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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 15 Dec 2014 23:24:36 +0000
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/Dec/15/t14186870565i7ugm4jijxujoe.htm/, Retrieved Thu, 16 May 2024 10:40:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269106, Retrieved Thu, 16 May 2024 10:40:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact34

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2014-12-15 23:24:36] [6993448de96b8662e47595bfdf466bf3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,35
12,7
18,1
17,85


17,1
19,1
16,1
13,35
18,4
14,7
10,6
12,6
16,2
13,6

14,1
14,5
16,15
14,75
14,8
12,45
12,65
17,35
8,6
18,4
16,1

17,75
15,25
17,65
16,35
17,65
13,6
14,35
14,75
18,25
9,9
16
18,25
16,85


18,95
15,6




17,1
16,1









15,4
15,4

13,35
19,1

7,6


19,1













14,75



19,25

13,6

12,75

9,85




15,25
11,9

16,35
12,4

18,15


17,75

12,35
15,6
19,3

17,1

18,4
19,05
18,55
19,1

12,85
9,5
4,5

13,6
11,7

13,35





17,6
14,05
16,1
13,35
11,85
11,95


13,2


7,7

















14,6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269106&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269106&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 time2 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14.35NANA0.702627NA
212.7NANA2.41582NA
318.1NANA0.607836NA
417.85NANA1.00784NA
517.1NANA0.544086NA
619.1NANA1.28659NA
716.115.085615.07290.01269681.01439
813.3514.495315.6042-1.10883-1.14534
918.413.743615.475-1.73144.6564
1014.715.31115.16880.142211-0.610961
1110.611.1714.9896-3.81959-0.569988
1212.614.708914.7687-0.0598727-2.10888
1316.215.23614.53330.7026270.964039
1413.616.857514.44172.41582-3.25749
1514.114.772414.16460.607836-0.672419
1614.515.043314.03541.00784-0.543252
1716.1514.606614.06250.5440861.54341
1814.7515.507414.22081.28659-0.757419
1914.814.47114.45830.01269680.32897
2012.4513.518314.6271-1.10883-1.06825
2112.6513.116514.8479-1.7314-0.466516
2217.3515.169315.02710.1422112.18071
238.611.347115.1667-3.81959-2.74707
2418.415.23615.2958-0.05987273.16404
2516.116.069315.36670.7026270.030706
2617.7517.811715.39582.41582-0.0616551
2715.2516.170315.56250.607836-0.920336
2817.6516.695315.68751.007840.954664
2916.3516.323315.77920.5440860.0267477
3017.6517.019915.73331.286590.630081
3113.615.735615.72290.0126968-2.13561
3214.3514.666215.775-1.10883-0.316169
3314.7514.160315.8917-1.73140.589734
3418.2516.102615.96040.1422112.14737
359.912.086715.9062-3.81959-2.18666
361615.81315.8729-0.05987270.186956
3718.2516.58615.88330.7026271.66404
3816.8518.417916.00212.41582-1.56791
3918.9516.595315.98750.6078362.35466
4015.616.972415.96461.00784-1.37242
4117.116.448315.90420.5440860.651748
4216.117.224115.93751.28659-1.12409
4315.415.933515.92080.0126968-0.53353
4415.414.766215.875-1.108830.633831
