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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 computationTue, 01 Dec 2009 11:48:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/01/t1259693359ie158i5ab19gj6c.htm/, Retrieved Fri, 29 Mar 2024 10:33:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62177, Retrieved Fri, 29 Mar 2024 10:33:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
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]
- RMP   [Decomposition by Loess] [] [2009-11-27 15:00:29] [b98453cac15ba1066b407e146608df68]
- R PD      [Decomposition by Loess] [] [2009-12-01 18:48:36] [791a4a78a0a7ca497fb8791b982a539e] [Current]
- R PD        [Decomposition by Loess] [] [2009-12-04 16:23:59] [fa71ec4c741ffec745cb91dcbd756720]
- R PD        [Decomposition by Loess] [] [2009-12-04 18:58:36] [eba9b8a72d680086d9ebbb043233c887]
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Dataseries X:
785.8
819.3
849.4
880.4
900.1
937.2
948.9
952.6
947.3
974.2
1000.8
1032.8
1050.7
1057.3
1075.4
1118.4
1179.8
1227
1257.8
1251.5
1236.3
1170.6
1213.1
1265.5
1300.8
1348.4
1371.9
1403.3
1451.8
1474.2
1438.2
1513.6
1562.2
1546.2
1527.5
1418.7
1448.5
1492.1
1395.4
1403.7
1316.6
1274.5
1264.4
1323.9
1332.1
1250.2
1096.7
1080.8
1039.2
792
746.6
688.8
715.8
672.9
629.5
681.2
755.4
760.6
765.9
836.8
904.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62177&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62177&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62177&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







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=62177&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=62177&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62177&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
Seasonal611062
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1785.8723.21899494725227.3330394632159821.047965589532-62.5810050527484
2819.3821.025514220258-21.7498038342756839.3242896140171.72551422025822
3849.4876.024055874798-34.8246695132999857.60061363850226.6240558747977
4880.4909.763209427012-24.936350625787875.97314119877529.3632094270116
5900.1918.182386593958-12.3280553530065894.34566875904918.0823865939581
6937.2970.891089530729-9.71992159381622913.22883206308733.6910895307291
7948.9986.53979861381-20.8517939809352932.11199536712637.6397986138093
8952.6936.33238611063217.6676867673212951.199927122047-16.2676138893681
9947.3882.82499112565941.4871499973735970.287858876968-64.4750088743414
10974.2939.73045353347819.3094748441347989.360071622387-34.4695464665216
111000.8989.2959120096583.871803622535981008.43228436781-11.5040879903420
121032.81017.8541246530414.74152034010731033.00435500685-14.9458753469578
131050.71016.4905348908927.33303946321591057.57642564589-34.2094651091106
141057.31052.98605890302-21.74980383427561083.36374493125-4.31394109697681
151075.41076.47360529669-34.82466951329991109.151064216611.07360529668995
161118.41130.57498556675-24.9363506257871131.1613650590412.1749855667499
171179.81218.75638945154-12.32805535300651153.1716659014638.9563894515418
1812271291.76599747425-9.719921593816221171.9539241195664.7659974742514
