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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, 12 Dec 2011 06:46:38 -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/2011/Dec/12/t13236904574yomz6qnvalw0wu.htm/, Retrieved Fri, 03 May 2024 08:07:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153920, Retrieved Fri, 03 May 2024 08:07:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [paper] [2011-11-24 11:53:20] [74be16979710d4c4e7c6647856088456]
- RMPD        [Classical Decomposition] [paper] [2011-12-12 11:46:38] [7a9891c1925ad1e8ddfe52b8c5887b5b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
374.92
375.63
376.51
377.75
378.54
378.21
376.65
374.28
373.12
373.1
374.67
375.97
377.03
377.87
378.88
380.42
380.62
379.66
377.48
376.07
374.1
374.47
376.15
377.51
378.43
379.7
380.91
382.2
382.45
382.14
380.6
378.6
376.72
376.98
378.29
380.07
381.36
382.19
382.65
384.65
384.94
384.01
382.15
380.33
378.81
379.06
380.17
381.85
382.88
383.77
384.42
386.36
386.53
386.01
384.45
381.96
380.81
381.09
382.37
383.84
385.42
385.72
385.96
387.18
388.5
387.88
386.38
384.15
383.07
382.98
384.11
385.54
386.92
387.41
388.77
389.46
390.18
389.43
387.74
385.91
384.77
384.38
385.99
387.26

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'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153920&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153920&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153920&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'Gwilym Jenkins' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1374.92NANA0.245873842592594NA
2375.63NANA0.858096064814809NA
3376.51NANA1.51809606481482NA
4377.75NANA2.80552662037037NA
5378.54NANA3.14024884259257NA
6378.21NANA2.30156828703702NA
7376.65376.365665509259375.8670833333330.4985821759259110.284334490740775
8374.28374.328443287037376.048333333333-1.71989004629631-0.0484432870370597
9373.12372.893582175926376.240416666667-3.346834490740720.226417824074076
10373.1373.112123842593376.450416666667-3.33829282407406-0.0121238425925299
11374.67374.494554398148376.648333333333-2.153778935185160.175445601851891
12375.97375.986221064815376.795416666667-0.809195601851852-0.016221064814772
13377.03377.136290509259376.8904166666670.245873842592594-0.106290509259225
14377.87377.857679398148376.9995833333330.8580960648148090.0123206018519681
15378.88378.633096064815377.1151.518096064814820.24690393518523
16380.42380.018443287037377.2129166666672.805526620370370.401556712962986
17380.62380.471915509259377.3316666666673.140248842592570.148084490740757
18379.66379.759068287037377.45752.30156828703702-0.099068287036971
19377.48378.078582175926377.580.498582175925911-0.598582175925856
20376.07375.994693287037377.714583333333-1.719890046296310.0753067129630267
21374.1374.528582175926377.875416666667-3.34683449074072-0.428582175925897
22374.47374.695873842593378.034166666667-3.33829282407406-0.225873842592534
23376.15376.030804398148378.184583333333-2.153778935185160.1191956018518
24377.51377.554971064815378.364166666667-0.809195601851852-0.0449710648147743
25378.43378.843373842593378.59750.245873842592594-0.413373842592534
26379.7379.691012731481378.8329166666670.8580960648148090.008987268518581
27380.91380.565596064815379.04751.518096064814820.344403935185312
28382.2382.06677662037379.261252.805526620370370.133223379629669
29382.45382.595248842593379.4553.14024884259257-0.145248842592594
30382.14381.95240162037379.6508333333332.301568287037020.187598379629605
31380.6380.378165509259379.8795833333330.4985821759259110.221834490740775
32378.6378.38552662037380.105416666667-1.719890046296310.214473379629624
33376.72376.934832175926380.281666666667-3.34683449074072-0.214832175925892
34376.98377.117957175926380.45625-3.33829282407406-0.137957175925862
35378.29378.508304398148380.662083333333-2.15377893518516-0.218304398148064
36380.07380.034554398148380.84375-0.8091956018518520.0354456018518476
