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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationSat, 10 Nov 2012 15:10:23 -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/2012/Nov/10/t135257896380cvygmv76x92lr.htm/, Retrieved Sat, 10 Dec 2022 04:31:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187422, Retrieved Sat, 10 Dec 2022 04:31:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP           [Decomposition by Loess] [ws82] [2012-11-10 20:10:23] [5821104a6123eb5bf529ba8614395dc8] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187422&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187422&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187422&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal751076
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 751 & 0 & 76 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187422&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]751[/C][C]0[/C][C]76[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187422&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187422&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal751076
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
197009665.38145341098216.6597249533499517.95882163567-34.618546589023
290819165.77198053844-524.3262785862899520.5542980478584.771980538444
390848819.30488240033-174.4546568603529523.14977446002-264.695117599666
497439815.13667288808148.1342581676289522.7290689442972.1366728880803
585878390.3144979431-738.6228613716639522.30836342857-196.685502056904
697319725.13807465835217.6548137857959519.20711155585-5.86192534164547
795639776.79463081457-166.9004904976979516.10585968313213.794630814566
8999810477.39159839494.899093226310299513.70930837883479.391598394865
994379409.98788403842-47.30064111293519511.31275707452-27.0121159615846
101003810051.5200681786519.3361529156139505.1437789057613.5200681786318
1199189947.88590851771389.1392907452949498.9748007369929.8859085177137
1292528874.86319978722155.7816679040929473.35513230869-377.136800212782
1397379809.60481116626216.6597249533499447.7354638803972.6048111662622
1490359183.97766975103-524.3262785862899410.34860883526148.977669751028
1591339067.49290307022-174.4546568603529372.96175379014-65.5070969297849
1694879479.52410990384148.1342581676289346.34163192853-7.47589009615876
1787008818.90135130474-738.6228613716639319.72151006693118.901351304736
1896279732.83020877481217.6548137857959303.51497743939105.830208774814
1989478773.59204568585-166.9004904976979287.30844481185-173.407954314154
2092839285.245003453764.899093226310299275.855903319932.24500345375782
2188298440.89727928492-47.30064111293519264.40336182801-388.102720715078
22994710115.1181643594519.3361529156139259.54568272501168.118164359377
2396289612.1727056327389.1392907452949254.68800362201-15.8272943673019
2493189223.30241590875155.7816679040929256.91591618715-94.6975840912455
2596059734.19644629435216.6597249533499259.1438287523129.196446294349
2686408534.35586053479-524.3262785862899269.9704180515-105.644139465214
2792149321.65764950965-174.4546568603529280.7970073507107.657649509649
2895679693.8923956418148.1342581676289291.97334619057126.892395641802
2985478529.47317634122-738.6228613716639303.14968503044-17.5268236587763
3091858843.67047554606217.6548137857959308.67471066814-341.329524453937
3194709792.70075419185-166.9004904976979314.19973630584322.700754191854
3291238918.073575426254.899093226310299323.02733134744-204.926424573749
3392789271.4457147239-47.30064111293519331.85492638903-6.55428527609911
341017010468.1371071333519.3361529156139352.52673995109298.137107133296
3594349105.66215574156389.1392907452949373.19855351315-328.337844258442
3696559760.51674400655155.7816679040929393.70158808936105.516744006552
3794299227.13565238109216.6597249533499414.20462266556-201.864347618912
