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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 computationThu, 24 Nov 2011 09:46:52 -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/Nov/24/t1322146028swz6sy9dt2ozogp.htm/, Retrieved Thu, 28 Mar 2024 15:28:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146891, Retrieved Thu, 28 Mar 2024 15:28:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Univariate Data Series] [] [2011-11-24 13:29:35] [22f8bc702946f784836540059d0d9516]
- RMP     [Classical Decomposition] [] [2011-11-24 14:18:49] [22f8bc702946f784836540059d0d9516]
- RMP         [Decomposition by Loess] [] [2011-11-24 14:46:52] [76a85a4cc6ea7903d92a0f5b9d2872d3] [Current]
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Dataseries X:
135094
135411
135698
135880
135891
135971
136173
136358
136514
136506
136711
136891
137094
137182
137400
137479
137620
137687
137638
137612
137681
137772
137899
137983
137996
137913
137841
137656
137423
137245
137014
136747
136313
135804
135002
134383
133563
132837
132041
131381
130995
130493
130193
129962
129726
129505
129450
129320
129281
129246
129438
129715
130173
129981
129932
129873
129844
130015
130108
130260




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=146891&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=146891&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146891&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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal601061
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146891&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]601[/C][C]0[/C][C]61[/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=146891&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146891&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
Seasonal601061
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1135094134935.21597610511.7529678853309135241.031056009-158.784023894637
2135411135425.2887581430.91194715315098135395.79929470314.2887581434916
3135698135801.76158096143.6708856420494135550.567533397103.761580960592
4135880136009.76106570745.4199428221291135704.818991471129.761065706523
5135891135816.160521698106.769028756549135859.070449545-74.8394783018739
6135971135907.03934422322.2737536480584136012.686902129-63.9606557774532
7136173136182.318238633-2.62159334637468136166.3033547139.31823863292811
8136358136414.055365647-16.2672706406742136318.21190499456.055365646811
9136514136602.992524813-45.1129800876134136470.12045527488.9925248133368
10136506136457.118887039-59.6061235599095136614.487236521-48.8811129608366
11136711136728.445334744-65.2993525111729136758.85401776717.4453347439412
12136891136931.94885822-41.8911458621321136891.94228764240.9488582201593
13137094137151.21647459811.7529678853309137025.03055751757.2164745979244
14137182137223.8980231760.91194715315098137139.1900296741.8980231764144
15137400137502.97961253443.6708856420494137253.349501824102.979612533847
16137479137560.13496785645.4199428221291137352.44508932181.1349678563711
17137620137681.690294425106.769028756549137451.54067681961.6902944245667
18137687137815.93539546822.2737536480584137535.790850884128.93539546759
19137638137658.580568397-2.62159334637468137620.0410249520.5805683965737
20137612137561.853305945-16.2672706406742137678.413964696-50.1466940554383
21137681137670.326075645-45.1129800876134137736.786904442-10.6739243547781
22137772137848.434063369-59.6061235599095137755.17206019176.4340633687971
23137899138089.742136571-65.2993525111729137773.55721594190.742136571353
