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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, 04 Dec 2009 12:15:18 -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/04/t1259954238jg2bzae8v528qyu.htm/, Retrieved Sun, 28 Apr 2024 13:30:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64055, Retrieved Sun, 28 Apr 2024 13:30:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

Tree of Dependent Computations

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] [Seizoenale decomp...] [2009-12-01 19:51:41] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-    D        [Decomposition by Loess] [seizoenale decomp...] [2009-12-04 19:15:18] [d1818fb1d9a1b0f34f8553ada228d3d5] [Current]
-    D          [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 15:57:17] [4f1a20f787b3465111b61213cdeef1a9]
-    D            [Decomposition by Loess] [Seizoenale decomp...] [2009-12-11 16:44:09] [4f1a20f787b3465111b61213cdeef1a9]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
107.11
107.57
107.81
108.75
109.43
109.62
109.54
109.53
109.84
109.67
109.79
109.56
110.22
110.40
110.69
110.72
110.89
110.58
110.94
110.91
111.22
111.09
111.00
111.06
111.55
112.32
112.64
112.36
112.04
112.37
112.59
112.89
113.22
112.85
113.06
112.99
113.32
113.74
113.91
114.52
114.96
114.91
115.30
115.44
115.52
116.08
115.94
115.56
115.88
116.66
117.41
117.68
117.85
118.21
118.92
119.03
119.17
118.95
118.92
118.90

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'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=64055&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=64055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64055&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'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=64055&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=64055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64055&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
1107.11106.932775372002-0.529359630251722107.81658425825-0.177224627998356
2107.57107.304423756218-0.206289705843261108.041865949625-0.265576243782192
3107.81107.404072211013-0.0512198520137605108.267147641001-0.405927788987043
4108.75108.9107558837580.101624283069029108.4876198331730.160755883757943
5109.43109.9834394767750.168468497879841108.7080920253450.553439476774912
6109.62110.1831701877570.128697690262538108.9281321219810.563170187756796
7109.54109.6269008033270.304926978057394109.1481722186160.0869008033265288
8109.53109.4418545622920.249133088672037109.369012349036-0.0881454377084196
9109.84109.7648083683090.325339152234056109.589852479457-0.0751916316907426
10109.67109.5010378197990.0664675831393001109.772494597062-0.168962180201362
11109.79109.737267331411-0.112404046078121109.955136714667-0.0527326685893144
12109.56109.482784872249-0.445384027043047110.082599154794-0.0772151277513728
13110.22110.759298035330-0.529359630251722110.2100615949210.539298035330305
14110.4110.682964144308-0.206289705843261110.3233255615360.282964144307726
15110.69110.994630323864-0.0512198520137605110.4365895281500.304630323864089
16110.72110.8011492960940.101624283069029110.5372264208370.0811492960944662
17110.89110.9736681885970.168468497879841110.6378633135230.0836681885968034
