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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 computationThu, 03 Dec 2009 13:05:50 -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/t1259870825esjrrmonragnxbj.htm/, Retrieved Fri, 29 Mar 2024 04:57:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63104, Retrieved Fri, 29 Mar 2024 04:57:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
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]
- R PD      [Classical Decomposition] [] [2009-12-03 20:05:50] [ed082d38031561faed979d8cebfeba4d] [Current]
-   PD        [Classical Decomposition] [] [2009-12-11 13:04:40] [a4642ac6536e7ce898d9b031a7452eab]
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Dataseries X:
1915
1843
1761
2858
3968
5061
4661
4269
3857
3568
3274
2987
1683
1381
1071
2772
4485
6181
5479
4782
4067
3489
2903
2330
1736
1483
1242
2334
3423
4523
3986
3462
2908
2575
2237
1904
1610
1251
941
2450
3946
5409
4741
4069
3539
3189
2960
2704
1697
1598
1456
2316
3083
4158
3469
2892
2578
2233
1947
2049




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63104&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63104&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63104&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' @ 72.249.127.135







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11915NANA0.55395025528304NA
21843NANA0.476287260569315NA
31761NANA0.399508434052524NA
42858NANA0.834526828269122NA
53968NANA1.26664061052328NA
65061NANA1.73103390556832NA
746615017.811835604723325.51.508889440867460.928890949422833
842694374.044482702783296.583333333331.326841775384450.975984587464033
938573738.342709785163248.583333333331.150760909048101.03174061326808
1035683312.392160620013216.251.029892626698801.07716714295452
1132742966.964914397033234.208333333330.9173697574822361.10348456906690
1229872656.127768277073302.416666666670.8042981962533721.12456939597358
1316831874.106038665073383.166666666670.553950255283040.89802816130874
1413811637.773281375163438.6250.4762872605693150.843218054479702
1510711385.794880619693468.750.3995084340525240.772841648484857
1627722899.320061162823474.208333333330.8345268282691220.956086234538813
1744854376.823852971083455.458333333331.266640610523281.02471567297722
1861815907.369581990083412.6251.731033905568321.04632017926289
1954795111.300110545143387.458333333331.508889440867461.07193862256224
2047824503.190415506873393.916666666671.326841775384451.06191378972851
2140673918.676533907253405.291666666671.150760909048101.03785039791097
2234893495.627223786843394.166666666671.029892626698800.998104138867628
2329033056.370242011653331.666666666670.9173697574822360.949819481977843
2423302588.49969494213218.333333333330.8042981962533720.900135319526131
2517361710.067519319383087.041666666670.553950255283041.01516459460673
2614831414.493782680772969.833333333330.4762872605693151.04843161430473
2712421145.207572396312866.541666666670.3995084340525241.08451954906406
2823342320.123670392872780.166666666670.8345268282691221.00598085773798
2934233438.084830497012714.333333333331.266640610523280.995612432141521
3045234619.840988310912668.833333333331.731033905568320.979038025647217
3139863992.269978961812645.833333333331.508889440867460.998429470202454
3234623490.810140888532630.916666666671.326841775384450.991746861122273
3329083001.999573108012608.708333333331.150760909048100.968687679388743
3425752678.7507220435726011.029892626698800.961268989611537
3522372410.503709004262627.6250.9173697574822360.928021803759874
3619042160.613054535312686.333333333330.8042981962533720.881231369033592
