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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 09 Dec 2013 03:23:42 -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/t1386577471b41oq0ct5oukb1r.htm/, Retrieved Thu, 28 Mar 2024 17:52:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231558, Retrieved Thu, 28 Mar 2024 17:52:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-09 08:23:42] [41a3153726230bb9de171cb3fce0abfe] [Current]
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Dataseries X:
155,28
173,24
180,16
181,52
182,25
182,19
182
181,65
180,07
182,62
180,38
181,15
180,5
181,14
180,93
211,91
223,81
226,88
226,8
231,81
232,06
232,32
228,37
226,31
225,72
219,98
219,31
215,19
213,81
213,7
213,6
213,52
218,39
219,97
221,09
219,17
219,17
218,45
216,88
216,19
214,59
269,87
272,71
280,35
274,5
268,86
261,7
263,98
263,01
262,79
263,59
267
267,89
267,86
266,84
268,24
267,67
269,07
270,87
271,68
271,63
275,21
276,66
276,08
278,3
279,06
279,28
279,12
262,72
262,55
260,7
259,14
260,61
260,53
259,07
257,01
257,08
256,83
256,75
257,61
258,58
259,57
259,29
258,51




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231558&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231558&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'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1155.28NANA-3.75641NA
2173.24NANA-5.22634NA
3180.16NANA-6.57571NA
4181.52NANA-3.1653NA
5182.25NANA-2.23099NA
6182.19NANA6.47047NA
7182185.952179.5936.35859-3.95193
8181.65188.238180.9737.26429-6.58762
9180.07184.231181.3352.89679-4.16137
10182.62184.454182.6331.82123-1.83415
11180.38184.362185.631-1.26932-3.98151
12181.15186.637189.225-2.58731-5.48727
13180.5189.197192.953-3.75641-8.69693
14181.14191.684196.91-5.22634-10.5437
15180.93194.591201.166-6.57571-13.6605
16211.91202.238205.403-3.16539.67196
17223.81207.243209.474-2.2309916.5672
18226.88219.825213.3556.470477.05453
19226.8223.479217.1216.358593.32057
20231.81227.888220.6237.264293.92238
21232.06226.738223.8412.896795.32238
22232.32227.398225.5771.821234.9221
23228.37224.027225.297-1.269324.34266
24226.31221.744224.331-2.587314.56648
25225.72219.475223.232-3.756416.24474
26219.98216.693221.92-5.226343.28675
27219.31214.012220.588-6.575715.2978
28215.19216.338219.504-3.1653-1.14845
29213.81216.455218.686-2.23099-2.64484
30213.7224.555218.0856.47047-10.8555
31213.6223.873217.5156.35859-10.2732
32213.52224.442217.1787.26429-10.9222
33218.39219.91217.0132.89679-1.5197
34219.97218.775216.9531.821231.19543
35221.09215.758217.028-1.269325.33182
36219.17216.813219.4-2.587312.35689
37219.17220.447224.204-3.75641-1.27734
38218.45224.225229.451-5.22634-5.77491
39216.88227.998234.574-6.57571-11.118
40216.19235.783238.949-3.1653-19.5935
41214.59240.447242.678-2.23099-25.8569
42269.87252.708246.2376.4704717.1624
43272.71256.289249.9316.3585916.4206
44280.35260.869253.6057.2642919.4807
45274.5260.296257.3992.8967914.2045
46268.86263.283261.4621.821235.57668