18110.58110.2893956409970.128697690262538110.741906668741-0.290604359003240
19110.94110.7291229979850.304926978057394110.845950023958-0.210877002015437
20110.91110.5902694905360.249133088672037110.980597420792-0.319730509464279
21111.22110.9994160301400.325339152234056111.115244817626-0.220583969860485
22111.09110.8440356178920.0664675831393001111.269496798969-0.245964382107871
23111110.688655265767-0.112404046078121111.423748780311-0.311344734232591
24111.06110.988050609003-0.445384027043047111.577333418040-0.0719493909973039
25111.55111.898441574482-0.529359630251722111.730918055770.34844157448174
26112.32112.962119469035-0.206289705843261111.8841702368090.642119469034668
27112.64113.293797434167-0.0512198520137605112.0374224178470.653797434166549
28112.36112.4342008899320.101624283069029112.1841748269990.0742008899320155
29112.04111.5806042659690.168468497879841112.330927236151-0.459395734030537
30112.37112.1442730915280.128697690262538112.467029218210-0.225726908472367
31112.59112.2719418216740.304926978057394112.603131200269-0.318058178326353
32112.89112.7867689806350.249133088672037112.744097930693-0.103231019364685
33113.22113.2295961866500.325339152234056112.8850646611160.00959618664963102
34112.85112.564903542230.0664675831393001113.068628874631-0.285096457769967
35113.06112.980210957933-0.112404046078121113.252193088145-0.0797890420668921
36112.99112.955020940767-0.445384027043047113.470363086276-0.0349790592326968
37113.32113.480826545845-0.529359630251722113.6885330844060.160826545845239
38113.74113.775404677598-0.206289705843261113.9108850282450.0354046775980095
39113.91113.737982879930-0.0512198520137605114.133236972084-0.172017120070265
40114.52114.5778610160800.101624283069029114.3605147008510.0578610160804374
41114.96115.1637390725030.168468497879841114.5877924296170.203739072503126
42114.91114.8746414180240.128697690262538114.816660891713-0.0353585819757285
43115.3115.2495436681330.304926978057394115.045529353809-0.050456331866755
44115.44115.3433556295030.249133088672037115.287511281825-0.096644370497188
45115.52115.1851676379250.325339152234056115.529493209841-0.334832362074991
46116.08116.3026523463640.0664675831393001115.7908800704960.222652346364328
47115.94115.940137114926-0.112404046078121116.0522669311520.000137114926303639
48115.56115.232567890156-0.445384027043047116.332816136887-0.327432109844025
49115.88115.675994287629-0.529359630251722116.613365342622-0.204005712370616
50116.66116.621028634447-0.206289705843261116.905261071396-0.0389713655529818
51117.41117.674063051844-0.0512198520137605117.1971568001700.264063051843607
52117.68117.7953628198300.101624283069029117.4630128971010.115362819829642
53117.85117.8026625080880.168468497879841117.728868994033-0.0473374919123728
54118.21118.3018643568780.128697690262538117.9894379528590.0918643568781761
55118.92119.2850661102570.304926978057394118.2500069116860.365066110256564
56119.03119.3012094868050.249133088672037118.5096574245230.271209486805333
57119.17119.2453529104070.325339152234056118.7693079373590.0753529104067354
58118.95118.8083515529850.0664675831393001119.025180863876-0.141648447015243
59118.92118.671350255685-0.112404046078121119.281053790393-0.248649744314548
