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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationThu, 10 Dec 2009 09:56:36 -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/10/t1260464232k93um4bo4ndxkx4.htm/, Retrieved Thu, 28 Mar 2024 18:47:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65591, Retrieved Thu, 28 Mar 2024 18:47:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
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]
-    D    [Decomposition by Loess] [ws9(1)] [2009-12-02 21:46:29] [cd6314e7e707a6546bd4604c9d1f2b69]
-    D      [Decomposition by Loess] [ws 9 ad] [2009-12-09 19:09:07] [626f1d98f4a7f05bcb9f17666b672c60]
-    D          [Decomposition by Loess] [WS 9 adh] [2009-12-10 16:56:36] [dd4f17965cad1d38de7a1c062d32d75d] [Current]
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Dataseries X:
1901
1395
1639
1643
1751
1797
1373
1558
1555
2061
2010
2119
1985
1963
2017
1975
1589
1679
1392
1511
1449
1767
1899
2179
2217
2049
2343
2175
1607
1702
1764
1766
1615
1953
2091
2411
2550
2351
2786
2525
2474
2332
1978
1789
1904
1997
2207
2453
1948
1384
1989
2140
2100
2045
2083
2022
1950
1422
1859
2147




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65591&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65591&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65591&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







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

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 601 & 0 & 61 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65591&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]601[/C][C]0[/C][C]61[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65591&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65591&T=1

As an alternative you can also use a QR Code:  

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

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







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
119012064.41089920635218.2139045373471519.37519625631163.410899206348
213951318.97132895013-85.64878982343411556.67746087331-76.0286710498722
316391455.33204676043228.6882277492671593.97972549031-183.667953239574
416431496.43158022568160.2748662910101629.29355348331-146.568419774322
517511869.73121302426-32.33859450057431664.60738147632118.731213024258
617971923.91682559587-27.64047028308411697.72364468722126.916825595869
713731237.90242522798-222.7423331260941730.83990789811-135.097574772020
815581567.52465330036-214.7903268874991763.265673587149.52465330036284
915551566.94685327191-252.6382925480661795.6914392761611.9468532719086
1020612417.46546724876-114.771017560951819.30555031219356.465467248765
1120102126.1842491773250.89608947447061842.91966134821116.184249177317
1221192105.43550890742292.4967489182161840.06774217436-13.5644910925760
1319851914.57027246215218.2139045373471837.21582300051-70.429727537854
1419632187.76201217304-85.64878982343411823.88677765039224.762012173040
1520171994.75403995045228.6882277492671810.55773230028-22.2459600495474
1619751993.26819115769160.2748662910101796.4569425513018.2681911576869
1715891427.98244169825-32.33859450057431782.35615280233-161.017558301751
1816791603.67592750730-27.64047028308411781.96454277578-75.3240724926954
1913921225.16940037686-222.7423331260941781.57293274923-166.830599623140
2015111438.63577105709-214.7903268874991798.15455583041-72.364228942907
2114491335.90211363649-252.6382925480661814.73617891158-113.097886363511
