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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 17:03:06 -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/t1386885935na8u3ehx35xbuvl.htm/, Retrieved Tue, 07 Dec 2021 12:25:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232273, Retrieved Tue, 07 Dec 2021 12:25:02 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact41
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-12 22:03:06] [354a1e9bb909abf3036d55f04d250334] [Current]
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Dataseries X:
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644
19195
19650
20830
23595
22937
21814
21928
21777
21383
21467
22052
22680
24320
24977
25204
25739
26434
27525
30695
32436
30160
30236
31293
31077
32226
33865
32810
32242
32700
32819
33947
34148
35261
39506
41591
39148
41216
40225
41126
42362
40740
40256
39804
41002
41702
42254
43605
43271
43221
41373




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232273&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232273&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
115579NANA412.042NA
216348NANA917.45NA
315928NANA236.233NA
416171NANA-593.017NA
515937NANA-260.983NA
615713NANA-82.7917NA
71559415833.616481-647.317-239.642
81568316400.316798.2-397.992-717.258
91643817286.417130.8155.658-848.408
101703217098.417405.5-307.017-66.4417
111769617918.117610.3307.783-222.117
121774518086.217826.2259.95-341.2
131939418499.218087.2412.042894.792
142014819253.818336.3917.45894.217
152010818757.618521.4236.2331350.39
161858418122.418715.4-593.017461.6
171844118625.718886.7-260.983-184.725
181839118945.819028.5-82.7917-554.75
191917818520.419167.8-647.317657.567
201807918973.219371.2-397.992-894.217
211848319788.419632.7155.658-1305.37
221964419578.119885.2-307.01765.85
231919520472.820165307.783-1277.83
241965020711.420451.4259.95-1061.37
252083021096.420684.4412.042-266.417
262359521834.920917.4917.451760.13
272293721443.521207.3236.2331493.48
282181420889.521482.5-593.017924.517
292192821561.621822.5-260.983366.442
302177722175.322258-82.7917-398.25
312138322014.922662.2-647.317-631.933
322146722535.822933.8-397.992-1068.84
332205223324.523168.9155.658-1272.53
342268023245.523552.5-307.017-565.525
352432024463.624155.8307.783-143.575
362497725225.224965.2259.95-248.158
372520426187.125775412.042-983.083
382573927423.626506.1917.45-1684.58
392643427492.827256.5236.233-1058.78
402752527398.427991.5-593.017126.558
413069528409.828670.8-260.9832285.23
423243629287.729370.5-82.79173148.29
433016029410.430057.8-647.317749.567
443023630247.630645.6-397.992-11.6333
453129331333.331177.7155.658-40.325
463107731352.331659.3-307.017-275.317
473222632323.232015.4307.783-97.2
483386532482.232222.2259.951382.8
493281032918.232506.1412.042-108.167
503224234022.433104.9917.45-1780.37
513270034156.533920.2236.233-1456.48
523281934092.634685.6-593.017-1273.61