37381.36381.232123842593380.986250.2458738425925940.127876157407457
38382.19381.981012731481381.1229166666670.8580960648148090.208987268518626
39382.65382.800179398148381.2820833333331.51809606481482-0.150179398148111
40384.65384.261359953704381.4558333333332.805526620370370.388640046296359
41384.94384.761082175926381.6208333333333.140248842592570.178917824074063
42384.01384.07490162037381.7733333333332.30156828703702-0.0649016203703354
43382.15382.409415509259381.9108333333330.498582175925911-0.25941550925927
44380.33380.320109953704382.04-1.719890046296310.00989004629633428
45378.81378.832748842593382.179583333333-3.34683449074072-0.0227488425925912
46379.06378.986290509259382.324583333333-3.338292824074060.0737094907407823
47380.17380.308304398148382.462083333333-2.15377893518516-0.138304398148193
48381.85381.802471064815382.611666666667-0.8091956018518520.0475289351853121
49382.88383.036707175926382.7908333333330.245873842592594-0.156707175925817
50383.77383.812679398148382.9545833333330.858096064814809-0.0426793981480955
51384.42384.623929398148383.1058333333331.51809606481482-0.203929398148091
52386.36386.07927662037383.273752.805526620370370.280723379629706
53386.53386.590248842593383.453.14024884259257-0.060248842592614
54386.01385.92615162037383.6245833333332.301568287037020.0838483796296714
55384.45384.311915509259383.8133333333330.4985821759259110.138084490740823
56381.96382.28052662037384.000416666667-1.71989004629631-0.320526620370288
57380.81380.798998842593384.145833333333-3.346834490740720.0110011574074633
58381.09380.905873842593384.244166666667-3.338292824074060.184126157407377
59382.37382.206637731482384.360416666667-2.153778935185160.163362268518483
60383.84383.711221064815384.520416666667-0.8091956018518520.128778935185153
61385.42384.924623842593384.678750.2458738425925940.495376157407463
62385.72385.708512731482384.8504166666670.8580960648148090.0114872685185219
63385.96386.553929398148385.0358333333331.51809606481482-0.593929398148134
64387.18388.01427662037385.208752.80552662037037-0.83427662037036
65388.5388.500248842593385.363.14024884259257-0.000248842592554865
66387.88387.80490162037385.5033333333332.301568287037020.0750983796296509
67386.38386.135248842592385.6366666666670.4985821759259110.244751157407507
68384.15384.049693287037385.769583333333-1.719890046296310.100306712963004
69383.07382.610248842593385.957083333333-3.346834490740720.459751157407482
70382.98382.830873842593386.169166666667-3.338292824074060.149126157407466
71384.11384.180387731481386.334166666667-2.15377893518516-0.0703877314814463
72385.54385.659554398148386.46875-0.809195601851852-0.119554398148125
73386.92386.835873842593386.590.2458738425925940.084126157407411
74387.41387.578096064815386.720.858096064814809-0.168096064814847
75388.77388.382262731481386.8641666666671.518096064814820.387737268518492
76389.46389.798859953704386.9933333333332.80552662037037-0.338859953703661
77390.18390.270248842593387.133.14024884259257-0.0902488425925867
78389.43389.581568287037387.282.30156828703702-0.15156828703698
79387.74NANA0.498582175925911NA
80385.91NANA-1.71989004629631NA
81384.77NANA-3.34683449074072NA
82384.38NANA-3.33829282407406NA
83385.99NANA-2.15377893518516NA
84387.26NANA-0.809195601851852NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 374.92 & NA & NA & 0.245873842592594 & NA \tabularnewline
2 & 375.63 & NA & NA & 0.858096064814809 & NA \tabularnewline
3 & 376.51 & NA & NA & 1.51809606481482 & NA \tabularnewline
4 & 377.75 & NA & NA & 2.80552662037037 & NA \tabularnewline
5 & 378.54 & NA & NA & 3.14024884259257 & NA \tabularnewline
6 & 378.21 & NA & NA & 2.30156828703702 & NA \tabularnewline
7 & 376.65 & 376.365665509259 & 375.867083333333 & 0.498582175925911 & 0.284334490740775 \tabularnewline
8 & 374.28 & 374.328443287037 & 376.048333333333 & -1.71989004629631 & -0.0484432870370597 \tabularnewline
9 & 373.12 & 372.893582175926 & 376.240416666667 & -3.34683449074072 & 0.226417824074076 \tabularnewline