4513.3514.020715.7521-1.7314-0.670683
4619.115.552615.41040.1422113.54737
477.611.1714.9896-3.81959-3.56999
4819.114.592214.6521-0.05987274.50779
4914.7515.173514.47080.702627-0.423461
5019.2516.780414.36462.415822.46959
5113.614.972414.36460.607836-1.37242
5212.7515.293314.28541.00784-2.54325
539.8515.212814.66870.544086-5.36284
5415.2516.09714.81041.28659-0.847002
5511.914.577314.56460.0126968-2.67728
5616.3513.493314.6021-1.108832.85675
5712.413.018614.75-1.7314-0.6186
5818.1515.273515.13120.1422112.87654
5917.7511.930415.75-3.819595.81959
6012.3516.21116.2708-0.0598727-3.86096
6115.617.41116.70830.702627-1.81096
6219.319.278316.86252.415820.0216782
6317.117.203716.59580.607836-0.103669
6418.416.914115.90631.007841.48591
6519.0515.708715.16460.5440863.34133
6618.5516.251214.96461.286592.29883
6719.114.856414.84380.01269684.24355
6812.8513.570314.6792-1.10883-0.720336
699.512.749814.4812-1.7314-3.24985
704.514.400514.25830.142211-9.90054
7113.610.105413.925-3.819593.49459
7211.713.348513.4083-0.0598727-1.64846
7313.3513.533912.83120.702627-0.183877
7417.614.963712.54792.415822.63626
7514.0513.095312.48750.6078360.954664
7616.113.841212.83331.007842.25883
7713.35NANA0.544086NA
7811.85NANA1.28659NA
7911.95NANA0.0126968NA
8013.2NANA-1.10883NA
817.7NANA-1.7314NA
8214.6NANA0.142211NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 4.35 & NA & NA & 0.702627 & NA \tabularnewline
2 & 12.7 & NA & NA & 2.41582 & NA \tabularnewline
3 & 18.1 & NA & NA & 0.607836 & NA \tabularnewline
4 & 17.85 & NA & NA & 1.00784 & NA \tabularnewline
5 & 17.1 & NA & NA & 0.544086 & NA \tabularnewline
6 & 19.1 & NA & NA & 1.28659 & NA \tabularnewline
7 & 16.1 & 15.0856 & 15.0729 & 0.0126968 & 1.01439 \tabularnewline
8 & 13.35 & 14.4953 & 15.6042 & -1.10883 & -1.14534 \tabularnewline
9 & 18.4 & 13.7436 & 15.475 & -1.7314 & 4.6564 \tabularnewline
10 & 14.7 & 15.311 & 15.1688 & 0.142211 & -0.610961 \tabularnewline
11 & 10.6 & 11.17 & 14.9896 & -3.81959 & -0.569988 \tabularnewline
12 & 12.6 & 14.7089 & 14.7687 & -0.0598727 & -2.10888 \tabularnewline
13 & 16.2 & 15.236 & 14.5333 & 0.702627 & 0.964039 \tabularnewline
14 & 13.6 & 16.8575 & 14.4417 & 2.41582 & -3.25749 \tabularnewline
15 & 14.1 & 14.7724 & 14.1646 & 0.607836 & -0.672419 \tabularnewline
16 & 14.5 & 15.0433 & 14.0354 & 1.00784 & -0.543252 \tabularnewline
17 & 16.15 & 14.6066 & 14.0625 & 0.544086 & 1.54341 \tabularnewline
18 & 14.75 & 15.5074 & 14.2208 & 1.28659 & -0.757419 \tabularnewline
19 & 14.8 & 14.471 & 14.4583 & 0.0126968 & 0.32897 \tabularnewline
20 & 12.45 & 13.5183 & 14.6271 & -1.10883 & -1.06825 \tabularnewline
21 & 12.65 & 13.1165 & 14.8479 & -1.7314 & -0.466516 \tabularnewline
22 & 17.35 & 15.1693 & 15.0271 & 0.142211 & 2.18071 \tabularnewline
23 & 8.6 & 11.3471 & 15.1667 & -3.81959 & -2.74707 \tabularnewline
24 & 18.4 & 15.236 & 15.2958 & -0.0598727 & 3.16404 \tabularnewline