533394735135.535396.5-260.983-1188.52
543414835953.336036.1-82.7917-1805.29
553526136000.336647.6-647.317-739.267
563950637017.837415.8-397.9922488.24
574159138328.138172.4155.6583262.93
583914838510.338817.3-307.017637.725
59412163967939371.2307.7831537.01
604022540160.839900.8259.9564.2167
614112640866.840454.8412.042259.167
624236241755.140837.7917.45606.883
634074041272.341036.1236.233-532.317
644025640698.841291.8-593.017-442.775
653980441286.141547.1-260.983-1482.14
664100241595.741678.5-82.7917-593.708
6741702NANA-647.317NA
6842254NANA-397.992NA
6943605NANA155.658NA
7043271NANA-307.017NA
7143221NANA307.783NA
7241373NANA259.95NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 15579 & NA & NA & 412.042 & NA \tabularnewline
2 & 16348 & NA & NA & 917.45 & NA \tabularnewline
3 & 15928 & NA & NA & 236.233 & NA \tabularnewline
4 & 16171 & NA & NA & -593.017 & NA \tabularnewline
5 & 15937 & NA & NA & -260.983 & NA \tabularnewline
6 & 15713 & NA & NA & -82.7917 & NA \tabularnewline
7 & 15594 & 15833.6 & 16481 & -647.317 & -239.642 \tabularnewline
8 & 15683 & 16400.3 & 16798.2 & -397.992 & -717.258 \tabularnewline
9 & 16438 & 17286.4 & 17130.8 & 155.658 & -848.408 \tabularnewline
10 & 17032 & 17098.4 & 17405.5 & -307.017 & -66.4417 \tabularnewline
11 & 17696 & 17918.1 & 17610.3 & 307.783 & -222.117 \tabularnewline
12 & 17745 & 18086.2 & 17826.2 & 259.95 & -341.2 \tabularnewline
13 & 19394 & 18499.2 & 18087.2 & 412.042 & 894.792 \tabularnewline
14 & 20148 & 19253.8 & 18336.3 & 917.45 & 894.217 \tabularnewline
15 & 20108 & 18757.6 & 18521.4 & 236.233 & 1350.39 \tabularnewline
16 & 18584 & 18122.4 & 18715.4 & -593.017 & 461.6 \tabularnewline
17 & 18441 & 18625.7 & 18886.7 & -260.983 & -184.725 \tabularnewline
18 & 18391 & 18945.8 & 19028.5 & -82.7917 & -554.75 \tabularnewline
19 & 19178 & 18520.4 & 19167.8 & -647.317 & 657.567 \tabularnewline
20 & 18079 & 18973.2 & 19371.2 & -397.992 & -894.217 \tabularnewline
21 & 18483 & 19788.4 & 19632.7 & 155.658 & -1305.37 \tabularnewline
22 & 19644 & 19578.1 & 19885.2 & -307.017 & 65.85 \tabularnewline
23 & 19195 & 20472.8 & 20165 & 307.783 & -1277.83 \tabularnewline
24 & 19650 & 20711.4 & 20451.4 & 259.95 & -1061.37 \tabularnewline
25 & 20830 & 21096.4 & 20684.4 & 412.042 & -266.417 \tabularnewline
26 & 23595 & 21834.9 & 20917.4 & 917.45 & 1760.13 \tabularnewline
27 & 22937 & 21443.5 & 21207.3 & 236.233 & 1493.48 \tabularnewline
28 & 21814 & 20889.5 & 21482.5 & -593.017 & 924.517 \tabularnewline
29 & 21928 & 21561.6 & 21822.5 & -260.983 & 366.442 \tabularnewline
30 & 21777 & 22175.3 & 22258 & -82.7917 & -398.25 \tabularnewline
31 & 21383 & 22014.9 & 22662.2 & -647.317 & -631.933 \tabularnewline
32 & 21467 & 22535.8 & 22933.8 & -397.992 & -1068.84 \tabularnewline
33 & 22052 & 23324.5 & 23168.9 & 155.658 & -1272.53 \tabularnewline
34 & 22680 & 23245.5 & 23552.5 & -307.017 & -565.525 \tabularnewline