2217671813.05374455668-114.771017560951835.7172730042746.0537445566822
2318991890.4055434285750.89608947447061856.69836709696-8.59445657142896
2421792190.81118739763292.4967489182161874.6920636841611.811187397627
2522172323.10033519130218.2139045373471892.68576027136106.100335191297
2620492276.82477741351-85.64878982343411906.82401240993227.824777413509
2723432536.34950770224228.6882277492671920.96226454850193.349507702238
2821752257.08099473867160.2748662910101932.6441389703282.080994738667
2916071302.01258110842-32.33859450057431944.32601339215-304.987418891576
3017021472.43427313654-27.64047028308411959.20619714654-229.565726863457
3117641776.65595222516-222.7423331260941974.0863809009312.6559522251612
3217661742.33570518474-214.7903268874992004.45462170276-23.6642948152632
3316151447.81543004348-252.6382925480662034.82286250459-167.184569956525
3419531937.46344745476-114.771017560952083.30757010619-15.5365525452366
3520911999.3116328177550.89608947447062131.79227770778-91.6883671822532
3624112350.94570929048292.4967489182162178.55754179130-60.0542907095196
3725502656.46328958783218.2139045373472225.32280587482106.463289587829
3823512538.51480898551-85.64878982343412249.13398083792187.514808985514
3927863070.36661644972228.6882277492672272.94515580102284.366616449717
4025252611.73542231799160.2748662910102277.98971139186.7354223179877
4124742697.30432751959-32.33859450057432283.03426698099223.304327519587
4223322431.74492886365-27.64047028308412259.8955414194499.7449288636453
4319781941.98551726820-222.7423331260942236.75681585789-36.0144827317968
4417891609.54354022380-214.7903268874992183.24678666370-179.456459776205
4519041930.90153507855-252.6382925480662129.7367574695226.9015350785498
4619972028.49697897619-114.771017560952080.2740385847631.4969789761894
4722072332.2925908255250.89608947447062030.81131970000125.292590825525
4824532602.47319399733292.4967489182162011.03005708445149.473193997335
4919481686.53730099376218.2139045373471991.24879446889-261.46269900624
501384863.170973067734-85.64878982343411990.4778167557-520.829026932266
5119891759.60493320823228.6882277492671989.70683904251-229.395066791774
5221402137.01848590348160.2748662910101982.70664780551-2.98151409651564
5321002256.63213793207-32.33859450057431975.70645656850156.632137932071
5420452148.95550552802-27.64047028308411968.68496475506103.955505528023
5520832427.07886018448-222.7423331260941961.66347294162344.078860184476
5620222300.77756012426-214.7903268874991958.01276676324278.77756012426
5719502198.27623196321-252.6382925480661954.36206058486248.276231963207
5814221008.60408040375-114.771017560951950.1669371572-413.395919596250
5918591721.1320967959950.89608947447061945.97181372954-137.867903204011
6021472062.08536691704292.4967489182161939.41788416474-84.9146330829583

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 1901 & 2064.41089920635 & 218.213904537347 & 1519.37519625631 & 163.410899206348 \tabularnewline
2 & 1395 & 1318.97132895013 & -85.6487898234341 & 1556.67746087331 & -76.0286710498722 \tabularnewline
3 & 1639 & 1455.33204676043 & 228.688227749267 & 1593.97972549031 & -183.667953239574 \tabularnewline
4 & 1643 & 1496.43158022568 & 160.274866291010 & 1629.29355348331 & -146.568419774322 \tabularnewline
5 & 1751 & 1869.73121302426 & -32.3385945005743 & 1664.60738147632 & 118.731213024258 \tabularnewline
6 & 1797 & 1923.91682559587 & -27.6404702830841 & 1697.72364468722 & 126.916825595869 \tabularnewline
7 & 1373 & 1237.90242522798 & -222.742333126094 & 1730.83990789811 & -135.097574772020 \tabularnewline
