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Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 03 Dec 2009 10:14:39 -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/03/t12598607234gicc79acg1yz84.htm/, Retrieved Fri, 29 Mar 2024 10:35:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62936, Retrieved Fri, 29 Mar 2024 10:35:14 +0000
QR Codes:

Original text written by user:Uitleg in Word document
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
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] [Ad hoc forecasting 1] [2009-12-03 17:14:39] [8eb8270f5a1cfdf0409dcfcbf10be18b] [Current]
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Dataseries X:
96.96
93.11
95.62
98.30
96.38
100.82
99.06
94.03
102.07
99.31
98.64
101.82
99.14
97.63
100.06
101.32
101.49
105.43
105.09
99.48
108.53
104.34
106.10
107.35
103.00
104.50
105.17
104.84
106.18
108.86
107.77
102.74
112.63
106.26
108.86
111.38
106.85
107.86
107.94
111.38
111.29
113.72
111.88
109.87
113.72
111.71
114.81
112.05
111.54
110.87
110.87
115.48
111.63
116.24
113.56
106.01
110.45
107.77
108.61
108.19




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62936&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62936&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62936&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
196.96NANA0.982441725464283NA
293.11NANA0.98046891946843NA
395.62NANA0.986208918331083NA
498.3NANA1.00490527685339NA
596.38NANA0.99781901202507NA
6100.82NANA1.02784167491245NA
799.0698.979892424566198.10083333333331.008960770886071.00080933180944
894.0394.763163697412798.380.9632360611650.992263199445791
9102.07102.07711366910998.75333333333331.033657398931090.999930310832145
1099.3198.486616013920899.06416666666670.99416993376941.00836036427491
1198.64100.03713167392799.40291666666671.006380245454840.986033869118907
12101.82101.19625104933799.80791666666671.013910062738881.00616375551658
1399.1498.4910110299512100.251250.9824417254642831.00658932184026
1497.6398.7622257293386100.7295833333330.980468919468430.988535842312409
15100.0699.8298195988292101.2258333333330.9862089183310831.00230572790871
16101.32102.203472471842101.7045833333331.004905276853390.991355748973346
17101.49102.002048504263102.2250.997819012025070.994980017443067
18105.43105.627434524471102.766251.027841674912450.998130840483251
19105.09104.08187072268103.15751.008960770886071.00968592580360
2099.4899.795670768641103.6045833333330.9632360611650.996836829030662
21108.53107.607611443973104.103751.033657398931091.00857177799646
22104.34103.854305181331104.4633333333330.99416993376941.00467669412280
23106.1105.474100949997104.8054166666671.006380245454841.00593414918322
24107.35106.606306159102105.143751.013910062738881.00697607737940
25103103.547720461060105.3983333333330.9824417254642830.994710453705587
26104.5103.582456054675105.6458333333330.980468919468431.00885810184729
27105.17104.491300419474105.95250.9862089183310831.00649527355676
28104.84106.724290086086106.2033333333331.004905276853390.98234431838744
29106.18106.166279847781106.3983333333330.997819012025071.00012923267387
30108.86109.651434681753106.681251.027841674912450.99278226788322
31107.77107.968471692197107.0095833333331.008960770886070.9981617625119
32102.74103.364861723616107.310.9632360611650.993954795534996
33112.63111.185788806606107.5654166666671.033657398931091.01298917072852
34106.26107.323958250186107.9533333333330.99416993376940.990086479593813
35108.86109.130615841816108.438751.006380245454840.997520257356483
36111.38110.368334954389108.8541666666671.013910062738881.00916626173648