191257.81345.71561164327-20.85179398093521190.7361823376687.9156116432705
201251.51274.7716904071617.66768676732121210.5606228255223.2716904071588
211236.31200.7277866892541.48714999737351230.38506331338-35.572213310749
221170.61070.4857337448219.30947484413471251.40479141105-100.114266255180
231213.11149.903676868753.871803622535981272.42451950872-63.1963231312513
241265.51221.7019225450514.74152034010731294.55655711484-43.7980774549462
251300.81257.5783658158227.33303946321591316.68859472096-43.2216341841784
261348.41376.32053727159-21.74980383427561342.2292665626927.9205372715892
271371.91410.85473110889-34.82466951329991367.7699384044138.9547311088891
281403.31437.2454601717-24.9363506257871394.2908904540933.9454601717
291451.81495.11621284924-12.32805535300651420.8118425037643.3162128492431
301474.21519.21982442130-9.719921593816221438.9000971725245.0198244212959
311438.21440.26344213966-20.85179398093521456.988351841282.06344213965804
321513.61544.9902560553617.66768676732121464.5420571773231.3902560553586
331562.21610.8170874892641.48714999737351472.0957625133648.6170874892632
341546.21603.8185997852519.30947484413471469.2719253706257.6185997852472
351527.51584.680108149593.871803622535981466.4480882278757.180108149591
361418.71368.7648214514214.74152034010731453.89365820847-49.9351785485808
371448.51428.3277323477127.33303946321591441.33922818907-20.1722676522900
381492.11583.54646958150-21.74980383427561422.4033342527891.4464695814966
391395.41422.15722919682-34.82466951329991403.4674403164826.7572291968161
401403.71453.85520586307-24.9363506257871378.4811447627250.15520586307
411316.61292.03320614406-12.32805535300651353.49484920895-24.5667938559441
421274.51238.92156726318-9.719921593816221319.79835433064-35.5784327368226
431264.41263.54993452861-20.85179398093521286.10185945233-0.850065471391872
441323.91390.4832954718517.66768676732121239.6490177608366.5832954718464
451332.11429.5166739332941.48714999737351193.1961760693497.4166739332886
461250.21342.4226140196619.30947484413471138.6679111362192.2226140196594
471096.71105.388550174393.871803622535981084.139646203078.68855017438977
481080.81119.1965060090014.74152034010731027.6619736508938.3965060090018
491039.21079.8826594380827.3330394632159971.18430109870740.6826594380768
50792687.860123409571-21.7498038342756917.889680424704-104.139876590429
51746.6663.429609762598-34.8246695132999864.595059750702-83.1703902374019
52688.8569.727230508675-24.936350625787832.809120117112-119.072769491325
53715.8642.904874869483-12.3280553530065801.023180483523-72.8951251305165
54672.9567.390088258918-9.71992159381622788.129833334898-105.509911741082
55629.5504.615307794662-20.8517939809352775.236486186273-124.884692205338
56681.2580.21686473510517.6676867673212764.515448497574-100.983135264895
57755.4715.51843919375141.4871499973735753.794410808875-39.8815608062486
58760.6755.70626115929319.3094748441347746.184263996573-4.89373884070721
59765.9789.3540791931943.87180362253598738.5741171842723.4540791931942
60836.8924.16670921133114.7415203401073734.69177044856187.3667092113311
61904.91051.6575368239327.3330394632159730.809423712853146.757536823931

