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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 computationMon, 09 Dec 2013 04:08:53 -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/t13865803156hrppg3f4oiklzl.htm/, Retrieved Sat, 20 Apr 2024 01:03:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231588, Retrieved Sat, 20 Apr 2024 01:03:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-09 09:08:53] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106,68
109,73
108,06
111,33
105,66
103,65
100,34
100,56
102,67
101,5
102,35
104,98
106,31
103,73
106,62
108,54
105,12
105,29
104,62
104,34
108,23
107,6
106,87
107,96
108,34
109,04
106,95
105,59
108,08
108,48
106,84
105,6
106,9
106,84
106,81
106,98
107,53
107,37
106,98
108,94
106,38
109,02
106,53
105,02
109,7
108,39
110,18
109,54
109,1
110,85
112,23
110,58
110,77
108,08
108,05
108,87
109,61
111,27
107,61
110,98
106,63
106,83
108,77
106,12
106,8
106,34
105,16
107,97
106,76
108,78
105,58
109,22
105,67
109,04
106,59
109,66
108,05
109,91
107,63
107,15
103,8
103,43
103,59
107,63

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=231588&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=231588&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231588&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
1106.68NANA0.0241782NA
2109.73NANA0.474456NA
3108.06NANA0.634178NA
4111.33NANA0.827928NA
5105.66NANA0.100914NA
6103.65NANA0.3939NA
7100.34102.905104.777-1.87193-2.56515
8100.56102.788104.512-1.72346-2.22821
9102.67104.412104.2020.209873-1.74154
10101.5104.342104.0250.316678-2.84209
11102.35103.368103.887-0.518322-1.01834
12104.98105.064103.9321.13161-0.0841088
13106.31104.203104.1790.02417822.10666
14103.73104.989104.5150.474456-1.25946
15106.62105.538104.9040.6341781.08166
16108.54106.218105.390.8279282.32207
17105.12105.933105.8320.100914-0.813414
18105.29106.539106.1450.3939-1.2489
19104.62104.482106.354-1.871930.138183
20104.34104.936106.66-1.72346-0.596123
21108.23107.104106.8950.2098731.12554
22107.6107.102106.7850.3166780.497905
23106.87106.268106.786-0.5183220.602488
24107.96108.174107.0421.13161-0.213692
25108.34107.292107.2680.02417821.04832
26109.04107.887107.4120.4744561.15304
27106.95108.044107.410.634178-1.09376
28105.59108.15107.3220.827928-2.56043
29108.08107.389107.2880.1009140.690752
30108.48107.639107.2450.39390.8411
31106.84105.298107.17-1.871931.54152
32105.6105.344107.067-1.723460.256377
33106.9107.209106.9990.209873-0.308623
34106.84107.456107.140.316678-0.616262
35106.81106.69107.208-0.5183220.119988
36106.98108.292107.161.13161-1.31161
37107.53107.194107.170.02417820.336238
38107.37107.607107.1330.474456-0.236956
39106.98107.859107.2250.634178-0.879178
40108.94108.234107.4060.8279280.705822
41106.38107.712107.6110.100914-1.33216
42109.02108.252107.8580.39390.767766
43106.53106.158108.03-1.871930.371516
44105.02106.517108.241-1.72346-1.49737
45109.7108.814108.6050.2098730.885544
46108.39109.208108.8920.316678-0.818345
47110.18108.625109.143-0.5183221.55541