24137983138262.50170063-41.8911458621321137745.389445232279.501700629888
25137996138263.0253575911.7529678853309137717.221674525267.025357589999
26137913138193.3709942770.91194715315098137631.71705857280.370994276658
27137841138092.11667174243.6708856420494137546.212442616251.116671742289
28137656137894.33707527445.4199428221291137372.242981904238.337075273565
29137423137540.957450051106.769028756549137198.273521193117.957450050511
30137245137561.62721194522.2737536480584136906.099034407316.627211944578
31137014137416.697045725-2.62159334637468136613.924547622402.697045724664
32136747137311.505294713-16.2672706406742136198.761975928564.505294713046
33136313136887.513575854-45.1129800876134135783.599404234574.5135758541
34135804136401.720979954-59.6061235599095135265.885143606597.720979954291
35135002135321.128469533-65.2993525111729134748.170882978319.128469533462
36134383134636.866704067-41.8911458621321134171.024441795253.866704066779
37133563133520.36903150211.7529678853309133593.878000613-42.6309684983571
38132837132657.5296649120.91194715315098133015.558387935-179.470335087739
39132041131601.09033910243.6708856420494132437.238775256-439.909660898178
40131381130795.41491659845.4199428221291131921.16514058-585.585083402373
41130995130478.139465339106.769028756549131405.091505904-516.860534660911
42130493129966.97800230922.2737536480584130996.748244043-526.021997690565
43130193129800.216611166-2.62159334637468130588.404982181-392.783388834272
44129962129632.196823078-16.2672706406742130308.070447562-329.803176921559
45129726129469.377067144-45.1129800876134130027.735912944-256.622932856233
46129505129191.671759752-59.6061235599095129877.934363808-313.328240248113
47129450129237.166537839-65.2993525111729129728.132814672-212.833462161012
48129320129004.900707305-41.8911458621321129676.990438557-315.099292694533
49129281128924.39896967411.7529678853309129625.848062441-356.601030326477
50129246128858.9194081440.91194715315098129632.168644703-387.080591855964
51129438129193.83988739343.6708856420494129638.489226964-244.160112606522
52129715129687.97544342245.4199428221291129696.604613755-27.024556577584
53130173130484.510970697106.769028756549129754.720000546311.51097069704
54129981130119.43507076422.2737536480584129820.291175588138.435070764273
55129932129980.759242717-2.62159334637468129885.86235062948.7592427174386
56129873129807.528147409-16.2672706406742129954.739123232-65.4718525909993
57129844129709.497084253-45.1129800876134130023.615895834-134.502915746809
58130015129996.01437927-59.6061235599095130093.59174429-18.9856207303965
59130108130117.731759765-65.2993525111729130163.5675927469.73175976498169
60130260130327.89019236-41.8911458621321130234.00095350267.890192360006

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 135094 & 134935.215976105 & 11.7529678853309 & 135241.031056009 & -158.784023894637 \tabularnewline
2 & 135411 & 135425.288758143 & 0.91194715315098 & 135395.799294703 & 14.2887581434916 \tabularnewline
3 & 135698 & 135801.761580961 & 43.6708856420494 & 135550.567533397 & 103.761580960592 \tabularnewline
4 & 135880 & 136009.761065707 & 45.4199428221291 & 135704.818991471 & 129.761065706523 \tabularnewline
5 & 135891 & 135816.160521698 & 106.769028756549 & 135859.070449545 & -74.8394783018739 \tabularnewline
6 & 135971 & 135907.039344223 & 22.2737536480584 & 136012.686902129 & -63.9606557774532 \tabularnewline
7 & 136173 & 136182.318238633 & -2.62159334637468 & 136166.303354713 & 9.31823863292811 \tabularnewline
8 & 136358 & 136414.055365647 & -16.2672706406742 & 136318.211904994 & 56.055365646811 \tabularnewline
