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Description of Statistical Computation

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 computationTue, 08 Dec 2015 15:45:08 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/08/t1449589577cb51pvr2hqjoq16.htm/, Retrieved Thu, 16 May 2024 20:54:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285536, Retrieved Thu, 16 May 2024 20:54:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [HPC Retail Sales] [2008-03-02 16:19:32] [74be16979710d4c4e7c6647856088456]
- R  D  [Classical Decomposition] [classical decompo...] [2015-12-08 14:52:05] [22b6f4a061c8797aa483199554a73d13]
- RMP       [Decomposition by Loess] [Loess totaal mannen] [2015-12-08 15:45:08] [20fcaaf1d4bc4a12bf87c6c50d624c14] [Current]
- RMP         [Structural Time Series Models] [structural time s...] [2015-12-08 16:02:26] [22b6f4a061c8797aa483199554a73d13]
- RMPD          [Classical Decomposition] [classical decompo...] [2015-12-08 17:17:05] [22b6f4a061c8797aa483199554a73d13]
- RMP             [Decomposition by Loess] [Loess totaal vrouwen] [2015-12-08 17:53:15] [22b6f4a061c8797aa483199554a73d13]
- RMP               [Structural Time Series Models] [structural time s...] [2015-12-08 18:02:34] [22b6f4a061c8797aa483199554a73d13]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
287224
279998
283495
285775
282329
277799
271980
266730
262433
285378
286692
282917
277686
274371
277466
290604
290770
283654
278601
274405
272817
294292
300562
298982
296917
295008
297295
305671
303853
300708
298194
292254
290646
314707
317009
317706
313312
311048
315917
326174
322116
317092
310468
302438
298493
320124
321873
321676
316696
312612
313307
320883
318749
315126
304600
295245
293619
309700
310597
307416
301126

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'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285536&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285536&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'George Udny Yule' @ yule.wessa.net






Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal611062
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 611 & 0 & 62 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285536&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]611[/C][C]0[/C][C]62[/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=285536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285536&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:

Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal611062
Trend1912
Low-pass1312






Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1287224290864.9268493751596.39048834567281986.6826622793640.92684937548
2279998280870.914048442-2358.54095419199281483.62690575872.914048442093
3283495285901.132538284108.296312494754280980.5711492212406.13253828441
4285775282885.5062481248141.89291088331280522.600840993-2889.49375187582
5282329279001.2820485775592.0874186587280064.630532764-3327.71795142285
6277799275294.993357256678.609155837559279624.397486907-2504.00664274412
7271980270430.30241283-5654.46685387867279184.164441049-1549.69758717029
8266730267123.231313486-12431.6231935571278768.391880071393.23131348606
9262433261780.760463851-15267.3797829444278352.619319093-652.239536148671
