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R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 09 Dec 2013 11:27:47 -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/09/t1386606506xy15h42h1p3nuhn.htm/, Retrieved Fri, 29 Mar 2024 06:52:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231682, Retrieved Fri, 29 Mar 2024 06:52:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-09 16:27:47] [c5b0870324cee902d4892ab6fd83304c] [Current]
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Dataseries X:
86,86
86,79
82,52
86,87
81,62
82,66
89,87
92,04
79,74
77,75
79,12
76,37
75,01
77,6
77,81
81,7
76,47
74,72
84,43
86,72
70,99
75,43
74,14
73,3
71,97
69,27
74,13
76,4
72,26
72,1
87,82
91,62
82,69
85,76
86,87
93,09
83,73
84,49
87,37
89,13
83,2
83,77
93,68
93,09
88,59
87,88
87,89
89,38
89,13
89,58
90,22
91,44
91,04
92,1
97,54
99,12
100
99,68
100,08
99,9
99,63
99,45
99,63
99,46
96,91
97,65
102,1
103,57
104,59
104,79
101,31
104,8
104,56
104,15
102,73
101,86
101,9
102,33
105,71
106,1
102,81
103,23
102,35
104,11




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231682&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]6 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=231682&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231682&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
186.86NANA-1.80113NA
286.79NANA-1.92377NA
382.52NANA-0.956615NA
486.87NANA0.0562326NA
581.62NANA-3.31703NA
682.66NANA-3.52266NA
789.8787.975483.02384.951651.8946
892.0488.641982.14716.494843.39807
979.7481.208581.5679-0.359392-1.46852
1077.7581.334181.15620.17783-3.58408
1179.1280.345880.7262-0.380434-1.22582
1276.3780.761380.18080.580469-4.3913
1375.0177.822279.6233-1.80113-2.8122
1477.677.251279.175-1.923770.348767
1577.8177.632178.5888-0.9566150.177865
1681.778.183778.12750.05623263.51627
1776.4774.506377.8233-3.317031.9637
1874.7273.965377.4879-3.522660.75474
1984.4382.18577.23334.951652.24502
2086.7283.254476.75966.494843.46557
2170.9975.899876.2592-0.359392-4.90977
2275.4376.062875.8850.17783-0.63283
2374.1475.108375.4888-0.380434-0.968316
2473.375.784675.20420.580469-2.48464
2571.9773.435175.2362-1.80113-1.46512
2669.2773.657975.5817-1.92377-4.3879
2774.1375.316776.2733-0.956615-1.18672
2876.477.247577.19120.0562326-0.847483
2972.2674.835178.1521-3.31703-2.57505
3072.175.984479.5071-3.52266-3.88443
3187.8285.773380.82174.951652.04668
3291.6288.440781.94586.494843.17932
3382.6982.772383.1317-0.359392-0.0822743
3485.7684.391684.21370.177831.36842
3586.8784.819685.2-0.3804342.05043
3693.0986.722686.14210.5804696.36745
3783.7385.071486.8725-1.80113-1.34137
