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R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationTue, 10 Dec 2013 04:14:29 -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/t1386667003ynap2fld3wqoqem.htm/, Retrieved Fri, 29 Mar 2024 08:59:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231832, Retrieved Fri, 29 Mar 2024 08:59:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-10 09:14:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
78,7
75,7
77,1
86,1
86,8
86,3
91,5
90,7
78,2
73
73,7
77,3
67,5
72,7
76,6
82,4
82,3
86,3
93
88,8
96,9
103,9
115,7
112,8
114,7
118
129,3
137
156
166,2
167,8
144,3
126
90,4
67,5
52,4
54,6
52,9
59,1
63,3
73,8
87,6
81,8
90,7
86,3
93,6
98
94,3
97,6
94,2
100,2
106,7
95,7
94,6
94,7
96,2
96,3
103,3
106,8
113,7
117,4
123,6
137,6
147,4
137,2
133,8
136,7
127,3
128,7
127
133,7
132
135,1
142,6
149,3
143,5
131,4
114,7
122,3
133,4
134,6
130,9
127,9
128




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231832&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231832&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
178.7NANA-7.57234NA
275.7NANA-5.23275NA
377.1NANA2.09572NA
486.1NANA6.00197NA
586.8NANA4.5735NA
686.3NANA4.97836NA
791.590.667980.79179.876270.83206
890.784.636780.24.436696.06331
978.279.258280.0542-0.795949-1.05822
107374.649979.8792-5.22928-1.64988
1173.774.299979.5375-5.23762-0.599884
1277.371.455479.35-7.894565.84456
1367.571.840279.4125-7.57234-4.34016
1472.774.163179.3958-5.23275-1.46308
1576.682.191680.09582.09572-5.59155
1682.488.164582.16256.00197-5.76447
1782.389.773585.24.5735-7.4735
1886.393.407588.42924.97836-7.10752
1993101.75191.8759.87627-8.75127
2088.8100.16695.72924.43669-11.3659
2196.999.016699.8125-0.795949-2.11655
22103.999.0541104.283-5.229284.84595
23115.7104.392109.629-5.2376211.3084
24112.8108.135116.029-7.894564.66539
25114.7114.903122.475-7.57234-0.202662
26118122.671127.904-5.23275-4.67141
27129.3133.525131.4292.09572-4.22488
28137138.081132.0796.00197-1.08113
29156134.082129.5084.573521.9182
30166.2129.962124.9834.9783636.2383
31167.8129.839119.9629.8762737.9612
32144.3119.183114.7464.4366925.1175
33126108.312109.108-0.79594917.6876
3490.497.8832103.112-5.22928-7.48322
3567.591.379196.6167-5.23762-23.8791
3652.482.022189.9167-7.89456-29.6221
3754.675.48683.0583-7.57234-20.886
3852.972.008977.2417-5.23275-19.1089
3959.175.449973.35422.09572-16.3499
4063.377.835371.83336.00197-14.5353
4173.877.81173.23754.5735-4.011
4287.681.232576.25424.978366.36748
4381.889.667979.79179.87627-7.86794
4490.787.740983.30424.436692.95914
4586.385.941686.7375-0.7959490.358449
4693.685.029190.2583-5.229288.57095
479887.741692.9792-5.2376210.2584
4894.386.288894.1833-7.894568.01123
4997.687.440295.0125-7.5723410.1598
5094.290.546495.7792-5.232753.65359
51100.298.520796.4252.095721.67928
52106.7103.24897.24586.001973.4522