47261.7264.531265.8-1.26932-2.83068
48263.98265.35267.937-2.58731-1.36977
49263.01263.852267.609-3.75641-0.842344
50262.79261.633266.86-5.226341.15675
51263.59259.495266.07-6.575714.0953
52267262.629265.795-3.16534.37071
53267.89263.954266.185-2.230993.93557
54267.86273.359266.8886.47047-5.4988
55266.84273.927267.5686.35859-7.08693
56268.24275.709268.4457.26429-7.46929
57267.67272.404269.5072.89679-4.73387
58269.07272.251270.431.82123-3.18123
59270.87269.973271.242-1.269320.89724
60271.68269.555272.142-2.587312.12481
61271.63269.371273.128-3.756412.25891
62275.21268.873274.099-5.226346.33717
63276.66267.771274.346-6.575718.88946
64276.08270.703273.868-3.16535.37696
65278.3270.942273.173-2.230997.35807
66279.06278.697272.2276.470470.362865
67279.28277.604271.2456.358591.67641
68279.12277.438270.1747.264291.68155
69262.72271.726268.832.89679-9.00637
70262.55269.123267.3021.82123-6.57332
71260.7264.354265.623-1.26932-3.65401
72259.14261.226263.813-2.58731-2.08561
73260.61258.192261.948-3.756412.41849
74260.53254.887260.113-5.226345.64342
75259.07252.468259.044-6.575716.60155
76257.01255.582258.748-3.16531.4278
77257.08256.334258.565-2.230990.746406
78256.83264.95258.486.47047-8.12005
79256.75NANA6.35859NA
80257.61NANA7.26429NA
81258.58NANA2.89679NA
82259.57NANA1.82123NA
83259.29NANA-1.26932NA
84258.51NANA-2.58731NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 155.28 & NA & NA & -3.75641 & NA \tabularnewline
2 & 173.24 & NA & NA & -5.22634 & NA \tabularnewline
3 & 180.16 & NA & NA & -6.57571 & NA \tabularnewline
4 & 181.52 & NA & NA & -3.1653 & NA \tabularnewline
5 & 182.25 & NA & NA & -2.23099 & NA \tabularnewline
6 & 182.19 & NA & NA & 6.47047 & NA \tabularnewline
7 & 182 & 185.952 & 179.593 & 6.35859 & -3.95193 \tabularnewline
8 & 181.65 & 188.238 & 180.973 & 7.26429 & -6.58762 \tabularnewline
9 & 180.07 & 184.231 & 181.335 & 2.89679 & -4.16137 \tabularnewline
10 & 182.62 & 184.454 & 182.633 & 1.82123 & -1.83415 \tabularnewline
11 & 180.38 & 184.362 & 185.631 & -1.26932 & -3.98151 \tabularnewline
12 & 181.15 & 186.637 & 189.225 & -2.58731 & -5.48727 \tabularnewline
13 & 180.5 & 189.197 & 192.953 & -3.75641 & -8.69693 \tabularnewline
14 & 181.14 & 191.684 & 196.91 & -5.22634 & -10.5437 \tabularnewline
15 & 180.93 & 194.591 & 201.166 & -6.57571 & -13.6605 \tabularnewline
16 & 211.91 & 202.238 & 205.403 & -3.1653 & 9.67196 \tabularnewline
17 & 223.81 & 207.243 & 209.474 & -2.23099 & 16.5672 \tabularnewline
18 & 226.88 & 219.825 & 213.355 & 6.47047 & 7.05453 \tabularnewline
19 & 226.8 & 223.479 & 217.121 & 6.35859 & 3.32057 \tabularnewline
20 & 231.81 & 227.888 & 220.623 & 7.26429 & 3.92238 \tabularnewline
21 & 232.06 & 226.738 & 223.841 & 2.89679 & 5.32238 \tabularnewline
22 & 232.32 & 227.398 & 225.577 & 1.82123 & 4.9221 \tabularnewline
23 & 228.37 & 224.027 & 225.297 & -1.26932 & 4.34266 \tabularnewline
24 & 226.31 & 221.744 & 224.331 & -2.58731 & 4.56648 \tabularnewline
