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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationTue, 10 Dec 2013 04:24:22 -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/10/t1386667474qe4e3esqypuxhg8.htm/, Retrieved Fri, 29 Mar 2024 10:23:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231835, Retrieved Fri, 29 Mar 2024 10:23:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-10 09:24:22] [ce63f6e18bf6a7ced484324fd1839a76] [Current]
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Dataseries X:
83,7
86,4
85,9
80,4
81,8
87,5
83,7
87
99,7
101,4
101,9
115,7
123,2
136,9
146,8
149,6
146,5
157
147,9
133,6
128,7
100,8
91,8
89,3
96,7
91,6
93,3
93,3
101
100,4
86,9
83,9
80,3
87,7
92,7
95,5
92
87,4
86,8
83,7
85
81,7
90,9
101,5
113,8
120,1
122,1
132,5
140
149,4
144,3
154,4
151,4
145,5
136,8
146,6
145,1
133,6
131,4
127,5
130,1
131,1
132,3
128,6
125,1
128,7
156,1
163,2
159,8
157,4
156,2
152,5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231835&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231835&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231835&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'Herman Ole Andreas Wold' @ wold.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
183.7NANA0.941806NA
286.4NANA2.58347NA
385.9NANA2.86764NA
480.4NANA3.12014NA
581.8NANA2.08097NA
687.5NANA2.18181NA
783.791.40692.9042-1.49819-7.70597
88795.676896.6542-0.977361-8.67681
999.7102.559101.2961.26347-2.85931
10101.4102.392106.717-4.32486-0.991806
11101.9106.468112.296-5.82736-4.56847
12115.7115.476117.887-2.411530.224028
13123.2124.4123.4580.941806-1.20014
14136.9130.658128.0752.583476.24153
15146.8134.093131.2252.8676412.7074
16149.6135.528132.4083.1201414.0715
17146.5134.043131.9622.0809712.4565
18157132.623130.4422.1818124.3765
19147.9126.739128.237-1.4981921.1607
20133.6124.268125.246-0.9773619.33153
21128.7122.393121.1291.263476.30736
22100.8112.229116.554-4.32486-11.4293
2391.8106.485112.312-5.82736-14.6851
2489.3105.647108.058-2.41153-16.3468
2596.7104.1103.1580.941806-7.40014
2691.6101.12998.54582.58347-9.52931
2793.397.32694.45832.86764-4.02597
2893.395.01691.89583.12014-1.71597
2910193.468591.38752.080977.53153
30100.493.865191.68332.181816.53486
3186.990.247691.7458-1.49819-3.34764
3283.990.397691.375-0.977361-6.49764
3380.392.192690.92921.26347-11.8926
3487.785.933590.2583-4.324861.76653
3592.783.364389.1917-5.827369.33569
3695.585.334387.7458-2.4115310.1657
379288.075187.13330.9418063.92486
3887.490.616888.03332.58347-3.21681
3986.893.030190.16252.86764-6.23014
4083.796.028592.90833.12014-12.3285
418597.564395.48332.08097-12.5643
4281.7100.43298.252.18181-18.7318
4390.9100.293101.792-1.49819-9.39347
44101.5105.398106.375-0.977361-3.89764
45113.8112.618111.3541.263471.18236
46120.1112.371116.696-4.324867.72903
47122.1116.581122.408-5.827365.51903
48132.5125.422127.833-2.411537.07819
49140133.346132.4040.9418066.65403