3887398573.70419384917-524.3262785862899428.62208473712-165.295806150829
3995529835.41511005168-174.4546568603529443.03954680868283.415110051677
4096879758.56511370315148.1342581676289467.3006281292271.5651137031509
4190199285.06115192189-738.6228613716639491.56170944977266.061151921895
4296729605.97213280895217.6548137857959520.37305340526-66.0278671910546
4392069029.71609313695-166.9004904976979549.18439736075-176.283906863051
4490698558.394271843344.899093226310299574.70663493035-510.605728156655
45978810023.071768613-47.30064111293519600.22887249994235.071768612994
461031210480.8282002468519.3361529156139623.8356468376168.828200246789
471010510173.4182880795389.1392907452949647.4424211752668.4182880794506
4898639890.35914461412155.7816679040929679.8591874817927.3591446141199
4996569383.06432125833216.6597249533499712.27595378832-272.935678741671
5092959374.16734837944-524.3262785862899740.1589302068579.167348379442
51994610298.412750235-174.4546568603529768.04190662537352.412750234978
5297019472.4536957728148.1342581676289781.41204605957-228.5463042272
5390499041.84067587789-738.6228613716639794.78218549377-7.15932412210896
541019010354.4579532181217.6548137857959807.88723299606164.457953218143
5597069757.90820999935-166.9004904976979820.9922804983551.9082099993466
5697659692.770733741144.899093226310299832.33017303255-72.2292662588588
5798939989.63257554619-47.30064111293519843.6680655667596.6325755461876
5899949615.54519313482519.3361529156139853.11865394957-378.454806865184
591043310614.2914669223389.1392907452949862.5692423324181.29146692231
601007310111.7158882826155.7816679040929878.5024438133138.7158882825952
611011210112.9046297524216.6597249533499894.435645294230.904629752420078
6292669136.50938088752-524.3262785862899919.81689769877-129.490619112479
6398209869.25650675705-174.4546568603529945.1981501033149.2565067570467
641009710071.7159847449148.1342581676289974.14975708747-25.2840152550998
6591158965.52149730002-738.62286137166310003.1013640716-149.478502699976
661041110567.8772587978217.65481378579510036.4679274164156.877258797789
6796789453.06599973651-166.90049049769710069.8344907612-224.934000263493
681040810720.29864439034.8990932263102910090.8022623834312.29864439033
691015310241.5306071074-47.300641112935110111.770034005588.5306071074065
701036810087.1032512669519.33615291561310129.5605958175-280.896748733068
711058110625.5095516253389.13929074529410147.351157629444.5095516253241
721059710873.4076819219155.78166790409210164.810650174276.407681921864
731068010961.0701323279216.65972495334910182.2701427187281.070132327946
7497389801.49489133259-524.32627858628910198.831387253763.4948913325898
7595569071.06202507166-174.45465686035210215.3926317887-484.937974928343

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 9700 & 9665.38145341098 & 216.659724953349 & 9517.95882163567 & -34.618546589023 \tabularnewline
2 & 9081 & 9165.77198053844 & -524.326278586289 & 9520.55429804785 & 84.771980538444 \tabularnewline
3 & 9084 & 8819.30488240033 & -174.454656860352 & 9523.14977446002 & -264.695117599666 \tabularnewline
4 & 9743 & 9815.13667288808 & 148.134258167628 & 9522.72906894429 & 72.1366728880803 \tabularnewline
5 & 8587 & 8390.3144979431 & -738.622861371663 & 9522.30836342857 & -196.685502056904 \tabularnewline
6 & 9731 & 9725.13807465835 & 217.654813785795 & 9519.20711155585 & -5.86192534164547 \tabularnewline
7 & 9563 & 9776.79463081457 & -166.900490497697 & 9516.10585968313 & 213.794630814566 \tabularnewline
8 & 9998 & 10477.3915983949 & 4.89909322631029 & 9513.70930837883 & 479.391598394865 \tabularnewline
9 & 9437 & 9409.98788403842 & -47.3006411129351 & 9511.31275707452 & -27.0121159615846 \tabularnewline
10 & 10038 & 10051.5200681786 & 519.336152915613 & 9505.14377890576 & 13.5200681786318 \tabularnewline