60118.9118.712694436132-0.445384027043047119.532689590911-0.187305563868236

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 107.11 & 106.932775372002 & -0.529359630251722 & 107.81658425825 & -0.177224627998356 \tabularnewline
2 & 107.57 & 107.304423756218 & -0.206289705843261 & 108.041865949625 & -0.265576243782192 \tabularnewline
3 & 107.81 & 107.404072211013 & -0.0512198520137605 & 108.267147641001 & -0.405927788987043 \tabularnewline
4 & 108.75 & 108.910755883758 & 0.101624283069029 & 108.487619833173 & 0.160755883757943 \tabularnewline
5 & 109.43 & 109.983439476775 & 0.168468497879841 & 108.708092025345 & 0.553439476774912 \tabularnewline
6 & 109.62 & 110.183170187757 & 0.128697690262538 & 108.928132121981 & 0.563170187756796 \tabularnewline
7 & 109.54 & 109.626900803327 & 0.304926978057394 & 109.148172218616 & 0.0869008033265288 \tabularnewline
8 & 109.53 & 109.441854562292 & 0.249133088672037 & 109.369012349036 & -0.0881454377084196 \tabularnewline
9 & 109.84 & 109.764808368309 & 0.325339152234056 & 109.589852479457 & -0.0751916316907426 \tabularnewline
10 & 109.67 & 109.501037819799 & 0.0664675831393001 & 109.772494597062 & -0.168962180201362 \tabularnewline
11 & 109.79 & 109.737267331411 & -0.112404046078121 & 109.955136714667 & -0.0527326685893144 \tabularnewline
12 & 109.56 & 109.482784872249 & -0.445384027043047 & 110.082599154794 & -0.0772151277513728 \tabularnewline
13 & 110.22 & 110.759298035330 & -0.529359630251722 & 110.210061594921 & 0.539298035330305 \tabularnewline
14 & 110.4 & 110.682964144308 & -0.206289705843261 & 110.323325561536 & 0.282964144307726 \tabularnewline
15 & 110.69 & 110.994630323864 & -0.0512198520137605 & 110.436589528150 & 0.304630323864089 \tabularnewline
16 & 110.72 & 110.801149296094 & 0.101624283069029 & 110.537226420837 & 0.0811492960944662 \tabularnewline
17 & 110.89 & 110.973668188597 & 0.168468497879841 & 110.637863313523 & 0.0836681885968034 \tabularnewline
18 & 110.58 & 110.289395640997 & 0.128697690262538 & 110.741906668741 & -0.290604359003240 \tabularnewline
19 & 110.94 & 110.729122997985 & 0.304926978057394 & 110.845950023958 & -0.210877002015437 \tabularnewline
20 & 110.91 & 110.590269490536 & 0.249133088672037 & 110.980597420792 & -0.319730509464279 \tabularnewline
21 & 111.22 & 110.999416030140 & 0.325339152234056 & 111.115244817626 & -0.220583969860485 \tabularnewline
22 & 111.09 & 110.844035617892 & 0.0664675831393001 & 111.269496798969 & -0.245964382107871 \tabularnewline
23 & 111 & 110.688655265767 & -0.112404046078121 & 111.423748780311 & -0.311344734232591 \tabularnewline
24 & 111.06 & 110.988050609003 & -0.445384027043047 & 111.577333418040 & -0.0719493909973039 \tabularnewline
25 & 111.55 & 111.898441574482 & -0.529359630251722 & 111.73091805577 & 0.34844157448174 \tabularnewline
26 & 112.32 & 112.962119469035 & -0.206289705843261 & 111.884170236809 & 0.642119469034668 \tabularnewline
27 & 112.64 & 113.293797434167 & -0.0512198520137605 & 112.037422417847 & 0.653797434166549 \tabularnewline
28 & 112.36 & 112.434200889932 & 0.101624283069029 & 112.184174826999 & 0.0742008899320155 \tabularnewline
