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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 computationMon, 19 Dec 2011 15:02:25 -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/Dec/19/t1324325046herj3f855l0c6dv.htm/, Retrieved Thu, 02 May 2024 14:34:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157661, Retrieved Thu, 02 May 2024 14:34:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
-  M D  [Classical Decomposition] [Workshop 8, Class...] [2010-11-28 20:55:54] [d946de7cca328fbcf207448a112523ab]
- R  D    [Classical Decomposition] [Paper Decompositi...] [2010-12-18 18:53:33] [8081b8996d5947580de3eb171e82db4f]
-    D      [Classical Decomposition] [Paper Decompositi...] [2010-12-18 18:57:16] [8081b8996d5947580de3eb171e82db4f]
-    D        [Classical Decomposition] [Paper Decompositi...] [2010-12-18 19:01:17] [8081b8996d5947580de3eb171e82db4f]
-    D          [Classical Decomposition] [Paper Decompositi...] [2010-12-18 19:04:54] [8081b8996d5947580de3eb171e82db4f]
- R  D              [Classical Decomposition] [] [2011-12-19 20:02:25] [eab7a71f9dca93a99c212b8372c46924] [Current]
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Dataseries X:
58608
46865
51378
46235
47206
45382
41227
33795
31295
42625
33625
21538
56421
53152
53536
52408
41454
38271
35306
26414
31917
38030
27534
18387
50556
43901
48572
43899
37532
40357
35489
29027
34485
42598
30306
26451
47460
50104
61465
53726
39477
43895
31481
29896
33842
39120
33702
25094
51442
45594
52518
48564
41745
49585
32747
33379
35645
37034
35681
20972




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157661&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157661&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'Gertrude Mary Cox' @ cox.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
158608NANA11498.4765625NA
246865NANA8309.14322916667NA
351378NANA14103.1640625NA
446235NANA9742.59114583333NA
547206NANA182.164062500001NA
645382NANA3141.64322916667NA
74122737318.205729166741557.125-4238.919270833333908.79427083334
83379531484.174479166741727.9583333333-10243.78385416672310.82552083334
93129534939.164062542079.8333333333-7140.66927083333-3644.16406249999
104262542958.653645833342426.9583333333531.695312499996-333.653645833336
113362533707.320312542444.5-8737.1796875-82.3203125
122153824760.216145833341908.5416666667-17148.3255208333-3222.21614583334
135642152864.018229166741365.541666666711498.47656253556.98177083334
145315249120.434895833340811.29166666678309.143229166674031.56510416666
155353654632.830729166740529.666666666714103.1640625-1096.83072916667
165240850106.716145833340364.1259742.591145833332301.28385416666
174145440101.039062539918.875182.1640625000011352.96093750001
183827142675.434895833339533.79166666673141.64322916667-4404.43489583332
193530634919.205729166739158.125-4238.91927083333386.794270833336
202641428284.507812538528.2916666667-10243.7838541667-1870.50781249999
213191730795.330729166737936-7140.669270833331121.66927083334
223803037906.320312537374.625531.695312499996123.679687500007
232753428119.486979166736856.6666666667-8737.1796875-585.486979166664
241838719631.841145833336780.1666666667-17148.3255208333-1244.84114583334
255055648373.184895833336874.708333333311498.47656252182.81510416667
264390145300.351562536991.20833333338309.14322916667-1399.35156250001
274857251310.247395833337207.083333333314103.1640625-2738.24739583334
284389947247.007812537504.41666666679742.59114583333-3348.00781250001
293753237992.414062537810.25182.164062500001-460.4140625
304035741403.393229166738261.753141.64322916667-1046.39322916666