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 785.8 & 723.218994947252 & 27.3330394632159 & 821.047965589532 & -62.5810050527484 \tabularnewline
2 & 819.3 & 821.025514220258 & -21.7498038342756 & 839.324289614017 & 1.72551422025822 \tabularnewline
3 & 849.4 & 876.024055874798 & -34.8246695132999 & 857.600613638502 & 26.6240558747977 \tabularnewline
4 & 880.4 & 909.763209427012 & -24.936350625787 & 875.973141198775 & 29.3632094270116 \tabularnewline
5 & 900.1 & 918.182386593958 & -12.3280553530065 & 894.345668759049 & 18.0823865939581 \tabularnewline
6 & 937.2 & 970.891089530729 & -9.71992159381622 & 913.228832063087 & 33.6910895307291 \tabularnewline
7 & 948.9 & 986.53979861381 & -20.8517939809352 & 932.111995367126 & 37.6397986138093 \tabularnewline
8 & 952.6 & 936.332386110632 & 17.6676867673212 & 951.199927122047 & -16.2676138893681 \tabularnewline
9 & 947.3 & 882.824991125659 & 41.4871499973735 & 970.287858876968 & -64.4750088743414 \tabularnewline
10 & 974.2 & 939.730453533478 & 19.3094748441347 & 989.360071622387 & -34.4695464665216 \tabularnewline
11 & 1000.8 & 989.295912009658 & 3.87180362253598 & 1008.43228436781 & -11.5040879903420 \tabularnewline
12 & 1032.8 & 1017.85412465304 & 14.7415203401073 & 1033.00435500685 & -14.9458753469578 \tabularnewline
13 & 1050.7 & 1016.49053489089 & 27.3330394632159 & 1057.57642564589 & -34.2094651091106 \tabularnewline
14 & 1057.3 & 1052.98605890302 & -21.7498038342756 & 1083.36374493125 & -4.31394109697681 \tabularnewline
15 & 1075.4 & 1076.47360529669 & -34.8246695132999 & 1109.15106421661 & 1.07360529668995 \tabularnewline
16 & 1118.4 & 1130.57498556675 & -24.936350625787 & 1131.16136505904 & 12.1749855667499 \tabularnewline
17 & 1179.8 & 1218.75638945154 & -12.3280553530065 & 1153.17166590146 & 38.9563894515418 \tabularnewline
18 & 1227 & 1291.76599747425 & -9.71992159381622 & 1171.95392411956 & 64.7659974742514 \tabularnewline
19 & 1257.8 & 1345.71561164327 & -20.8517939809352 & 1190.73618233766 & 87.9156116432705 \tabularnewline
20 & 1251.5 & 1274.77169040716 & 17.6676867673212 & 1210.56062282552 & 23.2716904071588 \tabularnewline
21 & 1236.3 & 1200.72778668925 & 41.4871499973735 & 1230.38506331338 & -35.572213310749 \tabularnewline
22 & 1170.6 & 1070.48573374482 & 19.3094748441347 & 1251.40479141105 & -100.114266255180 \tabularnewline
23 & 1213.1 & 1149.90367686875 & 3.87180362253598 & 1272.42451950872 & -63.1963231312513 \tabularnewline
24 & 1265.5 & 1221.70192254505 & 14.7415203401073 & 1294.55655711484 & -43.7980774549462 \tabularnewline
25 & 1300.8 & 1257.57836581582 & 27.3330394632159 & 1316.68859472096 & -43.2216341841784 \tabularnewline
26 & 1348.4 & 1376.32053727159 & -21.7498038342756 & 1342.22926656269 & 27.9205372715892 \tabularnewline
27 & 1371.9 & 1410.85473110889 & -34.8246695132999 & 1367.76993840441 & 38.9547311088891 \tabularnewline
28 & 1403.3 & 1437.2454601717 & -24.936350625787 & 1394.29089045409 & 33.9454601717 \tabularnewline
29 & 1451.8 & 1495.11621284924 & -12.3280553530065 & 1420.81184250376 & 43.3162128492431 \tabularnewline
30 & 1474.2 & 1519.21982442130 & -9.71992159381622 & 1438.90009717252 & 45.0198244212959 \tabularnewline