3884.4985.254187.1779-1.92377-0.764149
3987.3786.528487.485-0.9566150.841615
4089.1387.875487.81920.05623261.2546
4183.284.63387.95-3.31703-1.43297
4283.7784.315387.8379-3.52266-0.54526
4393.6892.8687.90834.951650.820017
4493.0994.840388.34546.49484-1.75026
4588.5988.316988.6762-0.3593920.273142
4687.8889.069188.89120.17783-1.18908
4787.8988.933789.3142-0.380434-1.04373
4889.3890.568489.98790.580469-1.18839
4989.1388.694790.4958-1.801130.435295
5089.5888.984190.9079-1.923770.595851
5190.2290.67891.6346-0.956615-0.457969
5291.4492.657992.60170.0562326-1.2179
5391.0490.284293.6012-3.317030.755781
5492.191.024894.5475-3.522661.07516
5597.54100.37595.42334.95165-2.83498
5699.12102.76796.27216.49484-3.64693
5710096.71697.0754-0.3593923.28398
5899.6897.979597.80170.177831.7005
59100.089898.3804-0.3804342.08002
6099.999.436798.85620.5804690.463281
6199.6397.476499.2775-1.801132.15363
6299.4597.729199.6529-1.923771.72085
6399.6399.073100.03-0.9566150.557031
6499.46100.49100.4340.0562326-1.02998
6596.9197.3809100.698-3.31703-0.470885
6697.6597.4307100.953-3.522660.219323
67102.1106.315101.3634.95165-4.21457
68103.57108.259101.7646.49484-4.68901
69104.59101.73102.089-0.3593922.86023
70104.79102.496102.3180.177832.29384
71101.31102.246102.626-0.380434-0.935816
72104.8103.61103.0290.5804691.19036
73104.56101.573103.375-1.801132.98655
74104.15101.707103.63-1.923772.44335
75102.73102.705103.662-0.9566150.0249479
76101.86103.579103.5220.0562326-1.71873
77101.9100.184103.501-3.317031.7162
78102.3399.9928103.515-3.522662.33724
79105.71NANA4.95165NA
80106.1NANA6.49484NA
81102.81NANA-0.359392NA
82103.23NANA0.17783NA
83102.35NANA-0.380434NA
84104.11NANA0.580469NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 86.86 & NA & NA & -1.80113 & NA \tabularnewline
2 & 86.79 & NA & NA & -1.92377 & NA \tabularnewline
3 & 82.52 & NA & NA & -0.956615 & NA \tabularnewline
4 & 86.87 & NA & NA & 0.0562326 & NA \tabularnewline
5 & 81.62 & NA & NA & -3.31703 & NA \tabularnewline
6 & 82.66 & NA & NA & -3.52266 & NA \tabularnewline
7 & 89.87 & 87.9754 & 83.0238 & 4.95165 & 1.8946 \tabularnewline
8 & 92.04 & 88.6419 & 82.1471 & 6.49484 & 3.39807 \tabularnewline
9 & 79.74 & 81.2085 & 81.5679 & -0.359392 & -1.46852 \tabularnewline
10 & 77.75 & 81.3341 & 81.1562 & 0.17783 & -3.58408 \tabularnewline
11 & 79.12 & 80.3458 & 80.7262 & -0.380434 & -1.22582 \tabularnewline
12 & 76.37 & 80.7613 & 80.1808 & 0.580469 & -4.3913 \tabularnewline
13 & 75.01 & 77.8222 & 79.6233 & -1.80113 & -2.8122 \tabularnewline
14 & 77.6 & 77.2512 & 79.175 & -1.92377 & 0.348767 \tabularnewline
