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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 07 Dec 2013 13:31:50 -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/07/t1386441120ofpsdfa7p3gvpdf.htm/, Retrieved Fri, 29 Mar 2024 12:31:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231405, Retrieved Fri, 29 Mar 2024 12:31:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-07 18:31:50] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
55,7
59,2
59,8
61,6
65,8
64,2
67
62,8
65,5
75,2
80,9
83,2
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'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=231405&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=231405&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231405&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
155.7NANA1.61435NA
259.2NANA3.14838NA
359.8NANA2.8963NA
461.6NANA1.7706NA
565.8NANA0.810185NA
664.2NANA1.47269NA
76766.563767.9083-1.344680.436343
862.868.214470.2083-1.99398-5.41435
965.572.382472.4292-0.0467593-6.88241
1075.270.901274.3-3.398844.29884
1180.971.807475.75-3.942599.09259
1283.276.401977.3875-0.9856486.79815
1383.780.668579.05421.614353.03148
1486.483.906780.75833.148382.49329
1585.986.08883.19172.8963-0.187963
1680.487.478985.70831.7706-7.07894
1781.888.485287.6750.810185-6.68519
1887.591.376989.90421.47269-3.87685
1983.791.559592.9042-1.34468-7.85949
208794.660296.6542-1.99398-7.66019
2199.7101.249101.296-0.0467593-1.54907
22101.4103.318106.717-3.39884-1.91782
23101.9108.353112.296-3.94259-6.45324
24115.7116.902117.887-0.985648-1.20185
25123.2125.073123.4581.61435-1.87269
26136.9131.223128.0753.148385.67662
27146.8134.121131.2252.896312.6787
28149.6134.179132.4081.770615.4211
29146.5132.773131.9620.81018513.7273
30157131.914130.4421.4726925.0856
31147.9126.893128.237-1.3446821.0072
32133.6123.252125.246-1.9939810.3481
33128.7121.082121.129-0.04675937.61759
34100.8113.155116.554-3.39884-12.3553
3591.8108.37112.312-3.94259-16.5699
3689.3107.073108.058-0.985648-17.7727
3796.7104.773103.1581.61435-8.07269
3891.6101.69498.54583.14838-10.0942
3993.397.354694.45832.8963-4.05463
4093.393.666491.89581.7706-0.366435
4110192.197791.38750.8101858.80231
42100.493.15691.68331.472697.24398
4386.990.401291.7458-1.34468-3.50116
4483.989.38191.375-1.99398-5.48102
4580.390.882490.9292-0.0467593-10.5824
4687.786.859590.2583-3.398840.840509
4792.785.249189.1917-3.942597.45093
4895.586.760287.7458-0.9856488.73981
499288.747787.13331.614353.25231
5087.491.181788.03333.14838-3.78171
5186.893.058890.16252.8963-6.2588
5283.794.678992.90831.7706-10.9789
538596.293595.48330.810185-11.2935
5481.799.722798.251.47269-18.0227
5590.9100.447101.792-1.34468-9.54699
56101.5104.381106.375-1.99398-2.88102
57113.8111.307111.354-0.04675932.49259
58120.1113.297116.696-3.398846.80301
59122.1118.466122.408-3.942593.63426