313548934229.830729166738468.75-4238.919270833331259.16927083334
322902728354.424479166738598.2083333333-10243.7838541667672.575520833336
333448532253.205729166739393.875-7140.669270833332231.79427083334
344259840872.236979166740340.5416666667531.6953124999961725.76302083334
353030632093.861979166740831.0416666667-8737.1796875-1787.86197916666
362645123911.174479166741059.5-17148.32552083332539.82552083334
374746052538.393229166741039.916666666711498.4765625-5078.39322916666
385010449218.268229166740909.1258309.14322916667885.731770833328
396146555021.705729166740918.541666666714103.16406256443.29427083334
405372650489.424479166740746.83333333339742.591145833333236.57552083334
413947740925.580729166740743.4166666667182.164062500001-1448.58072916666
424389543970.018229166740828.3753141.64322916667-75.0182291666642
433148136698.830729166740937.75-4238.91927083333-5217.83072916667
442989630671.966145833340915.75-10243.7838541667-775.966145833328
453384233214.372395833340355.0416666667-7140.66927083333627.627604166672
463912040298.861979166739767.1666666667531.695312499996-1178.86197916666
473370230909.403645833339646.5833333333-8737.17968752792.59635416667
482509422829.841145833339978.1666666667-17148.32552083332264.15885416666
495144251766.47656254026811498.4765625-324.4765625
504559448775.018229166740465.8758309.14322916667-3181.01822916666
515251854789.289062540686.12514103.1640625-2271.2890625
524856450416.924479166740674.33333333339742.59114583333-1852.92447916666
534174540852.039062540669.875182.164062500001892.9609375
544958543722.226562540580.58333333333141.643229166675862.7734375
5532747NANA-4238.91927083333NA
5633379NANA-10243.7838541667NA
5735645NANA-7140.66927083333NA
5837034NANA531.695312499996NA
5935681NANA-8737.1796875NA
6020972NANA-17148.3255208333NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 58608 & NA & NA & 11498.4765625 & NA \tabularnewline
2 & 46865 & NA & NA & 8309.14322916667 & NA \tabularnewline
3 & 51378 & NA & NA & 14103.1640625 & NA \tabularnewline
4 & 46235 & NA & NA & 9742.59114583333 & NA \tabularnewline
5 & 47206 & NA & NA & 182.164062500001 & NA \tabularnewline
6 & 45382 & NA & NA & 3141.64322916667 & NA \tabularnewline
7 & 41227 & 37318.2057291667 & 41557.125 & -4238.91927083333 & 3908.79427083334 \tabularnewline
8 & 33795 & 31484.1744791667 & 41727.9583333333 & -10243.7838541667 & 2310.82552083334 \tabularnewline
9 & 31295 & 34939.1640625 & 42079.8333333333 & -7140.66927083333 & -3644.16406249999 \tabularnewline
10 & 42625 & 42958.6536458333 & 42426.9583333333 & 531.695312499996 & -333.653645833336 \tabularnewline
11 & 33625 & 33707.3203125 & 42444.5 & -8737.1796875 & -82.3203125 \tabularnewline
12 & 21538 & 24760.2161458333 & 41908.5416666667 & -17148.3255208333 & -3222.21614583334 \tabularnewline
13 & 56421 & 52864.0182291667 & 41365.5416666667 & 11498.4765625 & 3556.98177083334 \tabularnewline
14 & 53152 & 49120.4348958333 & 40811.2916666667 & 8309.14322916667 & 4031.56510416666 \tabularnewline
15 & 53536 & 54632.8307291667 & 40529.6666666667 & 14103.1640625 & -1096.83072916667 \tabularnewline
16 & 52408 & 50106.7161458333 & 40364.125 & 9742.59114583333 & 2301.28385416666 \tabularnewline
17 & 41454 & 40101.0390625 & 39918.875 & 182.164062500001 & 1352.96093750001 \tabularnewline
18 & 38271 & 42675.4348958333 & 39533.7916666667 & 3141.64322916667 & -4404.43489583332 \tabularnewline
19 & 35306 & 34919.2057291667 & 39158.125 & -4238.91927083333 & 386.794270833336 \tabularnewline
20 & 26414 & 28284.5078125 & 38528.2916666667 & -10243.7838541667 & -1870.50781249999 \tabularnewline
21 & 31917 & 30795.3307291667 & 37936 & -7140.66927083333 & 1121.66927083334 \tabularnewline