10285378286721.1976350495691.45157393469278343.3507910161343.19763504912
11286692287131.8354252327918.0823118285278334.082262939439.835425232188
12282917281030.9414931365985.19890482799278817.859602036-1886.05850686372
13277686274473.9725705221596.39048834567279301.636941132-3212.02742947778
14274371271106.706546336-2358.54095419199279993.834407856-3264.29345366429
15277466274137.671812925108.296312494754280686.03187458-3328.32818707515
16290604291385.6636031748141.89291088331281680.443485942781.663603174384
17290770293273.0574840375592.0874186587282674.8550973042503.05748403701
18283654282585.261153459678.609155837559284044.129690703-1068.73884654086
19278601277443.062569776-5654.46685387867285413.404284102-1157.93743022362
20274405274287.573373582-12431.6231935571286954.049819975-117.426626418077
21272817272406.684427096-15267.3797829444288494.695355848-410.315572903608
22294292292960.4213589495691.45157393469289932.127067117-1331.57864105137
23300562301836.3589097867918.0823118285291369.5587783851274.35890978621
24298982299200.811118525985.19890482799292777.989976652218.811118519749
25296917298051.1883367351596.39048834567294186.4211749191134.18833673513
26295008296707.057059591-2358.54095419199295667.4838946011699.05705959105
27297295297333.157073223108.296312494754297148.54661428338.1570732226246
28305671304559.0254889778141.89291088331298641.08160014-1111.97451102349
29303853301980.2959953445592.0874186587300133.616585998-1872.70400465641
30300708299134.998600826678.609155837559301602.392243337-1573.0013991741
31298194298971.298953203-5654.46685387867303071.167900675777.298953203252
32292254292356.065905083-12431.6231935571304583.557288474102.065905082796
33290646290463.433106671-15267.3797829444306095.946676273-182.566893328796
34314707316079.2389392235691.45157393469307643.3094868421372.23893922305
35317009316909.245390767918.0823118285309190.672297411-99.7546092397533
36317706318902.3788620375985.19890482799310524.4222331351196.37886203662
37313312313169.4373427951596.39048834567311858.17216886-142.562657205155
38311048311686.750632441-2358.54095419199312767.790321751638.750632440613
39315917318048.295212862108.296312494754313677.4084746432131.29521286197
40326174330001.2484712788141.89291088331314204.8586178393827.24847127765
41322116323907.6038203065592.0874186587314732.3087610351791.60382030642
42317092318525.029104241678.609155837559314980.3617399221433.0291042407
43310468311362.05213507-5654.46685387867315228.414718809894.052135070087
44302438302101.893860977-12431.6231935571315205.72933258-336.10613902309
45298493297070.335836593-15267.3797829444315183.043946352-1422.66416340735
46320124319609.5800615585691.45157393469314946.968364507-514.419938441948
47321873321117.0249055097918.0823118285314710.892782663-755.975094491267
48321676322967.8502008225985.19890482799314398.950894351291.8502008224
49316696317708.6005056181596.39048834567314087.0090060361012.60050561791
50312612313952.918236175-2358.54095419199313629.6227180171340.91823617468
51313307313333.467257507108.296312494754313172.23642999826.467257507029
52320883321364.8048440468141.89291088331312259.302245071481.804844045721
53318749320559.5445211985592.0874186587311346.3680601441810.54452119756
