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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, 29 Nov 2011 08:40:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322574058z5uxzhx9lgmyx98.htm/, Retrieved Thu, 18 Apr 2024 05:00:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148344, Retrieved Thu, 18 Apr 2024 05:00:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

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]
- R P         [Univariate Data Series] [WS8 Time Series A...] [2010-11-30 09:10:08] [afe9379cca749d06b3d6872e02cc47ed]
- R PD          [Univariate Data Series] [ws8-1] [2011-11-29 11:47:38] [f7a862281046b7153543b12c78921b36]
- RMPD              [Decomposition by Loess] [ws8-3] [2011-11-29 13:40:38] [47995d3a8fac585eeb070a274b466f8c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12008
9169
8788
8417
8247
8197
8236
8253
7733
8366
8626
8863
10102
8463
9114
8563
8872
8301
8301
8278
7736
7973
8268
9476
11100
8962
9173
8738
8459
8078
8411
8291
7810
8616
8312
9692
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148344&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]3 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=148344&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148344&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 time3 seconds
R Server'George Udny Yule' @ yule.wessa.net






Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal961097
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 961 & 0 & 97 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148344&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]961[/C][C]0[/C][C]97[/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=148344&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148344&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
Seasonal961097
Trend1912
Low-pass1312






Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11200813251.01005047471665.306447301869099.683502223421243.01005047472
291699069.2147052992236.6579545665729032.12734013424-99.7852947008068
387887973.66913452228637.7596874326698964.57117804505-814.330865477718
484178028.1807749118-93.17707487061328898.99629995881-388.819225088193
582477951.8173120407-291.238733913268833.42142187256-295.182687959301
681978175.7373008356-549.3198143223848767.58251348679-21.2626991644047
782368139.03224123255-368.7758463335668701.74360510102-96.9677587674505
882538463.02944203181-601.9525028996788644.92306086787210.02944203181
977337726.90171897526-849.0042356099778588.10251663472-6.09828102474421
1083668425.72465323181-276.9743817849928583.2497285531859.7246532318131
1186268912.1728962229-238.5698366945338578.39694047164286.172896222895
1288638406.86732471246729.2883557900238589.84431949752-456.132675287539
13101029937.401854174741665.306447301868601.2916985234-164.598145825255
1484638080.84031276675236.6579545665728608.50173266668-382.159687233254
1591148974.52854575736637.7596874326698615.71176680997-139.471454242639
1685638599.68955852511-93.17707487061328619.487516345536.6895585251132
1788729411.97546803223-291.238733913268623.26326588103539.975468032228
1883018503.1755894842-549.3198143223848648.14422483818202.175589484201
1983018297.75066253823-368.7758463335668673.02518379533-3.24933746176976
2082788457.87443198398-601.9525028996788700.0780709157179.874431983983
2177367593.87327757392-849.0042356099778727.13095803606-142.126722426079
2279737492.23678920662-276.9743817849928730.73759257837-480.763210793375
2382688040.22560957386-238.5698366945338734.34422712068-227.774390426144
2494769492.74874263702729.2883557900238729.9629015729616.7487426370153
251110011809.11197667291665.306447301868725.58157602525709.111976672892
2689628950.97522736594236.6579545665728736.36681806749-11.024772634064
2791738961.0882524576637.7596874326698747.15206010973-211.911747542405
2887388810.94873910594-93.17707487061328758.2283357646872.9487391059356
2984598439.93412249364-291.238733913268769.30461141962-19.0658775063584
3080787945.57697207037-549.3198143223848759.74284225201-132.42302792963
3184118440.59477324916-368.7758463335668750.1810730844129.5947732491568
3282918445.25546694407-601.9525028996788738.69703595561154.255466944071
3378107741.79123678317-849.0042356099778727.2129988268-68.2087632168314
3486168774.27947921438-276.9743817849928734.6949025706158.279479214385
3583128120.39303038013-238.5698366945338742.1768063144-191.606969619874
3696929901.38159563134729.2883557900238753.33004857864209.381595631336
3799119392.210261855261665.306447301868764.48329084288-518.789738144738
3889158811.06532623422236.6579545665728782.27671919921-103.934673765782
3994529466.1701650118637.7596874326698800.0701475555414.1701650117921