11 & 9918 & 9947.88590851771 & 389.139290745294 & 9498.97480073699 & 29.8859085177137 \tabularnewline
12 & 9252 & 8874.86319978722 & 155.781667904092 & 9473.35513230869 & -377.136800212782 \tabularnewline
13 & 9737 & 9809.60481116626 & 216.659724953349 & 9447.73546388039 & 72.6048111662622 \tabularnewline
14 & 9035 & 9183.97766975103 & -524.326278586289 & 9410.34860883526 & 148.977669751028 \tabularnewline
15 & 9133 & 9067.49290307022 & -174.454656860352 & 9372.96175379014 & -65.5070969297849 \tabularnewline
16 & 9487 & 9479.52410990384 & 148.134258167628 & 9346.34163192853 & -7.47589009615876 \tabularnewline
17 & 8700 & 8818.90135130474 & -738.622861371663 & 9319.72151006693 & 118.901351304736 \tabularnewline
18 & 9627 & 9732.83020877481 & 217.654813785795 & 9303.51497743939 & 105.830208774814 \tabularnewline
19 & 8947 & 8773.59204568585 & -166.900490497697 & 9287.30844481185 & -173.407954314154 \tabularnewline
20 & 9283 & 9285.24500345376 & 4.89909322631029 & 9275.85590331993 & 2.24500345375782 \tabularnewline
21 & 8829 & 8440.89727928492 & -47.3006411129351 & 9264.40336182801 & -388.102720715078 \tabularnewline
22 & 9947 & 10115.1181643594 & 519.336152915613 & 9259.54568272501 & 168.118164359377 \tabularnewline
23 & 9628 & 9612.1727056327 & 389.139290745294 & 9254.68800362201 & -15.8272943673019 \tabularnewline
24 & 9318 & 9223.30241590875 & 155.781667904092 & 9256.91591618715 & -94.6975840912455 \tabularnewline
25 & 9605 & 9734.19644629435 & 216.659724953349 & 9259.1438287523 & 129.196446294349 \tabularnewline
26 & 8640 & 8534.35586053479 & -524.326278586289 & 9269.9704180515 & -105.644139465214 \tabularnewline
27 & 9214 & 9321.65764950965 & -174.454656860352 & 9280.7970073507 & 107.657649509649 \tabularnewline
28 & 9567 & 9693.8923956418 & 148.134258167628 & 9291.97334619057 & 126.892395641802 \tabularnewline
29 & 8547 & 8529.47317634122 & -738.622861371663 & 9303.14968503044 & -17.5268236587763 \tabularnewline
30 & 9185 & 8843.67047554606 & 217.654813785795 & 9308.67471066814 & -341.329524453937 \tabularnewline
31 & 9470 & 9792.70075419185 & -166.900490497697 & 9314.19973630584 & 322.700754191854 \tabularnewline
32 & 9123 & 8918.07357542625 & 4.89909322631029 & 9323.02733134744 & -204.926424573749 \tabularnewline
33 & 9278 & 9271.4457147239 & -47.3006411129351 & 9331.85492638903 & -6.55428527609911 \tabularnewline
34 & 10170 & 10468.1371071333 & 519.336152915613 & 9352.52673995109 & 298.137107133296 \tabularnewline
35 & 9434 & 9105.66215574156 & 389.139290745294 & 9373.19855351315 & -328.337844258442 \tabularnewline
36 & 9655 & 9760.51674400655 & 155.781667904092 & 9393.70158808936 & 105.516744006552 \tabularnewline
37 & 9429 & 9227.13565238109 & 216.659724953349 & 9414.20462266556 & -201.864347618912 \tabularnewline
38 & 8739 & 8573.70419384917 & -524.326278586289 & 9428.62208473712 & -165.295806150829 \tabularnewline
39 & 9552 & 9835.41511005168 & -174.454656860352 & 9443.03954680868 & 283.415110051677 \tabularnewline
40 & 9687 & 9758.56511370315 & 148.134258167628 & 9467.30062812922 & 71.5651137031509 \tabularnewline
41 & 9019 & 9285.06115192189 & -738.622861371663 & 9491.56170944977 & 266.061151921895 \tabularnewline
42 & 9672 & 9605.97213280895 & 217.654813785795 & 9520.37305340526 & -66.0278671910546 \tabularnewline
43 & 9206 & 9029.71609313695 & -166.900490497697 & 9549.18439736075 & -176.283906863051 \tabularnewline
44 & 9069 & 8558.39427184334 & 4.89909322631029 & 9574.70663493035 & -510.605728156655 \tabularnewline
45 & 9788 & 10023.071768613 & -47.3006411129351 & 9600.22887249994 & 235.071768612994 \tabularnewline
46 & 10312 & 10480.8282002468 & 519.336152915613 & 9623.8356468376 & 168.828200246789 \tabularnewline
47 & 10105 & 10173.4182880795 & 389.139290745294 & 9647.44242117526 & 68.4182880794506 \tabularnewline
48 & 9863 & 9890.35914461412 & 155.781667904092 & 9679.85918748179 & 27.3591446141199 \tabularnewline
