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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 computationFri, 17 Dec 2010 17:37:41 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/17/t1292607376lpe599jgafdor8o.htm/, Retrieved Mon, 06 May 2024 21:48:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111603, Retrieved Mon, 06 May 2024 21:48:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

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]
- RMP         [Decomposition by Loess] [Births] [2010-11-30 13:47:21] [b98453cac15ba1066b407e146608df68]
-    D          [Decomposition by Loess] [Loess Decompositi...] [2010-12-17 16:48:07] [f4dc4aa51d65be851b8508203d9f6001]
- R                 [Decomposition by Loess] [Loess Decompositi...] [2010-12-17 17:37:41] [7a87ed98a7b21a29d6a45388a9b7b229] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
989236
1008380
1207763
1368839
1469798
1498721
1761769
1653214
1599104
1421179
1163995
1037735
1015407
1039210
1258049
1469445
1552346
1549144
1785895
1662335
1629440
1467430
1202209
1076982
1039367
1063449
1335135
1491602
1591972
1641248
1898849
1798580
1762444
1622044
1368955
1262973
1195650
1269530
1479279
1607819
1712466
1721766
1949843
1821326
1757802
1590367
1260647
1149235
1016367
1027885
1262159
1520854
1544144
1564709
1821776
1741365
1623386
1498658
1241822
1136029

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' @ 72.249.76.132

\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' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111603&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' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111603&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111603&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' @ 72.249.76.132






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=111603&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=111603&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111603&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
Seasonal601061
Trend1912
Low-pass1312






Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
19892361019020.12362652-379171.7049339531338623.5813074429784.1236265176
210083801026393.68733991-350271.4402490661340637.7529091518013.6873399124
312077631197944.44842319-125070.3729340581342651.92451087-9818.55157681275
413688391336622.2674160156142.81311448951344912.91946951-32216.732583995
514697981455867.44373968136554.6418321751347173.91442814-13930.5562603155
614987211491369.78432243156508.4812994531349563.73437812-7351.21567756962
717617691767585.73351502403998.7121568851351953.554328095816.73351502209
816532141656361.42821317295352.7051366971354713.866650143147.42821316607
915991041606693.49021367234040.3308141491357474.178972187589.49021366914
1014211791401445.0745904478443.9814873061362468.94392226-19733.9254095615
1111639951155589.06787322-195062.7767455481367463.70887233-8405.9321267812
1210377351014761.94494923-311465.3340935381372173.38914431-22973.0550507708
1310154071033102.63551766-379171.7049339531376883.0694162917695.6355176647
1410392101048653.16042606-350271.4402490661380038.279823019443.1604260567
