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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 03 May 2017 07:45:55 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/May/03/t14937939895g8zskm4gtah2pf.htm/, Retrieved Fri, 17 May 2024 08:27:48 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 17 May 2024 08:27:48 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
92,49
92,46
92,55
92,24
92,41
92,83
92,85
93,04
93,04
92,83
92,96
92,83
93,01
93,21
93,58
94,07
94,57
95,03
95,21
95,89
96,43
96,35
96,71
96,32
97,23
97,88
98,2
98,56
99,31
99,69
99,77
101,06
101,77
101,91
102,52
102,09
102,22
102,74
103,56
104,4
104,76
104,86
104,84
104,96
104,83
104,58
104,8
104,17
104,63
105,32
106,16
107,22
107,51
107,87
107,79
108,04
107,74
107,71
111,19
110,82
113,65
114,72
114,32
116,76
116,47
117,34
116,92
116,48
115,07
116,45
116,84
114,31




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]2 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=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
192.49NANA-0.293569NA
292.46NANA-0.0634861NA
392.55NANA-0.0524028NA
492.24NANA0.605181NA
592.41NANA0.531347NA
692.83NANA0.587347NA
792.8592.711892.7325-0.02065280.138153
893.0492.908992.78540.1235140.131069
993.0492.780292.8596-0.07940280.259819
1092.8392.427692.9787-0.5511530.402403
1192.9693.14993.1450.00401389-0.189014
1292.8392.535993.3267-0.7907360.294069
1393.0193.223193.5167-0.293569-0.213097
1493.2193.670393.7337-0.0634861-0.460264
1593.5893.941393.9937-0.0524028-0.361347
1694.0794.886894.28170.605181-0.816847
1794.5795.115994.58460.531347-0.545931
1895.0395.473694.88620.587347-0.443597
1995.2195.186895.2075-0.02065280.0231528
2095.8995.701495.57790.1235140.188569
2196.4395.885695.965-0.07940280.544403
2296.3595.793496.3446-0.5511530.556569
2396.7196.733296.72920.00401389-0.0231806
2496.3296.330197.1208-0.790736-0.0100972
2597.2397.211497.505-0.2935690.0185694
2697.8897.846997.9104-0.06348610.0330694
2798.298.295998.3483-0.0524028-0.0959306
2898.5699.407798.80250.605181-0.847681
2999.3199.807699.27620.531347-0.497597
3099.69100.34699.75880.587347-0.656097
3199.77100.186100.207-0.0206528-0.416431
32101.06100.741100.6180.1235140.318986
33101.77100.964101.043-0.07940280.806069
34101.91100.959101.51-0.5511530.951153
35102.52101.984101.980.004013890.535569
36102.09101.632102.423-0.7907360.457819
37102.22102.556102.85-0.293569-0.336014
38102.74103.16103.223-0.0634861-0.419847
39103.56103.461103.513-0.05240280.0990694
40104.4104.357103.7520.6051810.0427361
41104.76104.49103.9580.5313470.270319
42104.86104.727104.140.5873470.132653
43104.84104.306104.327-0.02065280.533569
44104.96104.659104.5350.1235140.301486
45104.83104.671104.751-0.07940280.158569
46104.58104.426104.977-0.5511530.154486
47104.8105.213105.2090.00401389-0.412764
48104.17104.658105.449-0.790736-0.488014
49104.63105.404105.697-0.293569-0.773514
50105.32105.885105.948-0.0634861-0.564847
51106.16106.146106.198-0.05240280.0144861
52107.22107.055106.450.6051810.165236
53107.51107.378106.8460.5313470.132403