49 & 9656 & 9383.06432125833 & 216.659724953349 & 9712.27595378832 & -272.935678741671 \tabularnewline
50 & 9295 & 9374.16734837944 & -524.326278586289 & 9740.15893020685 & 79.167348379442 \tabularnewline
51 & 9946 & 10298.412750235 & -174.454656860352 & 9768.04190662537 & 352.412750234978 \tabularnewline
52 & 9701 & 9472.4536957728 & 148.134258167628 & 9781.41204605957 & -228.5463042272 \tabularnewline
53 & 9049 & 9041.84067587789 & -738.622861371663 & 9794.78218549377 & -7.15932412210896 \tabularnewline
54 & 10190 & 10354.4579532181 & 217.654813785795 & 9807.88723299606 & 164.457953218143 \tabularnewline
55 & 9706 & 9757.90820999935 & -166.900490497697 & 9820.99228049835 & 51.9082099993466 \tabularnewline
56 & 9765 & 9692.77073374114 & 4.89909322631029 & 9832.33017303255 & -72.2292662588588 \tabularnewline
57 & 9893 & 9989.63257554619 & -47.3006411129351 & 9843.66806556675 & 96.6325755461876 \tabularnewline
58 & 9994 & 9615.54519313482 & 519.336152915613 & 9853.11865394957 & -378.454806865184 \tabularnewline
59 & 10433 & 10614.2914669223 & 389.139290745294 & 9862.5692423324 & 181.29146692231 \tabularnewline
60 & 10073 & 10111.7158882826 & 155.781667904092 & 9878.50244381331 & 38.7158882825952 \tabularnewline
61 & 10112 & 10112.9046297524 & 216.659724953349 & 9894.43564529423 & 0.904629752420078 \tabularnewline
62 & 9266 & 9136.50938088752 & -524.326278586289 & 9919.81689769877 & -129.490619112479 \tabularnewline
63 & 9820 & 9869.25650675705 & -174.454656860352 & 9945.19815010331 & 49.2565067570467 \tabularnewline
64 & 10097 & 10071.7159847449 & 148.134258167628 & 9974.14975708747 & -25.2840152550998 \tabularnewline
65 & 9115 & 8965.52149730002 & -738.622861371663 & 10003.1013640716 & -149.478502699976 \tabularnewline
66 & 10411 & 10567.8772587978 & 217.654813785795 & 10036.4679274164 & 156.877258797789 \tabularnewline
67 & 9678 & 9453.06599973651 & -166.900490497697 & 10069.8344907612 & -224.934000263493 \tabularnewline
68 & 10408 & 10720.2986443903 & 4.89909322631029 & 10090.8022623834 & 312.29864439033 \tabularnewline
69 & 10153 & 10241.5306071074 & -47.3006411129351 & 10111.7700340055 & 88.5306071074065 \tabularnewline
70 & 10368 & 10087.1032512669 & 519.336152915613 & 10129.5605958175 & -280.896748733068 \tabularnewline
71 & 10581 & 10625.5095516253 & 389.139290745294 & 10147.3511576294 & 44.5095516253241 \tabularnewline
72 & 10597 & 10873.4076819219 & 155.781667904092 & 10164.810650174 & 276.407681921864 \tabularnewline
73 & 10680 & 10961.0701323279 & 216.659724953349 & 10182.2701427187 & 281.070132327946 \tabularnewline
74 & 9738 & 9801.49489133259 & -524.326278586289 & 10198.8313872537 & 63.4948913325898 \tabularnewline
75 & 9556 & 9071.06202507166 & -174.454656860352 & 10215.3926317887 & -484.937974928343 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187422&T=2

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Time Series Components[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Seasonal[/C][C]Trend[/C][C]Remainder[/C][/ROW]
[ROW][C]1[/C][C]9700[/C][C]9665.38145341098[/C][C]216.659724953349[/C][C]9517.95882163567[/C][C]-34.618546589023[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9165.77198053844[/C][C]-524.326278586289[/C][C]9520.55429804785[/C][C]84.771980538444[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]8819.30488240033[/C][C]-174.454656860352[/C][C]9523.14977446002[/C][C]-264.695117599666[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9815.13667288808[/C][C]148.134258167628[/C][C]9522.72906894429[/C][C]72.1366728880803[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]8390.3144979431[/C][C]-738.622861371663[/C][C]9522.30836342857[/C][C]-196.685502056904[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9725.13807465835[/C][C]217.654813785795[/C][C]9519.20711155585[/C][C]-5.86192534