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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 computationSat, 25 Dec 2010 23:02:54 +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/2010/Dec/26/t12933180615l2ifgh8dikqle7.htm/, Retrieved Mon, 06 May 2024 21:57:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115473, Retrieved Mon, 06 May 2024 21:57:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [] [2010-12-15 19:20:11] [d39e5c40c631ed6c22677d2e41dbfc7d]
-    D  [Classical Decomposition] [paper trend depos...] [2010-12-25 22:53:53] [eeb33d252044f8583501f5ba0605ad6d]
-    D      [Classical Decomposition] [paper trend dow j...] [2010-12-25 23:02:54] [6df2229e3f2091de42c4a9cf9a617420] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10554,27
10532,54
10324,31
10695,25
10827,81
10872,48
10971,19
11145,65
11234,68
11333,88
10997,97
11036,89
11257,35
11533,59
11963,12
12185,15
12377,62
12512,89
12631,48
12268,53
12754,8
13407,75
13480,21
13673,28
13239,71
13557,69
13901,28
13200,58
13406,97
12538,12
12419,57
12193,88
12656,63
12812,48
12056,67
11322,38
11530,75
11114,08
9181,73
8614,55
8595,56
8396,2
7690,5
7235,47
7992,12
8398,37
8593
8679,75
9374,63
9634,97
9857,34
10238,83
10433,44
10471,24
10214,51
10677,52
11052,15
10500,19
10159,27
10222,24
10350,4

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 Ronald Aylmer Fisher' @ 193.190.124.24

\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 Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115473&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 Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115473&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 Ronald Aylmer Fisher' @ 193.190.124.24






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110554.27NANA178.343706597222NA
210532.54NANA300.574644097223NA
310324.31NANA73.1373524305562NA
410695.25NANA-82.3670225694447NA
510827.81NANA78.6737065972228NA
610872.48NANA-127.888897569444NA
710971.1910555.985269097210906.5383333333-350.553064236112415.204730902778
811145.6510437.252039930610977.54375-540.291710069445708.397960069444
911234.6811041.239852430611087.5379166667-46.2980642361116193.440147569445
1011333.8811512.333602430611217.9008333333294.432769097223-178.453602430558
1110997.9711549.543498263911344.5554166667204.988081597221-551.57349826389
1211036.8911494.729748263911477.4812517.2484982638883-457.83974826389
1311257.3511793.354123263911615.0104166667178.343706597222-536.004123263889
1411533.5912031.550477430611730.9758333333300.574644097223-497.960477430554
1511963.1211914.238185763911841.100833333373.137352430556248.8818142361142
1612185.1511908.483394097211990.8504166667-82.3670225694447276.666605902778
1712377.6212259.362039930612180.688333333378.6737065972228118.257960069444
1812512.8912266.075685763912393.9645833333-127.888897569444246.814314236113
1912631.4812235.859435763912586.4125-350.553064236112395.620564236113
2012268.5312213.056623263912753.3483333333-540.29171006944555.4733767361122
2112754.812872.144435763912918.4425-46.2980642361116-117.344435763889
2213407.7513335.941519097213041.50875294.43276909722371.8084809027769
2313480.2113331.695998263913126.7079166667204.988081597221148.51400173611
2413673.2813187.897248263913170.6487517.2484982638883485.382751736111