54315126319436.074373478678.609155837559310137.3164706854310.07437347772
55304600305926.201972653-5654.46685387867308928.2648812261326.20197265293
56295245295228.843844665-12431.6231935571307692.779348892-16.1561553348438
57293619296048.085966386-15267.3797829444306457.2938165582429.08596638631
58309700308537.8349098735691.45157393469305170.713516192-1162.16509012674
59310597309391.7844723467918.0823118285303884.133215826-1205.21552765445
60307416306309.9428853865985.19890482799302536.858209786-1106.05711461435
61301126299466.0263079081596.39048834567301189.583203747-1659.97369209246

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 287224 & 290864.926849375 & 1596.39048834567 & 281986.682662279 & 3640.92684937548 \tabularnewline
2 & 279998 & 280870.914048442 & -2358.54095419199 & 281483.62690575 & 872.914048442093 \tabularnewline
3 & 283495 & 285901.132538284 & 108.296312494754 & 280980.571149221 & 2406.13253828441 \tabularnewline
4 & 285775 & 282885.506248124 & 8141.89291088331 & 280522.600840993 & -2889.49375187582 \tabularnewline
5 & 282329 & 279001.282048577 & 5592.0874186587 & 280064.630532764 & -3327.71795142285 \tabularnewline
6 & 277799 & 275294.993357256 & 678.609155837559 & 279624.397486907 & -2504.00664274412 \tabularnewline
7 & 271980 & 270430.30241283 & -5654.46685387867 & 279184.164441049 & -1549.69758717029 \tabularnewline
8 & 266730 & 267123.231313486 & -12431.6231935571 & 278768.391880071 & 393.23131348606 \tabularnewline
9 & 262433 & 261780.760463851 & -15267.3797829444 & 278352.619319093 & -652.239536148671 \tabularnewline
10 & 285378 & 286721.197635049 & 5691.45157393469 & 278343.350791016 & 1343.19763504912 \tabularnewline
11 & 286692 & 287131.835425232 & 7918.0823118285 & 278334.082262939 & 439.835425232188 \tabularnewline
12 & 282917 & 281030.941493136 & 5985.19890482799 & 278817.859602036 & -1886.05850686372 \tabularnewline
13 & 277686 & 274473.972570522 & 1596.39048834567 & 279301.636941132 & -3212.02742947778 \tabularnewline
14 & 274371 & 271106.706546336 & -2358.54095419199 & 279993.834407856 & -3264.29345366429 \tabularnewline
15 & 277466 & 274137.671812925 & 108.296312494754 & 280686.03187458 & -3328.32818707515 \tabularnewline
16 & 290604 & 291385.663603174 & 8141.89291088331 & 281680.443485942 & 781.663603174384 \tabularnewline
17 & 290770 & 293273.057484037 & 5592.0874186587 & 282674.855097304 & 2503.05748403701 \tabularnewline
18 & 283654 & 282585.261153459 & 678.609155837559 & 284044.129690703 & -1068.73884654086 \tabularnewline
19 & 278601 & 277443.062569776 & -5654.46685387867 & 285413.404284102 & -1157.93743022362 \tabularnewline
20 & 274405 & 274287.573373582 & -12431.6231935571 & 286954.049819975 & -117.426626418077 \tabularnewline
21 & 272817 & 272406.684427096 & -15267.3797829444 & 288494.695355848 & -410.315572903608 \tabularnewline
22 & 294292 & 292960.421358949 & 5691.45157393469 & 289932.127067117 & -1331.57864105137 \tabularnewline
23 & 300562 & 301836.358909786 & 7918.0823118285 & 291369.558778385 & 1274.35890978621 \tabularnewline
24 & 298982 & 299200.81111852 & 5985.19890482799 & 292777.989976652 & 218.811118519749 \tabularnewline
25 & 296917 & 298051.188336735 & 1596.39048834567 & 294186.421174919 & 1134.18833673513 \tabularnewline