10 & 373.1 & 373.112123842593 & 376.450416666667 & -3.33829282407406 & -0.0121238425925299 \tabularnewline
11 & 374.67 & 374.494554398148 & 376.648333333333 & -2.15377893518516 & 0.175445601851891 \tabularnewline
12 & 375.97 & 375.986221064815 & 376.795416666667 & -0.809195601851852 & -0.016221064814772 \tabularnewline
13 & 377.03 & 377.136290509259 & 376.890416666667 & 0.245873842592594 & -0.106290509259225 \tabularnewline
14 & 377.87 & 377.857679398148 & 376.999583333333 & 0.858096064814809 & 0.0123206018519681 \tabularnewline
15 & 378.88 & 378.633096064815 & 377.115 & 1.51809606481482 & 0.24690393518523 \tabularnewline
16 & 380.42 & 380.018443287037 & 377.212916666667 & 2.80552662037037 & 0.401556712962986 \tabularnewline
17 & 380.62 & 380.471915509259 & 377.331666666667 & 3.14024884259257 & 0.148084490740757 \tabularnewline
18 & 379.66 & 379.759068287037 & 377.4575 & 2.30156828703702 & -0.099068287036971 \tabularnewline
19 & 377.48 & 378.078582175926 & 377.58 & 0.498582175925911 & -0.598582175925856 \tabularnewline
20 & 376.07 & 375.994693287037 & 377.714583333333 & -1.71989004629631 & 0.0753067129630267 \tabularnewline
21 & 374.1 & 374.528582175926 & 377.875416666667 & -3.34683449074072 & -0.428582175925897 \tabularnewline
22 & 374.47 & 374.695873842593 & 378.034166666667 & -3.33829282407406 & -0.225873842592534 \tabularnewline
23 & 376.15 & 376.030804398148 & 378.184583333333 & -2.15377893518516 & 0.1191956018518 \tabularnewline
24 & 377.51 & 377.554971064815 & 378.364166666667 & -0.809195601851852 & -0.0449710648147743 \tabularnewline
25 & 378.43 & 378.843373842593 & 378.5975 & 0.245873842592594 & -0.413373842592534 \tabularnewline
26 & 379.7 & 379.691012731481 & 378.832916666667 & 0.858096064814809 & 0.008987268518581 \tabularnewline
27 & 380.91 & 380.565596064815 & 379.0475 & 1.51809606481482 & 0.344403935185312 \tabularnewline
28 & 382.2 & 382.06677662037 & 379.26125 & 2.80552662037037 & 0.133223379629669 \tabularnewline
29 & 382.45 & 382.595248842593 & 379.455 & 3.14024884259257 & -0.145248842592594 \tabularnewline
30 & 382.14 & 381.95240162037 & 379.650833333333 & 2.30156828703702 & 0.187598379629605 \tabularnewline
31 & 380.6 & 380.378165509259 & 379.879583333333 & 0.498582175925911 & 0.221834490740775 \tabularnewline
32 & 378.6 & 378.38552662037 & 380.105416666667 & -1.71989004629631 & 0.214473379629624 \tabularnewline
33 & 376.72 & 376.934832175926 & 380.281666666667 & -3.34683449074072 & -0.214832175925892 \tabularnewline
34 & 376.98 & 377.117957175926 & 380.45625 & -3.33829282407406 & -0.137957175925862 \tabularnewline
35 & 378.29 & 378.508304398148 & 380.662083333333 & -2.15377893518516 & -0.218304398148064 \tabularnewline
36 & 380.07 & 380.034554398148 & 380.84375 & -0.809195601851852 & 0.0354456018518476 \tabularnewline
37 & 381.36 & 381.232123842593 & 380.98625 & 0.245873842592594 & 0.127876157407457 \tabularnewline
38 & 382.19 & 381.981012731481 & 381.122916666667 & 0.858096064814809 & 0.208987268518626 \tabularnewline
39 & 382.65 & 382.800179398148 & 381.282083333333 & 1.51809606481482 & -0.150179398148111 \tabularnewline
40 & 384.65 & 384.261359953704 & 381.455833333333 & 2.80552662037037 & 0.388640046296359 \tabularnewline
41 & 384.94 & 384.761082175926 & 381.620833333333 & 3.14024884259257 & 0.178917824074063 \tabularnewline
42 & 384.01 & 384.07490162037 & 381.773333333333 & 2.30156828703702 & -0.0649016203703354 \tabularnewline
43 & 382.15 & 382.409415509259 & 381.910833333333 & 0.498582175925911 & -0.25941550925927 \tabularnewline
44 & 380.33 & 380.320109953704 & 382.04 & -1.71989004629631 & 0.00989004629633428 \tabularnewline
45 & 378.81 & 378.832748842593 & 382.179583333333 & -3.34683449074072 & -0.0227488425925912 \tabularnewline
46 & 379.06 & 378.986290509259 & 382.324583333333 & -3.33829282407406 & 0.0737094907407823 \tabularnewline
47 & 380.17 & 380.308304398148 & 382.462083333333 & -2.15377893518516 & -0.138304398148193 \tabularnewline
