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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationFri, 10 Jan 2014 12:38:23 -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/2014/Jan/10/t1389375535lvafgs43peufqy7.htm/, Retrieved Sun, 19 May 2024 11:30:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232869, Retrieved Sun, 19 May 2024 11:30:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2014-01-10 17:38:23] [328aaa3853be379f8fe8661c9ebb5323] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,12
112,28
113,77
114,38
119,06
119,94
120,98
122,33
121,7
123,73
121,73
119,75
117,4
120,99
125,18
126,41
129,38
131,93
129,34
128,58
125,37
123,25
122,78
120,37
116,83
116,39
120,69
123,51
127,43
125,99
120,62
113,71
110,79
108,15
111,22
112,65
112,47
117,48
122,46
123,46
122,33
129,2
129,22
131,17
120,22
120,38
115,32
112,25
109,83
107,05
112,87
113,68
115,08
120,61
119,14
118,63
115,78
117,26
117,61
113,92
113,65
115,89
116,55
117,78
117,36
121,09
124,26
121,88
119,52
122,49
120,86
120,31

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232869&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232869&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232869&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1110.12NANA-5.6684NA
2112.28NANA-4.16798NA
3113.77NANA-0.156063NA
4114.38NANA1.29044NA
5119.06NANA2.65602NA
6119.94NANA6.1066NA
7120.98122.984118.6184.36635-2.00385
8122.33122.615119.2843.33085-0.284604
9121.7119.288120.122-0.8343962.41231
10123.73119.995121.099-1.10393.73515
11121.73120.09122.03-1.940061.64006
12119.75119.08122.96-3.879480.669896
13117.4118.139123.807-5.6684-0.739104
14120.99120.248124.416-4.167980.741729
15125.18124.674124.83-0.1560630.506479
16126.41126.253124.9621.290440.157062
17129.38127.642124.9862.656021.73773
18131.93131.162125.0566.10660.767563
19129.34129.424125.0584.36635-0.0842708
20128.58128.173124.8433.330850.406646
21125.37123.629124.464-0.8343961.74065
22123.25123.052124.156-1.10390.198062
23122.78122.014123.954-1.940060.766312
24120.37119.746123.625-3.879480.624479
25116.83117.346123.014-5.6684-0.515771
26116.39117.863122.031-4.16798-1.47327
27120.69120.648120.804-0.1560630.0418958
28123.51120.858119.5681.290442.65206
29127.43121.113118.4572.656026.31731
30125.99123.76117.6536.10662.23006
31120.62121.516117.154.36635-0.896354
32113.71120.345117.0143.33085-6.6346
33110.79116.299117.133-0.834396-5.50852
34108.15116.101117.205-1.1039-7.95069
35111.22115.05116.99-1.94006-3.82994
36112.65113.032116.911-3.87948-0.381771
37112.47111.735117.403-5.66840.735062
38117.48114.321118.489-4.167983.15881
39122.46119.454119.61-0.1560633.00648
40123.46121.803120.5121.290441.65748
41122.33123.849121.1922.65602-1.51852
42129.2127.453121.3476.10661.74673
43129.22125.586121.224.366353.63365
44131.17124.006120.6753.330857.16373
45120.22119.007119.841-0.8343961.21315
46120.38117.93119.034-1.10392.44973
47115.32116.385118.325-1.94006-1.06452
48112.25113.785117.665-3.87948-1.5351
49109.83111.218116.887-5.6684-1.38827