25 & 16.1 & 16.0693 & 15.3667 & 0.702627 & 0.030706 \tabularnewline
26 & 17.75 & 17.8117 & 15.3958 & 2.41582 & -0.0616551 \tabularnewline
27 & 15.25 & 16.1703 & 15.5625 & 0.607836 & -0.920336 \tabularnewline
28 & 17.65 & 16.6953 & 15.6875 & 1.00784 & 0.954664 \tabularnewline
29 & 16.35 & 16.3233 & 15.7792 & 0.544086 & 0.0267477 \tabularnewline
30 & 17.65 & 17.0199 & 15.7333 & 1.28659 & 0.630081 \tabularnewline
31 & 13.6 & 15.7356 & 15.7229 & 0.0126968 & -2.13561 \tabularnewline
32 & 14.35 & 14.6662 & 15.775 & -1.10883 & -0.316169 \tabularnewline
33 & 14.75 & 14.1603 & 15.8917 & -1.7314 & 0.589734 \tabularnewline
34 & 18.25 & 16.1026 & 15.9604 & 0.142211 & 2.14737 \tabularnewline
35 & 9.9 & 12.0867 & 15.9062 & -3.81959 & -2.18666 \tabularnewline
36 & 16 & 15.813 & 15.8729 & -0.0598727 & 0.186956 \tabularnewline
37 & 18.25 & 16.586 & 15.8833 & 0.702627 & 1.66404 \tabularnewline
38 & 16.85 & 18.4179 & 16.0021 & 2.41582 & -1.56791 \tabularnewline
39 & 18.95 & 16.5953 & 15.9875 & 0.607836 & 2.35466 \tabularnewline
40 & 15.6 & 16.9724 & 15.9646 & 1.00784 & -1.37242 \tabularnewline
41 & 17.1 & 16.4483 & 15.9042 & 0.544086 & 0.651748 \tabularnewline
42 & 16.1 & 17.2241 & 15.9375 & 1.28659 & -1.12409 \tabularnewline
43 & 15.4 & 15.9335 & 15.9208 & 0.0126968 & -0.53353 \tabularnewline
44 & 15.4 & 14.7662 & 15.875 & -1.10883 & 0.633831 \tabularnewline
45 & 13.35 & 14.0207 & 15.7521 & -1.7314 & -0.670683 \tabularnewline
46 & 19.1 & 15.5526 & 15.4104 & 0.142211 & 3.54737 \tabularnewline
47 & 7.6 & 11.17 & 14.9896 & -3.81959 & -3.56999 \tabularnewline
48 & 19.1 & 14.5922 & 14.6521 & -0.0598727 & 4.50779 \tabularnewline
49 & 14.75 & 15.1735 & 14.4708 & 0.702627 & -0.423461 \tabularnewline
50 & 19.25 & 16.7804 & 14.3646 & 2.41582 & 2.46959 \tabularnewline
51 & 13.6 & 14.9724 & 14.3646 & 0.607836 & -1.37242 \tabularnewline
52 & 12.75 & 15.2933 & 14.2854 & 1.00784 & -2.54325 \tabularnewline
53 & 9.85 & 15.2128 & 14.6687 & 0.544086 & -5.36284 \tabularnewline
54 & 15.25 & 16.097 & 14.8104 & 1.28659 & -0.847002 \tabularnewline
55 & 11.9 & 14.5773 & 14.5646 & 0.0126968 & -2.67728 \tabularnewline
56 & 16.35 & 13.4933 & 14.6021 & -1.10883 & 2.85675 \tabularnewline
57 & 12.4 & 13.0186 & 14.75 & -1.7314 & -0.6186 \tabularnewline
58 & 18.15 & 15.2735 & 15.1312 & 0.142211 & 2.87654 \tabularnewline
59 & 17.75 & 11.9304 & 15.75 & -3.81959 & 5.81959 \tabularnewline
60 & 12.35 & 16.211 & 16.2708 & -0.0598727 & -3.86096 \tabularnewline
61 & 15.6 & 17.411 & 16.7083 & 0.702627 & -1.81096 \tabularnewline
62 & 19.3 & 19.2783 & 16.8625 & 2.41582 & 0.0216782 \tabularnewline
63 & 17.1 & 17.2037 & 16.5958 & 0.607836 & -0.103669 \tabularnewline
64 & 18.4 & 16.9141 & 15.9063 & 1.00784 & 1.48591 \tabularnewline
65 & 19.05 & 15.7087 & 15.1646 & 0.544086 & 3.34133 \tabularnewline
66 & 18.55 & 16.2512 & 14.9646 & 1.28659 & 2.29883 \tabularnewline