9 & 136514 & 136602.992524813 & -45.1129800876134 & 136470.120455274 & 88.9925248133368 \tabularnewline
10 & 136506 & 136457.118887039 & -59.6061235599095 & 136614.487236521 & -48.8811129608366 \tabularnewline
11 & 136711 & 136728.445334744 & -65.2993525111729 & 136758.854017767 & 17.4453347439412 \tabularnewline
12 & 136891 & 136931.94885822 & -41.8911458621321 & 136891.942287642 & 40.9488582201593 \tabularnewline
13 & 137094 & 137151.216474598 & 11.7529678853309 & 137025.030557517 & 57.2164745979244 \tabularnewline
14 & 137182 & 137223.898023176 & 0.91194715315098 & 137139.19002967 & 41.8980231764144 \tabularnewline
15 & 137400 & 137502.979612534 & 43.6708856420494 & 137253.349501824 & 102.979612533847 \tabularnewline
16 & 137479 & 137560.134967856 & 45.4199428221291 & 137352.445089321 & 81.1349678563711 \tabularnewline
17 & 137620 & 137681.690294425 & 106.769028756549 & 137451.540676819 & 61.6902944245667 \tabularnewline
18 & 137687 & 137815.935395468 & 22.2737536480584 & 137535.790850884 & 128.93539546759 \tabularnewline
19 & 137638 & 137658.580568397 & -2.62159334637468 & 137620.04102495 & 20.5805683965737 \tabularnewline
20 & 137612 & 137561.853305945 & -16.2672706406742 & 137678.413964696 & -50.1466940554383 \tabularnewline
21 & 137681 & 137670.326075645 & -45.1129800876134 & 137736.786904442 & -10.6739243547781 \tabularnewline
22 & 137772 & 137848.434063369 & -59.6061235599095 & 137755.172060191 & 76.4340633687971 \tabularnewline
23 & 137899 & 138089.742136571 & -65.2993525111729 & 137773.55721594 & 190.742136571353 \tabularnewline
24 & 137983 & 138262.50170063 & -41.8911458621321 & 137745.389445232 & 279.501700629888 \tabularnewline
25 & 137996 & 138263.02535759 & 11.7529678853309 & 137717.221674525 & 267.025357589999 \tabularnewline
26 & 137913 & 138193.370994277 & 0.91194715315098 & 137631.71705857 & 280.370994276658 \tabularnewline
27 & 137841 & 138092.116671742 & 43.6708856420494 & 137546.212442616 & 251.116671742289 \tabularnewline
28 & 137656 & 137894.337075274 & 45.4199428221291 & 137372.242981904 & 238.337075273565 \tabularnewline
29 & 137423 & 137540.957450051 & 106.769028756549 & 137198.273521193 & 117.957450050511 \tabularnewline
30 & 137245 & 137561.627211945 & 22.2737536480584 & 136906.099034407 & 316.627211944578 \tabularnewline
31 & 137014 & 137416.697045725 & -2.62159334637468 & 136613.924547622 & 402.697045724664 \tabularnewline
32 & 136747 & 137311.505294713 & -16.2672706406742 & 136198.761975928 & 564.505294713046 \tabularnewline
33 & 136313 & 136887.513575854 & -45.1129800876134 & 135783.599404234 & 574.5135758541 \tabularnewline
34 & 135804 & 136401.720979954 & -59.6061235599095 & 135265.885143606 & 597.720979954291 \tabularnewline
35 & 135002 & 135321.128469533 & -65.2993525111729 & 134748.170882978 & 319.128469533462 \tabularnewline
36 & 134383 & 134636.866704067 & -41.8911458621321 & 134171.024441795 & 253.866704066779 \tabularnewline
37 & 133563 & 133520.369031502 & 11.7529678853309 & 133593.878000613 & -42.6309684983571 \tabularnewline
38 & 132837 & 132657.529664912 & 0.91194715315098 & 133015.558387935 & -179.470335087739 \tabularnewline
39 & 132041 & 131601.090339102 & 43.6708856420494 & 132437.238775256 & -439.909660898178 \tabularnewline
40 & 131381 & 130795.414916598 & 45.4199428221291 & 131921.16514058 & -585.585083402373 \tabularnewline
41 & 130995 & 130478.139465339 & 106.769028756549 & 131405.091505904 & -516.860534660911 \tabularnewline
42 & 130493 & 129966.978002309 & 22.2737536480584 & 130996.748244043 & -526.021997690565 \tabularnewline