26 & 295008 & 296707.057059591 & -2358.54095419199 & 295667.483894601 & 1699.05705959105 \tabularnewline
27 & 297295 & 297333.157073223 & 108.296312494754 & 297148.546614283 & 38.1570732226246 \tabularnewline
28 & 305671 & 304559.025488977 & 8141.89291088331 & 298641.08160014 & -1111.97451102349 \tabularnewline
29 & 303853 & 301980.295995344 & 5592.0874186587 & 300133.616585998 & -1872.70400465641 \tabularnewline
30 & 300708 & 299134.998600826 & 678.609155837559 & 301602.392243337 & -1573.0013991741 \tabularnewline
31 & 298194 & 298971.298953203 & -5654.46685387867 & 303071.167900675 & 777.298953203252 \tabularnewline
32 & 292254 & 292356.065905083 & -12431.6231935571 & 304583.557288474 & 102.065905082796 \tabularnewline
33 & 290646 & 290463.433106671 & -15267.3797829444 & 306095.946676273 & -182.566893328796 \tabularnewline
34 & 314707 & 316079.238939223 & 5691.45157393469 & 307643.309486842 & 1372.23893922305 \tabularnewline
35 & 317009 & 316909.24539076 & 7918.0823118285 & 309190.672297411 & -99.7546092397533 \tabularnewline
36 & 317706 & 318902.378862037 & 5985.19890482799 & 310524.422233135 & 1196.37886203662 \tabularnewline
37 & 313312 & 313169.437342795 & 1596.39048834567 & 311858.17216886 & -142.562657205155 \tabularnewline
38 & 311048 & 311686.750632441 & -2358.54095419199 & 312767.790321751 & 638.750632440613 \tabularnewline
39 & 315917 & 318048.295212862 & 108.296312494754 & 313677.408474643 & 2131.29521286197 \tabularnewline
40 & 326174 & 330001.248471278 & 8141.89291088331 & 314204.858617839 & 3827.24847127765 \tabularnewline
41 & 322116 & 323907.603820306 & 5592.0874186587 & 314732.308761035 & 1791.60382030642 \tabularnewline
42 & 317092 & 318525.029104241 & 678.609155837559 & 314980.361739922 & 1433.0291042407 \tabularnewline
43 & 310468 & 311362.05213507 & -5654.46685387867 & 315228.414718809 & 894.052135070087 \tabularnewline
44 & 302438 & 302101.893860977 & -12431.6231935571 & 315205.72933258 & -336.10613902309 \tabularnewline
45 & 298493 & 297070.335836593 & -15267.3797829444 & 315183.043946352 & -1422.66416340735 \tabularnewline
46 & 320124 & 319609.580061558 & 5691.45157393469 & 314946.968364507 & -514.419938441948 \tabularnewline
47 & 321873 & 321117.024905509 & 7918.0823118285 & 314710.892782663 & -755.975094491267 \tabularnewline
48 & 321676 & 322967.850200822 & 5985.19890482799 & 314398.95089435 & 1291.8502008224 \tabularnewline
49 & 316696 & 317708.600505618 & 1596.39048834567 & 314087.009006036 & 1012.60050561791 \tabularnewline
50 & 312612 & 313952.918236175 & -2358.54095419199 & 313629.622718017 & 1340.91823617468 \tabularnewline
51 & 313307 & 313333.467257507 & 108.296312494754 & 313172.236429998 & 26.467257507029 \tabularnewline
52 & 320883 & 321364.804844046 & 8141.89291088331 & 312259.302245071 & 481.804844045721 \tabularnewline
53 & 318749 & 320559.544521198 & 5592.0874186587 & 311346.368060144 & 1810.54452119756 \tabularnewline
54 & 315126 & 319436.074373478 & 678.609155837559 & 310137.316470685 & 4310.07437347772 \tabularnewline
55 & 304600 & 305926.201972653 & -5654.46685387867 & 308928.264881226 & 1326.20197265293 \tabularnewline
56 & 295245 & 295228.843844665 & -12431.6231935571 & 307692.779348892 & -16.1561553348438 \tabularnewline
