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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 computationFri, 25 Nov 2011 11:52:48 -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/2011/Nov/25/t1322239998czl06og5eg77dne.htm/, Retrieved Fri, 29 Mar 2024 08:38:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147338, Retrieved Fri, 29 Mar 2024 08:38:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
- RMPD    [Classical Decomposition] [WS8] [2011-11-25 16:52:48] [84449ea5bbe6e767918d59f07903f9b5] [Current]
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Dataseries X:
89924
31795
27922
59954
52150
39964
34604
51106
52593
68794
47124
32315
42248
36088
52744
72586
92334
80761
71078
63713
57122
55243
62143
62708
62474
64250
71866
69886
58724
55298
52594
54854
54694
49298
44659
43657
47002
47042
48959
49750
54048
60067
68929
74617
75940
72762
75621
73008
74196
78878
83812
91624
89388
110410
113857
112060
117236
132810
137699
146409




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147338&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147338&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147338&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'Herman Ole Andreas Wold' @ wold.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
189924NANA0.900510120162767NA
231795NANA0.877921261066405NA
327922NANA0.987848807627763NA
459954NANA1.07381727242246NA
552150NANA1.1063078010585NA
639964NANA1.09173258007438NA
73460446116.84837241347033.91666666670.9805019790132950.750354831721328
85110648421.476625263245226.29166666671.070648838117131.05544075814772
95259348286.058415258146439.41666666671.039764533690131.08919637937109
106879451144.9959185401480001.065520748302921.3450778275468
114712447786.610243363450200.66666666670.9519118652480730.986133976861114
123231545726.916212282553574.8750.8535141932161770.706695370621122
134224851144.022019584356794.50.9005101201627670.826059397202321
143608851656.484620569358839.54166666670.8779212610664050.69861509673134
155274458829.895125426959553.54166666670.9878488076277630.896550977824257
167258663545.955865939459177.6251.073817272422461.14225994417539
179233465536.337346112959238.79166666671.10630780105851.40889777700518
188076166738.658863669261130.95833333331.091732580074381.21010822475434
197107862007.02686129963240.08333333330.9805019790132951.14628943843722
206371369866.528242381165256.251.070648838117130.911924516686542
215712269899.643777074867226.41666666671.039764533690130.817200158875981
225524372360.224364416767910.66666666671.065520748302920.763444288422713
236214363204.806050775266397.750.9519118652480730.983200548864556
246270854570.603525291963936.3750.8535141932161771.14911684953121
256247455926.406140238762105.250.9005101201627671.11707517631909
266425053523.311022121960965.95833333330.8779212610664051.20041153607714
277186659760.572183313260495.66666666670.9878488076277631.20256546037668
286988664586.663772462160146.79166666671.073817272422461.08205000719974
295872465460.87793484959170.58333333331.10630780105850.897085432591448
305529862936.518198130257648.29166666671.091732580074380.878631382592799
315259455113.852823340856209.83333333330.9805019790132950.954279138651079
325485458723.125914521554848.16666666671.070648838117130.934112398577803
335469455291.255343384253176.70833333331.039764533690130.989198014411593
344929854749.918990235951383.251.065520748302920.900421423615106
354465947928.207133319150349.41666666670.9519118652480730.931789496648074
364365742977.249112653850353.29166666670.8535141932161771.01581652854431
374700246135.49729500451232.6250.9005101201627671.01878169209829