43 & 130193 & 129800.216611166 & -2.62159334637468 & 130588.404982181 & -392.783388834272 \tabularnewline
44 & 129962 & 129632.196823078 & -16.2672706406742 & 130308.070447562 & -329.803176921559 \tabularnewline
45 & 129726 & 129469.377067144 & -45.1129800876134 & 130027.735912944 & -256.622932856233 \tabularnewline
46 & 129505 & 129191.671759752 & -59.6061235599095 & 129877.934363808 & -313.328240248113 \tabularnewline
47 & 129450 & 129237.166537839 & -65.2993525111729 & 129728.132814672 & -212.833462161012 \tabularnewline
48 & 129320 & 129004.900707305 & -41.8911458621321 & 129676.990438557 & -315.099292694533 \tabularnewline
49 & 129281 & 128924.398969674 & 11.7529678853309 & 129625.848062441 & -356.601030326477 \tabularnewline
50 & 129246 & 128858.919408144 & 0.91194715315098 & 129632.168644703 & -387.080591855964 \tabularnewline
51 & 129438 & 129193.839887393 & 43.6708856420494 & 129638.489226964 & -244.160112606522 \tabularnewline
52 & 129715 & 129687.975443422 & 45.4199428221291 & 129696.604613755 & -27.024556577584 \tabularnewline
53 & 130173 & 130484.510970697 & 106.769028756549 & 129754.720000546 & 311.51097069704 \tabularnewline
54 & 129981 & 130119.435070764 & 22.2737536480584 & 129820.291175588 & 138.435070764273 \tabularnewline
55 & 129932 & 129980.759242717 & -2.62159334637468 & 129885.862350629 & 48.7592427174386 \tabularnewline
56 & 129873 & 129807.528147409 & -16.2672706406742 & 129954.739123232 & -65.4718525909993 \tabularnewline
57 & 129844 & 129709.497084253 & -45.1129800876134 & 130023.615895834 & -134.502915746809 \tabularnewline
58 & 130015 & 129996.01437927 & -59.6061235599095 & 130093.59174429 & -18.9856207303965 \tabularnewline
59 & 130108 & 130117.731759765 & -65.2993525111729 & 130163.567592746 & 9.73175976498169 \tabularnewline
60 & 130260 & 130327.89019236 & -41.8911458621321 & 130234.000953502 & 67.890192360006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146891&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]135094[/C][C]134935.215976105[/C][C]11.7529678853309[/C][C]135241.031056009[/C][C]-158.784023894637[/C][/ROW]
[ROW][C]2[/C][C]135411[/C][C]135425.288758143[/C][C]0.91194715315098[/C][C]135395.799294703[/C][C]14.2887581434916[/C][/ROW]
[ROW][C]3[/C][C]135698[/C][C]135801.761580961[/C][C]43.6708856420494[/C][C]135550.567533397[/C][C]103.761580960592[/C][/ROW]
[ROW][C]4[/C][C]135880[/C][C]136009.761065707[/C][C]45.4199428221291[/C][C]135704.818991471[/C][C]129.761065706523[/C][/ROW]
[ROW][C]5[/C][C]135891[/C][C]135816.160521698[/C][C]106.769028756549[/C][C]135859.070449545[/C][C]-74.8394783018739[/C][/ROW]
[ROW][C]6[/C][C]135971[/C][C]135907.039344223[/C][C]22.2737536480584[/C][C]136012.686902129[/C][C]-63.9606557774532[/C][/ROW]
[ROW][C]7[/C][C]136173[/C][C]136182.318238633[/C][C]-2.62159334637468[/C][C]136166.303354713[/C][C]9.31823863292811[/C][/ROW]
[ROW][C]8[/C][C]136358[/C][C]136414.055365647[/C][C]-16.2672706406742[/C][C]136318.211904994[/C][C]56.055365646811[/C][/ROW]
[ROW][C]9[/C][C]136514[/C][C]136602.992524813[/C][C]-45.1129800876134[/C][C]136470.120455274[/C][C]88.9925248133368[/C][/ROW]
[ROW][C]10[/C][C]136506[/C][C]136457.118887039[/C][C]-59.6061235599095[/C][C]136614.487236521[/C][C]-48.8811129608366[/C][/ROW]
[ROW][C]11[/C][C]136711[/C][C]136728.445334744[/C][C]-65.2993525111729[/C][C]136758.854017767[/C][C]17.4453347439412[/C][/ROW]
[ROW][C]12[/C][C]136891[/C][C]136931.94885822[/C][C]-41.8911458621321[/C][C]136891.942287642[/C][C]40.9488582201593[/C][/ROW]
