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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 12 Dec 2013 14:07:22 -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/12/t138687525491awq0my29t6gwk.htm/, Retrieved Tue, 07 Dec 2021 11:11:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232267, Retrieved Tue, 07 Dec 2021 11:11:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-12 19:07:22] [ef6034d7f955a1620c5a7f0f2c706f38] [Current]
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Dataseries X:
101,16
101,16
101,16
101,16
101,16
101,16
101,16
101,16
101,16
101,21
101,21
101,21
103,16
103,16
103,16
103,16
101,13
101,13
100,53
100,53
100,53
100,53
100,53
100,53
100,53
100,53
100,53
99,42
99,42
99,42
99,42
100,31
100,31
102,25
102,25
102,25
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,81
101,94
101,94
101,94
101,94
101,94
102
102
102
102
102
102
102
102
102
102
102
102
109,67
109,67
109,67
109,67
109,67
109,67
109,67
109,67
109,67
109,67
109,67
109,67




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232267&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]4 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=232267&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232267&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1101.16NANA0.910365NA
2101.16NANA0.79217NA
3101.16NANA0.673976NA
4101.16NANA0.371128NA
5101.16NANA-0.0847049NA
6101.16NANA-0.202205NA
7101.16100.834101.256-0.4221350.326302
8101.16101.052101.422-0.370330.10783
9101.16101.101101.589-0.4885240.0593576
10101.21101.481101.756-0.275052-0.270781
11101.21101.445101.838-0.393247-0.23467
12101.21101.324101.835-0.511441-0.113976
13103.16102.718101.8080.9103650.441719
14103.16102.548101.7550.792170.612413
15103.16102.377101.7030.6739760.783108
16103.16102.019101.6480.3711281.14054
17101.13101.507101.592-0.0847049-0.376962
18101.13101.333101.535-0.202205-0.202795
19100.53100.975101.397-0.422135-0.444948
20100.53100.808101.178-0.37033-0.277587
21100.53100.47100.959-0.4885240.0597743
22100.53100.418100.693-0.2750520.111719
23100.53100.073100.466-0.3932470.456997
24100.5399.8123100.324-0.5114410.717691
25100.53101.117100.2060.910365-0.586615
26100.53100.943100.1510.79217-0.413003
27100.53100.806100.1320.673976-0.276476
2899.42100.566100.1950.371128-1.14613
2999.42100.254100.338-0.0847049-0.833628
3099.42100.279100.482-0.202205-0.859462
3199.42100.185100.607-0.422135-0.764531
32100.31100.343100.713-0.37033-0.0330035
33100.31100.331100.82-0.488524-0.0214757
34102.25100.698100.973-0.2750521.55214
35102.25100.779101.172-0.3932471.47116
36102.25100.86101.371-0.5114411.39019
37101.81102.481101.570.910365-0.670781
38101.81102.525101.7320.79217-0.71467
39101.81102.531101.8570.673976-0.721476
40101.81102.273101.9020.371128-0.462795
41101.81101.78101.865-0.08470490.0297049
42101.81101.626101.828-0.2022050.183872
43101.81101.388101.81-0.4221350.422135
44101.81101.44101.81-0.370330.37033
45101.81101.321101.81-0.4885240.488524
46101.81101.535101.81-0.2750520.275052
