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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 12 Dec 2013 10:34:59 -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/2013/Dec/12/t13868631761h8hsdr2m7b9ujw.htm/, Retrieved Tue, 07 Dec 2021 11:33:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232255, Retrieved Tue, 07 Dec 2021 11:33:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-12 15:34:59] [5e7911d8fd88d8bc3975d02d8918deef] [Current]
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Dataseries X:
41086
39690
43129
37863
35953
29133
24693
22205
21725
27192
21790
13253
37702
30364
32609
30212
29965
28352
25814
22414
20506
28806
22228
13971
36845
35338
35022
34777
26887
23970
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232255&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232255&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 time5 seconds
R Server'George Udny Yule' @ yule.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
141086NANA8179.45NA
239690NANA5047.24NA
343129NANA8069.2NA
437863NANA5544.54NA
535953NANA578.453NA
629133NANA1266.34NA
72469326610.729668.3-3057.66-1917.67
82220523213.129138.7-5925.65-1008.1
9217252405028311.8-4261.81-2325.02
102719228287.527554.7732.77-1095.48
112179021855.526986.4-5130.89-65.5285
121325315662.426704.4-11042-2409.4
13377023489826718.58179.452804
143036431821.2267745047.24-1457.2
153260934801.126731.98069.2-2192.08
163021232292.926748.35544.54-2080.87
172996527412.326833.8578.4532552.71
182835228148.3268821266.34203.663
192581423818.526876.2-3057.661995.45
202241421122.127047.8-5925.651291.9
212050623093.727355.5-4261.81-2587.73
222880628379.127646.3732.77426.938
232222822577.427708.2-5130.89-349.362
241397116355.427397.4-11042-2384.44
253684535267.927088.48179.451577.13
263533831798.3267515047.243539.72
273502234645.826576.68069.2376.213
283477731980.726436.25544.542796.25
29268872663726058.5578.453250.005
302397027021.725755.41266.34-3051.71
312278022373.225430.9-3057.66406.788
321735118938.624864.3-5925.65-1587.65
332138220026.424288.2-4261.811355.65
342456124427.923695.2732.77133.063
351740918015.923146.8-5130.89-606.862
361151411912.422954.4-11042-398.395
373151431083.722904.38179.45430.255
382707127937.522890.25047.24-866.487
392946230964.522895.28069.2-1502.45
402610528445.5229015544.54-2340.5
412239723523.222944.7578.453-1126.16
422384324391.423125.11266.34-548.42
432170520090.323148-3057.661614.7
441808917173.423099.1-5925.65915.563
452076419224.323486.1-4261.811539.73
462531624756.624023.8732.77559.438
471770419151.724282.6-5130.89-1447.7
481554813272.224314.2-110422275.81
492802932359.224179.88179.45-4330.25
502938329049.824002.65047.24333.18
513643832032.2239638069.24405.8
523203429385.423840.85544.542648.63
532267924374.723796.3578.453-1695.75
542431925051.123784.81266.34-732.128
55180042072623783.7-3057.66-2722