29 & 112.04 & 111.580604265969 & 0.168468497879841 & 112.330927236151 & -0.459395734030537 \tabularnewline
30 & 112.37 & 112.144273091528 & 0.128697690262538 & 112.467029218210 & -0.225726908472367 \tabularnewline
31 & 112.59 & 112.271941821674 & 0.304926978057394 & 112.603131200269 & -0.318058178326353 \tabularnewline
32 & 112.89 & 112.786768980635 & 0.249133088672037 & 112.744097930693 & -0.103231019364685 \tabularnewline
33 & 113.22 & 113.229596186650 & 0.325339152234056 & 112.885064661116 & 0.00959618664963102 \tabularnewline
34 & 112.85 & 112.56490354223 & 0.0664675831393001 & 113.068628874631 & -0.285096457769967 \tabularnewline
35 & 113.06 & 112.980210957933 & -0.112404046078121 & 113.252193088145 & -0.0797890420668921 \tabularnewline
36 & 112.99 & 112.955020940767 & -0.445384027043047 & 113.470363086276 & -0.0349790592326968 \tabularnewline
37 & 113.32 & 113.480826545845 & -0.529359630251722 & 113.688533084406 & 0.160826545845239 \tabularnewline
38 & 113.74 & 113.775404677598 & -0.206289705843261 & 113.910885028245 & 0.0354046775980095 \tabularnewline
39 & 113.91 & 113.737982879930 & -0.0512198520137605 & 114.133236972084 & -0.172017120070265 \tabularnewline
40 & 114.52 & 114.577861016080 & 0.101624283069029 & 114.360514700851 & 0.0578610160804374 \tabularnewline
41 & 114.96 & 115.163739072503 & 0.168468497879841 & 114.587792429617 & 0.203739072503126 \tabularnewline
42 & 114.91 & 114.874641418024 & 0.128697690262538 & 114.816660891713 & -0.0353585819757285 \tabularnewline
43 & 115.3 & 115.249543668133 & 0.304926978057394 & 115.045529353809 & -0.050456331866755 \tabularnewline
44 & 115.44 & 115.343355629503 & 0.249133088672037 & 115.287511281825 & -0.096644370497188 \tabularnewline
45 & 115.52 & 115.185167637925 & 0.325339152234056 & 115.529493209841 & -0.334832362074991 \tabularnewline
46 & 116.08 & 116.302652346364 & 0.0664675831393001 & 115.790880070496 & 0.222652346364328 \tabularnewline
47 & 115.94 & 115.940137114926 & -0.112404046078121 & 116.052266931152 & 0.000137114926303639 \tabularnewline
48 & 115.56 & 115.232567890156 & -0.445384027043047 & 116.332816136887 & -0.327432109844025 \tabularnewline
49 & 115.88 & 115.675994287629 & -0.529359630251722 & 116.613365342622 & -0.204005712370616 \tabularnewline
50 & 116.66 & 116.621028634447 & -0.206289705843261 & 116.905261071396 & -0.0389713655529818 \tabularnewline
51 & 117.41 & 117.674063051844 & -0.0512198520137605 & 117.197156800170 & 0.264063051843607 \tabularnewline
52 & 117.68 & 117.795362819830 & 0.101624283069029 & 117.463012897101 & 0.115362819829642 \tabularnewline
53 & 117.85 & 117.802662508088 & 0.168468497879841 & 117.728868994033 & -0.0473374919123728 \tabularnewline
54 & 118.21 & 118.301864356878 & 0.128697690262538 & 117.989437952859 & 0.0918643568781761 \tabularnewline
55 & 118.92 & 119.285066110257 & 0.304926978057394 & 118.250006911686 & 0.365066110256564 \tabularnewline
56 & 119.03 & 119.301209486805 & 0.249133088672037 & 118.509657424523 & 0.271209486805333 \tabularnewline
57 & 119.17 & 119.245352910407 & 0.325339152234056 & 118.769307937359 & 0.0753529104067354 \tabularnewline
58 & 118.95 & 118.808351552985 & 0.0664675831393001 & 119.025180863876 & -0.141648447015243 \tabularnewline