25 & 225.72 & 219.475 & 223.232 & -3.75641 & 6.24474 \tabularnewline
26 & 219.98 & 216.693 & 221.92 & -5.22634 & 3.28675 \tabularnewline
27 & 219.31 & 214.012 & 220.588 & -6.57571 & 5.2978 \tabularnewline
28 & 215.19 & 216.338 & 219.504 & -3.1653 & -1.14845 \tabularnewline
29 & 213.81 & 216.455 & 218.686 & -2.23099 & -2.64484 \tabularnewline
30 & 213.7 & 224.555 & 218.085 & 6.47047 & -10.8555 \tabularnewline
31 & 213.6 & 223.873 & 217.515 & 6.35859 & -10.2732 \tabularnewline
32 & 213.52 & 224.442 & 217.178 & 7.26429 & -10.9222 \tabularnewline
33 & 218.39 & 219.91 & 217.013 & 2.89679 & -1.5197 \tabularnewline
34 & 219.97 & 218.775 & 216.953 & 1.82123 & 1.19543 \tabularnewline
35 & 221.09 & 215.758 & 217.028 & -1.26932 & 5.33182 \tabularnewline
36 & 219.17 & 216.813 & 219.4 & -2.58731 & 2.35689 \tabularnewline
37 & 219.17 & 220.447 & 224.204 & -3.75641 & -1.27734 \tabularnewline
38 & 218.45 & 224.225 & 229.451 & -5.22634 & -5.77491 \tabularnewline
39 & 216.88 & 227.998 & 234.574 & -6.57571 & -11.118 \tabularnewline
40 & 216.19 & 235.783 & 238.949 & -3.1653 & -19.5935 \tabularnewline
41 & 214.59 & 240.447 & 242.678 & -2.23099 & -25.8569 \tabularnewline
42 & 269.87 & 252.708 & 246.237 & 6.47047 & 17.1624 \tabularnewline
43 & 272.71 & 256.289 & 249.931 & 6.35859 & 16.4206 \tabularnewline
44 & 280.35 & 260.869 & 253.605 & 7.26429 & 19.4807 \tabularnewline
45 & 274.5 & 260.296 & 257.399 & 2.89679 & 14.2045 \tabularnewline
46 & 268.86 & 263.283 & 261.462 & 1.82123 & 5.57668 \tabularnewline
47 & 261.7 & 264.531 & 265.8 & -1.26932 & -2.83068 \tabularnewline
48 & 263.98 & 265.35 & 267.937 & -2.58731 & -1.36977 \tabularnewline
49 & 263.01 & 263.852 & 267.609 & -3.75641 & -0.842344 \tabularnewline
50 & 262.79 & 261.633 & 266.86 & -5.22634 & 1.15675 \tabularnewline
51 & 263.59 & 259.495 & 266.07 & -6.57571 & 4.0953 \tabularnewline
52 & 267 & 262.629 & 265.795 & -3.1653 & 4.37071 \tabularnewline
53 & 267.89 & 263.954 & 266.185 & -2.23099 & 3.93557 \tabularnewline
54 & 267.86 & 273.359 & 266.888 & 6.47047 & -5.4988 \tabularnewline
55 & 266.84 & 273.927 & 267.568 & 6.35859 & -7.08693 \tabularnewline
56 & 268.24 & 275.709 & 268.445 & 7.26429 & -7.46929 \tabularnewline
57 & 267.67 & 272.404 & 269.507 & 2.89679 & -4.73387 \tabularnewline
58 & 269.07 & 272.251 & 270.43 & 1.82123 & -3.18123 \tabularnewline
59 & 270.87 & 269.973 & 271.242 & -1.26932 & 0.89724 \tabularnewline
60 & 271.68 & 269.555 & 272.142 & -2.58731 & 2.12481 \tabularnewline
61 & 271.63 & 269.371 & 273.128 & -3.75641 & 2.25891 \tabularnewline
62 & 275.21 & 268.873 & 274.099 & -5.22634 & 6.33717 \tabularnewline
63 & 276.66 & 267.771 & 274.346 & -6.57571 & 8.88946 \tabularnewline
64 & 276.08 & 270.703 & 273.868 & -3.1653 & 5.37696 \tabularnewline
65 & 278.3 & 270.942 & 273.173 & -2.23099 & 7.35807 \tabularnewline
66 & 279.06 & 278.697 & 272.227 & 6.47047 & 0.362865 \tabularnewline
67 & 279.28 & 277.604 & 271.245 & 6.35859 & 1.67641 \tabularnewline
68 & 279.12 & 277.438 & 270.174 & 7.26429 & 1.68155 \tabularnewline
