FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 30 Nov 2014 11:10:10 +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/Nov/30/t14173458575gqh237rbbqz7d5.htm/, Retrieved Sun, 19 May 2024 16:32:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=261346, Retrieved Sun, 19 May 2024 16:32:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2014-11-30 11:10:10] [23e6dab13f5783a368d8e0aa48b4f84f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,86
105,97
106,08
106,04
106,65
106,85
106,85
106,95
107,29
107,65
107,87
107,98
107,98
107,83
108,69
108,91
109,67
109,72
109,72
109,72
109,74
109,78
110,49
110,37
110,37
110,41
110,64
110,88
110,91
110,99
110,99
110,99
111,28
112,37
112,35
112,24
112,24
112,21
112,35
112,71
113,08
113,26
113,26
113,27
113,85
114,92
115,24
115,21
115,21
115,18
115,24
116,24
116,68
116,77
116,77
116,84
116,94
117,83
118,16
118,27
113,62
113,72
113,53
113,69
114,61
114,46
114,68
114,72
115,62
115,4
115,43
115,44

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'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261346&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261346&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261346&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'Gertrude Mary Cox' @ cox.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1105.86NANA-0.460181NA
2105.97NANA-0.604181NA
3106.08NANA-0.518347NA
4106.04NANA-0.256347NA
5106.65NANA0.120069NA
6106.85NANA0.0449028NA
7106.85106.869106.925-0.0559306-0.0190694
8106.95106.942107.091-0.1491810.00834722
9107.29107.267107.277-0.009847220.0227639
10107.65108.06107.5050.554319-0.409736
11107.87108.487107.7510.736236-0.617069
12107.98108.595107.9960.598486-0.614736
13107.98107.775108.235-0.4601810.204764
14107.83107.866108.47-0.604181-0.0362361
15108.69108.17108.688-0.5183470.520431
16108.91108.622108.879-0.2563470.287597
17109.67109.197109.0770.1200690.473264
18109.72109.33109.2850.04490280.389681
19109.72109.429109.485-0.05593060.291347
20109.72109.542109.692-0.1491810.177514
21109.74109.871109.88-0.00984722-0.130569
22109.78110.598110.0440.554319-0.818069
23110.49110.914110.1770.736236-0.423736
24110.37110.881110.2820.598486-0.510569
25110.37109.928110.388-0.4601810.442264
26110.41109.89110.494-0.6041810.520431
27110.64110.092110.611-0.5183470.547514
28110.88110.527110.783-0.2563470.353431
29110.91111.088110.9680.120069-0.178403
30110.99111.169111.1240.0449028-0.178653
31110.99111.224111.28-0.0559306-0.233653
32110.99111.283111.432-0.149181-0.293319
33111.28111.569111.579-0.00984722-0.288903
34112.37112.281111.7260.5543190.0894306
35112.35112.629111.8930.736236-0.279153
36112.24112.676112.0780.598486-0.436403
37112.24111.807112.267-0.4601810.433097
38112.21111.852112.457-0.6041810.357514
39112.35112.14112.659-0.5183470.209597
40112.71112.616112.872-0.2563470.0942639
41113.08113.219113.0990.120069-0.138819
42113.26113.388113.3430.0449028-0.127819
43113.26113.534113.59-0.0559306-0.274486
44113.27113.689113.838-0.149181-0.418736
45113.85114.072114.082-0.00984722-0.222236
46114.92114.904114.350.5543190.0160972
47115.24115.383114.6470.736236-0.142903