3716101525.971384480322754.708333333330.553950255283041.05506565612847
3812511339.061787788102811.458333333330.4762872605693150.934236202846497
399411143.809292877132863.041666666670.3995084340525240.82268959157782
4024502432.576160502132914.916666666670.8345268282691221.00716271078406
4139463762.714263635712970.6251.266640610523281.04871104301903
4254095252.101122319743034.083333333331.731033905568321.02987354470642
4347414633.862343297333071.041666666671.508889440867461.02312059546992
4440694098.780099384483089.1251.326841775384450.992734399342636
4535393596.175789146523125.041666666671.150760909048100.984100947089662
4631893234.806916075373140.916666666671.029892626698800.985839366223768
4729602843.272892096513099.3750.9173697574822361.04105378285284
4827042421.976455892813011.291666666670.8042981962533721.11644355312415
4916971609.871766895062906.166666666670.553950255283041.05412122561351
5015981335.569014543932804.1250.4762872605693151.19649376602652
5114561084.682044637362715.041666666670.3995084340525241.34232884853071
5223162199.117280293852635.166666666670.8345268282691221.05314983459660
5330833233.891808742242553.1251.266640610523280.953340489519677
5441584299.239083717112483.6251.731033905568320.967147888040924
553469NANA1.50888944086746NA
562892NANA1.32684177538445NA
572578NANA1.15076090904810NA
582233NANA1.02989262669880NA
591947NANA0.917369757482236NA
602049NANA0.804298196253372NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1915 & NA & NA & 0.55395025528304 & NA \tabularnewline
2 & 1843 & NA & NA & 0.476287260569315 & NA \tabularnewline
3 & 1761 & NA & NA & 0.399508434052524 & NA \tabularnewline
4 & 2858 & NA & NA & 0.834526828269122 & NA \tabularnewline
5 & 3968 & NA & NA & 1.26664061052328 & NA \tabularnewline
6 & 5061 & NA & NA & 1.73103390556832 & NA \tabularnewline
7 & 4661 & 5017.81183560472 & 3325.5 & 1.50888944086746 & 0.928890949422833 \tabularnewline
8 & 4269 & 4374.04448270278 & 3296.58333333333 & 1.32684177538445 & 0.975984587464033 \tabularnewline
9 & 3857 & 3738.34270978516 & 3248.58333333333 & 1.15076090904810 & 1.03174061326808 \tabularnewline
10 & 3568 & 3312.39216062001 & 3216.25 & 1.02989262669880 & 1.07716714295452 \tabularnewline
11 & 3274 & 2966.96491439703 & 3234.20833333333 & 0.917369757482236 & 1.10348456906690 \tabularnewline
12 & 2987 & 2656.12776827707 & 3302.41666666667 & 0.804298196253372 & 1.12456939597358 \tabularnewline
13 & 1683 & 1874.10603866507 & 3383.16666666667 & 0.55395025528304 & 0.89802816130874 \tabularnewline
14 & 1381 & 1637.77328137516 & 3438.625 & 0.476287260569315 & 0.843218054479702 \tabularnewline
15 & 1071 & 1385.79488061969 & 3468.75 & 0.399508434052524 & 0.772841648484857 \tabularnewline
16 & 2772 & 2899.32006116282 & 3474.20833333333 & 0.834526828269122 & 0.956086234538813 \tabularnewline
17 & 4485 & 4376.82385297108 & 3455.45833333333 & 1.26664061052328 & 1.02471567297722 \tabularnewline
18 & 6181 & 5907.36958199008 & 3412.625 & 1.73103390556832 & 1.04632017926289 \tabularnewline
19 & 5479 & 5111.30011054514 & 3387.45833333333 & 1.50888944086746 & 1.07193862256224 \tabularnewline
20 & 4782 & 4503.19041550687 & 3393.91666666667 & 1.32684177538445 & 1.06191378972851 \tabularnewline
21 & 4067 & 3918.67653390725 & 3405.29166666667 & 1.15076090904810 & 1.03785039791097 \tabularnewline
22 & 3489 & 3495.62722378684 & 3394.16666666667 & 1.02989262669880 & 0.998104138867628 \tabularnewline
23 & 2903 & 3056.37024201165 & 3331.66666666667 & 0.917369757482236 & 0.949819481977843 \tabularnewline
24 & 2330 & 2588.4996949421 & 3218.33333333333 & 0.804298196253372 & 0.900135319526131 \tabularnewline