384704246298.677484491252736.70833333330.8779212610664051.01605494057056
394895953783.839934963454445.41666666670.9878488076277630.910292014463867
404975060464.860914654956308.33333333331.073817272422460.822791936464077
415404864803.177947119758576.08333333331.10630780105850.834033171090836
426006766692.988050736161089.1251.091732580074380.900649404916519
436892962208.11147549563445.16666666670.9805019790132951.10803878087751
447461770560.844013965904.751.070648838117131.05748451627507
457594071414.624026183568683.45833333331.039764533690131.06336763702848
467276276590.075354992371880.41666666671.065520748302920.95001865010252
477562171486.359952444775097.66666666670.9519118652480731.0578381673134
487300867144.076736473378667.79166666670.8535141932161771.0873334409905
497419674415.830012440682637.41666666670.9005101201627670.997045924067448
507887875562.280559407486069.54166666670.8779212610664051.0438806163081
518381288264.620244476589350.33333333330.9878488076277630.949553737022336
5291624100480.303632387935731.073817272422460.911860301847928
5389388109150.0793064598661.58333333331.10630780105850.818945808999677
54110410113874.849852385104306.5416666671.091732580074380.969573177423488
55113857NANA0.980501979013295NA
56112060NANA1.07064883811713NA
57117236NANA1.03976453369013NA
58132810NANA1.06552074830292NA
59137699NANA0.951911865248073NA
60146409NANA0.853514193216177NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 89924 & NA & NA & 0.900510120162767 & NA \tabularnewline
2 & 31795 & NA & NA & 0.877921261066405 & NA \tabularnewline
3 & 27922 & NA & NA & 0.987848807627763 & NA \tabularnewline
4 & 59954 & NA & NA & 1.07381727242246 & NA \tabularnewline
5 & 52150 & NA & NA & 1.1063078010585 & NA \tabularnewline
6 & 39964 & NA & NA & 1.09173258007438 & NA \tabularnewline
7 & 34604 & 46116.848372413 & 47033.9166666667 & 0.980501979013295 & 0.750354831721328 \tabularnewline
8 & 51106 & 48421.4766252632 & 45226.2916666667 & 1.07064883811713 & 1.05544075814772 \tabularnewline
9 & 52593 & 48286.0584152581 & 46439.4166666667 & 1.03976453369013 & 1.08919637937109 \tabularnewline
10 & 68794 & 51144.9959185401 & 48000 & 1.06552074830292 & 1.3450778275468 \tabularnewline
11 & 47124 & 47786.6102433634 & 50200.6666666667 & 0.951911865248073 & 0.986133976861114 \tabularnewline
12 & 32315 & 45726.9162122825 & 53574.875 & 0.853514193216177 & 0.706695370621122 \tabularnewline
13 & 42248 & 51144.0220195843 & 56794.5 & 0.900510120162767 & 0.826059397202321 \tabularnewline
14 & 36088 & 51656.4846205693 & 58839.5416666667 & 0.877921261066405 & 0.69861509673134 \tabularnewline
15 & 52744 & 58829.8951254269 & 59553.5416666667 & 0.987848807627763 & 0.896550977824257 \tabularnewline
16 & 72586 & 63545.9558659394 & 59177.625 & 1.07381727242246 & 1.14225994417539 \tabularnewline
17 & 92334 & 65536.3373461129 & 59238.7916666667 & 1.1063078010585 & 1.40889777700518 \tabularnewline
18 & 80761 & 66738.6588636692 & 61130.9583333333 & 1.09173258007438 & 1.21010822475434 \tabularnewline
19 & 71078 & 62007.026861299 & 63240.0833333333 & 0.980501979013295 & 1.14628943843722 \tabularnewline
20 & 63713 & 69866.5282423811 & 65256.25 & 1.07064883811713 & 0.911924516686542 \tabularnewline
21 & 57122 & 69899.6437770748 & 67226.4166666667 & 1.03976453369013 & 0.817200158875981 \tabularnewline
22 & 55243 & 72360.2243644167 & 67910.6666666667 & 1.06552074830292 & 0.763444288422713 \tabularnewline
23 & 62143 & 63204.8060507752 & 66397.75 & 0.951911865248073 & 0.983200548864556 \tabularnewline
24 & 62708 & 54570.6035252919 & 63936.375 & 0.853514193216177 & 1.14911684953121 \tabularnewline