57 & 293619 & 296048.085966386 & -15267.3797829444 & 306457.293816558 & 2429.08596638631 \tabularnewline
58 & 309700 & 308537.834909873 & 5691.45157393469 & 305170.713516192 & -1162.16509012674 \tabularnewline
59 & 310597 & 309391.784472346 & 7918.0823118285 & 303884.133215826 & -1205.21552765445 \tabularnewline
60 & 307416 & 306309.942885386 & 5985.19890482799 & 302536.858209786 & -1106.05711461435 \tabularnewline
61 & 301126 & 299466.026307908 & 1596.39048834567 & 301189.583203747 & -1659.97369209246 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285536&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]287224[/C][C]290864.926849375[/C][C]1596.39048834567[/C][C]281986.682662279[/C][C]3640.92684937548[/C][/ROW]
[ROW][C]2[/C][C]279998[/C][C]280870.914048442[/C][C]-2358.54095419199[/C][C]281483.62690575[/C][C]872.914048442093[/C][/ROW]
[ROW][C]3[/C][C]283495[/C][C]285901.132538284[/C][C]108.296312494754[/C][C]280980.571149221[/C][C]2406.13253828441[/C][/ROW]
[ROW][C]4[/C][C]285775[/C][C]282885.506248124[/C][C]8141.89291088331[/C][C]280522.600840993[/C][C]-2889.49375187582[/C][/ROW]
[ROW][C]5[/C][C]282329[/C][C]279001.282048577[/C][C]5592.0874186587[/C][C]280064.630532764[/C][C]-3327.71795142285[/C][/ROW]
[ROW][C]6[/C][C]277799[/C][C]275294.993357256[/C][C]678.609155837559[/C][C]279624.397486907[/C][C]-2504.00664274412[/C][/ROW]
[ROW][C]7[/C][C]271980[/C][C]270430.30241283[/C][C]-5654.46685387867[/C][C]279184.164441049[/C][C]-1549.69758717029[/C][/ROW]
[ROW][C]8[/C][C]266730[/C][C]267123.231313486[/C][C]-12431.6231935571[/C][C]278768.391880071[/C][C]393.23131348606[/C][/ROW]
[ROW][C]9[/C][C]262433[/C][C]261780.760463851[/C][C]-15267.3797829444[/C][C]278352.619319093[/C][C]-652.239536148671[/C][/ROW]
[ROW][C]10[/C][C]285378[/C][C]286721.197635049[/C][C]5691.45157393469[/C][C]278343.350791016[/C][C]1343.19763504912[/C][/ROW]
[ROW][C]11[/C][C]286692[/C][C]287131.835425232[/C][C]7918.0823118285[/C][C]278334.082262939[/C][C]439.835425232188[/C][/ROW]
[ROW][C]12[/C][C]282917[/C][C]281030.941493136[/C][C]5985.19890482799[/C][C]278817.859602036[/C][C]-1886.05850686372[/C][/ROW]
[ROW][C]13[/C][C]277686[/C][C]274473.972570522[/C][C]1596.39048834567[/C][C]279301.636941132[/C][C]-3212.02742947778[/C][/ROW]
[ROW][C]14[/C][C]274371[/C][C]271106.706546336[/C][C]-2358.54095419199[/C][C]279993.834407856[/C][C]-3264.29345366429[/C][/ROW]
[ROW][C]15[/C][C]277466[/C][C]274137.671812925[/C][C]108.296312494754[/C][C]280686.03187458[/C][C]-3328.32818707515[/C][/ROW]
[ROW][C]16[/C][C]290604[/C][C]291385.663603174[/C][C]8141.89291088331[/C][C]281680.443485942[/C][C]781.663603174384[/C][/ROW]
[ROW][C]17[/C][C]290770[/C][C]293273.057484037[/C][C]5592.0874186587[/C][C]282674.855097304[/C][C]2503.05748403701[/C][/ROW]
[ROW][C]18[/C][C]283654[/C][C]282585.261153459[/C][C]678.609155837559[/C][C]284044.129690703[/C][C]-1068.73884654086[/C][/ROW]
[ROW][C]19[/C][C]278601[/C][C]277443.062569776[/C][C]-5654.46685387867[/C][C]285413.404284102[/C][C]-1157.93743022362[/C][/ROW]
[ROW][C]20[/C][C]274405[/C][C]274287.573373582[/C][C]-12431.6231935571[/C][C]286954.049819975[/C][C]-117.426626418077[/C][/ROW]
[ROW][C]21[/C][C]272817[/C][C]272406.684427096[/C][C]-15267.3797829444[/C][C]288494.695355848[/C][C]-410.315572903608[/C][/ROW]