60132.5126.848127.833-0.9856485.65231
61140134.019132.4041.614355.98148
62149.4139.344136.1963.1483810.0558
63144.3142.275139.3792.89632.02454
64154.4143.016141.2461.770611.3836
65151.4143.006142.1960.8101858.39398
66145.5143.848142.3751.472691.65231
67136.8140.409141.754-1.34468-3.60949
68146.6138.585140.579-1.993988.01481
69145.1139.27139.317-0.04675935.83009
70133.6134.343137.742-3.39884-0.742824
71131.4131.628135.571-3.94259-0.228241
72127.5132.789133.775-0.985648-5.28935
73130.1135.494133.8791.61435-5.39352
74131.1138.523135.3753.14838-7.42338
75132.3139.575136.6792.8963-7.27546
76128.6140.054138.2831.7706-11.4539
77125.1141.119140.3080.810185-16.0185
78128.7143.856142.3831.47269-15.156
79156.1NANA-1.34468NA
80163.2NANA-1.99398NA
81159.8NANA-0.0467593NA
82157.4NANA-3.39884NA
83156.2NANA-3.94259NA
84152.5NANA-0.985648NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 55.7 & NA & NA & 1.61435 & NA \tabularnewline
2 & 59.2 & NA & NA & 3.14838 & NA \tabularnewline
3 & 59.8 & NA & NA & 2.8963 & NA \tabularnewline
4 & 61.6 & NA & NA & 1.7706 & NA \tabularnewline
5 & 65.8 & NA & NA & 0.810185 & NA \tabularnewline
6 & 64.2 & NA & NA & 1.47269 & NA \tabularnewline
7 & 67 & 66.5637 & 67.9083 & -1.34468 & 0.436343 \tabularnewline
8 & 62.8 & 68.2144 & 70.2083 & -1.99398 & -5.41435 \tabularnewline
9 & 65.5 & 72.3824 & 72.4292 & -0.0467593 & -6.88241 \tabularnewline
10 & 75.2 & 70.9012 & 74.3 & -3.39884 & 4.29884 \tabularnewline
11 & 80.9 & 71.8074 & 75.75 & -3.94259 & 9.09259 \tabularnewline
12 & 83.2 & 76.4019 & 77.3875 & -0.985648 & 6.79815 \tabularnewline
13 & 83.7 & 80.6685 & 79.0542 & 1.61435 & 3.03148 \tabularnewline
14 & 86.4 & 83.9067 & 80.7583 & 3.14838 & 2.49329 \tabularnewline
15 & 85.9 & 86.088 & 83.1917 & 2.8963 & -0.187963 \tabularnewline
16 & 80.4 & 87.4789 & 85.7083 & 1.7706 & -7.07894 \tabularnewline
17 & 81.8 & 88.4852 & 87.675 & 0.810185 & -6.68519 \tabularnewline
18 & 87.5 & 91.3769 & 89.9042 & 1.47269 & -3.87685 \tabularnewline
19 & 83.7 & 91.5595 & 92.9042 & -1.34468 & -7.85949 \tabularnewline
20 & 87 & 94.6602 & 96.6542 & -1.99398 & -7.66019 \tabularnewline
21 & 99.7 & 101.249 & 101.296 & -0.0467593 & -1.54907 \tabularnewline
22 & 101.4 & 103.318 & 106.717 & -3.39884 & -1.91782 \tabularnewline
23 & 101.9 & 108.353 & 112.296 & -3.94259 & -6.45324 \tabularnewline
24 & 115.7 & 116.902 & 117.887 & -0.985648 & -1.20185 \tabularnewline
25 & 123.2 & 125.073 & 123.458 & 1.61435 & -1.87269 \tabularnewline
26 & 136.9 & 131.223 & 128.075 & 3.14838 & 5.67662 \tabularnewline
27 & 146.8 & 134.121 & 131.225 & 2.8963 & 12.6787 \tabularnewline
28 & 149.6 & 134.179 & 132.408 & 1.7706 & 15.4211 \tabularnewline
29 & 146.5 & 132.773 & 131.962 & 0.810185 & 13.7273 \tabularnewline
30 & 157 & 131.914 & 130.442 & 1.47269 & 25.0856 \tabularnewline
31 & 147.9 & 126.893 & 128.237 & -1.34468 & 21.0072 \tabularnewline