47101.81101.417101.81-0.3932470.393247
48101.81101.299101.81-0.5114410.511441
49101.81102.72101.810.910365-0.910365
50101.81102.608101.8150.79217-0.797587
51101.81102.5101.8260.673976-0.690226
52101.81102.208101.8370.371128-0.398212
53101.81101.763101.848-0.08470490.0467882
54101.81101.657101.859-0.2022050.153455
55101.81101.45101.872-0.4221350.360052
56101.94101.518101.888-0.370330.422413
57101.94101.415101.904-0.4885240.524774
58101.94101.645101.92-0.2750520.295469
59101.94101.542101.935-0.3932470.39783
60101.94101.44101.951-0.5114410.500191
61102102.877101.9670.910365-0.877448
62102102.77101.9780.79217-0.76967
63102102.656101.9830.673976-0.656476
64102102.359101.9880.371128-0.358628
65102101.908101.992-0.08470490.0922049
66102101.795101.998-0.2022050.204705
67102101.897102.32-0.4221350.102552
68102102.588102.959-0.37033-0.58842
69102103.109103.598-0.488524-1.10939
70102103.962104.237-0.275052-1.96203
71102104.483104.876-0.393247-2.483
72102105.004105.515-0.511441-3.00398
73109.67107.065106.1550.9103652.60505
74109.67107.586106.7940.792172.08408
75109.67108.107107.4330.6739761.56311
76109.67108.443108.0720.3711281.22679
77109.67108.627108.711-0.08470491.04345
78109.67109.148109.35-0.2022050.521788
79109.67NANA-0.422135NA
80109.67NANA-0.37033NA
81109.67NANA-0.488524NA
82109.67NANA-0.275052NA
83109.67NANA-0.393247NA
84109.67NANA-0.511441NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 101.16 & NA & NA & 0.910365 & NA \tabularnewline
2 & 101.16 & NA & NA & 0.79217 & NA \tabularnewline
3 & 101.16 & NA & NA & 0.673976 & NA \tabularnewline
4 & 101.16 & NA & NA & 0.371128 & NA \tabularnewline
5 & 101.16 & NA & NA & -0.0847049 & NA \tabularnewline
6 & 101.16 & NA & NA & -0.202205 & NA \tabularnewline
7 & 101.16 & 100.834 & 101.256 & -0.422135 & 0.326302 \tabularnewline
8 & 101.16 & 101.052 & 101.422 & -0.37033 & 0.10783 \tabularnewline
9 & 101.16 & 101.101 & 101.589 & -0.488524 & 0.0593576 \tabularnewline
10 & 101.21 & 101.481 & 101.756 & -0.275052 & -0.270781 \tabularnewline
11 & 101.21 & 101.445 & 101.838 & -0.393247 & -0.23467 \tabularnewline
12 & 101.21 & 101.324 & 101.835 & -0.511441 & -0.113976 \tabularnewline
13 & 103.16 & 102.718 & 101.808 & 0.910365 & 0.441719 \tabularnewline
14 & 103.16 & 102.548 & 101.755 & 0.79217 & 0.612413 \tabularnewline
15 & 103.16 & 102.377 & 101.703 & 0.673976 & 0.783108 \tabularnewline
16 & 103.16 & 102.019 & 101.648 & 0.371128 & 1.14054 \tabularnewline
17 & 101.13 & 101.507 & 101.592 & -0.0847049 & -0.376962 \tabularnewline
18 & 101.13 & 101.333 & 101.535 & -0.202205 & -0.202795 \tabularnewline
19 & 100.53 & 100.975 & 101.397 & -0.422135 & -0.444948 \tabularnewline
20 & 100.53 & 100.808 & 101.178 & -0.37033 & -0.277587 \tabularnewline
21 & 100.53 & 100.47 & 100.959 & -0.488524 & 0.0597743 \tabularnewline
22 & 100.53 & 100.418 & 100.693 & -0.275052 & 0.111719 \tabularnewline
23 & 100.53 & 100.073 & 100.466 & -0.393247 & 0.456997 \tabularnewline
24 & 100.53 & 99.8123 & 100.324 & -0.511441 & 0.717691 \tabularnewline