67 & 19.1 & 14.8564 & 14.8438 & 0.0126968 & 4.24355 \tabularnewline
68 & 12.85 & 13.5703 & 14.6792 & -1.10883 & -0.720336 \tabularnewline
69 & 9.5 & 12.7498 & 14.4812 & -1.7314 & -3.24985 \tabularnewline
70 & 4.5 & 14.4005 & 14.2583 & 0.142211 & -9.90054 \tabularnewline
71 & 13.6 & 10.1054 & 13.925 & -3.81959 & 3.49459 \tabularnewline
72 & 11.7 & 13.3485 & 13.4083 & -0.0598727 & -1.64846 \tabularnewline
73 & 13.35 & 13.5339 & 12.8312 & 0.702627 & -0.183877 \tabularnewline
74 & 17.6 & 14.9637 & 12.5479 & 2.41582 & 2.63626 \tabularnewline
75 & 14.05 & 13.0953 & 12.4875 & 0.607836 & 0.954664 \tabularnewline
76 & 16.1 & 13.8412 & 12.8333 & 1.00784 & 2.25883 \tabularnewline
77 & 13.35 & NA & NA & 0.544086 & NA \tabularnewline
78 & 11.85 & NA & NA & 1.28659 & NA \tabularnewline
79 & 11.95 & NA & NA & 0.0126968 & NA \tabularnewline
80 & 13.2 & NA & NA & -1.10883 & NA \tabularnewline
81 & 7.7 & NA & NA & -1.7314 & NA \tabularnewline
82 & 14.6 & NA & NA & 0.142211 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269106&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]4.35[/C][C]NA[/C][C]NA[/C][C]0.702627[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]NA[/C][C]NA[/C][C]2.41582[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]NA[/C][C]NA[/C][C]0.607836[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]NA[/C][C]NA[/C][C]1.00784[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]17.1[/C][C]NA[/C][C]NA[/C][C]0.544086[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]19.1[/C][C]NA[/C][C]NA[/C][C]1.28659[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]16.1[/C][C]15.0856[/C][C]15.0729[/C][C]0.0126968[/C][C]1.01439[/C][/ROW]
[ROW][C]8[/C][C]13.35[/C][C]14.4953[/C][C]15.6042[/C][C]-1.10883[/C][C]-1.14534[/C][/ROW]
[ROW][C]9[/C][C]18.4[/C][C]13.7436[/C][C]15.475[/C][C]-1.7314[/C][C]4.6564[/C][/ROW]
[ROW][C]10[/C][C]14.7[/C][C]15.311[/C][C]15.1688[/C][C]0.142211[/C][C]-0.610961[/C][/ROW]
[ROW][C]11[/C][C]10.6[/C][C]11.17[/C][C]14.9896[/C][C]-3.81959[/C][C]-0.569988[/C][/ROW]
[ROW][C]12[/C][C]12.6[/C][C]14.7089[/C][C]14.7687[/C][C]-0.0598727[/C][C]-2.10888[/C][/ROW]
[ROW][C]13[/C][C]16.2[/C][C]15.236[/C][C]14.5333[/C][C]0.702627[/C][C]0.964039[/C][/ROW]
[ROW][C]14[/C][C]13.6[/C][C]16.8575[/C][C]14.4417[/C][C]2.41582[/C][C]-3.25749[/C][/ROW]
[ROW][C]15[/C][C]14.1[/C][C]14.7724[/C][C]14.1646[/C][C]0.607836[/C][C]-0.672419[/C][/ROW]
[ROW][C]16[/C][C]14.5[/C][C]15.0433[/C][C]14.0354[/C][C]1.00784[/C][C]-0.543252[/C][/ROW]
[ROW][C]17[/C][C]16.15[/C][C]14.6066[/C][C]14.0625[/C][C]0.544086[/C][C]1.54341[/C][/ROW]
[ROW][C]18[/C][C]14.75[/C][C]15.5074[/C][C]14.2208[/C][C]1.28659[/C][C]-0.757419[/C][/ROW]
[ROW][C]19[/C][C]14.8[/C][C]14.471[/C][C]14.4583[/C][C]0.0126968[/C][C]0.32897[/C][/ROW]
[ROW][C]20[/C][C]12.45[/C][C]13.5183[/C][C]14.6271[/C][C]-1.10883[/C][C]-1.06825[/C][/ROW]