164547[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9776.79463081457[/C][C]-166.900490497697[/C][C]9516.10585968313[/C][C]213.794630814566[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]10477.3915983949[/C][C]4.89909322631029[/C][C]9513.70930837883[/C][C]479.391598394865[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9409.98788403842[/C][C]-47.3006411129351[/C][C]9511.31275707452[/C][C]-27.0121159615846[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]10051.5200681786[/C][C]519.336152915613[/C][C]9505.14377890576[/C][C]13.5200681786318[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9947.88590851771[/C][C]389.139290745294[/C][C]9498.97480073699[/C][C]29.8859085177137[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]8874.86319978722[/C][C]155.781667904092[/C][C]9473.35513230869[/C][C]-377.136800212782[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9809.60481116626[/C][C]216.659724953349[/C][C]9447.73546388039[/C][C]72.6048111662622[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9183.97766975103[/C][C]-524.326278586289[/C][C]9410.34860883526[/C][C]148.977669751028[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9067.49290307022[/C][C]-174.454656860352[/C][C]9372.96175379014[/C][C]-65.5070969297849[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9479.52410990384[/C][C]148.134258167628[/C][C]9346.34163192853[/C][C]-7.47589009615876[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8818.90135130474[/C][C]-738.622861371663[/C][C]9319.72151006693[/C][C]118.901351304736[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9732.83020877481[/C][C]217.654813785795[/C][C]9303.51497743939[/C][C]105.830208774814[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]8773.59204568585[/C][C]-166.900490497697[/C][C]9287.30844481185[/C][C]-173.407954314154[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9285.24500345376[/C][C]4.89909322631029[/C][C]9275.85590331993[/C][C]2.24500345375782[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]8440.89727928492[/C][C]-47.3006411129351[/C][C]9264.40336182801[/C][C]-388.102720715078[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]10115.1181643594[/C][C]519.336152915613[/C][C]9259.54568272501[/C][C]168.118164359377[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9612.1727056327[/C][C]389.139290745294[/C][C]9254.68800362201[/C][C]-15.8272943673019[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9223.30241590875[/C][C]155.781667904092[/C][C]9256.91591618715[/C][C]-94.6975840912455[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9734.19644629435[/C][C]216.659724953349[/C][C]9259.1438287523[/C][C]129.196446294349[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8534.35586053479[/C][C]-524.326278586289[/C][C]9269.9704180515[/C][C]-105.644139465214[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9321.65764950965[/C][C]-174.454656860352[/C][C]9280.7970073507[/C][C]107.657649509649[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9693.8923956418[/C][C]148.134258167628[/C][C]9291.97334619057[/C][C]126.892395641802[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8529.47317634122[/C][C]-738.622861371663[/C][C]9303.14968503044[/C][C]-17.5268236587763[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8843.67047554606[/C][C]217.654813785795[/C][C]9308.67471066814[/C][C]-341.329524453937[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9792.70075419185[/C][C]-166.900490497697[/C][C]9314.19973630584[/C][C]322.700754191854[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]8918.07357542625[/C][C]4.89909322631029[/C][C]9323.02733134744[/C][C]-204.926424573749[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9271.4457147239[/C][C]-47.3006411129351[/C][C]9331.85492638903[/C][C]-6.55428527609911[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