25 & 100.53 & 101.117 & 100.206 & 0.910365 & -0.586615 \tabularnewline
26 & 100.53 & 100.943 & 100.151 & 0.79217 & -0.413003 \tabularnewline
27 & 100.53 & 100.806 & 100.132 & 0.673976 & -0.276476 \tabularnewline
28 & 99.42 & 100.566 & 100.195 & 0.371128 & -1.14613 \tabularnewline
29 & 99.42 & 100.254 & 100.338 & -0.0847049 & -0.833628 \tabularnewline
30 & 99.42 & 100.279 & 100.482 & -0.202205 & -0.859462 \tabularnewline
31 & 99.42 & 100.185 & 100.607 & -0.422135 & -0.764531 \tabularnewline
32 & 100.31 & 100.343 & 100.713 & -0.37033 & -0.0330035 \tabularnewline
33 & 100.31 & 100.331 & 100.82 & -0.488524 & -0.0214757 \tabularnewline
34 & 102.25 & 100.698 & 100.973 & -0.275052 & 1.55214 \tabularnewline
35 & 102.25 & 100.779 & 101.172 & -0.393247 & 1.47116 \tabularnewline
36 & 102.25 & 100.86 & 101.371 & -0.511441 & 1.39019 \tabularnewline
37 & 101.81 & 102.481 & 101.57 & 0.910365 & -0.670781 \tabularnewline
38 & 101.81 & 102.525 & 101.732 & 0.79217 & -0.71467 \tabularnewline
39 & 101.81 & 102.531 & 101.857 & 0.673976 & -0.721476 \tabularnewline
40 & 101.81 & 102.273 & 101.902 & 0.371128 & -0.462795 \tabularnewline
41 & 101.81 & 101.78 & 101.865 & -0.0847049 & 0.0297049 \tabularnewline
42 & 101.81 & 101.626 & 101.828 & -0.202205 & 0.183872 \tabularnewline
43 & 101.81 & 101.388 & 101.81 & -0.422135 & 0.422135 \tabularnewline
44 & 101.81 & 101.44 & 101.81 & -0.37033 & 0.37033 \tabularnewline
45 & 101.81 & 101.321 & 101.81 & -0.488524 & 0.488524 \tabularnewline
46 & 101.81 & 101.535 & 101.81 & -0.275052 & 0.275052 \tabularnewline
47 & 101.81 & 101.417 & 101.81 & -0.393247 & 0.393247 \tabularnewline
48 & 101.81 & 101.299 & 101.81 & -0.511441 & 0.511441 \tabularnewline
49 & 101.81 & 102.72 & 101.81 & 0.910365 & -0.910365 \tabularnewline
50 & 101.81 & 102.608 & 101.815 & 0.79217 & -0.797587 \tabularnewline
51 & 101.81 & 102.5 & 101.826 & 0.673976 & -0.690226 \tabularnewline
52 & 101.81 & 102.208 & 101.837 & 0.371128 & -0.398212 \tabularnewline
53 & 101.81 & 101.763 & 101.848 & -0.0847049 & 0.0467882 \tabularnewline
54 & 101.81 & 101.657 & 101.859 & -0.202205 & 0.153455 \tabularnewline
55 & 101.81 & 101.45 & 101.872 & -0.422135 & 0.360052 \tabularnewline
56 & 101.94 & 101.518 & 101.888 & -0.37033 & 0.422413 \tabularnewline
57 & 101.94 & 101.415 & 101.904 & -0.488524 & 0.524774 \tabularnewline
58 & 101.94 & 101.645 & 101.92 & -0.275052 & 0.295469 \tabularnewline
59 & 101.94 & 101.542 & 101.935 & -0.393247 & 0.39783 \tabularnewline
60 & 101.94 & 101.44 & 101.951 & -0.511441 & 0.500191 \tabularnewline
61 & 102 & 102.877 & 101.967 & 0.910365 & -0.877448 \tabularnewline
62 & 102 & 102.77 & 101.978 & 0.79217 & -0.76967 \tabularnewline
63 & 102 & 102.656 & 101.983 & 0.673976 & -0.656476 \tabularnewline
64 & 102 & 102.359 & 101.988 & 0.371128 & -0.358628 \tabularnewline
65 & 102 & 101.908 & 101.992 & -0.0847049 & 0.0922049 \tabularnewline
66 & 102 & 101.795 & 101.998 & -0.202205 & 0.204705 \tabularnewline
67 & 102 & 101.897 & 102.32 & -0.422135 & 0.102552 \tabularnewline
