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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, 03 Dec 2009 11:30:51 -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/03/t1259865094aieaatenena33ty.htm/, Retrieved Fri, 29 Mar 2024 13:18:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63041, Retrieved Fri, 29 Mar 2024 13:18:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
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(3)] [2009-12-03 18:30:51] [5edea6bc5a9a9483633d9320282a2734] [Current]
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Dataseries X:
10.9
10
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9
9
9
9.8
10
9.8
9.3
9
9
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.7
7.9
7.9
8
7.9
7.6
7.1
6.8
6.5
6.9
8.2
8.7
8.3
7.9
7.5
7.8
8.3
8.4
8.2
7.7
7.2
7.3
8.1
8.5




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=63041&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=63041&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63041&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=63041&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=63041&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63041&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
110.911.52483561717620.5255505143788469.749613868444960.624835617176194
21010.16470918009450.1159480028668639.719342817038640.164709180094496
39.28.94458271687243-0.2336544825047569.68907176563232-0.255417283127565
49.28.84070085167392-0.1028840241920439.66218317251812-0.359299148326079
59.59.256819117177080.1078863034190029.63529457940392-0.243180882822925
69.69.478704326167540.1126267080574809.60866896577498-0.121295673832458
79.59.48058945673697-0.06263280888300259.58204335214603-0.0194105432630316
89.18.93903032088975-0.2968872636000649.55785694271032-0.160969679110252
98.98.81747134188463-0.551141875159239.5336705332746-0.0825286581153684
1098.97744563718292-0.5079565680513019.53051093086838-0.0225543628170843
1110.110.33741999783210.3352286737057519.527351328462170.237419997832076
1210.310.52633719664830.5579168069907739.51574599636090.226337196648336
1310.210.37030882136150.5255505143788469.504140664259610.170308821361543
149.69.595798141040680.1159480028668639.48825385609246-0.00420185895932335
159.29.16128743457945-0.2336544825047569.4723670479253-0.0387125654205516
169.39.24680196828296-0.1028840241920439.45608205590908-0.053198031717038
179.49.252316632688140.1078863034190029.43979706389286-0.147683367311862
189.49.26695141597990.1126267080574809.42042187596261-0.133048584020093
199.29.06158612085063-0.06263280888300259.40104668803237-0.138413879149367
2098.91350407130043-0.2968872636000649.38338319229963-0.0864959286995681
2199.18542217859233-0.551141875159239.36571969656690.185422178592333
2299.1613657235499-0.5079565680513019.346590844501390.161365723549906
239.89.937309333858360.3352286737057519.327461992435890.137309333858358
241010.13553765959380.5579168069907739.306545533415430.135537659593799
259.89.78882041122620.5255505143788469.28562907439496-0.0111795887738069
269.39.22021841359080.1159480028668639.26383358354234-0.0797815864092026
2798.99161638981504-0.2336544825047569.24203809268972-0.00838361018496236
2898.91696059152913-0.1028840241920439.18592343266292-0.0830394084708743
299.18.962304923944880.1078863034190029.12980877263612-0.137695076055120
309.19.071710694584030.1126267080574809.01566259735849-0.0282893054159654
319.19.36111638680215-0.06263280888300258.901516422080850.261116386802151
329.29.9276759048647-0.2968872636000648.769211358735370.727675904864698
338.89.51423557976935-0.551141875159238.636906295389880.71423557976935
348.38.59549649605017-0.5079565680513018.512460072001140.295496496050164
358.48.076757477681860.3352286737057518.38801384861239-0.323242522318143
368.17.39703019109890.5579168069907738.24505300191033-0.702969808901102
377.76.772357330412890.5255505143788468.10209215520826-0.92764266958711
387.97.737873327995670.1159480028668637.94617866913747-0.162126672004335
397.98.24338929943808-0.2336544825047567.790265183066680.343389299438078
4088.40429447349083-0.1028840241920437.698589550701220.404294473490825
417.98.085199778245240.1078863034190027.606913918335760.185199778245240
427.67.491217188819790.1126267080574807.59615610312273-0.108782811180209