48109.54110.418109.2871.13161-0.878275
49109.1109.335109.3110.0241782-0.235012
50110.85110.009109.5350.4744560.840961
51112.23110.325109.6910.6341781.90457
52110.58110.635109.8070.827928-0.0554282
53110.77109.921109.820.1009140.848669
54108.08110.167109.7730.3939-2.08723
55108.05107.858109.73-1.871930.191516
56108.87107.737109.46-1.723461.13346
57109.61109.358109.1480.2098730.251794
58111.27109.135108.8180.3166782.13499
59107.61107.949108.467-0.518322-0.338762
60110.98109.361108.2291.131611.61922
61106.63108.06108.0360.0241782-1.43043
62106.83108.353107.8780.474456-1.52279
63108.77108.356107.7220.6341780.413738
64106.12108.328107.50.827928-2.20751
65106.8107.412107.3110.100914-0.612164
66106.34107.547107.1530.3939-1.20723
67105.16105.168107.04-1.87193-0.00806713
68107.97105.369107.092-1.723462.60138
69106.76107.303107.0930.209873-0.543206
70108.78107.467107.150.3166781.31332
71105.58106.831107.35-0.518322-1.25126
72109.22108.682107.551.131610.537975
73105.67107.826107.8020.0241782-2.15626
74109.04108.345107.8710.4744560.694711
75106.59108.348107.7130.634178-1.75751
76109.66108.195107.3670.8279281.46499
77108.05107.162107.0610.1009140.887836
78109.91107.306106.9120.39392.60402
79107.63NANA-1.87193NA
80107.15NANA-1.72346NA
81103.8NANA0.209873NA
82103.43NANA0.316678NA
83103.59NANA-0.518322NA
84107.63NANA1.13161NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 106.68 & NA & NA & 0.0241782 & NA \tabularnewline
2 & 109.73 & NA & NA & 0.474456 & NA \tabularnewline
3 & 108.06 & NA & NA & 0.634178 & NA \tabularnewline
4 & 111.33 & NA & NA & 0.827928 & NA \tabularnewline
5 & 105.66 & NA & NA & 0.100914 & NA \tabularnewline
6 & 103.65 & NA & NA & 0.3939 & NA \tabularnewline
7 & 100.34 & 102.905 & 104.777 & -1.87193 & -2.56515 \tabularnewline
8 & 100.56 & 102.788 & 104.512 & -1.72346 & -2.22821 \tabularnewline
9 & 102.67 & 104.412 & 104.202 & 0.209873 & -1.74154 \tabularnewline
10 & 101.5 & 104.342 & 104.025 & 0.316678 & -2.84209 \tabularnewline
11 & 102.35 & 103.368 & 103.887 & -0.518322 & -1.01834 \tabularnewline
12 & 104.98 & 105.064 & 103.932 & 1.13161 & -0.0841088 \tabularnewline
13 & 106.31 & 104.203 & 104.179 & 0.0241782 & 2.10666 \tabularnewline
14 & 103.73 & 104.989 & 104.515 & 0.474456 & -1.25946 \tabularnewline
15 & 106.62 & 105.538 & 104.904 & 0.634178 & 1.08166 \tabularnewline
16 & 108.54 & 106.218 & 105.39 & 0.827928 & 2.32207 \tabularnewline
17 & 105.12 & 105.933 & 105.832 & 0.100914 & -0.813414 \tabularnewline
18 & 105.29 & 106.539 & 106.145 & 0.3939 & -1.2489 \tabularnewline
19 & 104.62 & 104.482 & 106.354 & -1.87193 & 0.138183 \tabularnewline
20 & 104.34 & 104.936 & 106.66 & -1.72346 & -0.596123 \tabularnewline
21 & 108.23 & 107.104 & 106.895 & 0.209873 & 1.12554 \tabularnewline
22 & 107.6 & 107.102 & 106.785 & 0.316678 & 0.497905 \tabularnewline
23 & 106.87 & 106.268 & 106.786 & -0.518322 & 0.602488 \tabularnewline
24 & 107.96 & 108.174 & 107.042 & 1.13161 & -0.213692 \tabularnewline
25 & 108.34 & 107.292 & 107.268 & 0.0241782 & 1.04832 \tabularnewline