68 & 102 & 102.588 & 102.959 & -0.37033 & -0.58842 \tabularnewline
69 & 102 & 103.109 & 103.598 & -0.488524 & -1.10939 \tabularnewline
70 & 102 & 103.962 & 104.237 & -0.275052 & -1.96203 \tabularnewline
71 & 102 & 104.483 & 104.876 & -0.393247 & -2.483 \tabularnewline
72 & 102 & 105.004 & 105.515 & -0.511441 & -3.00398 \tabularnewline
73 & 109.67 & 107.065 & 106.155 & 0.910365 & 2.60505 \tabularnewline
74 & 109.67 & 107.586 & 106.794 & 0.79217 & 2.08408 \tabularnewline
75 & 109.67 & 108.107 & 107.433 & 0.673976 & 1.56311 \tabularnewline
76 & 109.67 & 108.443 & 108.072 & 0.371128 & 1.22679 \tabularnewline
77 & 109.67 & 108.627 & 108.711 & -0.0847049 & 1.04345 \tabularnewline
78 & 109.67 & 109.148 & 109.35 & -0.202205 & 0.521788 \tabularnewline
79 & 109.67 & NA & NA & -0.422135 & NA \tabularnewline
80 & 109.67 & NA & NA & -0.37033 & NA \tabularnewline
81 & 109.67 & NA & NA & -0.488524 & NA \tabularnewline
82 & 109.67 & NA & NA & -0.275052 & NA \tabularnewline
83 & 109.67 & NA & NA & -0.393247 & NA \tabularnewline
84 & 109.67 & NA & NA & -0.511441 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232267&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]101.16[/C][C]NA[/C][C]NA[/C][C]0.910365[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]101.16[/C][C]NA[/C][C]NA[/C][C]0.79217[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]101.16[/C][C]NA[/C][C]NA[/C][C]0.673976[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]101.16[/C][C]NA[/C][C]NA[/C][C]0.371128[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]101.16[/C][C]NA[/C][C]NA[/C][C]-0.0847049[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]101.16[/C][C]NA[/C][C]NA[/C][C]-0.202205[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]101.16[/C][C]100.834[/C][C]101.256[/C][C]-0.422135[/C][C]0.326302[/C][/ROW]
[ROW][C]8[/C][C]101.16[/C][C]101.052[/C][C]101.422[/C][C]-0.37033[/C][C]0.10783[/C][/ROW]
[ROW][C]9[/C][C]101.16[/C][C]101.101[/C][C]101.589[/C][C]-0.488524[/C][C]0.0593576[/C][/ROW]
[ROW][C]10[/C][C]101.21[/C][C]101.481[/C][C]101.756[/C][C]-0.275052[/C][C]-0.270781[/C][/ROW]
[ROW][C]11[/C][C]101.21[/C][C]101.445[/C][C]101.838[/C][C]-0.393247[/C][C]-0.23467[/C][/ROW]
[ROW][C]12[/C][C]101.21[/C][C]101.324[/C][C]101.835[/C][C]-0.511441[/C][C]-0.113976[/C][/ROW]
[ROW][C]13[/C][C]103.16[/C][C]102.718[/C][C]101.808[/C][C]0.910365[/C][C]0.441719[/C][/ROW]
[ROW][C]14[/C][C]103.16[/C][C]102.548[/C][C]101.755[/C][C]0.79217[/C][C]0.612413[/C][/ROW]
[ROW][C]15[/C][C]103.16[/C][C]102.377[/C][C]101.703[/C][C]0.673976[/C][C]0.783108[/C][/ROW]
[ROW][C]16[/C][C]103.16[/C][C]102.019[/C][C]101.648[/C][C]0.371128[/C][C]1.14054[/C][/ROW]
[ROW][C]17[/C][C]101.13[/C][C]101.507[/C][C]101.592[/C][C]-0.0847049[/C][C]-0.376962[/C][/ROW]
[ROW][C]18[/C][C]101.13[/C][C]101.333[/C][C]101.535[/C][C]-0.202205[/C][C]-0.202795[/C][/ROW]
[ROW][C]19[/C][C]100.53[/C][C]100.975[/C][C]101.397[/C][C]-0.422135[/C][C]-0.444948[/C][/ROW]
[ROW][C]20[/C][C]100.53[/C][C]100.808[/C][C]101.178[/C][C]-0.37033[/C][C]-0.277587[/C][/ROW]