22 & 38030 & 37906.3203125 & 37374.625 & 531.695312499996 & 123.679687500007 \tabularnewline
23 & 27534 & 28119.4869791667 & 36856.6666666667 & -8737.1796875 & -585.486979166664 \tabularnewline
24 & 18387 & 19631.8411458333 & 36780.1666666667 & -17148.3255208333 & -1244.84114583334 \tabularnewline
25 & 50556 & 48373.1848958333 & 36874.7083333333 & 11498.4765625 & 2182.81510416667 \tabularnewline
26 & 43901 & 45300.3515625 & 36991.2083333333 & 8309.14322916667 & -1399.35156250001 \tabularnewline
27 & 48572 & 51310.2473958333 & 37207.0833333333 & 14103.1640625 & -2738.24739583334 \tabularnewline
28 & 43899 & 47247.0078125 & 37504.4166666667 & 9742.59114583333 & -3348.00781250001 \tabularnewline
29 & 37532 & 37992.4140625 & 37810.25 & 182.164062500001 & -460.4140625 \tabularnewline
30 & 40357 & 41403.3932291667 & 38261.75 & 3141.64322916667 & -1046.39322916666 \tabularnewline
31 & 35489 & 34229.8307291667 & 38468.75 & -4238.91927083333 & 1259.16927083334 \tabularnewline
32 & 29027 & 28354.4244791667 & 38598.2083333333 & -10243.7838541667 & 672.575520833336 \tabularnewline
33 & 34485 & 32253.2057291667 & 39393.875 & -7140.66927083333 & 2231.79427083334 \tabularnewline
34 & 42598 & 40872.2369791667 & 40340.5416666667 & 531.695312499996 & 1725.76302083334 \tabularnewline
35 & 30306 & 32093.8619791667 & 40831.0416666667 & -8737.1796875 & -1787.86197916666 \tabularnewline
36 & 26451 & 23911.1744791667 & 41059.5 & -17148.3255208333 & 2539.82552083334 \tabularnewline
37 & 47460 & 52538.3932291667 & 41039.9166666667 & 11498.4765625 & -5078.39322916666 \tabularnewline
38 & 50104 & 49218.2682291667 & 40909.125 & 8309.14322916667 & 885.731770833328 \tabularnewline
39 & 61465 & 55021.7057291667 & 40918.5416666667 & 14103.1640625 & 6443.29427083334 \tabularnewline
40 & 53726 & 50489.4244791667 & 40746.8333333333 & 9742.59114583333 & 3236.57552083334 \tabularnewline
41 & 39477 & 40925.5807291667 & 40743.4166666667 & 182.164062500001 & -1448.58072916666 \tabularnewline
42 & 43895 & 43970.0182291667 & 40828.375 & 3141.64322916667 & -75.0182291666642 \tabularnewline
43 & 31481 & 36698.8307291667 & 40937.75 & -4238.91927083333 & -5217.83072916667 \tabularnewline
44 & 29896 & 30671.9661458333 & 40915.75 & -10243.7838541667 & -775.966145833328 \tabularnewline
45 & 33842 & 33214.3723958333 & 40355.0416666667 & -7140.66927083333 & 627.627604166672 \tabularnewline
46 & 39120 & 40298.8619791667 & 39767.1666666667 & 531.695312499996 & -1178.86197916666 \tabularnewline
47 & 33702 & 30909.4036458333 & 39646.5833333333 & -8737.1796875 & 2792.59635416667 \tabularnewline
48 & 25094 & 22829.8411458333 & 39978.1666666667 & -17148.3255208333 & 2264.15885416666 \tabularnewline
49 & 51442 & 51766.4765625 & 40268 & 11498.4765625 & -324.4765625 \tabularnewline
50 & 45594 & 48775.0182291667 & 40465.875 & 8309.14322916667 & -3181.01822916666 \tabularnewline
51 & 52518 & 54789.2890625 & 40686.125 & 14103.1640625 & -2271.2890625 \tabularnewline
52 & 48564 & 50416.9244791667 & 40674.3333333333 & 9742.59114583333 & -1852.92447916666 \tabularnewline
53 & 41745 & 40852.0390625 & 40669.875 & 182.164062500001 & 892.9609375 \tabularnewline
54 & 49585 & 43722.2265625 & 40580.5833333333 & 3141.64322916667 & 5862.7734375 \tabularnewline
55 & 32747 & NA & NA & -4238.91927083333 & NA \tabularnewline
56 & 33379 & NA & NA & -10243.7838541667 & NA \tabularnewline
57 & 35645 & NA & NA & -7140.66927083333 & NA \tabularnewline
58 & 37034 & NA & NA & 531.695312499996 & NA \tabularnewline
59 & 35681 & NA & NA & -8737.1796875 & NA \tabularnewline