561753717771.423697.1-5925.65-234.437
572036618971.423233.2-4261.811394.65
582278223428.722695.9732.77-646.687
591916917322.322453.2-5130.891846.72
601380711513.322555.3-110422293.69
612974330846.922667.48179.45-1103.87
622559127762.922715.75047.24-2171.95
632909630806.2227378069.2-1710.2
642648228128.222583.75544.54-1646.25
652240523008.522430.1578.453-603.537
662704423538.122271.81266.343505.87
6717970NANA-3057.66NA
6818730NANA-5925.65NA
6919684NANA-4261.81NA
7019785NANA732.77NA
7118479NANA-5130.89NA
7210698NANA-11042NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 41086 & NA & NA & 8179.45 & NA \tabularnewline
2 & 39690 & NA & NA & 5047.24 & NA \tabularnewline
3 & 43129 & NA & NA & 8069.2 & NA \tabularnewline
4 & 37863 & NA & NA & 5544.54 & NA \tabularnewline
5 & 35953 & NA & NA & 578.453 & NA \tabularnewline
6 & 29133 & NA & NA & 1266.34 & NA \tabularnewline
7 & 24693 & 26610.7 & 29668.3 & -3057.66 & -1917.67 \tabularnewline
8 & 22205 & 23213.1 & 29138.7 & -5925.65 & -1008.1 \tabularnewline
9 & 21725 & 24050 & 28311.8 & -4261.81 & -2325.02 \tabularnewline
10 & 27192 & 28287.5 & 27554.7 & 732.77 & -1095.48 \tabularnewline
11 & 21790 & 21855.5 & 26986.4 & -5130.89 & -65.5285 \tabularnewline
12 & 13253 & 15662.4 & 26704.4 & -11042 & -2409.4 \tabularnewline
13 & 37702 & 34898 & 26718.5 & 8179.45 & 2804 \tabularnewline
14 & 30364 & 31821.2 & 26774 & 5047.24 & -1457.2 \tabularnewline
15 & 32609 & 34801.1 & 26731.9 & 8069.2 & -2192.08 \tabularnewline
16 & 30212 & 32292.9 & 26748.3 & 5544.54 & -2080.87 \tabularnewline
17 & 29965 & 27412.3 & 26833.8 & 578.453 & 2552.71 \tabularnewline
18 & 28352 & 28148.3 & 26882 & 1266.34 & 203.663 \tabularnewline
19 & 25814 & 23818.5 & 26876.2 & -3057.66 & 1995.45 \tabularnewline
20 & 22414 & 21122.1 & 27047.8 & -5925.65 & 1291.9 \tabularnewline
21 & 20506 & 23093.7 & 27355.5 & -4261.81 & -2587.73 \tabularnewline
22 & 28806 & 28379.1 & 27646.3 & 732.77 & 426.938 \tabularnewline
23 & 22228 & 22577.4 & 27708.2 & -5130.89 & -349.362 \tabularnewline
24 & 13971 & 16355.4 & 27397.4 & -11042 & -2384.44 \tabularnewline
25 & 36845 & 35267.9 & 27088.4 & 8179.45 & 1577.13 \tabularnewline
26 & 35338 & 31798.3 & 26751 & 5047.24 & 3539.72 \tabularnewline
27 & 35022 & 34645.8 & 26576.6 & 8069.2 & 376.213 \tabularnewline
28 & 34777 & 31980.7 & 26436.2 & 5544.54 & 2796.25 \tabularnewline
29 & 26887 & 26637 & 26058.5 & 578.453 & 250.005 \tabularnewline
30 & 23970 & 27021.7 & 25755.4 & 1266.34 & -3051.71 \tabularnewline
31 & 22780 & 22373.2 & 25430.9 & -3057.66 & 406.788 \tabularnewline
32 & 17351 & 18938.6 & 24864.3 & -5925.65 & -1587.65 \tabularnewline
33 & 21382 & 20026.4 & 24288.2 & -4261.81 & 1355.65 \tabularnewline
34 & 24561 & 24427.9 & 23695.2 & 732.77 & 133.063 \tabularnewline
35 & 17409 & 18015.9 & 23146.8 & -5130.89 & -606.862 \tabularnewline
36 & 11514 & 11912.4 & 22954.4 & -11042 & -398.395 \tabularnewline