69 & 262.72 & 271.726 & 268.83 & 2.89679 & -9.00637 \tabularnewline
70 & 262.55 & 269.123 & 267.302 & 1.82123 & -6.57332 \tabularnewline
71 & 260.7 & 264.354 & 265.623 & -1.26932 & -3.65401 \tabularnewline
72 & 259.14 & 261.226 & 263.813 & -2.58731 & -2.08561 \tabularnewline
73 & 260.61 & 258.192 & 261.948 & -3.75641 & 2.41849 \tabularnewline
74 & 260.53 & 254.887 & 260.113 & -5.22634 & 5.64342 \tabularnewline
75 & 259.07 & 252.468 & 259.044 & -6.57571 & 6.60155 \tabularnewline
76 & 257.01 & 255.582 & 258.748 & -3.1653 & 1.4278 \tabularnewline
77 & 257.08 & 256.334 & 258.565 & -2.23099 & 0.746406 \tabularnewline
78 & 256.83 & 264.95 & 258.48 & 6.47047 & -8.12005 \tabularnewline
79 & 256.75 & NA & NA & 6.35859 & NA \tabularnewline
80 & 257.61 & NA & NA & 7.26429 & NA \tabularnewline
81 & 258.58 & NA & NA & 2.89679 & NA \tabularnewline
82 & 259.57 & NA & NA & 1.82123 & NA \tabularnewline
83 & 259.29 & NA & NA & -1.26932 & NA \tabularnewline
84 & 258.51 & NA & NA & -2.58731 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231558&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]155.28[/C][C]NA[/C][C]NA[/C][C]-3.75641[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]173.24[/C][C]NA[/C][C]NA[/C][C]-5.22634[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]180.16[/C][C]NA[/C][C]NA[/C][C]-6.57571[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]181.52[/C][C]NA[/C][C]NA[/C][C]-3.1653[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]182.25[/C][C]NA[/C][C]NA[/C][C]-2.23099[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]182.19[/C][C]NA[/C][C]NA[/C][C]6.47047[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]182[/C][C]185.952[/C][C]179.593[/C][C]6.35859[/C][C]-3.95193[/C][/ROW]
[ROW][C]8[/C][C]181.65[/C][C]188.238[/C][C]180.973[/C][C]7.26429[/C][C]-6.58762[/C][/ROW]
[ROW][C]9[/C][C]180.07[/C][C]184.231[/C][C]181.335[/C][C]2.89679[/C][C]-4.16137[/C][/ROW]
[ROW][C]10[/C][C]182.62[/C][C]184.454[/C][C]182.633[/C][C]1.82123[/C][C]-1.83415[/C][/ROW]
[ROW][C]11[/C][C]180.38[/C][C]184.362[/C][C]185.631[/C][C]-1.26932[/C][C]-3.98151[/C][/ROW]
[ROW][C]12[/C][C]181.15[/C][C]186.637[/C][C]189.225[/C][C]-2.58731[/C][C]-5.48727[/C][/ROW]
[ROW][C]13[/C][C]180.5[/C][C]189.197[/C][C]192.953[/C][C]-3.75641[/C][C]-8.69693[/C][/ROW]
[ROW][C]14[/C][C]181.14[/C][C]191.684[/C][C]196.91[/C][C]-5.22634[/C][C]-10.5437[/C][/ROW]
[ROW][C]15[/C][C]180.93[/C][C]194.591[/C][C]201.166[/C][C]-6.57571[/C][C]-13.6605[/C][/ROW]
[ROW][C]16[/C][C]211.91[/C][C]202.238[/C][C]205.403[/C][C]-3.1653[/C][C]9.67196[/C][/ROW]
[ROW][C]17[/C][C]223.81[/C][C]207.243[/C][C]209.474[/C][C]-2.23099[/C][C]16.5672[/C][/ROW]
[ROW][C]18[/C][C]226.88[/C][C]219.825[/C][C]213.355[/C][C]6.47047[/C][C]7.05453[/C][/ROW]
[ROW][C]19[/C][C]226.8[/C][C]223.479[/C][C]217.121[/C][C]6.35859[/C][C]3.32057[/C][/ROW]
[ROW][C]20[/C][C]231.81[/C][C]227.888[/C][C]220.623[/C][C]7.26429[/C][C]3.92238[/C][/ROW]