5395.7102.5998.01674.5735-6.89016
5494.6104.1799.19174.97836-9.57002
5594.7110.701100.8259.87627-16.0013
5696.2107.312102.8754.43669-11.1117
5796.3104.862105.658-0.795949-8.56238
58103.3103.683108.912-5.22928-0.383218
59106.8107.1112.337-5.23762-0.299884
60113.7107.805115.7-7.894565.89456
61117.4111.511119.083-7.572345.889
62123.6116.896122.129-5.232756.70359
63137.6126.871124.7752.0957210.7293
64147.4133.114127.1126.0019714.2855
65137.2133.794129.2214.57353.40567
66133.8136.083131.1044.97836-2.28252
67136.7142.48132.6049.87627-5.78044
68127.3138.57134.1334.43669-11.27
69128.7134.617135.412-0.795949-5.91655
70127130.508135.738-5.22928-3.50822
71133.7130.096135.333-5.237623.60428
72132126.401134.296-7.894565.59873
73135.1125.328132.9-7.572349.77234
74142.6127.321132.554-5.2327515.2786
75149.3135.15133.0542.0957214.1501
76143.5139.464133.4626.001974.03553
77131.4137.957133.3834.5735-6.55683
78114.7137.953132.9754.97836-23.2534
79122.3NANA9.87627NA
80133.4NANA4.43669NA
81134.6NANA-0.795949NA
82130.9NANA-5.22928NA
83127.9NANA-5.23762NA
84128NANA-7.89456NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 78.7 & NA & NA & -7.57234 & NA \tabularnewline
2 & 75.7 & NA & NA & -5.23275 & NA \tabularnewline
3 & 77.1 & NA & NA & 2.09572 & NA \tabularnewline
4 & 86.1 & NA & NA & 6.00197 & NA \tabularnewline
5 & 86.8 & NA & NA & 4.5735 & NA \tabularnewline
6 & 86.3 & NA & NA & 4.97836 & NA \tabularnewline
7 & 91.5 & 90.6679 & 80.7917 & 9.87627 & 0.83206 \tabularnewline
8 & 90.7 & 84.6367 & 80.2 & 4.43669 & 6.06331 \tabularnewline
9 & 78.2 & 79.2582 & 80.0542 & -0.795949 & -1.05822 \tabularnewline
10 & 73 & 74.6499 & 79.8792 & -5.22928 & -1.64988 \tabularnewline
11 & 73.7 & 74.2999 & 79.5375 & -5.23762 & -0.599884 \tabularnewline
12 & 77.3 & 71.4554 & 79.35 & -7.89456 & 5.84456 \tabularnewline
13 & 67.5 & 71.8402 & 79.4125 & -7.57234 & -4.34016 \tabularnewline
14 & 72.7 & 74.1631 & 79.3958 & -5.23275 & -1.46308 \tabularnewline
15 & 76.6 & 82.1916 & 80.0958 & 2.09572 & -5.59155 \tabularnewline
16 & 82.4 & 88.1645 & 82.1625 & 6.00197 & -5.76447 \tabularnewline
17 & 82.3 & 89.7735 & 85.2 & 4.5735 & -7.4735 \tabularnewline
18 & 86.3 & 93.4075 & 88.4292 & 4.97836 & -7.10752 \tabularnewline
19 & 93 & 101.751 & 91.875 & 9.87627 & -8.75127 \tabularnewline
20 & 88.8 & 100.166 & 95.7292 & 4.43669 & -11.3659 \tabularnewline
21 & 96.9 & 99.0166 & 99.8125 & -0.795949 & -2.11655 \tabularnewline
22 & 103.9 & 99.0541 & 104.283 & -5.22928 & 4.84595 \tabularnewline
23 & 115.7 & 104.392 & 109.629 & -5.23762 & 11.3084 \tabularnewline
24 & 112.8 & 108.135 & 116.029 & -7.89456 & 4.66539 \tabularnewline
25 & 114.7 & 114.903 & 122.475 & -7.57234 & -0.202662 \tabularnewline
26 & 118 & 122.671 & 127.904 & -5.23275 & -4.67141 \tabularnewline
27 & 129.3 & 133.525 & 131.429 & 2.09572 & -4.22488 \tabularnewline