26 & 109.04 & 107.887 & 107.412 & 0.474456 & 1.15304 \tabularnewline
27 & 106.95 & 108.044 & 107.41 & 0.634178 & -1.09376 \tabularnewline
28 & 105.59 & 108.15 & 107.322 & 0.827928 & -2.56043 \tabularnewline
29 & 108.08 & 107.389 & 107.288 & 0.100914 & 0.690752 \tabularnewline
30 & 108.48 & 107.639 & 107.245 & 0.3939 & 0.8411 \tabularnewline
31 & 106.84 & 105.298 & 107.17 & -1.87193 & 1.54152 \tabularnewline
32 & 105.6 & 105.344 & 107.067 & -1.72346 & 0.256377 \tabularnewline
33 & 106.9 & 107.209 & 106.999 & 0.209873 & -0.308623 \tabularnewline
34 & 106.84 & 107.456 & 107.14 & 0.316678 & -0.616262 \tabularnewline
35 & 106.81 & 106.69 & 107.208 & -0.518322 & 0.119988 \tabularnewline
36 & 106.98 & 108.292 & 107.16 & 1.13161 & -1.31161 \tabularnewline
37 & 107.53 & 107.194 & 107.17 & 0.0241782 & 0.336238 \tabularnewline
38 & 107.37 & 107.607 & 107.133 & 0.474456 & -0.236956 \tabularnewline
39 & 106.98 & 107.859 & 107.225 & 0.634178 & -0.879178 \tabularnewline
40 & 108.94 & 108.234 & 107.406 & 0.827928 & 0.705822 \tabularnewline
41 & 106.38 & 107.712 & 107.611 & 0.100914 & -1.33216 \tabularnewline
42 & 109.02 & 108.252 & 107.858 & 0.3939 & 0.767766 \tabularnewline
43 & 106.53 & 106.158 & 108.03 & -1.87193 & 0.371516 \tabularnewline
44 & 105.02 & 106.517 & 108.241 & -1.72346 & -1.49737 \tabularnewline
45 & 109.7 & 108.814 & 108.605 & 0.209873 & 0.885544 \tabularnewline
46 & 108.39 & 109.208 & 108.892 & 0.316678 & -0.818345 \tabularnewline
47 & 110.18 & 108.625 & 109.143 & -0.518322 & 1.55541 \tabularnewline
48 & 109.54 & 110.418 & 109.287 & 1.13161 & -0.878275 \tabularnewline
49 & 109.1 & 109.335 & 109.311 & 0.0241782 & -0.235012 \tabularnewline
50 & 110.85 & 110.009 & 109.535 & 0.474456 & 0.840961 \tabularnewline
51 & 112.23 & 110.325 & 109.691 & 0.634178 & 1.90457 \tabularnewline
52 & 110.58 & 110.635 & 109.807 & 0.827928 & -0.0554282 \tabularnewline
53 & 110.77 & 109.921 & 109.82 & 0.100914 & 0.848669 \tabularnewline
54 & 108.08 & 110.167 & 109.773 & 0.3939 & -2.08723 \tabularnewline
55 & 108.05 & 107.858 & 109.73 & -1.87193 & 0.191516 \tabularnewline
56 & 108.87 & 107.737 & 109.46 & -1.72346 & 1.13346 \tabularnewline
57 & 109.61 & 109.358 & 109.148 & 0.209873 & 0.251794 \tabularnewline
58 & 111.27 & 109.135 & 108.818 & 0.316678 & 2.13499 \tabularnewline
59 & 107.61 & 107.949 & 108.467 & -0.518322 & -0.338762 \tabularnewline
60 & 110.98 & 109.361 & 108.229 & 1.13161 & 1.61922 \tabularnewline
61 & 106.63 & 108.06 & 108.036 & 0.0241782 & -1.43043 \tabularnewline
62 & 106.83 & 108.353 & 107.878 & 0.474456 & -1.52279 \tabularnewline
63 & 108.77 & 108.356 & 107.722 & 0.634178 & 0.413738 \tabularnewline
64 & 106.12 & 108.328 & 107.5 & 0.827928 & -2.20751 \tabularnewline
65 & 106.8 & 107.412 & 107.311 & 0.100914 & -0.612164 \tabularnewline
66 & 106.34 & 107.547 & 107.153 & 0.3939 & -1.20723 \tabularnewline
67 & 105.16 & 105.168 & 107.04 & -1.87193 & -0.00806713 \tabularnewline
68 & 107.97 & 105.369 & 107.092 & -1.72346 & 2.60138 \tabularnewline
69 & 106.76 & 107.303 & 107.093 & 0.209873 & -0.543206 \tabularnewline