37 & 31514 & 31083.7 & 22904.3 & 8179.45 & 430.255 \tabularnewline
38 & 27071 & 27937.5 & 22890.2 & 5047.24 & -866.487 \tabularnewline
39 & 29462 & 30964.5 & 22895.2 & 8069.2 & -1502.45 \tabularnewline
40 & 26105 & 28445.5 & 22901 & 5544.54 & -2340.5 \tabularnewline
41 & 22397 & 23523.2 & 22944.7 & 578.453 & -1126.16 \tabularnewline
42 & 23843 & 24391.4 & 23125.1 & 1266.34 & -548.42 \tabularnewline
43 & 21705 & 20090.3 & 23148 & -3057.66 & 1614.7 \tabularnewline
44 & 18089 & 17173.4 & 23099.1 & -5925.65 & 915.563 \tabularnewline
45 & 20764 & 19224.3 & 23486.1 & -4261.81 & 1539.73 \tabularnewline
46 & 25316 & 24756.6 & 24023.8 & 732.77 & 559.438 \tabularnewline
47 & 17704 & 19151.7 & 24282.6 & -5130.89 & -1447.7 \tabularnewline
48 & 15548 & 13272.2 & 24314.2 & -11042 & 2275.81 \tabularnewline
49 & 28029 & 32359.2 & 24179.8 & 8179.45 & -4330.25 \tabularnewline
50 & 29383 & 29049.8 & 24002.6 & 5047.24 & 333.18 \tabularnewline
51 & 36438 & 32032.2 & 23963 & 8069.2 & 4405.8 \tabularnewline
52 & 32034 & 29385.4 & 23840.8 & 5544.54 & 2648.63 \tabularnewline
53 & 22679 & 24374.7 & 23796.3 & 578.453 & -1695.75 \tabularnewline
54 & 24319 & 25051.1 & 23784.8 & 1266.34 & -732.128 \tabularnewline
55 & 18004 & 20726 & 23783.7 & -3057.66 & -2722 \tabularnewline
56 & 17537 & 17771.4 & 23697.1 & -5925.65 & -234.437 \tabularnewline
57 & 20366 & 18971.4 & 23233.2 & -4261.81 & 1394.65 \tabularnewline
58 & 22782 & 23428.7 & 22695.9 & 732.77 & -646.687 \tabularnewline
59 & 19169 & 17322.3 & 22453.2 & -5130.89 & 1846.72 \tabularnewline
60 & 13807 & 11513.3 & 22555.3 & -11042 & 2293.69 \tabularnewline
61 & 29743 & 30846.9 & 22667.4 & 8179.45 & -1103.87 \tabularnewline
62 & 25591 & 27762.9 & 22715.7 & 5047.24 & -2171.95 \tabularnewline
63 & 29096 & 30806.2 & 22737 & 8069.2 & -1710.2 \tabularnewline
64 & 26482 & 28128.2 & 22583.7 & 5544.54 & -1646.25 \tabularnewline
65 & 22405 & 23008.5 & 22430.1 & 578.453 & -603.537 \tabularnewline
66 & 27044 & 23538.1 & 22271.8 & 1266.34 & 3505.87 \tabularnewline
67 & 17970 & NA & NA & -3057.66 & NA \tabularnewline
68 & 18730 & NA & NA & -5925.65 & NA \tabularnewline
69 & 19684 & NA & NA & -4261.81 & NA \tabularnewline
70 & 19785 & NA & NA & 732.77 & NA \tabularnewline
71 & 18479 & NA & NA & -5130.89 & NA \tabularnewline
72 & 10698 & NA & NA & -11042 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232255&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]41086[/C][C]NA[/C][C]NA[/C][C]8179.45[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]39690[/C][C]NA[/C][C]NA[/C][C]5047.24[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]43129[/C][C]NA[/C][C]NA[/C][C]8069.2[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]37863[/C][C]NA[/C][C]NA[/C][C]5544.54[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]35953[/C][C]NA[/C][C]NA[/C][C]578.453[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]29133[/C][C]NA[/C][C]NA[/C][C]1266.34[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]24693[/C][C]26610.7[/C][C]29668.3[/C][C]-3057.66[/C][C]-1917.67[/C][/ROW]