28 & 137 & 138.081 & 132.079 & 6.00197 & -1.08113 \tabularnewline
29 & 156 & 134.082 & 129.508 & 4.5735 & 21.9182 \tabularnewline
30 & 166.2 & 129.962 & 124.983 & 4.97836 & 36.2383 \tabularnewline
31 & 167.8 & 129.839 & 119.962 & 9.87627 & 37.9612 \tabularnewline
32 & 144.3 & 119.183 & 114.746 & 4.43669 & 25.1175 \tabularnewline
33 & 126 & 108.312 & 109.108 & -0.795949 & 17.6876 \tabularnewline
34 & 90.4 & 97.8832 & 103.112 & -5.22928 & -7.48322 \tabularnewline
35 & 67.5 & 91.3791 & 96.6167 & -5.23762 & -23.8791 \tabularnewline
36 & 52.4 & 82.0221 & 89.9167 & -7.89456 & -29.6221 \tabularnewline
37 & 54.6 & 75.486 & 83.0583 & -7.57234 & -20.886 \tabularnewline
38 & 52.9 & 72.0089 & 77.2417 & -5.23275 & -19.1089 \tabularnewline
39 & 59.1 & 75.4499 & 73.3542 & 2.09572 & -16.3499 \tabularnewline
40 & 63.3 & 77.8353 & 71.8333 & 6.00197 & -14.5353 \tabularnewline
41 & 73.8 & 77.811 & 73.2375 & 4.5735 & -4.011 \tabularnewline
42 & 87.6 & 81.2325 & 76.2542 & 4.97836 & 6.36748 \tabularnewline
43 & 81.8 & 89.6679 & 79.7917 & 9.87627 & -7.86794 \tabularnewline
44 & 90.7 & 87.7409 & 83.3042 & 4.43669 & 2.95914 \tabularnewline
45 & 86.3 & 85.9416 & 86.7375 & -0.795949 & 0.358449 \tabularnewline
46 & 93.6 & 85.0291 & 90.2583 & -5.22928 & 8.57095 \tabularnewline
47 & 98 & 87.7416 & 92.9792 & -5.23762 & 10.2584 \tabularnewline
48 & 94.3 & 86.2888 & 94.1833 & -7.89456 & 8.01123 \tabularnewline
49 & 97.6 & 87.4402 & 95.0125 & -7.57234 & 10.1598 \tabularnewline
50 & 94.2 & 90.5464 & 95.7792 & -5.23275 & 3.65359 \tabularnewline
51 & 100.2 & 98.5207 & 96.425 & 2.09572 & 1.67928 \tabularnewline
52 & 106.7 & 103.248 & 97.2458 & 6.00197 & 3.4522 \tabularnewline
53 & 95.7 & 102.59 & 98.0167 & 4.5735 & -6.89016 \tabularnewline
54 & 94.6 & 104.17 & 99.1917 & 4.97836 & -9.57002 \tabularnewline
55 & 94.7 & 110.701 & 100.825 & 9.87627 & -16.0013 \tabularnewline
56 & 96.2 & 107.312 & 102.875 & 4.43669 & -11.1117 \tabularnewline
57 & 96.3 & 104.862 & 105.658 & -0.795949 & -8.56238 \tabularnewline
58 & 103.3 & 103.683 & 108.912 & -5.22928 & -0.383218 \tabularnewline
59 & 106.8 & 107.1 & 112.337 & -5.23762 & -0.299884 \tabularnewline
60 & 113.7 & 107.805 & 115.7 & -7.89456 & 5.89456 \tabularnewline
61 & 117.4 & 111.511 & 119.083 & -7.57234 & 5.889 \tabularnewline
62 & 123.6 & 116.896 & 122.129 & -5.23275 & 6.70359 \tabularnewline
63 & 137.6 & 126.871 & 124.775 & 2.09572 & 10.7293 \tabularnewline
64 & 147.4 & 133.114 & 127.112 & 6.00197 & 14.2855 \tabularnewline
65 & 137.2 & 133.794 & 129.221 & 4.5735 & 3.40567 \tabularnewline
66 & 133.8 & 136.083 & 131.104 & 4.97836 & -2.28252 \tabularnewline
67 & 136.7 & 142.48 & 132.604 & 9.87627 & -5.78044 \tabularnewline
68 & 127.3 & 138.57 & 134.133 & 4.43669 & -11.27 \tabularnewline
69 & 128.7 & 134.617 & 135.412 & -0.795949 & -5.91655 \tabularnewline
70 & 127 & 130.508 & 135.738 & -5.22928 & -3.50822 \tabularnewline