437.16.6772345209733-0.06263280888300257.5853982879097-0.422765479026698
446.86.3064196494693-0.2968872636000647.59046761413077-0.493580350530706
456.55.95560493480739-0.551141875159237.59553694035184-0.544395065192608
466.96.6945638032315-0.5079565680513017.6133927648198-0.205436196768494
478.28.43352273700650.3352286737057517.631248589287750.233522737006498
488.79.14727303213740.5579168069907737.694810160871830.447273032137394
498.38.316077753165240.5255505143788467.758371732455910.0160777531652405
507.97.85657207299390.1159480028668637.82747992413924-0.0434279270061042
517.57.33706636668219-0.2336544825047567.89658811582257-0.162933633317812
527.87.79549219800881-0.1028840241920437.90739182618323-0.00450780199118661
538.38.57391816003710.1078863034190027.918195536543890.273918160037107
548.48.75929639035260.1126267080574807.928076901589930.359296390352594
558.28.52467454224704-0.06263280888300257.937958266635960.32467454224704
567.77.75117223170349-0.2968872636000647.945715031896570.0511722317034895
577.26.99767007800204-0.551141875159237.95347179715719-0.202329921997956
587.37.15179431584823-0.5079565680513017.95616225220307-0.148205684151768
598.17.90591861904530.3352286737057517.95885270724895-0.194081380954702
608.58.484592156106870.5579168069907737.95749103690236-0.0154078438931329

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 10.9 & 11.5248356171762 & 0.525550514378846 & 9.74961386844496 & 0.624835617176194 \tabularnewline
2 & 10 & 10.1647091800945 & 0.115948002866863 & 9.71934281703864 & 0.164709180094496 \tabularnewline
3 & 9.2 & 8.94458271687243 & -0.233654482504756 & 9.68907176563232 & -0.255417283127565 \tabularnewline
4 & 9.2 & 8.84070085167392 & -0.102884024192043 & 9.66218317251812 & -0.359299148326079 \tabularnewline
5 & 9.5 & 9.25681911717708 & 0.107886303419002 & 9.63529457940392 & -0.243180882822925 \tabularnewline
6 & 9.6 & 9.47870432616754 & 0.112626708057480 & 9.60866896577498 & -0.121295673832458 \tabularnewline
7 & 9.5 & 9.48058945673697 & -0.0626328088830025 & 9.58204335214603 & -0.0194105432630316 \tabularnewline
8 & 9.1 & 8.93903032088975 & -0.296887263600064 & 9.55785694271032 & -0.160969679110252 \tabularnewline
9 & 8.9 & 8.81747134188463 & -0.55114187515923 & 9.5336705332746 & -0.0825286581153684 \tabularnewline
10 & 9 & 8.97744563718292 & -0.507956568051301 & 9.53051093086838 & -0.0225543628170843 \tabularnewline
11 & 10.1 & 10.3374199978321 & 0.335228673705751 & 9.52735132846217 & 0.237419997832076 \tabularnewline
12 & 10.3 & 10.5263371966483 & 0.557916806990773 & 9.5157459963609 & 0.226337196648336 \tabularnewline
13 & 10.2 & 10.3703088213615 & 0.525550514378846 & 9.50414066425961 & 0.170308821361543 \tabularnewline
14 & 9.6 & 9.59579814104068 & 0.115948002866863 & 9.48825385609246 & -0.00420185895932335 \tabularnewline
15 & 9.2 & 9.16128743457945 & -0.233654482504756 & 9.4723670479253 & -0.0387125654205516 \tabularnewline
16 & 9.3 & 9.24680196828296 & -0.102884024192043 & 9.45608205590908 & -0.053198031717038 \tabularnewline
17 & 9.4 & 9.25231663268814 & 0.107886303419002 & 9.43979706389286 & -0.147683367311862 \tabularnewline
18 & 9.4 & 9.2669514159799 & 0.112626708057480 & 9.42042187596261 & -0.133048584020093 \tabularnewline
19 & 9.2 & 9.06158612085063 & -0.0626328088830025 & 9.40104668803237 & -0.138413879149367 \tabularnewline
20 & 9 & 8.91350407130043 & -0.296887263600064 & 9.38338319229963 & -0.0864959286995681 \tabularnewline
21 & 9 & 9.18542217859233 & -0.55114187515923 & 9.3657196965669 & 0.185422178592333 \tabularnewline
22 & 9 & 9.1613657235499 & -0.507956568051301 & 9.34659084450139 & 0.161365723549906 \tabularnewline
23 & 9.8 & 9.93730933385836 & 0.335228673705751 & 9.32746199243589 & 0.137309333858358 \tabularnewline
24 & 10 & 10.1355376595938 & 0.557916806990773 & 9.30654553341543 & 0.135537659593799 \tabularnewline
25 & 9.8 & 9.7888204112262 & 0.525550514378846 & 9.28562907439496 & -0.0111795887738069 \tabularnewline
26 & 9.3 & 9.2202184135908 & 0.115948002866863 & 9.26383358354234 & -0.0797815864092026 \tabularnewline
27 & 9 & 8.99161638981504 & -0.233654482504756 & 9.24203809268972 & -0.00838361018496236 \tabularnewline
28 & 9 & 8.91696059152913 & -0.102884024192043 & 9.18592343266292 & -0.0830394084708743 \tabularnewline