[ROW][C]13[/C][C]137094[/C][C]137151.216474598[/C][C]11.7529678853309[/C][C]137025.030557517[/C][C]57.2164745979244[/C][/ROW]
[ROW][C]14[/C][C]137182[/C][C]137223.898023176[/C][C]0.91194715315098[/C][C]137139.19002967[/C][C]41.8980231764144[/C][/ROW]
[ROW][C]15[/C][C]137400[/C][C]137502.979612534[/C][C]43.6708856420494[/C][C]137253.349501824[/C][C]102.979612533847[/C][/ROW]
[ROW][C]16[/C][C]137479[/C][C]137560.134967856[/C][C]45.4199428221291[/C][C]137352.445089321[/C][C]81.1349678563711[/C][/ROW]
[ROW][C]17[/C][C]137620[/C][C]137681.690294425[/C][C]106.769028756549[/C][C]137451.540676819[/C][C]61.6902944245667[/C][/ROW]
[ROW][C]18[/C][C]137687[/C][C]137815.935395468[/C][C]22.2737536480584[/C][C]137535.790850884[/C][C]128.93539546759[/C][/ROW]
[ROW][C]19[/C][C]137638[/C][C]137658.580568397[/C][C]-2.62159334637468[/C][C]137620.04102495[/C][C]20.5805683965737[/C][/ROW]
[ROW][C]20[/C][C]137612[/C][C]137561.853305945[/C][C]-16.2672706406742[/C][C]137678.413964696[/C][C]-50.1466940554383[/C][/ROW]
[ROW][C]21[/C][C]137681[/C][C]137670.326075645[/C][C]-45.1129800876134[/C][C]137736.786904442[/C][C]-10.6739243547781[/C][/ROW]
[ROW][C]22[/C][C]137772[/C][C]137848.434063369[/C][C]-59.6061235599095[/C][C]137755.172060191[/C][C]76.4340633687971[/C][/ROW]
[ROW][C]23[/C][C]137899[/C][C]138089.742136571[/C][C]-65.2993525111729[/C][C]137773.55721594[/C][C]190.742136571353[/C][/ROW]
[ROW][C]24[/C][C]137983[/C][C]138262.50170063[/C][C]-41.8911458621321[/C][C]137745.389445232[/C][C]279.501700629888[/C][/ROW]
[ROW][C]25[/C][C]137996[/C][C]138263.02535759[/C][C]11.7529678853309[/C][C]137717.221674525[/C][C]267.025357589999[/C][/ROW]
[ROW][C]26[/C][C]137913[/C][C]138193.370994277[/C][C]0.91194715315098[/C][C]137631.71705857[/C][C]280.370994276658[/C][/ROW]
[ROW][C]27[/C][C]137841[/C][C]138092.116671742[/C][C]43.6708856420494[/C][C]137546.212442616[/C][C]251.116671742289[/C][/ROW]
[ROW][C]28[/C][C]137656[/C][C]137894.337075274[/C][C]45.4199428221291[/C][C]137372.242981904[/C][C]238.337075273565[/C][/ROW]
[ROW][C]29[/C][C]137423[/C][C]137540.957450051[/C][C]106.769028756549[/C][C]137198.273521193[/C][C]117.957450050511[/C][/ROW]
[ROW][C]30[/C][C]137245[/C][C]137561.627211945[/C][C]22.2737536480584[/C][C]136906.099034407[/C][C]316.627211944578[/C][/ROW]
[ROW][C]31[/C][C]137014[/C][C]137416.697045725[/C][C]-2.62159334637468[/C][C]136613.924547622[/C][C]402.697045724664[/C][/ROW]
[ROW][C]32[/C][C]136747[/C][C]137311.505294713[/C][C]-16.2672706406742[/C][C]136198.761975928[/C][C]564.505294713046[/C][/ROW]
[ROW][C]33[/C][C]136313[/C][C]136887.513575854[/C][C]-45.1129800876134[/C][C]135783.599404234[/C][C]574.5135758541[/C][/ROW]
[ROW][C]34[/C][C]135804[/C][C]136401.720979954[/C][C]-59.6061235599095[/C][C]135265.885143606[/C][C]597.720979954291[/C][/ROW]
[ROW][C]35[/C][C]135002[/C][C]135321.128469533[/C][C]-65.2993525111729[/C][C]134748.170882978[/C][C]319.128469533462[/C][/ROW]
[ROW][C]36[/C][C]134383[/C][C]134636.866704067[/C][C]-41.8911458621321[/C][C]134171.024441795[/C][C]253.866704066779[/C][/ROW]
[ROW][C]37[/C][C]133563[/C][C]133520.369031502[/C][C]11.7529678853309[/C][C]133593.878000613[/C][C]-42.6309684983571[/C][/ROW]
[ROW][C]38[/C][C]132837[/C][C]132657.529664912[/C][C]0.91194715315098[/C][C]133015.558387935[/C][C]-179.470335087739[/C][/ROW]
[ROW][C]39[/C][C]132041[/C][C]131601.090339102[/C][C]43.6708856420494[/C][C]132437.238775256[/C][C]-439.909660898178[/C][/ROW]
[ROW][C]40[/C][C]131381[/C][C]130795.414916598[/C][C]45.4199428221291[/C][C]131921.16514058[/C][C]-585.585083402373[/C][/ROW]