[ROW][C]21[/C][C]232.06[/C][C]226.738[/C][C]223.841[/C][C]2.89679[/C][C]5.32238[/C][/ROW]
[ROW][C]22[/C][C]232.32[/C][C]227.398[/C][C]225.577[/C][C]1.82123[/C][C]4.9221[/C][/ROW]
[ROW][C]23[/C][C]228.37[/C][C]224.027[/C][C]225.297[/C][C]-1.26932[/C][C]4.34266[/C][/ROW]
[ROW][C]24[/C][C]226.31[/C][C]221.744[/C][C]224.331[/C][C]-2.58731[/C][C]4.56648[/C][/ROW]
[ROW][C]25[/C][C]225.72[/C][C]219.475[/C][C]223.232[/C][C]-3.75641[/C][C]6.24474[/C][/ROW]
[ROW][C]26[/C][C]219.98[/C][C]216.693[/C][C]221.92[/C][C]-5.22634[/C][C]3.28675[/C][/ROW]
[ROW][C]27[/C][C]219.31[/C][C]214.012[/C][C]220.588[/C][C]-6.57571[/C][C]5.2978[/C][/ROW]
[ROW][C]28[/C][C]215.19[/C][C]216.338[/C][C]219.504[/C][C]-3.1653[/C][C]-1.14845[/C][/ROW]
[ROW][C]29[/C][C]213.81[/C][C]216.455[/C][C]218.686[/C][C]-2.23099[/C][C]-2.64484[/C][/ROW]
[ROW][C]30[/C][C]213.7[/C][C]224.555[/C][C]218.085[/C][C]6.47047[/C][C]-10.8555[/C][/ROW]
[ROW][C]31[/C][C]213.6[/C][C]223.873[/C][C]217.515[/C][C]6.35859[/C][C]-10.2732[/C][/ROW]
[ROW][C]32[/C][C]213.52[/C][C]224.442[/C][C]217.178[/C][C]7.26429[/C][C]-10.9222[/C][/ROW]
[ROW][C]33[/C][C]218.39[/C][C]219.91[/C][C]217.013[/C][C]2.89679[/C][C]-1.5197[/C][/ROW]
[ROW][C]34[/C][C]219.97[/C][C]218.775[/C][C]216.953[/C][C]1.82123[/C][C]1.19543[/C][/ROW]
[ROW][C]35[/C][C]221.09[/C][C]215.758[/C][C]217.028[/C][C]-1.26932[/C][C]5.33182[/C][/ROW]
[ROW][C]36[/C][C]219.17[/C][C]216.813[/C][C]219.4[/C][C]-2.58731[/C][C]2.35689[/C][/ROW]
[ROW][C]37[/C][C]219.17[/C][C]220.447[/C][C]224.204[/C][C]-3.75641[/C][C]-1.27734[/C][/ROW]
[ROW][C]38[/C][C]218.45[/C][C]224.225[/C][C]229.451[/C][C]-5.22634[/C][C]-5.77491[/C][/ROW]
[ROW][C]39[/C][C]216.88[/C][C]227.998[/C][C]234.574[/C][C]-6.57571[/C][C]-11.118[/C][/ROW]
[ROW][C]40[/C][C]216.19[/C][C]235.783[/C][C]238.949[/C][C]-3.1653[/C][C]-19.5935[/C][/ROW]
[ROW][C]41[/C][C]214.59[/C][C]240.447[/C][C]242.678[/C][C]-2.23099[/C][C]-25.8569[/C][/ROW]
[ROW][C]42[/C][C]269.87[/C][C]252.708[/C][C]246.237[/C][C]6.47047[/C][C]17.1624[/C][/ROW]
[ROW][C]43[/C][C]272.71[/C][C]256.289[/C][C]249.931[/C][C]6.35859[/C][C]16.4206[/C][/ROW]
[ROW][C]44[/C][C]280.35[/C][C]260.869[/C][C]253.605[/C][C]7.26429[/C][C]19.4807[/C][/ROW]
[ROW][C]45[/C][C]274.5[/C][C]260.296[/C][C]257.399[/C][C]2.89679[/C][C]14.2045[/C][/ROW]
[ROW][C]46[/C][C]268.86[/C][C]263.283[/C][C]261.462[/C][C]1.82123[/C][C]5.57668[/C][/ROW]
[ROW][C]47[/C][C]261.7[/C][C]264.531[/C][C]265.8[/C][C]-1.26932[/C][C]-2.83068[/C][/ROW]
[ROW][C]48[/C][C]263.98[/C][C]265.35[/C][C]267.937[/C][C]-2.58731[/C][C]-1.36977[/C][/ROW]
[ROW][C]49[/C][C]263.01[/C][C]263.852[/C][C]267.609[/C][C]-3.75641[/C][C]-0.842344[/C][/ROW]
[ROW][C]50[/C][C]262.79[/C][C]261.633[/C][C]266.86[/C][C]-5.22634[/C][C]1.15675[/C][/ROW]
[ROW][C]51[/C][C]263.59[/C][C]259.495[/C][C]266.07[/C][C]-6.57571[/C][C]4.0953[/C][/ROW]
[ROW][C]52[/C][C]267[/C][C]262.629[/C][C]265.795[/C][C]-3.1653[/C][C]4.37071[/C][/ROW]
[ROW][C]53[/C][C]267.89[/C][C]263.954[/C][C]266.185[/C][C]-2.23099[/C][C]3.93557[/C][/ROW]