1512580491257974.88270433-125070.3729340581383193.49022973-74.1172956712544
1614694451496894.5151881356142.81311448951385852.6716973827449.5151881315
1715523461579625.50500280136554.6418321751388511.8531650327279.5050027960
1815491441551043.24055473156508.4812994531390736.278145821899.24055472505
1917858951774830.5847165403998.7121568851392960.70312662-11064.4152835002
2016623351633986.73966128295352.7051366971395330.55520202-28348.2603387162
2116294401627139.26190843234040.3308141491397700.40727742-2300.73809157289
2214674301454665.4671550178443.9814873061401750.55135768-12764.5328449898
2312022091193680.08130760-195062.7767455481405800.69543794-8528.91869239556
2410769821052403.95712870-311465.3340935381413025.37696484-24578.0428712976
2510393671037655.64644223-379171.7049339531420250.05849173-1711.35355777433
2610634491046311.25383751-350271.4402490661430858.18641155-17137.7461624874
2713351351353874.05860268-125070.3729340581441466.3143313818739.0586026793
2814916021472666.5947174756142.81311448951454394.59216804-18935.4052825328
2915919721580066.48816312136554.6418321751467322.87000471-11905.5118368834
3016412481644288.61801534156508.4812994531481698.900685213040.61801534053
3118988491897624.35647741403998.7121568851496074.93136570-1224.6435225897
3217985801791459.06519002295352.7051366971510348.22967328-7120.93480997579
3317624441766226.14120500234040.3308141491524621.527980853782.14120499743
3416220441629401.5552723978443.9814873061536242.463240307357.55527239153
3513689551385109.37824580-195062.7767455481547863.3984997516154.3782457965
3612629731282294.20546051-311465.3340935381555117.1286330319321.2054605111
3711956501208100.84616765-379171.7049339531562370.8587663012450.8461676508
3812695301325126.91088612-350271.4402490661564204.5293629555596.9108861184
3914792791517590.17297447-125070.3729340581566038.1999595938311.1729744661
4016078191597800.8393668856142.81311448951561694.34751863-10018.1606331184
4117124661731026.86309016136554.6418321751557350.4950776718560.8630901587
4217217661740938.35449029156508.4812994531546085.1642102619172.3544902881
4319498431960867.45450026403998.7121568851534819.8333428511024.4545002631
4418213261828268.97007591295352.7051366971519030.324787396942.9700759144
4517578021778322.85295393234040.3308141491503240.8162319320520.8529539250
4615903671614117.4189923678443.9814873061488172.5995203423750.4189923571
4712606471243252.3939368-195062.7767455481473104.38280875-17394.6060631997
4811492351149270.40734269-311465.3340935381460664.9267508535.4073426856194
491016367963680.234240996-379171.7049339531448225.47069296-52686.7657590038
501027885967237.27404457-350271.4402490661438804.16620450-60647.7259554304
5112621591220005.51121802-125070.3729340581429382.86171603-42153.488781977
5215208541558357.7148556356142.81311448951427207.4720298837503.7148556304
5315441441526701.2758241136554.6418321751425032.08234373-17442.7241759007
5415647091548874.22740985156508.4812994531424035.29129070-15834.7725901511
5518217761816514.78760544403998.7121568851423038.50023767-5261.21239455556
5617413651764680.01841877295352.7051366971422697.2764445323315.0184187696
5716233861590375.61653445234040.3308141491422356.05265140-33010.3834655460