70 & 108.78 & 107.467 & 107.15 & 0.316678 & 1.31332 \tabularnewline
71 & 105.58 & 106.831 & 107.35 & -0.518322 & -1.25126 \tabularnewline
72 & 109.22 & 108.682 & 107.55 & 1.13161 & 0.537975 \tabularnewline
73 & 105.67 & 107.826 & 107.802 & 0.0241782 & -2.15626 \tabularnewline
74 & 109.04 & 108.345 & 107.871 & 0.474456 & 0.694711 \tabularnewline
75 & 106.59 & 108.348 & 107.713 & 0.634178 & -1.75751 \tabularnewline
76 & 109.66 & 108.195 & 107.367 & 0.827928 & 1.46499 \tabularnewline
77 & 108.05 & 107.162 & 107.061 & 0.100914 & 0.887836 \tabularnewline
78 & 109.91 & 107.306 & 106.912 & 0.3939 & 2.60402 \tabularnewline
79 & 107.63 & NA & NA & -1.87193 & NA \tabularnewline
80 & 107.15 & NA & NA & -1.72346 & NA \tabularnewline
81 & 103.8 & NA & NA & 0.209873 & NA \tabularnewline
82 & 103.43 & NA & NA & 0.316678 & NA \tabularnewline
83 & 103.59 & NA & NA & -0.518322 & NA \tabularnewline
84 & 107.63 & NA & NA & 1.13161 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231588&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]106.68[/C][C]NA[/C][C]NA[/C][C]0.0241782[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]109.73[/C][C]NA[/C][C]NA[/C][C]0.474456[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]108.06[/C][C]NA[/C][C]NA[/C][C]0.634178[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]111.33[/C][C]NA[/C][C]NA[/C][C]0.827928[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]105.66[/C][C]NA[/C][C]NA[/C][C]0.100914[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]103.65[/C][C]NA[/C][C]NA[/C][C]0.3939[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.34[/C][C]102.905[/C][C]104.777[/C][C]-1.87193[/C][C]-2.56515[/C][/ROW]
[ROW][C]8[/C][C]100.56[/C][C]102.788[/C][C]104.512[/C][C]-1.72346[/C][C]-2.22821[/C][/ROW]
[ROW][C]9[/C][C]102.67[/C][C]104.412[/C][C]104.202[/C][C]0.209873[/C][C]-1.74154[/C][/ROW]
[ROW][C]10[/C][C]101.5[/C][C]104.342[/C][C]104.025[/C][C]0.316678[/C][C]-2.84209[/C][/ROW]
[ROW][C]11[/C][C]102.35[/C][C]103.368[/C][C]103.887[/C][C]-0.518322[/C][C]-1.01834[/C][/ROW]
[ROW][C]12[/C][C]104.98[/C][C]105.064[/C][C]103.932[/C][C]1.13161[/C][C]-0.0841088[/C][/ROW]
[ROW][C]13[/C][C]106.31[/C][C]104.203[/C][C]104.179[/C][C]0.0241782[/C][C]2.10666[/C][/ROW]
[ROW][C]14[/C][C]103.73[/C][C]104.989[/C][C]104.515[/C][C]0.474456[/C][C]-1.25946[/C][/ROW]
[ROW][C]15[/C][C]106.62[/C][C]105.538[/C][C]104.904[/C][C]0.634178[/C][C]1.08166[/C][/ROW]
[ROW][C]16[/C][C]108.54[/C][C]106.218[/C][C]105.39[/C][C]0.827928[/C][C]2.32207[/C][/ROW]
[ROW][C]17[/C][C]105.12[/C][C]105.933[/C][C]105.832[/C][C]0.100914[/C][C]-0.813414[/C][/ROW]
[ROW][C]18[/C][C]105.29[/C][C]106.539[/C][C]106.145[/C][C]0.3939[/C][C]-1.2489[/C][/ROW]
[ROW][C]19[/C][C]104.62[/C][C]104.482[/C][C]106.354[/C][C]-1.87193[/C][C]0.138183[/C][/ROW]
[ROW][C]20[/C][C]104.34[/C][C]104.936[/C][C]106.66[/C][C]-1.72346[/C][C]-0.596123[/C][/ROW]
[ROW][C]21[/C][C]108.23[/C][C]107.104[/C][C]106.895[/C][C]0.209873[/C][C]1.12554[/C][/ROW]