37106.85107.310062918869109.2279166666670.9824417254642830.995712770020303
38107.86107.553763707239109.696250.980468919468431.00284728569420
39107.94108.521196612005110.038750.9862089183310830.994644395471583
40111.38110.852357221294110.311251.004905276853391.00475986972160
41111.29110.544626520962110.786250.997819012025071.00674273822705
42113.72114.154237752599111.0620833333331.027841674912450.996196043518418
43111.88112.282619788378111.2854166666671.008960770886070.996414228763662
44109.87107.503164651396111.606250.9632360611651.02201642487715
45113.72115.618456285689111.853751.033657398931090.983579989331479
46111.71111.492844172459112.1466666666670.99416993376941.00194771089708
47114.81113.048370272351112.3316666666671.006380245454841.01558297322999
48112.05114.015031480040112.4508333333331.013910062738880.98276515425614
49111.54110.648318031853112.6258333333330.9824417254642831.00805870332245
50110.87110.337069852380112.5350.980468919468431.00483001903470
51110.87110.690034391568112.2379166666670.9862089183310831.00162585195155
52115.48112.486584427777111.93751.004905276853391.02661131180621
53111.63111.271787125976111.5150.997819012025071.00321926054462
54116.24114.188927409127111.0958333333331.027841674912451.01796209700371
55113.56NANA1.00896077088607NA
56106.01NANA0.963236061165NA
57110.45NANA1.03365739893109NA
58107.77NANA0.9941699337694NA
59108.61NANA1.00638024545484NA
60108.19NANA1.01391006273888NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.96 & NA & NA & 0.982441725464283 & NA \tabularnewline
2 & 93.11 & NA & NA & 0.98046891946843 & NA \tabularnewline
3 & 95.62 & NA & NA & 0.986208918331083 & NA \tabularnewline
4 & 98.3 & NA & NA & 1.00490527685339 & NA \tabularnewline
5 & 96.38 & NA & NA & 0.99781901202507 & NA \tabularnewline
6 & 100.82 & NA & NA & 1.02784167491245 & NA \tabularnewline
7 & 99.06 & 98.9798924245661 & 98.1008333333333 & 1.00896077088607 & 1.00080933180944 \tabularnewline
8 & 94.03 & 94.7631636974127 & 98.38 & 0.963236061165 & 0.992263199445791 \tabularnewline
9 & 102.07 & 102.077113669109 & 98.7533333333333 & 1.03365739893109 & 0.999930310832145 \tabularnewline
10 & 99.31 & 98.4866160139208 & 99.0641666666667 & 0.9941699337694 & 1.00836036427491 \tabularnewline
11 & 98.64 & 100.037131673927 & 99.4029166666667 & 1.00638024545484 & 0.986033869118907 \tabularnewline
12 & 101.82 & 101.196251049337 & 99.8079166666667 & 1.01391006273888 & 1.00616375551658 \tabularnewline
13 & 99.14 & 98.4910110299512 & 100.25125 & 0.982441725464283 & 1.00658932184026 \tabularnewline
14 & 97.63 & 98.7622257293386 & 100.729583333333 & 0.98046891946843 & 0.988535842312409 \tabularnewline
15 & 100.06 & 99.8298195988292 & 101.225833333333 & 0.986208918331083 & 1.00230572790871 \tabularnewline
16 & 101.32 & 102.203472471842 & 101.704583333333 & 1.00490527685339 & 0.991355748973346 \tabularnewline
17 & 101.49 & 102.002048504263 & 102.225 & 0.99781901202507 & 0.994980017443067 \tabularnewline
18 & 105.43 & 105.627434524471 & 102.76625 & 1.02784167491245 & 0.998130840483251 \tabularnewline
19 & 105.09 & 104.08187072268 & 103.1575 & 1.00896077088607 & 1.00968592580360 \tabularnewline
20 & 99.48 & 99.795670768641 & 103.604583333333 & 0.963236061165 & 0.996836829030662 \tabularnewline
21 & 108.53 & 107.607611443973 & 104.10375 & 1.03365739893109 & 1.00857177799646 \tabularnewline
22 & 104.34 & 103.854305181331 & 104.463333333333 & 0.9941699337694 & 1.00467669412280 \tabularnewline
23 & 106.1 & 105.474100949997 & 104.805416666667 & 1.00638024545484 & 1.00593414918322 \tabularnewline
24 & 107.35 & 106.606306159102 & 105.14375 & 1.01391006273888 & 1.00697607737940 \tabularnewline