4091129487.69616533108-93.17707487061328829.48090953953375.696165331083
4184728376.34706238974-291.238733913268858.89167152352-95.6529376102626
4282308118.7951741021-549.3198143223848890.52464022028-111.204825897896
4383848214.61823741653-368.7758463335668922.15760891704-169.381762583471
4486258932.6307078908-601.9525028996788919.32179500888307.630707890794
4582218374.51825450925-849.0042356099778916.48598110073153.518254509247
4686498697.23524969104-276.9743817849928877.7391320939548.2352496910389
4786258649.57755360736-238.5698366945338838.9922830871724.5775536073579
481044311364.8349337838729.2883557900238791.87671042619921.834933783786
491035710303.93241493291665.306447301868744.7611377652-53.0675850670668
5085868240.47236915639236.6579545665728694.86967627704-345.527630843613
5188928501.26209777846637.7596874326698644.97821478887-390.737902221543
5283298151.35254298267-93.17707487061328599.82453188795-177.647457017332
5381017938.56788492624-291.238733913268554.67084898702-162.432115073758
5479227859.63926898677-549.3198143223848533.68054533561-62.3607310132302
5581208096.08560464936-368.7758463335668512.6902416842-23.9143953506427
5678387731.53721859468-601.9525028996788546.415284305-106.462781405318
5777357738.86390868419-849.0042356099778580.140326925783.86390868419403
5884068458.93721526025-276.9743817849928630.0371665247452.9372152602518
5982097976.63583057083-238.5698366945338679.9340061237-232.364169429167
6094519476.05705356138729.2883557900238696.654590648625.0570535613751
61100419703.318377524631665.306447301868713.3751751735-337.681622475366
6294119887.58942709805236.6579545665728697.75261833538476.589427098052
631040511490.1102510701637.7596874326698682.130061497251085.11025107008
6484678369.92008151995-93.17707487061328657.25699335066-97.0799184800508
6584648586.85480870918-291.238733913268632.38392520408122.854808709177
6681028159.06157062724-549.3198143223848594.2582436951557.0615706272383
6776277066.64328414736-368.7758463335668556.1325621862-560.356715852643
6875137120.57863208808-601.9525028996788507.3738708116-392.421367911922
6975107410.38905617299-849.0042356099778458.61517943699-99.6109438270123
7082918425.7207349122-276.9743817849928433.25364687278134.720734912207
7180647958.67772238595-238.5698366945338407.89211430858-105.32227761405
7293839603.95163858882729.2883557900238432.76000562116220.951638588822
7397069289.065655764411665.306447301868457.62789693373-416.934344235587
7485798429.96406615424236.6579545665728491.37797927919-149.035933845762
7594749785.11225094268637.7596874326698525.12806162465311.112250942677
7683188198.20956353839-93.17707487061328530.96751133222-119.790436461608
7782138180.43177287347-291.238733913268536.80696103979-32.5682271265287
7880598147.9590899952-549.3198143223848519.3607243271888.9590899952
79911110088.861358719-368.7758463335668501.91448761458977.861358718987
8077087532.81758031397-601.9525028996788485.13492258571-175.182419686033
8176807740.64887805313-849.0042356099778468.3553575568460.6488780531345
8280147859.39849690276-276.9743817849928445.57588488224-154.601503097245
8380077829.7734244869-238.5698366945338422.79641220763-177.226575513097
8487188316.06157950339729.2883557900238390.6500647066-401.938420496614
8594868948.18983549261665.306447301868358.50371720555-537.81016450741
8691139649.9745304112236.6579545665728339.36751502222536.974530411209
8790259092.00899972844637.7596874326698320.2313128388967.0089997284449
8884768703.90884070193-93.17707487061328341.26823416868227.90884070193
8979527832.93357841478-291.238733913268362.30515549848-119.066421585221
9077597690.54987573-549.3198143223848376.76993859239-68.4501242700044
9178357647.54112464727-368.7758463335668391.23472168629-187.458875352728
9276007398.41888384599-601.9525028996788403.53361905369-201.581116154008
9376517735.1717191889-849.0042356099778415.8325164210884.1717191888984
9483198486.41748542095-276.9743817849928428.55689636404167.417485420954
9588129421.28856038753-238.5698366945338441.281276307609.288560387535
9686308076.02840257242729.2883557900238454.68324163756-553.971597427584

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 12008 & 13251.0100504747 & 1665.30644730186 & 9099.68350222342 & 1243.01005047472 \tabularnewline
2 & 9169 & 9069.2147052992 & 236.657954566572 & 9032.12734013424 & -99.7852947008068 \tabularnewline
3 & 8788 & 7973.66913452228 & 637.759687432669 & 8964.57117804505 & -814.330865477718 \tabularnewline
4 & 8417 & 8028.1807749118 & -93.1770748706132 & 8898.99629995881 & -388.819225088193 \tabularnewline
5 & 8247 & 7951.8173120407 & -291.23873391326 & 8833.42142187256 & -295.182687959301 \tabularnewline
6 & 8197 & 8175.7373008356 & -549.319814322384 & 8767.58251348679 & -21.2626991644047 \tabularnewline