31 & 1438.2 & 1440.26344213966 & -20.8517939809352 & 1456.98835184128 & 2.06344213965804 \tabularnewline
32 & 1513.6 & 1544.99025605536 & 17.6676867673212 & 1464.54205717732 & 31.3902560553586 \tabularnewline
33 & 1562.2 & 1610.81708748926 & 41.4871499973735 & 1472.09576251336 & 48.6170874892632 \tabularnewline
34 & 1546.2 & 1603.81859978525 & 19.3094748441347 & 1469.27192537062 & 57.6185997852472 \tabularnewline
35 & 1527.5 & 1584.68010814959 & 3.87180362253598 & 1466.44808822787 & 57.180108149591 \tabularnewline
36 & 1418.7 & 1368.76482145142 & 14.7415203401073 & 1453.89365820847 & -49.9351785485808 \tabularnewline
37 & 1448.5 & 1428.32773234771 & 27.3330394632159 & 1441.33922818907 & -20.1722676522900 \tabularnewline
38 & 1492.1 & 1583.54646958150 & -21.7498038342756 & 1422.40333425278 & 91.4464695814966 \tabularnewline
39 & 1395.4 & 1422.15722919682 & -34.8246695132999 & 1403.46744031648 & 26.7572291968161 \tabularnewline
40 & 1403.7 & 1453.85520586307 & -24.936350625787 & 1378.48114476272 & 50.15520586307 \tabularnewline
41 & 1316.6 & 1292.03320614406 & -12.3280553530065 & 1353.49484920895 & -24.5667938559441 \tabularnewline
42 & 1274.5 & 1238.92156726318 & -9.71992159381622 & 1319.79835433064 & -35.5784327368226 \tabularnewline
43 & 1264.4 & 1263.54993452861 & -20.8517939809352 & 1286.10185945233 & -0.850065471391872 \tabularnewline
44 & 1323.9 & 1390.48329547185 & 17.6676867673212 & 1239.64901776083 & 66.5832954718464 \tabularnewline
45 & 1332.1 & 1429.51667393329 & 41.4871499973735 & 1193.19617606934 & 97.4166739332886 \tabularnewline
46 & 1250.2 & 1342.42261401966 & 19.3094748441347 & 1138.66791113621 & 92.2226140196594 \tabularnewline
47 & 1096.7 & 1105.38855017439 & 3.87180362253598 & 1084.13964620307 & 8.68855017438977 \tabularnewline
48 & 1080.8 & 1119.19650600900 & 14.7415203401073 & 1027.66197365089 & 38.3965060090018 \tabularnewline
49 & 1039.2 & 1079.88265943808 & 27.3330394632159 & 971.184301098707 & 40.6826594380768 \tabularnewline
50 & 792 & 687.860123409571 & -21.7498038342756 & 917.889680424704 & -104.139876590429 \tabularnewline
51 & 746.6 & 663.429609762598 & -34.8246695132999 & 864.595059750702 & -83.1703902374019 \tabularnewline
52 & 688.8 & 569.727230508675 & -24.936350625787 & 832.809120117112 & -119.072769491325 \tabularnewline
53 & 715.8 & 642.904874869483 & -12.3280553530065 & 801.023180483523 & -72.8951251305165 \tabularnewline
54 & 672.9 & 567.390088258918 & -9.71992159381622 & 788.129833334898 & -105.509911741082 \tabularnewline
55 & 629.5 & 504.615307794662 & -20.8517939809352 & 775.236486186273 & -124.884692205338 \tabularnewline
56 & 681.2 & 580.216864735105 & 17.6676867673212 & 764.515448497574 & -100.983135264895 \tabularnewline
57 & 755.4 & 715.518439193751 & 41.4871499973735 & 753.794410808875 & -39.8815608062486 \tabularnewline
58 & 760.6 & 755.706261159293 & 19.3094748441347 & 746.184263996573 & -4.89373884070721 \tabularnewline
59 & 765.9 & 789.354079193194 & 3.87180362253598 & 738.57411718427 & 23.4540791931942 \tabularnewline
60 & 836.8 & 924.166709211331 & 14.7415203401073 & 734.691770448561 & 87.3667092113311 \tabularnewline