50149.4138.779136.1962.5834710.6207
51144.3142.247139.3792.867642.05319
52154.4144.366141.2463.1201410.034
53151.4144.277142.1962.080977.12319
54145.5144.557142.3752.181810.943194
55136.8140.256141.754-1.49819-3.45597
56146.6139.602140.579-0.9773616.99819
57145.1140.58139.3171.263474.51986
58133.6133.417137.742-4.324860.183194
59131.4129.743135.571-5.827361.65653
60127.5131.363133.775-2.41153-3.86347
61130.1134.821133.8790.941806-4.72097
62131.1137.958135.3752.58347-6.85847
63132.3139.547136.6792.86764-7.24681
64128.6141.403138.2833.12014-12.8035
65125.1142.389140.3082.08097-17.2893
66128.7144.565142.3832.18181-15.8651
67156.1NANA-1.49819NA
68163.2NANA-0.977361NA
69159.8NANA1.26347NA
70157.4NANA-4.32486NA
71156.2NANA-5.82736NA
72152.5NANA-2.41153NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 83.7 & NA & NA & 0.941806 & NA \tabularnewline
2 & 86.4 & NA & NA & 2.58347 & NA \tabularnewline
3 & 85.9 & NA & NA & 2.86764 & NA \tabularnewline
4 & 80.4 & NA & NA & 3.12014 & NA \tabularnewline
5 & 81.8 & NA & NA & 2.08097 & NA \tabularnewline
6 & 87.5 & NA & NA & 2.18181 & NA \tabularnewline
7 & 83.7 & 91.406 & 92.9042 & -1.49819 & -7.70597 \tabularnewline
8 & 87 & 95.6768 & 96.6542 & -0.977361 & -8.67681 \tabularnewline
9 & 99.7 & 102.559 & 101.296 & 1.26347 & -2.85931 \tabularnewline
10 & 101.4 & 102.392 & 106.717 & -4.32486 & -0.991806 \tabularnewline
11 & 101.9 & 106.468 & 112.296 & -5.82736 & -4.56847 \tabularnewline
12 & 115.7 & 115.476 & 117.887 & -2.41153 & 0.224028 \tabularnewline
13 & 123.2 & 124.4 & 123.458 & 0.941806 & -1.20014 \tabularnewline
14 & 136.9 & 130.658 & 128.075 & 2.58347 & 6.24153 \tabularnewline
15 & 146.8 & 134.093 & 131.225 & 2.86764 & 12.7074 \tabularnewline
16 & 149.6 & 135.528 & 132.408 & 3.12014 & 14.0715 \tabularnewline
17 & 146.5 & 134.043 & 131.962 & 2.08097 & 12.4565 \tabularnewline
18 & 157 & 132.623 & 130.442 & 2.18181 & 24.3765 \tabularnewline
19 & 147.9 & 126.739 & 128.237 & -1.49819 & 21.1607 \tabularnewline
20 & 133.6 & 124.268 & 125.246 & -0.977361 & 9.33153 \tabularnewline
21 & 128.7 & 122.393 & 121.129 & 1.26347 & 6.30736 \tabularnewline
22 & 100.8 & 112.229 & 116.554 & -4.32486 & -11.4293 \tabularnewline
23 & 91.8 & 106.485 & 112.312 & -5.82736 & -14.6851 \tabularnewline
24 & 89.3 & 105.647 & 108.058 & -2.41153 & -16.3468 \tabularnewline
25 & 96.7 & 104.1 & 103.158 & 0.941806 & -7.40014 \tabularnewline
26 & 91.6 & 101.129 & 98.5458 & 2.58347 & -9.52931 \tabularnewline
27 & 93.3 & 97.326 & 94.4583 & 2.86764 & -4.02597 \tabularnewline
28 & 93.3 & 95.016 & 91.8958 & 3.12014 & -1.71597 \tabularnewline
29 & 101 & 93.4685 & 91.3875 & 2.08097 & 7.53153 \tabularnewline
30 & 100.4 & 93.8651 & 91.6833 & 2.18181 & 6.53486 \tabularnewline
31 & 86.9 & 90.2476 & 91.7458 & -1.49819 & -3.34764 \tabularnewline
32 & 83.9 & 90.3976 & 91.375 & -0.977361 & -6.49764 \tabularnewline
33 & 80.3 & 92.1926 & 90.9292 & 1.26347 & -11.8926 \tabularnewline