48115.21115.541114.9430.598486-0.331403
49115.21114.775115.235-0.4601810.434764
50115.18114.926115.53-0.6041810.253764
51115.24115.29115.808-0.518347-0.0495694
52116.24115.802116.058-0.2563470.438431
53116.68116.421116.3010.1200690.259097
54116.77116.595116.550.04490280.175097
55116.77116.555116.611-0.05593060.214681
56116.84116.335116.484-0.1491810.505014
57116.94116.342116.352-0.009847220.597764
58117.83116.729116.1750.5543191.1011
59118.16116.718115.9820.7362361.44168
60118.27116.398115.80.5984861.87193
61113.62115.156115.616-0.460181-1.53607
62113.72114.837115.441-0.604181-1.11665
63113.53114.779115.297-0.518347-1.24915
64113.69114.885115.141-0.256347-1.1949
65114.61115.046114.9260.120069-0.436319
66114.46114.739114.6950.0449028-0.279486
67114.68NANA-0.0559306NA
68114.72NANA-0.149181NA
69115.62NANA-0.00984722NA
70115.4NANA0.554319NA
71115.43NANA0.736236NA
72115.44NANA0.598486NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 105.86 & NA & NA & -0.460181 & NA \tabularnewline
2 & 105.97 & NA & NA & -0.604181 & NA \tabularnewline
3 & 106.08 & NA & NA & -0.518347 & NA \tabularnewline
4 & 106.04 & NA & NA & -0.256347 & NA \tabularnewline
5 & 106.65 & NA & NA & 0.120069 & NA \tabularnewline
6 & 106.85 & NA & NA & 0.0449028 & NA \tabularnewline
7 & 106.85 & 106.869 & 106.925 & -0.0559306 & -0.0190694 \tabularnewline
8 & 106.95 & 106.942 & 107.091 & -0.149181 & 0.00834722 \tabularnewline
9 & 107.29 & 107.267 & 107.277 & -0.00984722 & 0.0227639 \tabularnewline
10 & 107.65 & 108.06 & 107.505 & 0.554319 & -0.409736 \tabularnewline
11 & 107.87 & 108.487 & 107.751 & 0.736236 & -0.617069 \tabularnewline
12 & 107.98 & 108.595 & 107.996 & 0.598486 & -0.614736 \tabularnewline
13 & 107.98 & 107.775 & 108.235 & -0.460181 & 0.204764 \tabularnewline
14 & 107.83 & 107.866 & 108.47 & -0.604181 & -0.0362361 \tabularnewline
15 & 108.69 & 108.17 & 108.688 & -0.518347 & 0.520431 \tabularnewline
16 & 108.91 & 108.622 & 108.879 & -0.256347 & 0.287597 \tabularnewline
17 & 109.67 & 109.197 & 109.077 & 0.120069 & 0.473264 \tabularnewline
18 & 109.72 & 109.33 & 109.285 & 0.0449028 & 0.389681 \tabularnewline
19 & 109.72 & 109.429 & 109.485 & -0.0559306 & 0.291347 \tabularnewline
20 & 109.72 & 109.542 & 109.692 & -0.149181 & 0.177514 \tabularnewline
21 & 109.74 & 109.871 & 109.88 & -0.00984722 & -0.130569 \tabularnewline
22 & 109.78 & 110.598 & 110.044 & 0.554319 & -0.818069 \tabularnewline
23 & 110.49 & 110.914 & 110.177 & 0.736236 & -0.423736 \tabularnewline
24 & 110.37 & 110.881 & 110.282 & 0.598486 & -0.510569 \tabularnewline
25 & 110.37 & 109.928 & 110.388 & -0.460181 & 0.442264 \tabularnewline
26 & 110.41 & 109.89 & 110.494 & -0.604181 & 0.520431 \tabularnewline
27 & 110.64 & 110.092 & 110.611 & -0.518347 & 0.547514 \tabularnewline
28 & 110.88 & 110.527 & 110.783 & -0.256347 & 0.353431 \tabularnewline
29 & 110.91 & 111.088 & 110.968 & 0.120069 & -0.178403 \tabularnewline
30 & 110.99 & 111.169 & 111.124 & 0.0449028 & -0.178653 \tabularnewline
31 & 110.99 & 111.224 & 111.28 & -0.0559306 & -0.233653 \tabularnewline
32 & 110.99 & 111.283 & 111.432 & -0.149181 & -0.293319 \tabularnewline