2513239.7113341.214123263913162.8704166667178.343706597222-101.504123263889
2613557.6913451.505060763913150.9304166667300.574644097223106.184939236111
2713901.2813216.866935763913143.729583333373.1373524305562684.41306423611
2813200.5813032.469227430613114.83625-82.3670225694447168.110772569446
2913406.9713109.392873263913030.719166666778.6737065972228297.577126736111
3012538.1212745.561935763912873.4508333333-127.888897569444-207.441935763889
3112419.5712353.736935763912704.29-350.55306423611265.8330642361107
3212193.8811990.974539930612531.26625-540.291710069445202.905460069444
3312656.6312186.503185763912232.80125-46.2980642361116470.126814236111
3412812.4812139.501519097211845.06875294.432769097223672.978480902779
3512056.6711658.496831597211453.50875204.988081597221398.17316840278
3611322.3811097.701831597211080.453333333317.2484982638883224.678168402779
3711530.7510889.172456597210710.82875178.343706597222641.577543402782
3811114.0810607.758394097210307.18375300.574644097223506.321605902778
399181.739979.366102430569906.2287573.1373524305562-797.636102430557
408614.559445.585894097229527.95291666667-82.3670225694447-831.035894097224
418595.569278.385789930559199.7120833333378.6737065972228-682.825789930555
428396.28817.394019097228945.28291666667-127.888897569444-421.194019097222
437690.58394.781935763898745.335-350.553064236112-704.281935763887
447235.478053.575373263898593.86708333333-540.291710069445-818.10537326389
457992.128514.089852430568560.38791666667-46.2980642361116-521.969852430555
468398.378950.649435763898656.21666666667294.432769097223-552.279435763888
4785939005.461414930558800.47333333333204.988081597221-412.461414930554
488679.758980.760164930568963.5116666666717.2484982638883-301.010164930556
499374.639333.482456597229155.13875178.34370659722241.1475434027780
509634.979704.299227430569403.72458333333300.574644097223-69.3292274305568
519857.349747.781935763899674.6445833333373.1373524305562109.558064236111
5210238.839807.354644097229889.72166666667-82.3670225694447431.475355902778
5310433.4410121.232456597210042.5587578.6737065972228312.207543402779
5410471.2410044.201519097210172.0904166667-127.888897569444427.038480902778
5510214.519926.4648524305610277.0179166667-350.553064236112288.045147569444
5610677.52NANA-540.291710069445NA
5711052.15NANA-46.2980642361116NA
5810500.19NANA294.432769097223NA
5910159.27NANA204.988081597221NA
6010222.24NANA17.2484982638883NA
6110350.4NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 10554.27 & NA & NA & 178.343706597222 & NA \tabularnewline
2 & 10532.54 & NA & NA & 300.574644097223 & NA \tabularnewline
3 & 10324.31 & NA & NA & 73.1373524305562 & NA \tabularnewline
4 & 10695.25 & NA & NA & -82.3670225694447 & NA \tabularnewline
5 & 10827.81 & NA & NA & 78.6737065972228 & NA \tabularnewline
6 & 10872.48 & NA & NA & -127.888897569444 & NA \tabularnewline
7 & 10971.19 & 10555.9852690972 & 10906.5383333333 & -350.553064236112 & 415.204730902778 \tabularnewline
8 & 11145.65 & 10437.2520399306 & 10977.54375 & -540.291710069445 & 708.397960069444 \tabularnewline
9 & 11234.68 & 11041.2398524306 & 11087.5379166667 & -46.2980642361116 & 193.440147569445 \tabularnewline
10 & 11333.88 & 11512.3336024306 & 11217.9008333333 & 294.432769097223 & -178.453602430558 \tabularnewline
11 & 10997.97 & 11549.5434982639 & 11344.5554166667 & 204.988081597221 & -551.57349826389 \tabularnewline