35 & 24320 & 24463.6 & 24155.8 & 307.783 & -143.575 \tabularnewline
36 & 24977 & 25225.2 & 24965.2 & 259.95 & -248.158 \tabularnewline
37 & 25204 & 26187.1 & 25775 & 412.042 & -983.083 \tabularnewline
38 & 25739 & 27423.6 & 26506.1 & 917.45 & -1684.58 \tabularnewline
39 & 26434 & 27492.8 & 27256.5 & 236.233 & -1058.78 \tabularnewline
40 & 27525 & 27398.4 & 27991.5 & -593.017 & 126.558 \tabularnewline
41 & 30695 & 28409.8 & 28670.8 & -260.983 & 2285.23 \tabularnewline
42 & 32436 & 29287.7 & 29370.5 & -82.7917 & 3148.29 \tabularnewline
43 & 30160 & 29410.4 & 30057.8 & -647.317 & 749.567 \tabularnewline
44 & 30236 & 30247.6 & 30645.6 & -397.992 & -11.6333 \tabularnewline
45 & 31293 & 31333.3 & 31177.7 & 155.658 & -40.325 \tabularnewline
46 & 31077 & 31352.3 & 31659.3 & -307.017 & -275.317 \tabularnewline
47 & 32226 & 32323.2 & 32015.4 & 307.783 & -97.2 \tabularnewline
48 & 33865 & 32482.2 & 32222.2 & 259.95 & 1382.8 \tabularnewline
49 & 32810 & 32918.2 & 32506.1 & 412.042 & -108.167 \tabularnewline
50 & 32242 & 34022.4 & 33104.9 & 917.45 & -1780.37 \tabularnewline
51 & 32700 & 34156.5 & 33920.2 & 236.233 & -1456.48 \tabularnewline
52 & 32819 & 34092.6 & 34685.6 & -593.017 & -1273.61 \tabularnewline
53 & 33947 & 35135.5 & 35396.5 & -260.983 & -1188.52 \tabularnewline
54 & 34148 & 35953.3 & 36036.1 & -82.7917 & -1805.29 \tabularnewline
55 & 35261 & 36000.3 & 36647.6 & -647.317 & -739.267 \tabularnewline
56 & 39506 & 37017.8 & 37415.8 & -397.992 & 2488.24 \tabularnewline
57 & 41591 & 38328.1 & 38172.4 & 155.658 & 3262.93 \tabularnewline
58 & 39148 & 38510.3 & 38817.3 & -307.017 & 637.725 \tabularnewline
59 & 41216 & 39679 & 39371.2 & 307.783 & 1537.01 \tabularnewline
60 & 40225 & 40160.8 & 39900.8 & 259.95 & 64.2167 \tabularnewline
61 & 41126 & 40866.8 & 40454.8 & 412.042 & 259.167 \tabularnewline
62 & 42362 & 41755.1 & 40837.7 & 917.45 & 606.883 \tabularnewline
63 & 40740 & 41272.3 & 41036.1 & 236.233 & -532.317 \tabularnewline
64 & 40256 & 40698.8 & 41291.8 & -593.017 & -442.775 \tabularnewline
65 & 39804 & 41286.1 & 41547.1 & -260.983 & -1482.14 \tabularnewline
66 & 41002 & 41595.7 & 41678.5 & -82.7917 & -593.708 \tabularnewline
67 & 41702 & NA & NA & -647.317 & NA \tabularnewline
68 & 42254 & NA & NA & -397.992 & NA \tabularnewline
69 & 43605 & NA & NA & 155.658 & NA \tabularnewline
70 & 43271 & NA & NA & -307.017 & NA \tabularnewline
71 & 43221 & NA & NA & 307.783 & NA \tabularnewline
72 & 41373 & NA & NA & 259.95 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232273&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]15579[/C][C]NA[/C][C]NA[/C][C]412.042[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]16348[/C][C]NA[/C][C]NA[/C][C]917.45[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]15928[/C][C]NA[/C][C]NA[/C][C]236.233[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]16171[/C][C]NA[/C][C]NA[/C][C]-593.017[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]15937[/C][C]NA[/C][C]NA[/C][C]-260.983[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]15713[/C][C]NA[/C][C]NA[/C][C]-82.7917[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]15594[/C][C]15833.6[/C][C]16481[/C][C]-647.317[/C][C]-239.642[/C][/ROW]