50107.05111.776115.944-4.16798-4.72619
51112.87115.081115.237-0.156063-2.2106
52113.68116.212114.9221.29044-2.5321
53115.08117.543114.8872.65602-2.4631
54120.61121.159115.0526.1066-0.548687
55119.14119.647115.2814.36635-0.507187
56118.63119.139115.8083.33085-0.509187
57115.78115.496116.33-0.8343960.284396
58117.26115.55116.654-1.10391.70973
59117.61114.98116.92-1.940062.63006
60113.92113.156117.035-3.879480.764479
61113.65111.6117.268-5.66842.05006
62115.89113.449117.617-4.167982.4409
63116.55117.752117.908-0.156063-1.20227
64117.78119.573118.2821.29044-1.79252
65117.36121.291118.6352.65602-3.93144
66121.09125.144119.0376.1066-4.05369
67124.26NANA4.36635NA
68121.88NANA3.33085NA
69119.52NANA-0.834396NA
70122.49NANA-1.1039NA
71120.86NANA-1.94006NA
72120.31NANA-3.87948NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 110.12 & NA & NA & -5.6684 & NA \tabularnewline
2 & 112.28 & NA & NA & -4.16798 & NA \tabularnewline
3 & 113.77 & NA & NA & -0.156063 & NA \tabularnewline
4 & 114.38 & NA & NA & 1.29044 & NA \tabularnewline
5 & 119.06 & NA & NA & 2.65602 & NA \tabularnewline
6 & 119.94 & NA & NA & 6.1066 & NA \tabularnewline
7 & 120.98 & 122.984 & 118.618 & 4.36635 & -2.00385 \tabularnewline
8 & 122.33 & 122.615 & 119.284 & 3.33085 & -0.284604 \tabularnewline
9 & 121.7 & 119.288 & 120.122 & -0.834396 & 2.41231 \tabularnewline
10 & 123.73 & 119.995 & 121.099 & -1.1039 & 3.73515 \tabularnewline
11 & 121.73 & 120.09 & 122.03 & -1.94006 & 1.64006 \tabularnewline
12 & 119.75 & 119.08 & 122.96 & -3.87948 & 0.669896 \tabularnewline
13 & 117.4 & 118.139 & 123.807 & -5.6684 & -0.739104 \tabularnewline
14 & 120.99 & 120.248 & 124.416 & -4.16798 & 0.741729 \tabularnewline
15 & 125.18 & 124.674 & 124.83 & -0.156063 & 0.506479 \tabularnewline
16 & 126.41 & 126.253 & 124.962 & 1.29044 & 0.157062 \tabularnewline
17 & 129.38 & 127.642 & 124.986 & 2.65602 & 1.73773 \tabularnewline
18 & 131.93 & 131.162 & 125.056 & 6.1066 & 0.767563 \tabularnewline
19 & 129.34 & 129.424 & 125.058 & 4.36635 & -0.0842708 \tabularnewline
20 & 128.58 & 128.173 & 124.843 & 3.33085 & 0.406646 \tabularnewline
21 & 125.37 & 123.629 & 124.464 & -0.834396 & 1.74065 \tabularnewline
22 & 123.25 & 123.052 & 124.156 & -1.1039 & 0.198062 \tabularnewline
23 & 122.78 & 122.014 & 123.954 & -1.94006 & 0.766312 \tabularnewline
24 & 120.37 & 119.746 & 123.625 & -3.87948 & 0.624479 \tabularnewline
25 & 116.83 & 117.346 & 123.014 & -5.6684 & -0.515771 \tabularnewline
26 & 116.39 & 117.863 & 122.031 & -4.16798 & -1.47327 \tabularnewline
27 & 120.69 & 120.648 & 120.804 & -0.156063 & 0.0418958 \tabularnewline
28 & 123.51 & 120.858 & 119.568 & 1.29044 & 2.65206 \tabularnewline
29 & 127.43 & 121.113 & 118.457 & 2.65602 & 6.31731 \tabularnewline
30 & 125.99 & 123.76 & 117.653 & 6.1066 & 2.23006 \tabularnewline
31 & 120.62 & 121.516 & 117.15 & 4.36635 & -0.896354 \tabularnewline
32 & 113.71 & 120.345 & 117.014 & 3.33085 & -6.6346 \tabularnewline
33 & 110.79 & 116.299 & 117.133 & -0.834396 & -5.50852 \tabularnewline