34 & 87.7 & 85.9335 & 90.2583 & -4.32486 & 1.76653 \tabularnewline
35 & 92.7 & 83.3643 & 89.1917 & -5.82736 & 9.33569 \tabularnewline
36 & 95.5 & 85.3343 & 87.7458 & -2.41153 & 10.1657 \tabularnewline
37 & 92 & 88.0751 & 87.1333 & 0.941806 & 3.92486 \tabularnewline
38 & 87.4 & 90.6168 & 88.0333 & 2.58347 & -3.21681 \tabularnewline
39 & 86.8 & 93.0301 & 90.1625 & 2.86764 & -6.23014 \tabularnewline
40 & 83.7 & 96.0285 & 92.9083 & 3.12014 & -12.3285 \tabularnewline
41 & 85 & 97.5643 & 95.4833 & 2.08097 & -12.5643 \tabularnewline
42 & 81.7 & 100.432 & 98.25 & 2.18181 & -18.7318 \tabularnewline
43 & 90.9 & 100.293 & 101.792 & -1.49819 & -9.39347 \tabularnewline
44 & 101.5 & 105.398 & 106.375 & -0.977361 & -3.89764 \tabularnewline
45 & 113.8 & 112.618 & 111.354 & 1.26347 & 1.18236 \tabularnewline
46 & 120.1 & 112.371 & 116.696 & -4.32486 & 7.72903 \tabularnewline
47 & 122.1 & 116.581 & 122.408 & -5.82736 & 5.51903 \tabularnewline
48 & 132.5 & 125.422 & 127.833 & -2.41153 & 7.07819 \tabularnewline
49 & 140 & 133.346 & 132.404 & 0.941806 & 6.65403 \tabularnewline
50 & 149.4 & 138.779 & 136.196 & 2.58347 & 10.6207 \tabularnewline
51 & 144.3 & 142.247 & 139.379 & 2.86764 & 2.05319 \tabularnewline
52 & 154.4 & 144.366 & 141.246 & 3.12014 & 10.034 \tabularnewline
53 & 151.4 & 144.277 & 142.196 & 2.08097 & 7.12319 \tabularnewline
54 & 145.5 & 144.557 & 142.375 & 2.18181 & 0.943194 \tabularnewline
55 & 136.8 & 140.256 & 141.754 & -1.49819 & -3.45597 \tabularnewline
56 & 146.6 & 139.602 & 140.579 & -0.977361 & 6.99819 \tabularnewline
57 & 145.1 & 140.58 & 139.317 & 1.26347 & 4.51986 \tabularnewline
58 & 133.6 & 133.417 & 137.742 & -4.32486 & 0.183194 \tabularnewline
59 & 131.4 & 129.743 & 135.571 & -5.82736 & 1.65653 \tabularnewline
60 & 127.5 & 131.363 & 133.775 & -2.41153 & -3.86347 \tabularnewline
61 & 130.1 & 134.821 & 133.879 & 0.941806 & -4.72097 \tabularnewline
62 & 131.1 & 137.958 & 135.375 & 2.58347 & -6.85847 \tabularnewline
63 & 132.3 & 139.547 & 136.679 & 2.86764 & -7.24681 \tabularnewline
64 & 128.6 & 141.403 & 138.283 & 3.12014 & -12.8035 \tabularnewline
65 & 125.1 & 142.389 & 140.308 & 2.08097 & -17.2893 \tabularnewline
66 & 128.7 & 144.565 & 142.383 & 2.18181 & -15.8651 \tabularnewline
67 & 156.1 & NA & NA & -1.49819 & NA \tabularnewline
68 & 163.2 & NA & NA & -0.977361 & NA \tabularnewline
69 & 159.8 & NA & NA & 1.26347 & NA \tabularnewline
70 & 157.4 & NA & NA & -4.32486 & NA \tabularnewline
71 & 156.2 & NA & NA & -5.82736 & NA \tabularnewline
72 & 152.5 & NA & NA & -2.41153 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231835&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]83.7[/C][C]NA[/C][C]NA[/C][C]0.941806[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]86.4[/C][C]NA[/C][C]NA[/C][C]2.58347[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]85.9[/C][C]NA[/C][C]NA[/C][C]2.86764[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]80.4[/C][C]NA[/C][C]NA[/C][C]3.12014[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]81.8[/C][C]NA[/C][C]NA[/C][C]2.08097[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]87.5[/C][C]NA[/C][C]NA[/C][C]2.18181[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]83.7[/C][C]91.406[/C][C]92.9042[/C][C]-1.49819[/C][C]-7.70597[/C][/ROW]