59 & 118.92 & 118.671350255685 & -0.112404046078121 & 119.281053790393 & -0.248649744314548 \tabularnewline
60 & 118.9 & 118.712694436132 & -0.445384027043047 & 119.532689590911 & -0.187305563868236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64055&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]107.11[/C][C]106.932775372002[/C][C]-0.529359630251722[/C][C]107.81658425825[/C][C]-0.177224627998356[/C][/ROW]
[ROW][C]2[/C][C]107.57[/C][C]107.304423756218[/C][C]-0.206289705843261[/C][C]108.041865949625[/C][C]-0.265576243782192[/C][/ROW]
[ROW][C]3[/C][C]107.81[/C][C]107.404072211013[/C][C]-0.0512198520137605[/C][C]108.267147641001[/C][C]-0.405927788987043[/C][/ROW]
[ROW][C]4[/C][C]108.75[/C][C]108.910755883758[/C][C]0.101624283069029[/C][C]108.487619833173[/C][C]0.160755883757943[/C][/ROW]
[ROW][C]5[/C][C]109.43[/C][C]109.983439476775[/C][C]0.168468497879841[/C][C]108.708092025345[/C][C]0.553439476774912[/C][/ROW]
[ROW][C]6[/C][C]109.62[/C][C]110.183170187757[/C][C]0.128697690262538[/C][C]108.928132121981[/C][C]0.563170187756796[/C][/ROW]
[ROW][C]7[/C][C]109.54[/C][C]109.626900803327[/C][C]0.304926978057394[/C][C]109.148172218616[/C][C]0.0869008033265288[/C][/ROW]
[ROW][C]8[/C][C]109.53[/C][C]109.441854562292[/C][C]0.249133088672037[/C][C]109.369012349036[/C][C]-0.0881454377084196[/C][/ROW]
[ROW][C]9[/C][C]109.84[/C][C]109.764808368309[/C][C]0.325339152234056[/C][C]109.589852479457[/C][C]-0.0751916316907426[/C][/ROW]
[ROW][C]10[/C][C]109.67[/C][C]109.501037819799[/C][C]0.0664675831393001[/C][C]109.772494597062[/C][C]-0.168962180201362[/C][/ROW]
[ROW][C]11[/C][C]109.79[/C][C]109.737267331411[/C][C]-0.112404046078121[/C][C]109.955136714667[/C][C]-0.0527326685893144[/C][/ROW]
[ROW][C]12[/C][C]109.56[/C][C]109.482784872249[/C][C]-0.445384027043047[/C][C]110.082599154794[/C][C]-0.0772151277513728[/C][/ROW]
[ROW][C]13[/C][C]110.22[/C][C]110.759298035330[/C][C]-0.529359630251722[/C][C]110.210061594921[/C][C]0.539298035330305[/C][/ROW]
[ROW][C]14[/C][C]110.4[/C][C]110.682964144308[/C][C]-0.206289705843261[/C][C]110.323325561536[/C][C]0.282964144307726[/C][/ROW]
[ROW][C]15[/C][C]110.69[/C][C]110.994630323864[/C][C]-0.0512198520137605[/C][C]110.436589528150[/C][C]0.304630323864089[/C][/ROW]
[ROW][C]16[/C][C]110.72[/C][C]110.801149296094[/C][C]0.101624283069029[/C][C]110.537226420837[/C][C]0.0811492960944662[/C][/ROW]
[ROW][C]17[/C][C]110.89[/C][C]110.973668188597[/C][C]0.168468497879841[/C][C]110.637863313523[/C][C]0.0836681885968034[/C][/ROW]
[ROW][C]18[/C][C]110.58[/C][C]110.289395640997[/C][C]0.128697690262538[/C][C]110.741906668741[/C][C]-0.290604359003240[/C][/ROW]
[ROW][C]19[/C][C]110.94[/C][C]110.729122997985[/C][C]0.304926978057394[/C][C]110.845950023958[/C][C]-0.210877002015437[/C][/ROW]
[ROW][C]20[/C][C]110.91[/C][C]110.590269490536[/C][C]0.249133088672037[/C][C]110.980597420792[/C][C]-0.319730509464279[/C][/ROW]
[ROW][C]21[/C][C]111.22[/C][C]110.999416030140[/C][C]0.325339152234056[/C][C]111.115244817626[/C][C]-0.220583969860485[/C][/ROW]
[ROW][C]22[/C][C]111.09[/C][C]110.844035617892[/C][C]0.0664675831393001[/C][C]111.269496798969[/C][C]-0.245964382107871[/C][/ROW]
[ROW][C]23[/C][C]111[/C][C]110.688655265767[/C][C]-0.112404046078121[/C][C]111.423748780311[/C][C]-0.311344734232591[/C][/ROW]