34 & 108.15 & 116.101 & 117.205 & -1.1039 & -7.95069 \tabularnewline
35 & 111.22 & 115.05 & 116.99 & -1.94006 & -3.82994 \tabularnewline
36 & 112.65 & 113.032 & 116.911 & -3.87948 & -0.381771 \tabularnewline
37 & 112.47 & 111.735 & 117.403 & -5.6684 & 0.735062 \tabularnewline
38 & 117.48 & 114.321 & 118.489 & -4.16798 & 3.15881 \tabularnewline
39 & 122.46 & 119.454 & 119.61 & -0.156063 & 3.00648 \tabularnewline
40 & 123.46 & 121.803 & 120.512 & 1.29044 & 1.65748 \tabularnewline
41 & 122.33 & 123.849 & 121.192 & 2.65602 & -1.51852 \tabularnewline
42 & 129.2 & 127.453 & 121.347 & 6.1066 & 1.74673 \tabularnewline
43 & 129.22 & 125.586 & 121.22 & 4.36635 & 3.63365 \tabularnewline
44 & 131.17 & 124.006 & 120.675 & 3.33085 & 7.16373 \tabularnewline
45 & 120.22 & 119.007 & 119.841 & -0.834396 & 1.21315 \tabularnewline
46 & 120.38 & 117.93 & 119.034 & -1.1039 & 2.44973 \tabularnewline
47 & 115.32 & 116.385 & 118.325 & -1.94006 & -1.06452 \tabularnewline
48 & 112.25 & 113.785 & 117.665 & -3.87948 & -1.5351 \tabularnewline
49 & 109.83 & 111.218 & 116.887 & -5.6684 & -1.38827 \tabularnewline
50 & 107.05 & 111.776 & 115.944 & -4.16798 & -4.72619 \tabularnewline
51 & 112.87 & 115.081 & 115.237 & -0.156063 & -2.2106 \tabularnewline
52 & 113.68 & 116.212 & 114.922 & 1.29044 & -2.5321 \tabularnewline
53 & 115.08 & 117.543 & 114.887 & 2.65602 & -2.4631 \tabularnewline
54 & 120.61 & 121.159 & 115.052 & 6.1066 & -0.548687 \tabularnewline
55 & 119.14 & 119.647 & 115.281 & 4.36635 & -0.507187 \tabularnewline
56 & 118.63 & 119.139 & 115.808 & 3.33085 & -0.509187 \tabularnewline
57 & 115.78 & 115.496 & 116.33 & -0.834396 & 0.284396 \tabularnewline
58 & 117.26 & 115.55 & 116.654 & -1.1039 & 1.70973 \tabularnewline
59 & 117.61 & 114.98 & 116.92 & -1.94006 & 2.63006 \tabularnewline
60 & 113.92 & 113.156 & 117.035 & -3.87948 & 0.764479 \tabularnewline
61 & 113.65 & 111.6 & 117.268 & -5.6684 & 2.05006 \tabularnewline
62 & 115.89 & 113.449 & 117.617 & -4.16798 & 2.4409 \tabularnewline
63 & 116.55 & 117.752 & 117.908 & -0.156063 & -1.20227 \tabularnewline
64 & 117.78 & 119.573 & 118.282 & 1.29044 & -1.79252 \tabularnewline
65 & 117.36 & 121.291 & 118.635 & 2.65602 & -3.93144 \tabularnewline
66 & 121.09 & 125.144 & 119.037 & 6.1066 & -4.05369 \tabularnewline
67 & 124.26 & NA & NA & 4.36635 & NA \tabularnewline
68 & 121.88 & NA & NA & 3.33085 & NA \tabularnewline
69 & 119.52 & NA & NA & -0.834396 & NA \tabularnewline
70 & 122.49 & NA & NA & -1.1039 & NA \tabularnewline
71 & 120.86 & NA & NA & -1.94006 & NA \tabularnewline
72 & 120.31 & NA & NA & -3.87948 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232869&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]110.12[/C][C]NA[/C][C]NA[/C][C]-5.6684[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]112.28[/C][C]NA[/C][C]NA[/C][C]-4.16798[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]113.77[/C][C]NA[/C][C]NA[/C][C]-0.156063[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]114.38[/C][C]NA[/C][C]NA[/C][C]1.29044[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]119.06[/C][C]NA[/C][C]NA[/C][C]2.65602[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]119.94[/C][C]NA[/C][C]NA[/C][C]6.1066[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]120.98[/C][C]122.984[/C][C]118.618[/C][C]4.36635[/C][C]-2.00385[/C][/ROW]
