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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationTue, 01 Dec 2009 14:12:47 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/01/t1259702143a4813n49d6q4jd0.htm/, Retrieved Fri, 29 Mar 2024 11:04:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62261, Retrieved Fri, 29 Mar 2024 11:04:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [] [2009-11-27 14:58:37] [b98453cac15ba1066b407e146608df68]
-    D      [Classical Decomposition] [] [2009-12-01 21:12:47] [fc845972e0ebdb725d2fb9537c0c51aa] [Current]
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Dataseries X:
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3
91
93.2
103.1
94.1
91.8
102.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62261&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]1 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=62261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62261&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1111.4NANA1.08581264005774NA
287.4NANA0.834380110345525NA
396.8NANA0.94236680559873NA
4114.1NANA1.07817693771933NA
5110.3NANA1.08557752225472NA
6103.9NANA1.02554157155640NA
7101.696.1501946622139101.9041666666670.9435354589251061.05668012796991
894.699.8945907805817101.73750.9818856447286570.946998223435228
995.998.4307566054336101.4208333333330.9705181210839350.974288965230875
10104.7104.701194659951101.33751.033192990353540.999988589815471
11102.8101.761359100205101.1166666666671.006375728698251.01020663352945
1298.1102.415520850928101.13751.012636468678070.95786262848568
13113.9110.069732166520101.3708333333331.085812640057741.03479855686107
1480.984.7591128759329101.5833333333330.8343801103455250.954469640549664
1595.796.090001944217101.9666666666670.942366805598730.995941284875366
16113.2110.638923425631102.6166666666671.078176937719331.02314806123444
17105.9111.791868593856102.9791666666671.085577522254720.947296089885915
18108.8106.023906139406103.3833333333331.025541571556401.02618365953188
19102.398.037265580064103.9041666666670.9435354589251061.04348075596269
2099102.234751567185104.1208333333330.9818856447286570.96835956934801
21100.7101.427231304280104.5083333333330.9705181210839350.99283001916814
22115.5108.252795564292104.7751.033192990353541.06694704185634
23100.7105.954591303115105.2833333333331.006375728698250.950407139148107
24109.9107.233982714388105.8958333333331.012636468678071.02486168300503
25114.6115.154954697457106.0541666666671.085812640057740.995180800522954
2685.488.7224184000741106.3333333333330.8343801103455250.96255266188651
27100.5100.660480951371106.8166666666670.942366805598730.998405720399364
28114.8115.522166472719107.1458333333331.078176937719330.9937486761652
29116.5116.708630121735107.5083333333331.085577522254720.998212384795218
30112.9110.459373436387107.7083333333331.025541571556401.02209524178605
31102101.705259676635107.7916666666670.9435354589251061.00289798506293
32106106.235935569454108.1958333333330.9818856447286570.997779135956307
33105.3105.442750030265108.6458333333330.9705181210839350.998646184491356
34118.8112.312383022222108.7041666666671.033192990353541.05776403993221
35106.1109.548191301008108.8541666666671.006375728698250.968523521383085
36109.3110.495516007255109.1166666666671.012636468678070.9891804115637
37117.2118.407868398297109.051.085812640057740.989799086710742
3892.591.0830187955933109.16250.8343801103455251.01555702943472
39104.2103.350152875684109.6708333333330.942366805598731.00822298855560
40112.5118.177177181852109.6083333333331.078176937719330.951960460410081