25 & 62474 & 55926.4061402387 & 62105.25 & 0.900510120162767 & 1.11707517631909 \tabularnewline
26 & 64250 & 53523.3110221219 & 60965.9583333333 & 0.877921261066405 & 1.20041153607714 \tabularnewline
27 & 71866 & 59760.5721833132 & 60495.6666666667 & 0.987848807627763 & 1.20256546037668 \tabularnewline
28 & 69886 & 64586.6637724621 & 60146.7916666667 & 1.07381727242246 & 1.08205000719974 \tabularnewline
29 & 58724 & 65460.877934849 & 59170.5833333333 & 1.1063078010585 & 0.897085432591448 \tabularnewline
30 & 55298 & 62936.5181981302 & 57648.2916666667 & 1.09173258007438 & 0.878631382592799 \tabularnewline
31 & 52594 & 55113.8528233408 & 56209.8333333333 & 0.980501979013295 & 0.954279138651079 \tabularnewline
32 & 54854 & 58723.1259145215 & 54848.1666666667 & 1.07064883811713 & 0.934112398577803 \tabularnewline
33 & 54694 & 55291.2553433842 & 53176.7083333333 & 1.03976453369013 & 0.989198014411593 \tabularnewline
34 & 49298 & 54749.9189902359 & 51383.25 & 1.06552074830292 & 0.900421423615106 \tabularnewline
35 & 44659 & 47928.2071333191 & 50349.4166666667 & 0.951911865248073 & 0.931789496648074 \tabularnewline
36 & 43657 & 42977.2491126538 & 50353.2916666667 & 0.853514193216177 & 1.01581652854431 \tabularnewline
37 & 47002 & 46135.497295004 & 51232.625 & 0.900510120162767 & 1.01878169209829 \tabularnewline
38 & 47042 & 46298.6774844912 & 52736.7083333333 & 0.877921261066405 & 1.01605494057056 \tabularnewline
39 & 48959 & 53783.8399349634 & 54445.4166666667 & 0.987848807627763 & 0.910292014463867 \tabularnewline
40 & 49750 & 60464.8609146549 & 56308.3333333333 & 1.07381727242246 & 0.822791936464077 \tabularnewline
41 & 54048 & 64803.1779471197 & 58576.0833333333 & 1.1063078010585 & 0.834033171090836 \tabularnewline
42 & 60067 & 66692.9880507361 & 61089.125 & 1.09173258007438 & 0.900649404916519 \tabularnewline
43 & 68929 & 62208.111475495 & 63445.1666666667 & 0.980501979013295 & 1.10803878087751 \tabularnewline
44 & 74617 & 70560.8440139 & 65904.75 & 1.07064883811713 & 1.05748451627507 \tabularnewline
45 & 75940 & 71414.6240261835 & 68683.4583333333 & 1.03976453369013 & 1.06336763702848 \tabularnewline
46 & 72762 & 76590.0753549923 & 71880.4166666667 & 1.06552074830292 & 0.95001865010252 \tabularnewline
47 & 75621 & 71486.3599524447 & 75097.6666666667 & 0.951911865248073 & 1.0578381673134 \tabularnewline
48 & 73008 & 67144.0767364733 & 78667.7916666667 & 0.853514193216177 & 1.0873334409905 \tabularnewline
49 & 74196 & 74415.8300124406 & 82637.4166666667 & 0.900510120162767 & 0.997045924067448 \tabularnewline
50 & 78878 & 75562.2805594074 & 86069.5416666667 & 0.877921261066405 & 1.0438806163081 \tabularnewline
51 & 83812 & 88264.6202444765 & 89350.3333333333 & 0.987848807627763 & 0.949553737022336 \tabularnewline
52 & 91624 & 100480.303632387 & 93573 & 1.07381727242246 & 0.911860301847928 \tabularnewline
53 & 89388 & 109150.07930645 & 98661.5833333333 & 1.1063078010585 & 0.818945808999677 \tabularnewline
54 & 110410 & 113874.849852385 & 104306.541666667 & 1.09173258007438 & 0.969573177423488 \tabularnewline
55 & 113857 & NA & NA & 0.980501979013295 & NA \tabularnewline
56 & 112060 & NA & NA & 1.07064883811713 & NA \tabularnewline
57 & 117236 & NA & NA & 1.03976453369013 & NA \tabularnewline
58 & 132810 & NA & NA & 1.06552074830292 & NA \tabularnewline
59 & 137699 & NA & NA & 0.951911865248073 & NA \tabularnewline