[ROW][C]24[/C][C]111.06[/C][C]110.988050609003[/C][C]-0.445384027043047[/C][C]111.577333418040[/C][C]-0.0719493909973039[/C][/ROW]
[ROW][C]25[/C][C]111.55[/C][C]111.898441574482[/C][C]-0.529359630251722[/C][C]111.73091805577[/C][C]0.34844157448174[/C][/ROW]
[ROW][C]26[/C][C]112.32[/C][C]112.962119469035[/C][C]-0.206289705843261[/C][C]111.884170236809[/C][C]0.642119469034668[/C][/ROW]
[ROW][C]27[/C][C]112.64[/C][C]113.293797434167[/C][C]-0.0512198520137605[/C][C]112.037422417847[/C][C]0.653797434166549[/C][/ROW]
[ROW][C]28[/C][C]112.36[/C][C]112.434200889932[/C][C]0.101624283069029[/C][C]112.184174826999[/C][C]0.0742008899320155[/C][/ROW]
[ROW][C]29[/C][C]112.04[/C][C]111.580604265969[/C][C]0.168468497879841[/C][C]112.330927236151[/C][C]-0.459395734030537[/C][/ROW]
[ROW][C]30[/C][C]112.37[/C][C]112.144273091528[/C][C]0.128697690262538[/C][C]112.467029218210[/C][C]-0.225726908472367[/C][/ROW]
[ROW][C]31[/C][C]112.59[/C][C]112.271941821674[/C][C]0.304926978057394[/C][C]112.603131200269[/C][C]-0.318058178326353[/C][/ROW]
[ROW][C]32[/C][C]112.89[/C][C]112.786768980635[/C][C]0.249133088672037[/C][C]112.744097930693[/C][C]-0.103231019364685[/C][/ROW]
[ROW][C]33[/C][C]113.22[/C][C]113.229596186650[/C][C]0.325339152234056[/C][C]112.885064661116[/C][C]0.00959618664963102[/C][/ROW]
[ROW][C]34[/C][C]112.85[/C][C]112.56490354223[/C][C]0.0664675831393001[/C][C]113.068628874631[/C][C]-0.285096457769967[/C][/ROW]
[ROW][C]35[/C][C]113.06[/C][C]112.980210957933[/C][C]-0.112404046078121[/C][C]113.252193088145[/C][C]-0.0797890420668921[/C][/ROW]
[ROW][C]36[/C][C]112.99[/C][C]112.955020940767[/C][C]-0.445384027043047[/C][C]113.470363086276[/C][C]-0.0349790592326968[/C][/ROW]
[ROW][C]37[/C][C]113.32[/C][C]113.480826545845[/C][C]-0.529359630251722[/C][C]113.688533084406[/C][C]0.160826545845239[/C][/ROW]
[ROW][C]38[/C][C]113.74[/C][C]113.775404677598[/C][C]-0.206289705843261[/C][C]113.910885028245[/C][C]0.0354046775980095[/C][/ROW]
[ROW][C]39[/C][C]113.91[/C][C]113.737982879930[/C][C]-0.0512198520137605[/C][C]114.133236972084[/C][C]-0.172017120070265[/C][/ROW]
[ROW][C]40[/C][C]114.52[/C][C]114.577861016080[/C][C]0.101624283069029[/C][C]114.360514700851[/C][C]0.0578610160804374[/C][/ROW]
[ROW][C]41[/C][C]114.96[/C][C]115.163739072503[/C][C]0.168468497879841[/C][C]114.587792429617[/C][C]0.203739072503126[/C][/ROW]
[ROW][C]42[/C][C]114.91[/C][C]114.874641418024[/C][C]0.128697690262538[/C][C]114.816660891713[/C][C]-0.0353585819757285[/C][/ROW]
[ROW][C]43[/C][C]115.3[/C][C]115.249543668133[/C][C]0.304926978057394[/C][C]115.045529353809[/C][C]-0.050456331866755[/C][/ROW]
[ROW][C]44[/C][C]115.44[/C][C]115.343355629503[/C][C]0.249133088672037[/C][C]115.287511281825[/C][C]-0.096644370497188[/C][/ROW]
[ROW][C]45[/C][C]115.52[/C][C]115.185167637925[/C][C]0.325339152234056[/C][C]115.529493209841[/C][C]-0.334832362074991[/C][/ROW]
[ROW][C]46[/C][C]116.08[/C][C]116.302652346364[/C][C]0.0664675831393001[/C][C]115.790880070496[/C][C]0.222652346364328[/C][/ROW]
[ROW][C]47[/C][C]115.94[/C][C]115.940137114926[/C][C]-0.112404046078121[/C][C]116.052266931152[/C][C]0.000137114926303639[/C][/ROW]
[ROW][C]48[/C][C]115.56[/C][C]115.232567890156[/C][C]-0.445384027043047[/C][C]116.332816136887[/C][C]-0.327432109844025[/C][/ROW]