5814986581496321.0147773178443.9814873061422551.00373539-2336.98522269237
5912418221255960.82192617-195062.7767455481422745.9548193814138.8219261719
6011360291160197.44818737-311465.3340935381423325.8859061624168.4481873738

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 989236 & 1019020.12362652 & -379171.704933953 & 1338623.58130744 & 29784.1236265176 \tabularnewline
2 & 1008380 & 1026393.68733991 & -350271.440249066 & 1340637.75290915 & 18013.6873399124 \tabularnewline
3 & 1207763 & 1197944.44842319 & -125070.372934058 & 1342651.92451087 & -9818.55157681275 \tabularnewline
4 & 1368839 & 1336622.26741601 & 56142.8131144895 & 1344912.91946951 & -32216.732583995 \tabularnewline
5 & 1469798 & 1455867.44373968 & 136554.641832175 & 1347173.91442814 & -13930.5562603155 \tabularnewline
6 & 1498721 & 1491369.78432243 & 156508.481299453 & 1349563.73437812 & -7351.21567756962 \tabularnewline
7 & 1761769 & 1767585.73351502 & 403998.712156885 & 1351953.55432809 & 5816.73351502209 \tabularnewline
8 & 1653214 & 1656361.42821317 & 295352.705136697 & 1354713.86665014 & 3147.42821316607 \tabularnewline
9 & 1599104 & 1606693.49021367 & 234040.330814149 & 1357474.17897218 & 7589.49021366914 \tabularnewline
10 & 1421179 & 1401445.07459044 & 78443.981487306 & 1362468.94392226 & -19733.9254095615 \tabularnewline
11 & 1163995 & 1155589.06787322 & -195062.776745548 & 1367463.70887233 & -8405.9321267812 \tabularnewline
12 & 1037735 & 1014761.94494923 & -311465.334093538 & 1372173.38914431 & -22973.0550507708 \tabularnewline
13 & 1015407 & 1033102.63551766 & -379171.704933953 & 1376883.06941629 & 17695.6355176647 \tabularnewline
14 & 1039210 & 1048653.16042606 & -350271.440249066 & 1380038.27982301 & 9443.1604260567 \tabularnewline
15 & 1258049 & 1257974.88270433 & -125070.372934058 & 1383193.49022973 & -74.1172956712544 \tabularnewline
16 & 1469445 & 1496894.51518813 & 56142.8131144895 & 1385852.67169738 & 27449.5151881315 \tabularnewline
17 & 1552346 & 1579625.50500280 & 136554.641832175 & 1388511.85316503 & 27279.5050027960 \tabularnewline
18 & 1549144 & 1551043.24055473 & 156508.481299453 & 1390736.27814582 & 1899.24055472505 \tabularnewline
19 & 1785895 & 1774830.5847165 & 403998.712156885 & 1392960.70312662 & -11064.4152835002 \tabularnewline
20 & 1662335 & 1633986.73966128 & 295352.705136697 & 1395330.55520202 & -28348.2603387162 \tabularnewline
21 & 1629440 & 1627139.26190843 & 234040.330814149 & 1397700.40727742 & -2300.73809157289 \tabularnewline
22 & 1467430 & 1454665.46715501 & 78443.981487306 & 1401750.55135768 & -12764.5328449898 \tabularnewline
23 & 1202209 & 1193680.08130760 & -195062.776745548 & 1405800.69543794 & -8528.91869239556 \tabularnewline
24 & 1076982 & 1052403.95712870 & -311465.334093538 & 1413025.37696484 & -24578.0428712976 \tabularnewline
25 & 1039367 & 1037655.64644223 & -379171.704933953 & 1420250.05849173 & -1711.35355777433 \tabularnewline
26 & 1063449 & 1046311.25383751 & -350271.440249066 & 1430858.18641155 & -17137.7461624874 \tabularnewline
27 & 1335135 & 1353874.05860268 & -125070.372934058 & 1441466.31433138 & 18739.0586026793 \tabularnewline
28 & 1491602 & 1472666.59471747 & 56142.8131144895 & 1454394.59216804 & -18935.4052825328 \tabularnewline