[ROW][C]21[/C][C]12.65[/C][C]13.1165[/C][C]14.8479[/C][C]-1.7314[/C][C]-0.466516[/C][/ROW]
[ROW][C]22[/C][C]17.35[/C][C]15.1693[/C][C]15.0271[/C][C]0.142211[/C][C]2.18071[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]11.3471[/C][C]15.1667[/C][C]-3.81959[/C][C]-2.74707[/C][/ROW]
[ROW][C]24[/C][C]18.4[/C][C]15.236[/C][C]15.2958[/C][C]-0.0598727[/C][C]3.16404[/C][/ROW]
[ROW][C]25[/C][C]16.1[/C][C]16.0693[/C][C]15.3667[/C][C]0.702627[/C][C]0.030706[/C][/ROW]
[ROW][C]26[/C][C]17.75[/C][C]17.8117[/C][C]15.3958[/C][C]2.41582[/C][C]-0.0616551[/C][/ROW]
[ROW][C]27[/C][C]15.25[/C][C]16.1703[/C][C]15.5625[/C][C]0.607836[/C][C]-0.920336[/C][/ROW]
[ROW][C]28[/C][C]17.65[/C][C]16.6953[/C][C]15.6875[/C][C]1.00784[/C][C]0.954664[/C][/ROW]
[ROW][C]29[/C][C]16.35[/C][C]16.3233[/C][C]15.7792[/C][C]0.544086[/C][C]0.0267477[/C][/ROW]
[ROW][C]30[/C][C]17.65[/C][C]17.0199[/C][C]15.7333[/C][C]1.28659[/C][C]0.630081[/C][/ROW]
[ROW][C]31[/C][C]13.6[/C][C]15.7356[/C][C]15.7229[/C][C]0.0126968[/C][C]-2.13561[/C][/ROW]
[ROW][C]32[/C][C]14.35[/C][C]14.6662[/C][C]15.775[/C][C]-1.10883[/C][C]-0.316169[/C][/ROW]
[ROW][C]33[/C][C]14.75[/C][C]14.1603[/C][C]15.8917[/C][C]-1.7314[/C][C]0.589734[/C][/ROW]
[ROW][C]34[/C][C]18.25[/C][C]16.1026[/C][C]15.9604[/C][C]0.142211[/C][C]2.14737[/C][/ROW]
[ROW][C]35[/C][C]9.9[/C][C]12.0867[/C][C]15.9062[/C][C]-3.81959[/C][C]-2.18666[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.813[/C][C]15.8729[/C][C]-0.0598727[/C][C]0.186956[/C][/ROW]
[ROW][C]37[/C][C]18.25[/C][C]16.586[/C][C]15.8833[/C][C]0.702627[/C][C]1.66404[/C][/ROW]
[ROW][C]38[/C][C]16.85[/C][C]18.4179[/C][C]16.0021[/C][C]2.41582[/C][C]-1.56791[/C][/ROW]
[ROW][C]39[/C][C]18.95[/C][C]16.5953[/C][C]15.9875[/C][C]0.607836[/C][C]2.35466[/C][/ROW]
[ROW][C]40[/C][C]15.6[/C][C]16.9724[/C][C]15.9646[/C][C]1.00784[/C][C]-1.37242[/C][/ROW]
[ROW][C]41[/C][C]17.1[/C][C]16.4483[/C][C]15.9042[/C][C]0.544086[/C][C]0.651748[/C][/ROW]
[ROW][C]42[/C][C]16.1[/C][C]17.2241[/C][C]15.9375[/C][C]1.28659[/C][C]-1.12409[/C][/ROW]
[ROW][C]43[/C][C]15.4[/C][C]15.9335[/C][C]15.9208[/C][C]0.0126968[/C][C]-0.53353[/C][/ROW]
[ROW][C]44[/C][C]15.4[/C][C]14.7662[/C][C]15.875[/C][C]-1.10883[/C][C]0.633831[/C][/ROW]
[ROW][C]45[/C][C]13.35[/C][C]14.0207[/C][C]15.7521[/C][C]-1.7314[/C][C]-0.670683[/C][/ROW]
[ROW][C]46[/C][C]19.1[/C][C]15.5526[/C][C]15.4104[/C][C]0.142211[/C][C]3.54737[/C][/ROW]
[ROW][C]47[/C][C]7.6[/C][C]11.17[/C][C]14.9896[/C][C]-3.81959[/C][C]-3.56999[/C][/ROW]
[ROW][C]48[/C][C]19.1[/C][C]14.5922[/C][C]14.6521[/C][C]-0.0598727[/C][C]4.50779[/C][/ROW]
[ROW][C]49[/C][C]14.75[/C][C]15.1735[/C][C]14.4708[/C][C]0.702627[/C][C]-0.423461[/C][/ROW]
[ROW][C]50[/C][C]19.25[/C][C]16.7804[/C][C]14.3646[/C][C]2.41582[/C][C]2.46959[/C][/ROW]
[ROW][C]51[/C][C]13.6[/C][C]14.9724[/C][C]14.3646[/C][C]0.607836[/C][C]-1.37242[/C][/ROW]
