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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationFri, 07 Dec 2012 09:04:44 -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/2012/Dec/07/t13548891045bk3826lokfjjzb.htm/, Retrieved Thu, 31 Oct 2024 23:34:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197382, Retrieved Thu, 31 Oct 2024 23:34:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Indexcijfers Cons...] [2012-12-07 14:04:44] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
98.01
99.2
100.7
106.41
107.51
107.1
99.75
98.96
107.26
107.11
107.2
107.65
104.78
105.56
107.95
107.11
107.47
107.06
99.71
99.6
107.19
107.26
113.24
113.52
110.48
111.41
115.5
118.32
118.42
117.5
110.23
109.19
118.41
118.3
116.1
114.11
113.41
114.33
116.61
123.64
123.77
123.39
116.03
114.95
123.4
123.53
114.45
114.26
114.35
112.77
115.31
114.93
116.38
115.07
105
103.43
114.52
115.04
117.16
115
116.22
112.92
116.56
114.32
113.22
111.56
103.87
102.85
112.27
112.76
118.55
122.73




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197382&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197382&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197382&T=0

As an alternative you can also use a QR Code:  

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

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







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
198.01NANA0.993345612711611NA
299.2NANA0.988868364468653NA
3100.7NANA1.01475726593845NA
4106.41NANA1.02459153416048NA
5107.51NANA1.02496816888972NA
6107.1NANA1.01466835807449NA
799.7599.0197936697668104.1870833333330.9504037400967021.00737434711961
898.9698.4461101380778104.7341666666670.9399617457347841.00522001185422
9107.26107.134047780567105.301251.017405280379551.00117565071088
10107.11107.359111212963105.63251.016345454409980.997679645349629
11107.2106.80867598347105.661.010871436527261.00366378492127
12107.65106.059539615892105.6566666666671.003813038608311.0149959201206
13104.78104.950275135024105.6533333333330.9933456127116110.998377563710004
14105.56104.501960643106105.6783333333330.9888683644686531.01012458857597
15107.95107.261957087332105.7020833333331.014757265938451.00641460338177
16107.11108.304875031573105.7054166666671.024591534160480.988967486170641
17107.47108.609043736118105.9633333333331.024968168889720.989512441165718
18107.06108.021170622128106.4595833333331.014668358074490.991102016238188
1999.71101.637759972175106.9416666666670.9504037400967020.981033033660889
2099.6100.973432281922107.4229166666670.9399617457347840.986398082635365
21107.19109.860693931984107.981251.017405280379550.975690177838876
22107.26110.540695962539108.7629166666671.016345454409980.970321374096917
23113.24110.878697104788109.686251.010871436527261.02129627202401
24113.52110.99913627671110.57751.003813038608311.02271066071186
25110.48110.70919632472111.4508333333330.9933456127116110.99792974448078
26111.41111.038792560729112.288750.9888683644686531.00334304282954
27115.5114.825704058321113.1558333333331.014757265938451.00587234319362
28118.32116.888817522141114.0833333333331.024591534160481.0122439640352
29118.42117.525412665317114.66251.024968168889721.00761186295282
30117.5116.490269184189114.806251.014668358074491.0086679413043