[ROW][C]8[/C][C]15683[/C][C]16400.3[/C][C]16798.2[/C][C]-397.992[/C][C]-717.258[/C][/ROW]
[ROW][C]9[/C][C]16438[/C][C]17286.4[/C][C]17130.8[/C][C]155.658[/C][C]-848.408[/C][/ROW]
[ROW][C]10[/C][C]17032[/C][C]17098.4[/C][C]17405.5[/C][C]-307.017[/C][C]-66.4417[/C][/ROW]
[ROW][C]11[/C][C]17696[/C][C]17918.1[/C][C]17610.3[/C][C]307.783[/C][C]-222.117[/C][/ROW]
[ROW][C]12[/C][C]17745[/C][C]18086.2[/C][C]17826.2[/C][C]259.95[/C][C]-341.2[/C][/ROW]
[ROW][C]13[/C][C]19394[/C][C]18499.2[/C][C]18087.2[/C][C]412.042[/C][C]894.792[/C][/ROW]
[ROW][C]14[/C][C]20148[/C][C]19253.8[/C][C]18336.3[/C][C]917.45[/C][C]894.217[/C][/ROW]
[ROW][C]15[/C][C]20108[/C][C]18757.6[/C][C]18521.4[/C][C]236.233[/C][C]1350.39[/C][/ROW]
[ROW][C]16[/C][C]18584[/C][C]18122.4[/C][C]18715.4[/C][C]-593.017[/C][C]461.6[/C][/ROW]
[ROW][C]17[/C][C]18441[/C][C]18625.7[/C][C]18886.7[/C][C]-260.983[/C][C]-184.725[/C][/ROW]
[ROW][C]18[/C][C]18391[/C][C]18945.8[/C][C]19028.5[/C][C]-82.7917[/C][C]-554.75[/C][/ROW]
[ROW][C]19[/C][C]19178[/C][C]18520.4[/C][C]19167.8[/C][C]-647.317[/C][C]657.567[/C][/ROW]
[ROW][C]20[/C][C]18079[/C][C]18973.2[/C][C]19371.2[/C][C]-397.992[/C][C]-894.217[/C][/ROW]
[ROW][C]21[/C][C]18483[/C][C]19788.4[/C][C]19632.7[/C][C]155.658[/C][C]-1305.37[/C][/ROW]
[ROW][C]22[/C][C]19644[/C][C]19578.1[/C][C]19885.2[/C][C]-307.017[/C][C]65.85[/C][/ROW]
[ROW][C]23[/C][C]19195[/C][C]20472.8[/C][C]20165[/C][C]307.783[/C][C]-1277.83[/C][/ROW]
[ROW][C]24[/C][C]19650[/C][C]20711.4[/C][C]20451.4[/C][C]259.95[/C][C]-1061.37[/C][/ROW]
[ROW][C]25[/C][C]20830[/C][C]21096.4[/C][C]20684.4[/C][C]412.042[/C][C]-266.417[/C][/ROW]
[ROW][C]26[/C][C]23595[/C][C]21834.9[/C][C]20917.4[/C][C]917.45[/C][C]1760.13[/C][/ROW]
[ROW][C]27[/C][C]22937[/C][C]21443.5[/C][C]21207.3[/C][C]236.233[/C][C]1493.48[/C][/ROW]
[ROW][C]28[/C][C]21814[/C][C]20889.5[/C][C]21482.5[/C][C]-593.017[/C][C]924.517[/C][/ROW]
[ROW][C]29[/C][C]21928[/C][C]21561.6[/C][C]21822.5[/C][C]-260.983[/C][C]366.442[/C][/ROW]
[ROW][C]30[/C][C]21777[/C][C]22175.3[/C][C]22258[/C][C]-82.7917[/C][C]-398.25[/C][/ROW]
[ROW][C]31[/C][C]21383[/C][C]22014.9[/C][C]22662.2[/C][C]-647.317[/C][C]-631.933[/C][/ROW]
[ROW][C]32[/C][C]21467[/C][C]22535.8[/C][C]22933.8[/C][C]-397.992[/C][C]-1068.84[/C][/ROW]
[ROW][C]33[/C][C]22052[/C][C]23324.5[/C][C]23168.9[/C][C]155.658[/C][C]-1272.53[/C][/ROW]
[ROW][C]34[/C][C]22680[/C][C]23245.5[/C][C]23552.5[/C][C]-307.017[/C][C]-565.525[/C][/ROW]
[ROW][C]35[/C][C]24320[/C][C]24463.6[/C][C]24155.8[/C][C]307.783[/C][C]-143.575[/C][/ROW]
[ROW][C]36[/C][C]24977[/C][C]25225.2[/C][C]24965.2[/C][C]259.95[/C][C]-248.158[/C][/ROW]