7 & 8236 & 8139.03224123255 & -368.775846333566 & 8701.74360510102 & -96.9677587674505 \tabularnewline
8 & 8253 & 8463.02944203181 & -601.952502899678 & 8644.92306086787 & 210.02944203181 \tabularnewline
9 & 7733 & 7726.90171897526 & -849.004235609977 & 8588.10251663472 & -6.09828102474421 \tabularnewline
10 & 8366 & 8425.72465323181 & -276.974381784992 & 8583.24972855318 & 59.7246532318131 \tabularnewline
11 & 8626 & 8912.1728962229 & -238.569836694533 & 8578.39694047164 & 286.172896222895 \tabularnewline
12 & 8863 & 8406.86732471246 & 729.288355790023 & 8589.84431949752 & -456.132675287539 \tabularnewline
13 & 10102 & 9937.40185417474 & 1665.30644730186 & 8601.2916985234 & -164.598145825255 \tabularnewline
14 & 8463 & 8080.84031276675 & 236.657954566572 & 8608.50173266668 & -382.159687233254 \tabularnewline
15 & 9114 & 8974.52854575736 & 637.759687432669 & 8615.71176680997 & -139.471454242639 \tabularnewline
16 & 8563 & 8599.68955852511 & -93.1770748706132 & 8619.4875163455 & 36.6895585251132 \tabularnewline
17 & 8872 & 9411.97546803223 & -291.23873391326 & 8623.26326588103 & 539.975468032228 \tabularnewline
18 & 8301 & 8503.1755894842 & -549.319814322384 & 8648.14422483818 & 202.175589484201 \tabularnewline
19 & 8301 & 8297.75066253823 & -368.775846333566 & 8673.02518379533 & -3.24933746176976 \tabularnewline
20 & 8278 & 8457.87443198398 & -601.952502899678 & 8700.0780709157 & 179.874431983983 \tabularnewline
21 & 7736 & 7593.87327757392 & -849.004235609977 & 8727.13095803606 & -142.126722426079 \tabularnewline
22 & 7973 & 7492.23678920662 & -276.974381784992 & 8730.73759257837 & -480.763210793375 \tabularnewline
23 & 8268 & 8040.22560957386 & -238.569836694533 & 8734.34422712068 & -227.774390426144 \tabularnewline
24 & 9476 & 9492.74874263702 & 729.288355790023 & 8729.96290157296 & 16.7487426370153 \tabularnewline
25 & 11100 & 11809.1119766729 & 1665.30644730186 & 8725.58157602525 & 709.111976672892 \tabularnewline
26 & 8962 & 8950.97522736594 & 236.657954566572 & 8736.36681806749 & -11.024772634064 \tabularnewline
27 & 9173 & 8961.0882524576 & 637.759687432669 & 8747.15206010973 & -211.911747542405 \tabularnewline
28 & 8738 & 8810.94873910594 & -93.1770748706132 & 8758.22833576468 & 72.9487391059356 \tabularnewline
29 & 8459 & 8439.93412249364 & -291.23873391326 & 8769.30461141962 & -19.0658775063584 \tabularnewline
30 & 8078 & 7945.57697207037 & -549.319814322384 & 8759.74284225201 & -132.42302792963 \tabularnewline
31 & 8411 & 8440.59477324916 & -368.775846333566 & 8750.18107308441 & 29.5947732491568 \tabularnewline
32 & 8291 & 8445.25546694407 & -601.952502899678 & 8738.69703595561 & 154.255466944071 \tabularnewline
33 & 7810 & 7741.79123678317 & -849.004235609977 & 8727.2129988268 & -68.2087632168314 \tabularnewline
34 & 8616 & 8774.27947921438 & -276.974381784992 & 8734.6949025706 & 158.279479214385 \tabularnewline
35 & 8312 & 8120.39303038013 & -238.569836694533 & 8742.1768063144 & -191.606969619874 \tabularnewline
36 & 9692 & 9901.38159563134 & 729.288355790023 & 8753.33004857864 & 209.381595631336 \tabularnewline
37 & 9911 & 9392.21026185526 & 1665.30644730186 & 8764.48329084288 & -518.789738144738 \tabularnewline
38 & 8915 & 8811.06532623422 & 236.657954566572 & 8782.27671919921 & -103.934673765782 \tabularnewline
39 & 9452 & 9466.1701650118 & 637.759687432669 & 8800.07014755554 & 14.1701650117921 \tabularnewline
40 & 9112 & 9487.69616533108 & -93.1770748706132 & 8829.48090953953 & 375.696165331083 \tabularnewline
41 & 8472 & 8376.34706238974 & -291.23873391326 & 8858.89167152352 & -95.6529376102626 \tabularnewline
42 & 8230 & 8118.7951741021 & -549.319814322384 & 8890.52464022028 & -111.204825897896 \tabularnewline
43 & 8384 & 8214.61823741653 & -368.775846333566 & 8922.15760891704 & -169.381762583471 \tabularnewline
44 & 8625 & 8932.6307078908 & -601.952502899678 & 8919.32179500888 & 307.630707890794 \tabularnewline
45 & 8221 & 8374.51825450925 & -849.004235609977 & 8916.48598110073 & 153.518254509247 \tabularnewline
46 & 8649 & 8697.23524969104 & -276.974381784992 & 8877.73913209395 & 48.2352496910389 \tabularnewline
47 & 8625 & 8649.57755360736 & -238.569836694533 & 8838.99228308717 & 24.5775536073579 \tabularnewline
48 & 10443 & 11364.8349337838 & 729.288355790023 & 8791.87671042619 & 921.834933783786 \tabularnewline
49 & 10357 & 10303.9324149329 & 1665.30644730186 & 8744.7611377652 & -53.0675850670668 \tabularnewline
50 & 8586 & 8240.47236915639 & 236.657954566572 & 8694.86967627704 & -345.527630843613 \tabularnewline
51 & 8892 & 8501.26209777846 & 637.759687432669 & 8644.97821478887 & -390.737902221543 \tabularnewline
52 & 8329 & 8151.35254298267 & -93.1770748706132 & 8599.82453188795 & -177.647457017332 \tabularnewline