31110.23109.251681935025114.9529166666670.9504037400967021.00895471856953
32109.19108.280459902828115.1966666666670.9399617457347841.00839985439652
33118.41117.37253625212115.3645833333331.017405280379551.00883906730661
34118.3117.522565757063115.63251.016345454409981.00661519120119
35116.1117.339007977061116.0770833333331.010871436527260.98944078360281
36114.11116.989808840038116.5454166666671.003813038608310.975384105089223
37113.41116.253720419672117.03250.9933456127116110.975538671713852
38114.33116.206041793563117.5141666666670.9888683644686530.983855901426398
39116.61119.702881167737117.9620833333331.014757265938450.97416201567109
40123.64121.299257163563118.3879166666671.024591534160481.01929725615121
41123.77121.496737249695118.5370833333331.024968168889721.01871048393368
42123.39120.212410944393118.4745833333331.014668358074491.02643311976396
43116.03112.641851276261118.520.9504037400967021.03007895098802
44114.95111.379983759388118.4941666666670.9399617457347841.03205258359818
45123.4120.435350064929118.3751.017405280379551.02461611091322
46123.53119.885992415838117.9579166666671.016345454409981.03039560761629
47114.45118.562162415259117.2870833333331.010871436527260.965316401696043
48114.26117.077224225484116.63251.003813038608310.975937042886686
49114.35115.055497274338115.826250.9933456127116110.993868200207279
50112.77113.607790165922114.8866666666670.9888683644686530.992625592270579
51115.31115.719536083401114.0366666666671.014757265938450.996460959858098
52114.93116.099455127699113.3129166666671.024591534160480.989927126476068
53116.38115.895286206712113.0720833333331.024968168889721.00418234260557
54115.07114.876523716368113.2158333333331.014668358074491.00168421081499
55105107.7041078449113.3245833333330.9504037400967020.974893178180404
56103.43106.5998866316113.408750.9399617457347840.970263696034175
57114.52115.4420097326113.4670833333331.017405280379550.992013221748864
58115.04115.348856916443113.493751.016345454409980.997322410254426
59117.16114.568799044545113.3366666666671.010871436527261.02261698627431
60115113.489847378757113.058751.003813038608311.01330649971008
61116.22112.114366472701112.8654166666670.9933456127116111.03662004840654
62112.92111.538583113271112.7941666666670.9888683644686531.01238510341597
63116.56114.339043386198112.676251.014757265938451.01942430641387
64114.32115.253740198877112.48751.024591534160480.99189839568533
65113.22115.258097661719112.4504166666671.024968168889720.982317097860654
66111.56114.485453620027112.8304166666671.014668358074490.974446940397016
67103.87NANA0.950403740096702NA
68102.85NANA0.939961745734784NA
69112.27NANA1.01740528037955NA
70112.76NANA1.01634545440998NA
71118.55NANA1.01087143652726NA
72122.73NANA1.00381303860831NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 98.01 & NA & NA & 0.993345612711611 & NA \tabularnewline
2 & 99.2 & NA & NA & 0.988868364468653 & NA \tabularnewline
3 & 100.7 & NA & NA & 1.01475726593845 & NA \tabularnewline
4 & 106.41 & NA & NA & 1.02459153416048 & NA \tabularnewline
5 & 107.51 & NA & NA & 1.02496816888972 & NA \tabularnewline
6 & 107.1 & NA & NA & 1.01466835807449 & NA \tabularnewline
7 & 99.75 & 99.0197936697668 & 104.187083333333 & 0.950403740096702 & 1.00737434711961 \tabularnewline
8 & 98.96 & 98.4461101380778 & 104.734166666667 & 0.939961745734784 & 1.00522001185422 \tabularnewline