54107.87107.977107.390.587347-0.106931
55107.79108.022108.042-0.0206528-0.231847
56108.04108.934108.810.123514-0.893514
57107.74109.462109.542-0.0794028-1.72226
58107.71109.728110.279-0.551153-2.01801
59111.19111.054111.050.004013890.135986
60110.82111.027111.818-0.790736-0.207181
61113.65112.299112.593-0.2935691.35065
62114.72113.262113.325-0.06348611.45849
63114.32113.93113.982-0.05240280.390319
64116.76115.257114.6520.6051811.50315
65116.47115.783115.2510.5313470.687403
66117.34116.219115.6320.5873471.12057
67116.92NANA-0.0206528NA
68116.48NANA0.123514NA
69115.07NANA-0.0794028NA
70116.45NANA-0.551153NA
71116.84NANA0.00401389NA
72114.31NANA-0.790736NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 92.49 & NA & NA & -0.293569 & NA \tabularnewline
2 & 92.46 & NA & NA & -0.0634861 & NA \tabularnewline
3 & 92.55 & NA & NA & -0.0524028 & NA \tabularnewline
4 & 92.24 & NA & NA & 0.605181 & NA \tabularnewline
5 & 92.41 & NA & NA & 0.531347 & NA \tabularnewline
6 & 92.83 & NA & NA & 0.587347 & NA \tabularnewline
7 & 92.85 & 92.7118 & 92.7325 & -0.0206528 & 0.138153 \tabularnewline
8 & 93.04 & 92.9089 & 92.7854 & 0.123514 & 0.131069 \tabularnewline
9 & 93.04 & 92.7802 & 92.8596 & -0.0794028 & 0.259819 \tabularnewline
10 & 92.83 & 92.4276 & 92.9787 & -0.551153 & 0.402403 \tabularnewline
11 & 92.96 & 93.149 & 93.145 & 0.00401389 & -0.189014 \tabularnewline
12 & 92.83 & 92.5359 & 93.3267 & -0.790736 & 0.294069 \tabularnewline
13 & 93.01 & 93.2231 & 93.5167 & -0.293569 & -0.213097 \tabularnewline
14 & 93.21 & 93.6703 & 93.7337 & -0.0634861 & -0.460264 \tabularnewline
15 & 93.58 & 93.9413 & 93.9937 & -0.0524028 & -0.361347 \tabularnewline
16 & 94.07 & 94.8868 & 94.2817 & 0.605181 & -0.816847 \tabularnewline
17 & 94.57 & 95.1159 & 94.5846 & 0.531347 & -0.545931 \tabularnewline
18 & 95.03 & 95.4736 & 94.8862 & 0.587347 & -0.443597 \tabularnewline
19 & 95.21 & 95.1868 & 95.2075 & -0.0206528 & 0.0231528 \tabularnewline
20 & 95.89 & 95.7014 & 95.5779 & 0.123514 & 0.188569 \tabularnewline
21 & 96.43 & 95.8856 & 95.965 & -0.0794028 & 0.544403 \tabularnewline
22 & 96.35 & 95.7934 & 96.3446 & -0.551153 & 0.556569 \tabularnewline
23 & 96.71 & 96.7332 & 96.7292 & 0.00401389 & -0.0231806 \tabularnewline
24 & 96.32 & 96.3301 & 97.1208 & -0.790736 & -0.0100972 \tabularnewline
25 & 97.23 & 97.2114 & 97.505 & -0.293569 & 0.0185694 \tabularnewline
26 & 97.88 & 97.8469 & 97.9104 & -0.0634861 & 0.0330694 \tabularnewline
27 & 98.2 & 98.2959 & 98.3483 & -0.0524028 & -0.0959306 \tabularnewline
28 & 98.56 & 99.4077 & 98.8025 & 0.605181 & -0.847681 \tabularnewline
29 & 99.31 & 99.8076 & 99.2762 & 0.531347 & -0.497597 \tabularnewline
30 & 99.69 & 100.346 & 99.7588 & 0.587347 & -0.656097 \tabularnewline
31 & 99.77 & 100.186 & 100.207 & -0.0206528 & -0.416431 \tabularnewline
32 & 101.06 & 100.741 & 100.618 & 0.123514 & 0.318986 \tabularnewline
33 & 101.77 & 100.964 & 101.043 & -0.0794028 & 0.806069 \tabularnewline
34 & 101.91 & 100.959 & 101.51 & -0.551153 & 0.951153 \tabularnewline