[ROW][C]8[/C][C]22205[/C][C]23213.1[/C][C]29138.7[/C][C]-5925.65[/C][C]-1008.1[/C][/ROW]
[ROW][C]9[/C][C]21725[/C][C]24050[/C][C]28311.8[/C][C]-4261.81[/C][C]-2325.02[/C][/ROW]
[ROW][C]10[/C][C]27192[/C][C]28287.5[/C][C]27554.7[/C][C]732.77[/C][C]-1095.48[/C][/ROW]
[ROW][C]11[/C][C]21790[/C][C]21855.5[/C][C]26986.4[/C][C]-5130.89[/C][C]-65.5285[/C][/ROW]
[ROW][C]12[/C][C]13253[/C][C]15662.4[/C][C]26704.4[/C][C]-11042[/C][C]-2409.4[/C][/ROW]
[ROW][C]13[/C][C]37702[/C][C]34898[/C][C]26718.5[/C][C]8179.45[/C][C]2804[/C][/ROW]
[ROW][C]14[/C][C]30364[/C][C]31821.2[/C][C]26774[/C][C]5047.24[/C][C]-1457.2[/C][/ROW]
[ROW][C]15[/C][C]32609[/C][C]34801.1[/C][C]26731.9[/C][C]8069.2[/C][C]-2192.08[/C][/ROW]
[ROW][C]16[/C][C]30212[/C][C]32292.9[/C][C]26748.3[/C][C]5544.54[/C][C]-2080.87[/C][/ROW]
[ROW][C]17[/C][C]29965[/C][C]27412.3[/C][C]26833.8[/C][C]578.453[/C][C]2552.71[/C][/ROW]
[ROW][C]18[/C][C]28352[/C][C]28148.3[/C][C]26882[/C][C]1266.34[/C][C]203.663[/C][/ROW]
[ROW][C]19[/C][C]25814[/C][C]23818.5[/C][C]26876.2[/C][C]-3057.66[/C][C]1995.45[/C][/ROW]
[ROW][C]20[/C][C]22414[/C][C]21122.1[/C][C]27047.8[/C][C]-5925.65[/C][C]1291.9[/C][/ROW]
[ROW][C]21[/C][C]20506[/C][C]23093.7[/C][C]27355.5[/C][C]-4261.81[/C][C]-2587.73[/C][/ROW]
[ROW][C]22[/C][C]28806[/C][C]28379.1[/C][C]27646.3[/C][C]732.77[/C][C]426.938[/C][/ROW]
[ROW][C]23[/C][C]22228[/C][C]22577.4[/C][C]27708.2[/C][C]-5130.89[/C][C]-349.362[/C][/ROW]
[ROW][C]24[/C][C]13971[/C][C]16355.4[/C][C]27397.4[/C][C]-11042[/C][C]-2384.44[/C][/ROW]
[ROW][C]25[/C][C]36845[/C][C]35267.9[/C][C]27088.4[/C][C]8179.45[/C][C]1577.13[/C][/ROW]
[ROW][C]26[/C][C]35338[/C][C]31798.3[/C][C]26751[/C][C]5047.24[/C][C]3539.72[/C][/ROW]
[ROW][C]27[/C][C]35022[/C][C]34645.8[/C][C]26576.6[/C][C]8069.2[/C][C]376.213[/C][/ROW]
[ROW][C]28[/C][C]34777[/C][C]31980.7[/C][C]26436.2[/C][C]5544.54[/C][C]2796.25[/C][/ROW]
[ROW][C]29[/C][C]26887[/C][C]26637[/C][C]26058.5[/C][C]578.453[/C][C]250.005[/C][/ROW]
[ROW][C]30[/C][C]23970[/C][C]27021.7[/C][C]25755.4[/C][C]1266.34[/C][C]-3051.71[/C][/ROW]
[ROW][C]31[/C][C]22780[/C][C]22373.2[/C][C]25430.9[/C][C]-3057.66[/C][C]406.788[/C][/ROW]
[ROW][C]32[/C][C]17351[/C][C]18938.6[/C][C]24864.3[/C][C]-5925.65[/C][C]-1587.65[/C][/ROW]
[ROW][C]33[/C][C]21382[/C][C]20026.4[/C][C]24288.2[/C][C]-4261.81[/C][C]1355.65[/C][/ROW]
[ROW][C]34[/C][C]24561[/C][C]24427.9[/C][C]23695.2[/C][C]732.77[/C][C]133.063[/C][/ROW]
[ROW][C]35[/C][C]17409[/C][C]18015.9[/C][C]23146.8[/C][C]-5130.89[/C][C]-606.862[/C][/ROW]
[ROW][C]36[/C][C]11514[/C][C]11912.4[/C][C]22954.4[/C][C]-11042[/C][C]-398.395[/C][/ROW]
[ROW][C]37[/C][C]31514[/C][C]31083.7[/C][C]22904.3[/C][C]8179.45[/C][C]430.255[/C][/ROW]