]10468.1371071333[/C][C]519.336152915613[/C][C]9352.52673995109[/C][C]298.137107133296[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9105.66215574156[/C][C]389.139290745294[/C][C]9373.19855351315[/C][C]-328.337844258442[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9760.51674400655[/C][C]155.781667904092[/C][C]9393.70158808936[/C][C]105.516744006552[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9227.13565238109[/C][C]216.659724953349[/C][C]9414.20462266556[/C][C]-201.864347618912[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]8573.70419384917[/C][C]-524.326278586289[/C][C]9428.62208473712[/C][C]-165.295806150829[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9835.41511005168[/C][C]-174.454656860352[/C][C]9443.03954680868[/C][C]283.415110051677[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9758.56511370315[/C][C]148.134258167628[/C][C]9467.30062812922[/C][C]71.5651137031509[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9285.06115192189[/C][C]-738.622861371663[/C][C]9491.56170944977[/C][C]266.061151921895[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9605.97213280895[/C][C]217.654813785795[/C][C]9520.37305340526[/C][C]-66.0278671910546[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9029.71609313695[/C][C]-166.900490497697[/C][C]9549.18439736075[/C][C]-176.283906863051[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]8558.39427184334[/C][C]4.89909322631029[/C][C]9574.70663493035[/C][C]-510.605728156655[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]10023.071768613[/C][C]-47.3006411129351[/C][C]9600.22887249994[/C][C]235.071768612994[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10480.8282002468[/C][C]519.336152915613[/C][C]9623.8356468376[/C][C]168.828200246789[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10173.4182880795[/C][C]389.139290745294[/C][C]9647.44242117526[/C][C]68.4182880794506[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9890.35914461412[/C][C]155.781667904092[/C][C]9679.85918748179[/C][C]27.3591446141199[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9383.06432125833[/C][C]216.659724953349[/C][C]9712.27595378832[/C][C]-272.935678741671[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9374.16734837944[/C][C]-524.326278586289[/C][C]9740.15893020685[/C][C]79.167348379442[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]10298.412750235[/C][C]-174.454656860352[/C][C]9768.04190662537[/C][C]352.412750234978[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9472.4536957728[/C][C]148.134258167628[/C][C]9781.41204605957[/C][C]-228.5463042272[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9041.84067587789[/C][C]-738.622861371663[/C][C]9794.78218549377[/C][C]-7.15932412210896[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10354.4579532181[/C][C]217.654813785795[/C][C]9807.88723299606[/C][C]164.457953218143[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9757.90820999935[/C][C]-166.900490497697[/C][C]9820.99228049835[/C][C]51.9082099993466[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9692.77073374114[/C][C]4.89909322631029[/C][C]9832.33017303255[/C][C]-72.2292662588588[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9989.63257554619[/C][C]-47.3006411129351[/C][C]9843.66806556675[/C][C]96.6325755461876[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9615.54519313482[/C][C]519.336152915613[/C][C]9853.11865394957[/C][C]-378.454806865184[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10614.2914669223[/C][C]389.139290745294[/C][C]9862.5692423324[/C][C]181.29146692231[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]10111.7158882826[/C][C]155.781667904092[/C][C]9878.50244381331[/C][C]38.7158882825952[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10112.9046297524[/C][C]216.659724953349[/C][C]9894.43564529423[/C][C]