[ROW][C]54[/C][C]267.86[/C][C]273.359[/C][C]266.888[/C][C]6.47047[/C][C]-5.4988[/C][/ROW]
[ROW][C]55[/C][C]266.84[/C][C]273.927[/C][C]267.568[/C][C]6.35859[/C][C]-7.08693[/C][/ROW]
[ROW][C]56[/C][C]268.24[/C][C]275.709[/C][C]268.445[/C][C]7.26429[/C][C]-7.46929[/C][/ROW]
[ROW][C]57[/C][C]267.67[/C][C]272.404[/C][C]269.507[/C][C]2.89679[/C][C]-4.73387[/C][/ROW]
[ROW][C]58[/C][C]269.07[/C][C]272.251[/C][C]270.43[/C][C]1.82123[/C][C]-3.18123[/C][/ROW]
[ROW][C]59[/C][C]270.87[/C][C]269.973[/C][C]271.242[/C][C]-1.26932[/C][C]0.89724[/C][/ROW]
[ROW][C]60[/C][C]271.68[/C][C]269.555[/C][C]272.142[/C][C]-2.58731[/C][C]2.12481[/C][/ROW]
[ROW][C]61[/C][C]271.63[/C][C]269.371[/C][C]273.128[/C][C]-3.75641[/C][C]2.25891[/C][/ROW]
[ROW][C]62[/C][C]275.21[/C][C]268.873[/C][C]274.099[/C][C]-5.22634[/C][C]6.33717[/C][/ROW]
[ROW][C]63[/C][C]276.66[/C][C]267.771[/C][C]274.346[/C][C]-6.57571[/C][C]8.88946[/C][/ROW]
[ROW][C]64[/C][C]276.08[/C][C]270.703[/C][C]273.868[/C][C]-3.1653[/C][C]5.37696[/C][/ROW]
[ROW][C]65[/C][C]278.3[/C][C]270.942[/C][C]273.173[/C][C]-2.23099[/C][C]7.35807[/C][/ROW]
[ROW][C]66[/C][C]279.06[/C][C]278.697[/C][C]272.227[/C][C]6.47047[/C][C]0.362865[/C][/ROW]
[ROW][C]67[/C][C]279.28[/C][C]277.604[/C][C]271.245[/C][C]6.35859[/C][C]1.67641[/C][/ROW]
[ROW][C]68[/C][C]279.12[/C][C]277.438[/C][C]270.174[/C][C]7.26429[/C][C]1.68155[/C][/ROW]
[ROW][C]69[/C][C]262.72[/C][C]271.726[/C][C]268.83[/C][C]2.89679[/C][C]-9.00637[/C][/ROW]
[ROW][C]70[/C][C]262.55[/C][C]269.123[/C][C]267.302[/C][C]1.82123[/C][C]-6.57332[/C][/ROW]
[ROW][C]71[/C][C]260.7[/C][C]264.354[/C][C]265.623[/C][C]-1.26932[/C][C]-3.65401[/C][/ROW]
[ROW][C]72[/C][C]259.14[/C][C]261.226[/C][C]263.813[/C][C]-2.58731[/C][C]-2.08561[/C][/ROW]
[ROW][C]73[/C][C]260.61[/C][C]258.192[/C][C]261.948[/C][C]-3.75641[/C][C]2.41849[/C][/ROW]
[ROW][C]74[/C][C]260.53[/C][C]254.887[/C][C]260.113[/C][C]-5.22634[/C][C]5.64342[/C][/ROW]
[ROW][C]75[/C][C]259.07[/C][C]252.468[/C][C]259.044[/C][C]-6.57571[/C][C]6.60155[/C][/ROW]
[ROW][C]76[/C][C]257.01[/C][C]255.582[/C][C]258.748[/C][C]-3.1653[/C][C]1.4278[/C][/ROW]
[ROW][C]77[/C][C]257.08[/C][C]256.334[/C][C]258.565[/C][C]-2.23099[/C][C]0.746406[/C][/ROW]
[ROW][C]78[/C][C]256.83[/C][C]264.95[/C][C]258.48[/C][C]6.47047[/C][C]-8.12005[/C][/ROW]
[ROW][C]79[/C][C]256.75[/C][C]NA[/C][C]NA[/C][C]6.35859[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]257.61[/C][C]NA[/C][C]NA[/C][C]7.26429[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]258.58[/C][C]NA[/C][C]NA[/C][C]2.89679[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]259.57[/C][C]NA[/C][C]NA[/C][C]1.82123[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]259.29[/C][C]NA[/C][C]NA[/C][C]-1.26932[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]258.51[/C][C]NA[/C][C]NA[/C][C]-2.58731[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231558&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
1155.28NANA-3.75641NA
2173.24NANA-5.22634NA
3180.16NANA-6.57571NA