[ROW][C]8[/C][C]87[/C][C]95.6768[/C][C]96.6542[/C][C]-0.977361[/C][C]-8.67681[/C][/ROW]
[ROW][C]9[/C][C]99.7[/C][C]102.559[/C][C]101.296[/C][C]1.26347[/C][C]-2.85931[/C][/ROW]
[ROW][C]10[/C][C]101.4[/C][C]102.392[/C][C]106.717[/C][C]-4.32486[/C][C]-0.991806[/C][/ROW]
[ROW][C]11[/C][C]101.9[/C][C]106.468[/C][C]112.296[/C][C]-5.82736[/C][C]-4.56847[/C][/ROW]
[ROW][C]12[/C][C]115.7[/C][C]115.476[/C][C]117.887[/C][C]-2.41153[/C][C]0.224028[/C][/ROW]
[ROW][C]13[/C][C]123.2[/C][C]124.4[/C][C]123.458[/C][C]0.941806[/C][C]-1.20014[/C][/ROW]
[ROW][C]14[/C][C]136.9[/C][C]130.658[/C][C]128.075[/C][C]2.58347[/C][C]6.24153[/C][/ROW]
[ROW][C]15[/C][C]146.8[/C][C]134.093[/C][C]131.225[/C][C]2.86764[/C][C]12.7074[/C][/ROW]
[ROW][C]16[/C][C]149.6[/C][C]135.528[/C][C]132.408[/C][C]3.12014[/C][C]14.0715[/C][/ROW]
[ROW][C]17[/C][C]146.5[/C][C]134.043[/C][C]131.962[/C][C]2.08097[/C][C]12.4565[/C][/ROW]
[ROW][C]18[/C][C]157[/C][C]132.623[/C][C]130.442[/C][C]2.18181[/C][C]24.3765[/C][/ROW]
[ROW][C]19[/C][C]147.9[/C][C]126.739[/C][C]128.237[/C][C]-1.49819[/C][C]21.1607[/C][/ROW]
[ROW][C]20[/C][C]133.6[/C][C]124.268[/C][C]125.246[/C][C]-0.977361[/C][C]9.33153[/C][/ROW]
[ROW][C]21[/C][C]128.7[/C][C]122.393[/C][C]121.129[/C][C]1.26347[/C][C]6.30736[/C][/ROW]
[ROW][C]22[/C][C]100.8[/C][C]112.229[/C][C]116.554[/C][C]-4.32486[/C][C]-11.4293[/C][/ROW]
[ROW][C]23[/C][C]91.8[/C][C]106.485[/C][C]112.312[/C][C]-5.82736[/C][C]-14.6851[/C][/ROW]
[ROW][C]24[/C][C]89.3[/C][C]105.647[/C][C]108.058[/C][C]-2.41153[/C][C]-16.3468[/C][/ROW]
[ROW][C]25[/C][C]96.7[/C][C]104.1[/C][C]103.158[/C][C]0.941806[/C][C]-7.40014[/C][/ROW]
[ROW][C]26[/C][C]91.6[/C][C]101.129[/C][C]98.5458[/C][C]2.58347[/C][C]-9.52931[/C][/ROW]
[ROW][C]27[/C][C]93.3[/C][C]97.326[/C][C]94.4583[/C][C]2.86764[/C][C]-4.02597[/C][/ROW]
[ROW][C]28[/C][C]93.3[/C][C]95.016[/C][C]91.8958[/C][C]3.12014[/C][C]-1.71597[/C][/ROW]
[ROW][C]29[/C][C]101[/C][C]93.4685[/C][C]91.3875[/C][C]2.08097[/C][C]7.53153[/C][/ROW]
[ROW][C]30[/C][C]100.4[/C][C]93.8651[/C][C]91.6833[/C][C]2.18181[/C][C]6.53486[/C][/ROW]
[ROW][C]31[/C][C]86.9[/C][C]90.2476[/C][C]91.7458[/C][C]-1.49819[/C][C]-3.34764[/C][/ROW]
[ROW][C]32[/C][C]83.9[/C][C]90.3976[/C][C]91.375[/C][C]-0.977361[/C][C]-6.49764[/C][/ROW]
[ROW][C]33[/C][C]80.3[/C][C]92.1926[/C][C]90.9292[/C][C]1.26347[/C][C]-11.8926[/C][/ROW]
[ROW][C]34[/C][C]87.7[/C][C]85.9335[/C][C]90.2583[/C][C]-4.32486[/C][C]1.76653[/C][/ROW]
[ROW][C]35[/C][C]92.7[/C][C]83.3643[/C][C]89.1917[/C][C]-5.82736[/C][C]9.33569[/C][/ROW]
[ROW][C]36[/C][C]95.5[/C][C]85.3343[/C][C]87.7458[/C][C]-2.41153[/C][C]10.1657[/C][/ROW]