8 & 1558 & 1567.52465330036 & -214.790326887499 & 1763.26567358714 & 9.52465330036284 \tabularnewline
9 & 1555 & 1566.94685327191 & -252.638292548066 & 1795.69143927616 & 11.9468532719086 \tabularnewline
10 & 2061 & 2417.46546724876 & -114.77101756095 & 1819.30555031219 & 356.465467248765 \tabularnewline
11 & 2010 & 2126.18424917732 & 50.8960894744706 & 1842.91966134821 & 116.184249177317 \tabularnewline
12 & 2119 & 2105.43550890742 & 292.496748918216 & 1840.06774217436 & -13.5644910925760 \tabularnewline
13 & 1985 & 1914.57027246215 & 218.213904537347 & 1837.21582300051 & -70.429727537854 \tabularnewline
14 & 1963 & 2187.76201217304 & -85.6487898234341 & 1823.88677765039 & 224.762012173040 \tabularnewline
15 & 2017 & 1994.75403995045 & 228.688227749267 & 1810.55773230028 & -22.2459600495474 \tabularnewline
16 & 1975 & 1993.26819115769 & 160.274866291010 & 1796.45694255130 & 18.2681911576869 \tabularnewline
17 & 1589 & 1427.98244169825 & -32.3385945005743 & 1782.35615280233 & -161.017558301751 \tabularnewline
18 & 1679 & 1603.67592750730 & -27.6404702830841 & 1781.96454277578 & -75.3240724926954 \tabularnewline
19 & 1392 & 1225.16940037686 & -222.742333126094 & 1781.57293274923 & -166.830599623140 \tabularnewline
20 & 1511 & 1438.63577105709 & -214.790326887499 & 1798.15455583041 & -72.364228942907 \tabularnewline
21 & 1449 & 1335.90211363649 & -252.638292548066 & 1814.73617891158 & -113.097886363511 \tabularnewline
22 & 1767 & 1813.05374455668 & -114.77101756095 & 1835.71727300427 & 46.0537445566822 \tabularnewline
23 & 1899 & 1890.40554342857 & 50.8960894744706 & 1856.69836709696 & -8.59445657142896 \tabularnewline
24 & 2179 & 2190.81118739763 & 292.496748918216 & 1874.69206368416 & 11.811187397627 \tabularnewline
25 & 2217 & 2323.10033519130 & 218.213904537347 & 1892.68576027136 & 106.100335191297 \tabularnewline
26 & 2049 & 2276.82477741351 & -85.6487898234341 & 1906.82401240993 & 227.824777413509 \tabularnewline
27 & 2343 & 2536.34950770224 & 228.688227749267 & 1920.96226454850 & 193.349507702238 \tabularnewline
28 & 2175 & 2257.08099473867 & 160.274866291010 & 1932.64413897032 & 82.080994738667 \tabularnewline
29 & 1607 & 1302.01258110842 & -32.3385945005743 & 1944.32601339215 & -304.987418891576 \tabularnewline
30 & 1702 & 1472.43427313654 & -27.6404702830841 & 1959.20619714654 & -229.565726863457 \tabularnewline
31 & 1764 & 1776.65595222516 & -222.742333126094 & 1974.08638090093 & 12.6559522251612 \tabularnewline
32 & 1766 & 1742.33570518474 & -214.790326887499 & 2004.45462170276 & -23.6642948152632 \tabularnewline
33 & 1615 & 1447.81543004348 & -252.638292548066 & 2034.82286250459 & -167.184569956525 \tabularnewline
34 & 1953 & 1937.46344745476 & -114.77101756095 & 2083.30757010619 & -15.5365525452366 \tabularnewline
35 & 2091 & 1999.31163281775 & 50.8960894744706 & 2131.79227770778 & -91.6883671822532 \tabularnewline
36 & 2411 & 2350.94570929048 & 292.496748918216 & 2178.55754179130 & -60.0542907095196 \tabularnewline
37 & 2550 & 2656.46328958783 & 218.213904537347 & 2225.32280587482 & 106.463289587829 \tabularnewline
38 & 2351 & 2538.51480898551 & -85.6487898234341 & 2249.13398083792 & 187.514808985514 \tabularnewline
39 & 2786 & 3070.36661644972 & 228.688227749267 & 2272.94515580102 & 284.366616449717 \tabularnewline
40 & 2525 & 2611.73542231799 & 160.274866291010 & 2277.989711391 & 86.7354223179877 \tabularnewline
41 & 2474 & 2697.30432751959 & -32.3385945005743 & 2283.03426698099 & 223.304327519587 \tabularnewline