25 & 1736 & 1710.06751931938 & 3087.04166666667 & 0.55395025528304 & 1.01516459460673 \tabularnewline
26 & 1483 & 1414.49378268077 & 2969.83333333333 & 0.476287260569315 & 1.04843161430473 \tabularnewline
27 & 1242 & 1145.20757239631 & 2866.54166666667 & 0.399508434052524 & 1.08451954906406 \tabularnewline
28 & 2334 & 2320.12367039287 & 2780.16666666667 & 0.834526828269122 & 1.00598085773798 \tabularnewline
29 & 3423 & 3438.08483049701 & 2714.33333333333 & 1.26664061052328 & 0.995612432141521 \tabularnewline
30 & 4523 & 4619.84098831091 & 2668.83333333333 & 1.73103390556832 & 0.979038025647217 \tabularnewline
31 & 3986 & 3992.26997896181 & 2645.83333333333 & 1.50888944086746 & 0.998429470202454 \tabularnewline
32 & 3462 & 3490.81014088853 & 2630.91666666667 & 1.32684177538445 & 0.991746861122273 \tabularnewline
33 & 2908 & 3001.99957310801 & 2608.70833333333 & 1.15076090904810 & 0.968687679388743 \tabularnewline
34 & 2575 & 2678.75072204357 & 2601 & 1.02989262669880 & 0.961268989611537 \tabularnewline
35 & 2237 & 2410.50370900426 & 2627.625 & 0.917369757482236 & 0.928021803759874 \tabularnewline
36 & 1904 & 2160.61305453531 & 2686.33333333333 & 0.804298196253372 & 0.881231369033592 \tabularnewline
37 & 1610 & 1525.97138448032 & 2754.70833333333 & 0.55395025528304 & 1.05506565612847 \tabularnewline
38 & 1251 & 1339.06178778810 & 2811.45833333333 & 0.476287260569315 & 0.934236202846497 \tabularnewline
39 & 941 & 1143.80929287713 & 2863.04166666667 & 0.399508434052524 & 0.82268959157782 \tabularnewline
40 & 2450 & 2432.57616050213 & 2914.91666666667 & 0.834526828269122 & 1.00716271078406 \tabularnewline
41 & 3946 & 3762.71426363571 & 2970.625 & 1.26664061052328 & 1.04871104301903 \tabularnewline
42 & 5409 & 5252.10112231974 & 3034.08333333333 & 1.73103390556832 & 1.02987354470642 \tabularnewline
43 & 4741 & 4633.86234329733 & 3071.04166666667 & 1.50888944086746 & 1.02312059546992 \tabularnewline
44 & 4069 & 4098.78009938448 & 3089.125 & 1.32684177538445 & 0.992734399342636 \tabularnewline
45 & 3539 & 3596.17578914652 & 3125.04166666667 & 1.15076090904810 & 0.984100947089662 \tabularnewline
46 & 3189 & 3234.80691607537 & 3140.91666666667 & 1.02989262669880 & 0.985839366223768 \tabularnewline
47 & 2960 & 2843.27289209651 & 3099.375 & 0.917369757482236 & 1.04105378285284 \tabularnewline
48 & 2704 & 2421.97645589281 & 3011.29166666667 & 0.804298196253372 & 1.11644355312415 \tabularnewline
49 & 1697 & 1609.87176689506 & 2906.16666666667 & 0.55395025528304 & 1.05412122561351 \tabularnewline
50 & 1598 & 1335.56901454393 & 2804.125 & 0.476287260569315 & 1.19649376602652 \tabularnewline
51 & 1456 & 1084.68204463736 & 2715.04166666667 & 0.399508434052524 & 1.34232884853071 \tabularnewline
52 & 2316 & 2199.11728029385 & 2635.16666666667 & 0.834526828269122 & 1.05314983459660 \tabularnewline
53 & 3083 & 3233.89180874224 & 2553.125 & 1.26664061052328 & 0.953340489519677 \tabularnewline
54 & 4158 & 4299.23908371711 & 2483.625 & 1.73103390556832 & 0.967147888040924 \tabularnewline
55 & 3469 & NA & NA & 1.50888944086746 & NA \tabularnewline
56 & 2892 & NA & NA & 1.32684177538445 & NA \tabularnewline
57 & 2578 & NA & NA & 1.15076090904810 & NA \tabularnewline
58 & 2233 & NA & NA & 1.02989262669880 & NA \tabularnewline
59 & 1947 & NA & NA & 0.917369757482236 & NA \tabularnewline