61 & 904.9 & 1051.65753682393 & 27.3330394632159 & 730.809423712853 & 146.757536823931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62177&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]785.8[/C][C]723.218994947252[/C][C]27.3330394632159[/C][C]821.047965589532[/C][C]-62.5810050527484[/C][/ROW]
[ROW][C]2[/C][C]819.3[/C][C]821.025514220258[/C][C]-21.7498038342756[/C][C]839.324289614017[/C][C]1.72551422025822[/C][/ROW]
[ROW][C]3[/C][C]849.4[/C][C]876.024055874798[/C][C]-34.8246695132999[/C][C]857.600613638502[/C][C]26.6240558747977[/C][/ROW]
[ROW][C]4[/C][C]880.4[/C][C]909.763209427012[/C][C]-24.936350625787[/C][C]875.973141198775[/C][C]29.3632094270116[/C][/ROW]
[ROW][C]5[/C][C]900.1[/C][C]918.182386593958[/C][C]-12.3280553530065[/C][C]894.345668759049[/C][C]18.0823865939581[/C][/ROW]
[ROW][C]6[/C][C]937.2[/C][C]970.891089530729[/C][C]-9.71992159381622[/C][C]913.228832063087[/C][C]33.6910895307291[/C][/ROW]
[ROW][C]7[/C][C]948.9[/C][C]986.53979861381[/C][C]-20.8517939809352[/C][C]932.111995367126[/C][C]37.6397986138093[/C][/ROW]
[ROW][C]8[/C][C]952.6[/C][C]936.332386110632[/C][C]17.6676867673212[/C][C]951.199927122047[/C][C]-16.2676138893681[/C][/ROW]
[ROW][C]9[/C][C]947.3[/C][C]882.824991125659[/C][C]41.4871499973735[/C][C]970.287858876968[/C][C]-64.4750088743414[/C][/ROW]
[ROW][C]10[/C][C]974.2[/C][C]939.730453533478[/C][C]19.3094748441347[/C][C]989.360071622387[/C][C]-34.4695464665216[/C][/ROW]
[ROW][C]11[/C][C]1000.8[/C][C]989.295912009658[/C][C]3.87180362253598[/C][C]1008.43228436781[/C][C]-11.5040879903420[/C][/ROW]
[ROW][C]12[/C][C]1032.8[/C][C]1017.85412465304[/C][C]14.7415203401073[/C][C]1033.00435500685[/C][C]-14.9458753469578[/C][/ROW]
[ROW][C]13[/C][C]1050.7[/C][C]1016.49053489089[/C][C]27.3330394632159[/C][C]1057.57642564589[/C][C]-34.2094651091106[/C][/ROW]
[ROW][C]14[/C][C]1057.3[/C][C]1052.98605890302[/C][C]-21.7498038342756[/C][C]1083.36374493125[/C][C]-4.31394109697681[/C][/ROW]
[ROW][C]15[/C][C]1075.4[/C][C]1076.47360529669[/C][C]-34.8246695132999[/C][C]1109.15106421661[/C][C]1.07360529668995[/C][/ROW]
[ROW][C]16[/C][C]1118.4[/C][C]1130.57498556675[/C][C]-24.936350625787[/C][C]1131.16136505904[/C][C]12.1749855667499[/C][/ROW]
[ROW][C]17[/C][C]1179.8[/C][C]1218.75638945154[/C][C]-12.3280553530065[/C][C]1153.17166590146[/C][C]38.9563894515418[/C][/ROW]
[ROW][C]18[/C][C]1227[/C][C]1291.76599747425[/C][C]-9.71992159381622[/C][C]1171.95392411956[/C][C]64.7659974742514[/C][/ROW]
[ROW][C]19[/C][C]1257.8[/C][C]1345.71561164327[/C][C]-20.8517939809352[/C][C]1190.73618233766[/C][C]87.9156116432705[/C][/ROW]
[ROW][C]20[/C][C]1251.5[/C][C]1274.77169040716[/C][C]17.6676867673212[/C][C]1210.56062282552[/C][C]23.2716904071588[/C][/ROW]
[ROW][C]21[/C][C]1236.3[/C][C]1200.72778668925[/C][C]41.4871499973735[/C][C]1230.38506331338[/C][C]-35.572213310749[/C][/ROW]
[ROW][C]22[/C][C]1170.6[/C][C]1070.48573374482[/C][C]19.3094748441347[/C][C]1251.40479141105[/C][C]-100.114266255180[/C][/ROW]
[ROW][C]23[/C][C]1213.1[/C][C]1149.90367686875[/C][C]3.87180362253598[/C][C]1272.42451950872[/C][C]-63.1963231312513[/C][/ROW]
[ROW][C]24[/C][C]1265.5[/C][C]1221.70192254505[/C][C]14.7415203401073[/C][C]1294.55655711484[/C][C]-43.7980774549462[/C][/ROW]