41122.4119.087854191343109.71.085577522254721.02781262481508
42113.3112.971950286700110.1583333333331.025541571556401.00290381561500
43100103.879322629909110.0958333333330.9435354589251060.962655487813204
44110.7108.191524478539110.18750.9818856447286571.02318550860200
45112.8106.902571037395110.150.9705181210839351.05516639034380
46109.8113.801902916649110.1458333333331.033192990353540.964834481550103
47117.3110.74745571104110.0458333333331.006375728698251.059166544702
48109.1110.567244423787109.18751.012636468678070.986729845430869
49115.9117.670420646924108.3708333333331.085812640057740.984954412186248
509689.5394155914541107.31250.8343801103455251.07215352441012
5199.899.5846121816457105.6750.942366805598731.00216286245069
52116.8112.754845665906104.5791666666671.078176937719331.03587565847130
53115.7112.176343966321103.3333333333331.085577522254721.03141175678480
5499.4104.242027658826101.6458333333331.025541571556400.953550139348078
5594.394.7073716896076100.3750.9435354589251060.995698627442195
5691NANA0.981885644728657NA
5793.2NANA0.970518121083935NA
58103.1NANA1.03319299035354NA
5994.1NANA1.00637572869825NA
6091.8NANA1.01263646867807NA
61102.7NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 111.4 & NA & NA & 1.08581264005774 & NA \tabularnewline
2 & 87.4 & NA & NA & 0.834380110345525 & NA \tabularnewline
3 & 96.8 & NA & NA & 0.94236680559873 & NA \tabularnewline
4 & 114.1 & NA & NA & 1.07817693771933 & NA \tabularnewline
5 & 110.3 & NA & NA & 1.08557752225472 & NA \tabularnewline
6 & 103.9 & NA & NA & 1.02554157155640 & NA \tabularnewline
7 & 101.6 & 96.1501946622139 & 101.904166666667 & 0.943535458925106 & 1.05668012796991 \tabularnewline
8 & 94.6 & 99.8945907805817 & 101.7375 & 0.981885644728657 & 0.946998223435228 \tabularnewline
9 & 95.9 & 98.4307566054336 & 101.420833333333 & 0.970518121083935 & 0.974288965230875 \tabularnewline
10 & 104.7 & 104.701194659951 & 101.3375 & 1.03319299035354 & 0.999988589815471 \tabularnewline
11 & 102.8 & 101.761359100205 & 101.116666666667 & 1.00637572869825 & 1.01020663352945 \tabularnewline
12 & 98.1 & 102.415520850928 & 101.1375 & 1.01263646867807 & 0.95786262848568 \tabularnewline
13 & 113.9 & 110.069732166520 & 101.370833333333 & 1.08581264005774 & 1.03479855686107 \tabularnewline
14 & 80.9 & 84.7591128759329 & 101.583333333333 & 0.834380110345525 & 0.954469640549664 \tabularnewline
15 & 95.7 & 96.090001944217 & 101.966666666667 & 0.94236680559873 & 0.995941284875366 \tabularnewline
16 & 113.2 & 110.638923425631 & 102.616666666667 & 1.07817693771933 & 1.02314806123444 \tabularnewline
17 & 105.9 & 111.791868593856 & 102.979166666667 & 1.08557752225472 & 0.947296089885915 \tabularnewline
18 & 108.8 & 106.023906139406 & 103.383333333333 & 1.02554157155640 & 1.02618365953188 \tabularnewline
19 & 102.3 & 98.037265580064 & 103.904166666667 & 0.943535458925106 & 1.04348075596269 \tabularnewline
20 & 99 & 102.234751567185 & 104.120833333333 & 0.981885644728657 & 0.96835956934801 \tabularnewline
21 & 100.7 & 101.427231304280 & 104.508333333333 & 0.970518121083935 & 0.99283001916814 \tabularnewline
22 & 115.5 & 108.252795564292 & 104.775 & 1.03319299035354 & 1.06694704185634 \tabularnewline
23 & 100.7 & 105.954591303115 & 105.283333333333 & 1.00637572869825 & 0.950407139148107 \tabularnewline
24 & 109.9 & 107.233982714388 & 105.895833333333 & 1.01263646867807 & 1.02486168300503 \tabularnewline
25 & 114.6 & 115.154954697457 & 106.054166666667 & 1.08581264005774 & 0.995180800522954 \tabularnewline
26 & 85.4 & 88.7224184000741 & 106.333333333333 & 0.834380110345525 & 0.96255266188651 \tabularnewline