[ROW][C]41[/C][C]130995[/C][C]130478.139465339[/C][C]106.769028756549[/C][C]131405.091505904[/C][C]-516.860534660911[/C][/ROW]
[ROW][C]42[/C][C]130493[/C][C]129966.978002309[/C][C]22.2737536480584[/C][C]130996.748244043[/C][C]-526.021997690565[/C][/ROW]
[ROW][C]43[/C][C]130193[/C][C]129800.216611166[/C][C]-2.62159334637468[/C][C]130588.404982181[/C][C]-392.783388834272[/C][/ROW]
[ROW][C]44[/C][C]129962[/C][C]129632.196823078[/C][C]-16.2672706406742[/C][C]130308.070447562[/C][C]-329.803176921559[/C][/ROW]
[ROW][C]45[/C][C]129726[/C][C]129469.377067144[/C][C]-45.1129800876134[/C][C]130027.735912944[/C][C]-256.622932856233[/C][/ROW]
[ROW][C]46[/C][C]129505[/C][C]129191.671759752[/C][C]-59.6061235599095[/C][C]129877.934363808[/C][C]-313.328240248113[/C][/ROW]
[ROW][C]47[/C][C]129450[/C][C]129237.166537839[/C][C]-65.2993525111729[/C][C]129728.132814672[/C][C]-212.833462161012[/C][/ROW]
[ROW][C]48[/C][C]129320[/C][C]129004.900707305[/C][C]-41.8911458621321[/C][C]129676.990438557[/C][C]-315.099292694533[/C][/ROW]
[ROW][C]49[/C][C]129281[/C][C]128924.398969674[/C][C]11.7529678853309[/C][C]129625.848062441[/C][C]-356.601030326477[/C][/ROW]
[ROW][C]50[/C][C]129246[/C][C]128858.919408144[/C][C]0.91194715315098[/C][C]129632.168644703[/C][C]-387.080591855964[/C][/ROW]
[ROW][C]51[/C][C]129438[/C][C]129193.839887393[/C][C]43.6708856420494[/C][C]129638.489226964[/C][C]-244.160112606522[/C][/ROW]
[ROW][C]52[/C][C]129715[/C][C]129687.975443422[/C][C]45.4199428221291[/C][C]129696.604613755[/C][C]-27.024556577584[/C][/ROW]
[ROW][C]53[/C][C]130173[/C][C]130484.510970697[/C][C]106.769028756549[/C][C]129754.720000546[/C][C]311.51097069704[/C][/ROW]
[ROW][C]54[/C][C]129981[/C][C]130119.435070764[/C][C]22.2737536480584[/C][C]129820.291175588[/C][C]138.435070764273[/C][/ROW]
[ROW][C]55[/C][C]129932[/C][C]129980.759242717[/C][C]-2.62159334637468[/C][C]129885.862350629[/C][C]48.7592427174386[/C][/ROW]
[ROW][C]56[/C][C]129873[/C][C]129807.528147409[/C][C]-16.2672706406742[/C][C]129954.739123232[/C][C]-65.4718525909993[/C][/ROW]
[ROW][C]57[/C][C]129844[/C][C]129709.497084253[/C][C]-45.1129800876134[/C][C]130023.615895834[/C][C]-134.502915746809[/C][/ROW]
[ROW][C]58[/C][C]130015[/C][C]129996.01437927[/C][C]-59.6061235599095[/C][C]130093.59174429[/C][C]-18.9856207303965[/C][/ROW]
[ROW][C]59[/C][C]130108[/C][C]130117.731759765[/C][C]-65.2993525111729[/C][C]130163.567592746[/C][C]9.73175976498169[/C][/ROW]
[ROW][C]60[/C][C]130260[/C][C]130327.89019236[/C][C]-41.8911458621321[/C][C]130234.000953502[/C][C]67.890192360006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146891&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146891&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
1135094134935.21597610511.7529678853309135241.031056009-158.784023894637
2135411135425.2887581430.91194715315098135395.79929470314.2887581434916
3135698135801.76158096143.6708856420494135550.567533397103.761580960592
4135880136009.76106570745.4199428221291135704.818991471129.761065706523
5135891135816.160521698106.769028756549135859.070449545-74.8394783018739
6135971135907.03934422322.2737536480584136012.686902129-63.9606557774532
7136173136182.318238633-2.62159334637468136166.3033547139.31823863292811
8136358136414.055365647-16.2672706406742136318.21190499456.055365646811
9136514136602.992524813-45.1129800876134136470.12045527488.9925248133368
10136506136457.118887039-59.6061235599095136614.487236521-48.8811129608366
11136711136728.445334744-65.2993525111729136758.85401776717.4453347439412
12136891136931.94885822-41.8911458621321136891.94228764240.9488582201593