48 & 381.85 & 381.802471064815 & 382.611666666667 & -0.809195601851852 & 0.0475289351853121 \tabularnewline
49 & 382.88 & 383.036707175926 & 382.790833333333 & 0.245873842592594 & -0.156707175925817 \tabularnewline
50 & 383.77 & 383.812679398148 & 382.954583333333 & 0.858096064814809 & -0.0426793981480955 \tabularnewline
51 & 384.42 & 384.623929398148 & 383.105833333333 & 1.51809606481482 & -0.203929398148091 \tabularnewline
52 & 386.36 & 386.07927662037 & 383.27375 & 2.80552662037037 & 0.280723379629706 \tabularnewline
53 & 386.53 & 386.590248842593 & 383.45 & 3.14024884259257 & -0.060248842592614 \tabularnewline
54 & 386.01 & 385.92615162037 & 383.624583333333 & 2.30156828703702 & 0.0838483796296714 \tabularnewline
55 & 384.45 & 384.311915509259 & 383.813333333333 & 0.498582175925911 & 0.138084490740823 \tabularnewline
56 & 381.96 & 382.28052662037 & 384.000416666667 & -1.71989004629631 & -0.320526620370288 \tabularnewline
57 & 380.81 & 380.798998842593 & 384.145833333333 & -3.34683449074072 & 0.0110011574074633 \tabularnewline
58 & 381.09 & 380.905873842593 & 384.244166666667 & -3.33829282407406 & 0.184126157407377 \tabularnewline
59 & 382.37 & 382.206637731482 & 384.360416666667 & -2.15377893518516 & 0.163362268518483 \tabularnewline
60 & 383.84 & 383.711221064815 & 384.520416666667 & -0.809195601851852 & 0.128778935185153 \tabularnewline
61 & 385.42 & 384.924623842593 & 384.67875 & 0.245873842592594 & 0.495376157407463 \tabularnewline
62 & 385.72 & 385.708512731482 & 384.850416666667 & 0.858096064814809 & 0.0114872685185219 \tabularnewline
63 & 385.96 & 386.553929398148 & 385.035833333333 & 1.51809606481482 & -0.593929398148134 \tabularnewline
64 & 387.18 & 388.01427662037 & 385.20875 & 2.80552662037037 & -0.83427662037036 \tabularnewline
65 & 388.5 & 388.500248842593 & 385.36 & 3.14024884259257 & -0.000248842592554865 \tabularnewline
66 & 387.88 & 387.80490162037 & 385.503333333333 & 2.30156828703702 & 0.0750983796296509 \tabularnewline
67 & 386.38 & 386.135248842592 & 385.636666666667 & 0.498582175925911 & 0.244751157407507 \tabularnewline
68 & 384.15 & 384.049693287037 & 385.769583333333 & -1.71989004629631 & 0.100306712963004 \tabularnewline
69 & 383.07 & 382.610248842593 & 385.957083333333 & -3.34683449074072 & 0.459751157407482 \tabularnewline
70 & 382.98 & 382.830873842593 & 386.169166666667 & -3.33829282407406 & 0.149126157407466 \tabularnewline
71 & 384.11 & 384.180387731481 & 386.334166666667 & -2.15377893518516 & -0.0703877314814463 \tabularnewline
72 & 385.54 & 385.659554398148 & 386.46875 & -0.809195601851852 & -0.119554398148125 \tabularnewline
73 & 386.92 & 386.835873842593 & 386.59 & 0.245873842592594 & 0.084126157407411 \tabularnewline
74 & 387.41 & 387.578096064815 & 386.72 & 0.858096064814809 & -0.168096064814847 \tabularnewline
75 & 388.77 & 388.382262731481 & 386.864166666667 & 1.51809606481482 & 0.387737268518492 \tabularnewline
76 & 389.46 & 389.798859953704 & 386.993333333333 & 2.80552662037037 & -0.338859953703661 \tabularnewline
77 & 390.18 & 390.270248842593 & 387.13 & 3.14024884259257 & -0.0902488425925867 \tabularnewline
78 & 389.43 & 389.581568287037 & 387.28 & 2.30156828703702 & -0.15156828703698 \tabularnewline
79 & 387.74 & NA & NA & 0.498582175925911 & NA \tabularnewline
80 & 385.91 & NA & NA & -1.71989004629631 & NA \tabularnewline
81 & 384.77 & NA & NA & -3.34683449074072 & NA \tabularnewline
82 & 384.38 & NA & NA & -3.33829282407406 & NA \tabularnewline
83 & 385.99 & NA & NA & -2.15377893518516 & NA \tabularnewline