0.904629752420078[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9136.50938088752[/C][C]-524.326278586289[/C][C]9919.81689769877[/C][C]-129.490619112479[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9869.25650675705[/C][C]-174.454656860352[/C][C]9945.19815010331[/C][C]49.2565067570467[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10071.7159847449[/C][C]148.134258167628[/C][C]9974.14975708747[/C][C]-25.2840152550998[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]8965.52149730002[/C][C]-738.622861371663[/C][C]10003.1013640716[/C][C]-149.478502699976[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10567.8772587978[/C][C]217.654813785795[/C][C]10036.4679274164[/C][C]156.877258797789[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9453.06599973651[/C][C]-166.900490497697[/C][C]10069.8344907612[/C][C]-224.934000263493[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10720.2986443903[/C][C]4.89909322631029[/C][C]10090.8022623834[/C][C]312.29864439033[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10241.5306071074[/C][C]-47.3006411129351[/C][C]10111.7700340055[/C][C]88.5306071074065[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10087.1032512669[/C][C]519.336152915613[/C][C]10129.5605958175[/C][C]-280.896748733068[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10625.5095516253[/C][C]389.139290745294[/C][C]10147.3511576294[/C][C]44.5095516253241[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10873.4076819219[/C][C]155.781667904092[/C][C]10164.810650174[/C][C]276.407681921864[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10961.0701323279[/C][C]216.659724953349[/C][C]10182.2701427187[/C][C]281.070132327946[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9801.49489133259[/C][C]-524.326278586289[/C][C]10198.8313872537[/C][C]63.4948913325898[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9071.06202507166[/C][C]-174.454656860352[/C][C]10215.3926317887[/C][C]-484.937974928343[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187422&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187422&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
197009665.38145341098216.6597249533499517.95882163567-34.618546589023
290819165.77198053844-524.3262785862899520.5542980478584.771980538444
390848819.30488240033-174.4546568603529523.14977446002-264.695117599666
497439815.13667288808148.1342581676289522.7290689442972.1366728880803
585878390.3144979431-738.6228613716639522.30836342857-196.685502056904
697319725.13807465835217.6548137857959519.20711155585-5.86192534164547
795639776.79463081457-166.9004904976979516.10585968313213.794630814566
8999810477.39159839494.899093226310299513.70930837883479.391598394865
994379409.98788403842-47.30064111293519511.31275707452-27.0121159615846
101003810051.5200681786519.3361529156139505.1437789057613.5200681786318
1199189947.88590851771389.1392907452949498.9748007369929.8859085177137
1292528874.86319978722155.7816679040929473.35513230869-377.136800212782
1397379809.60481116626216.6597249533499447.7354638803972.6048111662622
1490359183.97766975103-524.3262785862899410.34860883526148.977669751028
1591339067.49290307022-174.4546568603529372.96175379014-65.5070969297849
1694879479.52410990384148.1342581676289346.34163192853-7.47589009615876
1787008818.90135130474-738.6228613716639319.72151006693118.901351304736
1896279732.83020877481217.6548137857959303.51497743939105.830208774814
1989478773.59204568585-166.9004904976979287.30844481185-173.407954314154
2092839285.245003453764.899093226310299275.855903319932.24500345375782
2188298440.89727928492-47.30064111293519264.40336182801-388.102720715078
22994710115.1181643594519.3361529156139259.54568272501168.118164359377
2396289612.1727056327389.1392907452949254.68800362201-15.8272943673019
2493189223.30241590875155.7816679040929256.91591618715-94.6975840912455