13137094137151.21647459811.7529678853309137025.03055751757.2164745979244
14137182137223.8980231760.91194715315098137139.1900296741.8980231764144
15137400137502.97961253443.6708856420494137253.349501824102.979612533847
16137479137560.13496785645.4199428221291137352.44508932181.1349678563711
17137620137681.690294425106.769028756549137451.54067681961.6902944245667
18137687137815.93539546822.2737536480584137535.790850884128.93539546759
19137638137658.580568397-2.62159334637468137620.0410249520.5805683965737
20137612137561.853305945-16.2672706406742137678.413964696-50.1466940554383
21137681137670.326075645-45.1129800876134137736.786904442-10.6739243547781
22137772137848.434063369-59.6061235599095137755.17206019176.4340633687971
23137899138089.742136571-65.2993525111729137773.55721594190.742136571353
24137983138262.50170063-41.8911458621321137745.389445232279.501700629888
25137996138263.0253575911.7529678853309137717.221674525267.025357589999
26137913138193.3709942770.91194715315098137631.71705857280.370994276658
27137841138092.11667174243.6708856420494137546.212442616251.116671742289
28137656137894.33707527445.4199428221291137372.242981904238.337075273565
29137423137540.957450051106.769028756549137198.273521193117.957450050511
30137245137561.62721194522.2737536480584136906.099034407316.627211944578
31137014137416.697045725-2.62159334637468136613.924547622402.697045724664
32136747137311.505294713-16.2672706406742136198.761975928564.505294713046
33136313136887.513575854-45.1129800876134135783.599404234574.5135758541
34135804136401.720979954-59.6061235599095135265.885143606597.720979954291
35135002135321.128469533-65.2993525111729134748.170882978319.128469533462
36134383134636.866704067-41.8911458621321134171.024441795253.866704066779
37133563133520.36903150211.7529678853309133593.878000613-42.6309684983571
38132837132657.5296649120.91194715315098133015.558387935-179.470335087739
39132041131601.09033910243.6708856420494132437.238775256-439.909660898178
40131381130795.41491659845.4199428221291131921.16514058-585.585083402373
41130995130478.139465339106.769028756549131405.091505904-516.860534660911
42130493129966.97800230922.2737536480584130996.748244043-526.021997690565
43130193129800.216611166-2.62159334637468130588.404982181-392.783388834272
44129962129632.196823078-16.2672706406742130308.070447562-329.803176921559
45129726129469.377067144-45.1129800876134130027.735912944-256.622932856233
46129505129191.671759752-59.6061235599095129877.934363808-313.328240248113
47129450129237.166537839-65.2993525111729129728.132814672-212.833462161012
48129320129004.900707305-41.8911458621321129676.990438557-315.099292694533
49129281128924.39896967411.7529678853309129625.848062441-356.601030326477
50129246128858.9194081440.91194715315098129632.168644703-387.080591855964
51129438129193.83988739343.6708856420494129638.489226964-244.160112606522
52129715129687.97544342245.4199428221291129696.604613755-27.024556577584
53130173130484.510970697106.769028756549129754.720000546311.51097069704
54129981130119.43507076422.2737536480584129820.291175588138.435070764273
55129932129980.759242717-2.62159334637468129885.86235062948.7592427174386
56129873129807.528147409-16.2672706406742129954.739123232-65.4718525909993
57129844129709.497084253-45.1129800876134130023.615895834-134.502915746809
58130015129996.01437927-59.6061235599095130093.59174429-18.9856207303965
59130108130117.731759765-65.2993525111729130163.5675927469.73175976498169
60130260130327.89019236-41.8911458621321130234.00095350267.890192360006



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')