32 & 133.6 & 123.252 & 125.246 & -1.99398 & 10.3481 \tabularnewline
33 & 128.7 & 121.082 & 121.129 & -0.0467593 & 7.61759 \tabularnewline
34 & 100.8 & 113.155 & 116.554 & -3.39884 & -12.3553 \tabularnewline
35 & 91.8 & 108.37 & 112.312 & -3.94259 & -16.5699 \tabularnewline
36 & 89.3 & 107.073 & 108.058 & -0.985648 & -17.7727 \tabularnewline
37 & 96.7 & 104.773 & 103.158 & 1.61435 & -8.07269 \tabularnewline
38 & 91.6 & 101.694 & 98.5458 & 3.14838 & -10.0942 \tabularnewline
39 & 93.3 & 97.3546 & 94.4583 & 2.8963 & -4.05463 \tabularnewline
40 & 93.3 & 93.6664 & 91.8958 & 1.7706 & -0.366435 \tabularnewline
41 & 101 & 92.1977 & 91.3875 & 0.810185 & 8.80231 \tabularnewline
42 & 100.4 & 93.156 & 91.6833 & 1.47269 & 7.24398 \tabularnewline
43 & 86.9 & 90.4012 & 91.7458 & -1.34468 & -3.50116 \tabularnewline
44 & 83.9 & 89.381 & 91.375 & -1.99398 & -5.48102 \tabularnewline
45 & 80.3 & 90.8824 & 90.9292 & -0.0467593 & -10.5824 \tabularnewline
46 & 87.7 & 86.8595 & 90.2583 & -3.39884 & 0.840509 \tabularnewline
47 & 92.7 & 85.2491 & 89.1917 & -3.94259 & 7.45093 \tabularnewline
48 & 95.5 & 86.7602 & 87.7458 & -0.985648 & 8.73981 \tabularnewline
49 & 92 & 88.7477 & 87.1333 & 1.61435 & 3.25231 \tabularnewline
50 & 87.4 & 91.1817 & 88.0333 & 3.14838 & -3.78171 \tabularnewline
51 & 86.8 & 93.0588 & 90.1625 & 2.8963 & -6.2588 \tabularnewline
52 & 83.7 & 94.6789 & 92.9083 & 1.7706 & -10.9789 \tabularnewline
53 & 85 & 96.2935 & 95.4833 & 0.810185 & -11.2935 \tabularnewline
54 & 81.7 & 99.7227 & 98.25 & 1.47269 & -18.0227 \tabularnewline
55 & 90.9 & 100.447 & 101.792 & -1.34468 & -9.54699 \tabularnewline
56 & 101.5 & 104.381 & 106.375 & -1.99398 & -2.88102 \tabularnewline
57 & 113.8 & 111.307 & 111.354 & -0.0467593 & 2.49259 \tabularnewline
58 & 120.1 & 113.297 & 116.696 & -3.39884 & 6.80301 \tabularnewline
59 & 122.1 & 118.466 & 122.408 & -3.94259 & 3.63426 \tabularnewline
60 & 132.5 & 126.848 & 127.833 & -0.985648 & 5.65231 \tabularnewline
61 & 140 & 134.019 & 132.404 & 1.61435 & 5.98148 \tabularnewline
62 & 149.4 & 139.344 & 136.196 & 3.14838 & 10.0558 \tabularnewline
63 & 144.3 & 142.275 & 139.379 & 2.8963 & 2.02454 \tabularnewline
64 & 154.4 & 143.016 & 141.246 & 1.7706 & 11.3836 \tabularnewline
65 & 151.4 & 143.006 & 142.196 & 0.810185 & 8.39398 \tabularnewline
66 & 145.5 & 143.848 & 142.375 & 1.47269 & 1.65231 \tabularnewline
67 & 136.8 & 140.409 & 141.754 & -1.34468 & -3.60949 \tabularnewline
68 & 146.6 & 138.585 & 140.579 & -1.99398 & 8.01481 \tabularnewline
69 & 145.1 & 139.27 & 139.317 & -0.0467593 & 5.83009 \tabularnewline
70 & 133.6 & 134.343 & 137.742 & -3.39884 & -0.742824 \tabularnewline
71 & 131.4 & 131.628 & 135.571 & -3.94259 & -0.228241 \tabularnewline
72 & 127.5 & 132.789 & 133.775 & -0.985648 & -5.28935 \tabularnewline
73 & 130.1 & 135.494 & 133.879 & 1.61435 & -5.39352 \tabularnewline