35 & 102.52 & 101.984 & 101.98 & 0.00401389 & 0.535569 \tabularnewline
36 & 102.09 & 101.632 & 102.423 & -0.790736 & 0.457819 \tabularnewline
37 & 102.22 & 102.556 & 102.85 & -0.293569 & -0.336014 \tabularnewline
38 & 102.74 & 103.16 & 103.223 & -0.0634861 & -0.419847 \tabularnewline
39 & 103.56 & 103.461 & 103.513 & -0.0524028 & 0.0990694 \tabularnewline
40 & 104.4 & 104.357 & 103.752 & 0.605181 & 0.0427361 \tabularnewline
41 & 104.76 & 104.49 & 103.958 & 0.531347 & 0.270319 \tabularnewline
42 & 104.86 & 104.727 & 104.14 & 0.587347 & 0.132653 \tabularnewline
43 & 104.84 & 104.306 & 104.327 & -0.0206528 & 0.533569 \tabularnewline
44 & 104.96 & 104.659 & 104.535 & 0.123514 & 0.301486 \tabularnewline
45 & 104.83 & 104.671 & 104.751 & -0.0794028 & 0.158569 \tabularnewline
46 & 104.58 & 104.426 & 104.977 & -0.551153 & 0.154486 \tabularnewline
47 & 104.8 & 105.213 & 105.209 & 0.00401389 & -0.412764 \tabularnewline
48 & 104.17 & 104.658 & 105.449 & -0.790736 & -0.488014 \tabularnewline
49 & 104.63 & 105.404 & 105.697 & -0.293569 & -0.773514 \tabularnewline
50 & 105.32 & 105.885 & 105.948 & -0.0634861 & -0.564847 \tabularnewline
51 & 106.16 & 106.146 & 106.198 & -0.0524028 & 0.0144861 \tabularnewline
52 & 107.22 & 107.055 & 106.45 & 0.605181 & 0.165236 \tabularnewline
53 & 107.51 & 107.378 & 106.846 & 0.531347 & 0.132403 \tabularnewline
54 & 107.87 & 107.977 & 107.39 & 0.587347 & -0.106931 \tabularnewline
55 & 107.79 & 108.022 & 108.042 & -0.0206528 & -0.231847 \tabularnewline
56 & 108.04 & 108.934 & 108.81 & 0.123514 & -0.893514 \tabularnewline
57 & 107.74 & 109.462 & 109.542 & -0.0794028 & -1.72226 \tabularnewline
58 & 107.71 & 109.728 & 110.279 & -0.551153 & -2.01801 \tabularnewline
59 & 111.19 & 111.054 & 111.05 & 0.00401389 & 0.135986 \tabularnewline
60 & 110.82 & 111.027 & 111.818 & -0.790736 & -0.207181 \tabularnewline
61 & 113.65 & 112.299 & 112.593 & -0.293569 & 1.35065 \tabularnewline
62 & 114.72 & 113.262 & 113.325 & -0.0634861 & 1.45849 \tabularnewline
63 & 114.32 & 113.93 & 113.982 & -0.0524028 & 0.390319 \tabularnewline
64 & 116.76 & 115.257 & 114.652 & 0.605181 & 1.50315 \tabularnewline
65 & 116.47 & 115.783 & 115.251 & 0.531347 & 0.687403 \tabularnewline
66 & 117.34 & 116.219 & 115.632 & 0.587347 & 1.12057 \tabularnewline
67 & 116.92 & NA & NA & -0.0206528 & NA \tabularnewline
68 & 116.48 & NA & NA & 0.123514 & NA \tabularnewline
69 & 115.07 & NA & NA & -0.0794028 & NA \tabularnewline
70 & 116.45 & NA & NA & -0.551153 & NA \tabularnewline
71 & 116.84 & NA & NA & 0.00401389 & NA \tabularnewline
72 & 114.31 & NA & NA & -0.790736 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]92.49[/C][C]NA[/C][C]NA[/C][C]-0.293569[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]92.46[/C][C]NA[/C][C]NA[/C][C]-0.0634861[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]92.55[/C][C]NA[/C][C]NA[/C][C]-0.0524028[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]92.24[/C][C]NA[/C][C]NA[/C][C]0.605181[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.41[/C][C]NA[/C][C]NA[/C][C]0.531347[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.83[/C][C]NA[/C][C]NA[/C][C]0.587347[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.85[/C][C]92.7118[/C][C]92.7325[/C][C]-0.0206528[/C][C]0.138153[/C][/ROW]