[ROW][C]8[/C][C]122.33[/C][C]122.615[/C][C]119.284[/C][C]3.33085[/C][C]-0.284604[/C][/ROW]
[ROW][C]9[/C][C]121.7[/C][C]119.288[/C][C]120.122[/C][C]-0.834396[/C][C]2.41231[/C][/ROW]
[ROW][C]10[/C][C]123.73[/C][C]119.995[/C][C]121.099[/C][C]-1.1039[/C][C]3.73515[/C][/ROW]
[ROW][C]11[/C][C]121.73[/C][C]120.09[/C][C]122.03[/C][C]-1.94006[/C][C]1.64006[/C][/ROW]
[ROW][C]12[/C][C]119.75[/C][C]119.08[/C][C]122.96[/C][C]-3.87948[/C][C]0.669896[/C][/ROW]
[ROW][C]13[/C][C]117.4[/C][C]118.139[/C][C]123.807[/C][C]-5.6684[/C][C]-0.739104[/C][/ROW]
[ROW][C]14[/C][C]120.99[/C][C]120.248[/C][C]124.416[/C][C]-4.16798[/C][C]0.741729[/C][/ROW]
[ROW][C]15[/C][C]125.18[/C][C]124.674[/C][C]124.83[/C][C]-0.156063[/C][C]0.506479[/C][/ROW]
[ROW][C]16[/C][C]126.41[/C][C]126.253[/C][C]124.962[/C][C]1.29044[/C][C]0.157062[/C][/ROW]
[ROW][C]17[/C][C]129.38[/C][C]127.642[/C][C]124.986[/C][C]2.65602[/C][C]1.73773[/C][/ROW]
[ROW][C]18[/C][C]131.93[/C][C]131.162[/C][C]125.056[/C][C]6.1066[/C][C]0.767563[/C][/ROW]
[ROW][C]19[/C][C]129.34[/C][C]129.424[/C][C]125.058[/C][C]4.36635[/C][C]-0.0842708[/C][/ROW]
[ROW][C]20[/C][C]128.58[/C][C]128.173[/C][C]124.843[/C][C]3.33085[/C][C]0.406646[/C][/ROW]
[ROW][C]21[/C][C]125.37[/C][C]123.629[/C][C]124.464[/C][C]-0.834396[/C][C]1.74065[/C][/ROW]
[ROW][C]22[/C][C]123.25[/C][C]123.052[/C][C]124.156[/C][C]-1.1039[/C][C]0.198062[/C][/ROW]
[ROW][C]23[/C][C]122.78[/C][C]122.014[/C][C]123.954[/C][C]-1.94006[/C][C]0.766312[/C][/ROW]
[ROW][C]24[/C][C]120.37[/C][C]119.746[/C][C]123.625[/C][C]-3.87948[/C][C]0.624479[/C][/ROW]
[ROW][C]25[/C][C]116.83[/C][C]117.346[/C][C]123.014[/C][C]-5.6684[/C][C]-0.515771[/C][/ROW]
[ROW][C]26[/C][C]116.39[/C][C]117.863[/C][C]122.031[/C][C]-4.16798[/C][C]-1.47327[/C][/ROW]
[ROW][C]27[/C][C]120.69[/C][C]120.648[/C][C]120.804[/C][C]-0.156063[/C][C]0.0418958[/C][/ROW]
[ROW][C]28[/C][C]123.51[/C][C]120.858[/C][C]119.568[/C][C]1.29044[/C][C]2.65206[/C][/ROW]
[ROW][C]29[/C][C]127.43[/C][C]121.113[/C][C]118.457[/C][C]2.65602[/C][C]6.31731[/C][/ROW]
[ROW][C]30[/C][C]125.99[/C][C]123.76[/C][C]117.653[/C][C]6.1066[/C][C]2.23006[/C][/ROW]
[ROW][C]31[/C][C]120.62[/C][C]121.516[/C][C]117.15[/C][C]4.36635[/C][C]-0.896354[/C][/ROW]
[ROW][C]32[/C][C]113.71[/C][C]120.345[/C][C]117.014[/C][C]3.33085[/C][C]-6.6346[/C][/ROW]
[ROW][C]33[/C][C]110.79[/C][C]116.299[/C][C]117.133[/C][C]-0.834396[/C][C]-5.50852[/C][/ROW]
[ROW][C]34[/C][C]108.15[/C][C]116.101[/C][C]117.205[/C][C]-1.1039[/C][C]-7.95069[/C][/ROW]
[ROW][C]35[/C][C]111.22[/C][C]115.05[/C][C]116.99[/C][C]-1.94006[/C][C]-3.82994[/C][/ROW]
[ROW][C]36[/C][C]112.65[/C][C]113.032[/C][C]116.911[/C][C]-3.87948[/C][C]-0.381771[/C][/ROW]
[ROW][C]37[/C][C]112.47[/C][C]111.735[/C][C]117.403[/C][C]-5.6684[/C][C]0.735062[/C][/ROW]