74 & 131.1 & 138.523 & 135.375 & 3.14838 & -7.42338 \tabularnewline
75 & 132.3 & 139.575 & 136.679 & 2.8963 & -7.27546 \tabularnewline
76 & 128.6 & 140.054 & 138.283 & 1.7706 & -11.4539 \tabularnewline
77 & 125.1 & 141.119 & 140.308 & 0.810185 & -16.0185 \tabularnewline
78 & 128.7 & 143.856 & 142.383 & 1.47269 & -15.156 \tabularnewline
79 & 156.1 & NA & NA & -1.34468 & NA \tabularnewline
80 & 163.2 & NA & NA & -1.99398 & NA \tabularnewline
81 & 159.8 & NA & NA & -0.0467593 & NA \tabularnewline
82 & 157.4 & NA & NA & -3.39884 & NA \tabularnewline
83 & 156.2 & NA & NA & -3.94259 & NA \tabularnewline
84 & 152.5 & NA & NA & -0.985648 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231405&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]55.7[/C][C]NA[/C][C]NA[/C][C]1.61435[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]59.2[/C][C]NA[/C][C]NA[/C][C]3.14838[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]59.8[/C][C]NA[/C][C]NA[/C][C]2.8963[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]61.6[/C][C]NA[/C][C]NA[/C][C]1.7706[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]65.8[/C][C]NA[/C][C]NA[/C][C]0.810185[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]64.2[/C][C]NA[/C][C]NA[/C][C]1.47269[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]67[/C][C]66.5637[/C][C]67.9083[/C][C]-1.34468[/C][C]0.436343[/C][/ROW]
[ROW][C]8[/C][C]62.8[/C][C]68.2144[/C][C]70.2083[/C][C]-1.99398[/C][C]-5.41435[/C][/ROW]
[ROW][C]9[/C][C]65.5[/C][C]72.3824[/C][C]72.4292[/C][C]-0.0467593[/C][C]-6.88241[/C][/ROW]
[ROW][C]10[/C][C]75.2[/C][C]70.9012[/C][C]74.3[/C][C]-3.39884[/C][C]4.29884[/C][/ROW]
[ROW][C]11[/C][C]80.9[/C][C]71.8074[/C][C]75.75[/C][C]-3.94259[/C][C]9.09259[/C][/ROW]
[ROW][C]12[/C][C]83.2[/C][C]76.4019[/C][C]77.3875[/C][C]-0.985648[/C][C]6.79815[/C][/ROW]
[ROW][C]13[/C][C]83.7[/C][C]80.6685[/C][C]79.0542[/C][C]1.61435[/C][C]3.03148[/C][/ROW]
[ROW][C]14[/C][C]86.4[/C][C]83.9067[/C][C]80.7583[/C][C]3.14838[/C][C]2.49329[/C][/ROW]
[ROW][C]15[/C][C]85.9[/C][C]86.088[/C][C]83.1917[/C][C]2.8963[/C][C]-0.187963[/C][/ROW]
[ROW][C]16[/C][C]80.4[/C][C]87.4789[/C][C]85.7083[/C][C]1.7706[/C][C]-7.07894[/C][/ROW]
[ROW][C]17[/C][C]81.8[/C][C]88.4852[/C][C]87.675[/C][C]0.810185[/C][C]-6.68519[/C][/ROW]
[ROW][C]18[/C][C]87.5[/C][C]91.3769[/C][C]89.9042[/C][C]1.47269[/C][C]-3.87685[/C][/ROW]
[ROW][C]19[/C][C]83.7[/C][C]91.5595[/C][C]92.9042[/C][C]-1.34468[/C][C]-7.85949[/C][/ROW]
[ROW][C]20[/C][C]87[/C][C]94.6602[/C][C]96.6542[/C][C]-1.99398[/C][C]-7.66019[/C][/ROW]
[ROW][C]21[/C][C]99.7[/C][C]101.249[/C][C]101.296[/C][C]-0.0467593[/C][C]-1.54907[/C][/ROW]
[ROW][C]22[/C][C]101.4[/C][C]103.318[/C][C]106.717[/C][C]-3.39884[/C][C]-1.91782[/C][/ROW]
[ROW][C]23[/C][C]101.9[/C][C]108.353[/C][C]112.296[/C][C]-3.94259[/C][C]-6.45324[/C][/ROW]