[ROW][C]49[/C][C]115.88[/C][C]115.675994287629[/C][C]-0.529359630251722[/C][C]116.613365342622[/C][C]-0.204005712370616[/C][/ROW]
[ROW][C]50[/C][C]116.66[/C][C]116.621028634447[/C][C]-0.206289705843261[/C][C]116.905261071396[/C][C]-0.0389713655529818[/C][/ROW]
[ROW][C]51[/C][C]117.41[/C][C]117.674063051844[/C][C]-0.0512198520137605[/C][C]117.197156800170[/C][C]0.264063051843607[/C][/ROW]
[ROW][C]52[/C][C]117.68[/C][C]117.795362819830[/C][C]0.101624283069029[/C][C]117.463012897101[/C][C]0.115362819829642[/C][/ROW]
[ROW][C]53[/C][C]117.85[/C][C]117.802662508088[/C][C]0.168468497879841[/C][C]117.728868994033[/C][C]-0.0473374919123728[/C][/ROW]
[ROW][C]54[/C][C]118.21[/C][C]118.301864356878[/C][C]0.128697690262538[/C][C]117.989437952859[/C][C]0.0918643568781761[/C][/ROW]
[ROW][C]55[/C][C]118.92[/C][C]119.285066110257[/C][C]0.304926978057394[/C][C]118.250006911686[/C][C]0.365066110256564[/C][/ROW]
[ROW][C]56[/C][C]119.03[/C][C]119.301209486805[/C][C]0.249133088672037[/C][C]118.509657424523[/C][C]0.271209486805333[/C][/ROW]
[ROW][C]57[/C][C]119.17[/C][C]119.245352910407[/C][C]0.325339152234056[/C][C]118.769307937359[/C][C]0.0753529104067354[/C][/ROW]
[ROW][C]58[/C][C]118.95[/C][C]118.808351552985[/C][C]0.0664675831393001[/C][C]119.025180863876[/C][C]-0.141648447015243[/C][/ROW]
[ROW][C]59[/C][C]118.92[/C][C]118.671350255685[/C][C]-0.112404046078121[/C][C]119.281053790393[/C][C]-0.248649744314548[/C][/ROW]
[ROW][C]60[/C][C]118.9[/C][C]118.712694436132[/C][C]-0.445384027043047[/C][C]119.532689590911[/C][C]-0.187305563868236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64055&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64055&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
1107.11106.932775372002-0.529359630251722107.81658425825-0.177224627998356
2107.57107.304423756218-0.206289705843261108.041865949625-0.265576243782192
3107.81107.404072211013-0.0512198520137605108.267147641001-0.405927788987043
4108.75108.9107558837580.101624283069029108.4876198331730.160755883757943
5109.43109.9834394767750.168468497879841108.7080920253450.553439476774912
6109.62110.1831701877570.128697690262538108.9281321219810.563170187756796
7109.54109.6269008033270.304926978057394109.1481722186160.0869008033265288
8109.53109.4418545622920.249133088672037109.369012349036-0.0881454377084196
9109.84109.7648083683090.325339152234056109.589852479457-0.0751916316907426
10109.67109.5010378197990.0664675831393001109.772494597062-0.168962180201362
11109.79109.737267331411-0.112404046078121109.955136714667-0.0527326685893144
12109.56109.482784872249-0.445384027043047110.082599154794-0.0772151277513728
13110.22110.759298035330-0.529359630251722110.2100615949210.539298035330305
14110.4110.682964144308-0.206289705843261110.3233255615360.282964144307726
15110.69110.994630323864-0.0512198520137605110.4365895281500.304630323864089
16110.72110.8011492960940.101624283069029110.5372264208370.0811492960944662
17110.89110.9736681885970.168468497879841110.6378633135230.0836681885968034
18110.58110.2893956409970.128697690262538110.741906668741-0.290604359003240
19110.94110.7291229979850.304926978057394110.845950023958-0.210877002015437