[ROW][C]25[/C][C]1300.8[/C][C]1257.57836581582[/C][C]27.3330394632159[/C][C]1316.68859472096[/C][C]-43.2216341841784[/C][/ROW]
[ROW][C]26[/C][C]1348.4[/C][C]1376.32053727159[/C][C]-21.7498038342756[/C][C]1342.22926656269[/C][C]27.9205372715892[/C][/ROW]
[ROW][C]27[/C][C]1371.9[/C][C]1410.85473110889[/C][C]-34.8246695132999[/C][C]1367.76993840441[/C][C]38.9547311088891[/C][/ROW]
[ROW][C]28[/C][C]1403.3[/C][C]1437.2454601717[/C][C]-24.936350625787[/C][C]1394.29089045409[/C][C]33.9454601717[/C][/ROW]
[ROW][C]29[/C][C]1451.8[/C][C]1495.11621284924[/C][C]-12.3280553530065[/C][C]1420.81184250376[/C][C]43.3162128492431[/C][/ROW]
[ROW][C]30[/C][C]1474.2[/C][C]1519.21982442130[/C][C]-9.71992159381622[/C][C]1438.90009717252[/C][C]45.0198244212959[/C][/ROW]
[ROW][C]31[/C][C]1438.2[/C][C]1440.26344213966[/C][C]-20.8517939809352[/C][C]1456.98835184128[/C][C]2.06344213965804[/C][/ROW]
[ROW][C]32[/C][C]1513.6[/C][C]1544.99025605536[/C][C]17.6676867673212[/C][C]1464.54205717732[/C][C]31.3902560553586[/C][/ROW]
[ROW][C]33[/C][C]1562.2[/C][C]1610.81708748926[/C][C]41.4871499973735[/C][C]1472.09576251336[/C][C]48.6170874892632[/C][/ROW]
[ROW][C]34[/C][C]1546.2[/C][C]1603.81859978525[/C][C]19.3094748441347[/C][C]1469.27192537062[/C][C]57.6185997852472[/C][/ROW]
[ROW][C]35[/C][C]1527.5[/C][C]1584.68010814959[/C][C]3.87180362253598[/C][C]1466.44808822787[/C][C]57.180108149591[/C][/ROW]
[ROW][C]36[/C][C]1418.7[/C][C]1368.76482145142[/C][C]14.7415203401073[/C][C]1453.89365820847[/C][C]-49.9351785485808[/C][/ROW]
[ROW][C]37[/C][C]1448.5[/C][C]1428.32773234771[/C][C]27.3330394632159[/C][C]1441.33922818907[/C][C]-20.1722676522900[/C][/ROW]
[ROW][C]38[/C][C]1492.1[/C][C]1583.54646958150[/C][C]-21.7498038342756[/C][C]1422.40333425278[/C][C]91.4464695814966[/C][/ROW]
[ROW][C]39[/C][C]1395.4[/C][C]1422.15722919682[/C][C]-34.8246695132999[/C][C]1403.46744031648[/C][C]26.7572291968161[/C][/ROW]
[ROW][C]40[/C][C]1403.7[/C][C]1453.85520586307[/C][C]-24.936350625787[/C][C]1378.48114476272[/C][C]50.15520586307[/C][/ROW]
[ROW][C]41[/C][C]1316.6[/C][C]1292.03320614406[/C][C]-12.3280553530065[/C][C]1353.49484920895[/C][C]-24.5667938559441[/C][/ROW]
[ROW][C]42[/C][C]1274.5[/C][C]1238.92156726318[/C][C]-9.71992159381622[/C][C]1319.79835433064[/C][C]-35.5784327368226[/C][/ROW]
[ROW][C]43[/C][C]1264.4[/C][C]1263.54993452861[/C][C]-20.8517939809352[/C][C]1286.10185945233[/C][C]-0.850065471391872[/C][/ROW]
[ROW][C]44[/C][C]1323.9[/C][C]1390.48329547185[/C][C]17.6676867673212[/C][C]1239.64901776083[/C][C]66.5832954718464[/C][/ROW]
[ROW][C]45[/C][C]1332.1[/C][C]1429.51667393329[/C][C]41.4871499973735[/C][C]1193.19617606934[/C][C]97.4166739332886[/C][/ROW]
[ROW][C]46[/C][C]1250.2[/C][C]1342.42261401966[/C][C]19.3094748441347[/C][C]1138.66791113621[/C][C]92.2226140196594[/C][/ROW]
[ROW][C]47[/C][C]1096.7[/C][C]1105.38855017439[/C][C]3.87180362253598[/C][C]1084.13964620307[/C][C]8.68855017438977[/C][/ROW]
[ROW][C]48[/C][C]1080.8[/C][C]1119.19650600900[/C][C]14.7415203401073[/C][C]1027.66197365089[/C][C]38.3965060090018[/C][/ROW]
