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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 22 Dec 2013 18:47:40 -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/22/t13877560834e3lko18pyv9z5g.htm/, Retrieved Sun, 05 Dec 2021 18:20:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232577, Retrieved Sun, 05 Dec 2021 18:20:48 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact48
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-22 23:47:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
85,73
85,73
85,74
86,32
87,59
87,81
87,87
87,94
87,96
88,01
88,01
88,01
88,01
88,01
88,59
89,43
89,63
89,73
89,88
89,89
89,9
89,91
89,86
90,07
90,17
90,17
90,28
90,87
92,05
92,1
92,16
92,22
92,25
92,29
92,29
92,29
92,29
92,29
91,95
91,82
92,16
92,31
92,33
92,4
92,54
92,49
92,54
92,58
92,58
92,39
92,33
93,59
95,51
95,99
96,22
97,2
98,54
99,64
100,23
100,17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232577&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232577&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232577&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'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
185.73NANA0.996756NA
285.73NANA0.994258NA
385.74NANA0.992891NA
486.32NANA0.997389NA
587.59NANA1.0045NA
687.81NANA1.00386NA
787.8787.734987.32171.004731.00154
887.9487.838587.51171.003741.00116
987.9687.965487.72541.002740.999939
1088.0188.085287.97381.001270.999146
1188.0188.145388.18830.9995120.998465
1288.0188.208588.35330.998360.99775
1388.0188.229988.51710.9967560.997507
1488.0188.172888.68210.9942580.998153
1588.5988.212688.84420.9928911.00428
1689.4388.771889.00420.9973891.00741
1789.6389.561989.16041.00451.00076
1889.7389.668189.32331.003861.00069
1989.8889.922789.49921.004730.999525
2089.8990.014189.67921.003740.998621
2189.990.085389.83961.002740.997943
2289.9190.08489.971.001270.998068
2389.8690.086890.13080.9995120.997482
2490.0790.182390.33040.998360.998755
2590.1790.230590.52420.9967560.99933
2690.1790.195390.71620.9942580.999719
2790.2890.26590.91120.9928911.00017
2890.8790.870491.10830.9973890.999995
2992.0591.7291.30871.00451.0036
3092.191.855791.50251.003861.00266
3192.1692.117291.68331.004731.00046
3292.2292.203191.861.003741.00018
3392.2592.269692.01791.002740.999787
3492.2992.243892.12711.001271.0005
3592.2992.126392.17120.9995121.00178
3692.2992.033492.18460.998361.00279
3792.2991.901392.20040.9967561.00423
3892.2991.685592.2150.9942581.00659
3991.9591.578992.23460.9928911.00405
4091.8292.014192.2550.9973890.99789
4192.1692.689392.27371.00450.99429
4292.3192.652592.29621.003860.996303
4392.3392.757392.32041.004730.995393
4492.492.681692.33671.003740.996962
4592.5492.609392.35671.002740.999252
4692.4992.563492.44621.001270.999207
4792.5492.614492.65960.9995120.999197
4892.5892.800192.95250.998360.997628
4992.5892.965393.26790.9967560.995855
5092.3993.092393.630.9942580.992455
5192.3393.411294.080.9928910.988425
5293.5994.380894.62790.9973890.991621