[ROW][C]24[/C][C]115.7[/C][C]116.902[/C][C]117.887[/C][C]-0.985648[/C][C]-1.20185[/C][/ROW]
[ROW][C]25[/C][C]123.2[/C][C]125.073[/C][C]123.458[/C][C]1.61435[/C][C]-1.87269[/C][/ROW]
[ROW][C]26[/C][C]136.9[/C][C]131.223[/C][C]128.075[/C][C]3.14838[/C][C]5.67662[/C][/ROW]
[ROW][C]27[/C][C]146.8[/C][C]134.121[/C][C]131.225[/C][C]2.8963[/C][C]12.6787[/C][/ROW]
[ROW][C]28[/C][C]149.6[/C][C]134.179[/C][C]132.408[/C][C]1.7706[/C][C]15.4211[/C][/ROW]
[ROW][C]29[/C][C]146.5[/C][C]132.773[/C][C]131.962[/C][C]0.810185[/C][C]13.7273[/C][/ROW]
[ROW][C]30[/C][C]157[/C][C]131.914[/C][C]130.442[/C][C]1.47269[/C][C]25.0856[/C][/ROW]
[ROW][C]31[/C][C]147.9[/C][C]126.893[/C][C]128.237[/C][C]-1.34468[/C][C]21.0072[/C][/ROW]
[ROW][C]32[/C][C]133.6[/C][C]123.252[/C][C]125.246[/C][C]-1.99398[/C][C]10.3481[/C][/ROW]
[ROW][C]33[/C][C]128.7[/C][C]121.082[/C][C]121.129[/C][C]-0.0467593[/C][C]7.61759[/C][/ROW]
[ROW][C]34[/C][C]100.8[/C][C]113.155[/C][C]116.554[/C][C]-3.39884[/C][C]-12.3553[/C][/ROW]
[ROW][C]35[/C][C]91.8[/C][C]108.37[/C][C]112.312[/C][C]-3.94259[/C][C]-16.5699[/C][/ROW]
[ROW][C]36[/C][C]89.3[/C][C]107.073[/C][C]108.058[/C][C]-0.985648[/C][C]-17.7727[/C][/ROW]
[ROW][C]37[/C][C]96.7[/C][C]104.773[/C][C]103.158[/C][C]1.61435[/C][C]-8.07269[/C][/ROW]
[ROW][C]38[/C][C]91.6[/C][C]101.694[/C][C]98.5458[/C][C]3.14838[/C][C]-10.0942[/C][/ROW]
[ROW][C]39[/C][C]93.3[/C][C]97.3546[/C][C]94.4583[/C][C]2.8963[/C][C]-4.05463[/C][/ROW]
[ROW][C]40[/C][C]93.3[/C][C]93.6664[/C][C]91.8958[/C][C]1.7706[/C][C]-0.366435[/C][/ROW]
[ROW][C]41[/C][C]101[/C][C]92.1977[/C][C]91.3875[/C][C]0.810185[/C][C]8.80231[/C][/ROW]
[ROW][C]42[/C][C]100.4[/C][C]93.156[/C][C]91.6833[/C][C]1.47269[/C][C]7.24398[/C][/ROW]
[ROW][C]43[/C][C]86.9[/C][C]90.4012[/C][C]91.7458[/C][C]-1.34468[/C][C]-3.50116[/C][/ROW]
[ROW][C]44[/C][C]83.9[/C][C]89.381[/C][C]91.375[/C][C]-1.99398[/C][C]-5.48102[/C][/ROW]
[ROW][C]45[/C][C]80.3[/C][C]90.8824[/C][C]90.9292[/C][C]-0.0467593[/C][C]-10.5824[/C][/ROW]
[ROW][C]46[/C][C]87.7[/C][C]86.8595[/C][C]90.2583[/C][C]-3.39884[/C][C]0.840509[/C][/ROW]
[ROW][C]47[/C][C]92.7[/C][C]85.2491[/C][C]89.1917[/C][C]-3.94259[/C][C]7.45093[/C][/ROW]
[ROW][C]48[/C][C]95.5[/C][C]86.7602[/C][C]87.7458[/C][C]-0.985648[/C][C]8.73981[/C][/ROW]
[ROW][C]49[/C][C]92[/C][C]88.7477[/C][C]87.1333[/C][C]1.61435[/C][C]3.25231[/C][/ROW]
[ROW][C]50[/C][C]87.4[/C][C]91.1817[/C][C]88.0333[/C][C]3.14838[/C][C]-3.78171[/C][/ROW]
[ROW][C]51[/C][C]86.8[/C][C]93.0588[/C][C]90.1625[/C][C]2.8963[/C][C]-6.2588[/C][/ROW]
[ROW][C]52[/C][C]83.7[/C][C]94.6789[/C][C]92.9083[/C][C]1.7706[/C][C]-10.9789[/C][/ROW]
[ROW][C]53[/C][C]85[/C][C]96.2935[/C][C]95.4833[/C][C]0.810185[/C][C]-11.2935[/C][/ROW]
[ROW][C]54[/C][C]81.7[/C][C]99.7227[/C][C]98.25[/C][C]1.47269[/C][C]-18.0227[/C][/ROW]
[ROW][C]55[/C][C]90.9[/C][C]100.447[/C][C]101.792[/C][C]-1.34468[/C][C]-9.54699[/C][/ROW]