84 & 387.26 & NA & NA & -0.809195601851852 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153920&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]374.92[/C][C]NA[/C][C]NA[/C][C]0.245873842592594[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]375.63[/C][C]NA[/C][C]NA[/C][C]0.858096064814809[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]376.51[/C][C]NA[/C][C]NA[/C][C]1.51809606481482[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]377.75[/C][C]NA[/C][C]NA[/C][C]2.80552662037037[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]378.54[/C][C]NA[/C][C]NA[/C][C]3.14024884259257[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]378.21[/C][C]NA[/C][C]NA[/C][C]2.30156828703702[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]376.65[/C][C]376.365665509259[/C][C]375.867083333333[/C][C]0.498582175925911[/C][C]0.284334490740775[/C][/ROW]
[ROW][C]8[/C][C]374.28[/C][C]374.328443287037[/C][C]376.048333333333[/C][C]-1.71989004629631[/C][C]-0.0484432870370597[/C][/ROW]
[ROW][C]9[/C][C]373.12[/C][C]372.893582175926[/C][C]376.240416666667[/C][C]-3.34683449074072[/C][C]0.226417824074076[/C][/ROW]
[ROW][C]10[/C][C]373.1[/C][C]373.112123842593[/C][C]376.450416666667[/C][C]-3.33829282407406[/C][C]-0.0121238425925299[/C][/ROW]
[ROW][C]11[/C][C]374.67[/C][C]374.494554398148[/C][C]376.648333333333[/C][C]-2.15377893518516[/C][C]0.175445601851891[/C][/ROW]
[ROW][C]12[/C][C]375.97[/C][C]375.986221064815[/C][C]376.795416666667[/C][C]-0.809195601851852[/C][C]-0.016221064814772[/C][/ROW]
[ROW][C]13[/C][C]377.03[/C][C]377.136290509259[/C][C]376.890416666667[/C][C]0.245873842592594[/C][C]-0.106290509259225[/C][/ROW]
[ROW][C]14[/C][C]377.87[/C][C]377.857679398148[/C][C]376.999583333333[/C][C]0.858096064814809[/C][C]0.0123206018519681[/C][/ROW]
[ROW][C]15[/C][C]378.88[/C][C]378.633096064815[/C][C]377.115[/C][C]1.51809606481482[/C][C]0.24690393518523[/C][/ROW]
[ROW][C]16[/C][C]380.42[/C][C]380.018443287037[/C][C]377.212916666667[/C][C]2.80552662037037[/C][C]0.401556712962986[/C][/ROW]
[ROW][C]17[/C][C]380.62[/C][C]380.471915509259[/C][C]377.331666666667[/C][C]3.14024884259257[/C][C]0.148084490740757[/C][/ROW]
[ROW][C]18[/C][C]379.66[/C][C]379.759068287037[/C][C]377.4575[/C][C]2.30156828703702[/C][C]-0.099068287036971[/C][/ROW]
[ROW][C]19[/C][C]377.48[/C][C]378.078582175926[/C][C]377.58[/C][C]0.498582175925911[/C][C]-0.598582175925856[/C][/ROW]
[ROW][C]20[/C][C]376.07[/C][C]375.994693287037[/C][C]377.714583333333[/C][C]-1.71989004629631[/C][C]0.0753067129630267[/C][/ROW]
[ROW][C]21[/C][C]374.1[/C][C]374.528582175926[/C][C]377.875416666667[/C][C]-3.34683449074072[/C][C]-0.428582175925897[/C][/ROW]
[ROW][C]22[/C][C]374.47[/C][C]374.695873842593[/C][C]378.034166666667[/C][C]-3.33829282407406[/C][C]-0.225873842592534[/C][/ROW]
[ROW][C]23[/C][C]376.15[/C][C]376.030804398148[/C][C]378.184583333333[/C][C]-2.15377893518516[/C][C]0.1191956018518[/C][/ROW]
[ROW][C]24[/C][C]377.51[/C][C]377.554971064815[/C][C]378.364166666667[/C][C]-0.809195601851852[/C][C]-0.0449710648147743[/C][/ROW]
[ROW][C]25[/C][C]378.43[/C][C]378.843373842593[/C][C]378.5975[/C][C]0.245873842592594[/C][C]-0.413373842592534[/C][/ROW]
[ROW][C]26[/C][C]379.7[/C][C]379.691012731481[/C][C]378.832916666667[/C][C]0.858096064814809[/C][C]0.008987268518581[/C][/ROW]
[ROW][C]27[/C][C]380.91[/C][C]380.565596064815[/C][C]379.0475[/C][C]1.51809606481482[/C][C]0.344403935185312[/C][/ROW]
[ROW][C]28[/C][C]382.2[/C][C]382.06677662037[/C][C]379.26125[/C][C]2.80552662037037[/C][C]0.133223379629669[/C][/ROW]
[ROW][C]29[/C][C]382.45[/C][C]382.595248842593[/C][C]379.455[/C][C]3.14024884259257[/C][C]-0.145248842592594[/C][/ROW]
[ROW][C]30[/C][C]382.14[/C][C]381.95240162037[/C][C]379.650833333333[/C][C]2.30156828703702[/C][C]0.187598379629605[/C][/ROW]
[ROW][C]31[/C][C]380.6[/C][C]380.378165509259[/C][C]379.879583333333[/C][C]0.498582175925911[/C][C]0.221834490740775[/C][/ROW]
[ROW][C]32[/C][C]378.6[/C][C]378.38552662037[/C][C]380.105416666667[/C][C]-1.71989004629631[/C][C]0.214473379629624[/C][/ROW]
[ROW][C]33[/C][C]376.72[/C][C]376.934832175926[/C][C]380.281666666667[/C][C]-3.34683449074072[/C][C]-0.214832175925892[/C][/ROW]
[ROW][C]34[/C][C]376.98[/C][C]377.117957175926[/C][C]380.45625[/C][C]-3.33829282407406[/C][C]-0.137957175925862[/C][/ROW]
[ROW][C]35[/C][C]378.29[/C][C]378.508304398148[/C][C]380.662083333333[/C][C]-2.15377893518516[/C][C]-0.218304398148064[/C][/ROW]