42 & 2332 & 2431.74492886365 & -27.6404702830841 & 2259.89554141944 & 99.7449288636453 \tabularnewline
43 & 1978 & 1941.98551726820 & -222.742333126094 & 2236.75681585789 & -36.0144827317968 \tabularnewline
44 & 1789 & 1609.54354022380 & -214.790326887499 & 2183.24678666370 & -179.456459776205 \tabularnewline
45 & 1904 & 1930.90153507855 & -252.638292548066 & 2129.73675746952 & 26.9015350785498 \tabularnewline
46 & 1997 & 2028.49697897619 & -114.77101756095 & 2080.27403858476 & 31.4969789761894 \tabularnewline
47 & 2207 & 2332.29259082552 & 50.8960894744706 & 2030.81131970000 & 125.292590825525 \tabularnewline
48 & 2453 & 2602.47319399733 & 292.496748918216 & 2011.03005708445 & 149.473193997335 \tabularnewline
49 & 1948 & 1686.53730099376 & 218.213904537347 & 1991.24879446889 & -261.46269900624 \tabularnewline
50 & 1384 & 863.170973067734 & -85.6487898234341 & 1990.4778167557 & -520.829026932266 \tabularnewline
51 & 1989 & 1759.60493320823 & 228.688227749267 & 1989.70683904251 & -229.395066791774 \tabularnewline
52 & 2140 & 2137.01848590348 & 160.274866291010 & 1982.70664780551 & -2.98151409651564 \tabularnewline
53 & 2100 & 2256.63213793207 & -32.3385945005743 & 1975.70645656850 & 156.632137932071 \tabularnewline
54 & 2045 & 2148.95550552802 & -27.6404702830841 & 1968.68496475506 & 103.955505528023 \tabularnewline
55 & 2083 & 2427.07886018448 & -222.742333126094 & 1961.66347294162 & 344.078860184476 \tabularnewline
56 & 2022 & 2300.77756012426 & -214.790326887499 & 1958.01276676324 & 278.77756012426 \tabularnewline
57 & 1950 & 2198.27623196321 & -252.638292548066 & 1954.36206058486 & 248.276231963207 \tabularnewline
58 & 1422 & 1008.60408040375 & -114.77101756095 & 1950.1669371572 & -413.395919596250 \tabularnewline
59 & 1859 & 1721.13209679599 & 50.8960894744706 & 1945.97181372954 & -137.867903204011 \tabularnewline
60 & 2147 & 2062.08536691704 & 292.496748918216 & 1939.41788416474 & -84.9146330829583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65591&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]1901[/C][C]2064.41089920635[/C][C]218.213904537347[/C][C]1519.37519625631[/C][C]163.410899206348[/C][/ROW]
[ROW][C]2[/C][C]1395[/C][C]1318.97132895013[/C][C]-85.6487898234341[/C][C]1556.67746087331[/C][C]-76.0286710498722[/C][/ROW]
[ROW][C]3[/C][C]1639[/C][C]1455.33204676043[/C][C]228.688227749267[/C][C]1593.97972549031[/C][C]-183.667953239574[/C][/ROW]
[ROW][C]4[/C][C]1643[/C][C]1496.43158022568[/C][C]160.274866291010[/C][C]1629.29355348331[/C][C]-146.568419774322[/C][/ROW]
[ROW][C]5[/C][C]1751[/C][C]1869.73121302426[/C][C]-32.3385945005743[/C][C]1664.60738147632[/C][C]118.731213024258[/C][/ROW]
[ROW][C]6[/C][C]1797[/C][C]1923.91682559587[/C][C]-27.6404702830841[/C][C]1697.72364468722[/C][C]126.916825595869[/C][/ROW]
[ROW][C]7[/C][C]1373[/C][C]1237.90242522798[/C][C]-222.742333126094[/C][C]1730.83990789811[/C][C]-135.097574772020[/C][/ROW]
[ROW][C]8[/C][C]1558[/C][C]1567.52465330036[/C][C]-214.790326887499[/C][C]1763.26567358714[/C][C]9.52465330036284[/C][/ROW]
[ROW][C]9[/C][C]1555[/C][C]1566.94685327191[/C][C]-252.638292548066[/C][C]1795.69143927616[/C][C]11.9468532719086[/C][/ROW]
[ROW][C]10[/C][C]2061[/C][C]2417.46546724876[/C][C]-114.77101756095[/C][C]1819.30555031219[/C][C]356.465467248765[/C][/ROW]
[ROW][C]11[/C][C]2010[/C][C]2126.18424917732[/C][C]50.8960894744706[/C][C]1842.91966134821[/C][C]116.184249177317[/C][/ROW]
[ROW][C]12[/C][C]2119[/C][C]2105.43550890742[/C][C]292.496748918216[/C][C]1840.06774217436[/C][C]-13.5644910925760[/C][/ROW]