2596059734.19644629435216.6597249533499259.1438287523129.196446294349
2686408534.35586053479-524.3262785862899269.9704180515-105.644139465214
2792149321.65764950965-174.4546568603529280.7970073507107.657649509649
2895679693.8923956418148.1342581676289291.97334619057126.892395641802
2985478529.47317634122-738.6228613716639303.14968503044-17.5268236587763
3091858843.67047554606217.6548137857959308.67471066814-341.329524453937
3194709792.70075419185-166.9004904976979314.19973630584322.700754191854
3291238918.073575426254.899093226310299323.02733134744-204.926424573749
3392789271.4457147239-47.30064111293519331.85492638903-6.55428527609911
341017010468.1371071333519.3361529156139352.52673995109298.137107133296
3594349105.66215574156389.1392907452949373.19855351315-328.337844258442
3696559760.51674400655155.7816679040929393.70158808936105.516744006552
3794299227.13565238109216.6597249533499414.20462266556-201.864347618912
3887398573.70419384917-524.3262785862899428.62208473712-165.295806150829
3995529835.41511005168-174.4546568603529443.03954680868283.415110051677
4096879758.56511370315148.1342581676289467.3006281292271.5651137031509
4190199285.06115192189-738.6228613716639491.56170944977266.061151921895
4296729605.97213280895217.6548137857959520.37305340526-66.0278671910546
4392069029.71609313695-166.9004904976979549.18439736075-176.283906863051
4490698558.394271843344.899093226310299574.70663493035-510.605728156655
45978810023.071768613-47.30064111293519600.22887249994235.071768612994
461031210480.8282002468519.3361529156139623.8356468376168.828200246789
471010510173.4182880795389.1392907452949647.4424211752668.4182880794506
4898639890.35914461412155.7816679040929679.8591874817927.3591446141199
4996569383.06432125833216.6597249533499712.27595378832-272.935678741671
5092959374.16734837944-524.3262785862899740.1589302068579.167348379442
51994610298.412750235-174.4546568603529768.04190662537352.412750234978
5297019472.4536957728148.1342581676289781.41204605957-228.5463042272
5390499041.84067587789-738.6228613716639794.78218549377-7.15932412210896
541019010354.4579532181217.6548137857959807.88723299606164.457953218143
5597069757.90820999935-166.9004904976979820.9922804983551.9082099993466
5697659692.770733741144.899093226310299832.33017303255-72.2292662588588
5798939989.63257554619-47.30064111293519843.6680655667596.6325755461876
5899949615.54519313482519.3361529156139853.11865394957-378.454806865184
591043310614.2914669223389.1392907452949862.5692423324181.29146692231
601007310111.7158882826155.7816679040929878.5024438133138.7158882825952
611011210112.9046297524216.6597249533499894.435645294230.904629752420078
6292669136.50938088752-524.3262785862899919.81689769877-129.490619112479
6398209869.25650675705-174.4546568603529945.1981501033149.2565067570467
641009710071.7159847449148.1342581676289974.14975708747-25.2840152550998
6591158965.52149730002-738.62286137166310003.1013640716-149.478502699976
661041110567.8772587978217.65481378579510036.4679274164156.877258797789
6796789453.06599973651-166.90049049769710069.8344907612-224.934000263493
681040810720.29864439034.8990932263102910090.8022623834312.29864439033
691015310241.5306071074-47.300641112935110111.770034005588.5306071074065
701036810087.1032512669519.33615291561310129.5605958175-280.896748733068
711058110625.5095516253389.13929074529410147.351157629444.5095516253241
721059710873.4076819219155.78166790409210164.810650174276.407681921864
731068010961.0701323279216.65972495334910182.2701427187281.070132327946
7497389801.49489133259-524.32627858628910198.831387253763.4948913325898
7595569071.06202507166-174.45465686035210215.3926317887-484.937974928343



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = ; par5 = 1 ; par6 = ; par7 = 1 ; par8 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')