29 & 1591972 & 1580066.48816312 & 136554.641832175 & 1467322.87000471 & -11905.5118368834 \tabularnewline
30 & 1641248 & 1644288.61801534 & 156508.481299453 & 1481698.90068521 & 3040.61801534053 \tabularnewline
31 & 1898849 & 1897624.35647741 & 403998.712156885 & 1496074.93136570 & -1224.6435225897 \tabularnewline
32 & 1798580 & 1791459.06519002 & 295352.705136697 & 1510348.22967328 & -7120.93480997579 \tabularnewline
33 & 1762444 & 1766226.14120500 & 234040.330814149 & 1524621.52798085 & 3782.14120499743 \tabularnewline
34 & 1622044 & 1629401.55527239 & 78443.981487306 & 1536242.46324030 & 7357.55527239153 \tabularnewline
35 & 1368955 & 1385109.37824580 & -195062.776745548 & 1547863.39849975 & 16154.3782457965 \tabularnewline
36 & 1262973 & 1282294.20546051 & -311465.334093538 & 1555117.12863303 & 19321.2054605111 \tabularnewline
37 & 1195650 & 1208100.84616765 & -379171.704933953 & 1562370.85876630 & 12450.8461676508 \tabularnewline
38 & 1269530 & 1325126.91088612 & -350271.440249066 & 1564204.52936295 & 55596.9108861184 \tabularnewline
39 & 1479279 & 1517590.17297447 & -125070.372934058 & 1566038.19995959 & 38311.1729744661 \tabularnewline
40 & 1607819 & 1597800.83936688 & 56142.8131144895 & 1561694.34751863 & -10018.1606331184 \tabularnewline
41 & 1712466 & 1731026.86309016 & 136554.641832175 & 1557350.49507767 & 18560.8630901587 \tabularnewline
42 & 1721766 & 1740938.35449029 & 156508.481299453 & 1546085.16421026 & 19172.3544902881 \tabularnewline
43 & 1949843 & 1960867.45450026 & 403998.712156885 & 1534819.83334285 & 11024.4545002631 \tabularnewline
44 & 1821326 & 1828268.97007591 & 295352.705136697 & 1519030.32478739 & 6942.9700759144 \tabularnewline
45 & 1757802 & 1778322.85295393 & 234040.330814149 & 1503240.81623193 & 20520.8529539250 \tabularnewline
46 & 1590367 & 1614117.41899236 & 78443.981487306 & 1488172.59952034 & 23750.4189923571 \tabularnewline
47 & 1260647 & 1243252.3939368 & -195062.776745548 & 1473104.38280875 & -17394.6060631997 \tabularnewline
48 & 1149235 & 1149270.40734269 & -311465.334093538 & 1460664.92675085 & 35.4073426856194 \tabularnewline
49 & 1016367 & 963680.234240996 & -379171.704933953 & 1448225.47069296 & -52686.7657590038 \tabularnewline
50 & 1027885 & 967237.27404457 & -350271.440249066 & 1438804.16620450 & -60647.7259554304 \tabularnewline
51 & 1262159 & 1220005.51121802 & -125070.372934058 & 1429382.86171603 & -42153.488781977 \tabularnewline
52 & 1520854 & 1558357.71485563 & 56142.8131144895 & 1427207.47202988 & 37503.7148556304 \tabularnewline
53 & 1544144 & 1526701.2758241 & 136554.641832175 & 1425032.08234373 & -17442.7241759007 \tabularnewline
54 & 1564709 & 1548874.22740985 & 156508.481299453 & 1424035.29129070 & -15834.7725901511 \tabularnewline
55 & 1821776 & 1816514.78760544 & 403998.712156885 & 1423038.50023767 & -5261.21239455556 \tabularnewline
56 & 1741365 & 1764680.01841877 & 295352.705136697 & 1422697.27644453 & 23315.0184187696 \tabularnewline
57 & 1623386 & 1590375.61653445 & 234040.330814149 & 1422356.05265140 & -33010.3834655460 \tabularnewline
58 & 1498658 & 1496321.01477731 & 78443.981487306 & 1422551.00373539 & -2336.98522269237 \tabularnewline