53 & 8101 & 7938.56788492624 & -291.23873391326 & 8554.67084898702 & -162.432115073758 \tabularnewline
54 & 7922 & 7859.63926898677 & -549.319814322384 & 8533.68054533561 & -62.3607310132302 \tabularnewline
55 & 8120 & 8096.08560464936 & -368.775846333566 & 8512.6902416842 & -23.9143953506427 \tabularnewline
56 & 7838 & 7731.53721859468 & -601.952502899678 & 8546.415284305 & -106.462781405318 \tabularnewline
57 & 7735 & 7738.86390868419 & -849.004235609977 & 8580.14032692578 & 3.86390868419403 \tabularnewline
58 & 8406 & 8458.93721526025 & -276.974381784992 & 8630.03716652474 & 52.9372152602518 \tabularnewline
59 & 8209 & 7976.63583057083 & -238.569836694533 & 8679.9340061237 & -232.364169429167 \tabularnewline
60 & 9451 & 9476.05705356138 & 729.288355790023 & 8696.6545906486 & 25.0570535613751 \tabularnewline
61 & 10041 & 9703.31837752463 & 1665.30644730186 & 8713.3751751735 & -337.681622475366 \tabularnewline
62 & 9411 & 9887.58942709805 & 236.657954566572 & 8697.75261833538 & 476.589427098052 \tabularnewline
63 & 10405 & 11490.1102510701 & 637.759687432669 & 8682.13006149725 & 1085.11025107008 \tabularnewline
64 & 8467 & 8369.92008151995 & -93.1770748706132 & 8657.25699335066 & -97.0799184800508 \tabularnewline
65 & 8464 & 8586.85480870918 & -291.23873391326 & 8632.38392520408 & 122.854808709177 \tabularnewline
66 & 8102 & 8159.06157062724 & -549.319814322384 & 8594.25824369515 & 57.0615706272383 \tabularnewline
67 & 7627 & 7066.64328414736 & -368.775846333566 & 8556.1325621862 & -560.356715852643 \tabularnewline
68 & 7513 & 7120.57863208808 & -601.952502899678 & 8507.3738708116 & -392.421367911922 \tabularnewline
69 & 7510 & 7410.38905617299 & -849.004235609977 & 8458.61517943699 & -99.6109438270123 \tabularnewline
70 & 8291 & 8425.7207349122 & -276.974381784992 & 8433.25364687278 & 134.720734912207 \tabularnewline
71 & 8064 & 7958.67772238595 & -238.569836694533 & 8407.89211430858 & -105.32227761405 \tabularnewline
72 & 9383 & 9603.95163858882 & 729.288355790023 & 8432.76000562116 & 220.951638588822 \tabularnewline
73 & 9706 & 9289.06565576441 & 1665.30644730186 & 8457.62789693373 & -416.934344235587 \tabularnewline
74 & 8579 & 8429.96406615424 & 236.657954566572 & 8491.37797927919 & -149.035933845762 \tabularnewline
75 & 9474 & 9785.11225094268 & 637.759687432669 & 8525.12806162465 & 311.112250942677 \tabularnewline
76 & 8318 & 8198.20956353839 & -93.1770748706132 & 8530.96751133222 & -119.790436461608 \tabularnewline
77 & 8213 & 8180.43177287347 & -291.23873391326 & 8536.80696103979 & -32.5682271265287 \tabularnewline
78 & 8059 & 8147.9590899952 & -549.319814322384 & 8519.36072432718 & 88.9590899952 \tabularnewline
79 & 9111 & 10088.861358719 & -368.775846333566 & 8501.91448761458 & 977.861358718987 \tabularnewline
80 & 7708 & 7532.81758031397 & -601.952502899678 & 8485.13492258571 & -175.182419686033 \tabularnewline
81 & 7680 & 7740.64887805313 & -849.004235609977 & 8468.35535755684 & 60.6488780531345 \tabularnewline
82 & 8014 & 7859.39849690276 & -276.974381784992 & 8445.57588488224 & -154.601503097245 \tabularnewline
83 & 8007 & 7829.7734244869 & -238.569836694533 & 8422.79641220763 & -177.226575513097 \tabularnewline
84 & 8718 & 8316.06157950339 & 729.288355790023 & 8390.6500647066 & -401.938420496614 \tabularnewline
85 & 9486 & 8948.1898354926 & 1665.30644730186 & 8358.50371720555 & -537.81016450741 \tabularnewline
86 & 9113 & 9649.9745304112 & 236.657954566572 & 8339.36751502222 & 536.974530411209 \tabularnewline
87 & 9025 & 9092.00899972844 & 637.759687432669 & 8320.23131283889 & 67.0089997284449 \tabularnewline
88 & 8476 & 8703.90884070193 & -93.1770748706132 & 8341.26823416868 & 227.90884070193 \tabularnewline
89 & 7952 & 7832.93357841478 & -291.23873391326 & 8362.30515549848 & -119.066421585221 \tabularnewline
90 & 7759 & 7690.54987573 & -549.319814322384 & 8376.76993859239 & -68.4501242700044 \tabularnewline
91 & 7835 & 7647.54112464727 & -368.775846333566 & 8391.23472168629 & -187.458875352728 \tabularnewline
92 & 7600 & 7398.41888384599 & -601.952502899678 & 8403.53361905369 & -201.581116154008 \tabularnewline
93 & 7651 & 7735.1717191889 & -849.004235609977 & 8415.83251642108 & 84.1717191888984 \tabularnewline
94 & 8319 & 8486.41748542095 & -276.974381784992 & 8428.55689636404 & 167.417485420954 \tabularnewline
95 & 8812 & 9421.28856038753 & -238.569836694533 & 8441.281276307 & 609.288560387535 \tabularnewline
96 & 8630 & 8076.02840257242 & 729.288355790023 & 8454.68324163756 & -553.971597427584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148344&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]12008[/C][C]13251.0100504747[/C][C]1665.30644730186[/C][C]9099.68350222342[/C][C]1243.01005047472[/C][/ROW]