9 & 107.26 & 107.134047780567 & 105.30125 & 1.01740528037955 & 1.00117565071088 \tabularnewline
10 & 107.11 & 107.359111212963 & 105.6325 & 1.01634545440998 & 0.997679645349629 \tabularnewline
11 & 107.2 & 106.80867598347 & 105.66 & 1.01087143652726 & 1.00366378492127 \tabularnewline
12 & 107.65 & 106.059539615892 & 105.656666666667 & 1.00381303860831 & 1.0149959201206 \tabularnewline
13 & 104.78 & 104.950275135024 & 105.653333333333 & 0.993345612711611 & 0.998377563710004 \tabularnewline
14 & 105.56 & 104.501960643106 & 105.678333333333 & 0.988868364468653 & 1.01012458857597 \tabularnewline
15 & 107.95 & 107.261957087332 & 105.702083333333 & 1.01475726593845 & 1.00641460338177 \tabularnewline
16 & 107.11 & 108.304875031573 & 105.705416666667 & 1.02459153416048 & 0.988967486170641 \tabularnewline
17 & 107.47 & 108.609043736118 & 105.963333333333 & 1.02496816888972 & 0.989512441165718 \tabularnewline
18 & 107.06 & 108.021170622128 & 106.459583333333 & 1.01466835807449 & 0.991102016238188 \tabularnewline
19 & 99.71 & 101.637759972175 & 106.941666666667 & 0.950403740096702 & 0.981033033660889 \tabularnewline
20 & 99.6 & 100.973432281922 & 107.422916666667 & 0.939961745734784 & 0.986398082635365 \tabularnewline
21 & 107.19 & 109.860693931984 & 107.98125 & 1.01740528037955 & 0.975690177838876 \tabularnewline
22 & 107.26 & 110.540695962539 & 108.762916666667 & 1.01634545440998 & 0.970321374096917 \tabularnewline
23 & 113.24 & 110.878697104788 & 109.68625 & 1.01087143652726 & 1.02129627202401 \tabularnewline
24 & 113.52 & 110.99913627671 & 110.5775 & 1.00381303860831 & 1.02271066071186 \tabularnewline
25 & 110.48 & 110.70919632472 & 111.450833333333 & 0.993345612711611 & 0.99792974448078 \tabularnewline
26 & 111.41 & 111.038792560729 & 112.28875 & 0.988868364468653 & 1.00334304282954 \tabularnewline
27 & 115.5 & 114.825704058321 & 113.155833333333 & 1.01475726593845 & 1.00587234319362 \tabularnewline
28 & 118.32 & 116.888817522141 & 114.083333333333 & 1.02459153416048 & 1.0122439640352 \tabularnewline
29 & 118.42 & 117.525412665317 & 114.6625 & 1.02496816888972 & 1.00761186295282 \tabularnewline
30 & 117.5 & 116.490269184189 & 114.80625 & 1.01466835807449 & 1.0086679413043 \tabularnewline
31 & 110.23 & 109.251681935025 & 114.952916666667 & 0.950403740096702 & 1.00895471856953 \tabularnewline
32 & 109.19 & 108.280459902828 & 115.196666666667 & 0.939961745734784 & 1.00839985439652 \tabularnewline
33 & 118.41 & 117.37253625212 & 115.364583333333 & 1.01740528037955 & 1.00883906730661 \tabularnewline
34 & 118.3 & 117.522565757063 & 115.6325 & 1.01634545440998 & 1.00661519120119 \tabularnewline
35 & 116.1 & 117.339007977061 & 116.077083333333 & 1.01087143652726 & 0.98944078360281 \tabularnewline
36 & 114.11 & 116.989808840038 & 116.545416666667 & 1.00381303860831 & 0.975384105089223 \tabularnewline
37 & 113.41 & 116.253720419672 & 117.0325 & 0.993345612711611 & 0.975538671713852 \tabularnewline
38 & 114.33 & 116.206041793563 & 117.514166666667 & 0.988868364468653 & 0.983855901426398 \tabularnewline
39 & 116.61 & 119.702881167737 & 117.962083333333 & 1.01475726593845 & 0.97416201567109 \tabularnewline
40 & 123.64 & 121.299257163563 & 118.387916666667 & 1.02459153416048 & 1.01929725615121 \tabularnewline