60 & 20972 & NA & NA & -17148.3255208333 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157661&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]58608[/C][C]NA[/C][C]NA[/C][C]11498.4765625[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]46865[/C][C]NA[/C][C]NA[/C][C]8309.14322916667[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]51378[/C][C]NA[/C][C]NA[/C][C]14103.1640625[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]46235[/C][C]NA[/C][C]NA[/C][C]9742.59114583333[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]47206[/C][C]NA[/C][C]NA[/C][C]182.164062500001[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]45382[/C][C]NA[/C][C]NA[/C][C]3141.64322916667[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]41227[/C][C]37318.2057291667[/C][C]41557.125[/C][C]-4238.91927083333[/C][C]3908.79427083334[/C][/ROW]
[ROW][C]8[/C][C]33795[/C][C]31484.1744791667[/C][C]41727.9583333333[/C][C]-10243.7838541667[/C][C]2310.82552083334[/C][/ROW]
[ROW][C]9[/C][C]31295[/C][C]34939.1640625[/C][C]42079.8333333333[/C][C]-7140.66927083333[/C][C]-3644.16406249999[/C][/ROW]
[ROW][C]10[/C][C]42625[/C][C]42958.6536458333[/C][C]42426.9583333333[/C][C]531.695312499996[/C][C]-333.653645833336[/C][/ROW]
[ROW][C]11[/C][C]33625[/C][C]33707.3203125[/C][C]42444.5[/C][C]-8737.1796875[/C][C]-82.3203125[/C][/ROW]
[ROW][C]12[/C][C]21538[/C][C]24760.2161458333[/C][C]41908.5416666667[/C][C]-17148.3255208333[/C][C]-3222.21614583334[/C][/ROW]
[ROW][C]13[/C][C]56421[/C][C]52864.0182291667[/C][C]41365.5416666667[/C][C]11498.4765625[/C][C]3556.98177083334[/C][/ROW]
[ROW][C]14[/C][C]53152[/C][C]49120.4348958333[/C][C]40811.2916666667[/C][C]8309.14322916667[/C][C]4031.56510416666[/C][/ROW]
[ROW][C]15[/C][C]53536[/C][C]54632.8307291667[/C][C]40529.6666666667[/C][C]14103.1640625[/C][C]-1096.83072916667[/C][/ROW]
[ROW][C]16[/C][C]52408[/C][C]50106.7161458333[/C][C]40364.125[/C][C]9742.59114583333[/C][C]2301.28385416666[/C][/ROW]
[ROW][C]17[/C][C]41454[/C][C]40101.0390625[/C][C]39918.875[/C][C]182.164062500001[/C][C]1352.96093750001[/C][/ROW]
[ROW][C]18[/C][C]38271[/C][C]42675.4348958333[/C][C]39533.7916666667[/C][C]3141.64322916667[/C][C]-4404.43489583332[/C][/ROW]
[ROW][C]19[/C][C]35306[/C][C]34919.2057291667[/C][C]39158.125[/C][C]-4238.91927083333[/C][C]386.794270833336[/C][/ROW]
[ROW][C]20[/C][C]26414[/C][C]28284.5078125[/C][C]38528.2916666667[/C][C]-10243.7838541667[/C][C]-1870.50781249999[/C][/ROW]
[ROW][C]21[/C][C]31917[/C][C]30795.3307291667[/C][C]37936[/C][C]-7140.66927083333[/C][C]1121.66927083334[/C][/ROW]
[ROW][C]22[/C][C]38030[/C][C]37906.3203125[/C][C]37374.625[/C][C]531.695312499996[/C][C]123.679687500007[/C][/ROW]
[ROW][C]23[/C][C]27534[/C][C]28119.4869791667[/C][C]36856.6666666667[/C][C]-8737.1796875[/C][C]-585.486979166664[/C][/ROW]
[ROW][C]24[/C][C]18387[/C][C]19631.8411458333[/C][C]36780.1666666667[/C][C]-17148.3255208333[/C][C]-1244.84114583334[/C][/ROW]
[ROW][C]25[/C][C]50556[/C][C]48373.1848958333[/C][C]36874.7083333333[/C][C]11498.4765625[/C][C]2182.81510416667[/C][/ROW]
[ROW][C]26[/C][C]43901[/C][C]45300.3515625[/C][C]36991.2083333333[/C][C]8309.14322916667[/C][C]-1399.35156250001[/C][/ROW]
[ROW][C]27[/C][C]48572[/C][C]51310.2473958333[/C][C]37207.0833333333[/C][C]14103.1640625[/C][C]-2738.24739583334[/C][/ROW]
[ROW][C]28[/C][C]43899[/C][C]47247.0078125[/C][C]37504.4166666667[/C][C]9742.59114583333[/C][C]-3348.00781250001[/C][/ROW]
[ROW][C]29[/C][C]37532[/C][C]37992.4140625[/C][C]37810.25[/C][C]182.164062500001[/C][C]-460.4140625[/C][/ROW]
[ROW][C]30[/C][C]40357[/C][C]41403.3932291667[/C][C]38261.75[/C][C]3141.64322916667[/C][C]-1046.39322916666[/C][/ROW]