71 & 133.7 & 130.096 & 135.333 & -5.23762 & 3.60428 \tabularnewline
72 & 132 & 126.401 & 134.296 & -7.89456 & 5.59873 \tabularnewline
73 & 135.1 & 125.328 & 132.9 & -7.57234 & 9.77234 \tabularnewline
74 & 142.6 & 127.321 & 132.554 & -5.23275 & 15.2786 \tabularnewline
75 & 149.3 & 135.15 & 133.054 & 2.09572 & 14.1501 \tabularnewline
76 & 143.5 & 139.464 & 133.462 & 6.00197 & 4.03553 \tabularnewline
77 & 131.4 & 137.957 & 133.383 & 4.5735 & -6.55683 \tabularnewline
78 & 114.7 & 137.953 & 132.975 & 4.97836 & -23.2534 \tabularnewline
79 & 122.3 & NA & NA & 9.87627 & NA \tabularnewline
80 & 133.4 & NA & NA & 4.43669 & NA \tabularnewline
81 & 134.6 & NA & NA & -0.795949 & NA \tabularnewline
82 & 130.9 & NA & NA & -5.22928 & NA \tabularnewline
83 & 127.9 & NA & NA & -5.23762 & NA \tabularnewline
84 & 128 & NA & NA & -7.89456 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231832&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]78.7[/C][C]NA[/C][C]NA[/C][C]-7.57234[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]75.7[/C][C]NA[/C][C]NA[/C][C]-5.23275[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]77.1[/C][C]NA[/C][C]NA[/C][C]2.09572[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]86.1[/C][C]NA[/C][C]NA[/C][C]6.00197[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]86.8[/C][C]NA[/C][C]NA[/C][C]4.5735[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]86.3[/C][C]NA[/C][C]NA[/C][C]4.97836[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]91.5[/C][C]90.6679[/C][C]80.7917[/C][C]9.87627[/C][C]0.83206[/C][/ROW]
[ROW][C]8[/C][C]90.7[/C][C]84.6367[/C][C]80.2[/C][C]4.43669[/C][C]6.06331[/C][/ROW]
[ROW][C]9[/C][C]78.2[/C][C]79.2582[/C][C]80.0542[/C][C]-0.795949[/C][C]-1.05822[/C][/ROW]
[ROW][C]10[/C][C]73[/C][C]74.6499[/C][C]79.8792[/C][C]-5.22928[/C][C]-1.64988[/C][/ROW]
[ROW][C]11[/C][C]73.7[/C][C]74.2999[/C][C]79.5375[/C][C]-5.23762[/C][C]-0.599884[/C][/ROW]
[ROW][C]12[/C][C]77.3[/C][C]71.4554[/C][C]79.35[/C][C]-7.89456[/C][C]5.84456[/C][/ROW]
[ROW][C]13[/C][C]67.5[/C][C]71.8402[/C][C]79.4125[/C][C]-7.57234[/C][C]-4.34016[/C][/ROW]
[ROW][C]14[/C][C]72.7[/C][C]74.1631[/C][C]79.3958[/C][C]-5.23275[/C][C]-1.46308[/C][/ROW]
[ROW][C]15[/C][C]76.6[/C][C]82.1916[/C][C]80.0958[/C][C]2.09572[/C][C]-5.59155[/C][/ROW]
[ROW][C]16[/C][C]82.4[/C][C]88.1645[/C][C]82.1625[/C][C]6.00197[/C][C]-5.76447[/C][/ROW]
[ROW][C]17[/C][C]82.3[/C][C]89.7735[/C][C]85.2[/C][C]4.5735[/C][C]-7.4735[/C][/ROW]
[ROW][C]18[/C][C]86.3[/C][C]93.4075[/C][C]88.4292[/C][C]4.97836[/C][C]-7.10752[/C][/ROW]
[ROW][C]19[/C][C]93[/C][C]101.751[/C][C]91.875[/C][C]9.87627[/C][C]-8.75127[/C][/ROW]
[ROW][C]20[/C][C]88.8[/C][C]100.166[/C][C]95.7292[/C][C]4.43669[/C][C]-11.3659[/C][/ROW]
[ROW][C]21[/C][C]96.9[/C][C]99.0166[/C][C]99.8125[/C][C]-0.795949[/C][C]-2.11655[/C][/ROW]
[ROW][C]22[/C][C]103.9[/C][C]99.0541[/C][C]104.283[/C][C]-5.22928[/C][C]4.84595[/C][/ROW]