[ROW][C]38[/C][C]27071[/C][C]27937.5[/C][C]22890.2[/C][C]5047.24[/C][C]-866.487[/C][/ROW]
[ROW][C]39[/C][C]29462[/C][C]30964.5[/C][C]22895.2[/C][C]8069.2[/C][C]-1502.45[/C][/ROW]
[ROW][C]40[/C][C]26105[/C][C]28445.5[/C][C]22901[/C][C]5544.54[/C][C]-2340.5[/C][/ROW]
[ROW][C]41[/C][C]22397[/C][C]23523.2[/C][C]22944.7[/C][C]578.453[/C][C]-1126.16[/C][/ROW]
[ROW][C]42[/C][C]23843[/C][C]24391.4[/C][C]23125.1[/C][C]1266.34[/C][C]-548.42[/C][/ROW]
[ROW][C]43[/C][C]21705[/C][C]20090.3[/C][C]23148[/C][C]-3057.66[/C][C]1614.7[/C][/ROW]
[ROW][C]44[/C][C]18089[/C][C]17173.4[/C][C]23099.1[/C][C]-5925.65[/C][C]915.563[/C][/ROW]
[ROW][C]45[/C][C]20764[/C][C]19224.3[/C][C]23486.1[/C][C]-4261.81[/C][C]1539.73[/C][/ROW]
[ROW][C]46[/C][C]25316[/C][C]24756.6[/C][C]24023.8[/C][C]732.77[/C][C]559.438[/C][/ROW]
[ROW][C]47[/C][C]17704[/C][C]19151.7[/C][C]24282.6[/C][C]-5130.89[/C][C]-1447.7[/C][/ROW]
[ROW][C]48[/C][C]15548[/C][C]13272.2[/C][C]24314.2[/C][C]-11042[/C][C]2275.81[/C][/ROW]
[ROW][C]49[/C][C]28029[/C][C]32359.2[/C][C]24179.8[/C][C]8179.45[/C][C]-4330.25[/C][/ROW]
[ROW][C]50[/C][C]29383[/C][C]29049.8[/C][C]24002.6[/C][C]5047.24[/C][C]333.18[/C][/ROW]
[ROW][C]51[/C][C]36438[/C][C]32032.2[/C][C]23963[/C][C]8069.2[/C][C]4405.8[/C][/ROW]
[ROW][C]52[/C][C]32034[/C][C]29385.4[/C][C]23840.8[/C][C]5544.54[/C][C]2648.63[/C][/ROW]
[ROW][C]53[/C][C]22679[/C][C]24374.7[/C][C]23796.3[/C][C]578.453[/C][C]-1695.75[/C][/ROW]
[ROW][C]54[/C][C]24319[/C][C]25051.1[/C][C]23784.8[/C][C]1266.34[/C][C]-732.128[/C][/ROW]
[ROW][C]55[/C][C]18004[/C][C]20726[/C][C]23783.7[/C][C]-3057.66[/C][C]-2722[/C][/ROW]
[ROW][C]56[/C][C]17537[/C][C]17771.4[/C][C]23697.1[/C][C]-5925.65[/C][C]-234.437[/C][/ROW]
[ROW][C]57[/C][C]20366[/C][C]18971.4[/C][C]23233.2[/C][C]-4261.81[/C][C]1394.65[/C][/ROW]
[ROW][C]58[/C][C]22782[/C][C]23428.7[/C][C]22695.9[/C][C]732.77[/C][C]-646.687[/C][/ROW]
[ROW][C]59[/C][C]19169[/C][C]17322.3[/C][C]22453.2[/C][C]-5130.89[/C][C]1846.72[/C][/ROW]
[ROW][C]60[/C][C]13807[/C][C]11513.3[/C][C]22555.3[/C][C]-11042[/C][C]2293.69[/C][/ROW]
[ROW][C]61[/C][C]29743[/C][C]30846.9[/C][C]22667.4[/C][C]8179.45[/C][C]-1103.87[/C][/ROW]
[ROW][C]62[/C][C]25591[/C][C]27762.9[/C][C]22715.7[/C][C]5047.24[/C][C]-2171.95[/C][/ROW]
[ROW][C]63[/C][C]29096[/C][C]30806.2[/C][C]22737[/C][C]8069.2[/C][C]-1710.2[/C][/ROW]
[ROW][C]64[/C][C]26482[/C][C]28128.2[/C][C]22583.7[/C][C]5544.54[/C][C]-1646.25[/C][/ROW]
[ROW][C]65[/C][C]22405[/C][C]23008.5[/C][C]22430.1[/C][C]578.453[/C][C]-603.537[/C][/ROW]
[ROW][C]66[/C][C]27044[/C][C]23538.1[/C][C]22271.8[/C][C]1266.34[/C][C]3505.87[/C][/ROW]