59 & 1241822 & 1255960.82192617 & -195062.776745548 & 1422745.95481938 & 14138.8219261719 \tabularnewline
60 & 1136029 & 1160197.44818737 & -311465.334093538 & 1423325.88590616 & 24168.4481873738 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111603&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]989236[/C][C]1019020.12362652[/C][C]-379171.704933953[/C][C]1338623.58130744[/C][C]29784.1236265176[/C][/ROW]
[ROW][C]2[/C][C]1008380[/C][C]1026393.68733991[/C][C]-350271.440249066[/C][C]1340637.75290915[/C][C]18013.6873399124[/C][/ROW]
[ROW][C]3[/C][C]1207763[/C][C]1197944.44842319[/C][C]-125070.372934058[/C][C]1342651.92451087[/C][C]-9818.55157681275[/C][/ROW]
[ROW][C]4[/C][C]1368839[/C][C]1336622.26741601[/C][C]56142.8131144895[/C][C]1344912.91946951[/C][C]-32216.732583995[/C][/ROW]
[ROW][C]5[/C][C]1469798[/C][C]1455867.44373968[/C][C]136554.641832175[/C][C]1347173.91442814[/C][C]-13930.5562603155[/C][/ROW]
[ROW][C]6[/C][C]1498721[/C][C]1491369.78432243[/C][C]156508.481299453[/C][C]1349563.73437812[/C][C]-7351.21567756962[/C][/ROW]
[ROW][C]7[/C][C]1761769[/C][C]1767585.73351502[/C][C]403998.712156885[/C][C]1351953.55432809[/C][C]5816.73351502209[/C][/ROW]
[ROW][C]8[/C][C]1653214[/C][C]1656361.42821317[/C][C]295352.705136697[/C][C]1354713.86665014[/C][C]3147.42821316607[/C][/ROW]
[ROW][C]9[/C][C]1599104[/C][C]1606693.49021367[/C][C]234040.330814149[/C][C]1357474.17897218[/C][C]7589.49021366914[/C][/ROW]
[ROW][C]10[/C][C]1421179[/C][C]1401445.07459044[/C][C]78443.981487306[/C][C]1362468.94392226[/C][C]-19733.9254095615[/C][/ROW]
[ROW][C]11[/C][C]1163995[/C][C]1155589.06787322[/C][C]-195062.776745548[/C][C]1367463.70887233[/C][C]-8405.9321267812[/C][/ROW]
[ROW][C]12[/C][C]1037735[/C][C]1014761.94494923[/C][C]-311465.334093538[/C][C]1372173.38914431[/C][C]-22973.0550507708[/C][/ROW]
[ROW][C]13[/C][C]1015407[/C][C]1033102.63551766[/C][C]-379171.704933953[/C][C]1376883.06941629[/C][C]17695.6355176647[/C][/ROW]
[ROW][C]14[/C][C]1039210[/C][C]1048653.16042606[/C][C]-350271.440249066[/C][C]1380038.27982301[/C][C]9443.1604260567[/C][/ROW]
[ROW][C]15[/C][C]1258049[/C][C]1257974.88270433[/C][C]-125070.372934058[/C][C]1383193.49022973[/C][C]-74.1172956712544[/C][/ROW]
[ROW][C]16[/C][C]1469445[/C][C]1496894.51518813[/C][C]56142.8131144895[/C][C]1385852.67169738[/C][C]27449.5151881315[/C][/ROW]
[ROW][C]17[/C][C]1552346[/C][C]1579625.50500280[/C][C]136554.641832175[/C][C]1388511.85316503[/C][C]27279.5050027960[/C][/ROW]
[ROW][C]18[/C][C]1549144[/C][C]1551043.24055473[/C][C]156508.481299453[/C][C]1390736.27814582[/C][C]1899.24055472505[/C][/ROW]
[ROW][C]19[/C][C]1785895[/C][C]1774830.5847165[/C][C]403998.712156885[/C][C]1392960.70312662[/C][C]-11064.4152835002[/C][/ROW]
[ROW][C]20[/C][C]1662335[/C][C]1633986.73966128[/C][C]295352.705136697[/C][C]1395330.55520202[/C][C]-28348.2603387162[/C][/ROW]
[ROW][C]21[/C][C]1629440[/C][C]1627139.26190843[/C][C]234040.330814149[/C][C]1397700.40727742[/C][C]-2300.73809157289[/C][/ROW]
[ROW][C]22[/C][C]1467430[/C][C]1454665.46715501[/C][C]78443.981487306[/C][C]1401750.55135768[/C][C]-12764.5328449898[/C][/ROW]
[ROW][C]23[/C][C]1202209[/C][C]1193680.08130760[/C][C]-195062.776745548[/C][C]1405800.69543794[/C][C]-8528.91869239556[/C][/ROW]