12 & 11036.89 & 11494.7297482639 & 11477.48125 & 17.2484982638883 & -457.83974826389 \tabularnewline
13 & 11257.35 & 11793.3541232639 & 11615.0104166667 & 178.343706597222 & -536.004123263889 \tabularnewline
14 & 11533.59 & 12031.5504774306 & 11730.9758333333 & 300.574644097223 & -497.960477430554 \tabularnewline
15 & 11963.12 & 11914.2381857639 & 11841.1008333333 & 73.1373524305562 & 48.8818142361142 \tabularnewline
16 & 12185.15 & 11908.4833940972 & 11990.8504166667 & -82.3670225694447 & 276.666605902778 \tabularnewline
17 & 12377.62 & 12259.3620399306 & 12180.6883333333 & 78.6737065972228 & 118.257960069444 \tabularnewline
18 & 12512.89 & 12266.0756857639 & 12393.9645833333 & -127.888897569444 & 246.814314236113 \tabularnewline
19 & 12631.48 & 12235.8594357639 & 12586.4125 & -350.553064236112 & 395.620564236113 \tabularnewline
20 & 12268.53 & 12213.0566232639 & 12753.3483333333 & -540.291710069445 & 55.4733767361122 \tabularnewline
21 & 12754.8 & 12872.1444357639 & 12918.4425 & -46.2980642361116 & -117.344435763889 \tabularnewline
22 & 13407.75 & 13335.9415190972 & 13041.50875 & 294.432769097223 & 71.8084809027769 \tabularnewline
23 & 13480.21 & 13331.6959982639 & 13126.7079166667 & 204.988081597221 & 148.51400173611 \tabularnewline
24 & 13673.28 & 13187.8972482639 & 13170.64875 & 17.2484982638883 & 485.382751736111 \tabularnewline
25 & 13239.71 & 13341.2141232639 & 13162.8704166667 & 178.343706597222 & -101.504123263889 \tabularnewline
26 & 13557.69 & 13451.5050607639 & 13150.9304166667 & 300.574644097223 & 106.184939236111 \tabularnewline
27 & 13901.28 & 13216.8669357639 & 13143.7295833333 & 73.1373524305562 & 684.41306423611 \tabularnewline
28 & 13200.58 & 13032.4692274306 & 13114.83625 & -82.3670225694447 & 168.110772569446 \tabularnewline
29 & 13406.97 & 13109.3928732639 & 13030.7191666667 & 78.6737065972228 & 297.577126736111 \tabularnewline
30 & 12538.12 & 12745.5619357639 & 12873.4508333333 & -127.888897569444 & -207.441935763889 \tabularnewline
31 & 12419.57 & 12353.7369357639 & 12704.29 & -350.553064236112 & 65.8330642361107 \tabularnewline
32 & 12193.88 & 11990.9745399306 & 12531.26625 & -540.291710069445 & 202.905460069444 \tabularnewline
33 & 12656.63 & 12186.5031857639 & 12232.80125 & -46.2980642361116 & 470.126814236111 \tabularnewline
34 & 12812.48 & 12139.5015190972 & 11845.06875 & 294.432769097223 & 672.978480902779 \tabularnewline
35 & 12056.67 & 11658.4968315972 & 11453.50875 & 204.988081597221 & 398.17316840278 \tabularnewline
36 & 11322.38 & 11097.7018315972 & 11080.4533333333 & 17.2484982638883 & 224.678168402779 \tabularnewline
37 & 11530.75 & 10889.1724565972 & 10710.82875 & 178.343706597222 & 641.577543402782 \tabularnewline
38 & 11114.08 & 10607.7583940972 & 10307.18375 & 300.574644097223 & 506.321605902778 \tabularnewline
39 & 9181.73 & 9979.36610243056 & 9906.22875 & 73.1373524305562 & -797.636102430557 \tabularnewline
40 & 8614.55 & 9445.58589409722 & 9527.95291666667 & -82.3670225694447 & -831.035894097224 \tabularnewline
41 & 8595.56 & 9278.38578993055 & 9199.71208333333 & 78.6737065972228 & -682.825789930555 \tabularnewline
42 & 8396.2 & 8817.39401909722 & 8945.28291666667 & -127.888897569444 & -421.194019097222 \tabularnewline
43 & 7690.5 & 8394.78193576389 & 8745.335 & -350.553064236112 & -704.281935763887 \tabularnewline