[ROW][C]37[/C][C]25204[/C][C]26187.1[/C][C]25775[/C][C]412.042[/C][C]-983.083[/C][/ROW]
[ROW][C]38[/C][C]25739[/C][C]27423.6[/C][C]26506.1[/C][C]917.45[/C][C]-1684.58[/C][/ROW]
[ROW][C]39[/C][C]26434[/C][C]27492.8[/C][C]27256.5[/C][C]236.233[/C][C]-1058.78[/C][/ROW]
[ROW][C]40[/C][C]27525[/C][C]27398.4[/C][C]27991.5[/C][C]-593.017[/C][C]126.558[/C][/ROW]
[ROW][C]41[/C][C]30695[/C][C]28409.8[/C][C]28670.8[/C][C]-260.983[/C][C]2285.23[/C][/ROW]
[ROW][C]42[/C][C]32436[/C][C]29287.7[/C][C]29370.5[/C][C]-82.7917[/C][C]3148.29[/C][/ROW]
[ROW][C]43[/C][C]30160[/C][C]29410.4[/C][C]30057.8[/C][C]-647.317[/C][C]749.567[/C][/ROW]
[ROW][C]44[/C][C]30236[/C][C]30247.6[/C][C]30645.6[/C][C]-397.992[/C][C]-11.6333[/C][/ROW]
[ROW][C]45[/C][C]31293[/C][C]31333.3[/C][C]31177.7[/C][C]155.658[/C][C]-40.325[/C][/ROW]
[ROW][C]46[/C][C]31077[/C][C]31352.3[/C][C]31659.3[/C][C]-307.017[/C][C]-275.317[/C][/ROW]
[ROW][C]47[/C][C]32226[/C][C]32323.2[/C][C]32015.4[/C][C]307.783[/C][C]-97.2[/C][/ROW]
[ROW][C]48[/C][C]33865[/C][C]32482.2[/C][C]32222.2[/C][C]259.95[/C][C]1382.8[/C][/ROW]
[ROW][C]49[/C][C]32810[/C][C]32918.2[/C][C]32506.1[/C][C]412.042[/C][C]-108.167[/C][/ROW]
[ROW][C]50[/C][C]32242[/C][C]34022.4[/C][C]33104.9[/C][C]917.45[/C][C]-1780.37[/C][/ROW]
[ROW][C]51[/C][C]32700[/C][C]34156.5[/C][C]33920.2[/C][C]236.233[/C][C]-1456.48[/C][/ROW]
[ROW][C]52[/C][C]32819[/C][C]34092.6[/C][C]34685.6[/C][C]-593.017[/C][C]-1273.61[/C][/ROW]
[ROW][C]53[/C][C]33947[/C][C]35135.5[/C][C]35396.5[/C][C]-260.983[/C][C]-1188.52[/C][/ROW]
[ROW][C]54[/C][C]34148[/C][C]35953.3[/C][C]36036.1[/C][C]-82.7917[/C][C]-1805.29[/C][/ROW]
[ROW][C]55[/C][C]35261[/C][C]36000.3[/C][C]36647.6[/C][C]-647.317[/C][C]-739.267[/C][/ROW]
[ROW][C]56[/C][C]39506[/C][C]37017.8[/C][C]37415.8[/C][C]-397.992[/C][C]2488.24[/C][/ROW]
[ROW][C]57[/C][C]41591[/C][C]38328.1[/C][C]38172.4[/C][C]155.658[/C][C]3262.93[/C][/ROW]
[ROW][C]58[/C][C]39148[/C][C]38510.3[/C][C]38817.3[/C][C]-307.017[/C][C]637.725[/C][/ROW]
[ROW][C]59[/C][C]41216[/C][C]39679[/C][C]39371.2[/C][C]307.783[/C][C]1537.01[/C][/ROW]
[ROW][C]60[/C][C]40225[/C][C]40160.8[/C][C]39900.8[/C][C]259.95[/C][C]64.2167[/C][/ROW]
[ROW][C]61[/C][C]41126[/C][C]40866.8[/C][C]40454.8[/C][C]412.042[/C][C]259.167[/C][/ROW]
[ROW][C]62[/C][C]42362[/C][C]41755.1[/C][C]40837.7[/C][C]917.45[/C][C]606.883[/C][/ROW]
[ROW][C]63[/C][C]40740[/C][C]41272.3[/C][C]41036.1[/C][C]236.233[/C][C]-532.317[/C][/ROW]
[ROW][C]64[/C][C]40256[/C][C]40698.8[/C][C]41291.8[/C][C]-593.017[/C][C]-442.775[/C][/ROW]
[ROW][C]65[/C][C]39804[/C][C]41286.1[/C][C]41547.1[/C][C]-260.983[/C][C]-1482.14[/C][/ROW]
[ROW][C]66[/C][C]41002[/C][C]41595.7[/C][C]41678.5[/C][C]-82.7917[/C][C]-593.708[/C][/ROW]