[ROW][C]8[/C][C]93.04[/C][C]92.9089[/C][C]92.7854[/C][C]0.123514[/C][C]0.131069[/C][/ROW]
[ROW][C]9[/C][C]93.04[/C][C]92.7802[/C][C]92.8596[/C][C]-0.0794028[/C][C]0.259819[/C][/ROW]
[ROW][C]10[/C][C]92.83[/C][C]92.4276[/C][C]92.9787[/C][C]-0.551153[/C][C]0.402403[/C][/ROW]
[ROW][C]11[/C][C]92.96[/C][C]93.149[/C][C]93.145[/C][C]0.00401389[/C][C]-0.189014[/C][/ROW]
[ROW][C]12[/C][C]92.83[/C][C]92.5359[/C][C]93.3267[/C][C]-0.790736[/C][C]0.294069[/C][/ROW]
[ROW][C]13[/C][C]93.01[/C][C]93.2231[/C][C]93.5167[/C][C]-0.293569[/C][C]-0.213097[/C][/ROW]
[ROW][C]14[/C][C]93.21[/C][C]93.6703[/C][C]93.7337[/C][C]-0.0634861[/C][C]-0.460264[/C][/ROW]
[ROW][C]15[/C][C]93.58[/C][C]93.9413[/C][C]93.9937[/C][C]-0.0524028[/C][C]-0.361347[/C][/ROW]
[ROW][C]16[/C][C]94.07[/C][C]94.8868[/C][C]94.2817[/C][C]0.605181[/C][C]-0.816847[/C][/ROW]
[ROW][C]17[/C][C]94.57[/C][C]95.1159[/C][C]94.5846[/C][C]0.531347[/C][C]-0.545931[/C][/ROW]
[ROW][C]18[/C][C]95.03[/C][C]95.4736[/C][C]94.8862[/C][C]0.587347[/C][C]-0.443597[/C][/ROW]
[ROW][C]19[/C][C]95.21[/C][C]95.1868[/C][C]95.2075[/C][C]-0.0206528[/C][C]0.0231528[/C][/ROW]
[ROW][C]20[/C][C]95.89[/C][C]95.7014[/C][C]95.5779[/C][C]0.123514[/C][C]0.188569[/C][/ROW]
[ROW][C]21[/C][C]96.43[/C][C]95.8856[/C][C]95.965[/C][C]-0.0794028[/C][C]0.544403[/C][/ROW]
[ROW][C]22[/C][C]96.35[/C][C]95.7934[/C][C]96.3446[/C][C]-0.551153[/C][C]0.556569[/C][/ROW]
[ROW][C]23[/C][C]96.71[/C][C]96.7332[/C][C]96.7292[/C][C]0.00401389[/C][C]-0.0231806[/C][/ROW]
[ROW][C]24[/C][C]96.32[/C][C]96.3301[/C][C]97.1208[/C][C]-0.790736[/C][C]-0.0100972[/C][/ROW]
[ROW][C]25[/C][C]97.23[/C][C]97.2114[/C][C]97.505[/C][C]-0.293569[/C][C]0.0185694[/C][/ROW]
[ROW][C]26[/C][C]97.88[/C][C]97.8469[/C][C]97.9104[/C][C]-0.0634861[/C][C]0.0330694[/C][/ROW]
[ROW][C]27[/C][C]98.2[/C][C]98.2959[/C][C]98.3483[/C][C]-0.0524028[/C][C]-0.0959306[/C][/ROW]
[ROW][C]28[/C][C]98.56[/C][C]99.4077[/C][C]98.8025[/C][C]0.605181[/C][C]-0.847681[/C][/ROW]
[ROW][C]29[/C][C]99.31[/C][C]99.8076[/C][C]99.2762[/C][C]0.531347[/C][C]-0.497597[/C][/ROW]
[ROW][C]30[/C][C]99.69[/C][C]100.346[/C][C]99.7588[/C][C]0.587347[/C][C]-0.656097[/C][/ROW]
[ROW][C]31[/C][C]99.77[/C][C]100.186[/C][C]100.207[/C][C]-0.0206528[/C][C]-0.416431[/C][/ROW]
[ROW][C]32[/C][C]101.06[/C][C]100.741[/C][C]100.618[/C][C]0.123514[/C][C]0.318986[/C][/ROW]
[ROW][C]33[/C][C]101.77[/C][C]100.964[/C][C]101.043[/C][C]-0.0794028[/C][C]0.806069[/C][/ROW]
[ROW][C]34[/C][C]101.91[/C][C]100.959[/C][C]101.51[/C][C]-0.551153[/C][C]0.951153[/C][/ROW]
[ROW][C]35[/C][C]102.52[/C][C]101.984[/C][C]101.98[/C][C]0.00401389[/C][C]0.535569[/C][/ROW]
[ROW][C]36[/C][C]102.09[/C][C]101.632[/C][C]102.423[/C][C]-0.790736[/C][C]0.457819[/C][/ROW]
[ROW][C]37[/C][C]102.22[/C][C]102.556[/C][C]102.85[/C][C]-0.293569[/C][C]-0.336014[/C][/ROW]