44 & 7235.47 & 8053.57537326389 & 8593.86708333333 & -540.291710069445 & -818.10537326389 \tabularnewline
45 & 7992.12 & 8514.08985243056 & 8560.38791666667 & -46.2980642361116 & -521.969852430555 \tabularnewline
46 & 8398.37 & 8950.64943576389 & 8656.21666666667 & 294.432769097223 & -552.279435763888 \tabularnewline
47 & 8593 & 9005.46141493055 & 8800.47333333333 & 204.988081597221 & -412.461414930554 \tabularnewline
48 & 8679.75 & 8980.76016493056 & 8963.51166666667 & 17.2484982638883 & -301.010164930556 \tabularnewline
49 & 9374.63 & 9333.48245659722 & 9155.13875 & 178.343706597222 & 41.1475434027780 \tabularnewline
50 & 9634.97 & 9704.29922743056 & 9403.72458333333 & 300.574644097223 & -69.3292274305568 \tabularnewline
51 & 9857.34 & 9747.78193576389 & 9674.64458333333 & 73.1373524305562 & 109.558064236111 \tabularnewline
52 & 10238.83 & 9807.35464409722 & 9889.72166666667 & -82.3670225694447 & 431.475355902778 \tabularnewline
53 & 10433.44 & 10121.2324565972 & 10042.55875 & 78.6737065972228 & 312.207543402779 \tabularnewline
54 & 10471.24 & 10044.2015190972 & 10172.0904166667 & -127.888897569444 & 427.038480902778 \tabularnewline
55 & 10214.51 & 9926.46485243056 & 10277.0179166667 & -350.553064236112 & 288.045147569444 \tabularnewline
56 & 10677.52 & NA & NA & -540.291710069445 & NA \tabularnewline
57 & 11052.15 & NA & NA & -46.2980642361116 & NA \tabularnewline
58 & 10500.19 & NA & NA & 294.432769097223 & NA \tabularnewline
59 & 10159.27 & NA & NA & 204.988081597221 & NA \tabularnewline
60 & 10222.24 & NA & NA & 17.2484982638883 & NA \tabularnewline
61 & 10350.4 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115473&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]10554.27[/C][C]NA[/C][C]NA[/C][C]178.343706597222[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]10532.54[/C][C]NA[/C][C]NA[/C][C]300.574644097223[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]10324.31[/C][C]NA[/C][C]NA[/C][C]73.1373524305562[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]10695.25[/C][C]NA[/C][C]NA[/C][C]-82.3670225694447[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]10827.81[/C][C]NA[/C][C]NA[/C][C]78.6737065972228[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]10872.48[/C][C]NA[/C][C]NA[/C][C]-127.888897569444[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]10971.19[/C][C]10555.9852690972[/C][C]10906.5383333333[/C][C]-350.553064236112[/C][C]415.204730902778[/C][/ROW]
[ROW][C]8[/C][C]11145.65[/C][C]10437.2520399306[/C][C]10977.54375[/C][C]-540.291710069445[/C][C]708.397960069444[/C][/ROW]
[ROW][C]9[/C][C]11234.68[/C][C]11041.2398524306[/C][C]11087.5379166667[/C][C]-46.2980642361116[/C][C]193.440147569445[/C][/ROW]
[ROW][C]10[/C][C]11333.88[/C][C]11512.3336024306[/C][C]11217.9008333333[/C][C]294.432769097223[/C][C]-178.453602430558[/C][/ROW]
[ROW][C]11[/C][C]10997.97[/C][C]11549.5434982639[/C][C]11344.5554166667[/C][C]204.988081597221[/C][C]-551.57349826389[/C][/ROW]
[ROW][C]12[/C][C]11036.89[/C][C]11494.7297482639[/C][C]11477.48125[/C][C]17.2484982638883[/C][C]-457.83974826389[/C][/ROW]
[ROW][C]13[/C][C]11257.35[/C][C]11793.3541232639[/C][C]11615.0104166667[/C][C]178.343706597222[/C][C]-536.004123263889[/C][/ROW]
[ROW][C]14[/C][C]11533.59[/C][C]12031.5504774306[/C][C]11730.9758333333[/C][C]300.574644097223[/C][C]-497.960477430554[/C][/ROW]