27 & 100.5 & 100.660480951371 & 106.816666666667 & 0.94236680559873 & 0.998405720399364 \tabularnewline
28 & 114.8 & 115.522166472719 & 107.145833333333 & 1.07817693771933 & 0.9937486761652 \tabularnewline
29 & 116.5 & 116.708630121735 & 107.508333333333 & 1.08557752225472 & 0.998212384795218 \tabularnewline
30 & 112.9 & 110.459373436387 & 107.708333333333 & 1.02554157155640 & 1.02209524178605 \tabularnewline
31 & 102 & 101.705259676635 & 107.791666666667 & 0.943535458925106 & 1.00289798506293 \tabularnewline
32 & 106 & 106.235935569454 & 108.195833333333 & 0.981885644728657 & 0.997779135956307 \tabularnewline
33 & 105.3 & 105.442750030265 & 108.645833333333 & 0.970518121083935 & 0.998646184491356 \tabularnewline
34 & 118.8 & 112.312383022222 & 108.704166666667 & 1.03319299035354 & 1.05776403993221 \tabularnewline
35 & 106.1 & 109.548191301008 & 108.854166666667 & 1.00637572869825 & 0.968523521383085 \tabularnewline
36 & 109.3 & 110.495516007255 & 109.116666666667 & 1.01263646867807 & 0.9891804115637 \tabularnewline
37 & 117.2 & 118.407868398297 & 109.05 & 1.08581264005774 & 0.989799086710742 \tabularnewline
38 & 92.5 & 91.0830187955933 & 109.1625 & 0.834380110345525 & 1.01555702943472 \tabularnewline
39 & 104.2 & 103.350152875684 & 109.670833333333 & 0.94236680559873 & 1.00822298855560 \tabularnewline
40 & 112.5 & 118.177177181852 & 109.608333333333 & 1.07817693771933 & 0.951960460410081 \tabularnewline
41 & 122.4 & 119.087854191343 & 109.7 & 1.08557752225472 & 1.02781262481508 \tabularnewline
42 & 113.3 & 112.971950286700 & 110.158333333333 & 1.02554157155640 & 1.00290381561500 \tabularnewline
43 & 100 & 103.879322629909 & 110.095833333333 & 0.943535458925106 & 0.962655487813204 \tabularnewline
44 & 110.7 & 108.191524478539 & 110.1875 & 0.981885644728657 & 1.02318550860200 \tabularnewline
45 & 112.8 & 106.902571037395 & 110.15 & 0.970518121083935 & 1.05516639034380 \tabularnewline
46 & 109.8 & 113.801902916649 & 110.145833333333 & 1.03319299035354 & 0.964834481550103 \tabularnewline
47 & 117.3 & 110.74745571104 & 110.045833333333 & 1.00637572869825 & 1.059166544702 \tabularnewline
48 & 109.1 & 110.567244423787 & 109.1875 & 1.01263646867807 & 0.986729845430869 \tabularnewline
49 & 115.9 & 117.670420646924 & 108.370833333333 & 1.08581264005774 & 0.984954412186248 \tabularnewline
50 & 96 & 89.5394155914541 & 107.3125 & 0.834380110345525 & 1.07215352441012 \tabularnewline
51 & 99.8 & 99.5846121816457 & 105.675 & 0.94236680559873 & 1.00216286245069 \tabularnewline
52 & 116.8 & 112.754845665906 & 104.579166666667 & 1.07817693771933 & 1.03587565847130 \tabularnewline
53 & 115.7 & 112.176343966321 & 103.333333333333 & 1.08557752225472 & 1.03141175678480 \tabularnewline
54 & 99.4 & 104.242027658826 & 101.645833333333 & 1.02554157155640 & 0.953550139348078 \tabularnewline
55 & 94.3 & 94.7073716896076 & 100.375 & 0.943535458925106 & 0.995698627442195 \tabularnewline
56 & 91 & NA & NA & 0.981885644728657 & NA \tabularnewline
57 & 93.2 & NA & NA & 0.970518121083935 & NA \tabularnewline
58 & 103.1 & NA & NA & 1.03319299035354 & NA \tabularnewline
59 & 94.1 & NA & NA & 1.00637572869825 & NA \tabularnewline
60 & 91.8 & NA & NA & 1.01263646867807 & NA \tabularnewline