60 & 146409 & NA & NA & 0.853514193216177 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147338&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]89924[/C][C]NA[/C][C]NA[/C][C]0.900510120162767[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]31795[/C][C]NA[/C][C]NA[/C][C]0.877921261066405[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]27922[/C][C]NA[/C][C]NA[/C][C]0.987848807627763[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]59954[/C][C]NA[/C][C]NA[/C][C]1.07381727242246[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]52150[/C][C]NA[/C][C]NA[/C][C]1.1063078010585[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]39964[/C][C]NA[/C][C]NA[/C][C]1.09173258007438[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]34604[/C][C]46116.848372413[/C][C]47033.9166666667[/C][C]0.980501979013295[/C][C]0.750354831721328[/C][/ROW]
[ROW][C]8[/C][C]51106[/C][C]48421.4766252632[/C][C]45226.2916666667[/C][C]1.07064883811713[/C][C]1.05544075814772[/C][/ROW]
[ROW][C]9[/C][C]52593[/C][C]48286.0584152581[/C][C]46439.4166666667[/C][C]1.03976453369013[/C][C]1.08919637937109[/C][/ROW]
[ROW][C]10[/C][C]68794[/C][C]51144.9959185401[/C][C]48000[/C][C]1.06552074830292[/C][C]1.3450778275468[/C][/ROW]
[ROW][C]11[/C][C]47124[/C][C]47786.6102433634[/C][C]50200.6666666667[/C][C]0.951911865248073[/C][C]0.986133976861114[/C][/ROW]
[ROW][C]12[/C][C]32315[/C][C]45726.9162122825[/C][C]53574.875[/C][C]0.853514193216177[/C][C]0.706695370621122[/C][/ROW]
[ROW][C]13[/C][C]42248[/C][C]51144.0220195843[/C][C]56794.5[/C][C]0.900510120162767[/C][C]0.826059397202321[/C][/ROW]
[ROW][C]14[/C][C]36088[/C][C]51656.4846205693[/C][C]58839.5416666667[/C][C]0.877921261066405[/C][C]0.69861509673134[/C][/ROW]
[ROW][C]15[/C][C]52744[/C][C]58829.8951254269[/C][C]59553.5416666667[/C][C]0.987848807627763[/C][C]0.896550977824257[/C][/ROW]
[ROW][C]16[/C][C]72586[/C][C]63545.9558659394[/C][C]59177.625[/C][C]1.07381727242246[/C][C]1.14225994417539[/C][/ROW]
[ROW][C]17[/C][C]92334[/C][C]65536.3373461129[/C][C]59238.7916666667[/C][C]1.1063078010585[/C][C]1.40889777700518[/C][/ROW]
[ROW][C]18[/C][C]80761[/C][C]66738.6588636692[/C][C]61130.9583333333[/C][C]1.09173258007438[/C][C]1.21010822475434[/C][/ROW]
[ROW][C]19[/C][C]71078[/C][C]62007.026861299[/C][C]63240.0833333333[/C][C]0.980501979013295[/C][C]1.14628943843722[/C][/ROW]
[ROW][C]20[/C][C]63713[/C][C]69866.5282423811[/C][C]65256.25[/C][C]1.07064883811713[/C][C]0.911924516686542[/C][/ROW]
[ROW][C]21[/C][C]57122[/C][C]69899.6437770748[/C][C]67226.4166666667[/C][C]1.03976453369013[/C][C]0.817200158875981[/C][/ROW]
[ROW][C]22[/C][C]55243[/C][C]72360.2243644167[/C][C]67910.6666666667[/C][C]1.06552074830292[/C][C]0.763444288422713[/C][/ROW]
[ROW][C]23[/C][C]62143[/C][C]63204.8060507752[/C][C]66397.75[/C][C]0.951911865248073[/C][C]0.983200548864556[/C][/ROW]
[ROW][C]24[/C][C]62708[/C][C]54570.6035252919[/C][C]63936.375[/C][C]0.853514193216177[/C][C]1.14911684953121[/C][/ROW]
[ROW][C]25[/C][C]62474[/C][C]55926.4061402387[/C][C]62105.25[/C][C]0.900510120162767[/C][C]1.11707517631909[/C][/ROW]
[ROW][C]26[/C][C]64250[/C][C]53523.3110221219[/C][C]60965.9583333333[/C][C]0.877921261066405[/C][C]1.20041153607714[/C][/ROW]
[ROW][C]27[/C][C]71866[/C][C]59760.5721833132[/C][C]60495.6666666667[/C][C]0.987848807627763[/C][C]1.20256546037668[/C][/ROW]
[ROW][C]28[/C][C]69886[/C][C]64586.6637724621[/C][C]60146.7916666667[/C][C]1.07381727242246[/C][C]1.08205000719974[/C][/ROW]
[ROW][C]29[/C][C]58724[/C][C]65460.877934849[/C][C]59170.5833333333[/C][C]1.1063078010585[/C][C]0.897085432591448[/C][/ROW]
[ROW][C]30[/C][C]55298[/C][C]62936.5181981302[/C][C]57648.2916666667[/C][C]1.09173258007438[/C][C]0.878631382592799[/C][/ROW]
[ROW][C]31[/C][C]52594[/C][C]55113.8528233408[/C][C]56209.8333333333[/C][C]0.980501979013295[/C][C]0.954279138651079[/C][/ROW]
[ROW][C]32[/C][C]54854[/C][C]58723.1259145215[/C][C]54848.1666666667[/C][C]1.07064883811713[/C][C]0.934112398577803[/C][/ROW]