25 & 103 & 103.547720461060 & 105.398333333333 & 0.982441725464283 & 0.994710453705587 \tabularnewline
26 & 104.5 & 103.582456054675 & 105.645833333333 & 0.98046891946843 & 1.00885810184729 \tabularnewline
27 & 105.17 & 104.491300419474 & 105.9525 & 0.986208918331083 & 1.00649527355676 \tabularnewline
28 & 104.84 & 106.724290086086 & 106.203333333333 & 1.00490527685339 & 0.98234431838744 \tabularnewline
29 & 106.18 & 106.166279847781 & 106.398333333333 & 0.99781901202507 & 1.00012923267387 \tabularnewline
30 & 108.86 & 109.651434681753 & 106.68125 & 1.02784167491245 & 0.99278226788322 \tabularnewline
31 & 107.77 & 107.968471692197 & 107.009583333333 & 1.00896077088607 & 0.9981617625119 \tabularnewline
32 & 102.74 & 103.364861723616 & 107.31 & 0.963236061165 & 0.993954795534996 \tabularnewline
33 & 112.63 & 111.185788806606 & 107.565416666667 & 1.03365739893109 & 1.01298917072852 \tabularnewline
34 & 106.26 & 107.323958250186 & 107.953333333333 & 0.9941699337694 & 0.990086479593813 \tabularnewline
35 & 108.86 & 109.130615841816 & 108.43875 & 1.00638024545484 & 0.997520257356483 \tabularnewline
36 & 111.38 & 110.368334954389 & 108.854166666667 & 1.01391006273888 & 1.00916626173648 \tabularnewline
37 & 106.85 & 107.310062918869 & 109.227916666667 & 0.982441725464283 & 0.995712770020303 \tabularnewline
38 & 107.86 & 107.553763707239 & 109.69625 & 0.98046891946843 & 1.00284728569420 \tabularnewline
39 & 107.94 & 108.521196612005 & 110.03875 & 0.986208918331083 & 0.994644395471583 \tabularnewline
40 & 111.38 & 110.852357221294 & 110.31125 & 1.00490527685339 & 1.00475986972160 \tabularnewline
41 & 111.29 & 110.544626520962 & 110.78625 & 0.99781901202507 & 1.00674273822705 \tabularnewline
42 & 113.72 & 114.154237752599 & 111.062083333333 & 1.02784167491245 & 0.996196043518418 \tabularnewline
43 & 111.88 & 112.282619788378 & 111.285416666667 & 1.00896077088607 & 0.996414228763662 \tabularnewline
44 & 109.87 & 107.503164651396 & 111.60625 & 0.963236061165 & 1.02201642487715 \tabularnewline
45 & 113.72 & 115.618456285689 & 111.85375 & 1.03365739893109 & 0.983579989331479 \tabularnewline
46 & 111.71 & 111.492844172459 & 112.146666666667 & 0.9941699337694 & 1.00194771089708 \tabularnewline
47 & 114.81 & 113.048370272351 & 112.331666666667 & 1.00638024545484 & 1.01558297322999 \tabularnewline
48 & 112.05 & 114.015031480040 & 112.450833333333 & 1.01391006273888 & 0.98276515425614 \tabularnewline
49 & 111.54 & 110.648318031853 & 112.625833333333 & 0.982441725464283 & 1.00805870332245 \tabularnewline
50 & 110.87 & 110.337069852380 & 112.535 & 0.98046891946843 & 1.00483001903470 \tabularnewline
51 & 110.87 & 110.690034391568 & 112.237916666667 & 0.986208918331083 & 1.00162585195155 \tabularnewline
52 & 115.48 & 112.486584427777 & 111.9375 & 1.00490527685339 & 1.02661131180621 \tabularnewline
53 & 111.63 & 111.271787125976 & 111.515 & 0.99781901202507 & 1.00321926054462 \tabularnewline
54 & 116.24 & 114.188927409127 & 111.095833333333 & 1.02784167491245 & 1.01796209700371 \tabularnewline
55 & 113.56 & NA & NA & 1.00896077088607 & NA \tabularnewline
56 & 106.01 & NA & NA & 0.963236061165 & NA \tabularnewline
57 & 110.45 & NA & NA & 1.03365739893109 & NA \tabularnewline
58 & 107.77 & NA & NA & 0.9941699337694 & NA \tabularnewline
59 & 108.61 & NA & NA & 1.00638024545484 & NA \tabularnewline