41 & 123.77 & 121.496737249695 & 118.537083333333 & 1.02496816888972 & 1.01871048393368 \tabularnewline
42 & 123.39 & 120.212410944393 & 118.474583333333 & 1.01466835807449 & 1.02643311976396 \tabularnewline
43 & 116.03 & 112.641851276261 & 118.52 & 0.950403740096702 & 1.03007895098802 \tabularnewline
44 & 114.95 & 111.379983759388 & 118.494166666667 & 0.939961745734784 & 1.03205258359818 \tabularnewline
45 & 123.4 & 120.435350064929 & 118.375 & 1.01740528037955 & 1.02461611091322 \tabularnewline
46 & 123.53 & 119.885992415838 & 117.957916666667 & 1.01634545440998 & 1.03039560761629 \tabularnewline
47 & 114.45 & 118.562162415259 & 117.287083333333 & 1.01087143652726 & 0.965316401696043 \tabularnewline
48 & 114.26 & 117.077224225484 & 116.6325 & 1.00381303860831 & 0.975937042886686 \tabularnewline
49 & 114.35 & 115.055497274338 & 115.82625 & 0.993345612711611 & 0.993868200207279 \tabularnewline
50 & 112.77 & 113.607790165922 & 114.886666666667 & 0.988868364468653 & 0.992625592270579 \tabularnewline
51 & 115.31 & 115.719536083401 & 114.036666666667 & 1.01475726593845 & 0.996460959858098 \tabularnewline
52 & 114.93 & 116.099455127699 & 113.312916666667 & 1.02459153416048 & 0.989927126476068 \tabularnewline
53 & 116.38 & 115.895286206712 & 113.072083333333 & 1.02496816888972 & 1.00418234260557 \tabularnewline
54 & 115.07 & 114.876523716368 & 113.215833333333 & 1.01466835807449 & 1.00168421081499 \tabularnewline
55 & 105 & 107.7041078449 & 113.324583333333 & 0.950403740096702 & 0.974893178180404 \tabularnewline
56 & 103.43 & 106.5998866316 & 113.40875 & 0.939961745734784 & 0.970263696034175 \tabularnewline
57 & 114.52 & 115.4420097326 & 113.467083333333 & 1.01740528037955 & 0.992013221748864 \tabularnewline
58 & 115.04 & 115.348856916443 & 113.49375 & 1.01634545440998 & 0.997322410254426 \tabularnewline
59 & 117.16 & 114.568799044545 & 113.336666666667 & 1.01087143652726 & 1.02261698627431 \tabularnewline
60 & 115 & 113.489847378757 & 113.05875 & 1.00381303860831 & 1.01330649971008 \tabularnewline
61 & 116.22 & 112.114366472701 & 112.865416666667 & 0.993345612711611 & 1.03662004840654 \tabularnewline
62 & 112.92 & 111.538583113271 & 112.794166666667 & 0.988868364468653 & 1.01238510341597 \tabularnewline
63 & 116.56 & 114.339043386198 & 112.67625 & 1.01475726593845 & 1.01942430641387 \tabularnewline
64 & 114.32 & 115.253740198877 & 112.4875 & 1.02459153416048 & 0.99189839568533 \tabularnewline
65 & 113.22 & 115.258097661719 & 112.450416666667 & 1.02496816888972 & 0.982317097860654 \tabularnewline
66 & 111.56 & 114.485453620027 & 112.830416666667 & 1.01466835807449 & 0.974446940397016 \tabularnewline
67 & 103.87 & NA & NA & 0.950403740096702 & NA \tabularnewline
68 & 102.85 & NA & NA & 0.939961745734784 & NA \tabularnewline
69 & 112.27 & NA & NA & 1.01740528037955 & NA \tabularnewline
70 & 112.76 & NA & NA & 1.01634545440998 & NA \tabularnewline
71 & 118.55 & NA & NA & 1.01087143652726 & NA \tabularnewline