5395.5195.675295.24631.00450.998273
5495.9996.25395.88291.003860.997268
5596.22NANA1.00473NA
5697.2NANA1.00374NA
5798.54NANA1.00274NA
5899.64NANA1.00127NA
59100.23NANA0.999512NA
60100.17NANA0.99836NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 85.73 & NA & NA & 0.996756 & NA \tabularnewline
2 & 85.73 & NA & NA & 0.994258 & NA \tabularnewline
3 & 85.74 & NA & NA & 0.992891 & NA \tabularnewline
4 & 86.32 & NA & NA & 0.997389 & NA \tabularnewline
5 & 87.59 & NA & NA & 1.0045 & NA \tabularnewline
6 & 87.81 & NA & NA & 1.00386 & NA \tabularnewline
7 & 87.87 & 87.7349 & 87.3217 & 1.00473 & 1.00154 \tabularnewline
8 & 87.94 & 87.8385 & 87.5117 & 1.00374 & 1.00116 \tabularnewline
9 & 87.96 & 87.9654 & 87.7254 & 1.00274 & 0.999939 \tabularnewline
10 & 88.01 & 88.0852 & 87.9738 & 1.00127 & 0.999146 \tabularnewline
11 & 88.01 & 88.1453 & 88.1883 & 0.999512 & 0.998465 \tabularnewline
12 & 88.01 & 88.2085 & 88.3533 & 0.99836 & 0.99775 \tabularnewline
13 & 88.01 & 88.2299 & 88.5171 & 0.996756 & 0.997507 \tabularnewline
14 & 88.01 & 88.1728 & 88.6821 & 0.994258 & 0.998153 \tabularnewline
15 & 88.59 & 88.2126 & 88.8442 & 0.992891 & 1.00428 \tabularnewline
16 & 89.43 & 88.7718 & 89.0042 & 0.997389 & 1.00741 \tabularnewline
17 & 89.63 & 89.5619 & 89.1604 & 1.0045 & 1.00076 \tabularnewline
18 & 89.73 & 89.6681 & 89.3233 & 1.00386 & 1.00069 \tabularnewline
19 & 89.88 & 89.9227 & 89.4992 & 1.00473 & 0.999525 \tabularnewline
20 & 89.89 & 90.0141 & 89.6792 & 1.00374 & 0.998621 \tabularnewline
21 & 89.9 & 90.0853 & 89.8396 & 1.00274 & 0.997943 \tabularnewline
22 & 89.91 & 90.084 & 89.97 & 1.00127 & 0.998068 \tabularnewline
23 & 89.86 & 90.0868 & 90.1308 & 0.999512 & 0.997482 \tabularnewline
24 & 90.07 & 90.1823 & 90.3304 & 0.99836 & 0.998755 \tabularnewline
25 & 90.17 & 90.2305 & 90.5242 & 0.996756 & 0.99933 \tabularnewline
26 & 90.17 & 90.1953 & 90.7162 & 0.994258 & 0.999719 \tabularnewline
27 & 90.28 & 90.265 & 90.9112 & 0.992891 & 1.00017 \tabularnewline
28 & 90.87 & 90.8704 & 91.1083 & 0.997389 & 0.999995 \tabularnewline
29 & 92.05 & 91.72 & 91.3087 & 1.0045 & 1.0036 \tabularnewline
30 & 92.1 & 91.8557 & 91.5025 & 1.00386 & 1.00266 \tabularnewline
31 & 92.16 & 92.1172 & 91.6833 & 1.00473 & 1.00046 \tabularnewline
32 & 92.22 & 92.2031 & 91.86 & 1.00374 & 1.00018 \tabularnewline
33 & 92.25 & 92.2696 & 92.0179 & 1.00274 & 0.999787 \tabularnewline
34 & 92.29 & 92.2438 & 92.1271 & 1.00127 & 1.0005 \tabularnewline
35 & 92.29 & 92.1263 & 92.1712 & 0.999512 & 1.00178 \tabularnewline
36 & 92.29 & 92.0334 & 92.1846 & 0.99836 & 1.00279 \tabularnewline
37 & 92.29 & 91.9013 & 92.2004 & 0.996756 & 1.00423 \tabularnewline
38 & 92.29 & 91.6855 & 92.215 & 0.994258 & 1.00659 \tabularnewline
39 & 91.95 & 91.5789 & 92.2346 & 0.992891 & 1.00405 \tabularnewline
40 & 91.82 & 92.0141 & 92.255 & 0.997389 & 0.99789 \tabularnewline
41 & 92.16 & 92.6893 & 92.2737 & 1.0045 & 0.99429 \tabularnewline
42 & 92.31 & 92.6525 & 92.2962 & 1.00386 & 0.996303 \tabularnewline