60 & 108.19 & NA & NA & 1.01391006273888 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62936&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]96.96[/C][C]NA[/C][C]NA[/C][C]0.982441725464283[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]93.11[/C][C]NA[/C][C]NA[/C][C]0.98046891946843[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]95.62[/C][C]NA[/C][C]NA[/C][C]0.986208918331083[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]98.3[/C][C]NA[/C][C]NA[/C][C]1.00490527685339[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]96.38[/C][C]NA[/C][C]NA[/C][C]0.99781901202507[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.82[/C][C]NA[/C][C]NA[/C][C]1.02784167491245[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]99.06[/C][C]98.9798924245661[/C][C]98.1008333333333[/C][C]1.00896077088607[/C][C]1.00080933180944[/C][/ROW]
[ROW][C]8[/C][C]94.03[/C][C]94.7631636974127[/C][C]98.38[/C][C]0.963236061165[/C][C]0.992263199445791[/C][/ROW]
[ROW][C]9[/C][C]102.07[/C][C]102.077113669109[/C][C]98.7533333333333[/C][C]1.03365739893109[/C][C]0.999930310832145[/C][/ROW]
[ROW][C]10[/C][C]99.31[/C][C]98.4866160139208[/C][C]99.0641666666667[/C][C]0.9941699337694[/C][C]1.00836036427491[/C][/ROW]
[ROW][C]11[/C][C]98.64[/C][C]100.037131673927[/C][C]99.4029166666667[/C][C]1.00638024545484[/C][C]0.986033869118907[/C][/ROW]
[ROW][C]12[/C][C]101.82[/C][C]101.196251049337[/C][C]99.8079166666667[/C][C]1.01391006273888[/C][C]1.00616375551658[/C][/ROW]
[ROW][C]13[/C][C]99.14[/C][C]98.4910110299512[/C][C]100.25125[/C][C]0.982441725464283[/C][C]1.00658932184026[/C][/ROW]
[ROW][C]14[/C][C]97.63[/C][C]98.7622257293386[/C][C]100.729583333333[/C][C]0.98046891946843[/C][C]0.988535842312409[/C][/ROW]
[ROW][C]15[/C][C]100.06[/C][C]99.8298195988292[/C][C]101.225833333333[/C][C]0.986208918331083[/C][C]1.00230572790871[/C][/ROW]
[ROW][C]16[/C][C]101.32[/C][C]102.203472471842[/C][C]101.704583333333[/C][C]1.00490527685339[/C][C]0.991355748973346[/C][/ROW]
[ROW][C]17[/C][C]101.49[/C][C]102.002048504263[/C][C]102.225[/C][C]0.99781901202507[/C][C]0.994980017443067[/C][/ROW]
[ROW][C]18[/C][C]105.43[/C][C]105.627434524471[/C][C]102.76625[/C][C]1.02784167491245[/C][C]0.998130840483251[/C][/ROW]
[ROW][C]19[/C][C]105.09[/C][C]104.08187072268[/C][C]103.1575[/C][C]1.00896077088607[/C][C]1.00968592580360[/C][/ROW]
[ROW][C]20[/C][C]99.48[/C][C]99.795670768641[/C][C]103.604583333333[/C][C]0.963236061165[/C][C]0.996836829030662[/C][/ROW]
[ROW][C]21[/C][C]108.53[/C][C]107.607611443973[/C][C]104.10375[/C][C]1.03365739893109[/C][C]1.00857177799646[/C][/ROW]
[ROW][C]22[/C][C]104.34[/C][C]103.854305181331[/C][C]104.463333333333[/C][C]0.9941699337694[/C][C]1.00467669412280[/C][/ROW]
[ROW][C]23[/C][C]106.1[/C][C]105.474100949997[/C][C]104.805416666667[/C][C]1.00638024545484[/C][C]1.00593414918322[/C][/ROW]
[ROW][C]24[/C][C]107.35[/C][C]106.606306159102[/C][C]105.14375[/C][C]1.01391006273888[/C][C]1.00697607737940[/C][/ROW]
[ROW][C]25[/C][C]103[/C][C]103.547720461060[/C][C]105.398333333333[/C][C]0.982441725464283[/C][C]0.994710453705587[/C][/ROW]
[ROW][C]26[/C][C]104.5[/C][C]103.582456054675[/C][C]105.645833333333[/C][C]0.98046891946843[/C][C]1.00885810184729[/C][/ROW]
[ROW][C]27[/C][C]105.17[/C][C]104.491300419474[/C][C]105.9525[/C][C]0.986208918331083[/C][C]1.00649527355676[/C][/ROW]
[ROW][C]28[/C][C]104.84[/C][C]106.724290086086[/C][C]106.203333333333[/C][C]1.00490527685339[/C][C]0.98234431838744[/C][/ROW]
[ROW][C]29[/C][C]106.18[/C][C]106.166279847781[/C][C]106.398333333333[/C][C]0.99781901202507[/C][C]1.00012923267387[/C][/ROW]
[ROW][C]30[/C][C]108.86[/C][C]109.651434681753[/C][C]106.68125[/C][C]1.02784167491245[/C][C]0.99278226788322[/C][/ROW]
[ROW][C]31[/C][C]107.77[/C][C]107.968471692197[/C][C]107.009583333333[/C][C]1.00896077088607[/C][C]0.9981617625119[/C][/ROW]
[ROW][C]32[/C][C]102.74[/C][C]103.364861723616[/C][C]107.31[/C][C]0.963236061165[/C][C]0.993954795534996[/C][/ROW]
