468.791666666666670.999820339510461.00226537554489
538.828.8245407517448.808751.001792621171450.999485440447073
548.838.851067170281138.822083333333331.003285373290260.997619815794432
558.84NANA1.00160193800162NA
568.83NANA0.999631991799417NA
578.82NANA0.999356003103178NA
588.87NANA0.999964480039686NA
598.87NANA0.999620029550465NA
608.87NANA0.99912570559201NA


Figures (Output of Computation)

Input Parameters & R Code

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')