[ROW][C]23[/C][C]115.7[/C][C]104.392[/C][C]109.629[/C][C]-5.23762[/C][C]11.3084[/C][/ROW]
[ROW][C]24[/C][C]112.8[/C][C]108.135[/C][C]116.029[/C][C]-7.89456[/C][C]4.66539[/C][/ROW]
[ROW][C]25[/C][C]114.7[/C][C]114.903[/C][C]122.475[/C][C]-7.57234[/C][C]-0.202662[/C][/ROW]
[ROW][C]26[/C][C]118[/C][C]122.671[/C][C]127.904[/C][C]-5.23275[/C][C]-4.67141[/C][/ROW]
[ROW][C]27[/C][C]129.3[/C][C]133.525[/C][C]131.429[/C][C]2.09572[/C][C]-4.22488[/C][/ROW]
[ROW][C]28[/C][C]137[/C][C]138.081[/C][C]132.079[/C][C]6.00197[/C][C]-1.08113[/C][/ROW]
[ROW][C]29[/C][C]156[/C][C]134.082[/C][C]129.508[/C][C]4.5735[/C][C]21.9182[/C][/ROW]
[ROW][C]30[/C][C]166.2[/C][C]129.962[/C][C]124.983[/C][C]4.97836[/C][C]36.2383[/C][/ROW]
[ROW][C]31[/C][C]167.8[/C][C]129.839[/C][C]119.962[/C][C]9.87627[/C][C]37.9612[/C][/ROW]
[ROW][C]32[/C][C]144.3[/C][C]119.183[/C][C]114.746[/C][C]4.43669[/C][C]25.1175[/C][/ROW]
[ROW][C]33[/C][C]126[/C][C]108.312[/C][C]109.108[/C][C]-0.795949[/C][C]17.6876[/C][/ROW]
[ROW][C]34[/C][C]90.4[/C][C]97.8832[/C][C]103.112[/C][C]-5.22928[/C][C]-7.48322[/C][/ROW]
[ROW][C]35[/C][C]67.5[/C][C]91.3791[/C][C]96.6167[/C][C]-5.23762[/C][C]-23.8791[/C][/ROW]
[ROW][C]36[/C][C]52.4[/C][C]82.0221[/C][C]89.9167[/C][C]-7.89456[/C][C]-29.6221[/C][/ROW]
[ROW][C]37[/C][C]54.6[/C][C]75.486[/C][C]83.0583[/C][C]-7.57234[/C][C]-20.886[/C][/ROW]
[ROW][C]38[/C][C]52.9[/C][C]72.0089[/C][C]77.2417[/C][C]-5.23275[/C][C]-19.1089[/C][/ROW]
[ROW][C]39[/C][C]59.1[/C][C]75.4499[/C][C]73.3542[/C][C]2.09572[/C][C]-16.3499[/C][/ROW]
[ROW][C]40[/C][C]63.3[/C][C]77.8353[/C][C]71.8333[/C][C]6.00197[/C][C]-14.5353[/C][/ROW]
[ROW][C]41[/C][C]73.8[/C][C]77.811[/C][C]73.2375[/C][C]4.5735[/C][C]-4.011[/C][/ROW]
[ROW][C]42[/C][C]87.6[/C][C]81.2325[/C][C]76.2542[/C][C]4.97836[/C][C]6.36748[/C][/ROW]
[ROW][C]43[/C][C]81.8[/C][C]89.6679[/C][C]79.7917[/C][C]9.87627[/C][C]-7.86794[/C][/ROW]
[ROW][C]44[/C][C]90.7[/C][C]87.7409[/C][C]83.3042[/C][C]4.43669[/C][C]2.95914[/C][/ROW]
[ROW][C]45[/C][C]86.3[/C][C]85.9416[/C][C]86.7375[/C][C]-0.795949[/C][C]0.358449[/C][/ROW]
[ROW][C]46[/C][C]93.6[/C][C]85.0291[/C][C]90.2583[/C][C]-5.22928[/C][C]8.57095[/C][/ROW]
[ROW][C]47[/C][C]98[/C][C]87.7416[/C][C]92.9792[/C][C]-5.23762[/C][C]10.2584[/C][/ROW]
[ROW][C]48[/C][C]94.3[/C][C]86.2888[/C][C]94.1833[/C][C]-7.89456[/C][C]8.01123[/C][/ROW]
[ROW][C]49[/C][C]97.6[/C][C]87.4402[/C][C]95.0125[/C][C]-7.57234[/C][C]10.1598[/C][/ROW]
[ROW][C]50[/C][C]94.2[/C][C]90.5464[/C][C]95.7792[/C][C]-5.23275[/C][C]3.65359[/C][/ROW]
[ROW][C]51[/C][C]100.2[/C][C]98.5207[/C][C]96.425[/C][C]2.09572[/C][C]1.67928[/C][/ROW]
[ROW][C]52[/C][C]106.7[/C][C]103.248[/C][C]97.2458[/C][C]6.00197[/C][C]3.4522[/C][/ROW]
[ROW][C]53[/C][C]95.7[/C][C]102.59[/C][C]98.0167[/C][C]4.5735[/C][C]-6.89016[/C][/ROW]
[ROW][C]54[/C][C]94.6[/C][C]104.17[/C][C]99.1917[/C][C]4.97836[/C][C]-9.57002[/C][/ROW]