72 & 122.73 & NA & NA & 1.00381303860831 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197382&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]98.01[/C][C]NA[/C][C]NA[/C][C]0.993345612711611[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]99.2[/C][C]NA[/C][C]NA[/C][C]0.988868364468653[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]NA[/C][C]NA[/C][C]1.01475726593845[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]106.41[/C][C]NA[/C][C]NA[/C][C]1.02459153416048[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]107.51[/C][C]NA[/C][C]NA[/C][C]1.02496816888972[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]107.1[/C][C]NA[/C][C]NA[/C][C]1.01466835807449[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]99.75[/C][C]99.0197936697668[/C][C]104.187083333333[/C][C]0.950403740096702[/C][C]1.00737434711961[/C][/ROW]
[ROW][C]8[/C][C]98.96[/C][C]98.4461101380778[/C][C]104.734166666667[/C][C]0.939961745734784[/C][C]1.00522001185422[/C][/ROW]
[ROW][C]9[/C][C]107.26[/C][C]107.134047780567[/C][C]105.30125[/C][C]1.01740528037955[/C][C]1.00117565071088[/C][/ROW]
[ROW][C]10[/C][C]107.11[/C][C]107.359111212963[/C][C]105.6325[/C][C]1.01634545440998[/C][C]0.997679645349629[/C][/ROW]
[ROW][C]11[/C][C]107.2[/C][C]106.80867598347[/C][C]105.66[/C][C]1.01087143652726[/C][C]1.00366378492127[/C][/ROW]
[ROW][C]12[/C][C]107.65[/C][C]106.059539615892[/C][C]105.656666666667[/C][C]1.00381303860831[/C][C]1.0149959201206[/C][/ROW]
[ROW][C]13[/C][C]104.78[/C][C]104.950275135024[/C][C]105.653333333333[/C][C]0.993345612711611[/C][C]0.998377563710004[/C][/ROW]
[ROW][C]14[/C][C]105.56[/C][C]104.501960643106[/C][C]105.678333333333[/C][C]0.988868364468653[/C][C]1.01012458857597[/C][/ROW]
[ROW][C]15[/C][C]107.95[/C][C]107.261957087332[/C][C]105.702083333333[/C][C]1.01475726593845[/C][C]1.00641460338177[/C][/ROW]
[ROW][C]16[/C][C]107.11[/C][C]108.304875031573[/C][C]105.705416666667[/C][C]1.02459153416048[/C][C]0.988967486170641[/C][/ROW]
[ROW][C]17[/C][C]107.47[/C][C]108.609043736118[/C][C]105.963333333333[/C][C]1.02496816888972[/C][C]0.989512441165718[/C][/ROW]
[ROW][C]18[/C][C]107.06[/C][C]108.021170622128[/C][C]106.459583333333[/C][C]1.01466835807449[/C][C]0.991102016238188[/C][/ROW]
[ROW][C]19[/C][C]99.71[/C][C]101.637759972175[/C][C]106.941666666667[/C][C]0.950403740096702[/C][C]0.981033033660889[/C][/ROW]
[ROW][C]20[/C][C]99.6[/C][C]100.973432281922[/C][C]107.422916666667[/C][C]0.939961745734784[/C][C]0.986398082635365[/C][/ROW]
[ROW][C]21[/C][C]107.19[/C][C]109.860693931984[/C][C]107.98125[/C][C]1.01740528037955[/C][C]0.975690177838876[/C][/ROW]
[ROW][C]22[/C][C]107.26[/C][C]110.540695962539[/C][C]108.762916666667[/C][C]1.01634545440998[/C][C]0.970321374096917[/C][/ROW]
[ROW][C]23[/C][C]113.24[/C][C]110.878697104788[/C][C]109.68625[/C][C]1.01087143652726[/C][C]1.02129627202401[/C][/ROW]
[ROW][C]24[/C][C]113.52[/C][C]110.99913627671[/C][C]110.5775[/C][C]1.00381303860831[/C][C]1.02271066071186[/C][/ROW]
[ROW][C]25[/C][C]110.48[/C][C]110.70919632472[/C][C]111.450833333333[/C][C]0.993345612711611[/C][C]0.99792974448078[/C][/ROW]
[ROW][C]26[/C][C]111.41[/C][C]111.038792560729[/C][C]112.28875[/C][C]0.988868364468653[/C][C]1.00334304282954[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]114.825704058321[/C][C]113.155833333333[/C][C]1.01475726593845[/C][C]1.00587234319362[/C][/ROW]
[ROW][C]28[/C][C]118.32[/C][C]116.888817522141[/C][C]114.083333333333[/C][C]1.02459153416048[/C][C]1.0122439640352[/C][/ROW]
[ROW][C]29[/C][C]118.42[/C][C]117.525412665317[/C][C]114.6625[/C][C]1.02496816888972[/C][C]1.00761186295282[/C][/ROW]
[ROW][C]30[/C][C]117.5[/C][C]116.490269184189[/C][C]114.80625[/C][C]1.01466835807449[/C][C]1.0086679413043[/C][/ROW]