[ROW][C]21[/C][C]100.53[/C][C]100.47[/C][C]100.959[/C][C]-0.488524[/C][C]0.0597743[/C][/ROW]
[ROW][C]22[/C][C]100.53[/C][C]100.418[/C][C]100.693[/C][C]-0.275052[/C][C]0.111719[/C][/ROW]
[ROW][C]23[/C][C]100.53[/C][C]100.073[/C][C]100.466[/C][C]-0.393247[/C][C]0.456997[/C][/ROW]
[ROW][C]24[/C][C]100.53[/C][C]99.8123[/C][C]100.324[/C][C]-0.511441[/C][C]0.717691[/C][/ROW]
[ROW][C]25[/C][C]100.53[/C][C]101.117[/C][C]100.206[/C][C]0.910365[/C][C]-0.586615[/C][/ROW]
[ROW][C]26[/C][C]100.53[/C][C]100.943[/C][C]100.151[/C][C]0.79217[/C][C]-0.413003[/C][/ROW]
[ROW][C]27[/C][C]100.53[/C][C]100.806[/C][C]100.132[/C][C]0.673976[/C][C]-0.276476[/C][/ROW]
[ROW][C]28[/C][C]99.42[/C][C]100.566[/C][C]100.195[/C][C]0.371128[/C][C]-1.14613[/C][/ROW]
[ROW][C]29[/C][C]99.42[/C][C]100.254[/C][C]100.338[/C][C]-0.0847049[/C][C]-0.833628[/C][/ROW]
[ROW][C]30[/C][C]99.42[/C][C]100.279[/C][C]100.482[/C][C]-0.202205[/C][C]-0.859462[/C][/ROW]
[ROW][C]31[/C][C]99.42[/C][C]100.185[/C][C]100.607[/C][C]-0.422135[/C][C]-0.764531[/C][/ROW]
[ROW][C]32[/C][C]100.31[/C][C]100.343[/C][C]100.713[/C][C]-0.37033[/C][C]-0.0330035[/C][/ROW]
[ROW][C]33[/C][C]100.31[/C][C]100.331[/C][C]100.82[/C][C]-0.488524[/C][C]-0.0214757[/C][/ROW]
[ROW][C]34[/C][C]102.25[/C][C]100.698[/C][C]100.973[/C][C]-0.275052[/C][C]1.55214[/C][/ROW]
[ROW][C]35[/C][C]102.25[/C][C]100.779[/C][C]101.172[/C][C]-0.393247[/C][C]1.47116[/C][/ROW]
[ROW][C]36[/C][C]102.25[/C][C]100.86[/C][C]101.371[/C][C]-0.511441[/C][C]1.39019[/C][/ROW]
[ROW][C]37[/C][C]101.81[/C][C]102.481[/C][C]101.57[/C][C]0.910365[/C][C]-0.670781[/C][/ROW]
[ROW][C]38[/C][C]101.81[/C][C]102.525[/C][C]101.732[/C][C]0.79217[/C][C]-0.71467[/C][/ROW]
[ROW][C]39[/C][C]101.81[/C][C]102.531[/C][C]101.857[/C][C]0.673976[/C][C]-0.721476[/C][/ROW]
[ROW][C]40[/C][C]101.81[/C][C]102.273[/C][C]101.902[/C][C]0.371128[/C][C]-0.462795[/C][/ROW]
[ROW][C]41[/C][C]101.81[/C][C]101.78[/C][C]101.865[/C][C]-0.0847049[/C][C]0.0297049[/C][/ROW]
[ROW][C]42[/C][C]101.81[/C][C]101.626[/C][C]101.828[/C][C]-0.202205[/C][C]0.183872[/C][/ROW]
[ROW][C]43[/C][C]101.81[/C][C]101.388[/C][C]101.81[/C][C]-0.422135[/C][C]0.422135[/C][/ROW]
[ROW][C]44[/C][C]101.81[/C][C]101.44[/C][C]101.81[/C][C]-0.37033[/C][C]0.37033[/C][/ROW]
[ROW][C]45[/C][C]101.81[/C][C]101.321[/C][C]101.81[/C][C]-0.488524[/C][C]0.488524[/C][/ROW]
[ROW][C]46[/C][C]101.81[/C][C]101.535[/C][C]101.81[/C][C]-0.275052[/C][C]0.275052[/C][/ROW]
[ROW][C]47[/C][C]101.81[/C][C]101.417[/C][C]101.81[/C][C]-0.393247[/C][C]0.393247[/C][/ROW]
[ROW][C]48[/C][C]101.81[/C][C]101.299[/C][C]101.81[/C][C]-0.511441[/C][C]0.511441[/C][/ROW]
[ROW][C]49[/C][C]101.81[/C][C]102.72[/C][C]101.81[/C][C]0.910365[/C][C]-0.910365[/C][/ROW]
[ROW][C]50[/C][C]101.81[/C][C]102.608[/C][C]101.815[/C][C]0.79217[/C][C]-0.797587[/C][/ROW]
[ROW][C]51[/C][C]101.81[/C][C]102.5[/C][C]101.826[/C][C]0.673976[/C][C]-0.690226[/C][/ROW]
[ROW][C]52[/C][C]101.81[/C][C]102.208[/C][C]101.837[/C][C]0.371128[/C][C]-0.398212[/C][/ROW]
[ROW][C]53[/C][C]101.81[/C][C]101.763[/C][C]101.848[/C][C]-0.0847049[/C][C]0.0467882[/C][/ROW]