[ROW][C]22[/C][C]107.6[/C][C]107.102[/C][C]106.785[/C][C]0.316678[/C][C]0.497905[/C][/ROW]
[ROW][C]23[/C][C]106.87[/C][C]106.268[/C][C]106.786[/C][C]-0.518322[/C][C]0.602488[/C][/ROW]
[ROW][C]24[/C][C]107.96[/C][C]108.174[/C][C]107.042[/C][C]1.13161[/C][C]-0.213692[/C][/ROW]
[ROW][C]25[/C][C]108.34[/C][C]107.292[/C][C]107.268[/C][C]0.0241782[/C][C]1.04832[/C][/ROW]
[ROW][C]26[/C][C]109.04[/C][C]107.887[/C][C]107.412[/C][C]0.474456[/C][C]1.15304[/C][/ROW]
[ROW][C]27[/C][C]106.95[/C][C]108.044[/C][C]107.41[/C][C]0.634178[/C][C]-1.09376[/C][/ROW]
[ROW][C]28[/C][C]105.59[/C][C]108.15[/C][C]107.322[/C][C]0.827928[/C][C]-2.56043[/C][/ROW]
[ROW][C]29[/C][C]108.08[/C][C]107.389[/C][C]107.288[/C][C]0.100914[/C][C]0.690752[/C][/ROW]
[ROW][C]30[/C][C]108.48[/C][C]107.639[/C][C]107.245[/C][C]0.3939[/C][C]0.8411[/C][/ROW]
[ROW][C]31[/C][C]106.84[/C][C]105.298[/C][C]107.17[/C][C]-1.87193[/C][C]1.54152[/C][/ROW]
[ROW][C]32[/C][C]105.6[/C][C]105.344[/C][C]107.067[/C][C]-1.72346[/C][C]0.256377[/C][/ROW]
[ROW][C]33[/C][C]106.9[/C][C]107.209[/C][C]106.999[/C][C]0.209873[/C][C]-0.308623[/C][/ROW]
[ROW][C]34[/C][C]106.84[/C][C]107.456[/C][C]107.14[/C][C]0.316678[/C][C]-0.616262[/C][/ROW]
[ROW][C]35[/C][C]106.81[/C][C]106.69[/C][C]107.208[/C][C]-0.518322[/C][C]0.119988[/C][/ROW]
[ROW][C]36[/C][C]106.98[/C][C]108.292[/C][C]107.16[/C][C]1.13161[/C][C]-1.31161[/C][/ROW]
[ROW][C]37[/C][C]107.53[/C][C]107.194[/C][C]107.17[/C][C]0.0241782[/C][C]0.336238[/C][/ROW]
[ROW][C]38[/C][C]107.37[/C][C]107.607[/C][C]107.133[/C][C]0.474456[/C][C]-0.236956[/C][/ROW]
[ROW][C]39[/C][C]106.98[/C][C]107.859[/C][C]107.225[/C][C]0.634178[/C][C]-0.879178[/C][/ROW]
[ROW][C]40[/C][C]108.94[/C][C]108.234[/C][C]107.406[/C][C]0.827928[/C][C]0.705822[/C][/ROW]
[ROW][C]41[/C][C]106.38[/C][C]107.712[/C][C]107.611[/C][C]0.100914[/C][C]-1.33216[/C][/ROW]
[ROW][C]42[/C][C]109.02[/C][C]108.252[/C][C]107.858[/C][C]0.3939[/C][C]0.767766[/C][/ROW]
[ROW][C]43[/C][C]106.53[/C][C]106.158[/C][C]108.03[/C][C]-1.87193[/C][C]0.371516[/C][/ROW]
[ROW][C]44[/C][C]105.02[/C][C]106.517[/C][C]108.241[/C][C]-1.72346[/C][C]-1.49737[/C][/ROW]
[ROW][C]45[/C][C]109.7[/C][C]108.814[/C][C]108.605[/C][C]0.209873[/C][C]0.885544[/C][/ROW]
[ROW][C]46[/C][C]108.39[/C][C]109.208[/C][C]108.892[/C][C]0.316678[/C][C]-0.818345[/C][/ROW]
[ROW][C]47[/C][C]110.18[/C][C]108.625[/C][C]109.143[/C][C]-0.518322[/C][C]1.55541[/C][/ROW]
[ROW][C]48[/C][C]109.54[/C][C]110.418[/C][C]109.287[/C][C]1.13161[/C][C]-0.878275[/C][/ROW]
[ROW][C]49[/C][C]109.1[/C][C]109.335[/C][C]109.311[/C][C]0.0241782[/C][C]-0.235012[/C][/ROW]
[ROW][C]50[/C][C]110.85[/C][C]110.009[/C][C]109.535[/C][C]0.474456[/C][C]0.840961[/C][/ROW]
[ROW][C]51[/C][C]112.23[/C][C]110.325[/C][C]109.691[/C][C]0.634178[/C][C]1.90457[/C][/ROW]
[ROW][C]52[/C][C]110.58[/C][C]110.635[/C][C]109.807[/C][C]0.827928[/C][C]-0.0554282[/C][/ROW]
[ROW][C]53[/C][C]110.77[/C][C]109.921[/C][C]109.82[/C][C]0.100914[/C][C]0.848669[/C][/ROW]
[ROW][C]54[/C][C]108.08[/C][C]110.167[/C][C]109.773[/C][C]0.3939[/C][C]-2.08723[/C][/ROW]