[ROW][C]31[/C][C]110.23[/C][C]109.251681935025[/C][C]114.952916666667[/C][C]0.950403740096702[/C][C]1.00895471856953[/C][/ROW]
[ROW][C]32[/C][C]109.19[/C][C]108.280459902828[/C][C]115.196666666667[/C][C]0.939961745734784[/C][C]1.00839985439652[/C][/ROW]
[ROW][C]33[/C][C]118.41[/C][C]117.37253625212[/C][C]115.364583333333[/C][C]1.01740528037955[/C][C]1.00883906730661[/C][/ROW]
[ROW][C]34[/C][C]118.3[/C][C]117.522565757063[/C][C]115.6325[/C][C]1.01634545440998[/C][C]1.00661519120119[/C][/ROW]
[ROW][C]35[/C][C]116.1[/C][C]117.339007977061[/C][C]116.077083333333[/C][C]1.01087143652726[/C][C]0.98944078360281[/C][/ROW]
[ROW][C]36[/C][C]114.11[/C][C]116.989808840038[/C][C]116.545416666667[/C][C]1.00381303860831[/C][C]0.975384105089223[/C][/ROW]
[ROW][C]37[/C][C]113.41[/C][C]116.253720419672[/C][C]117.0325[/C][C]0.993345612711611[/C][C]0.975538671713852[/C][/ROW]
[ROW][C]38[/C][C]114.33[/C][C]116.206041793563[/C][C]117.514166666667[/C][C]0.988868364468653[/C][C]0.983855901426398[/C][/ROW]
[ROW][C]39[/C][C]116.61[/C][C]119.702881167737[/C][C]117.962083333333[/C][C]1.01475726593845[/C][C]0.97416201567109[/C][/ROW]
[ROW][C]40[/C][C]123.64[/C][C]121.299257163563[/C][C]118.387916666667[/C][C]1.02459153416048[/C][C]1.01929725615121[/C][/ROW]
[ROW][C]41[/C][C]123.77[/C][C]121.496737249695[/C][C]118.537083333333[/C][C]1.02496816888972[/C][C]1.01871048393368[/C][/ROW]
[ROW][C]42[/C][C]123.39[/C][C]120.212410944393[/C][C]118.474583333333[/C][C]1.01466835807449[/C][C]1.02643311976396[/C][/ROW]
[ROW][C]43[/C][C]116.03[/C][C]112.641851276261[/C][C]118.52[/C][C]0.950403740096702[/C][C]1.03007895098802[/C][/ROW]
[ROW][C]44[/C][C]114.95[/C][C]111.379983759388[/C][C]118.494166666667[/C][C]0.939961745734784[/C][C]1.03205258359818[/C][/ROW]
[ROW][C]45[/C][C]123.4[/C][C]120.435350064929[/C][C]118.375[/C][C]1.01740528037955[/C][C]1.02461611091322[/C][/ROW]
[ROW][C]46[/C][C]123.53[/C][C]119.885992415838[/C][C]117.957916666667[/C][C]1.01634545440998[/C][C]1.03039560761629[/C][/ROW]
[ROW][C]47[/C][C]114.45[/C][C]118.562162415259[/C][C]117.287083333333[/C][C]1.01087143652726[/C][C]0.965316401696043[/C][/ROW]
[ROW][C]48[/C][C]114.26[/C][C]117.077224225484[/C][C]116.6325[/C][C]1.00381303860831[/C][C]0.975937042886686[/C][/ROW]
[ROW][C]49[/C][C]114.35[/C][C]115.055497274338[/C][C]115.82625[/C][C]0.993345612711611[/C][C]0.993868200207279[/C][/ROW]
[ROW][C]50[/C][C]112.77[/C][C]113.607790165922[/C][C]114.886666666667[/C][C]0.988868364468653[/C][C]0.992625592270579[/C][/ROW]
[ROW][C]51[/C][C]115.31[/C][C]115.719536083401[/C][C]114.036666666667[/C][C]1.01475726593845[/C][C]0.996460959858098[/C][/ROW]
[ROW][C]52[/C][C]114.93[/C][C]116.099455127699[/C][C]113.312916666667[/C][C]1.02459153416048[/C][C]0.989927126476068[/C][/ROW]
[ROW][C]53[/C][C]116.38[/C][C]115.895286206712[/C][C]113.072083333333[/C][C]1.02496816888972[/C][C]1.00418234260557[/C][/ROW]
[ROW][C]54[/C][C]115.07[/C][C]114.876523716368[/C][C]113.215833333333[/C][C]1.01466835807449[/C][C]1.00168421081499[/C][/ROW]
[ROW][C]55[/C][C]105[/C][C]107.7041078449[/C][C]113.324583333333[/C][C]0.950403740096702[/C][C]0.974893178180404[/C][/ROW]
[ROW][C]56[/C][C]103.43[/C][C]106.5998866316[/C][C]113.40875[/C][C]0.939961745734784[/C][C]0.970263696034175[/C][/ROW]