[ROW][C]24[/C][C]1076982[/C][C]1052403.95712870[/C][C]-311465.334093538[/C][C]1413025.37696484[/C][C]-24578.0428712976[/C][/ROW]
[ROW][C]25[/C][C]1039367[/C][C]1037655.64644223[/C][C]-379171.704933953[/C][C]1420250.05849173[/C][C]-1711.35355777433[/C][/ROW]
[ROW][C]26[/C][C]1063449[/C][C]1046311.25383751[/C][C]-350271.440249066[/C][C]1430858.18641155[/C][C]-17137.7461624874[/C][/ROW]
[ROW][C]27[/C][C]1335135[/C][C]1353874.05860268[/C][C]-125070.372934058[/C][C]1441466.31433138[/C][C]18739.0586026793[/C][/ROW]
[ROW][C]28[/C][C]1491602[/C][C]1472666.59471747[/C][C]56142.8131144895[/C][C]1454394.59216804[/C][C]-18935.4052825328[/C][/ROW]
[ROW][C]29[/C][C]1591972[/C][C]1580066.48816312[/C][C]136554.641832175[/C][C]1467322.87000471[/C][C]-11905.5118368834[/C][/ROW]
[ROW][C]30[/C][C]1641248[/C][C]1644288.61801534[/C][C]156508.481299453[/C][C]1481698.90068521[/C][C]3040.61801534053[/C][/ROW]
[ROW][C]31[/C][C]1898849[/C][C]1897624.35647741[/C][C]403998.712156885[/C][C]1496074.93136570[/C][C]-1224.6435225897[/C][/ROW]
[ROW][C]32[/C][C]1798580[/C][C]1791459.06519002[/C][C]295352.705136697[/C][C]1510348.22967328[/C][C]-7120.93480997579[/C][/ROW]
[ROW][C]33[/C][C]1762444[/C][C]1766226.14120500[/C][C]234040.330814149[/C][C]1524621.52798085[/C][C]3782.14120499743[/C][/ROW]
[ROW][C]34[/C][C]1622044[/C][C]1629401.55527239[/C][C]78443.981487306[/C][C]1536242.46324030[/C][C]7357.55527239153[/C][/ROW]
[ROW][C]35[/C][C]1368955[/C][C]1385109.37824580[/C][C]-195062.776745548[/C][C]1547863.39849975[/C][C]16154.3782457965[/C][/ROW]
[ROW][C]36[/C][C]1262973[/C][C]1282294.20546051[/C][C]-311465.334093538[/C][C]1555117.12863303[/C][C]19321.2054605111[/C][/ROW]
[ROW][C]37[/C][C]1195650[/C][C]1208100.84616765[/C][C]-379171.704933953[/C][C]1562370.85876630[/C][C]12450.8461676508[/C][/ROW]
[ROW][C]38[/C][C]1269530[/C][C]1325126.91088612[/C][C]-350271.440249066[/C][C]1564204.52936295[/C][C]55596.9108861184[/C][/ROW]
[ROW][C]39[/C][C]1479279[/C][C]1517590.17297447[/C][C]-125070.372934058[/C][C]1566038.19995959[/C][C]38311.1729744661[/C][/ROW]
[ROW][C]40[/C][C]1607819[/C][C]1597800.83936688[/C][C]56142.8131144895[/C][C]1561694.34751863[/C][C]-10018.1606331184[/C][/ROW]
[ROW][C]41[/C][C]1712466[/C][C]1731026.86309016[/C][C]136554.641832175[/C][C]1557350.49507767[/C][C]18560.8630901587[/C][/ROW]
[ROW][C]42[/C][C]1721766[/C][C]1740938.35449029[/C][C]156508.481299453[/C][C]1546085.16421026[/C][C]19172.3544902881[/C][/ROW]
[ROW][C]43[/C][C]1949843[/C][C]1960867.45450026[/C][C]403998.712156885[/C][C]1534819.83334285[/C][C]11024.4545002631[/C][/ROW]
[ROW][C]44[/C][C]1821326[/C][C]1828268.97007591[/C][C]295352.705136697[/C][C]1519030.32478739[/C][C]6942.9700759144[/C][/ROW]
[ROW][C]45[/C][C]1757802[/C][C]1778322.85295393[/C][C]234040.330814149[/C][C]1503240.81623193[/C][C]20520.8529539250[/C][/ROW]
[ROW][C]46[/C][C]1590367[/C][C]1614117.41899236[/C][C]78443.981487306[/C][C]1488172.59952034[/C][C]23750.4189923571[/C][/ROW]
[ROW][C]47[/C][C]1260647[/C][C]1243252.3939368[/C][C]-195062.776745548[/C][C]1473104.38280875[/C][C]-17394.6060631997[/C][/ROW]
[ROW][C]48[/C][C]1149235[/C][C]1149270.40734269[/C][C]-311465.334093538[/C][C]1460664.92675085[/C][C]35.4073426856194[/C][/ROW]