15 & 77.81 & 77.6321 & 78.5888 & -0.956615 & 0.177865 \tabularnewline
16 & 81.7 & 78.1837 & 78.1275 & 0.0562326 & 3.51627 \tabularnewline
17 & 76.47 & 74.5063 & 77.8233 & -3.31703 & 1.9637 \tabularnewline
18 & 74.72 & 73.9653 & 77.4879 & -3.52266 & 0.75474 \tabularnewline
19 & 84.43 & 82.185 & 77.2333 & 4.95165 & 2.24502 \tabularnewline
20 & 86.72 & 83.2544 & 76.7596 & 6.49484 & 3.46557 \tabularnewline
21 & 70.99 & 75.8998 & 76.2592 & -0.359392 & -4.90977 \tabularnewline
22 & 75.43 & 76.0628 & 75.885 & 0.17783 & -0.63283 \tabularnewline
23 & 74.14 & 75.1083 & 75.4888 & -0.380434 & -0.968316 \tabularnewline
24 & 73.3 & 75.7846 & 75.2042 & 0.580469 & -2.48464 \tabularnewline
25 & 71.97 & 73.4351 & 75.2362 & -1.80113 & -1.46512 \tabularnewline
26 & 69.27 & 73.6579 & 75.5817 & -1.92377 & -4.3879 \tabularnewline
27 & 74.13 & 75.3167 & 76.2733 & -0.956615 & -1.18672 \tabularnewline
28 & 76.4 & 77.2475 & 77.1912 & 0.0562326 & -0.847483 \tabularnewline
29 & 72.26 & 74.8351 & 78.1521 & -3.31703 & -2.57505 \tabularnewline
30 & 72.1 & 75.9844 & 79.5071 & -3.52266 & -3.88443 \tabularnewline
31 & 87.82 & 85.7733 & 80.8217 & 4.95165 & 2.04668 \tabularnewline
32 & 91.62 & 88.4407 & 81.9458 & 6.49484 & 3.17932 \tabularnewline
33 & 82.69 & 82.7723 & 83.1317 & -0.359392 & -0.0822743 \tabularnewline
34 & 85.76 & 84.3916 & 84.2137 & 0.17783 & 1.36842 \tabularnewline
35 & 86.87 & 84.8196 & 85.2 & -0.380434 & 2.05043 \tabularnewline
36 & 93.09 & 86.7226 & 86.1421 & 0.580469 & 6.36745 \tabularnewline
37 & 83.73 & 85.0714 & 86.8725 & -1.80113 & -1.34137 \tabularnewline
38 & 84.49 & 85.2541 & 87.1779 & -1.92377 & -0.764149 \tabularnewline
39 & 87.37 & 86.5284 & 87.485 & -0.956615 & 0.841615 \tabularnewline
40 & 89.13 & 87.8754 & 87.8192 & 0.0562326 & 1.2546 \tabularnewline
41 & 83.2 & 84.633 & 87.95 & -3.31703 & -1.43297 \tabularnewline
42 & 83.77 & 84.3153 & 87.8379 & -3.52266 & -0.54526 \tabularnewline
43 & 93.68 & 92.86 & 87.9083 & 4.95165 & 0.820017 \tabularnewline
44 & 93.09 & 94.8403 & 88.3454 & 6.49484 & -1.75026 \tabularnewline
45 & 88.59 & 88.3169 & 88.6762 & -0.359392 & 0.273142 \tabularnewline
46 & 87.88 & 89.0691 & 88.8912 & 0.17783 & -1.18908 \tabularnewline
47 & 87.89 & 88.9337 & 89.3142 & -0.380434 & -1.04373 \tabularnewline
48 & 89.38 & 90.5684 & 89.9879 & 0.580469 & -1.18839 \tabularnewline
49 & 89.13 & 88.6947 & 90.4958 & -1.80113 & 0.435295 \tabularnewline
50 & 89.58 & 88.9841 & 90.9079 & -1.92377 & 0.595851 \tabularnewline
51 & 90.22 & 90.678 & 91.6346 & -0.956615 & -0.457969 \tabularnewline
52 & 91.44 & 92.6579 & 92.6017 & 0.0562326 & -1.2179 \tabularnewline