[ROW][C]49[/C][C]1039.2[/C][C]1079.88265943808[/C][C]27.3330394632159[/C][C]971.184301098707[/C][C]40.6826594380768[/C][/ROW]
[ROW][C]50[/C][C]792[/C][C]687.860123409571[/C][C]-21.7498038342756[/C][C]917.889680424704[/C][C]-104.139876590429[/C][/ROW]
[ROW][C]51[/C][C]746.6[/C][C]663.429609762598[/C][C]-34.8246695132999[/C][C]864.595059750702[/C][C]-83.1703902374019[/C][/ROW]
[ROW][C]52[/C][C]688.8[/C][C]569.727230508675[/C][C]-24.936350625787[/C][C]832.809120117112[/C][C]-119.072769491325[/C][/ROW]
[ROW][C]53[/C][C]715.8[/C][C]642.904874869483[/C][C]-12.3280553530065[/C][C]801.023180483523[/C][C]-72.8951251305165[/C][/ROW]
[ROW][C]54[/C][C]672.9[/C][C]567.390088258918[/C][C]-9.71992159381622[/C][C]788.129833334898[/C][C]-105.509911741082[/C][/ROW]
[ROW][C]55[/C][C]629.5[/C][C]504.615307794662[/C][C]-20.8517939809352[/C][C]775.236486186273[/C][C]-124.884692205338[/C][/ROW]
[ROW][C]56[/C][C]681.2[/C][C]580.216864735105[/C][C]17.6676867673212[/C][C]764.515448497574[/C][C]-100.983135264895[/C][/ROW]
[ROW][C]57[/C][C]755.4[/C][C]715.518439193751[/C][C]41.4871499973735[/C][C]753.794410808875[/C][C]-39.8815608062486[/C][/ROW]
[ROW][C]58[/C][C]760.6[/C][C]755.706261159293[/C][C]19.3094748441347[/C][C]746.184263996573[/C][C]-4.89373884070721[/C][/ROW]
[ROW][C]59[/C][C]765.9[/C][C]789.354079193194[/C][C]3.87180362253598[/C][C]738.57411718427[/C][C]23.4540791931942[/C][/ROW]
[ROW][C]60[/C][C]836.8[/C][C]924.166709211331[/C][C]14.7415203401073[/C][C]734.691770448561[/C][C]87.3667092113311[/C][/ROW]
[ROW][C]61[/C][C]904.9[/C][C]1051.65753682393[/C][C]27.3330394632159[/C][C]730.809423712853[/C][C]146.757536823931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62177&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62177&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
1785.8723.21899494725227.3330394632159821.047965589532-62.5810050527484
2819.3821.025514220258-21.7498038342756839.3242896140171.72551422025822
3849.4876.024055874798-34.8246695132999857.60061363850226.6240558747977
4880.4909.763209427012-24.936350625787875.97314119877529.3632094270116
5900.1918.182386593958-12.3280553530065894.34566875904918.0823865939581
6937.2970.891089530729-9.71992159381622913.22883206308733.6910895307291
7948.9986.53979861381-20.8517939809352932.11199536712637.6397986138093
8952.6936.33238611063217.6676867673212951.199927122047-16.2676138893681
9947.3882.82499112565941.4871499973735970.287858876968-64.4750088743414
10974.2939.73045353347819.3094748441347989.360071622387-34.4695464665216
111000.8989.2959120096583.871803622535981008.43228436781-11.5040879903420
121032.81017.8541246530414.74152034010731033.00435500685-14.9458753469578
131050.71016.4905348908927.33303946321591057.57642564589-34.2094651091106
141057.31052.98605890302-21.74980383427561083.36374493125-4.31394109697681
151075.41076.47360529669-34.82466951329991109.151064216611.07360529668995
161118.41130.57498556675-24.9363506257871131.1613650590412.1749855667499
171179.81218.75638945154-12.32805535300651153.1716659014638.9563894515418
1812271291.76599747425-9.719921593816221171.9539241195664.7659974742514
191257.81345.71561164327-20.85179398093521190.7361823376687.9156116432705