[ROW][C]13[/C][C]1985[/C][C]1914.57027246215[/C][C]218.213904537347[/C][C]1837.21582300051[/C][C]-70.429727537854[/C][/ROW]
[ROW][C]14[/C][C]1963[/C][C]2187.76201217304[/C][C]-85.6487898234341[/C][C]1823.88677765039[/C][C]224.762012173040[/C][/ROW]
[ROW][C]15[/C][C]2017[/C][C]1994.75403995045[/C][C]228.688227749267[/C][C]1810.55773230028[/C][C]-22.2459600495474[/C][/ROW]
[ROW][C]16[/C][C]1975[/C][C]1993.26819115769[/C][C]160.274866291010[/C][C]1796.45694255130[/C][C]18.2681911576869[/C][/ROW]
[ROW][C]17[/C][C]1589[/C][C]1427.98244169825[/C][C]-32.3385945005743[/C][C]1782.35615280233[/C][C]-161.017558301751[/C][/ROW]
[ROW][C]18[/C][C]1679[/C][C]1603.67592750730[/C][C]-27.6404702830841[/C][C]1781.96454277578[/C][C]-75.3240724926954[/C][/ROW]
[ROW][C]19[/C][C]1392[/C][C]1225.16940037686[/C][C]-222.742333126094[/C][C]1781.57293274923[/C][C]-166.830599623140[/C][/ROW]
[ROW][C]20[/C][C]1511[/C][C]1438.63577105709[/C][C]-214.790326887499[/C][C]1798.15455583041[/C][C]-72.364228942907[/C][/ROW]
[ROW][C]21[/C][C]1449[/C][C]1335.90211363649[/C][C]-252.638292548066[/C][C]1814.73617891158[/C][C]-113.097886363511[/C][/ROW]
[ROW][C]22[/C][C]1767[/C][C]1813.05374455668[/C][C]-114.77101756095[/C][C]1835.71727300427[/C][C]46.0537445566822[/C][/ROW]
[ROW][C]23[/C][C]1899[/C][C]1890.40554342857[/C][C]50.8960894744706[/C][C]1856.69836709696[/C][C]-8.59445657142896[/C][/ROW]
[ROW][C]24[/C][C]2179[/C][C]2190.81118739763[/C][C]292.496748918216[/C][C]1874.69206368416[/C][C]11.811187397627[/C][/ROW]
[ROW][C]25[/C][C]2217[/C][C]2323.10033519130[/C][C]218.213904537347[/C][C]1892.68576027136[/C][C]106.100335191297[/C][/ROW]
[ROW][C]26[/C][C]2049[/C][C]2276.82477741351[/C][C]-85.6487898234341[/C][C]1906.82401240993[/C][C]227.824777413509[/C][/ROW]
[ROW][C]27[/C][C]2343[/C][C]2536.34950770224[/C][C]228.688227749267[/C][C]1920.96226454850[/C][C]193.349507702238[/C][/ROW]
[ROW][C]28[/C][C]2175[/C][C]2257.08099473867[/C][C]160.274866291010[/C][C]1932.64413897032[/C][C]82.080994738667[/C][/ROW]
[ROW][C]29[/C][C]1607[/C][C]1302.01258110842[/C][C]-32.3385945005743[/C][C]1944.32601339215[/C][C]-304.987418891576[/C][/ROW]
[ROW][C]30[/C][C]1702[/C][C]1472.43427313654[/C][C]-27.6404702830841[/C][C]1959.20619714654[/C][C]-229.565726863457[/C][/ROW]
[ROW][C]31[/C][C]1764[/C][C]1776.65595222516[/C][C]-222.742333126094[/C][C]1974.08638090093[/C][C]12.6559522251612[/C][/ROW]
[ROW][C]32[/C][C]1766[/C][C]1742.33570518474[/C][C]-214.790326887499[/C][C]2004.45462170276[/C][C]-23.6642948152632[/C][/ROW]
[ROW][C]33[/C][C]1615[/C][C]1447.81543004348[/C][C]-252.638292548066[/C][C]2034.82286250459[/C][C]-167.184569956525[/C][/ROW]
[ROW][C]34[/C][C]1953[/C][C]1937.46344745476[/C][C]-114.77101756095[/C][C]2083.30757010619[/C][C]-15.5365525452366[/C][/ROW]
[ROW][C]35[/C][C]2091[/C][C]1999.31163281775[/C][C]50.8960894744706[/C][C]2131.79227770778[/C][C]-91.6883671822532[/C][/ROW]
[ROW][C]36[/C][C]2411[/C][C]2350.94570929048[/C][C]292.496748918216[/C][C]2178.55754179130[/C][C]-60.0542907095196[/C][/ROW]
[ROW][C]37[/C][C]2550[/C][C]2656.46328958783[/C][C]218.213904537347[/C][C]2225.32280587482[/C][C]106.463289587829[/C][/ROW]
[ROW][C]38[/C][C]2351[/C][C]2538.51480898551[/C][C]-85.6487898234341[/C][C]2249.13398083792[/C][C]187.514808985514[/C][/ROW]
[ROW][C]39[/C][C]2786[/C][C]3070.36661644972[/C][C]228.688227749267[/C][C]2272.94515580102[/C][C]284.366616449717[/C][/ROW]
[ROW][C]40[/C][C]2525[/C][C]2611.73542231799[/C][C]160.274866291010[/C][C]2277.989711391[/C][C]86.7354223179877[/C][/ROW]