43 & 92.33 & 92.7573 & 92.3204 & 1.00473 & 0.995393 \tabularnewline
44 & 92.4 & 92.6816 & 92.3367 & 1.00374 & 0.996962 \tabularnewline
45 & 92.54 & 92.6093 & 92.3567 & 1.00274 & 0.999252 \tabularnewline
46 & 92.49 & 92.5634 & 92.4462 & 1.00127 & 0.999207 \tabularnewline
47 & 92.54 & 92.6144 & 92.6596 & 0.999512 & 0.999197 \tabularnewline
48 & 92.58 & 92.8001 & 92.9525 & 0.99836 & 0.997628 \tabularnewline
49 & 92.58 & 92.9653 & 93.2679 & 0.996756 & 0.995855 \tabularnewline
50 & 92.39 & 93.0923 & 93.63 & 0.994258 & 0.992455 \tabularnewline
51 & 92.33 & 93.4112 & 94.08 & 0.992891 & 0.988425 \tabularnewline
52 & 93.59 & 94.3808 & 94.6279 & 0.997389 & 0.991621 \tabularnewline
53 & 95.51 & 95.6752 & 95.2463 & 1.0045 & 0.998273 \tabularnewline
54 & 95.99 & 96.253 & 95.8829 & 1.00386 & 0.997268 \tabularnewline
55 & 96.22 & NA & NA & 1.00473 & NA \tabularnewline
56 & 97.2 & NA & NA & 1.00374 & NA \tabularnewline
57 & 98.54 & NA & NA & 1.00274 & NA \tabularnewline
58 & 99.64 & NA & NA & 1.00127 & NA \tabularnewline
59 & 100.23 & NA & NA & 0.999512 & NA \tabularnewline
60 & 100.17 & NA & NA & 0.99836 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232577&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]85.73[/C][C]NA[/C][C]NA[/C][C]0.996756[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]85.73[/C][C]NA[/C][C]NA[/C][C]0.994258[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]85.74[/C][C]NA[/C][C]NA[/C][C]0.992891[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]86.32[/C][C]NA[/C][C]NA[/C][C]0.997389[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]87.59[/C][C]NA[/C][C]NA[/C][C]1.0045[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]87.81[/C][C]NA[/C][C]NA[/C][C]1.00386[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]87.87[/C][C]87.7349[/C][C]87.3217[/C][C]1.00473[/C][C]1.00154[/C][/ROW]
[ROW][C]8[/C][C]87.94[/C][C]87.8385[/C][C]87.5117[/C][C]1.00374[/C][C]1.00116[/C][/ROW]
[ROW][C]9[/C][C]87.96[/C][C]87.9654[/C][C]87.7254[/C][C]1.00274[/C][C]0.999939[/C][/ROW]
[ROW][C]10[/C][C]88.01[/C][C]88.0852[/C][C]87.9738[/C][C]1.00127[/C][C]0.999146[/C][/ROW]
[ROW][C]11[/C][C]88.01[/C][C]88.1453[/C][C]88.1883[/C][C]0.999512[/C][C]0.998465[/C][/ROW]
[ROW][C]12[/C][C]88.01[/C][C]88.2085[/C][C]88.3533[/C][C]0.99836[/C][C]0.99775[/C][/ROW]
[ROW][C]13[/C][C]88.01[/C][C]88.2299[/C][C]88.5171[/C][C]0.996756[/C][C]0.997507[/C][/ROW]
[ROW][C]14[/C][C]88.01[/C][C]88.1728[/C][C]88.6821[/C][C]0.994258[/C][C]0.998153[/C][/ROW]
[ROW][C]15[/C][C]88.59[/C][C]88.2126[/C][C]88.8442[/C][C]0.992891[/C][C]1.00428[/C][/ROW]
[ROW][C]16[/C][C]89.43[/C][C]88.7718[/C][C]89.0042[/C][C]0.997389[/C][C]1.00741[/C][/ROW]
[ROW][C]17[/C][C]89.63[/C][C]89.5619[/C][C]89.1604[/C][C]1.0045[/C][C]1.00076[/C][/ROW]
[ROW][C]18[/C][C]89.73[/C][C]89.6681[/C][C]89.3233[/C][C]1.00386[/C][C]1.00069[/C][/ROW]
[ROW][C]19[/C][C]89.88[/C][C]89.9227[/C][C]89.4992[/C][C]1.00473[/C][C]0.999525[/C][/ROW]