[ROW][C]38[/C][C]102.74[/C][C]103.16[/C][C]103.223[/C][C]-0.0634861[/C][C]-0.419847[/C][/ROW]
[ROW][C]39[/C][C]103.56[/C][C]103.461[/C][C]103.513[/C][C]-0.0524028[/C][C]0.0990694[/C][/ROW]
[ROW][C]40[/C][C]104.4[/C][C]104.357[/C][C]103.752[/C][C]0.605181[/C][C]0.0427361[/C][/ROW]
[ROW][C]41[/C][C]104.76[/C][C]104.49[/C][C]103.958[/C][C]0.531347[/C][C]0.270319[/C][/ROW]
[ROW][C]42[/C][C]104.86[/C][C]104.727[/C][C]104.14[/C][C]0.587347[/C][C]0.132653[/C][/ROW]
[ROW][C]43[/C][C]104.84[/C][C]104.306[/C][C]104.327[/C][C]-0.0206528[/C][C]0.533569[/C][/ROW]
[ROW][C]44[/C][C]104.96[/C][C]104.659[/C][C]104.535[/C][C]0.123514[/C][C]0.301486[/C][/ROW]
[ROW][C]45[/C][C]104.83[/C][C]104.671[/C][C]104.751[/C][C]-0.0794028[/C][C]0.158569[/C][/ROW]
[ROW][C]46[/C][C]104.58[/C][C]104.426[/C][C]104.977[/C][C]-0.551153[/C][C]0.154486[/C][/ROW]
[ROW][C]47[/C][C]104.8[/C][C]105.213[/C][C]105.209[/C][C]0.00401389[/C][C]-0.412764[/C][/ROW]
[ROW][C]48[/C][C]104.17[/C][C]104.658[/C][C]105.449[/C][C]-0.790736[/C][C]-0.488014[/C][/ROW]
[ROW][C]49[/C][C]104.63[/C][C]105.404[/C][C]105.697[/C][C]-0.293569[/C][C]-0.773514[/C][/ROW]
[ROW][C]50[/C][C]105.32[/C][C]105.885[/C][C]105.948[/C][C]-0.0634861[/C][C]-0.564847[/C][/ROW]
[ROW][C]51[/C][C]106.16[/C][C]106.146[/C][C]106.198[/C][C]-0.0524028[/C][C]0.0144861[/C][/ROW]
[ROW][C]52[/C][C]107.22[/C][C]107.055[/C][C]106.45[/C][C]0.605181[/C][C]0.165236[/C][/ROW]
[ROW][C]53[/C][C]107.51[/C][C]107.378[/C][C]106.846[/C][C]0.531347[/C][C]0.132403[/C][/ROW]
[ROW][C]54[/C][C]107.87[/C][C]107.977[/C][C]107.39[/C][C]0.587347[/C][C]-0.106931[/C][/ROW]
[ROW][C]55[/C][C]107.79[/C][C]108.022[/C][C]108.042[/C][C]-0.0206528[/C][C]-0.231847[/C][/ROW]
[ROW][C]56[/C][C]108.04[/C][C]108.934[/C][C]108.81[/C][C]0.123514[/C][C]-0.893514[/C][/ROW]
[ROW][C]57[/C][C]107.74[/C][C]109.462[/C][C]109.542[/C][C]-0.0794028[/C][C]-1.72226[/C][/ROW]
[ROW][C]58[/C][C]107.71[/C][C]109.728[/C][C]110.279[/C][C]-0.551153[/C][C]-2.01801[/C][/ROW]
[ROW][C]59[/C][C]111.19[/C][C]111.054[/C][C]111.05[/C][C]0.00401389[/C][C]0.135986[/C][/ROW]
[ROW][C]60[/C][C]110.82[/C][C]111.027[/C][C]111.818[/C][C]-0.790736[/C][C]-0.207181[/C][/ROW]
[ROW][C]61[/C][C]113.65[/C][C]112.299[/C][C]112.593[/C][C]-0.293569[/C][C]1.35065[/C][/ROW]
[ROW][C]62[/C][C]114.72[/C][C]113.262[/C][C]113.325[/C][C]-0.0634861[/C][C]1.45849[/C][/ROW]
[ROW][C]63[/C][C]114.32[/C][C]113.93[/C][C]113.982[/C][C]-0.0524028[/C][C]0.390319[/C][/ROW]
[ROW][C]64[/C][C]116.76[/C][C]115.257[/C][C]114.652[/C][C]0.605181[/C][C]1.50315[/C][/ROW]
[ROW][C]65[/C][C]116.47[/C][C]115.783[/C][C]115.251[/C][C]0.531347[/C][C]0.687403[/C][/ROW]
[ROW][C]66[/C][C]117.34[/C][C]116.219[/C][C]115.632[/C][C]0.587347[/C][C]1.12057[/C][/ROW]
[ROW][C]67[/C][C]116.92[/C][C]NA[/C][C]NA[/C][C]-0.0206528[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]116.48[/C][C]NA[/C][C]NA[/C][C]0.123514[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]115.07[/C][C]NA[/C][C]NA[/C][C]-0.0794028[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]116.45[/C][C]NA[/C][C]NA[/C][C]-0.551153[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]116.84[/C][C]NA[/C][C]NA[/C][C]0.00401389[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]114.31[/C][C]NA[/C][C]NA[/C][C]-0.790736[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