[ROW][C]54[/C][C]101.81[/C][C]101.657[/C][C]101.859[/C][C]-0.202205[/C][C]0.153455[/C][/ROW]
[ROW][C]55[/C][C]101.81[/C][C]101.45[/C][C]101.872[/C][C]-0.422135[/C][C]0.360052[/C][/ROW]
[ROW][C]56[/C][C]101.94[/C][C]101.518[/C][C]101.888[/C][C]-0.37033[/C][C]0.422413[/C][/ROW]
[ROW][C]57[/C][C]101.94[/C][C]101.415[/C][C]101.904[/C][C]-0.488524[/C][C]0.524774[/C][/ROW]
[ROW][C]58[/C][C]101.94[/C][C]101.645[/C][C]101.92[/C][C]-0.275052[/C][C]0.295469[/C][/ROW]
[ROW][C]59[/C][C]101.94[/C][C]101.542[/C][C]101.935[/C][C]-0.393247[/C][C]0.39783[/C][/ROW]
[ROW][C]60[/C][C]101.94[/C][C]101.44[/C][C]101.951[/C][C]-0.511441[/C][C]0.500191[/C][/ROW]
[ROW][C]61[/C][C]102[/C][C]102.877[/C][C]101.967[/C][C]0.910365[/C][C]-0.877448[/C][/ROW]
[ROW][C]62[/C][C]102[/C][C]102.77[/C][C]101.978[/C][C]0.79217[/C][C]-0.76967[/C][/ROW]
[ROW][C]63[/C][C]102[/C][C]102.656[/C][C]101.983[/C][C]0.673976[/C][C]-0.656476[/C][/ROW]
[ROW][C]64[/C][C]102[/C][C]102.359[/C][C]101.988[/C][C]0.371128[/C][C]-0.358628[/C][/ROW]
[ROW][C]65[/C][C]102[/C][C]101.908[/C][C]101.992[/C][C]-0.0847049[/C][C]0.0922049[/C][/ROW]
[ROW][C]66[/C][C]102[/C][C]101.795[/C][C]101.998[/C][C]-0.202205[/C][C]0.204705[/C][/ROW]
[ROW][C]67[/C][C]102[/C][C]101.897[/C][C]102.32[/C][C]-0.422135[/C][C]0.102552[/C][/ROW]
[ROW][C]68[/C][C]102[/C][C]102.588[/C][C]102.959[/C][C]-0.37033[/C][C]-0.58842[/C][/ROW]
[ROW][C]69[/C][C]102[/C][C]103.109[/C][C]103.598[/C][C]-0.488524[/C][C]-1.10939[/C][/ROW]
[ROW][C]70[/C][C]102[/C][C]103.962[/C][C]104.237[/C][C]-0.275052[/C][C]-1.96203[/C][/ROW]
[ROW][C]71[/C][C]102[/C][C]104.483[/C][C]104.876[/C][C]-0.393247[/C][C]-2.483[/C][/ROW]
[ROW][C]72[/C][C]102[/C][C]105.004[/C][C]105.515[/C][C]-0.511441[/C][C]-3.00398[/C][/ROW]
[ROW][C]73[/C][C]109.67[/C][C]107.065[/C][C]106.155[/C][C]0.910365[/C][C]2.60505[/C][/ROW]
[ROW][C]74[/C][C]109.67[/C][C]107.586[/C][C]106.794[/C][C]0.79217[/C][C]2.08408[/C][/ROW]
[ROW][C]75[/C][C]109.67[/C][C]108.107[/C][C]107.433[/C][C]0.673976[/C][C]1.56311[/C][/ROW]
[ROW][C]76[/C][C]109.67[/C][C]108.443[/C][C]108.072[/C][C]0.371128[/C][C]1.22679[/C][/ROW]
[ROW][C]77[/C][C]109.67[/C][C]108.627[/C][C]108.711[/C][C]-0.0847049[/C][C]1.04345[/C][/ROW]
[ROW][C]78[/C][C]109.67[/C][C]109.148[/C][C]109.35[/C][C]-0.202205[/C][C]0.521788[/C][/ROW]
[ROW][C]79[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.422135[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.37033[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.488524[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.275052[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.393247[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]109.67[/C][C]NA[/C][C]NA[/C][C]-0.511441[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232267&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232267&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
1101.16NANA0.910365NA
2101.16NANA0.79217NA
3101.16NANA0.673976NA
4101.16NANA0.371128NA
5101.16NANA-0.0847049NA
6101.16NANA-0.202205NA