53 & 91.04 & 90.2842 & 93.6012 & -3.31703 & 0.755781 \tabularnewline
54 & 92.1 & 91.0248 & 94.5475 & -3.52266 & 1.07516 \tabularnewline
55 & 97.54 & 100.375 & 95.4233 & 4.95165 & -2.83498 \tabularnewline
56 & 99.12 & 102.767 & 96.2721 & 6.49484 & -3.64693 \tabularnewline
57 & 100 & 96.716 & 97.0754 & -0.359392 & 3.28398 \tabularnewline
58 & 99.68 & 97.9795 & 97.8017 & 0.17783 & 1.7005 \tabularnewline
59 & 100.08 & 98 & 98.3804 & -0.380434 & 2.08002 \tabularnewline
60 & 99.9 & 99.4367 & 98.8562 & 0.580469 & 0.463281 \tabularnewline
61 & 99.63 & 97.4764 & 99.2775 & -1.80113 & 2.15363 \tabularnewline
62 & 99.45 & 97.7291 & 99.6529 & -1.92377 & 1.72085 \tabularnewline
63 & 99.63 & 99.073 & 100.03 & -0.956615 & 0.557031 \tabularnewline
64 & 99.46 & 100.49 & 100.434 & 0.0562326 & -1.02998 \tabularnewline
65 & 96.91 & 97.3809 & 100.698 & -3.31703 & -0.470885 \tabularnewline
66 & 97.65 & 97.4307 & 100.953 & -3.52266 & 0.219323 \tabularnewline
67 & 102.1 & 106.315 & 101.363 & 4.95165 & -4.21457 \tabularnewline
68 & 103.57 & 108.259 & 101.764 & 6.49484 & -4.68901 \tabularnewline
69 & 104.59 & 101.73 & 102.089 & -0.359392 & 2.86023 \tabularnewline
70 & 104.79 & 102.496 & 102.318 & 0.17783 & 2.29384 \tabularnewline
71 & 101.31 & 102.246 & 102.626 & -0.380434 & -0.935816 \tabularnewline
72 & 104.8 & 103.61 & 103.029 & 0.580469 & 1.19036 \tabularnewline
73 & 104.56 & 101.573 & 103.375 & -1.80113 & 2.98655 \tabularnewline
74 & 104.15 & 101.707 & 103.63 & -1.92377 & 2.44335 \tabularnewline
75 & 102.73 & 102.705 & 103.662 & -0.956615 & 0.0249479 \tabularnewline
76 & 101.86 & 103.579 & 103.522 & 0.0562326 & -1.71873 \tabularnewline
77 & 101.9 & 100.184 & 103.501 & -3.31703 & 1.7162 \tabularnewline
78 & 102.33 & 99.9928 & 103.515 & -3.52266 & 2.33724 \tabularnewline
79 & 105.71 & NA & NA & 4.95165 & NA \tabularnewline
80 & 106.1 & NA & NA & 6.49484 & NA \tabularnewline
81 & 102.81 & NA & NA & -0.359392 & NA \tabularnewline
82 & 103.23 & NA & NA & 0.17783 & NA \tabularnewline
83 & 102.35 & NA & NA & -0.380434 & NA \tabularnewline
84 & 104.11 & NA & NA & 0.580469 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231682&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]86.86[/C][C]NA[/C][C]NA[/C][C]-1.80113[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]86.79[/C][C]NA[/C][C]NA[/C][C]-1.92377[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]82.52[/C][C]NA[/C][C]NA[/C][C]-0.956615[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]86.87[/C][C]NA[/C][C]NA[/C][C]0.0562326[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]81.62[/C][C]NA[/C][C]NA[/C][C]-3.31703[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]82.66[/C][C]NA[/C][C]NA[/C][C]-3.52266[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]89.87[/C][C]87.9754[/C][C]83.0238[/C][C]4.95165[/C][C]1.8946[/C][/ROW]