60 & 2049 & NA & NA & 0.804298196253372 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63104&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]1915[/C][C]NA[/C][C]NA[/C][C]0.55395025528304[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1843[/C][C]NA[/C][C]NA[/C][C]0.476287260569315[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1761[/C][C]NA[/C][C]NA[/C][C]0.399508434052524[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]2858[/C][C]NA[/C][C]NA[/C][C]0.834526828269122[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]3968[/C][C]NA[/C][C]NA[/C][C]1.26664061052328[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]5061[/C][C]NA[/C][C]NA[/C][C]1.73103390556832[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]4661[/C][C]5017.81183560472[/C][C]3325.5[/C][C]1.50888944086746[/C][C]0.928890949422833[/C][/ROW]
[ROW][C]8[/C][C]4269[/C][C]4374.04448270278[/C][C]3296.58333333333[/C][C]1.32684177538445[/C][C]0.975984587464033[/C][/ROW]
[ROW][C]9[/C][C]3857[/C][C]3738.34270978516[/C][C]3248.58333333333[/C][C]1.15076090904810[/C][C]1.03174061326808[/C][/ROW]
[ROW][C]10[/C][C]3568[/C][C]3312.39216062001[/C][C]3216.25[/C][C]1.02989262669880[/C][C]1.07716714295452[/C][/ROW]
[ROW][C]11[/C][C]3274[/C][C]2966.96491439703[/C][C]3234.20833333333[/C][C]0.917369757482236[/C][C]1.10348456906690[/C][/ROW]
[ROW][C]12[/C][C]2987[/C][C]2656.12776827707[/C][C]3302.41666666667[/C][C]0.804298196253372[/C][C]1.12456939597358[/C][/ROW]
[ROW][C]13[/C][C]1683[/C][C]1874.10603866507[/C][C]3383.16666666667[/C][C]0.55395025528304[/C][C]0.89802816130874[/C][/ROW]
[ROW][C]14[/C][C]1381[/C][C]1637.77328137516[/C][C]3438.625[/C][C]0.476287260569315[/C][C]0.843218054479702[/C][/ROW]
[ROW][C]15[/C][C]1071[/C][C]1385.79488061969[/C][C]3468.75[/C][C]0.399508434052524[/C][C]0.772841648484857[/C][/ROW]
[ROW][C]16[/C][C]2772[/C][C]2899.32006116282[/C][C]3474.20833333333[/C][C]0.834526828269122[/C][C]0.956086234538813[/C][/ROW]
[ROW][C]17[/C][C]4485[/C][C]4376.82385297108[/C][C]3455.45833333333[/C][C]1.26664061052328[/C][C]1.02471567297722[/C][/ROW]
[ROW][C]18[/C][C]6181[/C][C]5907.36958199008[/C][C]3412.625[/C][C]1.73103390556832[/C][C]1.04632017926289[/C][/ROW]
[ROW][C]19[/C][C]5479[/C][C]5111.30011054514[/C][C]3387.45833333333[/C][C]1.50888944086746[/C][C]1.07193862256224[/C][/ROW]
[ROW][C]20[/C][C]4782[/C][C]4503.19041550687[/C][C]3393.91666666667[/C][C]1.32684177538445[/C][C]1.06191378972851[/C][/ROW]
[ROW][C]21[/C][C]4067[/C][C]3918.67653390725[/C][C]3405.29166666667[/C][C]1.15076090904810[/C][C]1.03785039791097[/C][/ROW]
[ROW][C]22[/C][C]3489[/C][C]3495.62722378684[/C][C]3394.16666666667[/C][C]1.02989262669880[/C][C]0.998104138867628[/C][/ROW]
[ROW][C]23[/C][C]2903[/C][C]3056.37024201165[/C][C]3331.66666666667[/C][C]0.917369757482236[/C][C]0.949819481977843[/C][/ROW]
[ROW][C]24[/C][C]2330[/C][C]2588.4996949421[/C][C]3218.33333333333[/C][C]0.804298196253372[/C][C]0.900135319526131[/C][/ROW]
[ROW][C]25[/C][C]1736[/C][C]1710.06751931938[/C][C]3087.04166666667[/C][C]0.55395025528304[/C][C]1.01516459460673[/C][/ROW]
[ROW][C]26[/C][C]1483[/C][C]1414.49378268077[/C][C]2969.83333333333[/C][C]0.476287260569315[/C][C]1.04843161430473[/C][/ROW]
[ROW][C]27[/C][C]1242[/C][C]1145.20757239631[/C][C]2866.54166666667[/C][C]0.399508434052524[/C][C]1.08451954906406[/C][/ROW]
[ROW][C]28[/C][C]2334[/C][C]2320.12367039287[/C][C]2780.16666666667[/C][C]0.834526828269122[/C][C]1.00598085773798[/C][/ROW]
[ROW][C]29[/C][C]3423[/C][C]3438.08483049701[/C][C]2714.33333333333[/C][C]1.26664061052328[/C][C]0.995612432141521[/C][/ROW]
[ROW][C]30[/C][C]4523[/C][C]4619.84098831091[/C][C]2668.83333333333[/C][C]1.73103390556832[/C][C]0.979038025647217[/C][/ROW]
[ROW][C]31[/C][C]3986[/C][C]3992.26997896181[/C][C]2645.83333333333[/C][C]1.50888944086746[/C][C]0.998429470202454[/C][/ROW]
[ROW][C]32[/C][C]3462[/C][C]3490.81014088853[/C][C]2630.91666666667[/C][C]1.32684177538445[/C][C]0.991746861122273[/C][/ROW]