[ROW][C]57[/C][C]114.52[/C][C]115.4420097326[/C][C]113.467083333333[/C][C]1.01740528037955[/C][C]0.992013221748864[/C][/ROW]
[ROW][C]58[/C][C]115.04[/C][C]115.348856916443[/C][C]113.49375[/C][C]1.01634545440998[/C][C]0.997322410254426[/C][/ROW]
[ROW][C]59[/C][C]117.16[/C][C]114.568799044545[/C][C]113.336666666667[/C][C]1.01087143652726[/C][C]1.02261698627431[/C][/ROW]
[ROW][C]60[/C][C]115[/C][C]113.489847378757[/C][C]113.05875[/C][C]1.00381303860831[/C][C]1.01330649971008[/C][/ROW]
[ROW][C]61[/C][C]116.22[/C][C]112.114366472701[/C][C]112.865416666667[/C][C]0.993345612711611[/C][C]1.03662004840654[/C][/ROW]
[ROW][C]62[/C][C]112.92[/C][C]111.538583113271[/C][C]112.794166666667[/C][C]0.988868364468653[/C][C]1.01238510341597[/C][/ROW]
[ROW][C]63[/C][C]116.56[/C][C]114.339043386198[/C][C]112.67625[/C][C]1.01475726593845[/C][C]1.01942430641387[/C][/ROW]
[ROW][C]64[/C][C]114.32[/C][C]115.253740198877[/C][C]112.4875[/C][C]1.02459153416048[/C][C]0.99189839568533[/C][/ROW]
[ROW][C]65[/C][C]113.22[/C][C]115.258097661719[/C][C]112.450416666667[/C][C]1.02496816888972[/C][C]0.982317097860654[/C][/ROW]
[ROW][C]66[/C][C]111.56[/C][C]114.485453620027[/C][C]112.830416666667[/C][C]1.01466835807449[/C][C]0.974446940397016[/C][/ROW]
[ROW][C]67[/C][C]103.87[/C][C]NA[/C][C]NA[/C][C]0.950403740096702[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]102.85[/C][C]NA[/C][C]NA[/C][C]0.939961745734784[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]112.27[/C][C]NA[/C][C]NA[/C][C]1.01740528037955[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]112.76[/C][C]NA[/C][C]NA[/C][C]1.01634545440998[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]118.55[/C][C]NA[/C][C]NA[/C][C]1.01087143652726[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]122.73[/C][C]NA[/C][C]NA[/C][C]1.00381303860831[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197382&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197382&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
198.01NANA0.993345612711611NA
299.2NANA0.988868364468653NA
3100.7NANA1.01475726593845NA
4106.41NANA1.02459153416048NA
5107.51NANA1.02496816888972NA
6107.1NANA1.01466835807449NA
799.7599.0197936697668104.1870833333330.9504037400967021.00737434711961
898.9698.4461101380778104.7341666666670.9399617457347841.00522001185422
9107.26107.134047780567105.301251.017405280379551.00117565071088
10107.11107.359111212963105.63251.016345454409980.997679645349629
11107.2106.80867598347105.661.010871436527261.00366378492127
12107.65106.059539615892105.6566666666671.003813038608311.0149959201206
13104.78104.950275135024105.6533333333330.9933456127116110.998377563710004
14105.56104.501960643106105.6783333333330.9888683644686531.01012458857597
15107.95107.261957087332105.7020833333331.014757265938451.00641460338177
16107.11108.304875031573105.7054166666671.024591534160480.988967486170641
17107.47108.609043736118105.9633333333331.024968168889720.989512441165718
18107.06108.021170622128106.4595833333331.014668358074490.991102016238188
1999.71101.637759972175106.9416666666670.9504037400967020.981033033660889
2099.6100.973432281922107.4229166666670.9399617457347840.986398082635365
21107.19109.860693931984107.981251.017405280379550.975690177838876
22107.26110.540695962539108.7629166666671.016345454409980.970321374096917
23113.24110.878697104788109.686251.010871436527261.02129627202401