[ROW][C]15[/C][C]11963.12[/C][C]11914.2381857639[/C][C]11841.1008333333[/C][C]73.1373524305562[/C][C]48.8818142361142[/C][/ROW]
[ROW][C]16[/C][C]12185.15[/C][C]11908.4833940972[/C][C]11990.8504166667[/C][C]-82.3670225694447[/C][C]276.666605902778[/C][/ROW]
[ROW][C]17[/C][C]12377.62[/C][C]12259.3620399306[/C][C]12180.6883333333[/C][C]78.6737065972228[/C][C]118.257960069444[/C][/ROW]
[ROW][C]18[/C][C]12512.89[/C][C]12266.0756857639[/C][C]12393.9645833333[/C][C]-127.888897569444[/C][C]246.814314236113[/C][/ROW]
[ROW][C]19[/C][C]12631.48[/C][C]12235.8594357639[/C][C]12586.4125[/C][C]-350.553064236112[/C][C]395.620564236113[/C][/ROW]
[ROW][C]20[/C][C]12268.53[/C][C]12213.0566232639[/C][C]12753.3483333333[/C][C]-540.291710069445[/C][C]55.4733767361122[/C][/ROW]
[ROW][C]21[/C][C]12754.8[/C][C]12872.1444357639[/C][C]12918.4425[/C][C]-46.2980642361116[/C][C]-117.344435763889[/C][/ROW]
[ROW][C]22[/C][C]13407.75[/C][C]13335.9415190972[/C][C]13041.50875[/C][C]294.432769097223[/C][C]71.8084809027769[/C][/ROW]
[ROW][C]23[/C][C]13480.21[/C][C]13331.6959982639[/C][C]13126.7079166667[/C][C]204.988081597221[/C][C]148.51400173611[/C][/ROW]
[ROW][C]24[/C][C]13673.28[/C][C]13187.8972482639[/C][C]13170.64875[/C][C]17.2484982638883[/C][C]485.382751736111[/C][/ROW]
[ROW][C]25[/C][C]13239.71[/C][C]13341.2141232639[/C][C]13162.8704166667[/C][C]178.343706597222[/C][C]-101.504123263889[/C][/ROW]
[ROW][C]26[/C][C]13557.69[/C][C]13451.5050607639[/C][C]13150.9304166667[/C][C]300.574644097223[/C][C]106.184939236111[/C][/ROW]
[ROW][C]27[/C][C]13901.28[/C][C]13216.8669357639[/C][C]13143.7295833333[/C][C]73.1373524305562[/C][C]684.41306423611[/C][/ROW]
[ROW][C]28[/C][C]13200.58[/C][C]13032.4692274306[/C][C]13114.83625[/C][C]-82.3670225694447[/C][C]168.110772569446[/C][/ROW]
[ROW][C]29[/C][C]13406.97[/C][C]13109.3928732639[/C][C]13030.7191666667[/C][C]78.6737065972228[/C][C]297.577126736111[/C][/ROW]
[ROW][C]30[/C][C]12538.12[/C][C]12745.5619357639[/C][C]12873.4508333333[/C][C]-127.888897569444[/C][C]-207.441935763889[/C][/ROW]
[ROW][C]31[/C][C]12419.57[/C][C]12353.7369357639[/C][C]12704.29[/C][C]-350.553064236112[/C][C]65.8330642361107[/C][/ROW]
[ROW][C]32[/C][C]12193.88[/C][C]11990.9745399306[/C][C]12531.26625[/C][C]-540.291710069445[/C][C]202.905460069444[/C][/ROW]
[ROW][C]33[/C][C]12656.63[/C][C]12186.5031857639[/C][C]12232.80125[/C][C]-46.2980642361116[/C][C]470.126814236111[/C][/ROW]
[ROW][C]34[/C][C]12812.48[/C][C]12139.5015190972[/C][C]11845.06875[/C][C]294.432769097223[/C][C]672.978480902779[/C][/ROW]
[ROW][C]35[/C][C]12056.67[/C][C]11658.4968315972[/C][C]11453.50875[/C][C]204.988081597221[/C][C]398.17316840278[/C][/ROW]
[ROW][C]36[/C][C]11322.38[/C][C]11097.7018315972[/C][C]11080.4533333333[/C][C]17.2484982638883[/C][C]224.678168402779[/C][/ROW]
[ROW][C]37[/C][C]11530.75[/C][C]10889.1724565972[/C][C]10710.82875[/C][C]178.343706597222[/C][C]641.577543402782[/C][/ROW]
[ROW][C]38[/C][C]11114.08[/C][C]10607.7583940972[/C][C]10307.18375[/C][C]300.574644097223[/C][C]506.321605902778[/C][/ROW]
[ROW][C]39[/C][C]9181.73[/C][C]9979.36610243056[/C][C]9906.22875[/C][C]73.1373524305562[/C][C]-797.636102430557[/C][/ROW]
[ROW][C]40[/C][C]8614.55[/C][C]9445.58589409722[/C][C]9527.95291666667[/C][C]-82.3670225694447[/C][C]-831.035894097224[/C][/ROW]