[ROW][C]33[/C][C]2908[/C][C]3001.99957310801[/C][C]2608.70833333333[/C][C]1.15076090904810[/C][C]0.968687679388743[/C][/ROW]
[ROW][C]34[/C][C]2575[/C][C]2678.75072204357[/C][C]2601[/C][C]1.02989262669880[/C][C]0.961268989611537[/C][/ROW]
[ROW][C]35[/C][C]2237[/C][C]2410.50370900426[/C][C]2627.625[/C][C]0.917369757482236[/C][C]0.928021803759874[/C][/ROW]
[ROW][C]36[/C][C]1904[/C][C]2160.61305453531[/C][C]2686.33333333333[/C][C]0.804298196253372[/C][C]0.881231369033592[/C][/ROW]
[ROW][C]37[/C][C]1610[/C][C]1525.97138448032[/C][C]2754.70833333333[/C][C]0.55395025528304[/C][C]1.05506565612847[/C][/ROW]
[ROW][C]38[/C][C]1251[/C][C]1339.06178778810[/C][C]2811.45833333333[/C][C]0.476287260569315[/C][C]0.934236202846497[/C][/ROW]
[ROW][C]39[/C][C]941[/C][C]1143.80929287713[/C][C]2863.04166666667[/C][C]0.399508434052524[/C][C]0.82268959157782[/C][/ROW]
[ROW][C]40[/C][C]2450[/C][C]2432.57616050213[/C][C]2914.91666666667[/C][C]0.834526828269122[/C][C]1.00716271078406[/C][/ROW]
[ROW][C]41[/C][C]3946[/C][C]3762.71426363571[/C][C]2970.625[/C][C]1.26664061052328[/C][C]1.04871104301903[/C][/ROW]
[ROW][C]42[/C][C]5409[/C][C]5252.10112231974[/C][C]3034.08333333333[/C][C]1.73103390556832[/C][C]1.02987354470642[/C][/ROW]
[ROW][C]43[/C][C]4741[/C][C]4633.86234329733[/C][C]3071.04166666667[/C][C]1.50888944086746[/C][C]1.02312059546992[/C][/ROW]
[ROW][C]44[/C][C]4069[/C][C]4098.78009938448[/C][C]3089.125[/C][C]1.32684177538445[/C][C]0.992734399342636[/C][/ROW]
[ROW][C]45[/C][C]3539[/C][C]3596.17578914652[/C][C]3125.04166666667[/C][C]1.15076090904810[/C][C]0.984100947089662[/C][/ROW]
[ROW][C]46[/C][C]3189[/C][C]3234.80691607537[/C][C]3140.91666666667[/C][C]1.02989262669880[/C][C]0.985839366223768[/C][/ROW]
[ROW][C]47[/C][C]2960[/C][C]2843.27289209651[/C][C]3099.375[/C][C]0.917369757482236[/C][C]1.04105378285284[/C][/ROW]
[ROW][C]48[/C][C]2704[/C][C]2421.97645589281[/C][C]3011.29166666667[/C][C]0.804298196253372[/C][C]1.11644355312415[/C][/ROW]
[ROW][C]49[/C][C]1697[/C][C]1609.87176689506[/C][C]2906.16666666667[/C][C]0.55395025528304[/C][C]1.05412122561351[/C][/ROW]
[ROW][C]50[/C][C]1598[/C][C]1335.56901454393[/C][C]2804.125[/C][C]0.476287260569315[/C][C]1.19649376602652[/C][/ROW]
[ROW][C]51[/C][C]1456[/C][C]1084.68204463736[/C][C]2715.04166666667[/C][C]0.399508434052524[/C][C]1.34232884853071[/C][/ROW]
[ROW][C]52[/C][C]2316[/C][C]2199.11728029385[/C][C]2635.16666666667[/C][C]0.834526828269122[/C][C]1.05314983459660[/C][/ROW]
[ROW][C]53[/C][C]3083[/C][C]3233.89180874224[/C][C]2553.125[/C][C]1.26664061052328[/C][C]0.953340489519677[/C][/ROW]
[ROW][C]54[/C][C]4158[/C][C]4299.23908371711[/C][C]2483.625[/C][C]1.73103390556832[/C][C]0.967147888040924[/C][/ROW]
[ROW][C]55[/C][C]3469[/C][C]NA[/C][C]NA[/C][C]1.50888944086746[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]2892[/C][C]NA[/C][C]NA[/C][C]1.32684177538445[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]2578[/C][C]NA[/C][C]NA[/C][C]1.15076090904810[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]2233[/C][C]NA[/C][C]NA[/C][C]1.02989262669880[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]1947[/C][C]NA[/C][C]NA[/C][C]0.917369757482236[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]2049[/C][C]NA[/C][C]NA[/C][C]0.804298196253372[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63104&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63104&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
11915NANA0.55395025528304NA
21843NANA0.476287260569315NA
31761NANA0.399508434052524NA
42858NANA0.834526828269122NA
53968NANA1.26664061052328NA
65061NANA1.73103390556832NA