[ROW][C]20[/C][C]89.89[/C][C]90.0141[/C][C]89.6792[/C][C]1.00374[/C][C]0.998621[/C][/ROW]
[ROW][C]21[/C][C]89.9[/C][C]90.0853[/C][C]89.8396[/C][C]1.00274[/C][C]0.997943[/C][/ROW]
[ROW][C]22[/C][C]89.91[/C][C]90.084[/C][C]89.97[/C][C]1.00127[/C][C]0.998068[/C][/ROW]
[ROW][C]23[/C][C]89.86[/C][C]90.0868[/C][C]90.1308[/C][C]0.999512[/C][C]0.997482[/C][/ROW]
[ROW][C]24[/C][C]90.07[/C][C]90.1823[/C][C]90.3304[/C][C]0.99836[/C][C]0.998755[/C][/ROW]
[ROW][C]25[/C][C]90.17[/C][C]90.2305[/C][C]90.5242[/C][C]0.996756[/C][C]0.99933[/C][/ROW]
[ROW][C]26[/C][C]90.17[/C][C]90.1953[/C][C]90.7162[/C][C]0.994258[/C][C]0.999719[/C][/ROW]
[ROW][C]27[/C][C]90.28[/C][C]90.265[/C][C]90.9112[/C][C]0.992891[/C][C]1.00017[/C][/ROW]
[ROW][C]28[/C][C]90.87[/C][C]90.8704[/C][C]91.1083[/C][C]0.997389[/C][C]0.999995[/C][/ROW]
[ROW][C]29[/C][C]92.05[/C][C]91.72[/C][C]91.3087[/C][C]1.0045[/C][C]1.0036[/C][/ROW]
[ROW][C]30[/C][C]92.1[/C][C]91.8557[/C][C]91.5025[/C][C]1.00386[/C][C]1.00266[/C][/ROW]
[ROW][C]31[/C][C]92.16[/C][C]92.1172[/C][C]91.6833[/C][C]1.00473[/C][C]1.00046[/C][/ROW]
[ROW][C]32[/C][C]92.22[/C][C]92.2031[/C][C]91.86[/C][C]1.00374[/C][C]1.00018[/C][/ROW]
[ROW][C]33[/C][C]92.25[/C][C]92.2696[/C][C]92.0179[/C][C]1.00274[/C][C]0.999787[/C][/ROW]
[ROW][C]34[/C][C]92.29[/C][C]92.2438[/C][C]92.1271[/C][C]1.00127[/C][C]1.0005[/C][/ROW]
[ROW][C]35[/C][C]92.29[/C][C]92.1263[/C][C]92.1712[/C][C]0.999512[/C][C]1.00178[/C][/ROW]
[ROW][C]36[/C][C]92.29[/C][C]92.0334[/C][C]92.1846[/C][C]0.99836[/C][C]1.00279[/C][/ROW]
[ROW][C]37[/C][C]92.29[/C][C]91.9013[/C][C]92.2004[/C][C]0.996756[/C][C]1.00423[/C][/ROW]
[ROW][C]38[/C][C]92.29[/C][C]91.6855[/C][C]92.215[/C][C]0.994258[/C][C]1.00659[/C][/ROW]
[ROW][C]39[/C][C]91.95[/C][C]91.5789[/C][C]92.2346[/C][C]0.992891[/C][C]1.00405[/C][/ROW]
[ROW][C]40[/C][C]91.82[/C][C]92.0141[/C][C]92.255[/C][C]0.997389[/C][C]0.99789[/C][/ROW]
[ROW][C]41[/C][C]92.16[/C][C]92.6893[/C][C]92.2737[/C][C]1.0045[/C][C]0.99429[/C][/ROW]
[ROW][C]42[/C][C]92.31[/C][C]92.6525[/C][C]92.2962[/C][C]1.00386[/C][C]0.996303[/C][/ROW]
[ROW][C]43[/C][C]92.33[/C][C]92.7573[/C][C]92.3204[/C][C]1.00473[/C][C]0.995393[/C][/ROW]
[ROW][C]44[/C][C]92.4[/C][C]92.6816[/C][C]92.3367[/C][C]1.00374[/C][C]0.996962[/C][/ROW]
[ROW][C]45[/C][C]92.54[/C][C]92.6093[/C][C]92.3567[/C][C]1.00274[/C][C]0.999252[/C][/ROW]
[ROW][C]46[/C][C]92.49[/C][C]92.5634[/C][C]92.4462[/C][C]1.00127[/C][C]0.999207[/C][/ROW]
[ROW][C]47[/C][C]92.54[/C][C]92.6144[/C][C]92.6596[/C][C]0.999512[/C][C]0.999197[/C][/ROW]
[ROW][C]48[/C][C]92.58[/C][C]92.8001[/C][C]92.9525[/C][C]0.99836[/C][C]0.997628[/C][/ROW]
[ROW][C]49[/C][C]92.58[/C][C]92.9653[/C][C]93.2679[/C][C]0.996756[/C][C]0.995855[/C][/ROW]
[ROW][C]50[/C][C]92.39[/C][C]93.0923[/C][C]93.63[/C][C]0.994258[/C][C]0.992455[/C][/ROW]
[ROW][C]51[/C][C]92.33[/C][C]93.4112[/C][C]94.08[/C][C]0.992891[/C][C]0.988425[/C][/ROW]
[ROW][C]52[/C][C]93.59[/C][C]94.3808[/C][C]94.6279[/C][C]0.997389[/C][C]0.991621[/C][/ROW]