[ROW][C]36[/C][C]380.07[/C][C]380.034554398148[/C][C]380.84375[/C][C]-0.809195601851852[/C][C]0.0354456018518476[/C][/ROW]
[ROW][C]37[/C][C]381.36[/C][C]381.232123842593[/C][C]380.98625[/C][C]0.245873842592594[/C][C]0.127876157407457[/C][/ROW]
[ROW][C]38[/C][C]382.19[/C][C]381.981012731481[/C][C]381.122916666667[/C][C]0.858096064814809[/C][C]0.208987268518626[/C][/ROW]
[ROW][C]39[/C][C]382.65[/C][C]382.800179398148[/C][C]381.282083333333[/C][C]1.51809606481482[/C][C]-0.150179398148111[/C][/ROW]
[ROW][C]40[/C][C]384.65[/C][C]384.261359953704[/C][C]381.455833333333[/C][C]2.80552662037037[/C][C]0.388640046296359[/C][/ROW]
[ROW][C]41[/C][C]384.94[/C][C]384.761082175926[/C][C]381.620833333333[/C][C]3.14024884259257[/C][C]0.178917824074063[/C][/ROW]
[ROW][C]42[/C][C]384.01[/C][C]384.07490162037[/C][C]381.773333333333[/C][C]2.30156828703702[/C][C]-0.0649016203703354[/C][/ROW]
[ROW][C]43[/C][C]382.15[/C][C]382.409415509259[/C][C]381.910833333333[/C][C]0.498582175925911[/C][C]-0.25941550925927[/C][/ROW]
[ROW][C]44[/C][C]380.33[/C][C]380.320109953704[/C][C]382.04[/C][C]-1.71989004629631[/C][C]0.00989004629633428[/C][/ROW]
[ROW][C]45[/C][C]378.81[/C][C]378.832748842593[/C][C]382.179583333333[/C][C]-3.34683449074072[/C][C]-0.0227488425925912[/C][/ROW]
[ROW][C]46[/C][C]379.06[/C][C]378.986290509259[/C][C]382.324583333333[/C][C]-3.33829282407406[/C][C]0.0737094907407823[/C][/ROW]
[ROW][C]47[/C][C]380.17[/C][C]380.308304398148[/C][C]382.462083333333[/C][C]-2.15377893518516[/C][C]-0.138304398148193[/C][/ROW]
[ROW][C]48[/C][C]381.85[/C][C]381.802471064815[/C][C]382.611666666667[/C][C]-0.809195601851852[/C][C]0.0475289351853121[/C][/ROW]
[ROW][C]49[/C][C]382.88[/C][C]383.036707175926[/C][C]382.790833333333[/C][C]0.245873842592594[/C][C]-0.156707175925817[/C][/ROW]
[ROW][C]50[/C][C]383.77[/C][C]383.812679398148[/C][C]382.954583333333[/C][C]0.858096064814809[/C][C]-0.0426793981480955[/C][/ROW]
[ROW][C]51[/C][C]384.42[/C][C]384.623929398148[/C][C]383.105833333333[/C][C]1.51809606481482[/C][C]-0.203929398148091[/C][/ROW]
[ROW][C]52[/C][C]386.36[/C][C]386.07927662037[/C][C]383.27375[/C][C]2.80552662037037[/C][C]0.280723379629706[/C][/ROW]
[ROW][C]53[/C][C]386.53[/C][C]386.590248842593[/C][C]383.45[/C][C]3.14024884259257[/C][C]-0.060248842592614[/C][/ROW]
[ROW][C]54[/C][C]386.01[/C][C]385.92615162037[/C][C]383.624583333333[/C][C]2.30156828703702[/C][C]0.0838483796296714[/C][/ROW]
[ROW][C]55[/C][C]384.45[/C][C]384.311915509259[/C][C]383.813333333333[/C][C]0.498582175925911[/C][C]0.138084490740823[/C][/ROW]
[ROW][C]56[/C][C]381.96[/C][C]382.28052662037[/C][C]384.000416666667[/C][C]-1.71989004629631[/C][C]-0.320526620370288[/C][/ROW]
[ROW][C]57[/C][C]380.81[/C][C]380.798998842593[/C][C]384.145833333333[/C][C]-3.34683449074072[/C][C]0.0110011574074633[/C][/ROW]
[ROW][C]58[/C][C]381.09[/C][C]380.905873842593[/C][C]384.244166666667[/C][C]-3.33829282407406[/C][C]0.184126157407377[/C][/ROW]
[ROW][C]59[/C][C]382.37[/C][C]382.206637731482[/C][C]384.360416666667[/C][C]-2.15377893518516[/C][C]0.163362268518483[/C][/ROW]
[ROW][C]60[/C][C]383.84[/C][C]383.711221064815[/C][C]384.520416666667[/C][C]-0.809195601851852[/C][C]0.128778935185153[/C][/ROW]
[ROW][C]61[/C][C]385.42[/C][C]384.924623842593[/C][C]384.67875[/C][C]0.245873842592594[/C][C]0.495376157407463[/C][/ROW]
[ROW][C]62[/C][C]385.72[/C][C]385.708512731482[/C][C]384.850416666667[/C][C]0.858096064814809[/C][C]0.0114872685185219[/C][/ROW]
[ROW][C]63[/C][C]385.96[/C][C]386.553929398148[/C][C]385.035833333333[/C][C]1.51809606481482[/C][C]-0.593929398148134[/C][/ROW]
[ROW][C]64[/C][C]387.18[/C][C]388.01427662037[/C][C]385.20875[/C][C]2.80552662037037[/C][C]-0.83427662037036[/C][/ROW]
[ROW][C]65[/C][C]388.5[/C][C]388.500248842593[/C][C]385.36[/C][C]3.14024884259257[/C][C]-0.000248842592554865[/C][/ROW]
[ROW][C]66[/C][C]387.88[/C][C]387.80490162037[/C][C]385.503333333333[/C][C]2.30156828703702[/C][C]0.0750983796296509[/C][/ROW]