[ROW][C]31[/C][C]35489[/C][C]34229.8307291667[/C][C]38468.75[/C][C]-4238.91927083333[/C][C]1259.16927083334[/C][/ROW]
[ROW][C]32[/C][C]29027[/C][C]28354.4244791667[/C][C]38598.2083333333[/C][C]-10243.7838541667[/C][C]672.575520833336[/C][/ROW]
[ROW][C]33[/C][C]34485[/C][C]32253.2057291667[/C][C]39393.875[/C][C]-7140.66927083333[/C][C]2231.79427083334[/C][/ROW]
[ROW][C]34[/C][C]42598[/C][C]40872.2369791667[/C][C]40340.5416666667[/C][C]531.695312499996[/C][C]1725.76302083334[/C][/ROW]
[ROW][C]35[/C][C]30306[/C][C]32093.8619791667[/C][C]40831.0416666667[/C][C]-8737.1796875[/C][C]-1787.86197916666[/C][/ROW]
[ROW][C]36[/C][C]26451[/C][C]23911.1744791667[/C][C]41059.5[/C][C]-17148.3255208333[/C][C]2539.82552083334[/C][/ROW]
[ROW][C]37[/C][C]47460[/C][C]52538.3932291667[/C][C]41039.9166666667[/C][C]11498.4765625[/C][C]-5078.39322916666[/C][/ROW]
[ROW][C]38[/C][C]50104[/C][C]49218.2682291667[/C][C]40909.125[/C][C]8309.14322916667[/C][C]885.731770833328[/C][/ROW]
[ROW][C]39[/C][C]61465[/C][C]55021.7057291667[/C][C]40918.5416666667[/C][C]14103.1640625[/C][C]6443.29427083334[/C][/ROW]
[ROW][C]40[/C][C]53726[/C][C]50489.4244791667[/C][C]40746.8333333333[/C][C]9742.59114583333[/C][C]3236.57552083334[/C][/ROW]
[ROW][C]41[/C][C]39477[/C][C]40925.5807291667[/C][C]40743.4166666667[/C][C]182.164062500001[/C][C]-1448.58072916666[/C][/ROW]
[ROW][C]42[/C][C]43895[/C][C]43970.0182291667[/C][C]40828.375[/C][C]3141.64322916667[/C][C]-75.0182291666642[/C][/ROW]
[ROW][C]43[/C][C]31481[/C][C]36698.8307291667[/C][C]40937.75[/C][C]-4238.91927083333[/C][C]-5217.83072916667[/C][/ROW]
[ROW][C]44[/C][C]29896[/C][C]30671.9661458333[/C][C]40915.75[/C][C]-10243.7838541667[/C][C]-775.966145833328[/C][/ROW]
[ROW][C]45[/C][C]33842[/C][C]33214.3723958333[/C][C]40355.0416666667[/C][C]-7140.66927083333[/C][C]627.627604166672[/C][/ROW]
[ROW][C]46[/C][C]39120[/C][C]40298.8619791667[/C][C]39767.1666666667[/C][C]531.695312499996[/C][C]-1178.86197916666[/C][/ROW]
[ROW][C]47[/C][C]33702[/C][C]30909.4036458333[/C][C]39646.5833333333[/C][C]-8737.1796875[/C][C]2792.59635416667[/C][/ROW]
[ROW][C]48[/C][C]25094[/C][C]22829.8411458333[/C][C]39978.1666666667[/C][C]-17148.3255208333[/C][C]2264.15885416666[/C][/ROW]
[ROW][C]49[/C][C]51442[/C][C]51766.4765625[/C][C]40268[/C][C]11498.4765625[/C][C]-324.4765625[/C][/ROW]
[ROW][C]50[/C][C]45594[/C][C]48775.0182291667[/C][C]40465.875[/C][C]8309.14322916667[/C][C]-3181.01822916666[/C][/ROW]
[ROW][C]51[/C][C]52518[/C][C]54789.2890625[/C][C]40686.125[/C][C]14103.1640625[/C][C]-2271.2890625[/C][/ROW]
[ROW][C]52[/C][C]48564[/C][C]50416.9244791667[/C][C]40674.3333333333[/C][C]9742.59114583333[/C][C]-1852.92447916666[/C][/ROW]
[ROW][C]53[/C][C]41745[/C][C]40852.0390625[/C][C]40669.875[/C][C]182.164062500001[/C][C]892.9609375[/C][/ROW]
[ROW][C]54[/C][C]49585[/C][C]43722.2265625[/C][C]40580.5833333333[/C][C]3141.64322916667[/C][C]5862.7734375[/C][/ROW]
[ROW][C]55[/C][C]32747[/C][C]NA[/C][C]NA[/C][C]-4238.91927083333[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]33379[/C][C]NA[/C][C]NA[/C][C]-10243.7838541667[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]35645[/C][C]NA[/C][C]NA[/C][C]-7140.66927083333[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]37034[/C][C]NA[/C][C]NA[/C][C]531.695312499996[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]35681[/C][C]NA[/C][C]NA[/C][C]-8737.1796875[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]20972[/C][C]NA[/C][C]NA[/C][C]-17148.3255208333[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157661&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
158608NANA11498.4765625NA
246865NANA8309.14322916667NA
351378NANA14103.1640625NA