[ROW][C]52[/C][C]12.75[/C][C]15.2933[/C][C]14.2854[/C][C]1.00784[/C][C]-2.54325[/C][/ROW]
[ROW][C]53[/C][C]9.85[/C][C]15.2128[/C][C]14.6687[/C][C]0.544086[/C][C]-5.36284[/C][/ROW]
[ROW][C]54[/C][C]15.25[/C][C]16.097[/C][C]14.8104[/C][C]1.28659[/C][C]-0.847002[/C][/ROW]
[ROW][C]55[/C][C]11.9[/C][C]14.5773[/C][C]14.5646[/C][C]0.0126968[/C][C]-2.67728[/C][/ROW]
[ROW][C]56[/C][C]16.35[/C][C]13.4933[/C][C]14.6021[/C][C]-1.10883[/C][C]2.85675[/C][/ROW]
[ROW][C]57[/C][C]12.4[/C][C]13.0186[/C][C]14.75[/C][C]-1.7314[/C][C]-0.6186[/C][/ROW]
[ROW][C]58[/C][C]18.15[/C][C]15.2735[/C][C]15.1312[/C][C]0.142211[/C][C]2.87654[/C][/ROW]
[ROW][C]59[/C][C]17.75[/C][C]11.9304[/C][C]15.75[/C][C]-3.81959[/C][C]5.81959[/C][/ROW]
[ROW][C]60[/C][C]12.35[/C][C]16.211[/C][C]16.2708[/C][C]-0.0598727[/C][C]-3.86096[/C][/ROW]
[ROW][C]61[/C][C]15.6[/C][C]17.411[/C][C]16.7083[/C][C]0.702627[/C][C]-1.81096[/C][/ROW]
[ROW][C]62[/C][C]19.3[/C][C]19.2783[/C][C]16.8625[/C][C]2.41582[/C][C]0.0216782[/C][/ROW]
[ROW][C]63[/C][C]17.1[/C][C]17.2037[/C][C]16.5958[/C][C]0.607836[/C][C]-0.103669[/C][/ROW]
[ROW][C]64[/C][C]18.4[/C][C]16.9141[/C][C]15.9063[/C][C]1.00784[/C][C]1.48591[/C][/ROW]
[ROW][C]65[/C][C]19.05[/C][C]15.7087[/C][C]15.1646[/C][C]0.544086[/C][C]3.34133[/C][/ROW]
[ROW][C]66[/C][C]18.55[/C][C]16.2512[/C][C]14.9646[/C][C]1.28659[/C][C]2.29883[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]14.8564[/C][C]14.8438[/C][C]0.0126968[/C][C]4.24355[/C][/ROW]
[ROW][C]68[/C][C]12.85[/C][C]13.5703[/C][C]14.6792[/C][C]-1.10883[/C][C]-0.720336[/C][/ROW]
[ROW][C]69[/C][C]9.5[/C][C]12.7498[/C][C]14.4812[/C][C]-1.7314[/C][C]-3.24985[/C][/ROW]
[ROW][C]70[/C][C]4.5[/C][C]14.4005[/C][C]14.2583[/C][C]0.142211[/C][C]-9.90054[/C][/ROW]
[ROW][C]71[/C][C]13.6[/C][C]10.1054[/C][C]13.925[/C][C]-3.81959[/C][C]3.49459[/C][/ROW]
[ROW][C]72[/C][C]11.7[/C][C]13.3485[/C][C]13.4083[/C][C]-0.0598727[/C][C]-1.64846[/C][/ROW]
[ROW][C]73[/C][C]13.35[/C][C]13.5339[/C][C]12.8312[/C][C]0.702627[/C][C]-0.183877[/C][/ROW]
[ROW][C]74[/C][C]17.6[/C][C]14.9637[/C][C]12.5479[/C][C]2.41582[/C][C]2.63626[/C][/ROW]
[ROW][C]75[/C][C]14.05[/C][C]13.0953[/C][C]12.4875[/C][C]0.607836[/C][C]0.954664[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]13.8412[/C][C]12.8333[/C][C]1.00784[/C][C]2.25883[/C][/ROW]
[ROW][C]77[/C][C]13.35[/C][C]NA[/C][C]NA[/C][C]0.544086[/C][C]NA[/C][/ROW]
[ROW][C]78[/C][C]11.85[/C][C]NA[/C][C]NA[/C][C]1.28659[/C][C]NA[/C][/ROW]
[ROW][C]79[/C][C]11.95[/C][C]NA[/C][C]NA[/C][C]0.0126968[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]13.2[/C][C]NA[/C][C]NA[/C][C]-1.10883[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]7.7[/C][C]NA[/C][C]NA[/C][C]-1.7314[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]14.6[/C][C]NA[/C][C]NA[/C][C]0.142211[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269106&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
14.35NANA0.702627NA