[ROW][C]49[/C][C]1016367[/C][C]963680.234240996[/C][C]-379171.704933953[/C][C]1448225.47069296[/C][C]-52686.7657590038[/C][/ROW]
[ROW][C]50[/C][C]1027885[/C][C]967237.27404457[/C][C]-350271.440249066[/C][C]1438804.16620450[/C][C]-60647.7259554304[/C][/ROW]
[ROW][C]51[/C][C]1262159[/C][C]1220005.51121802[/C][C]-125070.372934058[/C][C]1429382.86171603[/C][C]-42153.488781977[/C][/ROW]
[ROW][C]52[/C][C]1520854[/C][C]1558357.71485563[/C][C]56142.8131144895[/C][C]1427207.47202988[/C][C]37503.7148556304[/C][/ROW]
[ROW][C]53[/C][C]1544144[/C][C]1526701.2758241[/C][C]136554.641832175[/C][C]1425032.08234373[/C][C]-17442.7241759007[/C][/ROW]
[ROW][C]54[/C][C]1564709[/C][C]1548874.22740985[/C][C]156508.481299453[/C][C]1424035.29129070[/C][C]-15834.7725901511[/C][/ROW]
[ROW][C]55[/C][C]1821776[/C][C]1816514.78760544[/C][C]403998.712156885[/C][C]1423038.50023767[/C][C]-5261.21239455556[/C][/ROW]
[ROW][C]56[/C][C]1741365[/C][C]1764680.01841877[/C][C]295352.705136697[/C][C]1422697.27644453[/C][C]23315.0184187696[/C][/ROW]
[ROW][C]57[/C][C]1623386[/C][C]1590375.61653445[/C][C]234040.330814149[/C][C]1422356.05265140[/C][C]-33010.3834655460[/C][/ROW]
[ROW][C]58[/C][C]1498658[/C][C]1496321.01477731[/C][C]78443.981487306[/C][C]1422551.00373539[/C][C]-2336.98522269237[/C][/ROW]
[ROW][C]59[/C][C]1241822[/C][C]1255960.82192617[/C][C]-195062.776745548[/C][C]1422745.95481938[/C][C]14138.8219261719[/C][/ROW]
[ROW][C]60[/C][C]1136029[/C][C]1160197.44818737[/C][C]-311465.334093538[/C][C]1423325.88590616[/C][C]24168.4481873738[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111603&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111603&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
19892361019020.12362652-379171.7049339531338623.5813074429784.1236265176
210083801026393.68733991-350271.4402490661340637.7529091518013.6873399124
312077631197944.44842319-125070.3729340581342651.92451087-9818.55157681275
413688391336622.2674160156142.81311448951344912.91946951-32216.732583995
514697981455867.44373968136554.6418321751347173.91442814-13930.5562603155
614987211491369.78432243156508.4812994531349563.73437812-7351.21567756962
717617691767585.73351502403998.7121568851351953.554328095816.73351502209
816532141656361.42821317295352.7051366971354713.866650143147.42821316607
915991041606693.49021367234040.3308141491357474.178972187589.49021366914
1014211791401445.0745904478443.9814873061362468.94392226-19733.9254095615
1111639951155589.06787322-195062.7767455481367463.70887233-8405.9321267812
1210377351014761.94494923-311465.3340935381372173.38914431-22973.0550507708
1310154071033102.63551766-379171.7049339531376883.0694162917695.6355176647
1410392101048653.16042606-350271.4402490661380038.279823019443.1604260567
1512580491257974.88270433-125070.3729340581383193.49022973-74.1172956712544
1614694451496894.5151881356142.81311448951385852.6716973827449.5151881315
1715523461579625.50500280136554.6418321751388511.8531650327279.5050027960
1815491441551043.24055473156508.4812994531390736.278145821899.24055472505
1917858951774830.5847165403998.7121568851392960.70312662-11064.4152835002
2016623351633986.73966128295352.7051366971395330.55520202-28348.2603387162