[ROW][C]41[/C][C]2474[/C][C]2697.30432751959[/C][C]-32.3385945005743[/C][C]2283.03426698099[/C][C]223.304327519587[/C][/ROW]
[ROW][C]42[/C][C]2332[/C][C]2431.74492886365[/C][C]-27.6404702830841[/C][C]2259.89554141944[/C][C]99.7449288636453[/C][/ROW]
[ROW][C]43[/C][C]1978[/C][C]1941.98551726820[/C][C]-222.742333126094[/C][C]2236.75681585789[/C][C]-36.0144827317968[/C][/ROW]
[ROW][C]44[/C][C]1789[/C][C]1609.54354022380[/C][C]-214.790326887499[/C][C]2183.24678666370[/C][C]-179.456459776205[/C][/ROW]
[ROW][C]45[/C][C]1904[/C][C]1930.90153507855[/C][C]-252.638292548066[/C][C]2129.73675746952[/C][C]26.9015350785498[/C][/ROW]
[ROW][C]46[/C][C]1997[/C][C]2028.49697897619[/C][C]-114.77101756095[/C][C]2080.27403858476[/C][C]31.4969789761894[/C][/ROW]
[ROW][C]47[/C][C]2207[/C][C]2332.29259082552[/C][C]50.8960894744706[/C][C]2030.81131970000[/C][C]125.292590825525[/C][/ROW]
[ROW][C]48[/C][C]2453[/C][C]2602.47319399733[/C][C]292.496748918216[/C][C]2011.03005708445[/C][C]149.473193997335[/C][/ROW]
[ROW][C]49[/C][C]1948[/C][C]1686.53730099376[/C][C]218.213904537347[/C][C]1991.24879446889[/C][C]-261.46269900624[/C][/ROW]
[ROW][C]50[/C][C]1384[/C][C]863.170973067734[/C][C]-85.6487898234341[/C][C]1990.4778167557[/C][C]-520.829026932266[/C][/ROW]
[ROW][C]51[/C][C]1989[/C][C]1759.60493320823[/C][C]228.688227749267[/C][C]1989.70683904251[/C][C]-229.395066791774[/C][/ROW]
[ROW][C]52[/C][C]2140[/C][C]2137.01848590348[/C][C]160.274866291010[/C][C]1982.70664780551[/C][C]-2.98151409651564[/C][/ROW]
[ROW][C]53[/C][C]2100[/C][C]2256.63213793207[/C][C]-32.3385945005743[/C][C]1975.70645656850[/C][C]156.632137932071[/C][/ROW]
[ROW][C]54[/C][C]2045[/C][C]2148.95550552802[/C][C]-27.6404702830841[/C][C]1968.68496475506[/C][C]103.955505528023[/C][/ROW]
[ROW][C]55[/C][C]2083[/C][C]2427.07886018448[/C][C]-222.742333126094[/C][C]1961.66347294162[/C][C]344.078860184476[/C][/ROW]
[ROW][C]56[/C][C]2022[/C][C]2300.77756012426[/C][C]-214.790326887499[/C][C]1958.01276676324[/C][C]278.77756012426[/C][/ROW]
[ROW][C]57[/C][C]1950[/C][C]2198.27623196321[/C][C]-252.638292548066[/C][C]1954.36206058486[/C][C]248.276231963207[/C][/ROW]
[ROW][C]58[/C][C]1422[/C][C]1008.60408040375[/C][C]-114.77101756095[/C][C]1950.1669371572[/C][C]-413.395919596250[/C][/ROW]
[ROW][C]59[/C][C]1859[/C][C]1721.13209679599[/C][C]50.8960894744706[/C][C]1945.97181372954[/C][C]-137.867903204011[/C][/ROW]
[ROW][C]60[/C][C]2147[/C][C]2062.08536691704[/C][C]292.496748918216[/C][C]1939.41788416474[/C][C]-84.9146330829583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65591&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65591&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
119012064.41089920635218.2139045373471519.37519625631163.410899206348
213951318.97132895013-85.64878982343411556.67746087331-76.0286710498722
316391455.33204676043228.6882277492671593.97972549031-183.667953239574
416431496.43158022568160.2748662910101629.29355348331-146.568419774322
517511869.73121302426-32.33859450057431664.60738147632118.731213024258
617971923.91682559587-27.64047028308411697.72364468722126.916825595869
713731237.90242522798-222.7423331260941730.83990789811-135.097574772020
815581567.52465330036-214.7903268874991763.265673587149.52465330036284
915551566.94685327191-252.6382925480661795.6914392761611.9468532719086
1020612417.46546724876-114.771017560951819.30555031219356.465467248765
1120102126.1842491773250.89608947447061842.91966134821116.184249177317
1221192105.43550890742292.4967489182161840.06774217436-13.5644910925760