[ROW][C]33[/C][C]112.63[/C][C]111.185788806606[/C][C]107.565416666667[/C][C]1.03365739893109[/C][C]1.01298917072852[/C][/ROW]
[ROW][C]34[/C][C]106.26[/C][C]107.323958250186[/C][C]107.953333333333[/C][C]0.9941699337694[/C][C]0.990086479593813[/C][/ROW]
[ROW][C]35[/C][C]108.86[/C][C]109.130615841816[/C][C]108.43875[/C][C]1.00638024545484[/C][C]0.997520257356483[/C][/ROW]
[ROW][C]36[/C][C]111.38[/C][C]110.368334954389[/C][C]108.854166666667[/C][C]1.01391006273888[/C][C]1.00916626173648[/C][/ROW]
[ROW][C]37[/C][C]106.85[/C][C]107.310062918869[/C][C]109.227916666667[/C][C]0.982441725464283[/C][C]0.995712770020303[/C][/ROW]
[ROW][C]38[/C][C]107.86[/C][C]107.553763707239[/C][C]109.69625[/C][C]0.98046891946843[/C][C]1.00284728569420[/C][/ROW]
[ROW][C]39[/C][C]107.94[/C][C]108.521196612005[/C][C]110.03875[/C][C]0.986208918331083[/C][C]0.994644395471583[/C][/ROW]
[ROW][C]40[/C][C]111.38[/C][C]110.852357221294[/C][C]110.31125[/C][C]1.00490527685339[/C][C]1.00475986972160[/C][/ROW]
[ROW][C]41[/C][C]111.29[/C][C]110.544626520962[/C][C]110.78625[/C][C]0.99781901202507[/C][C]1.00674273822705[/C][/ROW]
[ROW][C]42[/C][C]113.72[/C][C]114.154237752599[/C][C]111.062083333333[/C][C]1.02784167491245[/C][C]0.996196043518418[/C][/ROW]
[ROW][C]43[/C][C]111.88[/C][C]112.282619788378[/C][C]111.285416666667[/C][C]1.00896077088607[/C][C]0.996414228763662[/C][/ROW]
[ROW][C]44[/C][C]109.87[/C][C]107.503164651396[/C][C]111.60625[/C][C]0.963236061165[/C][C]1.02201642487715[/C][/ROW]
[ROW][C]45[/C][C]113.72[/C][C]115.618456285689[/C][C]111.85375[/C][C]1.03365739893109[/C][C]0.983579989331479[/C][/ROW]
[ROW][C]46[/C][C]111.71[/C][C]111.492844172459[/C][C]112.146666666667[/C][C]0.9941699337694[/C][C]1.00194771089708[/C][/ROW]
[ROW][C]47[/C][C]114.81[/C][C]113.048370272351[/C][C]112.331666666667[/C][C]1.00638024545484[/C][C]1.01558297322999[/C][/ROW]
[ROW][C]48[/C][C]112.05[/C][C]114.015031480040[/C][C]112.450833333333[/C][C]1.01391006273888[/C][C]0.98276515425614[/C][/ROW]
[ROW][C]49[/C][C]111.54[/C][C]110.648318031853[/C][C]112.625833333333[/C][C]0.982441725464283[/C][C]1.00805870332245[/C][/ROW]
[ROW][C]50[/C][C]110.87[/C][C]110.337069852380[/C][C]112.535[/C][C]0.98046891946843[/C][C]1.00483001903470[/C][/ROW]
[ROW][C]51[/C][C]110.87[/C][C]110.690034391568[/C][C]112.237916666667[/C][C]0.986208918331083[/C][C]1.00162585195155[/C][/ROW]
[ROW][C]52[/C][C]115.48[/C][C]112.486584427777[/C][C]111.9375[/C][C]1.00490527685339[/C][C]1.02661131180621[/C][/ROW]
[ROW][C]53[/C][C]111.63[/C][C]111.271787125976[/C][C]111.515[/C][C]0.99781901202507[/C][C]1.00321926054462[/C][/ROW]
[ROW][C]54[/C][C]116.24[/C][C]114.188927409127[/C][C]111.095833333333[/C][C]1.02784167491245[/C][C]1.01796209700371[/C][/ROW]
[ROW][C]55[/C][C]113.56[/C][C]NA[/C][C]NA[/C][C]1.00896077088607[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]106.01[/C][C]NA[/C][C]NA[/C][C]0.963236061165[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]110.45[/C][C]NA[/C][C]NA[/C][C]1.03365739893109[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]107.77[/C][C]NA[/C][C]NA[/C][C]0.9941699337694[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]108.61[/C][C]NA[/C][C]NA[/C][C]1.00638024545484[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]108.19[/C][C]NA[/C][C]NA[/C][C]1.01391006273888[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62936&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62936&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
196.96NANA0.982441725464283NA
293.11NANA0.98046891946843NA
395.62NANA0.986208918331083NA
498.3NANA1.00490527685339NA
596.38NANA0.99781901202507NA