[ROW][C]37[/C][C]92[/C][C]88.0751[/C][C]87.1333[/C][C]0.941806[/C][C]3.92486[/C][/ROW]
[ROW][C]38[/C][C]87.4[/C][C]90.6168[/C][C]88.0333[/C][C]2.58347[/C][C]-3.21681[/C][/ROW]
[ROW][C]39[/C][C]86.8[/C][C]93.0301[/C][C]90.1625[/C][C]2.86764[/C][C]-6.23014[/C][/ROW]
[ROW][C]40[/C][C]83.7[/C][C]96.0285[/C][C]92.9083[/C][C]3.12014[/C][C]-12.3285[/C][/ROW]
[ROW][C]41[/C][C]85[/C][C]97.5643[/C][C]95.4833[/C][C]2.08097[/C][C]-12.5643[/C][/ROW]
[ROW][C]42[/C][C]81.7[/C][C]100.432[/C][C]98.25[/C][C]2.18181[/C][C]-18.7318[/C][/ROW]
[ROW][C]43[/C][C]90.9[/C][C]100.293[/C][C]101.792[/C][C]-1.49819[/C][C]-9.39347[/C][/ROW]
[ROW][C]44[/C][C]101.5[/C][C]105.398[/C][C]106.375[/C][C]-0.977361[/C][C]-3.89764[/C][/ROW]
[ROW][C]45[/C][C]113.8[/C][C]112.618[/C][C]111.354[/C][C]1.26347[/C][C]1.18236[/C][/ROW]
[ROW][C]46[/C][C]120.1[/C][C]112.371[/C][C]116.696[/C][C]-4.32486[/C][C]7.72903[/C][/ROW]
[ROW][C]47[/C][C]122.1[/C][C]116.581[/C][C]122.408[/C][C]-5.82736[/C][C]5.51903[/C][/ROW]
[ROW][C]48[/C][C]132.5[/C][C]125.422[/C][C]127.833[/C][C]-2.41153[/C][C]7.07819[/C][/ROW]
[ROW][C]49[/C][C]140[/C][C]133.346[/C][C]132.404[/C][C]0.941806[/C][C]6.65403[/C][/ROW]
[ROW][C]50[/C][C]149.4[/C][C]138.779[/C][C]136.196[/C][C]2.58347[/C][C]10.6207[/C][/ROW]
[ROW][C]51[/C][C]144.3[/C][C]142.247[/C][C]139.379[/C][C]2.86764[/C][C]2.05319[/C][/ROW]
[ROW][C]52[/C][C]154.4[/C][C]144.366[/C][C]141.246[/C][C]3.12014[/C][C]10.034[/C][/ROW]
[ROW][C]53[/C][C]151.4[/C][C]144.277[/C][C]142.196[/C][C]2.08097[/C][C]7.12319[/C][/ROW]
[ROW][C]54[/C][C]145.5[/C][C]144.557[/C][C]142.375[/C][C]2.18181[/C][C]0.943194[/C][/ROW]
[ROW][C]55[/C][C]136.8[/C][C]140.256[/C][C]141.754[/C][C]-1.49819[/C][C]-3.45597[/C][/ROW]
[ROW][C]56[/C][C]146.6[/C][C]139.602[/C][C]140.579[/C][C]-0.977361[/C][C]6.99819[/C][/ROW]
[ROW][C]57[/C][C]145.1[/C][C]140.58[/C][C]139.317[/C][C]1.26347[/C][C]4.51986[/C][/ROW]
[ROW][C]58[/C][C]133.6[/C][C]133.417[/C][C]137.742[/C][C]-4.32486[/C][C]0.183194[/C][/ROW]
[ROW][C]59[/C][C]131.4[/C][C]129.743[/C][C]135.571[/C][C]-5.82736[/C][C]1.65653[/C][/ROW]
[ROW][C]60[/C][C]127.5[/C][C]131.363[/C][C]133.775[/C][C]-2.41153[/C][C]-3.86347[/C][/ROW]
[ROW][C]61[/C][C]130.1[/C][C]134.821[/C][C]133.879[/C][C]0.941806[/C][C]-4.72097[/C][/ROW]
[ROW][C]62[/C][C]131.1[/C][C]137.958[/C][C]135.375[/C][C]2.58347[/C][C]-6.85847[/C][/ROW]
[ROW][C]63[/C][C]132.3[/C][C]139.547[/C][C]136.679[/C][C]2.86764[/C][C]-7.24681[/C][/ROW]
[ROW][C]64[/C][C]128.6[/C][C]141.403[/C][C]138.283[/C][C]3.12014[/C][C]-12.8035[/C][/ROW]
[ROW][C]65[/C][C]125.1[/C][C]142.389[/C][C]140.308[/C][C]2.08097[/C][C]-17.2893[/C][/ROW]
[ROW][C]66[/C][C]128.7[/C][C]144.565[/C][C]142.383[/C][C]2.18181[/C][C]-15.8651[/C][/ROW]