[ROW][C]53[/C][C]95.51[/C][C]95.6752[/C][C]95.2463[/C][C]1.0045[/C][C]0.998273[/C][/ROW]
[ROW][C]54[/C][C]95.99[/C][C]96.253[/C][C]95.8829[/C][C]1.00386[/C][C]0.997268[/C][/ROW]
[ROW][C]55[/C][C]96.22[/C][C]NA[/C][C]NA[/C][C]1.00473[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]97.2[/C][C]NA[/C][C]NA[/C][C]1.00374[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]98.54[/C][C]NA[/C][C]NA[/C][C]1.00274[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]99.64[/C][C]NA[/C][C]NA[/C][C]1.00127[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]100.23[/C][C]NA[/C][C]NA[/C][C]0.999512[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]100.17[/C][C]NA[/C][C]NA[/C][C]0.99836[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232577&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232577&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
185.73NANA0.996756NA
285.73NANA0.994258NA
385.74NANA0.992891NA
486.32NANA0.997389NA
587.59NANA1.0045NA
687.81NANA1.00386NA
787.8787.734987.32171.004731.00154
887.9487.838587.51171.003741.00116
987.9687.965487.72541.002740.999939
1088.0188.085287.97381.001270.999146
1188.0188.145388.18830.9995120.998465
1288.0188.208588.35330.998360.99775
1388.0188.229988.51710.9967560.997507
1488.0188.172888.68210.9942580.998153
1588.5988.212688.84420.9928911.00428
1689.4388.771889.00420.9973891.00741
1789.6389.561989.16041.00451.00076
1889.7389.668189.32331.003861.00069
1989.8889.922789.49921.004730.999525
2089.8990.014189.67921.003740.998621
2189.990.085389.83961.002740.997943
2289.9190.08489.971.001270.998068
2389.8690.086890.13080.9995120.997482
2490.0790.182390.33040.998360.998755
2590.1790.230590.52420.9967560.99933
2690.1790.195390.71620.9942580.999719
2790.2890.26590.91120.9928911.00017
2890.8790.870491.10830.9973890.999995
2992.0591.7291.30871.00451.0036
3092.191.855791.50251.003861.00266
3192.1692.117291.68331.004731.00046
3292.2292.203191.861.003741.00018
3392.2592.269692.01791.002740.999787
3492.2992.243892.12711.001271.0005
3592.2992.126392.17120.9995121.00178
3692.2992.033492.18460.998361.00279
3792.2991.901392.20040.9967561.00423
3892.2991.685592.2150.9942581.00659
3991.9591.578992.23460.9928911.00405
4091.8292.014192.2550.9973890.99789
4192.1692.689392.27371.00450.99429
4292.3192.652592.29621.003860.996303
4392.3392.757392.32041.004730.995393
4492.492.681692.33671.003740.996962
4592.5492.609392.35671.002740.999252
4692.4992.563492.44621.001270.999207
4792.5492.614492.65960.9995120.999197
4892.5892.800192.95250.998360.997628
4992.5892.965393.26790.9967560.995855
5092.3993.092393.630.9942580.992455
5192.3393.411294.080.9928910.988425
5293.5994.380894.62790.9973890.991621
5395.5195.675295.24631.00450.998273
5495.9996.25395.88291.003860.997268
5596.22NANA1.00473NA
5697.2NANA1.00374NA
5798.54NANA1.00274NA
5899.64NANA1.00127NA
59100.23NANA0.999512NA
60100.17NANA0.99836NA



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$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')