1319851914.57027246215218.2139045373471837.21582300051-70.429727537854
1419632187.76201217304-85.64878982343411823.88677765039224.762012173040
1520171994.75403995045228.6882277492671810.55773230028-22.2459600495474
1619751993.26819115769160.2748662910101796.4569425513018.2681911576869
1715891427.98244169825-32.33859450057431782.35615280233-161.017558301751
1816791603.67592750730-27.64047028308411781.96454277578-75.3240724926954
1913921225.16940037686-222.7423331260941781.57293274923-166.830599623140
2015111438.63577105709-214.7903268874991798.15455583041-72.364228942907
2114491335.90211363649-252.6382925480661814.73617891158-113.097886363511
2217671813.05374455668-114.771017560951835.7172730042746.0537445566822
2318991890.4055434285750.89608947447061856.69836709696-8.59445657142896
2421792190.81118739763292.4967489182161874.6920636841611.811187397627
2522172323.10033519130218.2139045373471892.68576027136106.100335191297
2620492276.82477741351-85.64878982343411906.82401240993227.824777413509
2723432536.34950770224228.6882277492671920.96226454850193.349507702238
2821752257.08099473867160.2748662910101932.6441389703282.080994738667
2916071302.01258110842-32.33859450057431944.32601339215-304.987418891576
3017021472.43427313654-27.64047028308411959.20619714654-229.565726863457
3117641776.65595222516-222.7423331260941974.0863809009312.6559522251612
3217661742.33570518474-214.7903268874992004.45462170276-23.6642948152632
3316151447.81543004348-252.6382925480662034.82286250459-167.184569956525
3419531937.46344745476-114.771017560952083.30757010619-15.5365525452366
3520911999.3116328177550.89608947447062131.79227770778-91.6883671822532
3624112350.94570929048292.4967489182162178.55754179130-60.0542907095196
3725502656.46328958783218.2139045373472225.32280587482106.463289587829
3823512538.51480898551-85.64878982343412249.13398083792187.514808985514
3927863070.36661644972228.6882277492672272.94515580102284.366616449717
4025252611.73542231799160.2748662910102277.98971139186.7354223179877
4124742697.30432751959-32.33859450057432283.03426698099223.304327519587
4223322431.74492886365-27.64047028308412259.8955414194499.7449288636453
4319781941.98551726820-222.7423331260942236.75681585789-36.0144827317968
4417891609.54354022380-214.7903268874992183.24678666370-179.456459776205
4519041930.90153507855-252.6382925480662129.7367574695226.9015350785498
4619972028.49697897619-114.771017560952080.2740385847631.4969789761894
4722072332.2925908255250.89608947447062030.81131970000125.292590825525
4824532602.47319399733292.4967489182162011.03005708445149.473193997335
4919481686.53730099376218.2139045373471991.24879446889-261.46269900624
501384863.170973067734-85.64878982343411990.4778167557-520.829026932266
5119891759.60493320823228.6882277492671989.70683904251-229.395066791774
5221402137.01848590348160.2748662910101982.70664780551-2.98151409651564
5321002256.63213793207-32.33859450057431975.70645656850156.632137932071
5420452148.95550552802-27.64047028308411968.68496475506103.955505528023
5520832427.07886018448-222.7423331260941961.66347294162344.078860184476
5620222300.77756012426-214.7903268874991958.01276676324278.77756012426
5719502198.27623196321-252.6382925480661954.36206058486248.276231963207
5814221008.60408040375-114.771017560951950.1669371572-413.395919596250
5918591721.1320967959950.89608947447061945.97181372954-137.867903204011
6021472062.08536691704292.4967489182161939.41788416474-84.9146330829583



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')