[ROW][C]67[/C][C]17970[/C][C]NA[/C][C]NA[/C][C]-3057.66[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]18730[/C][C]NA[/C][C]NA[/C][C]-5925.65[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]19684[/C][C]NA[/C][C]NA[/C][C]-4261.81[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]19785[/C][C]NA[/C][C]NA[/C][C]732.77[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]18479[/C][C]NA[/C][C]NA[/C][C]-5130.89[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]10698[/C][C]NA[/C][C]NA[/C][C]-11042[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232255&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
141086NANA8179.45NA
239690NANA5047.24NA
343129NANA8069.2NA
437863NANA5544.54NA
535953NANA578.453NA
629133NANA1266.34NA
72469326610.729668.3-3057.66-1917.67
82220523213.129138.7-5925.65-1008.1
9217252405028311.8-4261.81-2325.02
102719228287.527554.7732.77-1095.48
112179021855.526986.4-5130.89-65.5285
121325315662.426704.4-11042-2409.4
13377023489826718.58179.452804
143036431821.2267745047.24-1457.2
153260934801.126731.98069.2-2192.08
163021232292.926748.35544.54-2080.87
172996527412.326833.8578.4532552.71
182835228148.3268821266.34203.663
192581423818.526876.2-3057.661995.45
202241421122.127047.8-5925.651291.9
212050623093.727355.5-4261.81-2587.73
222880628379.127646.3732.77426.938
232222822577.427708.2-5130.89-349.362
241397116355.427397.4-11042-2384.44
253684535267.927088.48179.451577.13
263533831798.3267515047.243539.72
273502234645.826576.68069.2376.213
283477731980.726436.25544.542796.25
29268872663726058.5578.453250.005
302397027021.725755.41266.34-3051.71
312278022373.225430.9-3057.66406.788
321735118938.624864.3-5925.65-1587.65
332138220026.424288.2-4261.811355.65
342456124427.923695.2732.77133.063
351740918015.923146.8-5130.89-606.862
361151411912.422954.4-11042-398.395
373151431083.722904.38179.45430.255
382707127937.522890.25047.24-866.487
392946230964.522895.28069.2-1502.45
402610528445.5229015544.54-2340.5
412239723523.222944.7578.453-1126.16
422384324391.423125.11266.34-548.42
432170520090.323148-3057.661614.7
441808917173.423099.1-5925.65915.563
452076419224.323486.1-4261.811539.73
462531624756.624023.8732.77559.438
471770419151.724282.6-5130.89-1447.7
481554813272.224314.2-110422275.81
492802932359.224179.88179.45-4330.25
502938329049.824002.65047.24333.18
513643832032.2239638069.24405.8
523203429385.423840.85544.542648.63
532267924374.723796.3578.453-1695.75
542431925051.123784.81266.34-732.128
55180042072623783.7-3057.66-2722
561753717771.423697.1-5925.65-234.437
572036618971.423233.2-4261.811394.65
582278223428.722695.9732.77-646.687
591916917322.322453.2-5130.891846.72
601380711513.322555.3-110422293.69
612974330846.922667.48179.45-1103.87
622559127762.922715.75047.24-2171.95
632909630806.2227378069.2-1710.2
642648228128.222583.75544.54-1646.25
652240523008.522430.1578.453-603.537
662704423538.122271.81266.343505.87
6717970NANA-3057.66NA
6818730NANA-5925.65NA
6919684NANA-4261.81NA
7019785NANA732.77NA
7118479NANA-5130.89NA
7210698NANA-11042NA



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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')