[ROW][C]8[/C][C]92.04[/C][C]88.6419[/C][C]82.1471[/C][C]6.49484[/C][C]3.39807[/C][/ROW]
[ROW][C]9[/C][C]79.74[/C][C]81.2085[/C][C]81.5679[/C][C]-0.359392[/C][C]-1.46852[/C][/ROW]
[ROW][C]10[/C][C]77.75[/C][C]81.3341[/C][C]81.1562[/C][C]0.17783[/C][C]-3.58408[/C][/ROW]
[ROW][C]11[/C][C]79.12[/C][C]80.3458[/C][C]80.7262[/C][C]-0.380434[/C][C]-1.22582[/C][/ROW]
[ROW][C]12[/C][C]76.37[/C][C]80.7613[/C][C]80.1808[/C][C]0.580469[/C][C]-4.3913[/C][/ROW]
[ROW][C]13[/C][C]75.01[/C][C]77.8222[/C][C]79.6233[/C][C]-1.80113[/C][C]-2.8122[/C][/ROW]
[ROW][C]14[/C][C]77.6[/C][C]77.2512[/C][C]79.175[/C][C]-1.92377[/C][C]0.348767[/C][/ROW]
[ROW][C]15[/C][C]77.81[/C][C]77.6321[/C][C]78.5888[/C][C]-0.956615[/C][C]0.177865[/C][/ROW]
[ROW][C]16[/C][C]81.7[/C][C]78.1837[/C][C]78.1275[/C][C]0.0562326[/C][C]3.51627[/C][/ROW]
[ROW][C]17[/C][C]76.47[/C][C]74.5063[/C][C]77.8233[/C][C]-3.31703[/C][C]1.9637[/C][/ROW]
[ROW][C]18[/C][C]74.72[/C][C]73.9653[/C][C]77.4879[/C][C]-3.52266[/C][C]0.75474[/C][/ROW]
[ROW][C]19[/C][C]84.43[/C][C]82.185[/C][C]77.2333[/C][C]4.95165[/C][C]2.24502[/C][/ROW]
[ROW][C]20[/C][C]86.72[/C][C]83.2544[/C][C]76.7596[/C][C]6.49484[/C][C]3.46557[/C][/ROW]
[ROW][C]21[/C][C]70.99[/C][C]75.8998[/C][C]76.2592[/C][C]-0.359392[/C][C]-4.90977[/C][/ROW]
[ROW][C]22[/C][C]75.43[/C][C]76.0628[/C][C]75.885[/C][C]0.17783[/C][C]-0.63283[/C][/ROW]
[ROW][C]23[/C][C]74.14[/C][C]75.1083[/C][C]75.4888[/C][C]-0.380434[/C][C]-0.968316[/C][/ROW]
[ROW][C]24[/C][C]73.3[/C][C]75.7846[/C][C]75.2042[/C][C]0.580469[/C][C]-2.48464[/C][/ROW]
[ROW][C]25[/C][C]71.97[/C][C]73.4351[/C][C]75.2362[/C][C]-1.80113[/C][C]-1.46512[/C][/ROW]
[ROW][C]26[/C][C]69.27[/C][C]73.6579[/C][C]75.5817[/C][C]-1.92377[/C][C]-4.3879[/C][/ROW]
[ROW][C]27[/C][C]74.13[/C][C]75.3167[/C][C]76.2733[/C][C]-0.956615[/C][C]-1.18672[/C][/ROW]
[ROW][C]28[/C][C]76.4[/C][C]77.2475[/C][C]77.1912[/C][C]0.0562326[/C][C]-0.847483[/C][/ROW]
[ROW][C]29[/C][C]72.26[/C][C]74.8351[/C][C]78.1521[/C][C]-3.31703[/C][C]-2.57505[/C][/ROW]
[ROW][C]30[/C][C]72.1[/C][C]75.9844[/C][C]79.5071[/C][C]-3.52266[/C][C]-3.88443[/C][/ROW]
[ROW][C]31[/C][C]87.82[/C][C]85.7733[/C][C]80.8217[/C][C]4.95165[/C][C]2.04668[/C][/ROW]
[ROW][C]32[/C][C]91.62[/C][C]88.4407[/C][C]81.9458[/C][C]6.49484[/C][C]3.17932[/C][/ROW]
[ROW][C]33[/C][C]82.69[/C][C]82.7723[/C][C]83.1317[/C][C]-0.359392[/C][C]-0.0822743[/C][/ROW]