4181.52NANA-3.1653NA
5182.25NANA-2.23099NA
6182.19NANA6.47047NA
7182185.952179.5936.35859-3.95193
8181.65188.238180.9737.26429-6.58762
9180.07184.231181.3352.89679-4.16137
10182.62184.454182.6331.82123-1.83415
11180.38184.362185.631-1.26932-3.98151
12181.15186.637189.225-2.58731-5.48727
13180.5189.197192.953-3.75641-8.69693
14181.14191.684196.91-5.22634-10.5437
15180.93194.591201.166-6.57571-13.6605
16211.91202.238205.403-3.16539.67196
17223.81207.243209.474-2.2309916.5672
18226.88219.825213.3556.470477.05453
19226.8223.479217.1216.358593.32057
20231.81227.888220.6237.264293.92238
21232.06226.738223.8412.896795.32238
22232.32227.398225.5771.821234.9221
23228.37224.027225.297-1.269324.34266
24226.31221.744224.331-2.587314.56648
25225.72219.475223.232-3.756416.24474
26219.98216.693221.92-5.226343.28675
27219.31214.012220.588-6.575715.2978
28215.19216.338219.504-3.1653-1.14845
29213.81216.455218.686-2.23099-2.64484
30213.7224.555218.0856.47047-10.8555
31213.6223.873217.5156.35859-10.2732
32213.52224.442217.1787.26429-10.9222
33218.39219.91217.0132.89679-1.5197
34219.97218.775216.9531.821231.19543
35221.09215.758217.028-1.269325.33182
36219.17216.813219.4-2.587312.35689
37219.17220.447224.204-3.75641-1.27734
38218.45224.225229.451-5.22634-5.77491
39216.88227.998234.574-6.57571-11.118
40216.19235.783238.949-3.1653-19.5935
41214.59240.447242.678-2.23099-25.8569
42269.87252.708246.2376.4704717.1624
43272.71256.289249.9316.3585916.4206
44280.35260.869253.6057.2642919.4807
45274.5260.296257.3992.8967914.2045
46268.86263.283261.4621.821235.57668
47261.7264.531265.8-1.26932-2.83068
48263.98265.35267.937-2.58731-1.36977
49263.01263.852267.609-3.75641-0.842344
50262.79261.633266.86-5.226341.15675
51263.59259.495266.07-6.575714.0953
52267262.629265.795-3.16534.37071
53267.89263.954266.185-2.230993.93557
54267.86273.359266.8886.47047-5.4988
55266.84273.927267.5686.35859-7.08693
56268.24275.709268.4457.26429-7.46929
57267.67272.404269.5072.89679-4.73387
58269.07272.251270.431.82123-3.18123
59270.87269.973271.242-1.269320.89724
60271.68269.555272.142-2.587312.12481
61271.63269.371273.128-3.756412.25891
62275.21268.873274.099-5.226346.33717
63276.66267.771274.346-6.575718.88946
64276.08270.703273.868-3.16535.37696
65278.3270.942273.173-2.230997.35807
66279.06278.697272.2276.470470.362865
67279.28277.604271.2456.358591.67641
68279.12277.438270.1747.264291.68155
69262.72271.726268.832.89679-9.00637
70262.55269.123267.3021.82123-6.57332
71260.7264.354265.623-1.26932-3.65401
72259.14261.226263.813-2.58731-2.08561
73260.61258.192261.948-3.756412.41849
74260.53254.887260.113-5.226345.64342
75259.07252.468259.044-6.575716.60155
76257.01255.582258.748-3.16531.4278
77257.08256.334258.565-2.230990.746406
78256.83264.95258.486.47047-8.12005
79256.75NANA6.35859NA
80257.61NANA7.26429NA
81258.58NANA2.89679NA
82259.57NANA1.82123NA
83259.29NANA-1.26932NA
84258.51NANA-2.58731NA



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')