746615017.811835604723325.51.508889440867460.928890949422833
842694374.044482702783296.583333333331.326841775384450.975984587464033
938573738.342709785163248.583333333331.150760909048101.03174061326808
1035683312.392160620013216.251.029892626698801.07716714295452
1132742966.964914397033234.208333333330.9173697574822361.10348456906690
1229872656.127768277073302.416666666670.8042981962533721.12456939597358
1316831874.106038665073383.166666666670.553950255283040.89802816130874
1413811637.773281375163438.6250.4762872605693150.843218054479702
1510711385.794880619693468.750.3995084340525240.772841648484857
1627722899.320061162823474.208333333330.8345268282691220.956086234538813
1744854376.823852971083455.458333333331.266640610523281.02471567297722
1861815907.369581990083412.6251.731033905568321.04632017926289
1954795111.300110545143387.458333333331.508889440867461.07193862256224
2047824503.190415506873393.916666666671.326841775384451.06191378972851
2140673918.676533907253405.291666666671.150760909048101.03785039791097
2234893495.627223786843394.166666666671.029892626698800.998104138867628
2329033056.370242011653331.666666666670.9173697574822360.949819481977843
2423302588.49969494213218.333333333330.8042981962533720.900135319526131
2517361710.067519319383087.041666666670.553950255283041.01516459460673
2614831414.493782680772969.833333333330.4762872605693151.04843161430473
2712421145.207572396312866.541666666670.3995084340525241.08451954906406
2823342320.123670392872780.166666666670.8345268282691221.00598085773798
2934233438.084830497012714.333333333331.266640610523280.995612432141521
3045234619.840988310912668.833333333331.731033905568320.979038025647217
3139863992.269978961812645.833333333331.508889440867460.998429470202454
3234623490.810140888532630.916666666671.326841775384450.991746861122273
3329083001.999573108012608.708333333331.150760909048100.968687679388743
3425752678.7507220435726011.029892626698800.961268989611537
3522372410.503709004262627.6250.9173697574822360.928021803759874
3619042160.613054535312686.333333333330.8042981962533720.881231369033592
3716101525.971384480322754.708333333330.553950255283041.05506565612847
3812511339.061787788102811.458333333330.4762872605693150.934236202846497
399411143.809292877132863.041666666670.3995084340525240.82268959157782
4024502432.576160502132914.916666666670.8345268282691221.00716271078406
4139463762.714263635712970.6251.266640610523281.04871104301903
4254095252.101122319743034.083333333331.731033905568321.02987354470642
4347414633.862343297333071.041666666671.508889440867461.02312059546992
4440694098.780099384483089.1251.326841775384450.992734399342636
4535393596.175789146523125.041666666671.150760909048100.984100947089662
4631893234.806916075373140.916666666671.029892626698800.985839366223768
4729602843.272892096513099.3750.9173697574822361.04105378285284
4827042421.976455892813011.291666666670.8042981962533721.11644355312415
4916971609.871766895062906.166666666670.553950255283041.05412122561351
5015981335.569014543932804.1250.4762872605693151.19649376602652
5114561084.682044637362715.041666666670.3995084340525241.34232884853071
5223162199.117280293852635.166666666670.8345268282691221.05314983459660
5330833233.891808742242553.1251.266640610523280.953340489519677
5441584299.239083717112483.6251.731033905568320.967147888040924
553469NANA1.50888944086746NA
562892NANA1.32684177538445NA
572578NANA1.15076090904810NA
582233NANA1.02989262669880NA
591947NANA0.917369757482236NA
602049NANA0.804298196253372NA



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
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')