[ROW][C]38[/C][C]117.48[/C][C]114.321[/C][C]118.489[/C][C]-4.16798[/C][C]3.15881[/C][/ROW]
[ROW][C]39[/C][C]122.46[/C][C]119.454[/C][C]119.61[/C][C]-0.156063[/C][C]3.00648[/C][/ROW]
[ROW][C]40[/C][C]123.46[/C][C]121.803[/C][C]120.512[/C][C]1.29044[/C][C]1.65748[/C][/ROW]
[ROW][C]41[/C][C]122.33[/C][C]123.849[/C][C]121.192[/C][C]2.65602[/C][C]-1.51852[/C][/ROW]
[ROW][C]42[/C][C]129.2[/C][C]127.453[/C][C]121.347[/C][C]6.1066[/C][C]1.74673[/C][/ROW]
[ROW][C]43[/C][C]129.22[/C][C]125.586[/C][C]121.22[/C][C]4.36635[/C][C]3.63365[/C][/ROW]
[ROW][C]44[/C][C]131.17[/C][C]124.006[/C][C]120.675[/C][C]3.33085[/C][C]7.16373[/C][/ROW]
[ROW][C]45[/C][C]120.22[/C][C]119.007[/C][C]119.841[/C][C]-0.834396[/C][C]1.21315[/C][/ROW]
[ROW][C]46[/C][C]120.38[/C][C]117.93[/C][C]119.034[/C][C]-1.1039[/C][C]2.44973[/C][/ROW]
[ROW][C]47[/C][C]115.32[/C][C]116.385[/C][C]118.325[/C][C]-1.94006[/C][C]-1.06452[/C][/ROW]
[ROW][C]48[/C][C]112.25[/C][C]113.785[/C][C]117.665[/C][C]-3.87948[/C][C]-1.5351[/C][/ROW]
[ROW][C]49[/C][C]109.83[/C][C]111.218[/C][C]116.887[/C][C]-5.6684[/C][C]-1.38827[/C][/ROW]
[ROW][C]50[/C][C]107.05[/C][C]111.776[/C][C]115.944[/C][C]-4.16798[/C][C]-4.72619[/C][/ROW]
[ROW][C]51[/C][C]112.87[/C][C]115.081[/C][C]115.237[/C][C]-0.156063[/C][C]-2.2106[/C][/ROW]
[ROW][C]52[/C][C]113.68[/C][C]116.212[/C][C]114.922[/C][C]1.29044[/C][C]-2.5321[/C][/ROW]
[ROW][C]53[/C][C]115.08[/C][C]117.543[/C][C]114.887[/C][C]2.65602[/C][C]-2.4631[/C][/ROW]
[ROW][C]54[/C][C]120.61[/C][C]121.159[/C][C]115.052[/C][C]6.1066[/C][C]-0.548687[/C][/ROW]
[ROW][C]55[/C][C]119.14[/C][C]119.647[/C][C]115.281[/C][C]4.36635[/C][C]-0.507187[/C][/ROW]
[ROW][C]56[/C][C]118.63[/C][C]119.139[/C][C]115.808[/C][C]3.33085[/C][C]-0.509187[/C][/ROW]
[ROW][C]57[/C][C]115.78[/C][C]115.496[/C][C]116.33[/C][C]-0.834396[/C][C]0.284396[/C][/ROW]
[ROW][C]58[/C][C]117.26[/C][C]115.55[/C][C]116.654[/C][C]-1.1039[/C][C]1.70973[/C][/ROW]
[ROW][C]59[/C][C]117.61[/C][C]114.98[/C][C]116.92[/C][C]-1.94006[/C][C]2.63006[/C][/ROW]
[ROW][C]60[/C][C]113.92[/C][C]113.156[/C][C]117.035[/C][C]-3.87948[/C][C]0.764479[/C][/ROW]
[ROW][C]61[/C][C]113.65[/C][C]111.6[/C][C]117.268[/C][C]-5.6684[/C][C]2.05006[/C][/ROW]
[ROW][C]62[/C][C]115.89[/C][C]113.449[/C][C]117.617[/C][C]-4.16798[/C][C]2.4409[/C][/ROW]
[ROW][C]63[/C][C]116.55[/C][C]117.752[/C][C]117.908[/C][C]-0.156063[/C][C]-1.20227[/C][/ROW]
[ROW][C]64[/C][C]117.78[/C][C]119.573[/C][C]118.282[/C][C]1.29044[/C][C]-1.79252[/C][/ROW]
[ROW][C]65[/C][C]117.36[/C][C]121.291[/C][C]118.635[/C][C]2.65602[/C][C]-3.93144[/C][/ROW]
[ROW][C]66[/C][C]121.09[/C][C]125.144[/C][C]119.037[/C][C]6.1066[/C][C]-4.05369[/C][/ROW]