[ROW][C]2[/C][C]9169[/C][C]9069.2147052992[/C][C]236.657954566572[/C][C]9032.12734013424[/C][C]-99.7852947008068[/C][/ROW]
[ROW][C]3[/C][C]8788[/C][C]7973.66913452228[/C][C]637.759687432669[/C][C]8964.57117804505[/C][C]-814.330865477718[/C][/ROW]
[ROW][C]4[/C][C]8417[/C][C]8028.1807749118[/C][C]-93.1770748706132[/C][C]8898.99629995881[/C][C]-388.819225088193[/C][/ROW]
[ROW][C]5[/C][C]8247[/C][C]7951.8173120407[/C][C]-291.23873391326[/C][C]8833.42142187256[/C][C]-295.182687959301[/C][/ROW]
[ROW][C]6[/C][C]8197[/C][C]8175.7373008356[/C][C]-549.319814322384[/C][C]8767.58251348679[/C][C]-21.2626991644047[/C][/ROW]
[ROW][C]7[/C][C]8236[/C][C]8139.03224123255[/C][C]-368.775846333566[/C][C]8701.74360510102[/C][C]-96.9677587674505[/C][/ROW]
[ROW][C]8[/C][C]8253[/C][C]8463.02944203181[/C][C]-601.952502899678[/C][C]8644.92306086787[/C][C]210.02944203181[/C][/ROW]
[ROW][C]9[/C][C]7733[/C][C]7726.90171897526[/C][C]-849.004235609977[/C][C]8588.10251663472[/C][C]-6.09828102474421[/C][/ROW]
[ROW][C]10[/C][C]8366[/C][C]8425.72465323181[/C][C]-276.974381784992[/C][C]8583.24972855318[/C][C]59.7246532318131[/C][/ROW]
[ROW][C]11[/C][C]8626[/C][C]8912.1728962229[/C][C]-238.569836694533[/C][C]8578.39694047164[/C][C]286.172896222895[/C][/ROW]
[ROW][C]12[/C][C]8863[/C][C]8406.86732471246[/C][C]729.288355790023[/C][C]8589.84431949752[/C][C]-456.132675287539[/C][/ROW]
[ROW][C]13[/C][C]10102[/C][C]9937.40185417474[/C][C]1665.30644730186[/C][C]8601.2916985234[/C][C]-164.598145825255[/C][/ROW]
[ROW][C]14[/C][C]8463[/C][C]8080.84031276675[/C][C]236.657954566572[/C][C]8608.50173266668[/C][C]-382.159687233254[/C][/ROW]
[ROW][C]15[/C][C]9114[/C][C]8974.52854575736[/C][C]637.759687432669[/C][C]8615.71176680997[/C][C]-139.471454242639[/C][/ROW]
[ROW][C]16[/C][C]8563[/C][C]8599.68955852511[/C][C]-93.1770748706132[/C][C]8619.4875163455[/C][C]36.6895585251132[/C][/ROW]
[ROW][C]17[/C][C]8872[/C][C]9411.97546803223[/C][C]-291.23873391326[/C][C]8623.26326588103[/C][C]539.975468032228[/C][/ROW]
[ROW][C]18[/C][C]8301[/C][C]8503.1755894842[/C][C]-549.319814322384[/C][C]8648.14422483818[/C][C]202.175589484201[/C][/ROW]
[ROW][C]19[/C][C]8301[/C][C]8297.75066253823[/C][C]-368.775846333566[/C][C]8673.02518379533[/C][C]-3.24933746176976[/C][/ROW]
[ROW][C]20[/C][C]8278[/C][C]8457.87443198398[/C][C]-601.952502899678[/C][C]8700.0780709157[/C][C]179.874431983983[/C][/ROW]
[ROW][C]21[/C][C]7736[/C][C]7593.87327757392[/C][C]-849.004235609977[/C][C]8727.13095803606[/C][C]-142.126722426079[/C][/ROW]
[ROW][C]22[/C][C]7973[/C][C]7492.23678920662[/C][C]-276.974381784992[/C][C]8730.73759257837[/C][C]-480.763210793375[/C][/ROW]
[ROW][C]23[/C][C]8268[/C][C]8040.22560957386[/C][C]-238.569836694533[/C][C]8734.34422712068[/C][C]-227.774390426144[/C][/ROW]
[ROW][C]24[/C][C]9476[/C][C]9492.74874263702[/C][C]729.288355790023[/C][C]8729.96290157296[/C][C]16.7487426370153[/C][/ROW]
[ROW][C]25[/C][C]11100[/C][C]11809.1119766729[/C][C]1665.30644730186[/C][C]8725.58157602525[/C][C]709.111976672892[/C][/ROW]
[ROW][C]26[/C][C]8962[/C][C]8950.97522736594[/C][C]236.657954566572[/C][C]8736.36681806749[/C][C]-11.024772634064[/C][/ROW]
[ROW][C]27[/C][C]9173[/C][C]8961.0882524576[/C][C]637.759687432669[/C][C]8747.15206010973[/C][C]-211.911747542405[/C][/ROW]
[ROW][C]28[/C][C]8738[/C][C]8810.94873910594[/C][C]-93.1770748706132[/C][C]8758.22833576468[/C][C]72.9487391059356[/C][/ROW]
[ROW][C]29[/C][C]8459[/C][C]8439.93412249364[/C][C]-291.23873391326[/C][C]8769.30461141962[/C][C]-19.0658775063584[/C][/ROW]
[ROW][C]30[/C][C]8078[/C][C]7945.57697207037[/C][C]-549.319814322384[/C][C]8759.74284225201[/C][C]-132.42302792963[/C][/ROW]
[ROW][C]31[/C][C]8411[/C][C]8440.59477324916[/C][C]-368.775846333566[/C][C]8750.18107308441[/C][C]29.5947732491568[/C][/ROW]
[ROW][C]32[/C][C]8291[/C][C]8445.25546694407[/C][C]-601.952502899678[/C][C]8738.69703595561[/C][C]154.255466944071[/C][/ROW]
[ROW][C]33[/C][C]7810[/C][C]7741.79123678317[/C][C]-849.004235609977[/C][C]8727.2129988268[/C][C]-68.2087632168314[/C][/ROW]
[ROW][C]34[/C][C]8616[/C][C]8774.27947921438[/C][C]-276.974381784992[/C][C]8734.6949025706[/C][C]158.279479214385[/C][/ROW]
[ROW][C]35[/C][C]8312[/C][C]8120.39303038013[/C][C]-238.569836694533[/C][C]8742.1768063144[/C][C]-191.606969619874[/C][/ROW]
[ROW][C]36[/C][C]9692[/C][C]9901.38159563134[/C][C]729.288355790023[/C][C]8753.33004857864[/C][C]209.381595631336[/C][/ROW]
[ROW][C]37[/C][C]9911[/C][C]9392.21026185526[/C][C]1665.30644730186[/C][C]8764.48329084288[/C][C]-518.789738144738[/C][/ROW]
[ROW][C]38[/C][C]8915[/C][C]8811.06532623422[/C][C]236.657954566572[/C][C]8782.27671919921[/C][C]-103.934673765782[/C][/ROW]
[ROW][C]39[/C][C]9452[/C][C]9466.1701650118[/C][C]637.759687432669[/C][C]8800.07014755554[/C][C]14.1701650117921[/C][/ROW]