[ROW][C]67[/C][C]386.38[/C][C]386.135248842592[/C][C]385.636666666667[/C][C]0.498582175925911[/C][C]0.244751157407507[/C][/ROW]
[ROW][C]68[/C][C]384.15[/C][C]384.049693287037[/C][C]385.769583333333[/C][C]-1.71989004629631[/C][C]0.100306712963004[/C][/ROW]
[ROW][C]69[/C][C]383.07[/C][C]382.610248842593[/C][C]385.957083333333[/C][C]-3.34683449074072[/C][C]0.459751157407482[/C][/ROW]
[ROW][C]70[/C][C]382.98[/C][C]382.830873842593[/C][C]386.169166666667[/C][C]-3.33829282407406[/C][C]0.149126157407466[/C][/ROW]
[ROW][C]71[/C][C]384.11[/C][C]384.180387731481[/C][C]386.334166666667[/C][C]-2.15377893518516[/C][C]-0.0703877314814463[/C][/ROW]
[ROW][C]72[/C][C]385.54[/C][C]385.659554398148[/C][C]386.46875[/C][C]-0.809195601851852[/C][C]-0.119554398148125[/C][/ROW]
[ROW][C]73[/C][C]386.92[/C][C]386.835873842593[/C][C]386.59[/C][C]0.245873842592594[/C][C]0.084126157407411[/C][/ROW]
[ROW][C]74[/C][C]387.41[/C][C]387.578096064815[/C][C]386.72[/C][C]0.858096064814809[/C][C]-0.168096064814847[/C][/ROW]
[ROW][C]75[/C][C]388.77[/C][C]388.382262731481[/C][C]386.864166666667[/C][C]1.51809606481482[/C][C]0.387737268518492[/C][/ROW]
[ROW][C]76[/C][C]389.46[/C][C]389.798859953704[/C][C]386.993333333333[/C][C]2.80552662037037[/C][C]-0.338859953703661[/C][/ROW]
[ROW][C]77[/C][C]390.18[/C][C]390.270248842593[/C][C]387.13[/C][C]3.14024884259257[/C][C]-0.0902488425925867[/C][/ROW]
[ROW][C]78[/C][C]389.43[/C][C]389.581568287037[/C][C]387.28[/C][C]2.30156828703702[/C][C]-0.15156828703698[/C][/ROW]
[ROW][C]79[/C][C]387.74[/C][C]NA[/C][C]NA[/C][C]0.498582175925911[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]385.91[/C][C]NA[/C][C]NA[/C][C]-1.71989004629631[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]384.77[/C][C]NA[/C][C]NA[/C][C]-3.34683449074072[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]384.38[/C][C]NA[/C][C]NA[/C][C]-3.33829282407406[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]385.99[/C][C]NA[/C][C]NA[/C][C]-2.15377893518516[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]387.26[/C][C]NA[/C][C]NA[/C][C]-0.809195601851852[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153920&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153920&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
1374.92NANA0.245873842592594NA
2375.63NANA0.858096064814809NA
3376.51NANA1.51809606481482NA
4377.75NANA2.80552662037037NA
5378.54NANA3.14024884259257NA
6378.21NANA2.30156828703702NA
7376.65376.365665509259375.8670833333330.4985821759259110.284334490740775
8374.28374.328443287037376.048333333333-1.71989004629631-0.0484432870370597
9373.12372.893582175926376.240416666667-3.346834490740720.226417824074076
10373.1373.112123842593376.450416666667-3.33829282407406-0.0121238425925299
11374.67374.494554398148376.648333333333-2.153778935185160.175445601851891
12375.97375.986221064815376.795416666667-0.809195601851852-0.016221064814772
13377.03377.136290509259376.8904166666670.245873842592594-0.106290509259225
14377.87377.857679398148376.9995833333330.8580960648148090.0123206018519681
15378.88378.633096064815377.1151.518096064814820.24690393518523
16380.42380.018443287037377.2129166666672.805526620370370.401556712962986
17380.62380.471915509259377.3316666666673.140248842592570.148084490740757
18379.66379.759068287037377.45752.30156828703702-0.099068287036971
19377.48378.078582175926377.580.498582175925911-0.598582175925856
20376.07375.994693287037377.714583333333-1.719890046296310.0753067129630267
21374.1374.528582175926377.875416666667-3.34683449074072-0.428582175925897
22374.47374.695873842593378.034166666667-3.33829282407406-0.225873842592534
23376.15376.030804398148378.184583333333-2.153778935185160.1191956018518
24377.51377.554971064815378.364166666667-0.809195601851852-0.0449710648147743
25378.43378.843373842593378.59750.245873842592594-0.413373842592534
26379.7379.691012731481378.8329166666670.8580960648148090.008987268518581
27380.91380.565596064815379.04751.518096064814820.344403935185312