20110.91110.5902694905360.249133088672037110.980597420792-0.319730509464279
21111.22110.9994160301400.325339152234056111.115244817626-0.220583969860485
22111.09110.8440356178920.0664675831393001111.269496798969-0.245964382107871
23111110.688655265767-0.112404046078121111.423748780311-0.311344734232591
24111.06110.988050609003-0.445384027043047111.577333418040-0.0719493909973039
25111.55111.898441574482-0.529359630251722111.730918055770.34844157448174
26112.32112.962119469035-0.206289705843261111.8841702368090.642119469034668
27112.64113.293797434167-0.0512198520137605112.0374224178470.653797434166549
28112.36112.4342008899320.101624283069029112.1841748269990.0742008899320155
29112.04111.5806042659690.168468497879841112.330927236151-0.459395734030537
30112.37112.1442730915280.128697690262538112.467029218210-0.225726908472367
31112.59112.2719418216740.304926978057394112.603131200269-0.318058178326353
32112.89112.7867689806350.249133088672037112.744097930693-0.103231019364685
33113.22113.2295961866500.325339152234056112.8850646611160.00959618664963102
34112.85112.564903542230.0664675831393001113.068628874631-0.285096457769967
35113.06112.980210957933-0.112404046078121113.252193088145-0.0797890420668921
36112.99112.955020940767-0.445384027043047113.470363086276-0.0349790592326968
37113.32113.480826545845-0.529359630251722113.6885330844060.160826545845239
38113.74113.775404677598-0.206289705843261113.9108850282450.0354046775980095
39113.91113.737982879930-0.0512198520137605114.133236972084-0.172017120070265
40114.52114.5778610160800.101624283069029114.3605147008510.0578610160804374
41114.96115.1637390725030.168468497879841114.5877924296170.203739072503126
42114.91114.8746414180240.128697690262538114.816660891713-0.0353585819757285
43115.3115.2495436681330.304926978057394115.045529353809-0.050456331866755
44115.44115.3433556295030.249133088672037115.287511281825-0.096644370497188
45115.52115.1851676379250.325339152234056115.529493209841-0.334832362074991
46116.08116.3026523463640.0664675831393001115.7908800704960.222652346364328
47115.94115.940137114926-0.112404046078121116.0522669311520.000137114926303639
48115.56115.232567890156-0.445384027043047116.332816136887-0.327432109844025
49115.88115.675994287629-0.529359630251722116.613365342622-0.204005712370616
50116.66116.621028634447-0.206289705843261116.905261071396-0.0389713655529818
51117.41117.674063051844-0.0512198520137605117.1971568001700.264063051843607
52117.68117.7953628198300.101624283069029117.4630128971010.115362819829642
53117.85117.8026625080880.168468497879841117.728868994033-0.0473374919123728
54118.21118.3018643568780.128697690262538117.9894379528590.0918643568781761
55118.92119.2850661102570.304926978057394118.2500069116860.365066110256564
56119.03119.3012094868050.249133088672037118.5096574245230.271209486805333
57119.17119.2453529104070.325339152234056118.7693079373590.0753529104067354
58118.95118.8083515529850.0664675831393001119.025180863876-0.141648447015243
59118.92118.671350255685-0.112404046078121119.281053790393-0.248649744314548
60118.9118.712694436132-0.445384027043047119.532689590911-0.187305563868236


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')