[ROW][C]22[/C][C]294292[/C][C]292960.421358949[/C][C]5691.45157393469[/C][C]289932.127067117[/C][C]-1331.57864105137[/C][/ROW]
[ROW][C]23[/C][C]300562[/C][C]301836.358909786[/C][C]7918.0823118285[/C][C]291369.558778385[/C][C]1274.35890978621[/C][/ROW]
[ROW][C]24[/C][C]298982[/C][C]299200.81111852[/C][C]5985.19890482799[/C][C]292777.989976652[/C][C]218.811118519749[/C][/ROW]
[ROW][C]25[/C][C]296917[/C][C]298051.188336735[/C][C]1596.39048834567[/C][C]294186.421174919[/C][C]1134.18833673513[/C][/ROW]
[ROW][C]26[/C][C]295008[/C][C]296707.057059591[/C][C]-2358.54095419199[/C][C]295667.483894601[/C][C]1699.05705959105[/C][/ROW]
[ROW][C]27[/C][C]297295[/C][C]297333.157073223[/C][C]108.296312494754[/C][C]297148.546614283[/C][C]38.1570732226246[/C][/ROW]
[ROW][C]28[/C][C]305671[/C][C]304559.025488977[/C][C]8141.89291088331[/C][C]298641.08160014[/C][C]-1111.97451102349[/C][/ROW]
[ROW][C]29[/C][C]303853[/C][C]301980.295995344[/C][C]5592.0874186587[/C][C]300133.616585998[/C][C]-1872.70400465641[/C][/ROW]
[ROW][C]30[/C][C]300708[/C][C]299134.998600826[/C][C]678.609155837559[/C][C]301602.392243337[/C][C]-1573.0013991741[/C][/ROW]
[ROW][C]31[/C][C]298194[/C][C]298971.298953203[/C][C]-5654.46685387867[/C][C]303071.167900675[/C][C]777.298953203252[/C][/ROW]
[ROW][C]32[/C][C]292254[/C][C]292356.065905083[/C][C]-12431.6231935571[/C][C]304583.557288474[/C][C]102.065905082796[/C][/ROW]
[ROW][C]33[/C][C]290646[/C][C]290463.433106671[/C][C]-15267.3797829444[/C][C]306095.946676273[/C][C]-182.566893328796[/C][/ROW]
[ROW][C]34[/C][C]314707[/C][C]316079.238939223[/C][C]5691.45157393469[/C][C]307643.309486842[/C][C]1372.23893922305[/C][/ROW]
[ROW][C]35[/C][C]317009[/C][C]316909.24539076[/C][C]7918.0823118285[/C][C]309190.672297411[/C][C]-99.7546092397533[/C][/ROW]
[ROW][C]36[/C][C]317706[/C][C]318902.378862037[/C][C]5985.19890482799[/C][C]310524.422233135[/C][C]1196.37886203662[/C][/ROW]
[ROW][C]37[/C][C]313312[/C][C]313169.437342795[/C][C]1596.39048834567[/C][C]311858.17216886[/C][C]-142.562657205155[/C][/ROW]
[ROW][C]38[/C][C]311048[/C][C]311686.750632441[/C][C]-2358.54095419199[/C][C]312767.790321751[/C][C]638.750632440613[/C][/ROW]
[ROW][C]39[/C][C]315917[/C][C]318048.295212862[/C][C]108.296312494754[/C][C]313677.408474643[/C][C]2131.29521286197[/C][/ROW]
[ROW][C]40[/C][C]326174[/C][C]330001.248471278[/C][C]8141.89291088331[/C][C]314204.858617839[/C][C]3827.24847127765[/C][/ROW]
[ROW][C]41[/C][C]322116[/C][C]323907.603820306[/C][C]5592.0874186587[/C][C]314732.308761035[/C][C]1791.60382030642[/C][/ROW]
[ROW][C]42[/C][C]317092[/C][C]318525.029104241[/C][C]678.609155837559[/C][C]314980.361739922[/C][C]1433.0291042407[/C][/ROW]
[ROW][C]43[/C][C]310468[/C][C]311362.05213507[/C][C]-5654.46685387867[/C][C]315228.414718809[/C][C]894.052135070087[/C][/ROW]
[ROW][C]44[/C][C]302438[/C][C]302101.893860977[/C][C]-12431.6231935571[/C][C]315205.72933258[/C][C]-336.10613902309[/C][/ROW]
[ROW][C]45[/C][C]298493[/C][C]297070.335836593[/C][C]-15267.3797829444[/C][C]315183.043946352[/C][C]-1422.66416340735[/C][/ROW]
[ROW][C]46[/C][C]320124[/C][C]319609.580061558[/C][C]5691.45157393469[/C][C]314946.968364507[/C][C]-514.419938441948[/C][/ROW]
[ROW][C]47[/C][C]321873[/C][C]321117.024905509[/C][C]7918.0823118285[/C][C]314710.892782663[/C][C]-755.975094491267[/C][/ROW]