61 & 102.7 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62261&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]111.4[/C][C]NA[/C][C]NA[/C][C]1.08581264005774[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]87.4[/C][C]NA[/C][C]NA[/C][C]0.834380110345525[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]96.8[/C][C]NA[/C][C]NA[/C][C]0.94236680559873[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]114.1[/C][C]NA[/C][C]NA[/C][C]1.07817693771933[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]110.3[/C][C]NA[/C][C]NA[/C][C]1.08557752225472[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]103.9[/C][C]NA[/C][C]NA[/C][C]1.02554157155640[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]101.6[/C][C]96.1501946622139[/C][C]101.904166666667[/C][C]0.943535458925106[/C][C]1.05668012796991[/C][/ROW]
[ROW][C]8[/C][C]94.6[/C][C]99.8945907805817[/C][C]101.7375[/C][C]0.981885644728657[/C][C]0.946998223435228[/C][/ROW]
[ROW][C]9[/C][C]95.9[/C][C]98.4307566054336[/C][C]101.420833333333[/C][C]0.970518121083935[/C][C]0.974288965230875[/C][/ROW]
[ROW][C]10[/C][C]104.7[/C][C]104.701194659951[/C][C]101.3375[/C][C]1.03319299035354[/C][C]0.999988589815471[/C][/ROW]
[ROW][C]11[/C][C]102.8[/C][C]101.761359100205[/C][C]101.116666666667[/C][C]1.00637572869825[/C][C]1.01020663352945[/C][/ROW]
[ROW][C]12[/C][C]98.1[/C][C]102.415520850928[/C][C]101.1375[/C][C]1.01263646867807[/C][C]0.95786262848568[/C][/ROW]
[ROW][C]13[/C][C]113.9[/C][C]110.069732166520[/C][C]101.370833333333[/C][C]1.08581264005774[/C][C]1.03479855686107[/C][/ROW]
[ROW][C]14[/C][C]80.9[/C][C]84.7591128759329[/C][C]101.583333333333[/C][C]0.834380110345525[/C][C]0.954469640549664[/C][/ROW]
[ROW][C]15[/C][C]95.7[/C][C]96.090001944217[/C][C]101.966666666667[/C][C]0.94236680559873[/C][C]0.995941284875366[/C][/ROW]
[ROW][C]16[/C][C]113.2[/C][C]110.638923425631[/C][C]102.616666666667[/C][C]1.07817693771933[/C][C]1.02314806123444[/C][/ROW]
[ROW][C]17[/C][C]105.9[/C][C]111.791868593856[/C][C]102.979166666667[/C][C]1.08557752225472[/C][C]0.947296089885915[/C][/ROW]
[ROW][C]18[/C][C]108.8[/C][C]106.023906139406[/C][C]103.383333333333[/C][C]1.02554157155640[/C][C]1.02618365953188[/C][/ROW]
[ROW][C]19[/C][C]102.3[/C][C]98.037265580064[/C][C]103.904166666667[/C][C]0.943535458925106[/C][C]1.04348075596269[/C][/ROW]
[ROW][C]20[/C][C]99[/C][C]102.234751567185[/C][C]104.120833333333[/C][C]0.981885644728657[/C][C]0.96835956934801[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]101.427231304280[/C][C]104.508333333333[/C][C]0.970518121083935[/C][C]0.99283001916814[/C][/ROW]
[ROW][C]22[/C][C]115.5[/C][C]108.252795564292[/C][C]104.775[/C][C]1.03319299035354[/C][C]1.06694704185634[/C][/ROW]
[ROW][C]23[/C][C]100.7[/C][C]105.954591303115[/C][C]105.283333333333[/C][C]1.00637572869825[/C][C]0.950407139148107[/C][/ROW]
[ROW][C]24[/C][C]109.9[/C][C]107.233982714388[/C][C]105.895833333333[/C][C]1.01263646867807[/C][C]1.02486168300503[/C][/ROW]
[ROW][C]25[/C][C]114.6[/C][C]115.154954697457[/C][C]106.054166666667[/C][C]1.08581264005774[/C][C]0.995180800522954[/C][/ROW]
[ROW][C]26[/C][C]85.4[/C][C]88.7224184000741[/C][C]106.333333333333[/C][C]0.834380110345525[/C][C]0.96255266188651[/C][/ROW]
[ROW][C]27[/C][C]100.5[/C][C]100.660480951371[/C][C]106.816666666667[/C][C]0.94236680559873[/C][C]0.998405720399364[/C][/ROW]
[ROW][C]28[/C][C]114.8[/C][C]115.522166472719[/C][C]107.145833333333[/C][C]1.07817693771933[/C][C]0.9937486761652[/C][/ROW]
[ROW][C]29[/C][C]116.5[/C][C]116.708630121735[/C][C]107.508333333333[/C][C]1.08557752225472[/C][C]0.998212384795218[/C][/ROW]
[ROW][C]30[/C][C]112.9[/C][C]110.459373436387[/C][C]107.708333333333[/C][C]1.02554157155640[/C][C]1.02209524178605[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]101.705259676635[/C][C]107.791666666667[/C][C]0.943535458925106[/C][C]1.00289798506293[/C][/ROW]
[ROW][C]32[/C][C]106[/C][C]106.235935569454[/C][C]108.195833333333[/C][C]0.981885644728657[/C][C]0.997779135956307[/C][/ROW]