[ROW][C]34[/C][C]85.76[/C][C]84.3916[/C][C]84.2137[/C][C]0.17783[/C][C]1.36842[/C][/ROW]
[ROW][C]35[/C][C]86.87[/C][C]84.8196[/C][C]85.2[/C][C]-0.380434[/C][C]2.05043[/C][/ROW]
[ROW][C]36[/C][C]93.09[/C][C]86.7226[/C][C]86.1421[/C][C]0.580469[/C][C]6.36745[/C][/ROW]
[ROW][C]37[/C][C]83.73[/C][C]85.0714[/C][C]86.8725[/C][C]-1.80113[/C][C]-1.34137[/C][/ROW]
[ROW][C]38[/C][C]84.49[/C][C]85.2541[/C][C]87.1779[/C][C]-1.92377[/C][C]-0.764149[/C][/ROW]
[ROW][C]39[/C][C]87.37[/C][C]86.5284[/C][C]87.485[/C][C]-0.956615[/C][C]0.841615[/C][/ROW]
[ROW][C]40[/C][C]89.13[/C][C]87.8754[/C][C]87.8192[/C][C]0.0562326[/C][C]1.2546[/C][/ROW]
[ROW][C]41[/C][C]83.2[/C][C]84.633[/C][C]87.95[/C][C]-3.31703[/C][C]-1.43297[/C][/ROW]
[ROW][C]42[/C][C]83.77[/C][C]84.3153[/C][C]87.8379[/C][C]-3.52266[/C][C]-0.54526[/C][/ROW]
[ROW][C]43[/C][C]93.68[/C][C]92.86[/C][C]87.9083[/C][C]4.95165[/C][C]0.820017[/C][/ROW]
[ROW][C]44[/C][C]93.09[/C][C]94.8403[/C][C]88.3454[/C][C]6.49484[/C][C]-1.75026[/C][/ROW]
[ROW][C]45[/C][C]88.59[/C][C]88.3169[/C][C]88.6762[/C][C]-0.359392[/C][C]0.273142[/C][/ROW]
[ROW][C]46[/C][C]87.88[/C][C]89.0691[/C][C]88.8912[/C][C]0.17783[/C][C]-1.18908[/C][/ROW]
[ROW][C]47[/C][C]87.89[/C][C]88.9337[/C][C]89.3142[/C][C]-0.380434[/C][C]-1.04373[/C][/ROW]
[ROW][C]48[/C][C]89.38[/C][C]90.5684[/C][C]89.9879[/C][C]0.580469[/C][C]-1.18839[/C][/ROW]
[ROW][C]49[/C][C]89.13[/C][C]88.6947[/C][C]90.4958[/C][C]-1.80113[/C][C]0.435295[/C][/ROW]
[ROW][C]50[/C][C]89.58[/C][C]88.9841[/C][C]90.9079[/C][C]-1.92377[/C][C]0.595851[/C][/ROW]
[ROW][C]51[/C][C]90.22[/C][C]90.678[/C][C]91.6346[/C][C]-0.956615[/C][C]-0.457969[/C][/ROW]
[ROW][C]52[/C][C]91.44[/C][C]92.6579[/C][C]92.6017[/C][C]0.0562326[/C][C]-1.2179[/C][/ROW]
[ROW][C]53[/C][C]91.04[/C][C]90.2842[/C][C]93.6012[/C][C]-3.31703[/C][C]0.755781[/C][/ROW]
[ROW][C]54[/C][C]92.1[/C][C]91.0248[/C][C]94.5475[/C][C]-3.52266[/C][C]1.07516[/C][/ROW]
[ROW][C]55[/C][C]97.54[/C][C]100.375[/C][C]95.4233[/C][C]4.95165[/C][C]-2.83498[/C][/ROW]
[ROW][C]56[/C][C]99.12[/C][C]102.767[/C][C]96.2721[/C][C]6.49484[/C][C]-3.64693[/C][/ROW]
[ROW][C]57[/C][C]100[/C][C]96.716[/C][C]97.0754[/C][C]-0.359392[/C][C]3.28398[/C][/ROW]
[ROW][C]58[/C][C]99.68[/C][C]97.9795[/C][C]97.8017[/C][C]0.17783[/C][C]1.7005[/C][/ROW]
[ROW][C]59[/C][C]100.08[/C][C]98[/C][C]98.3804[/C][C]-0.380434[/C][C]2.08002[/C][/ROW]
[ROW][C]60[/C][C]99.9[/C][C]99.4367[/C][C]98.8562[/C][C]0.580469[/C][C]0.463281[/C][/ROW]
[ROW][C]61[/C][C]99.63[/C][C]97.4764[/C][C]99.2775[/C][C]-1.80113[/C][C]2.15363[/C][/ROW]
[ROW][C]62[/C][C]99.45[/C][C]97.7291[/C][C]99.6529[/C][C]-1.92377[/C][C]1.72085[/C][/ROW]