24113.52110.99913627671110.57751.003813038608311.02271066071186
25110.48110.70919632472111.4508333333330.9933456127116110.99792974448078
26111.41111.038792560729112.288750.9888683644686531.00334304282954
27115.5114.825704058321113.1558333333331.014757265938451.00587234319362
28118.32116.888817522141114.0833333333331.024591534160481.0122439640352
29118.42117.525412665317114.66251.024968168889721.00761186295282
30117.5116.490269184189114.806251.014668358074491.0086679413043
31110.23109.251681935025114.9529166666670.9504037400967021.00895471856953
32109.19108.280459902828115.1966666666670.9399617457347841.00839985439652
33118.41117.37253625212115.3645833333331.017405280379551.00883906730661
34118.3117.522565757063115.63251.016345454409981.00661519120119
35116.1117.339007977061116.0770833333331.010871436527260.98944078360281
36114.11116.989808840038116.5454166666671.003813038608310.975384105089223
37113.41116.253720419672117.03250.9933456127116110.975538671713852
38114.33116.206041793563117.5141666666670.9888683644686530.983855901426398
39116.61119.702881167737117.9620833333331.014757265938450.97416201567109
40123.64121.299257163563118.3879166666671.024591534160481.01929725615121
41123.77121.496737249695118.5370833333331.024968168889721.01871048393368
42123.39120.212410944393118.4745833333331.014668358074491.02643311976396
43116.03112.641851276261118.520.9504037400967021.03007895098802
44114.95111.379983759388118.4941666666670.9399617457347841.03205258359818
45123.4120.435350064929118.3751.017405280379551.02461611091322
46123.53119.885992415838117.9579166666671.016345454409981.03039560761629
47114.45118.562162415259117.2870833333331.010871436527260.965316401696043
48114.26117.077224225484116.63251.003813038608310.975937042886686
49114.35115.055497274338115.826250.9933456127116110.993868200207279
50112.77113.607790165922114.8866666666670.9888683644686530.992625592270579
51115.31115.719536083401114.0366666666671.014757265938450.996460959858098
52114.93116.099455127699113.3129166666671.024591534160480.989927126476068
53116.38115.895286206712113.0720833333331.024968168889721.00418234260557
54115.07114.876523716368113.2158333333331.014668358074491.00168421081499
55105107.7041078449113.3245833333330.9504037400967020.974893178180404
56103.43106.5998866316113.408750.9399617457347840.970263696034175
57114.52115.4420097326113.4670833333331.017405280379550.992013221748864
58115.04115.348856916443113.493751.016345454409980.997322410254426
59117.16114.568799044545113.3366666666671.010871436527261.02261698627431
60115113.489847378757113.058751.003813038608311.01330649971008
61116.22112.114366472701112.8654166666670.9933456127116111.03662004840654
62112.92111.538583113271112.7941666666670.9888683644686531.01238510341597
63116.56114.339043386198112.676251.014757265938451.01942430641387
64114.32115.253740198877112.48751.024591534160480.99189839568533
65113.22115.258097661719112.4504166666671.024968168889720.982317097860654
66111.56114.485453620027112.8304166666671.014668358074490.974446940397016
67103.87NANA0.950403740096702NA
68102.85NANA0.939961745734784NA
69112.27NANA1.01740528037955NA
70112.76NANA1.01634545440998NA
71118.55NANA1.01087143652726NA
72122.73NANA1.00381303860831NA



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')