[ROW][C]40[/C][C]9112[/C][C]9487.69616533108[/C][C]-93.1770748706132[/C][C]8829.48090953953[/C][C]375.696165331083[/C][/ROW]
[ROW][C]41[/C][C]8472[/C][C]8376.34706238974[/C][C]-291.23873391326[/C][C]8858.89167152352[/C][C]-95.6529376102626[/C][/ROW]
[ROW][C]42[/C][C]8230[/C][C]8118.7951741021[/C][C]-549.319814322384[/C][C]8890.52464022028[/C][C]-111.204825897896[/C][/ROW]
[ROW][C]43[/C][C]8384[/C][C]8214.61823741653[/C][C]-368.775846333566[/C][C]8922.15760891704[/C][C]-169.381762583471[/C][/ROW]
[ROW][C]44[/C][C]8625[/C][C]8932.6307078908[/C][C]-601.952502899678[/C][C]8919.32179500888[/C][C]307.630707890794[/C][/ROW]
[ROW][C]45[/C][C]8221[/C][C]8374.51825450925[/C][C]-849.004235609977[/C][C]8916.48598110073[/C][C]153.518254509247[/C][/ROW]
[ROW][C]46[/C][C]8649[/C][C]8697.23524969104[/C][C]-276.974381784992[/C][C]8877.73913209395[/C][C]48.2352496910389[/C][/ROW]
[ROW][C]47[/C][C]8625[/C][C]8649.57755360736[/C][C]-238.569836694533[/C][C]8838.99228308717[/C][C]24.5775536073579[/C][/ROW]
[ROW][C]48[/C][C]10443[/C][C]11364.8349337838[/C][C]729.288355790023[/C][C]8791.87671042619[/C][C]921.834933783786[/C][/ROW]
[ROW][C]49[/C][C]10357[/C][C]10303.9324149329[/C][C]1665.30644730186[/C][C]8744.7611377652[/C][C]-53.0675850670668[/C][/ROW]
[ROW][C]50[/C][C]8586[/C][C]8240.47236915639[/C][C]236.657954566572[/C][C]8694.86967627704[/C][C]-345.527630843613[/C][/ROW]
[ROW][C]51[/C][C]8892[/C][C]8501.26209777846[/C][C]637.759687432669[/C][C]8644.97821478887[/C][C]-390.737902221543[/C][/ROW]
[ROW][C]52[/C][C]8329[/C][C]8151.35254298267[/C][C]-93.1770748706132[/C][C]8599.82453188795[/C][C]-177.647457017332[/C][/ROW]
[ROW][C]53[/C][C]8101[/C][C]7938.56788492624[/C][C]-291.23873391326[/C][C]8554.67084898702[/C][C]-162.432115073758[/C][/ROW]
[ROW][C]54[/C][C]7922[/C][C]7859.63926898677[/C][C]-549.319814322384[/C][C]8533.68054533561[/C][C]-62.3607310132302[/C][/ROW]
[ROW][C]55[/C][C]8120[/C][C]8096.08560464936[/C][C]-368.775846333566[/C][C]8512.6902416842[/C][C]-23.9143953506427[/C][/ROW]
[ROW][C]56[/C][C]7838[/C][C]7731.53721859468[/C][C]-601.952502899678[/C][C]8546.415284305[/C][C]-106.462781405318[/C][/ROW]
[ROW][C]57[/C][C]7735[/C][C]7738.86390868419[/C][C]-849.004235609977[/C][C]8580.14032692578[/C][C]3.86390868419403[/C][/ROW]
[ROW][C]58[/C][C]8406[/C][C]8458.93721526025[/C][C]-276.974381784992[/C][C]8630.03716652474[/C][C]52.9372152602518[/C][/ROW]
[ROW][C]59[/C][C]8209[/C][C]7976.63583057083[/C][C]-238.569836694533[/C][C]8679.9340061237[/C][C]-232.364169429167[/C][/ROW]
[ROW][C]60[/C][C]9451[/C][C]9476.05705356138[/C][C]729.288355790023[/C][C]8696.6545906486[/C][C]25.0570535613751[/C][/ROW]
[ROW][C]61[/C][C]10041[/C][C]9703.31837752463[/C][C]1665.30644730186[/C][C]8713.3751751735[/C][C]-337.681622475366[/C][/ROW]
[ROW][C]62[/C][C]9411[/C][C]9887.58942709805[/C][C]236.657954566572[/C][C]8697.75261833538[/C][C]476.589427098052[/C][/ROW]
[ROW][C]63[/C][C]10405[/C][C]11490.1102510701[/C][C]637.759687432669[/C][C]8682.13006149725[/C][C]1085.11025107008[/C][/ROW]
[ROW][C]64[/C][C]8467[/C][C]8369.92008151995[/C][C]-93.1770748706132[/C][C]8657.25699335066[/C][C]-97.0799184800508[/C][/ROW]
[ROW][C]65[/C][C]8464[/C][C]8586.85480870918[/C][C]-291.23873391326[/C][C]8632.38392520408[/C][C]122.854808709177[/C][/ROW]
[ROW][C]66[/C][C]8102[/C][C]8159.06157062724[/C][C]-549.319814322384[/C][C]8594.25824369515[/C][C]57.0615706272383[/C][/ROW]
[ROW][C]67[/C][C]7627[/C][C]7066.64328414736[/C][C]-368.775846333566[/C][C]8556.1325621862[/C][C]-560.356715852643[/C][/ROW]
[ROW][C]68[/C][C]7513[/C][C]7120.57863208808[/C][C]-601.952502899678[/C][C]8507.3738708116[/C][C]-392.421367911922[/C][/ROW]
[ROW][C]69[/C][C]7510[/C][C]7410.38905617299[/C][C]-849.004235609977[/C][C]8458.61517943699[/C][C]-99.6109438270123[/C][/ROW]
[ROW][C]70[/C][C]8291[/C][C]8425.7207349122[/C][C]-276.974381784992[/C][C]8433.25364687278[/C][C]134.720734912207[/C][/ROW]
[ROW][C]71[/C][C]8064[/C][C]7958.67772238595[/C][C]-238.569836694533[/C][C]8407.89211430858[/C][C]-105.32227761405[/C][/ROW]
[ROW][C]72[/C][C]9383[/C][C]9603.95163858882[/C][C]729.288355790023[/C][C]8432.76000562116[/C][C]220.951638588822[/C][/ROW]
[ROW][C]73[/C][C]9706[/C][C]9289.06565576441[/C][C]1665.30644730186[/C][C]8457.62789693373[/C][C]-416.934344235587[/C][/ROW]
[ROW][C]74[/C][C]8579[/C][C]8429.96406615424[/C][C]236.657954566572[/C][C]8491.37797927919[/C][C]-149.035933845762[/C][/ROW]
[ROW][C]75[/C][C]9474[/C][C]9785.11225094268[/C][C]637.759687432669[/C][C]8525.12806162465[/C][C]311.112250942677[/C][/ROW]
[ROW][C]76[/C][C]8318[/C][C]8198.20956353839[/C][C]-93.1770748706132[/C][C]8530.96751133222[/C][C]-119.790436461608[/C][/ROW]
[ROW][C]77[/C][C]8213[/C][C]8180.43177287347[/C][C]-291.23873391326[/C][C]8536.80696103979[/C][C]-32.5682271265287[/C][/ROW]