33 & 111.28 & 111.569 & 111.579 & -0.00984722 & -0.288903 \tabularnewline
34 & 112.37 & 112.281 & 111.726 & 0.554319 & 0.0894306 \tabularnewline
35 & 112.35 & 112.629 & 111.893 & 0.736236 & -0.279153 \tabularnewline
36 & 112.24 & 112.676 & 112.078 & 0.598486 & -0.436403 \tabularnewline
37 & 112.24 & 111.807 & 112.267 & -0.460181 & 0.433097 \tabularnewline
38 & 112.21 & 111.852 & 112.457 & -0.604181 & 0.357514 \tabularnewline
39 & 112.35 & 112.14 & 112.659 & -0.518347 & 0.209597 \tabularnewline
40 & 112.71 & 112.616 & 112.872 & -0.256347 & 0.0942639 \tabularnewline
41 & 113.08 & 113.219 & 113.099 & 0.120069 & -0.138819 \tabularnewline
42 & 113.26 & 113.388 & 113.343 & 0.0449028 & -0.127819 \tabularnewline
43 & 113.26 & 113.534 & 113.59 & -0.0559306 & -0.274486 \tabularnewline
44 & 113.27 & 113.689 & 113.838 & -0.149181 & -0.418736 \tabularnewline
45 & 113.85 & 114.072 & 114.082 & -0.00984722 & -0.222236 \tabularnewline
46 & 114.92 & 114.904 & 114.35 & 0.554319 & 0.0160972 \tabularnewline
47 & 115.24 & 115.383 & 114.647 & 0.736236 & -0.142903 \tabularnewline
48 & 115.21 & 115.541 & 114.943 & 0.598486 & -0.331403 \tabularnewline
49 & 115.21 & 114.775 & 115.235 & -0.460181 & 0.434764 \tabularnewline
50 & 115.18 & 114.926 & 115.53 & -0.604181 & 0.253764 \tabularnewline
51 & 115.24 & 115.29 & 115.808 & -0.518347 & -0.0495694 \tabularnewline
52 & 116.24 & 115.802 & 116.058 & -0.256347 & 0.438431 \tabularnewline
53 & 116.68 & 116.421 & 116.301 & 0.120069 & 0.259097 \tabularnewline
54 & 116.77 & 116.595 & 116.55 & 0.0449028 & 0.175097 \tabularnewline
55 & 116.77 & 116.555 & 116.611 & -0.0559306 & 0.214681 \tabularnewline
56 & 116.84 & 116.335 & 116.484 & -0.149181 & 0.505014 \tabularnewline
57 & 116.94 & 116.342 & 116.352 & -0.00984722 & 0.597764 \tabularnewline
58 & 117.83 & 116.729 & 116.175 & 0.554319 & 1.1011 \tabularnewline
59 & 118.16 & 116.718 & 115.982 & 0.736236 & 1.44168 \tabularnewline
60 & 118.27 & 116.398 & 115.8 & 0.598486 & 1.87193 \tabularnewline
61 & 113.62 & 115.156 & 115.616 & -0.460181 & -1.53607 \tabularnewline
62 & 113.72 & 114.837 & 115.441 & -0.604181 & -1.11665 \tabularnewline
63 & 113.53 & 114.779 & 115.297 & -0.518347 & -1.24915 \tabularnewline
64 & 113.69 & 114.885 & 115.141 & -0.256347 & -1.1949 \tabularnewline
65 & 114.61 & 115.046 & 114.926 & 0.120069 & -0.436319 \tabularnewline
66 & 114.46 & 114.739 & 114.695 & 0.0449028 & -0.279486 \tabularnewline
67 & 114.68 & NA & NA & -0.0559306 & NA \tabularnewline
68 & 114.72 & NA & NA & -0.149181 & NA \tabularnewline
69 & 115.62 & NA & NA & -0.00984722 & NA \tabularnewline
70 & 115.4 & NA & NA & 0.554319 & NA \tabularnewline
71 & 115.43 & NA & NA & 0.736236 & NA \tabularnewline
72 & 115.44 & NA & NA & 0.598486 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261346&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]105.86[/C][C]NA[/C][C]NA[/C][C]-0.460181[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]105.97[/C][C]NA[/C][C]NA[/C][C]-0.604181[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]106.08[/C][C]NA[/C][C]NA[/C][C]-0.518347[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]106.04[/C][C]NA[/C][C]NA[/C][C]-0.256347[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]106.65[/C][C]NA[/C][C]NA[/C][C]0.120069[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]106.85[/C][C]NA[/C][C]NA[/C][C]0.0449028[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]106.85[/C][C]106.869[/C][C]106.925[/C][C]-0.0559306[/C][C]-0.0190694[/C][/ROW]