[ROW][C]41[/C][C]8595.56[/C][C]9278.38578993055[/C][C]9199.71208333333[/C][C]78.6737065972228[/C][C]-682.825789930555[/C][/ROW]
[ROW][C]42[/C][C]8396.2[/C][C]8817.39401909722[/C][C]8945.28291666667[/C][C]-127.888897569444[/C][C]-421.194019097222[/C][/ROW]
[ROW][C]43[/C][C]7690.5[/C][C]8394.78193576389[/C][C]8745.335[/C][C]-350.553064236112[/C][C]-704.281935763887[/C][/ROW]
[ROW][C]44[/C][C]7235.47[/C][C]8053.57537326389[/C][C]8593.86708333333[/C][C]-540.291710069445[/C][C]-818.10537326389[/C][/ROW]
[ROW][C]45[/C][C]7992.12[/C][C]8514.08985243056[/C][C]8560.38791666667[/C][C]-46.2980642361116[/C][C]-521.969852430555[/C][/ROW]
[ROW][C]46[/C][C]8398.37[/C][C]8950.64943576389[/C][C]8656.21666666667[/C][C]294.432769097223[/C][C]-552.279435763888[/C][/ROW]
[ROW][C]47[/C][C]8593[/C][C]9005.46141493055[/C][C]8800.47333333333[/C][C]204.988081597221[/C][C]-412.461414930554[/C][/ROW]
[ROW][C]48[/C][C]8679.75[/C][C]8980.76016493056[/C][C]8963.51166666667[/C][C]17.2484982638883[/C][C]-301.010164930556[/C][/ROW]
[ROW][C]49[/C][C]9374.63[/C][C]9333.48245659722[/C][C]9155.13875[/C][C]178.343706597222[/C][C]41.1475434027780[/C][/ROW]
[ROW][C]50[/C][C]9634.97[/C][C]9704.29922743056[/C][C]9403.72458333333[/C][C]300.574644097223[/C][C]-69.3292274305568[/C][/ROW]
[ROW][C]51[/C][C]9857.34[/C][C]9747.78193576389[/C][C]9674.64458333333[/C][C]73.1373524305562[/C][C]109.558064236111[/C][/ROW]
[ROW][C]52[/C][C]10238.83[/C][C]9807.35464409722[/C][C]9889.72166666667[/C][C]-82.3670225694447[/C][C]431.475355902778[/C][/ROW]
[ROW][C]53[/C][C]10433.44[/C][C]10121.2324565972[/C][C]10042.55875[/C][C]78.6737065972228[/C][C]312.207543402779[/C][/ROW]
[ROW][C]54[/C][C]10471.24[/C][C]10044.2015190972[/C][C]10172.0904166667[/C][C]-127.888897569444[/C][C]427.038480902778[/C][/ROW]
[ROW][C]55[/C][C]10214.51[/C][C]9926.46485243056[/C][C]10277.0179166667[/C][C]-350.553064236112[/C][C]288.045147569444[/C][/ROW]
[ROW][C]56[/C][C]10677.52[/C][C]NA[/C][C]NA[/C][C]-540.291710069445[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]11052.15[/C][C]NA[/C][C]NA[/C][C]-46.2980642361116[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]10500.19[/C][C]NA[/C][C]NA[/C][C]294.432769097223[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]10159.27[/C][C]NA[/C][C]NA[/C][C]204.988081597221[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]10222.24[/C][C]NA[/C][C]NA[/C][C]17.2484982638883[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]10350.4[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115473&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
110554.27NANA178.343706597222NA
210532.54NANA300.574644097223NA
310324.31NANA73.1373524305562NA
410695.25NANA-82.3670225694447NA
510827.81NANA78.6737065972228NA
610872.48NANA-127.888897569444NA
710971.1910555.985269097210906.5383333333-350.553064236112415.204730902778
811145.6510437.252039930610977.54375-540.291710069445708.397960069444
911234.6811041.239852430611087.5379166667-46.2980642361116193.440147569445
1011333.8811512.333602430611217.9008333333294.432769097223-178.453602430558
1110997.9711549.543498263911344.5554166667204.988081597221-551.57349826389
1211036.8911494.729748263911477.4812517.2484982638883-457.83974826389
1311257.3511793.354123263911615.0104166667178.343706597222-536.004123263889
1411533.5912031.550477430611730.9758333333300.574644097223-497.960477430554