[ROW][C]67[/C][C]41702[/C][C]NA[/C][C]NA[/C][C]-647.317[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]42254[/C][C]NA[/C][C]NA[/C][C]-397.992[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]43605[/C][C]NA[/C][C]NA[/C][C]155.658[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]43271[/C][C]NA[/C][C]NA[/C][C]-307.017[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]43221[/C][C]NA[/C][C]NA[/C][C]307.783[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]41373[/C][C]NA[/C][C]NA[/C][C]259.95[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232273&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
115579NANA412.042NA
216348NANA917.45NA
315928NANA236.233NA
416171NANA-593.017NA
515937NANA-260.983NA
615713NANA-82.7917NA
71559415833.616481-647.317-239.642
81568316400.316798.2-397.992-717.258
91643817286.417130.8155.658-848.408
101703217098.417405.5-307.017-66.4417
111769617918.117610.3307.783-222.117
121774518086.217826.2259.95-341.2
131939418499.218087.2412.042894.792
142014819253.818336.3917.45894.217
152010818757.618521.4236.2331350.39
161858418122.418715.4-593.017461.6
171844118625.718886.7-260.983-184.725
181839118945.819028.5-82.7917-554.75
191917818520.419167.8-647.317657.567
201807918973.219371.2-397.992-894.217
211848319788.419632.7155.658-1305.37
221964419578.119885.2-307.01765.85
231919520472.820165307.783-1277.83
241965020711.420451.4259.95-1061.37
252083021096.420684.4412.042-266.417
262359521834.920917.4917.451760.13
272293721443.521207.3236.2331493.48
282181420889.521482.5-593.017924.517
292192821561.621822.5-260.983366.442
302177722175.322258-82.7917-398.25
312138322014.922662.2-647.317-631.933
322146722535.822933.8-397.992-1068.84
332205223324.523168.9155.658-1272.53
342268023245.523552.5-307.017-565.525
352432024463.624155.8307.783-143.575
362497725225.224965.2259.95-248.158
372520426187.125775412.042-983.083
382573927423.626506.1917.45-1684.58
392643427492.827256.5236.233-1058.78
402752527398.427991.5-593.017126.558
413069528409.828670.8-260.9832285.23
423243629287.729370.5-82.79173148.29
433016029410.430057.8-647.317749.567
443023630247.630645.6-397.992-11.6333
453129331333.331177.7155.658-40.325
463107731352.331659.3-307.017-275.317
473222632323.232015.4307.783-97.2
483386532482.232222.2259.951382.8
493281032918.232506.1412.042-108.167
503224234022.433104.9917.45-1780.37
513270034156.533920.2236.233-1456.48
523281934092.634685.6-593.017-1273.61
533394735135.535396.5-260.983-1188.52
543414835953.336036.1-82.7917-1805.29
553526136000.336647.6-647.317-739.267
563950637017.837415.8-397.9922488.24
574159138328.138172.4155.6583262.93
583914838510.338817.3-307.017637.725
59412163967939371.2307.7831537.01
604022540160.839900.8259.9564.2167
614112640866.840454.8412.042259.167
624236241755.140837.7917.45606.883
634074041272.341036.1236.233-532.317
644025640698.841291.8-593.017-442.775
653980441286.141547.1-260.983-1482.14
664100241595.741678.5-82.7917-593.708
6741702NANA-647.317NA
6842254NANA-397.992NA
6943605NANA155.658NA
7043271NANA-307.017NA
7143221NANA307.783NA
7241373NANA259.95NA



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')