[ROW][C]78[/C][C]8059[/C][C]8147.9590899952[/C][C]-549.319814322384[/C][C]8519.36072432718[/C][C]88.9590899952[/C][/ROW]
[ROW][C]79[/C][C]9111[/C][C]10088.861358719[/C][C]-368.775846333566[/C][C]8501.91448761458[/C][C]977.861358718987[/C][/ROW]
[ROW][C]80[/C][C]7708[/C][C]7532.81758031397[/C][C]-601.952502899678[/C][C]8485.13492258571[/C][C]-175.182419686033[/C][/ROW]
[ROW][C]81[/C][C]7680[/C][C]7740.64887805313[/C][C]-849.004235609977[/C][C]8468.35535755684[/C][C]60.6488780531345[/C][/ROW]
[ROW][C]82[/C][C]8014[/C][C]7859.39849690276[/C][C]-276.974381784992[/C][C]8445.57588488224[/C][C]-154.601503097245[/C][/ROW]
[ROW][C]83[/C][C]8007[/C][C]7829.7734244869[/C][C]-238.569836694533[/C][C]8422.79641220763[/C][C]-177.226575513097[/C][/ROW]
[ROW][C]84[/C][C]8718[/C][C]8316.06157950339[/C][C]729.288355790023[/C][C]8390.6500647066[/C][C]-401.938420496614[/C][/ROW]
[ROW][C]85[/C][C]9486[/C][C]8948.1898354926[/C][C]1665.30644730186[/C][C]8358.50371720555[/C][C]-537.81016450741[/C][/ROW]
[ROW][C]86[/C][C]9113[/C][C]9649.9745304112[/C][C]236.657954566572[/C][C]8339.36751502222[/C][C]536.974530411209[/C][/ROW]
[ROW][C]87[/C][C]9025[/C][C]9092.00899972844[/C][C]637.759687432669[/C][C]8320.23131283889[/C][C]67.0089997284449[/C][/ROW]
[ROW][C]88[/C][C]8476[/C][C]8703.90884070193[/C][C]-93.1770748706132[/C][C]8341.26823416868[/C][C]227.90884070193[/C][/ROW]
[ROW][C]89[/C][C]7952[/C][C]7832.93357841478[/C][C]-291.23873391326[/C][C]8362.30515549848[/C][C]-119.066421585221[/C][/ROW]
[ROW][C]90[/C][C]7759[/C][C]7690.54987573[/C][C]-549.319814322384[/C][C]8376.76993859239[/C][C]-68.4501242700044[/C][/ROW]
[ROW][C]91[/C][C]7835[/C][C]7647.54112464727[/C][C]-368.775846333566[/C][C]8391.23472168629[/C][C]-187.458875352728[/C][/ROW]
[ROW][C]92[/C][C]7600[/C][C]7398.41888384599[/C][C]-601.952502899678[/C][C]8403.53361905369[/C][C]-201.581116154008[/C][/ROW]
[ROW][C]93[/C][C]7651[/C][C]7735.1717191889[/C][C]-849.004235609977[/C][C]8415.83251642108[/C][C]84.1717191888984[/C][/ROW]
[ROW][C]94[/C][C]8319[/C][C]8486.41748542095[/C][C]-276.974381784992[/C][C]8428.55689636404[/C][C]167.417485420954[/C][/ROW]
[ROW][C]95[/C][C]8812[/C][C]9421.28856038753[/C][C]-238.569836694533[/C][C]8441.281276307[/C][C]609.288560387535[/C][/ROW]
[ROW][C]96[/C][C]8630[/C][C]8076.02840257242[/C][C]729.288355790023[/C][C]8454.68324163756[/C][C]-553.971597427584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148344&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148344&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
11200813251.01005047471665.306447301869099.683502223421243.01005047472
291699069.2147052992236.6579545665729032.12734013424-99.7852947008068
387887973.66913452228637.7596874326698964.57117804505-814.330865477718
484178028.1807749118-93.17707487061328898.99629995881-388.819225088193
582477951.8173120407-291.238733913268833.42142187256-295.182687959301
681978175.7373008356-549.3198143223848767.58251348679-21.2626991644047
782368139.03224123255-368.7758463335668701.74360510102-96.9677587674505
882538463.02944203181-601.9525028996788644.92306086787210.02944203181
977337726.90171897526-849.0042356099778588.10251663472-6.09828102474421
1083668425.72465323181-276.9743817849928583.2497285531859.7246532318131
1186268912.1728962229-238.5698366945338578.39694047164286.172896222895
1288638406.86732471246729.2883557900238589.84431949752-456.132675287539
13101029937.401854174741665.306447301868601.2916985234-164.598145825255
1484638080.84031276675236.6579545665728608.50173266668-382.159687233254
1591148974.52854575736637.7596874326698615.71176680997-139.471454242639
1685638599.68955852511-93.17707487061328619.487516345536.6895585251132
1788729411.97546803223-291.238733913268623.26326588103539.975468032228
1883018503.1755894842-549.3198143223848648.14422483818202.175589484201
1983018297.75066253823-368.7758463335668673.02518379533-3.24933746176976
2082788457.87443198398-601.9525028996788700.0780709157179.874431983983
2177367593.87327757392-849.0042356099778727.13095803606-142.126722426079
2279737492.23678920662-276.9743817849928730.73759257837-480.763210793375
2382688040.22560957386-238.5698366945338734.34422712068-227.774390426144
2494769492.74874263702729.2883557900238729.9629015729616.7487426370153
251110011809.11197667291665.306447301868725.58157602525709.111976672892
2689628950.97522736594236.6579545665728736.36681806749-11.024772634064
2791738961.0882524576637.7596874326698747.15206010973-211.911747542405
2887388810.94873910594-93.17707487061328758.2283357646872.9487391059356
2984598439.93412249364-291.238733913268769.30461141962-19.0658775063584
3080787945.57697207037-549.3198143223848759.74284225201-132.42302792963
3184118440.59477324916-368.7758463335668750.1810730844129.5947732491568