[ROW][C]67[/C][C]156.1[/C][C]NA[/C][C]NA[/C][C]-1.49819[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]163.2[/C][C]NA[/C][C]NA[/C][C]-0.977361[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]159.8[/C][C]NA[/C][C]NA[/C][C]1.26347[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]157.4[/C][C]NA[/C][C]NA[/C][C]-4.32486[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]156.2[/C][C]NA[/C][C]NA[/C][C]-5.82736[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]152.5[/C][C]NA[/C][C]NA[/C][C]-2.41153[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231835&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231835&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
183.7NANA0.941806NA
286.4NANA2.58347NA
385.9NANA2.86764NA
480.4NANA3.12014NA
581.8NANA2.08097NA
687.5NANA2.18181NA
783.791.40692.9042-1.49819-7.70597
88795.676896.6542-0.977361-8.67681
999.7102.559101.2961.26347-2.85931
10101.4102.392106.717-4.32486-0.991806
11101.9106.468112.296-5.82736-4.56847
12115.7115.476117.887-2.411530.224028
13123.2124.4123.4580.941806-1.20014
14136.9130.658128.0752.583476.24153
15146.8134.093131.2252.8676412.7074
16149.6135.528132.4083.1201414.0715
17146.5134.043131.9622.0809712.4565
18157132.623130.4422.1818124.3765
19147.9126.739128.237-1.4981921.1607
20133.6124.268125.246-0.9773619.33153
21128.7122.393121.1291.263476.30736
22100.8112.229116.554-4.32486-11.4293
2391.8106.485112.312-5.82736-14.6851
2489.3105.647108.058-2.41153-16.3468
2596.7104.1103.1580.941806-7.40014
2691.6101.12998.54582.58347-9.52931
2793.397.32694.45832.86764-4.02597
2893.395.01691.89583.12014-1.71597
2910193.468591.38752.080977.53153
30100.493.865191.68332.181816.53486
3186.990.247691.7458-1.49819-3.34764
3283.990.397691.375-0.977361-6.49764
3380.392.192690.92921.26347-11.8926
3487.785.933590.2583-4.324861.76653
3592.783.364389.1917-5.827369.33569
3695.585.334387.7458-2.4115310.1657
379288.075187.13330.9418063.92486
3887.490.616888.03332.58347-3.21681
3986.893.030190.16252.86764-6.23014
4083.796.028592.90833.12014-12.3285
418597.564395.48332.08097-12.5643
4281.7100.43298.252.18181-18.7318
4390.9100.293101.792-1.49819-9.39347
44101.5105.398106.375-0.977361-3.89764
45113.8112.618111.3541.263471.18236
46120.1112.371116.696-4.324867.72903
47122.1116.581122.408-5.827365.51903
48132.5125.422127.833-2.411537.07819
49140133.346132.4040.9418066.65403
50149.4138.779136.1962.5834710.6207
51144.3142.247139.3792.867642.05319
52154.4144.366141.2463.1201410.034
53151.4144.277142.1962.080977.12319
54145.5144.557142.3752.181810.943194
55136.8140.256141.754-1.49819-3.45597
56146.6139.602140.579-0.9773616.99819
57145.1140.58139.3171.263474.51986
58133.6133.417137.742-4.324860.183194
59131.4129.743135.571-5.827361.65653
60127.5131.363133.775-2.41153-3.86347
61130.1134.821133.8790.941806-4.72097
62131.1137.958135.3752.58347-6.85847
63132.3139.547136.6792.86764-7.24681
64128.6141.403138.2833.12014-12.8035
65125.1142.389140.3082.08097-17.2893
66128.7144.565142.3832.18181-15.8651
67156.1NANA-1.49819NA
68163.2NANA-0.977361NA
69159.8NANA1.26347NA
70157.4NANA-4.32486NA
71156.2NANA-5.82736NA
72152.5NANA-2.41153NA



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')