[ROW][C]8[/C][C]106.95[/C][C]106.942[/C][C]107.091[/C][C]-0.149181[/C][C]0.00834722[/C][/ROW]
[ROW][C]9[/C][C]107.29[/C][C]107.267[/C][C]107.277[/C][C]-0.00984722[/C][C]0.0227639[/C][/ROW]
[ROW][C]10[/C][C]107.65[/C][C]108.06[/C][C]107.505[/C][C]0.554319[/C][C]-0.409736[/C][/ROW]
[ROW][C]11[/C][C]107.87[/C][C]108.487[/C][C]107.751[/C][C]0.736236[/C][C]-0.617069[/C][/ROW]
[ROW][C]12[/C][C]107.98[/C][C]108.595[/C][C]107.996[/C][C]0.598486[/C][C]-0.614736[/C][/ROW]
[ROW][C]13[/C][C]107.98[/C][C]107.775[/C][C]108.235[/C][C]-0.460181[/C][C]0.204764[/C][/ROW]
[ROW][C]14[/C][C]107.83[/C][C]107.866[/C][C]108.47[/C][C]-0.604181[/C][C]-0.0362361[/C][/ROW]
[ROW][C]15[/C][C]108.69[/C][C]108.17[/C][C]108.688[/C][C]-0.518347[/C][C]0.520431[/C][/ROW]
[ROW][C]16[/C][C]108.91[/C][C]108.622[/C][C]108.879[/C][C]-0.256347[/C][C]0.287597[/C][/ROW]
[ROW][C]17[/C][C]109.67[/C][C]109.197[/C][C]109.077[/C][C]0.120069[/C][C]0.473264[/C][/ROW]
[ROW][C]18[/C][C]109.72[/C][C]109.33[/C][C]109.285[/C][C]0.0449028[/C][C]0.389681[/C][/ROW]
[ROW][C]19[/C][C]109.72[/C][C]109.429[/C][C]109.485[/C][C]-0.0559306[/C][C]0.291347[/C][/ROW]
[ROW][C]20[/C][C]109.72[/C][C]109.542[/C][C]109.692[/C][C]-0.149181[/C][C]0.177514[/C][/ROW]
[ROW][C]21[/C][C]109.74[/C][C]109.871[/C][C]109.88[/C][C]-0.00984722[/C][C]-0.130569[/C][/ROW]
[ROW][C]22[/C][C]109.78[/C][C]110.598[/C][C]110.044[/C][C]0.554319[/C][C]-0.818069[/C][/ROW]
[ROW][C]23[/C][C]110.49[/C][C]110.914[/C][C]110.177[/C][C]0.736236[/C][C]-0.423736[/C][/ROW]
[ROW][C]24[/C][C]110.37[/C][C]110.881[/C][C]110.282[/C][C]0.598486[/C][C]-0.510569[/C][/ROW]
[ROW][C]25[/C][C]110.37[/C][C]109.928[/C][C]110.388[/C][C]-0.460181[/C][C]0.442264[/C][/ROW]
[ROW][C]26[/C][C]110.41[/C][C]109.89[/C][C]110.494[/C][C]-0.604181[/C][C]0.520431[/C][/ROW]
[ROW][C]27[/C][C]110.64[/C][C]110.092[/C][C]110.611[/C][C]-0.518347[/C][C]0.547514[/C][/ROW]
[ROW][C]28[/C][C]110.88[/C][C]110.527[/C][C]110.783[/C][C]-0.256347[/C][C]0.353431[/C][/ROW]
[ROW][C]29[/C][C]110.91[/C][C]111.088[/C][C]110.968[/C][C]0.120069[/C][C]-0.178403[/C][/ROW]
[ROW][C]30[/C][C]110.99[/C][C]111.169[/C][C]111.124[/C][C]0.0449028[/C][C]-0.178653[/C][/ROW]
[ROW][C]31[/C][C]110.99[/C][C]111.224[/C][C]111.28[/C][C]-0.0559306[/C][C]-0.233653[/C][/ROW]
[ROW][C]32[/C][C]110.99[/C][C]111.283[/C][C]111.432[/C][C]-0.149181[/C][C]-0.293319[/C][/ROW]
[ROW][C]33[/C][C]111.28[/C][C]111.569[/C][C]111.579[/C][C]-0.00984722[/C][C]-0.288903[/C][/ROW]
[ROW][C]34[/C][C]112.37[/C][C]112.281[/C][C]111.726[/C][C]0.554319[/C][C]0.0894306[/C][/ROW]
[ROW][C]35[/C][C]112.35[/C][C]112.629[/C][C]111.893[/C][C]0.736236[/C][C]-0.279153[/C][/ROW]
[ROW][C]36[/C][C]112.24[/C][C]112.676[/C][C]112.078[/C][C]0.598486[/C][C]-0.436403[/C][/ROW]