212.7NANA2.41582NA
318.1NANA0.607836NA
417.85NANA1.00784NA
517.1NANA0.544086NA
619.1NANA1.28659NA
716.115.085615.07290.01269681.01439
813.3514.495315.6042-1.10883-1.14534
918.413.743615.475-1.73144.6564
1014.715.31115.16880.142211-0.610961
1110.611.1714.9896-3.81959-0.569988
1212.614.708914.7687-0.0598727-2.10888
1316.215.23614.53330.7026270.964039
1413.616.857514.44172.41582-3.25749
1514.114.772414.16460.607836-0.672419
1614.515.043314.03541.00784-0.543252
1716.1514.606614.06250.5440861.54341
1814.7515.507414.22081.28659-0.757419
1914.814.47114.45830.01269680.32897
2012.4513.518314.6271-1.10883-1.06825
2112.6513.116514.8479-1.7314-0.466516
2217.3515.169315.02710.1422112.18071
238.611.347115.1667-3.81959-2.74707
2418.415.23615.2958-0.05987273.16404
2516.116.069315.36670.7026270.030706
2617.7517.811715.39582.41582-0.0616551
2715.2516.170315.56250.607836-0.920336
2817.6516.695315.68751.007840.954664
2916.3516.323315.77920.5440860.0267477
3017.6517.019915.73331.286590.630081
3113.615.735615.72290.0126968-2.13561
3214.3514.666215.775-1.10883-0.316169
3314.7514.160315.8917-1.73140.589734
3418.2516.102615.96040.1422112.14737
359.912.086715.9062-3.81959-2.18666
361615.81315.8729-0.05987270.186956
3718.2516.58615.88330.7026271.66404
3816.8518.417916.00212.41582-1.56791
3918.9516.595315.98750.6078362.35466
4015.616.972415.96461.00784-1.37242
4117.116.448315.90420.5440860.651748
4216.117.224115.93751.28659-1.12409
4315.415.933515.92080.0126968-0.53353
4415.414.766215.875-1.108830.633831
4513.3514.020715.7521-1.7314-0.670683
4619.115.552615.41040.1422113.54737
477.611.1714.9896-3.81959-3.56999
4819.114.592214.6521-0.05987274.50779
4914.7515.173514.47080.702627-0.423461
5019.2516.780414.36462.415822.46959
5113.614.972414.36460.607836-1.37242
5212.7515.293314.28541.00784-2.54325
539.8515.212814.66870.544086-5.36284
5415.2516.09714.81041.28659-0.847002
5511.914.577314.56460.0126968-2.67728
5616.3513.493314.6021-1.108832.85675
5712.413.018614.75-1.7314-0.6186
5818.1515.273515.13120.1422112.87654
5917.7511.930415.75-3.819595.81959
6012.3516.21116.2708-0.0598727-3.86096
6115.617.41116.70830.702627-1.81096
6219.319.278316.86252.415820.0216782
6317.117.203716.59580.607836-0.103669
6418.416.914115.90631.007841.48591
6519.0515.708715.16460.5440863.34133
6618.5516.251214.96461.286592.29883
6719.114.856414.84380.01269684.24355
6812.8513.570314.6792-1.10883-0.720336
699.512.749814.4812-1.7314-3.24985
704.514.400514.25830.142211-9.90054
7113.610.105413.925-3.819593.49459
7211.713.348513.4083-0.0598727-1.64846
7313.3513.533912.83120.702627-0.183877
7417.614.963712.54792.415822.63626
7514.0513.095312.48750.6078360.954664
7616.113.841212.83331.007842.25883
7713.35NANA0.544086NA
7811.85NANA1.28659NA
7911.95NANA0.0126968NA
8013.2NANA-1.10883NA
817.7NANA-1.7314NA
8214.6NANA0.142211NA


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')