2116294401627139.26190843234040.3308141491397700.40727742-2300.73809157289
2214674301454665.4671550178443.9814873061401750.55135768-12764.5328449898
2312022091193680.08130760-195062.7767455481405800.69543794-8528.91869239556
2410769821052403.95712870-311465.3340935381413025.37696484-24578.0428712976
2510393671037655.64644223-379171.7049339531420250.05849173-1711.35355777433
2610634491046311.25383751-350271.4402490661430858.18641155-17137.7461624874
2713351351353874.05860268-125070.3729340581441466.3143313818739.0586026793
2814916021472666.5947174756142.81311448951454394.59216804-18935.4052825328
2915919721580066.48816312136554.6418321751467322.87000471-11905.5118368834
3016412481644288.61801534156508.4812994531481698.900685213040.61801534053
3118988491897624.35647741403998.7121568851496074.93136570-1224.6435225897
3217985801791459.06519002295352.7051366971510348.22967328-7120.93480997579
3317624441766226.14120500234040.3308141491524621.527980853782.14120499743
3416220441629401.5552723978443.9814873061536242.463240307357.55527239153
3513689551385109.37824580-195062.7767455481547863.3984997516154.3782457965
3612629731282294.20546051-311465.3340935381555117.1286330319321.2054605111
3711956501208100.84616765-379171.7049339531562370.8587663012450.8461676508
3812695301325126.91088612-350271.4402490661564204.5293629555596.9108861184
3914792791517590.17297447-125070.3729340581566038.1999595938311.1729744661
4016078191597800.8393668856142.81311448951561694.34751863-10018.1606331184
4117124661731026.86309016136554.6418321751557350.4950776718560.8630901587
4217217661740938.35449029156508.4812994531546085.1642102619172.3544902881
4319498431960867.45450026403998.7121568851534819.8333428511024.4545002631
4418213261828268.97007591295352.7051366971519030.324787396942.9700759144
4517578021778322.85295393234040.3308141491503240.8162319320520.8529539250
4615903671614117.4189923678443.9814873061488172.5995203423750.4189923571
4712606471243252.3939368-195062.7767455481473104.38280875-17394.6060631997
4811492351149270.40734269-311465.3340935381460664.9267508535.4073426856194
491016367963680.234240996-379171.7049339531448225.47069296-52686.7657590038
501027885967237.27404457-350271.4402490661438804.16620450-60647.7259554304
5112621591220005.51121802-125070.3729340581429382.86171603-42153.488781977
5215208541558357.7148556356142.81311448951427207.4720298837503.7148556304
5315441441526701.2758241136554.6418321751425032.08234373-17442.7241759007
5415647091548874.22740985156508.4812994531424035.29129070-15834.7725901511
5518217761816514.78760544403998.7121568851423038.50023767-5261.21239455556
5617413651764680.01841877295352.7051366971422697.2764445323315.0184187696
5716233861590375.61653445234040.3308141491422356.05265140-33010.3834655460
5814986581496321.0147773178443.9814873061422551.00373539-2336.98522269237
5912418221255960.82192617-195062.7767455481422745.9548193814138.8219261719
6011360291160197.44818737-311465.3340935381423325.8859061624168.4481873738


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')
bitmap(file="test5.png")
myresid <- m$time.series[!is.na(m$time.series[,"remainder"]),"remainder"]
hist(as.numeric(myresid), main="Residual Histogram", xlab="Residual Value")
dev.off()