[ROW][C]48[/C][C]321676[/C][C]322967.850200822[/C][C]5985.19890482799[/C][C]314398.95089435[/C][C]1291.8502008224[/C][/ROW]
[ROW][C]49[/C][C]316696[/C][C]317708.600505618[/C][C]1596.39048834567[/C][C]314087.009006036[/C][C]1012.60050561791[/C][/ROW]
[ROW][C]50[/C][C]312612[/C][C]313952.918236175[/C][C]-2358.54095419199[/C][C]313629.622718017[/C][C]1340.91823617468[/C][/ROW]
[ROW][C]51[/C][C]313307[/C][C]313333.467257507[/C][C]108.296312494754[/C][C]313172.236429998[/C][C]26.467257507029[/C][/ROW]
[ROW][C]52[/C][C]320883[/C][C]321364.804844046[/C][C]8141.89291088331[/C][C]312259.302245071[/C][C]481.804844045721[/C][/ROW]
[ROW][C]53[/C][C]318749[/C][C]320559.544521198[/C][C]5592.0874186587[/C][C]311346.368060144[/C][C]1810.54452119756[/C][/ROW]
[ROW][C]54[/C][C]315126[/C][C]319436.074373478[/C][C]678.609155837559[/C][C]310137.316470685[/C][C]4310.07437347772[/C][/ROW]
[ROW][C]55[/C][C]304600[/C][C]305926.201972653[/C][C]-5654.46685387867[/C][C]308928.264881226[/C][C]1326.20197265293[/C][/ROW]
[ROW][C]56[/C][C]295245[/C][C]295228.843844665[/C][C]-12431.6231935571[/C][C]307692.779348892[/C][C]-16.1561553348438[/C][/ROW]
[ROW][C]57[/C][C]293619[/C][C]296048.085966386[/C][C]-15267.3797829444[/C][C]306457.293816558[/C][C]2429.08596638631[/C][/ROW]
[ROW][C]58[/C][C]309700[/C][C]308537.834909873[/C][C]5691.45157393469[/C][C]305170.713516192[/C][C]-1162.16509012674[/C][/ROW]
[ROW][C]59[/C][C]310597[/C][C]309391.784472346[/C][C]7918.0823118285[/C][C]303884.133215826[/C][C]-1205.21552765445[/C][/ROW]
[ROW][C]60[/C][C]307416[/C][C]306309.942885386[/C][C]5985.19890482799[/C][C]302536.858209786[/C][C]-1106.05711461435[/C][/ROW]
[ROW][C]61[/C][C]301126[/C][C]299466.026307908[/C][C]1596.39048834567[/C][C]301189.583203747[/C][C]-1659.97369209246[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285536&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285536&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1287224290864.9268493751596.39048834567281986.6826622793640.92684937548
2279998280870.914048442-2358.54095419199281483.62690575872.914048442093
3283495285901.132538284108.296312494754280980.5711492212406.13253828441
4285775282885.5062481248141.89291088331280522.600840993-2889.49375187582
5282329279001.2820485775592.0874186587280064.630532764-3327.71795142285
6277799275294.993357256678.609155837559279624.397486907-2504.00664274412
7271980270430.30241283-5654.46685387867279184.164441049-1549.69758717029
8266730267123.231313486-12431.6231935571278768.391880071393.23131348606
9262433261780.760463851-15267.3797829444278352.619319093-652.239536148671
10285378286721.1976350495691.45157393469278343.3507910161343.19763504912
11286692287131.8354252327918.0823118285278334.082262939439.835425232188
12282917281030.9414931365985.19890482799278817.859602036-1886.05850686372
13277686274473.9725705221596.39048834567279301.636941132-3212.02742947778
14274371271106.706546336-2358.54095419199279993.834407856-3264.29345366429
15277466274137.671812925108.296312494754280686.03187458-3328.32818707515
16290604291385.6636031748141.89291088331281680.443485942781.663603174384
17290770293273.0574840375592.0874186587282674.8550973042503.05748403701
18283654282585.261153459678.609155837559284044.129690703-1068.73884654086