[ROW][C]63[/C][C]99.63[/C][C]99.073[/C][C]100.03[/C][C]-0.956615[/C][C]0.557031[/C][/ROW]
[ROW][C]64[/C][C]99.46[/C][C]100.49[/C][C]100.434[/C][C]0.0562326[/C][C]-1.02998[/C][/ROW]
[ROW][C]65[/C][C]96.91[/C][C]97.3809[/C][C]100.698[/C][C]-3.31703[/C][C]-0.470885[/C][/ROW]
[ROW][C]66[/C][C]97.65[/C][C]97.4307[/C][C]100.953[/C][C]-3.52266[/C][C]0.219323[/C][/ROW]
[ROW][C]67[/C][C]102.1[/C][C]106.315[/C][C]101.363[/C][C]4.95165[/C][C]-4.21457[/C][/ROW]
[ROW][C]68[/C][C]103.57[/C][C]108.259[/C][C]101.764[/C][C]6.49484[/C][C]-4.68901[/C][/ROW]
[ROW][C]69[/C][C]104.59[/C][C]101.73[/C][C]102.089[/C][C]-0.359392[/C][C]2.86023[/C][/ROW]
[ROW][C]70[/C][C]104.79[/C][C]102.496[/C][C]102.318[/C][C]0.17783[/C][C]2.29384[/C][/ROW]
[ROW][C]71[/C][C]101.31[/C][C]102.246[/C][C]102.626[/C][C]-0.380434[/C][C]-0.935816[/C][/ROW]
[ROW][C]72[/C][C]104.8[/C][C]103.61[/C][C]103.029[/C][C]0.580469[/C][C]1.19036[/C][/ROW]
[ROW][C]73[/C][C]104.56[/C][C]101.573[/C][C]103.375[/C][C]-1.80113[/C][C]2.98655[/C][/ROW]
[ROW][C]74[/C][C]104.15[/C][C]101.707[/C][C]103.63[/C][C]-1.92377[/C][C]2.44335[/C][/ROW]
[ROW][C]75[/C][C]102.73[/C][C]102.705[/C][C]103.662[/C][C]-0.956615[/C][C]0.0249479[/C][/ROW]
[ROW][C]76[/C][C]101.86[/C][C]103.579[/C][C]103.522[/C][C]0.0562326[/C][C]-1.71873[/C][/ROW]
[ROW][C]77[/C][C]101.9[/C][C]100.184[/C][C]103.501[/C][C]-3.31703[/C][C]1.7162[/C][/ROW]
[ROW][C]78[/C][C]102.33[/C][C]99.9928[/C][C]103.515[/C][C]-3.52266[/C][C]2.33724[/C][/ROW]
[ROW][C]79[/C][C]105.71[/C][C]NA[/C][C]NA[/C][C]4.95165[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]106.1[/C][C]NA[/C][C]NA[/C][C]6.49484[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]102.81[/C][C]NA[/C][C]NA[/C][C]-0.359392[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]103.23[/C][C]NA[/C][C]NA[/C][C]0.17783[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]102.35[/C][C]NA[/C][C]NA[/C][C]-0.380434[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]104.11[/C][C]NA[/C][C]NA[/C][C]0.580469[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231682&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231682&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
186.86NANA-1.80113NA
286.79NANA-1.92377NA
382.52NANA-0.956615NA
486.87NANA0.0562326NA
581.62NANA-3.31703NA
682.66NANA-3.52266NA
789.8787.975483.02384.951651.8946
892.0488.641982.14716.494843.39807
979.7481.208581.5679-0.359392-1.46852
1077.7581.334181.15620.17783-3.58408
1179.1280.345880.7262-0.380434-1.22582
1276.3780.761380.18080.580469-4.3913
1375.0177.822279.6233-1.80113-2.8122
1477.677.251279.175-1.923770.348767