[ROW][C]37[/C][C]112.24[/C][C]111.807[/C][C]112.267[/C][C]-0.460181[/C][C]0.433097[/C][/ROW]
[ROW][C]38[/C][C]112.21[/C][C]111.852[/C][C]112.457[/C][C]-0.604181[/C][C]0.357514[/C][/ROW]
[ROW][C]39[/C][C]112.35[/C][C]112.14[/C][C]112.659[/C][C]-0.518347[/C][C]0.209597[/C][/ROW]
[ROW][C]40[/C][C]112.71[/C][C]112.616[/C][C]112.872[/C][C]-0.256347[/C][C]0.0942639[/C][/ROW]
[ROW][C]41[/C][C]113.08[/C][C]113.219[/C][C]113.099[/C][C]0.120069[/C][C]-0.138819[/C][/ROW]
[ROW][C]42[/C][C]113.26[/C][C]113.388[/C][C]113.343[/C][C]0.0449028[/C][C]-0.127819[/C][/ROW]
[ROW][C]43[/C][C]113.26[/C][C]113.534[/C][C]113.59[/C][C]-0.0559306[/C][C]-0.274486[/C][/ROW]
[ROW][C]44[/C][C]113.27[/C][C]113.689[/C][C]113.838[/C][C]-0.149181[/C][C]-0.418736[/C][/ROW]
[ROW][C]45[/C][C]113.85[/C][C]114.072[/C][C]114.082[/C][C]-0.00984722[/C][C]-0.222236[/C][/ROW]
[ROW][C]46[/C][C]114.92[/C][C]114.904[/C][C]114.35[/C][C]0.554319[/C][C]0.0160972[/C][/ROW]
[ROW][C]47[/C][C]115.24[/C][C]115.383[/C][C]114.647[/C][C]0.736236[/C][C]-0.142903[/C][/ROW]
[ROW][C]48[/C][C]115.21[/C][C]115.541[/C][C]114.943[/C][C]0.598486[/C][C]-0.331403[/C][/ROW]
[ROW][C]49[/C][C]115.21[/C][C]114.775[/C][C]115.235[/C][C]-0.460181[/C][C]0.434764[/C][/ROW]
[ROW][C]50[/C][C]115.18[/C][C]114.926[/C][C]115.53[/C][C]-0.604181[/C][C]0.253764[/C][/ROW]
[ROW][C]51[/C][C]115.24[/C][C]115.29[/C][C]115.808[/C][C]-0.518347[/C][C]-0.0495694[/C][/ROW]
[ROW][C]52[/C][C]116.24[/C][C]115.802[/C][C]116.058[/C][C]-0.256347[/C][C]0.438431[/C][/ROW]
[ROW][C]53[/C][C]116.68[/C][C]116.421[/C][C]116.301[/C][C]0.120069[/C][C]0.259097[/C][/ROW]
[ROW][C]54[/C][C]116.77[/C][C]116.595[/C][C]116.55[/C][C]0.0449028[/C][C]0.175097[/C][/ROW]
[ROW][C]55[/C][C]116.77[/C][C]116.555[/C][C]116.611[/C][C]-0.0559306[/C][C]0.214681[/C][/ROW]
[ROW][C]56[/C][C]116.84[/C][C]116.335[/C][C]116.484[/C][C]-0.149181[/C][C]0.505014[/C][/ROW]
[ROW][C]57[/C][C]116.94[/C][C]116.342[/C][C]116.352[/C][C]-0.00984722[/C][C]0.597764[/C][/ROW]
[ROW][C]58[/C][C]117.83[/C][C]116.729[/C][C]116.175[/C][C]0.554319[/C][C]1.1011[/C][/ROW]
[ROW][C]59[/C][C]118.16[/C][C]116.718[/C][C]115.982[/C][C]0.736236[/C][C]1.44168[/C][/ROW]
[ROW][C]60[/C][C]118.27[/C][C]116.398[/C][C]115.8[/C][C]0.598486[/C][C]1.87193[/C][/ROW]
[ROW][C]61[/C][C]113.62[/C][C]115.156[/C][C]115.616[/C][C]-0.460181[/C][C]-1.53607[/C][/ROW]
[ROW][C]62[/C][C]113.72[/C][C]114.837[/C][C]115.441[/C][C]-0.604181[/C][C]-1.11665[/C][/ROW]
[ROW][C]63[/C][C]113.53[/C][C]114.779[/C][C]115.297[/C][C]-0.518347[/C][C]-1.24915[/C][/ROW]
[ROW][C]64[/C][C]113.69[/C][C]114.885[/C][C]115.141[/C][C]-0.256347[/C][C]-1.1949[/C][/ROW]
[ROW][C]65[/C][C]114.61[/C][C]115.046[/C][C]114.926[/C][C]0.120069[/C][C]-0.436319[/C][/ROW]
[ROW][C]66[/C][C]114.46[/C][C]114.739[/C][C]114.695[/C][C]0.0449028[/C][C]-0.279486[/C][/ROW]