19278601277443.062569776-5654.46685387867285413.404284102-1157.93743022362
20274405274287.573373582-12431.6231935571286954.049819975-117.426626418077
21272817272406.684427096-15267.3797829444288494.695355848-410.315572903608
22294292292960.4213589495691.45157393469289932.127067117-1331.57864105137
23300562301836.3589097867918.0823118285291369.5587783851274.35890978621
24298982299200.811118525985.19890482799292777.989976652218.811118519749
25296917298051.1883367351596.39048834567294186.4211749191134.18833673513
26295008296707.057059591-2358.54095419199295667.4838946011699.05705959105
27297295297333.157073223108.296312494754297148.54661428338.1570732226246
28305671304559.0254889778141.89291088331298641.08160014-1111.97451102349
29303853301980.2959953445592.0874186587300133.616585998-1872.70400465641
30300708299134.998600826678.609155837559301602.392243337-1573.0013991741
31298194298971.298953203-5654.46685387867303071.167900675777.298953203252
32292254292356.065905083-12431.6231935571304583.557288474102.065905082796
33290646290463.433106671-15267.3797829444306095.946676273-182.566893328796
34314707316079.2389392235691.45157393469307643.3094868421372.23893922305
35317009316909.245390767918.0823118285309190.672297411-99.7546092397533
36317706318902.3788620375985.19890482799310524.4222331351196.37886203662
37313312313169.4373427951596.39048834567311858.17216886-142.562657205155
38311048311686.750632441-2358.54095419199312767.790321751638.750632440613
39315917318048.295212862108.296312494754313677.4084746432131.29521286197
40326174330001.2484712788141.89291088331314204.8586178393827.24847127765
41322116323907.6038203065592.0874186587314732.3087610351791.60382030642
42317092318525.029104241678.609155837559314980.3617399221433.0291042407
43310468311362.05213507-5654.46685387867315228.414718809894.052135070087
44302438302101.893860977-12431.6231935571315205.72933258-336.10613902309
45298493297070.335836593-15267.3797829444315183.043946352-1422.66416340735
46320124319609.5800615585691.45157393469314946.968364507-514.419938441948
47321873321117.0249055097918.0823118285314710.892782663-755.975094491267
48321676322967.8502008225985.19890482799314398.950894351291.8502008224
49316696317708.6005056181596.39048834567314087.0090060361012.60050561791
50312612313952.918236175-2358.54095419199313629.6227180171340.91823617468
51313307313333.467257507108.296312494754313172.23642999826.467257507029
52320883321364.8048440468141.89291088331312259.302245071481.804844045721
53318749320559.5445211985592.0874186587311346.3680601441810.54452119756
54315126319436.074373478678.609155837559310137.3164706854310.07437347772
55304600305926.201972653-5654.46685387867308928.2648812261326.20197265293
56295245295228.843844665-12431.6231935571307692.779348892-16.1561553348438
57293619296048.085966386-15267.3797829444306457.2938165582429.08596638631
58309700308537.8349098735691.45157393469305170.713516192-1162.16509012674
59310597309391.7844723467918.0823118285303884.133215826-1205.21552765445
60307416306309.9428853865985.19890482799302536.858209786-1106.05711461435
61301126299466.0263079081596.39048834567301189.583203747-1659.97369209246


Figures (Output of Computation)

Input Parameters & R Code

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')