[ROW][C]55[/C][C]108.05[/C][C]107.858[/C][C]109.73[/C][C]-1.87193[/C][C]0.191516[/C][/ROW]
[ROW][C]56[/C][C]108.87[/C][C]107.737[/C][C]109.46[/C][C]-1.72346[/C][C]1.13346[/C][/ROW]
[ROW][C]57[/C][C]109.61[/C][C]109.358[/C][C]109.148[/C][C]0.209873[/C][C]0.251794[/C][/ROW]
[ROW][C]58[/C][C]111.27[/C][C]109.135[/C][C]108.818[/C][C]0.316678[/C][C]2.13499[/C][/ROW]
[ROW][C]59[/C][C]107.61[/C][C]107.949[/C][C]108.467[/C][C]-0.518322[/C][C]-0.338762[/C][/ROW]
[ROW][C]60[/C][C]110.98[/C][C]109.361[/C][C]108.229[/C][C]1.13161[/C][C]1.61922[/C][/ROW]
[ROW][C]61[/C][C]106.63[/C][C]108.06[/C][C]108.036[/C][C]0.0241782[/C][C]-1.43043[/C][/ROW]
[ROW][C]62[/C][C]106.83[/C][C]108.353[/C][C]107.878[/C][C]0.474456[/C][C]-1.52279[/C][/ROW]
[ROW][C]63[/C][C]108.77[/C][C]108.356[/C][C]107.722[/C][C]0.634178[/C][C]0.413738[/C][/ROW]
[ROW][C]64[/C][C]106.12[/C][C]108.328[/C][C]107.5[/C][C]0.827928[/C][C]-2.20751[/C][/ROW]
[ROW][C]65[/C][C]106.8[/C][C]107.412[/C][C]107.311[/C][C]0.100914[/C][C]-0.612164[/C][/ROW]
[ROW][C]66[/C][C]106.34[/C][C]107.547[/C][C]107.153[/C][C]0.3939[/C][C]-1.20723[/C][/ROW]
[ROW][C]67[/C][C]105.16[/C][C]105.168[/C][C]107.04[/C][C]-1.87193[/C][C]-0.00806713[/C][/ROW]
[ROW][C]68[/C][C]107.97[/C][C]105.369[/C][C]107.092[/C][C]-1.72346[/C][C]2.60138[/C][/ROW]
[ROW][C]69[/C][C]106.76[/C][C]107.303[/C][C]107.093[/C][C]0.209873[/C][C]-0.543206[/C][/ROW]
[ROW][C]70[/C][C]108.78[/C][C]107.467[/C][C]107.15[/C][C]0.316678[/C][C]1.31332[/C][/ROW]
[ROW][C]71[/C][C]105.58[/C][C]106.831[/C][C]107.35[/C][C]-0.518322[/C][C]-1.25126[/C][/ROW]
[ROW][C]72[/C][C]109.22[/C][C]108.682[/C][C]107.55[/C][C]1.13161[/C][C]0.537975[/C][/ROW]
[ROW][C]73[/C][C]105.67[/C][C]107.826[/C][C]107.802[/C][C]0.0241782[/C][C]-2.15626[/C][/ROW]
[ROW][C]74[/C][C]109.04[/C][C]108.345[/C][C]107.871[/C][C]0.474456[/C][C]0.694711[/C][/ROW]
[ROW][C]75[/C][C]106.59[/C][C]108.348[/C][C]107.713[/C][C]0.634178[/C][C]-1.75751[/C][/ROW]
[ROW][C]76[/C][C]109.66[/C][C]108.195[/C][C]107.367[/C][C]0.827928[/C][C]1.46499[/C][/ROW]
[ROW][C]77[/C][C]108.05[/C][C]107.162[/C][C]107.061[/C][C]0.100914[/C][C]0.887836[/C][/ROW]
[ROW][C]78[/C][C]109.91[/C][C]107.306[/C][C]106.912[/C][C]0.3939[/C][C]2.60402[/C][/ROW]
[ROW][C]79[/C][C]107.63[/C][C]NA[/C][C]NA[/C][C]-1.87193[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]107.15[/C][C]NA[/C][C]NA[/C][C]-1.72346[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]103.8[/C][C]NA[/C][C]NA[/C][C]0.209873[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]103.43[/C][C]NA[/C][C]NA[/C][C]0.316678[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]103.59[/C][C]NA[/C][C]NA[/C][C]-0.518322[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]107.63[/C][C]NA[/C][C]NA[/C][C]1.13161[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231588&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231588&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
1106.68NANA0.0241782NA
2109.73NANA0.474456NA
3108.06NANA0.634178NA
4111.33NANA0.827928NA
5105.66NANA0.100914NA
6103.65NANA0.3939NA
7100.34102.905104.777-1.87193-2.56515