[ROW][C]56[/C][C]101.5[/C][C]104.381[/C][C]106.375[/C][C]-1.99398[/C][C]-2.88102[/C][/ROW]
[ROW][C]57[/C][C]113.8[/C][C]111.307[/C][C]111.354[/C][C]-0.0467593[/C][C]2.49259[/C][/ROW]
[ROW][C]58[/C][C]120.1[/C][C]113.297[/C][C]116.696[/C][C]-3.39884[/C][C]6.80301[/C][/ROW]
[ROW][C]59[/C][C]122.1[/C][C]118.466[/C][C]122.408[/C][C]-3.94259[/C][C]3.63426[/C][/ROW]
[ROW][C]60[/C][C]132.5[/C][C]126.848[/C][C]127.833[/C][C]-0.985648[/C][C]5.65231[/C][/ROW]
[ROW][C]61[/C][C]140[/C][C]134.019[/C][C]132.404[/C][C]1.61435[/C][C]5.98148[/C][/ROW]
[ROW][C]62[/C][C]149.4[/C][C]139.344[/C][C]136.196[/C][C]3.14838[/C][C]10.0558[/C][/ROW]
[ROW][C]63[/C][C]144.3[/C][C]142.275[/C][C]139.379[/C][C]2.8963[/C][C]2.02454[/C][/ROW]
[ROW][C]64[/C][C]154.4[/C][C]143.016[/C][C]141.246[/C][C]1.7706[/C][C]11.3836[/C][/ROW]
[ROW][C]65[/C][C]151.4[/C][C]143.006[/C][C]142.196[/C][C]0.810185[/C][C]8.39398[/C][/ROW]
[ROW][C]66[/C][C]145.5[/C][C]143.848[/C][C]142.375[/C][C]1.47269[/C][C]1.65231[/C][/ROW]
[ROW][C]67[/C][C]136.8[/C][C]140.409[/C][C]141.754[/C][C]-1.34468[/C][C]-3.60949[/C][/ROW]
[ROW][C]68[/C][C]146.6[/C][C]138.585[/C][C]140.579[/C][C]-1.99398[/C][C]8.01481[/C][/ROW]
[ROW][C]69[/C][C]145.1[/C][C]139.27[/C][C]139.317[/C][C]-0.0467593[/C][C]5.83009[/C][/ROW]
[ROW][C]70[/C][C]133.6[/C][C]134.343[/C][C]137.742[/C][C]-3.39884[/C][C]-0.742824[/C][/ROW]
[ROW][C]71[/C][C]131.4[/C][C]131.628[/C][C]135.571[/C][C]-3.94259[/C][C]-0.228241[/C][/ROW]
[ROW][C]72[/C][C]127.5[/C][C]132.789[/C][C]133.775[/C][C]-0.985648[/C][C]-5.28935[/C][/ROW]
[ROW][C]73[/C][C]130.1[/C][C]135.494[/C][C]133.879[/C][C]1.61435[/C][C]-5.39352[/C][/ROW]
[ROW][C]74[/C][C]131.1[/C][C]138.523[/C][C]135.375[/C][C]3.14838[/C][C]-7.42338[/C][/ROW]
[ROW][C]75[/C][C]132.3[/C][C]139.575[/C][C]136.679[/C][C]2.8963[/C][C]-7.27546[/C][/ROW]
[ROW][C]76[/C][C]128.6[/C][C]140.054[/C][C]138.283[/C][C]1.7706[/C][C]-11.4539[/C][/ROW]
[ROW][C]77[/C][C]125.1[/C][C]141.119[/C][C]140.308[/C][C]0.810185[/C][C]-16.0185[/C][/ROW]
[ROW][C]78[/C][C]128.7[/C][C]143.856[/C][C]142.383[/C][C]1.47269[/C][C]-15.156[/C][/ROW]
[ROW][C]79[/C][C]156.1[/C][C]NA[/C][C]NA[/C][C]-1.34468[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]163.2[/C][C]NA[/C][C]NA[/C][C]-1.99398[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]159.8[/C][C]NA[/C][C]NA[/C][C]-0.0467593[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]157.4[/C][C]NA[/C][C]NA[/C][C]-3.39884[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]156.2[/C][C]NA[/C][C]NA[/C][C]-3.94259[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]152.5[/C][C]NA[/C][C]NA[/C][C]-0.985648[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231405&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231405&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
155.7NANA1.61435NA
259.2NANA3.14838NA
359.8NANA2.8963NA
461.6NANA1.7706NA
565.8NANA0.810185NA