[ROW][C]55[/C][C]94.7[/C][C]110.701[/C][C]100.825[/C][C]9.87627[/C][C]-16.0013[/C][/ROW]
[ROW][C]56[/C][C]96.2[/C][C]107.312[/C][C]102.875[/C][C]4.43669[/C][C]-11.1117[/C][/ROW]
[ROW][C]57[/C][C]96.3[/C][C]104.862[/C][C]105.658[/C][C]-0.795949[/C][C]-8.56238[/C][/ROW]
[ROW][C]58[/C][C]103.3[/C][C]103.683[/C][C]108.912[/C][C]-5.22928[/C][C]-0.383218[/C][/ROW]
[ROW][C]59[/C][C]106.8[/C][C]107.1[/C][C]112.337[/C][C]-5.23762[/C][C]-0.299884[/C][/ROW]
[ROW][C]60[/C][C]113.7[/C][C]107.805[/C][C]115.7[/C][C]-7.89456[/C][C]5.89456[/C][/ROW]
[ROW][C]61[/C][C]117.4[/C][C]111.511[/C][C]119.083[/C][C]-7.57234[/C][C]5.889[/C][/ROW]
[ROW][C]62[/C][C]123.6[/C][C]116.896[/C][C]122.129[/C][C]-5.23275[/C][C]6.70359[/C][/ROW]
[ROW][C]63[/C][C]137.6[/C][C]126.871[/C][C]124.775[/C][C]2.09572[/C][C]10.7293[/C][/ROW]
[ROW][C]64[/C][C]147.4[/C][C]133.114[/C][C]127.112[/C][C]6.00197[/C][C]14.2855[/C][/ROW]
[ROW][C]65[/C][C]137.2[/C][C]133.794[/C][C]129.221[/C][C]4.5735[/C][C]3.40567[/C][/ROW]
[ROW][C]66[/C][C]133.8[/C][C]136.083[/C][C]131.104[/C][C]4.97836[/C][C]-2.28252[/C][/ROW]
[ROW][C]67[/C][C]136.7[/C][C]142.48[/C][C]132.604[/C][C]9.87627[/C][C]-5.78044[/C][/ROW]
[ROW][C]68[/C][C]127.3[/C][C]138.57[/C][C]134.133[/C][C]4.43669[/C][C]-11.27[/C][/ROW]
[ROW][C]69[/C][C]128.7[/C][C]134.617[/C][C]135.412[/C][C]-0.795949[/C][C]-5.91655[/C][/ROW]
[ROW][C]70[/C][C]127[/C][C]130.508[/C][C]135.738[/C][C]-5.22928[/C][C]-3.50822[/C][/ROW]
[ROW][C]71[/C][C]133.7[/C][C]130.096[/C][C]135.333[/C][C]-5.23762[/C][C]3.60428[/C][/ROW]
[ROW][C]72[/C][C]132[/C][C]126.401[/C][C]134.296[/C][C]-7.89456[/C][C]5.59873[/C][/ROW]
[ROW][C]73[/C][C]135.1[/C][C]125.328[/C][C]132.9[/C][C]-7.57234[/C][C]9.77234[/C][/ROW]
[ROW][C]74[/C][C]142.6[/C][C]127.321[/C][C]132.554[/C][C]-5.23275[/C][C]15.2786[/C][/ROW]
[ROW][C]75[/C][C]149.3[/C][C]135.15[/C][C]133.054[/C][C]2.09572[/C][C]14.1501[/C][/ROW]
[ROW][C]76[/C][C]143.5[/C][C]139.464[/C][C]133.462[/C][C]6.00197[/C][C]4.03553[/C][/ROW]
[ROW][C]77[/C][C]131.4[/C][C]137.957[/C][C]133.383[/C][C]4.5735[/C][C]-6.55683[/C][/ROW]
[ROW][C]78[/C][C]114.7[/C][C]137.953[/C][C]132.975[/C][C]4.97836[/C][C]-23.2534[/C][/ROW]
[ROW][C]79[/C][C]122.3[/C][C]NA[/C][C]NA[/C][C]9.87627[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]133.4[/C][C]NA[/C][C]NA[/C][C]4.43669[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]134.6[/C][C]NA[/C][C]NA[/C][C]-0.795949[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]130.9[/C][C]NA[/C][C]NA[/C][C]-5.22928[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]127.9[/C][C]NA[/C][C]NA[/C][C]-5.23762[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]128[/C][C]NA[/C][C]NA[/C][C]-7.89456[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231832&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
178.7NANA-7.57234NA
275.7NANA-5.23275NA