3282918445.25546694407-601.9525028996788738.69703595561154.255466944071
3378107741.79123678317-849.0042356099778727.2129988268-68.2087632168314
3486168774.27947921438-276.9743817849928734.6949025706158.279479214385
3583128120.39303038013-238.5698366945338742.1768063144-191.606969619874
3696929901.38159563134729.2883557900238753.33004857864209.381595631336
3799119392.210261855261665.306447301868764.48329084288-518.789738144738
3889158811.06532623422236.6579545665728782.27671919921-103.934673765782
3994529466.1701650118637.7596874326698800.0701475555414.1701650117921
4091129487.69616533108-93.17707487061328829.48090953953375.696165331083
4184728376.34706238974-291.238733913268858.89167152352-95.6529376102626
4282308118.7951741021-549.3198143223848890.52464022028-111.204825897896
4383848214.61823741653-368.7758463335668922.15760891704-169.381762583471
4486258932.6307078908-601.9525028996788919.32179500888307.630707890794
4582218374.51825450925-849.0042356099778916.48598110073153.518254509247
4686498697.23524969104-276.9743817849928877.7391320939548.2352496910389
4786258649.57755360736-238.5698366945338838.9922830871724.5775536073579
481044311364.8349337838729.2883557900238791.87671042619921.834933783786
491035710303.93241493291665.306447301868744.7611377652-53.0675850670668
5085868240.47236915639236.6579545665728694.86967627704-345.527630843613
5188928501.26209777846637.7596874326698644.97821478887-390.737902221543
5283298151.35254298267-93.17707487061328599.82453188795-177.647457017332
5381017938.56788492624-291.238733913268554.67084898702-162.432115073758
5479227859.63926898677-549.3198143223848533.68054533561-62.3607310132302
5581208096.08560464936-368.7758463335668512.6902416842-23.9143953506427
5678387731.53721859468-601.9525028996788546.415284305-106.462781405318
5777357738.86390868419-849.0042356099778580.140326925783.86390868419403
5884068458.93721526025-276.9743817849928630.0371665247452.9372152602518
5982097976.63583057083-238.5698366945338679.9340061237-232.364169429167
6094519476.05705356138729.2883557900238696.654590648625.0570535613751
61100419703.318377524631665.306447301868713.3751751735-337.681622475366
6294119887.58942709805236.6579545665728697.75261833538476.589427098052
631040511490.1102510701637.7596874326698682.130061497251085.11025107008
6484678369.92008151995-93.17707487061328657.25699335066-97.0799184800508
6584648586.85480870918-291.238733913268632.38392520408122.854808709177
6681028159.06157062724-549.3198143223848594.2582436951557.0615706272383
6776277066.64328414736-368.7758463335668556.1325621862-560.356715852643
6875137120.57863208808-601.9525028996788507.3738708116-392.421367911922
6975107410.38905617299-849.0042356099778458.61517943699-99.6109438270123
7082918425.7207349122-276.9743817849928433.25364687278134.720734912207
7180647958.67772238595-238.5698366945338407.89211430858-105.32227761405
7293839603.95163858882729.2883557900238432.76000562116220.951638588822
7397069289.065655764411665.306447301868457.62789693373-416.934344235587
7485798429.96406615424236.6579545665728491.37797927919-149.035933845762
7594749785.11225094268637.7596874326698525.12806162465311.112250942677
7683188198.20956353839-93.17707487061328530.96751133222-119.790436461608
7782138180.43177287347-291.238733913268536.80696103979-32.5682271265287
7880598147.9590899952-549.3198143223848519.3607243271888.9590899952
79911110088.861358719-368.7758463335668501.91448761458977.861358718987
8077087532.81758031397-601.9525028996788485.13492258571-175.182419686033
8176807740.64887805313-849.0042356099778468.3553575568460.6488780531345
8280147859.39849690276-276.9743817849928445.57588488224-154.601503097245
8380077829.7734244869-238.5698366945338422.79641220763-177.226575513097
8487188316.06157950339729.2883557900238390.6500647066-401.938420496614
8594868948.18983549261665.306447301868358.50371720555-537.81016450741
8691139649.9745304112236.6579545665728339.36751502222536.974530411209
8790259092.00899972844637.7596874326698320.2313128388967.0089997284449
8884768703.90884070193-93.17707487061328341.26823416868227.90884070193
8979527832.93357841478-291.238733913268362.30515549848-119.066421585221
9077597690.54987573-549.3198143223848376.76993859239-68.4501242700044
9178357647.54112464727-368.7758463335668391.23472168629-187.458875352728
9276007398.41888384599-601.9525028996788403.53361905369-201.581116154008
9376517735.1717191889-849.0042356099778415.8325164210884.1717191888984
9483198486.41748542095-276.9743817849928428.55689636404167.417485420954
9588129421.28856038753-238.5698366945338441.281276307609.288560387535
9686308076.02840257242729.2883557900238454.68324163756-553.971597427584


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')