192.49NANA-0.293569NA
292.46NANA-0.0634861NA
392.55NANA-0.0524028NA
492.24NANA0.605181NA
592.41NANA0.531347NA
692.83NANA0.587347NA
792.8592.711892.7325-0.02065280.138153
893.0492.908992.78540.1235140.131069
993.0492.780292.8596-0.07940280.259819
1092.8392.427692.9787-0.5511530.402403
1192.9693.14993.1450.00401389-0.189014
1292.8392.535993.3267-0.7907360.294069
1393.0193.223193.5167-0.293569-0.213097
1493.2193.670393.7337-0.0634861-0.460264
1593.5893.941393.9937-0.0524028-0.361347
1694.0794.886894.28170.605181-0.816847
1794.5795.115994.58460.531347-0.545931
1895.0395.473694.88620.587347-0.443597
1995.2195.186895.2075-0.02065280.0231528
2095.8995.701495.57790.1235140.188569
2196.4395.885695.965-0.07940280.544403
2296.3595.793496.3446-0.5511530.556569
2396.7196.733296.72920.00401389-0.0231806
2496.3296.330197.1208-0.790736-0.0100972
2597.2397.211497.505-0.2935690.0185694
2697.8897.846997.9104-0.06348610.0330694
2798.298.295998.3483-0.0524028-0.0959306
2898.5699.407798.80250.605181-0.847681
2999.3199.807699.27620.531347-0.497597
3099.69100.34699.75880.587347-0.656097
3199.77100.186100.207-0.0206528-0.416431
32101.06100.741100.6180.1235140.318986
33101.77100.964101.043-0.07940280.806069
34101.91100.959101.51-0.5511530.951153
35102.52101.984101.980.004013890.535569
36102.09101.632102.423-0.7907360.457819
37102.22102.556102.85-0.293569-0.336014
38102.74103.16103.223-0.0634861-0.419847
39103.56103.461103.513-0.05240280.0990694
40104.4104.357103.7520.6051810.0427361
41104.76104.49103.9580.5313470.270319
42104.86104.727104.140.5873470.132653
43104.84104.306104.327-0.02065280.533569
44104.96104.659104.5350.1235140.301486
45104.83104.671104.751-0.07940280.158569
46104.58104.426104.977-0.5511530.154486
47104.8105.213105.2090.00401389-0.412764
48104.17104.658105.449-0.790736-0.488014
49104.63105.404105.697-0.293569-0.773514
50105.32105.885105.948-0.0634861-0.564847
51106.16106.146106.198-0.05240280.0144861
52107.22107.055106.450.6051810.165236
53107.51107.378106.8460.5313470.132403
54107.87107.977107.390.587347-0.106931
55107.79108.022108.042-0.0206528-0.231847
56108.04108.934108.810.123514-0.893514
57107.74109.462109.542-0.0794028-1.72226
58107.71109.728110.279-0.551153-2.01801
59111.19111.054111.050.004013890.135986
60110.82111.027111.818-0.790736-0.207181
61113.65112.299112.593-0.2935691.35065
62114.72113.262113.325-0.06348611.45849
63114.32113.93113.982-0.05240280.390319
64116.76115.257114.6520.6051811.50315
65116.47115.783115.2510.5313470.687403
66117.34116.219115.6320.5873471.12057
67116.92NANA-0.0206528NA
68116.48NANA0.123514NA
69115.07NANA-0.0794028NA
70116.45NANA-0.551153NA
71116.84NANA0.00401389NA
72114.31NANA-0.790736NA



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')