377.1NANA2.09572NA
486.1NANA6.00197NA
586.8NANA4.5735NA
686.3NANA4.97836NA
791.590.667980.79179.876270.83206
890.784.636780.24.436696.06331
978.279.258280.0542-0.795949-1.05822
107374.649979.8792-5.22928-1.64988
1173.774.299979.5375-5.23762-0.599884
1277.371.455479.35-7.894565.84456
1367.571.840279.4125-7.57234-4.34016
1472.774.163179.3958-5.23275-1.46308
1576.682.191680.09582.09572-5.59155
1682.488.164582.16256.00197-5.76447
1782.389.773585.24.5735-7.4735
1886.393.407588.42924.97836-7.10752
1993101.75191.8759.87627-8.75127
2088.8100.16695.72924.43669-11.3659
2196.999.016699.8125-0.795949-2.11655
22103.999.0541104.283-5.229284.84595
23115.7104.392109.629-5.2376211.3084
24112.8108.135116.029-7.894564.66539
25114.7114.903122.475-7.57234-0.202662
26118122.671127.904-5.23275-4.67141
27129.3133.525131.4292.09572-4.22488
28137138.081132.0796.00197-1.08113
29156134.082129.5084.573521.9182
30166.2129.962124.9834.9783636.2383
31167.8129.839119.9629.8762737.9612
32144.3119.183114.7464.4366925.1175
33126108.312109.108-0.79594917.6876
3490.497.8832103.112-5.22928-7.48322
3567.591.379196.6167-5.23762-23.8791
3652.482.022189.9167-7.89456-29.6221
3754.675.48683.0583-7.57234-20.886
3852.972.008977.2417-5.23275-19.1089
3959.175.449973.35422.09572-16.3499
4063.377.835371.83336.00197-14.5353
4173.877.81173.23754.5735-4.011
4287.681.232576.25424.978366.36748
4381.889.667979.79179.87627-7.86794
4490.787.740983.30424.436692.95914
4586.385.941686.7375-0.7959490.358449
4693.685.029190.2583-5.229288.57095
479887.741692.9792-5.2376210.2584
4894.386.288894.1833-7.894568.01123
4997.687.440295.0125-7.5723410.1598
5094.290.546495.7792-5.232753.65359
51100.298.520796.4252.095721.67928
52106.7103.24897.24586.001973.4522
5395.7102.5998.01674.5735-6.89016
5494.6104.1799.19174.97836-9.57002
5594.7110.701100.8259.87627-16.0013
5696.2107.312102.8754.43669-11.1117
5796.3104.862105.658-0.795949-8.56238
58103.3103.683108.912-5.22928-0.383218
59106.8107.1112.337-5.23762-0.299884
60113.7107.805115.7-7.894565.89456
61117.4111.511119.083-7.572345.889
62123.6116.896122.129-5.232756.70359
63137.6126.871124.7752.0957210.7293
64147.4133.114127.1126.0019714.2855
65137.2133.794129.2214.57353.40567
66133.8136.083131.1044.97836-2.28252
67136.7142.48132.6049.87627-5.78044
68127.3138.57134.1334.43669-11.27
69128.7134.617135.412-0.795949-5.91655
70127130.508135.738-5.22928-3.50822
71133.7130.096135.333-5.237623.60428
72132126.401134.296-7.894565.59873
73135.1125.328132.9-7.572349.77234
74142.6127.321132.554-5.2327515.2786
75149.3135.15133.0542.0957214.1501
76143.5139.464133.4626.001974.03553
77131.4137.957133.3834.5735-6.55683
78114.7137.953132.9754.97836-23.2534
79122.3NANA9.87627NA
80133.4NANA4.43669NA
81134.6NANA-0.795949NA
82130.9NANA-5.22928NA
83127.9NANA-5.23762NA
84128NANA-7.89456NA



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')