[ROW][C]67[/C][C]114.68[/C][C]NA[/C][C]NA[/C][C]-0.0559306[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]114.72[/C][C]NA[/C][C]NA[/C][C]-0.149181[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]115.62[/C][C]NA[/C][C]NA[/C][C]-0.00984722[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]115.4[/C][C]NA[/C][C]NA[/C][C]0.554319[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]115.43[/C][C]NA[/C][C]NA[/C][C]0.736236[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]115.44[/C][C]NA[/C][C]NA[/C][C]0.598486[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261346&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261346&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
1105.86NANA-0.460181NA
2105.97NANA-0.604181NA
3106.08NANA-0.518347NA
4106.04NANA-0.256347NA
5106.65NANA0.120069NA
6106.85NANA0.0449028NA
7106.85106.869106.925-0.0559306-0.0190694
8106.95106.942107.091-0.1491810.00834722
9107.29107.267107.277-0.009847220.0227639
10107.65108.06107.5050.554319-0.409736
11107.87108.487107.7510.736236-0.617069
12107.98108.595107.9960.598486-0.614736
13107.98107.775108.235-0.4601810.204764
14107.83107.866108.47-0.604181-0.0362361
15108.69108.17108.688-0.5183470.520431
16108.91108.622108.879-0.2563470.287597
17109.67109.197109.0770.1200690.473264
18109.72109.33109.2850.04490280.389681
19109.72109.429109.485-0.05593060.291347
20109.72109.542109.692-0.1491810.177514
21109.74109.871109.88-0.00984722-0.130569
22109.78110.598110.0440.554319-0.818069
23110.49110.914110.1770.736236-0.423736
24110.37110.881110.2820.598486-0.510569
25110.37109.928110.388-0.4601810.442264
26110.41109.89110.494-0.6041810.520431
27110.64110.092110.611-0.5183470.547514
28110.88110.527110.783-0.2563470.353431
29110.91111.088110.9680.120069-0.178403
30110.99111.169111.1240.0449028-0.178653
31110.99111.224111.28-0.0559306-0.233653
32110.99111.283111.432-0.149181-0.293319
33111.28111.569111.579-0.00984722-0.288903
34112.37112.281111.7260.5543190.0894306
35112.35112.629111.8930.736236-0.279153
36112.24112.676112.0780.598486-0.436403
37112.24111.807112.267-0.4601810.433097
38112.21111.852112.457-0.6041810.357514
39112.35112.14112.659-0.5183470.209597
40112.71112.616112.872-0.2563470.0942639
41113.08113.219113.0990.120069-0.138819
42113.26113.388113.3430.0449028-0.127819
43113.26113.534113.59-0.0559306-0.274486
44113.27113.689113.838-0.149181-0.418736
45113.85114.072114.082-0.00984722-0.222236
46114.92114.904114.350.5543190.0160972
47115.24115.383114.6470.736236-0.142903
48115.21115.541114.9430.598486-0.331403
49115.21114.775115.235-0.4601810.434764
50115.18114.926115.53-0.6041810.253764
51115.24115.29115.808-0.518347-0.0495694
52116.24115.802116.058-0.2563470.438431
53116.68116.421116.3010.1200690.259097
54116.77116.595116.550.04490280.175097
55116.77116.555116.611-0.05593060.214681
56116.84116.335116.484-0.1491810.505014
57116.94116.342116.352-0.009847220.597764
58117.83116.729116.1750.5543191.1011
59118.16116.718115.9820.7362361.44168
60118.27116.398115.80.5984861.87193
61113.62115.156115.616-0.460181-1.53607
62113.72114.837115.441-0.604181-1.11665
63113.53114.779115.297-0.518347-1.24915
64113.69114.885115.141-0.256347-1.1949
65114.61115.046114.9260.120069-0.436319
66114.46114.739114.6950.0449028-0.279486
67114.68NANA-0.0559306NA
68114.72NANA-0.149181NA
69115.62NANA-0.00984722NA
70115.4NANA0.554319NA
71115.43NANA0.736236NA
72115.44NANA0.598486NA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')