[ROW][C]33[/C][C]105.3[/C][C]105.442750030265[/C][C]108.645833333333[/C][C]0.970518121083935[/C][C]0.998646184491356[/C][/ROW]
[ROW][C]34[/C][C]118.8[/C][C]112.312383022222[/C][C]108.704166666667[/C][C]1.03319299035354[/C][C]1.05776403993221[/C][/ROW]
[ROW][C]35[/C][C]106.1[/C][C]109.548191301008[/C][C]108.854166666667[/C][C]1.00637572869825[/C][C]0.968523521383085[/C][/ROW]
[ROW][C]36[/C][C]109.3[/C][C]110.495516007255[/C][C]109.116666666667[/C][C]1.01263646867807[/C][C]0.9891804115637[/C][/ROW]
[ROW][C]37[/C][C]117.2[/C][C]118.407868398297[/C][C]109.05[/C][C]1.08581264005774[/C][C]0.989799086710742[/C][/ROW]
[ROW][C]38[/C][C]92.5[/C][C]91.0830187955933[/C][C]109.1625[/C][C]0.834380110345525[/C][C]1.01555702943472[/C][/ROW]
[ROW][C]39[/C][C]104.2[/C][C]103.350152875684[/C][C]109.670833333333[/C][C]0.94236680559873[/C][C]1.00822298855560[/C][/ROW]
[ROW][C]40[/C][C]112.5[/C][C]118.177177181852[/C][C]109.608333333333[/C][C]1.07817693771933[/C][C]0.951960460410081[/C][/ROW]
[ROW][C]41[/C][C]122.4[/C][C]119.087854191343[/C][C]109.7[/C][C]1.08557752225472[/C][C]1.02781262481508[/C][/ROW]
[ROW][C]42[/C][C]113.3[/C][C]112.971950286700[/C][C]110.158333333333[/C][C]1.02554157155640[/C][C]1.00290381561500[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]103.879322629909[/C][C]110.095833333333[/C][C]0.943535458925106[/C][C]0.962655487813204[/C][/ROW]
[ROW][C]44[/C][C]110.7[/C][C]108.191524478539[/C][C]110.1875[/C][C]0.981885644728657[/C][C]1.02318550860200[/C][/ROW]
[ROW][C]45[/C][C]112.8[/C][C]106.902571037395[/C][C]110.15[/C][C]0.970518121083935[/C][C]1.05516639034380[/C][/ROW]
[ROW][C]46[/C][C]109.8[/C][C]113.801902916649[/C][C]110.145833333333[/C][C]1.03319299035354[/C][C]0.964834481550103[/C][/ROW]
[ROW][C]47[/C][C]117.3[/C][C]110.74745571104[/C][C]110.045833333333[/C][C]1.00637572869825[/C][C]1.059166544702[/C][/ROW]
[ROW][C]48[/C][C]109.1[/C][C]110.567244423787[/C][C]109.1875[/C][C]1.01263646867807[/C][C]0.986729845430869[/C][/ROW]
[ROW][C]49[/C][C]115.9[/C][C]117.670420646924[/C][C]108.370833333333[/C][C]1.08581264005774[/C][C]0.984954412186248[/C][/ROW]
[ROW][C]50[/C][C]96[/C][C]89.5394155914541[/C][C]107.3125[/C][C]0.834380110345525[/C][C]1.07215352441012[/C][/ROW]
[ROW][C]51[/C][C]99.8[/C][C]99.5846121816457[/C][C]105.675[/C][C]0.94236680559873[/C][C]1.00216286245069[/C][/ROW]
[ROW][C]52[/C][C]116.8[/C][C]112.754845665906[/C][C]104.579166666667[/C][C]1.07817693771933[/C][C]1.03587565847130[/C][/ROW]
[ROW][C]53[/C][C]115.7[/C][C]112.176343966321[/C][C]103.333333333333[/C][C]1.08557752225472[/C][C]1.03141175678480[/C][/ROW]
[ROW][C]54[/C][C]99.4[/C][C]104.242027658826[/C][C]101.645833333333[/C][C]1.02554157155640[/C][C]0.953550139348078[/C][/ROW]
[ROW][C]55[/C][C]94.3[/C][C]94.7073716896076[/C][C]100.375[/C][C]0.943535458925106[/C][C]0.995698627442195[/C][/ROW]
[ROW][C]56[/C][C]91[/C][C]NA[/C][C]NA[/C][C]0.981885644728657[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]93.2[/C][C]NA[/C][C]NA[/C][C]0.970518121083935[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]103.1[/C][C]NA[/C][C]NA[/C][C]1.03319299035354[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]94.1[/C][C]NA[/C][C]NA[/C][C]1.00637572869825[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]91.8[/C][C]NA[/C][C]NA[/C][C]1.01263646867807[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]102.7[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62261&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
1111.4NANA1.08581264005774NA
287.4NANA0.834380110345525NA
396.8NANA0.94236680559873NA
4114.1NANA1.07817693771933NA
5110.3NANA1.08557752225472NA
6103.9NANA1.02554157155640NA