[ROW][C]33[/C][C]54694[/C][C]55291.2553433842[/C][C]53176.7083333333[/C][C]1.03976453369013[/C][C]0.989198014411593[/C][/ROW]
[ROW][C]34[/C][C]49298[/C][C]54749.9189902359[/C][C]51383.25[/C][C]1.06552074830292[/C][C]0.900421423615106[/C][/ROW]
[ROW][C]35[/C][C]44659[/C][C]47928.2071333191[/C][C]50349.4166666667[/C][C]0.951911865248073[/C][C]0.931789496648074[/C][/ROW]
[ROW][C]36[/C][C]43657[/C][C]42977.2491126538[/C][C]50353.2916666667[/C][C]0.853514193216177[/C][C]1.01581652854431[/C][/ROW]
[ROW][C]37[/C][C]47002[/C][C]46135.497295004[/C][C]51232.625[/C][C]0.900510120162767[/C][C]1.01878169209829[/C][/ROW]
[ROW][C]38[/C][C]47042[/C][C]46298.6774844912[/C][C]52736.7083333333[/C][C]0.877921261066405[/C][C]1.01605494057056[/C][/ROW]
[ROW][C]39[/C][C]48959[/C][C]53783.8399349634[/C][C]54445.4166666667[/C][C]0.987848807627763[/C][C]0.910292014463867[/C][/ROW]
[ROW][C]40[/C][C]49750[/C][C]60464.8609146549[/C][C]56308.3333333333[/C][C]1.07381727242246[/C][C]0.822791936464077[/C][/ROW]
[ROW][C]41[/C][C]54048[/C][C]64803.1779471197[/C][C]58576.0833333333[/C][C]1.1063078010585[/C][C]0.834033171090836[/C][/ROW]
[ROW][C]42[/C][C]60067[/C][C]66692.9880507361[/C][C]61089.125[/C][C]1.09173258007438[/C][C]0.900649404916519[/C][/ROW]
[ROW][C]43[/C][C]68929[/C][C]62208.111475495[/C][C]63445.1666666667[/C][C]0.980501979013295[/C][C]1.10803878087751[/C][/ROW]
[ROW][C]44[/C][C]74617[/C][C]70560.8440139[/C][C]65904.75[/C][C]1.07064883811713[/C][C]1.05748451627507[/C][/ROW]
[ROW][C]45[/C][C]75940[/C][C]71414.6240261835[/C][C]68683.4583333333[/C][C]1.03976453369013[/C][C]1.06336763702848[/C][/ROW]
[ROW][C]46[/C][C]72762[/C][C]76590.0753549923[/C][C]71880.4166666667[/C][C]1.06552074830292[/C][C]0.95001865010252[/C][/ROW]
[ROW][C]47[/C][C]75621[/C][C]71486.3599524447[/C][C]75097.6666666667[/C][C]0.951911865248073[/C][C]1.0578381673134[/C][/ROW]
[ROW][C]48[/C][C]73008[/C][C]67144.0767364733[/C][C]78667.7916666667[/C][C]0.853514193216177[/C][C]1.0873334409905[/C][/ROW]
[ROW][C]49[/C][C]74196[/C][C]74415.8300124406[/C][C]82637.4166666667[/C][C]0.900510120162767[/C][C]0.997045924067448[/C][/ROW]
[ROW][C]50[/C][C]78878[/C][C]75562.2805594074[/C][C]86069.5416666667[/C][C]0.877921261066405[/C][C]1.0438806163081[/C][/ROW]
[ROW][C]51[/C][C]83812[/C][C]88264.6202444765[/C][C]89350.3333333333[/C][C]0.987848807627763[/C][C]0.949553737022336[/C][/ROW]
[ROW][C]52[/C][C]91624[/C][C]100480.303632387[/C][C]93573[/C][C]1.07381727242246[/C][C]0.911860301847928[/C][/ROW]
[ROW][C]53[/C][C]89388[/C][C]109150.07930645[/C][C]98661.5833333333[/C][C]1.1063078010585[/C][C]0.818945808999677[/C][/ROW]
[ROW][C]54[/C][C]110410[/C][C]113874.849852385[/C][C]104306.541666667[/C][C]1.09173258007438[/C][C]0.969573177423488[/C][/ROW]
[ROW][C]55[/C][C]113857[/C][C]NA[/C][C]NA[/C][C]0.980501979013295[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]112060[/C][C]NA[/C][C]NA[/C][C]1.07064883811713[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]117236[/C][C]NA[/C][C]NA[/C][C]1.03976453369013[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]132810[/C][C]NA[/C][C]NA[/C][C]1.06552074830292[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]137699[/C][C]NA[/C][C]NA[/C][C]0.951911865248073[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]146409[/C][C]NA[/C][C]NA[/C][C]0.853514193216177[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147338&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147338&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
189924NANA0.900510120162767NA
231795NANA0.877921261066405NA
327922NANA0.987848807627763NA
459954NANA1.07381727242246NA
552150NANA1.1063078010585NA