28382.2382.06677662037379.261252.805526620370370.133223379629669
29382.45382.595248842593379.4553.14024884259257-0.145248842592594
30382.14381.95240162037379.6508333333332.301568287037020.187598379629605
31380.6380.378165509259379.8795833333330.4985821759259110.221834490740775
32378.6378.38552662037380.105416666667-1.719890046296310.214473379629624
33376.72376.934832175926380.281666666667-3.34683449074072-0.214832175925892
34376.98377.117957175926380.45625-3.33829282407406-0.137957175925862
35378.29378.508304398148380.662083333333-2.15377893518516-0.218304398148064
36380.07380.034554398148380.84375-0.8091956018518520.0354456018518476
37381.36381.232123842593380.986250.2458738425925940.127876157407457
38382.19381.981012731481381.1229166666670.8580960648148090.208987268518626
39382.65382.800179398148381.2820833333331.51809606481482-0.150179398148111
40384.65384.261359953704381.4558333333332.805526620370370.388640046296359
41384.94384.761082175926381.6208333333333.140248842592570.178917824074063
42384.01384.07490162037381.7733333333332.30156828703702-0.0649016203703354
43382.15382.409415509259381.9108333333330.498582175925911-0.25941550925927
44380.33380.320109953704382.04-1.719890046296310.00989004629633428
45378.81378.832748842593382.179583333333-3.34683449074072-0.0227488425925912
46379.06378.986290509259382.324583333333-3.338292824074060.0737094907407823
47380.17380.308304398148382.462083333333-2.15377893518516-0.138304398148193
48381.85381.802471064815382.611666666667-0.8091956018518520.0475289351853121
49382.88383.036707175926382.7908333333330.245873842592594-0.156707175925817
50383.77383.812679398148382.9545833333330.858096064814809-0.0426793981480955
51384.42384.623929398148383.1058333333331.51809606481482-0.203929398148091
52386.36386.07927662037383.273752.805526620370370.280723379629706
53386.53386.590248842593383.453.14024884259257-0.060248842592614
54386.01385.92615162037383.6245833333332.301568287037020.0838483796296714
55384.45384.311915509259383.8133333333330.4985821759259110.138084490740823
56381.96382.28052662037384.000416666667-1.71989004629631-0.320526620370288
57380.81380.798998842593384.145833333333-3.346834490740720.0110011574074633
58381.09380.905873842593384.244166666667-3.338292824074060.184126157407377
59382.37382.206637731482384.360416666667-2.153778935185160.163362268518483
60383.84383.711221064815384.520416666667-0.8091956018518520.128778935185153
61385.42384.924623842593384.678750.2458738425925940.495376157407463
62385.72385.708512731482384.8504166666670.8580960648148090.0114872685185219
63385.96386.553929398148385.0358333333331.51809606481482-0.593929398148134
64387.18388.01427662037385.208752.80552662037037-0.83427662037036
65388.5388.500248842593385.363.14024884259257-0.000248842592554865
66387.88387.80490162037385.5033333333332.301568287037020.0750983796296509
67386.38386.135248842592385.6366666666670.4985821759259110.244751157407507
68384.15384.049693287037385.769583333333-1.719890046296310.100306712963004
69383.07382.610248842593385.957083333333-3.346834490740720.459751157407482
70382.98382.830873842593386.169166666667-3.338292824074060.149126157407466
71384.11384.180387731481386.334166666667-2.15377893518516-0.0703877314814463
72385.54385.659554398148386.46875-0.809195601851852-0.119554398148125
73386.92386.835873842593386.590.2458738425925940.084126157407411
74387.41387.578096064815386.720.858096064814809-0.168096064814847
75388.77388.382262731481386.8641666666671.518096064814820.387737268518492
76389.46389.798859953704386.9933333333332.80552662037037-0.338859953703661
77390.18390.270248842593387.133.14024884259257-0.0902488425925867
78389.43389.581568287037387.282.30156828703702-0.15156828703698
79387.74NANA0.498582175925911NA
80385.91NANA-1.71989004629631NA
81384.77NANA-3.34683449074072NA
82384.38NANA-3.33829282407406NA
83385.99NANA-2.15377893518516NA
84387.26NANA-0.809195601851852NA


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')