1511963.1211914.238185763911841.100833333373.137352430556248.8818142361142
1612185.1511908.483394097211990.8504166667-82.3670225694447276.666605902778
1712377.6212259.362039930612180.688333333378.6737065972228118.257960069444
1812512.8912266.075685763912393.9645833333-127.888897569444246.814314236113
1912631.4812235.859435763912586.4125-350.553064236112395.620564236113
2012268.5312213.056623263912753.3483333333-540.29171006944555.4733767361122
2112754.812872.144435763912918.4425-46.2980642361116-117.344435763889
2213407.7513335.941519097213041.50875294.43276909722371.8084809027769
2313480.2113331.695998263913126.7079166667204.988081597221148.51400173611
2413673.2813187.897248263913170.6487517.2484982638883485.382751736111
2513239.7113341.214123263913162.8704166667178.343706597222-101.504123263889
2613557.6913451.505060763913150.9304166667300.574644097223106.184939236111
2713901.2813216.866935763913143.729583333373.1373524305562684.41306423611
2813200.5813032.469227430613114.83625-82.3670225694447168.110772569446
2913406.9713109.392873263913030.719166666778.6737065972228297.577126736111
3012538.1212745.561935763912873.4508333333-127.888897569444-207.441935763889
3112419.5712353.736935763912704.29-350.55306423611265.8330642361107
3212193.8811990.974539930612531.26625-540.291710069445202.905460069444
3312656.6312186.503185763912232.80125-46.2980642361116470.126814236111
3412812.4812139.501519097211845.06875294.432769097223672.978480902779
3512056.6711658.496831597211453.50875204.988081597221398.17316840278
3611322.3811097.701831597211080.453333333317.2484982638883224.678168402779
3711530.7510889.172456597210710.82875178.343706597222641.577543402782
3811114.0810607.758394097210307.18375300.574644097223506.321605902778
399181.739979.366102430569906.2287573.1373524305562-797.636102430557
408614.559445.585894097229527.95291666667-82.3670225694447-831.035894097224
418595.569278.385789930559199.7120833333378.6737065972228-682.825789930555
428396.28817.394019097228945.28291666667-127.888897569444-421.194019097222
437690.58394.781935763898745.335-350.553064236112-704.281935763887
447235.478053.575373263898593.86708333333-540.291710069445-818.10537326389
457992.128514.089852430568560.38791666667-46.2980642361116-521.969852430555
468398.378950.649435763898656.21666666667294.432769097223-552.279435763888
4785939005.461414930558800.47333333333204.988081597221-412.461414930554
488679.758980.760164930568963.5116666666717.2484982638883-301.010164930556
499374.639333.482456597229155.13875178.34370659722241.1475434027780
509634.979704.299227430569403.72458333333300.574644097223-69.3292274305568
519857.349747.781935763899674.6445833333373.1373524305562109.558064236111
5210238.839807.354644097229889.72166666667-82.3670225694447431.475355902778
5310433.4410121.232456597210042.5587578.6737065972228312.207543402779
5410471.2410044.201519097210172.0904166667-127.888897569444427.038480902778
5510214.519926.4648524305610277.0179166667-350.553064236112288.045147569444
5610677.52NANA-540.291710069445NA
5711052.15NANA-46.2980642361116NA
5810500.19NANA294.432769097223NA
5910159.27NANA204.988081597221NA
6010222.24NANA17.2484982638883NA
6110350.4NANANANA


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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')