1577.8177.632178.5888-0.9566150.177865
1681.778.183778.12750.05623263.51627
1776.4774.506377.8233-3.317031.9637
1874.7273.965377.4879-3.522660.75474
1984.4382.18577.23334.951652.24502
2086.7283.254476.75966.494843.46557
2170.9975.899876.2592-0.359392-4.90977
2275.4376.062875.8850.17783-0.63283
2374.1475.108375.4888-0.380434-0.968316
2473.375.784675.20420.580469-2.48464
2571.9773.435175.2362-1.80113-1.46512
2669.2773.657975.5817-1.92377-4.3879
2774.1375.316776.2733-0.956615-1.18672
2876.477.247577.19120.0562326-0.847483
2972.2674.835178.1521-3.31703-2.57505
3072.175.984479.5071-3.52266-3.88443
3187.8285.773380.82174.951652.04668
3291.6288.440781.94586.494843.17932
3382.6982.772383.1317-0.359392-0.0822743
3485.7684.391684.21370.177831.36842
3586.8784.819685.2-0.3804342.05043
3693.0986.722686.14210.5804696.36745
3783.7385.071486.8725-1.80113-1.34137
3884.4985.254187.1779-1.92377-0.764149
3987.3786.528487.485-0.9566150.841615
4089.1387.875487.81920.05623261.2546
4183.284.63387.95-3.31703-1.43297
4283.7784.315387.8379-3.52266-0.54526
4393.6892.8687.90834.951650.820017
4493.0994.840388.34546.49484-1.75026
4588.5988.316988.6762-0.3593920.273142
4687.8889.069188.89120.17783-1.18908
4787.8988.933789.3142-0.380434-1.04373
4889.3890.568489.98790.580469-1.18839
4989.1388.694790.4958-1.801130.435295
5089.5888.984190.9079-1.923770.595851
5190.2290.67891.6346-0.956615-0.457969
5291.4492.657992.60170.0562326-1.2179
5391.0490.284293.6012-3.317030.755781
5492.191.024894.5475-3.522661.07516
5597.54100.37595.42334.95165-2.83498
5699.12102.76796.27216.49484-3.64693
5710096.71697.0754-0.3593923.28398
5899.6897.979597.80170.177831.7005
59100.089898.3804-0.3804342.08002
6099.999.436798.85620.5804690.463281
6199.6397.476499.2775-1.801132.15363
6299.4597.729199.6529-1.923771.72085
6399.6399.073100.03-0.9566150.557031
6499.46100.49100.4340.0562326-1.02998
6596.9197.3809100.698-3.31703-0.470885
6697.6597.4307100.953-3.522660.219323
67102.1106.315101.3634.95165-4.21457
68103.57108.259101.7646.49484-4.68901
69104.59101.73102.089-0.3593922.86023
70104.79102.496102.3180.177832.29384
71101.31102.246102.626-0.380434-0.935816
72104.8103.61103.0290.5804691.19036
73104.56101.573103.375-1.801132.98655
74104.15101.707103.63-1.923772.44335
75102.73102.705103.662-0.9566150.0249479
76101.86103.579103.5220.0562326-1.71873
77101.9100.184103.501-3.317031.7162
78102.3399.9928103.515-3.522662.33724
79105.71NANA4.95165NA
80106.1NANA6.49484NA
81102.81NANA-0.359392NA
82103.23NANA0.17783NA
83102.35NANA-0.380434NA
84104.11NANA0.580469NA



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')