6100.82NANA1.02784167491245NA
799.0698.979892424566198.10083333333331.008960770886071.00080933180944
894.0394.763163697412798.380.9632360611650.992263199445791
9102.07102.07711366910998.75333333333331.033657398931090.999930310832145
1099.3198.486616013920899.06416666666670.99416993376941.00836036427491
1198.64100.03713167392799.40291666666671.006380245454840.986033869118907
12101.82101.19625104933799.80791666666671.013910062738881.00616375551658
1399.1498.4910110299512100.251250.9824417254642831.00658932184026
1497.6398.7622257293386100.7295833333330.980468919468430.988535842312409
15100.0699.8298195988292101.2258333333330.9862089183310831.00230572790871
16101.32102.203472471842101.7045833333331.004905276853390.991355748973346
17101.49102.002048504263102.2250.997819012025070.994980017443067
18105.43105.627434524471102.766251.027841674912450.998130840483251
19105.09104.08187072268103.15751.008960770886071.00968592580360
2099.4899.795670768641103.6045833333330.9632360611650.996836829030662
21108.53107.607611443973104.103751.033657398931091.00857177799646
22104.34103.854305181331104.4633333333330.99416993376941.00467669412280
23106.1105.474100949997104.8054166666671.006380245454841.00593414918322
24107.35106.606306159102105.143751.013910062738881.00697607737940
25103103.547720461060105.3983333333330.9824417254642830.994710453705587
26104.5103.582456054675105.6458333333330.980468919468431.00885810184729
27105.17104.491300419474105.95250.9862089183310831.00649527355676
28104.84106.724290086086106.2033333333331.004905276853390.98234431838744
29106.18106.166279847781106.3983333333330.997819012025071.00012923267387
30108.86109.651434681753106.681251.027841674912450.99278226788322
31107.77107.968471692197107.0095833333331.008960770886070.9981617625119
32102.74103.364861723616107.310.9632360611650.993954795534996
33112.63111.185788806606107.5654166666671.033657398931091.01298917072852
34106.26107.323958250186107.9533333333330.99416993376940.990086479593813
35108.86109.130615841816108.438751.006380245454840.997520257356483
36111.38110.368334954389108.8541666666671.013910062738881.00916626173648
37106.85107.310062918869109.2279166666670.9824417254642830.995712770020303
38107.86107.553763707239109.696250.980468919468431.00284728569420
39107.94108.521196612005110.038750.9862089183310830.994644395471583
40111.38110.852357221294110.311251.004905276853391.00475986972160
41111.29110.544626520962110.786250.997819012025071.00674273822705
42113.72114.154237752599111.0620833333331.027841674912450.996196043518418
43111.88112.282619788378111.2854166666671.008960770886070.996414228763662
44109.87107.503164651396111.606250.9632360611651.02201642487715
45113.72115.618456285689111.853751.033657398931090.983579989331479
46111.71111.492844172459112.1466666666670.99416993376941.00194771089708
47114.81113.048370272351112.3316666666671.006380245454841.01558297322999
48112.05114.015031480040112.4508333333331.013910062738880.98276515425614
49111.54110.648318031853112.6258333333330.9824417254642831.00805870332245
50110.87110.337069852380112.5350.980468919468431.00483001903470
51110.87110.690034391568112.2379166666670.9862089183310831.00162585195155
52115.48112.486584427777111.93751.004905276853391.02661131180621
53111.63111.271787125976111.5150.997819012025071.00321926054462
54116.24114.188927409127111.0958333333331.027841674912451.01796209700371
55113.56NANA1.00896077088607NA
56106.01NANA0.963236061165NA
57110.45NANA1.03365739893109NA
58107.77NANA0.9941699337694NA
59108.61NANA1.00638024545484NA
60108.19NANA1.01391006273888NA



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')