8100.56102.788104.512-1.72346-2.22821
9102.67104.412104.2020.209873-1.74154
10101.5104.342104.0250.316678-2.84209
11102.35103.368103.887-0.518322-1.01834
12104.98105.064103.9321.13161-0.0841088
13106.31104.203104.1790.02417822.10666
14103.73104.989104.5150.474456-1.25946
15106.62105.538104.9040.6341781.08166
16108.54106.218105.390.8279282.32207
17105.12105.933105.8320.100914-0.813414
18105.29106.539106.1450.3939-1.2489
19104.62104.482106.354-1.871930.138183
20104.34104.936106.66-1.72346-0.596123
21108.23107.104106.8950.2098731.12554
22107.6107.102106.7850.3166780.497905
23106.87106.268106.786-0.5183220.602488
24107.96108.174107.0421.13161-0.213692
25108.34107.292107.2680.02417821.04832
26109.04107.887107.4120.4744561.15304
27106.95108.044107.410.634178-1.09376
28105.59108.15107.3220.827928-2.56043
29108.08107.389107.2880.1009140.690752
30108.48107.639107.2450.39390.8411
31106.84105.298107.17-1.871931.54152
32105.6105.344107.067-1.723460.256377
33106.9107.209106.9990.209873-0.308623
34106.84107.456107.140.316678-0.616262
35106.81106.69107.208-0.5183220.119988
36106.98108.292107.161.13161-1.31161
37107.53107.194107.170.02417820.336238
38107.37107.607107.1330.474456-0.236956
39106.98107.859107.2250.634178-0.879178
40108.94108.234107.4060.8279280.705822
41106.38107.712107.6110.100914-1.33216
42109.02108.252107.8580.39390.767766
43106.53106.158108.03-1.871930.371516
44105.02106.517108.241-1.72346-1.49737
45109.7108.814108.6050.2098730.885544
46108.39109.208108.8920.316678-0.818345
47110.18108.625109.143-0.5183221.55541
48109.54110.418109.2871.13161-0.878275
49109.1109.335109.3110.0241782-0.235012
50110.85110.009109.5350.4744560.840961
51112.23110.325109.6910.6341781.90457
52110.58110.635109.8070.827928-0.0554282
53110.77109.921109.820.1009140.848669
54108.08110.167109.7730.3939-2.08723
55108.05107.858109.73-1.871930.191516
56108.87107.737109.46-1.723461.13346
57109.61109.358109.1480.2098730.251794
58111.27109.135108.8180.3166782.13499
59107.61107.949108.467-0.518322-0.338762
60110.98109.361108.2291.131611.61922
61106.63108.06108.0360.0241782-1.43043
62106.83108.353107.8780.474456-1.52279
63108.77108.356107.7220.6341780.413738
64106.12108.328107.50.827928-2.20751
65106.8107.412107.3110.100914-0.612164
66106.34107.547107.1530.3939-1.20723
67105.16105.168107.04-1.87193-0.00806713
68107.97105.369107.092-1.723462.60138
69106.76107.303107.0930.209873-0.543206
70108.78107.467107.150.3166781.31332
71105.58106.831107.35-0.518322-1.25126
72109.22108.682107.551.131610.537975
73105.67107.826107.8020.0241782-2.15626
74109.04108.345107.8710.4744560.694711
75106.59108.348107.7130.634178-1.75751
76109.66108.195107.3670.8279281.46499
77108.05107.162107.0610.1009140.887836
78109.91107.306106.9120.39392.60402
79107.63NANA-1.87193NA
80107.15NANA-1.72346NA
81103.8NANA0.209873NA
82103.43NANA0.316678NA
83103.59NANA-0.518322NA
84107.63NANA1.13161NA


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 <- '12'
par1 <- 'additive'
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')