639964NANA1.09173258007438NA
73460446116.84837241347033.91666666670.9805019790132950.750354831721328
85110648421.476625263245226.29166666671.070648838117131.05544075814772
95259348286.058415258146439.41666666671.039764533690131.08919637937109
106879451144.9959185401480001.065520748302921.3450778275468
114712447786.610243363450200.66666666670.9519118652480730.986133976861114
123231545726.916212282553574.8750.8535141932161770.706695370621122
134224851144.022019584356794.50.9005101201627670.826059397202321
143608851656.484620569358839.54166666670.8779212610664050.69861509673134
155274458829.895125426959553.54166666670.9878488076277630.896550977824257
167258663545.955865939459177.6251.073817272422461.14225994417539
179233465536.337346112959238.79166666671.10630780105851.40889777700518
188076166738.658863669261130.95833333331.091732580074381.21010822475434
197107862007.02686129963240.08333333330.9805019790132951.14628943843722
206371369866.528242381165256.251.070648838117130.911924516686542
215712269899.643777074867226.41666666671.039764533690130.817200158875981
225524372360.224364416767910.66666666671.065520748302920.763444288422713
236214363204.806050775266397.750.9519118652480730.983200548864556
246270854570.603525291963936.3750.8535141932161771.14911684953121
256247455926.406140238762105.250.9005101201627671.11707517631909
266425053523.311022121960965.95833333330.8779212610664051.20041153607714
277186659760.572183313260495.66666666670.9878488076277631.20256546037668
286988664586.663772462160146.79166666671.073817272422461.08205000719974
295872465460.87793484959170.58333333331.10630780105850.897085432591448
305529862936.518198130257648.29166666671.091732580074380.878631382592799
315259455113.852823340856209.83333333330.9805019790132950.954279138651079
325485458723.125914521554848.16666666671.070648838117130.934112398577803
335469455291.255343384253176.70833333331.039764533690130.989198014411593
344929854749.918990235951383.251.065520748302920.900421423615106
354465947928.207133319150349.41666666670.9519118652480730.931789496648074
364365742977.249112653850353.29166666670.8535141932161771.01581652854431
374700246135.49729500451232.6250.9005101201627671.01878169209829
384704246298.677484491252736.70833333330.8779212610664051.01605494057056
394895953783.839934963454445.41666666670.9878488076277630.910292014463867
404975060464.860914654956308.33333333331.073817272422460.822791936464077
415404864803.177947119758576.08333333331.10630780105850.834033171090836
426006766692.988050736161089.1251.091732580074380.900649404916519
436892962208.11147549563445.16666666670.9805019790132951.10803878087751
447461770560.844013965904.751.070648838117131.05748451627507
457594071414.624026183568683.45833333331.039764533690131.06336763702848
467276276590.075354992371880.41666666671.065520748302920.95001865010252
477562171486.359952444775097.66666666670.9519118652480731.0578381673134
487300867144.076736473378667.79166666670.8535141932161771.0873334409905
497419674415.830012440682637.41666666670.9005101201627670.997045924067448
507887875562.280559407486069.54166666670.8779212610664051.0438806163081
518381288264.620244476589350.33333333330.9878488076277630.949553737022336
5291624100480.303632387935731.073817272422460.911860301847928
5389388109150.0793064598661.58333333331.10630780105850.818945808999677
54110410113874.849852385104306.5416666671.091732580074380.969573177423488
55113857NANA0.980501979013295NA
56112060NANA1.07064883811713NA
57117236NANA1.03976453369013NA
58132810NANA1.06552074830292NA
59137699NANA0.951911865248073NA
60146409NANA0.853514193216177NA



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')