7101.16100.834101.256-0.4221350.326302
8101.16101.052101.422-0.370330.10783
9101.16101.101101.589-0.4885240.0593576
10101.21101.481101.756-0.275052-0.270781
11101.21101.445101.838-0.393247-0.23467
12101.21101.324101.835-0.511441-0.113976
13103.16102.718101.8080.9103650.441719
14103.16102.548101.7550.792170.612413
15103.16102.377101.7030.6739760.783108
16103.16102.019101.6480.3711281.14054
17101.13101.507101.592-0.0847049-0.376962
18101.13101.333101.535-0.202205-0.202795
19100.53100.975101.397-0.422135-0.444948
20100.53100.808101.178-0.37033-0.277587
21100.53100.47100.959-0.4885240.0597743
22100.53100.418100.693-0.2750520.111719
23100.53100.073100.466-0.3932470.456997
24100.5399.8123100.324-0.5114410.717691
25100.53101.117100.2060.910365-0.586615
26100.53100.943100.1510.79217-0.413003
27100.53100.806100.1320.673976-0.276476
2899.42100.566100.1950.371128-1.14613
2999.42100.254100.338-0.0847049-0.833628
3099.42100.279100.482-0.202205-0.859462
3199.42100.185100.607-0.422135-0.764531
32100.31100.343100.713-0.37033-0.0330035
33100.31100.331100.82-0.488524-0.0214757
34102.25100.698100.973-0.2750521.55214
35102.25100.779101.172-0.3932471.47116
36102.25100.86101.371-0.5114411.39019
37101.81102.481101.570.910365-0.670781
38101.81102.525101.7320.79217-0.71467
39101.81102.531101.8570.673976-0.721476
40101.81102.273101.9020.371128-0.462795
41101.81101.78101.865-0.08470490.0297049
42101.81101.626101.828-0.2022050.183872
43101.81101.388101.81-0.4221350.422135
44101.81101.44101.81-0.370330.37033
45101.81101.321101.81-0.4885240.488524
46101.81101.535101.81-0.2750520.275052
47101.81101.417101.81-0.3932470.393247
48101.81101.299101.81-0.5114410.511441
49101.81102.72101.810.910365-0.910365
50101.81102.608101.8150.79217-0.797587
51101.81102.5101.8260.673976-0.690226
52101.81102.208101.8370.371128-0.398212
53101.81101.763101.848-0.08470490.0467882
54101.81101.657101.859-0.2022050.153455
55101.81101.45101.872-0.4221350.360052
56101.94101.518101.888-0.370330.422413
57101.94101.415101.904-0.4885240.524774
58101.94101.645101.92-0.2750520.295469
59101.94101.542101.935-0.3932470.39783
60101.94101.44101.951-0.5114410.500191
61102102.877101.9670.910365-0.877448
62102102.77101.9780.79217-0.76967
63102102.656101.9830.673976-0.656476
64102102.359101.9880.371128-0.358628
65102101.908101.992-0.08470490.0922049
66102101.795101.998-0.2022050.204705
67102101.897102.32-0.4221350.102552
68102102.588102.959-0.37033-0.58842
69102103.109103.598-0.488524-1.10939
70102103.962104.237-0.275052-1.96203
71102104.483104.876-0.393247-2.483
72102105.004105.515-0.511441-3.00398
73109.67107.065106.1550.9103652.60505
74109.67107.586106.7940.792172.08408
75109.67108.107107.4330.6739761.56311
76109.67108.443108.0720.3711281.22679
77109.67108.627108.711-0.08470491.04345
78109.67109.148109.35-0.2022050.521788
79109.67NANA-0.422135NA
80109.67NANA-0.37033NA
81109.67NANA-0.488524NA
82109.67NANA-0.275052NA
83109.67NANA-0.393247NA
84109.67NANA-0.511441NA



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')