7101.696.1501946622139101.9041666666670.9435354589251061.05668012796991
894.699.8945907805817101.73750.9818856447286570.946998223435228
995.998.4307566054336101.4208333333330.9705181210839350.974288965230875
10104.7104.701194659951101.33751.033192990353540.999988589815471
11102.8101.761359100205101.1166666666671.006375728698251.01020663352945
1298.1102.415520850928101.13751.012636468678070.95786262848568
13113.9110.069732166520101.3708333333331.085812640057741.03479855686107
1480.984.7591128759329101.5833333333330.8343801103455250.954469640549664
1595.796.090001944217101.9666666666670.942366805598730.995941284875366
16113.2110.638923425631102.6166666666671.078176937719331.02314806123444
17105.9111.791868593856102.9791666666671.085577522254720.947296089885915
18108.8106.023906139406103.3833333333331.025541571556401.02618365953188
19102.398.037265580064103.9041666666670.9435354589251061.04348075596269
2099102.234751567185104.1208333333330.9818856447286570.96835956934801
21100.7101.427231304280104.5083333333330.9705181210839350.99283001916814
22115.5108.252795564292104.7751.033192990353541.06694704185634
23100.7105.954591303115105.2833333333331.006375728698250.950407139148107
24109.9107.233982714388105.8958333333331.012636468678071.02486168300503
25114.6115.154954697457106.0541666666671.085812640057740.995180800522954
2685.488.7224184000741106.3333333333330.8343801103455250.96255266188651
27100.5100.660480951371106.8166666666670.942366805598730.998405720399364
28114.8115.522166472719107.1458333333331.078176937719330.9937486761652
29116.5116.708630121735107.5083333333331.085577522254720.998212384795218
30112.9110.459373436387107.7083333333331.025541571556401.02209524178605
31102101.705259676635107.7916666666670.9435354589251061.00289798506293
32106106.235935569454108.1958333333330.9818856447286570.997779135956307
33105.3105.442750030265108.6458333333330.9705181210839350.998646184491356
34118.8112.312383022222108.7041666666671.033192990353541.05776403993221
35106.1109.548191301008108.8541666666671.006375728698250.968523521383085
36109.3110.495516007255109.1166666666671.012636468678070.9891804115637
37117.2118.407868398297109.051.085812640057740.989799086710742
3892.591.0830187955933109.16250.8343801103455251.01555702943472
39104.2103.350152875684109.6708333333330.942366805598731.00822298855560
40112.5118.177177181852109.6083333333331.078176937719330.951960460410081
41122.4119.087854191343109.71.085577522254721.02781262481508
42113.3112.971950286700110.1583333333331.025541571556401.00290381561500
43100103.879322629909110.0958333333330.9435354589251060.962655487813204
44110.7108.191524478539110.18750.9818856447286571.02318550860200
45112.8106.902571037395110.150.9705181210839351.05516639034380
46109.8113.801902916649110.1458333333331.033192990353540.964834481550103
47117.3110.74745571104110.0458333333331.006375728698251.059166544702
48109.1110.567244423787109.18751.012636468678070.986729845430869
49115.9117.670420646924108.3708333333331.085812640057740.984954412186248
509689.5394155914541107.31250.8343801103455251.07215352441012
5199.899.5846121816457105.6750.942366805598731.00216286245069
52116.8112.754845665906104.5791666666671.078176937719331.03587565847130
53115.7112.176343966321103.3333333333331.085577522254721.03141175678480
5499.4104.242027658826101.6458333333331.025541571556400.953550139348078
5594.394.7073716896076100.3750.9435354589251060.995698627442195
5691NANA0.981885644728657NA
5793.2NANA0.970518121083935NA
58103.1NANA1.03319299035354NA
5994.1NANA1.00637572869825NA
6091.8NANA1.01263646867807NA
61102.7NANANANA



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')