664.2NANA1.47269NA
76766.563767.9083-1.344680.436343
862.868.214470.2083-1.99398-5.41435
965.572.382472.4292-0.0467593-6.88241
1075.270.901274.3-3.398844.29884
1180.971.807475.75-3.942599.09259
1283.276.401977.3875-0.9856486.79815
1383.780.668579.05421.614353.03148
1486.483.906780.75833.148382.49329
1585.986.08883.19172.8963-0.187963
1680.487.478985.70831.7706-7.07894
1781.888.485287.6750.810185-6.68519
1887.591.376989.90421.47269-3.87685
1983.791.559592.9042-1.34468-7.85949
208794.660296.6542-1.99398-7.66019
2199.7101.249101.296-0.0467593-1.54907
22101.4103.318106.717-3.39884-1.91782
23101.9108.353112.296-3.94259-6.45324
24115.7116.902117.887-0.985648-1.20185
25123.2125.073123.4581.61435-1.87269
26136.9131.223128.0753.148385.67662
27146.8134.121131.2252.896312.6787
28149.6134.179132.4081.770615.4211
29146.5132.773131.9620.81018513.7273
30157131.914130.4421.4726925.0856
31147.9126.893128.237-1.3446821.0072
32133.6123.252125.246-1.9939810.3481
33128.7121.082121.129-0.04675937.61759
34100.8113.155116.554-3.39884-12.3553
3591.8108.37112.312-3.94259-16.5699
3689.3107.073108.058-0.985648-17.7727
3796.7104.773103.1581.61435-8.07269
3891.6101.69498.54583.14838-10.0942
3993.397.354694.45832.8963-4.05463
4093.393.666491.89581.7706-0.366435
4110192.197791.38750.8101858.80231
42100.493.15691.68331.472697.24398
4386.990.401291.7458-1.34468-3.50116
4483.989.38191.375-1.99398-5.48102
4580.390.882490.9292-0.0467593-10.5824
4687.786.859590.2583-3.398840.840509
4792.785.249189.1917-3.942597.45093
4895.586.760287.7458-0.9856488.73981
499288.747787.13331.614353.25231
5087.491.181788.03333.14838-3.78171
5186.893.058890.16252.8963-6.2588
5283.794.678992.90831.7706-10.9789
538596.293595.48330.810185-11.2935
5481.799.722798.251.47269-18.0227
5590.9100.447101.792-1.34468-9.54699
56101.5104.381106.375-1.99398-2.88102
57113.8111.307111.354-0.04675932.49259
58120.1113.297116.696-3.398846.80301
59122.1118.466122.408-3.942593.63426
60132.5126.848127.833-0.9856485.65231
61140134.019132.4041.614355.98148
62149.4139.344136.1963.1483810.0558
63144.3142.275139.3792.89632.02454
64154.4143.016141.2461.770611.3836
65151.4143.006142.1960.8101858.39398
66145.5143.848142.3751.472691.65231
67136.8140.409141.754-1.34468-3.60949
68146.6138.585140.579-1.993988.01481
69145.1139.27139.317-0.04675935.83009
70133.6134.343137.742-3.39884-0.742824
71131.4131.628135.571-3.94259-0.228241
72127.5132.789133.775-0.985648-5.28935
73130.1135.494133.8791.61435-5.39352
74131.1138.523135.3753.14838-7.42338
75132.3139.575136.6792.8963-7.27546
76128.6140.054138.2831.7706-11.4539
77125.1141.119140.3080.810185-16.0185
78128.7143.856142.3831.47269-15.156
79156.1NANA-1.34468NA
80163.2NANA-1.99398NA
81159.8NANA-0.0467593NA
82157.4NANA-3.39884NA
83156.2NANA-3.94259NA
84152.5NANA-0.985648NA



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')