201251.51274.7716904071617.66768676732121210.5606228255223.2716904071588
211236.31200.7277866892541.48714999737351230.38506331338-35.572213310749
221170.61070.4857337448219.30947484413471251.40479141105-100.114266255180
231213.11149.903676868753.871803622535981272.42451950872-63.1963231312513
241265.51221.7019225450514.74152034010731294.55655711484-43.7980774549462
251300.81257.5783658158227.33303946321591316.68859472096-43.2216341841784
261348.41376.32053727159-21.74980383427561342.2292665626927.9205372715892
271371.91410.85473110889-34.82466951329991367.7699384044138.9547311088891
281403.31437.2454601717-24.9363506257871394.2908904540933.9454601717
291451.81495.11621284924-12.32805535300651420.8118425037643.3162128492431
301474.21519.21982442130-9.719921593816221438.9000971725245.0198244212959
311438.21440.26344213966-20.85179398093521456.988351841282.06344213965804
321513.61544.9902560553617.66768676732121464.5420571773231.3902560553586
331562.21610.8170874892641.48714999737351472.0957625133648.6170874892632
341546.21603.8185997852519.30947484413471469.2719253706257.6185997852472
351527.51584.680108149593.871803622535981466.4480882278757.180108149591
361418.71368.7648214514214.74152034010731453.89365820847-49.9351785485808
371448.51428.3277323477127.33303946321591441.33922818907-20.1722676522900
381492.11583.54646958150-21.74980383427561422.4033342527891.4464695814966
391395.41422.15722919682-34.82466951329991403.4674403164826.7572291968161
401403.71453.85520586307-24.9363506257871378.4811447627250.15520586307
411316.61292.03320614406-12.32805535300651353.49484920895-24.5667938559441
421274.51238.92156726318-9.719921593816221319.79835433064-35.5784327368226
431264.41263.54993452861-20.85179398093521286.10185945233-0.850065471391872
441323.91390.4832954718517.66768676732121239.6490177608366.5832954718464
451332.11429.5166739332941.48714999737351193.1961760693497.4166739332886
461250.21342.4226140196619.30947484413471138.6679111362192.2226140196594
471096.71105.388550174393.871803622535981084.139646203078.68855017438977
481080.81119.1965060090014.74152034010731027.6619736508938.3965060090018
491039.21079.8826594380827.3330394632159971.18430109870740.6826594380768
50792687.860123409571-21.7498038342756917.889680424704-104.139876590429
51746.6663.429609762598-34.8246695132999864.595059750702-83.1703902374019
52688.8569.727230508675-24.936350625787832.809120117112-119.072769491325
53715.8642.904874869483-12.3280553530065801.023180483523-72.8951251305165
54672.9567.390088258918-9.71992159381622788.129833334898-105.509911741082
55629.5504.615307794662-20.8517939809352775.236486186273-124.884692205338
56681.2580.21686473510517.6676867673212764.515448497574-100.983135264895
57755.4715.51843919375141.4871499973735753.794410808875-39.8815608062486
58760.6755.70626115929319.3094748441347746.184263996573-4.89373884070721
59765.9789.3540791931943.87180362253598738.5741171842723.4540791931942
60836.8924.16670921133114.7415203401073734.69177044856187.3667092113311
61904.91051.6575368239327.3330394632159730.809423712853146.757536823931



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
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')