446235NANA9742.59114583333NA
547206NANA182.164062500001NA
645382NANA3141.64322916667NA
74122737318.205729166741557.125-4238.919270833333908.79427083334
83379531484.174479166741727.9583333333-10243.78385416672310.82552083334
93129534939.164062542079.8333333333-7140.66927083333-3644.16406249999
104262542958.653645833342426.9583333333531.695312499996-333.653645833336
113362533707.320312542444.5-8737.1796875-82.3203125
122153824760.216145833341908.5416666667-17148.3255208333-3222.21614583334
135642152864.018229166741365.541666666711498.47656253556.98177083334
145315249120.434895833340811.29166666678309.143229166674031.56510416666
155353654632.830729166740529.666666666714103.1640625-1096.83072916667
165240850106.716145833340364.1259742.591145833332301.28385416666
174145440101.039062539918.875182.1640625000011352.96093750001
183827142675.434895833339533.79166666673141.64322916667-4404.43489583332
193530634919.205729166739158.125-4238.91927083333386.794270833336
202641428284.507812538528.2916666667-10243.7838541667-1870.50781249999
213191730795.330729166737936-7140.669270833331121.66927083334
223803037906.320312537374.625531.695312499996123.679687500007
232753428119.486979166736856.6666666667-8737.1796875-585.486979166664
241838719631.841145833336780.1666666667-17148.3255208333-1244.84114583334
255055648373.184895833336874.708333333311498.47656252182.81510416667
264390145300.351562536991.20833333338309.14322916667-1399.35156250001
274857251310.247395833337207.083333333314103.1640625-2738.24739583334
284389947247.007812537504.41666666679742.59114583333-3348.00781250001
293753237992.414062537810.25182.164062500001-460.4140625
304035741403.393229166738261.753141.64322916667-1046.39322916666
313548934229.830729166738468.75-4238.919270833331259.16927083334
322902728354.424479166738598.2083333333-10243.7838541667672.575520833336
333448532253.205729166739393.875-7140.669270833332231.79427083334
344259840872.236979166740340.5416666667531.6953124999961725.76302083334
353030632093.861979166740831.0416666667-8737.1796875-1787.86197916666
362645123911.174479166741059.5-17148.32552083332539.82552083334
374746052538.393229166741039.916666666711498.4765625-5078.39322916666
385010449218.268229166740909.1258309.14322916667885.731770833328
396146555021.705729166740918.541666666714103.16406256443.29427083334
405372650489.424479166740746.83333333339742.591145833333236.57552083334
413947740925.580729166740743.4166666667182.164062500001-1448.58072916666
424389543970.018229166740828.3753141.64322916667-75.0182291666642
433148136698.830729166740937.75-4238.91927083333-5217.83072916667
442989630671.966145833340915.75-10243.7838541667-775.966145833328
453384233214.372395833340355.0416666667-7140.66927083333627.627604166672
463912040298.861979166739767.1666666667531.695312499996-1178.86197916666
473370230909.403645833339646.5833333333-8737.17968752792.59635416667
482509422829.841145833339978.1666666667-17148.32552083332264.15885416666
495144251766.47656254026811498.4765625-324.4765625
504559448775.018229166740465.8758309.14322916667-3181.01822916666
515251854789.289062540686.12514103.1640625-2271.2890625
524856450416.924479166740674.33333333339742.59114583333-1852.92447916666
534174540852.039062540669.875182.164062500001892.9609375
544958543722.226562540580.58333333333141.643229166675862.7734375
5532747NANA-4238.91927083333NA
5633379NANA-10243.7838541667NA
5735645NANA-7140.66927083333NA
5837034NANA531.695312499996NA
5935681NANA-8737.1796875NA
6020972NANA-17148.3255208333NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; 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')