[ROW][C]67[/C][C]124.26[/C][C]NA[/C][C]NA[/C][C]4.36635[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]121.88[/C][C]NA[/C][C]NA[/C][C]3.33085[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]119.52[/C][C]NA[/C][C]NA[/C][C]-0.834396[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]122.49[/C][C]NA[/C][C]NA[/C][C]-1.1039[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]120.86[/C][C]NA[/C][C]NA[/C][C]-1.94006[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]120.31[/C][C]NA[/C][C]NA[/C][C]-3.87948[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232869&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232869&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1110.12NANA-5.6684NA
2112.28NANA-4.16798NA
3113.77NANA-0.156063NA
4114.38NANA1.29044NA
5119.06NANA2.65602NA
6119.94NANA6.1066NA
7120.98122.984118.6184.36635-2.00385
8122.33122.615119.2843.33085-0.284604
9121.7119.288120.122-0.8343962.41231
10123.73119.995121.099-1.10393.73515
11121.73120.09122.03-1.940061.64006
12119.75119.08122.96-3.879480.669896
13117.4118.139123.807-5.6684-0.739104
14120.99120.248124.416-4.167980.741729
15125.18124.674124.83-0.1560630.506479
16126.41126.253124.9621.290440.157062
17129.38127.642124.9862.656021.73773
18131.93131.162125.0566.10660.767563
19129.34129.424125.0584.36635-0.0842708
20128.58128.173124.8433.330850.406646
21125.37123.629124.464-0.8343961.74065
22123.25123.052124.156-1.10390.198062
23122.78122.014123.954-1.940060.766312
24120.37119.746123.625-3.879480.624479
25116.83117.346123.014-5.6684-0.515771
26116.39117.863122.031-4.16798-1.47327
27120.69120.648120.804-0.1560630.0418958
28123.51120.858119.5681.290442.65206
29127.43121.113118.4572.656026.31731
30125.99123.76117.6536.10662.23006
31120.62121.516117.154.36635-0.896354
32113.71120.345117.0143.33085-6.6346
33110.79116.299117.133-0.834396-5.50852
34108.15116.101117.205-1.1039-7.95069
35111.22115.05116.99-1.94006-3.82994
36112.65113.032116.911-3.87948-0.381771
37112.47111.735117.403-5.66840.735062
38117.48114.321118.489-4.167983.15881
39122.46119.454119.61-0.1560633.00648
40123.46121.803120.5121.290441.65748
41122.33123.849121.1922.65602-1.51852
42129.2127.453121.3476.10661.74673
43129.22125.586121.224.366353.63365
44131.17124.006120.6753.330857.16373
45120.22119.007119.841-0.8343961.21315
46120.38117.93119.034-1.10392.44973
47115.32116.385118.325-1.94006-1.06452
48112.25113.785117.665-3.87948-1.5351
49109.83111.218116.887-5.6684-1.38827
50107.05111.776115.944-4.16798-4.72619
51112.87115.081115.237-0.156063-2.2106
52113.68116.212114.9221.29044-2.5321
53115.08117.543114.8872.65602-2.4631
54120.61121.159115.0526.1066-0.548687
55119.14119.647115.2814.36635-0.507187
56118.63119.139115.8083.33085-0.509187
57115.78115.496116.33-0.8343960.284396
58117.26115.55116.654-1.10391.70973
59117.61114.98116.92-1.940062.63006
60113.92113.156117.035-3.879480.764479
61113.65111.6117.268-5.66842.05006
62115.89113.449117.617-4.167982.4409
63116.55117.752117.908-0.156063-1.20227
64117.78119.573118.2821.29044-1.79252
65117.36121.291118.6352.65602-3.93144
66121.09125.144119.0376.1066-4.05369
67124.26NANA4.36635NA
68121.88NANA3.33085NA
69119.52NANA-0.834396NA
70122.49NANA-1.1039NA
71120.86NANA-1.94006NA
72120.31NANA-3.87948NA


Figures (Output of Computation)

Input Parameters & R Code

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')