29 & 9.1 & 8.96230492394488 & 0.107886303419002 & 9.12980877263612 & -0.137695076055120 \tabularnewline
30 & 9.1 & 9.07171069458403 & 0.112626708057480 & 9.01566259735849 & -0.0282893054159654 \tabularnewline
31 & 9.1 & 9.36111638680215 & -0.0626328088830025 & 8.90151642208085 & 0.261116386802151 \tabularnewline
32 & 9.2 & 9.9276759048647 & -0.296887263600064 & 8.76921135873537 & 0.727675904864698 \tabularnewline
33 & 8.8 & 9.51423557976935 & -0.55114187515923 & 8.63690629538988 & 0.71423557976935 \tabularnewline
34 & 8.3 & 8.59549649605017 & -0.507956568051301 & 8.51246007200114 & 0.295496496050164 \tabularnewline
35 & 8.4 & 8.07675747768186 & 0.335228673705751 & 8.38801384861239 & -0.323242522318143 \tabularnewline
36 & 8.1 & 7.3970301910989 & 0.557916806990773 & 8.24505300191033 & -0.702969808901102 \tabularnewline
37 & 7.7 & 6.77235733041289 & 0.525550514378846 & 8.10209215520826 & -0.92764266958711 \tabularnewline
38 & 7.9 & 7.73787332799567 & 0.115948002866863 & 7.94617866913747 & -0.162126672004335 \tabularnewline
39 & 7.9 & 8.24338929943808 & -0.233654482504756 & 7.79026518306668 & 0.343389299438078 \tabularnewline
40 & 8 & 8.40429447349083 & -0.102884024192043 & 7.69858955070122 & 0.404294473490825 \tabularnewline
41 & 7.9 & 8.08519977824524 & 0.107886303419002 & 7.60691391833576 & 0.185199778245240 \tabularnewline
42 & 7.6 & 7.49121718881979 & 0.112626708057480 & 7.59615610312273 & -0.108782811180209 \tabularnewline
43 & 7.1 & 6.6772345209733 & -0.0626328088830025 & 7.5853982879097 & -0.422765479026698 \tabularnewline
44 & 6.8 & 6.3064196494693 & -0.296887263600064 & 7.59046761413077 & -0.493580350530706 \tabularnewline
45 & 6.5 & 5.95560493480739 & -0.55114187515923 & 7.59553694035184 & -0.544395065192608 \tabularnewline
46 & 6.9 & 6.6945638032315 & -0.507956568051301 & 7.6133927648198 & -0.205436196768494 \tabularnewline
47 & 8.2 & 8.4335227370065 & 0.335228673705751 & 7.63124858928775 & 0.233522737006498 \tabularnewline
48 & 8.7 & 9.1472730321374 & 0.557916806990773 & 7.69481016087183 & 0.447273032137394 \tabularnewline
49 & 8.3 & 8.31607775316524 & 0.525550514378846 & 7.75837173245591 & 0.0160777531652405 \tabularnewline
50 & 7.9 & 7.8565720729939 & 0.115948002866863 & 7.82747992413924 & -0.0434279270061042 \tabularnewline
51 & 7.5 & 7.33706636668219 & -0.233654482504756 & 7.89658811582257 & -0.162933633317812 \tabularnewline
52 & 7.8 & 7.79549219800881 & -0.102884024192043 & 7.90739182618323 & -0.00450780199118661 \tabularnewline
53 & 8.3 & 8.5739181600371 & 0.107886303419002 & 7.91819553654389 & 0.273918160037107 \tabularnewline
54 & 8.4 & 8.7592963903526 & 0.112626708057480 & 7.92807690158993 & 0.359296390352594 \tabularnewline
55 & 8.2 & 8.52467454224704 & -0.0626328088830025 & 7.93795826663596 & 0.32467454224704 \tabularnewline
56 & 7.7 & 7.75117223170349 & -0.296887263600064 & 7.94571503189657 & 0.0511722317034895 \tabularnewline
57 & 7.2 & 6.99767007800204 & -0.55114187515923 & 7.95347179715719 & -0.202329921997956 \tabularnewline
58 & 7.3 & 7.15179431584823 & -0.507956568051301 & 7.95616225220307 & -0.148205684151768 \tabularnewline
59 & 8.1 & 7.9059186190453 & 0.335228673705751 & 7.95885270724895 & -0.194081380954702 \tabularnewline
60 & 8.5 & 8.48459215610687 & 0.557916806990773 & 7.95749103690236 & -0.0154078438931329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63041&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]10.9[/C][C]11.5248356171762[/C][C]0.525550514378846[/C][C]9.74961386844496[/C][C]0.624835617176194[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]10.1647091800945[/C][C]0.115948002866863[/C][C]9.71934281703864[/C][C]0.164709180094496[/C][/ROW]
[ROW][C]3[/C][C]9.2[/C][C]8.94458271687243[/C][C]-0.233654482504756[/C][C]9.68907176563232[/C][C]-0.255417283127565[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]8.84070085167392[/C][C]-0.102884024192043[/C][C]9.66218317251812[/C][C]-0.359299148326079[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]9.25681911717708[/C][C]0.107886303419002[/C][C]9.63529457940392[/C][C]-0.243180882822925[/C][/ROW]
[ROW][C]6[/C][C]9.6[/C][C]9.47870432616754[/C][C]0.112626708057480[/C][C]9.60866896577498[/C][C]-0.121295673832458[/C][/ROW]
[ROW][C]7[/C][C]9.5[/C][C]9.48058945673697[/C][C]-0.0626328088830025[/C][C]9.58204335214603[/C][C]-0.0194105432630316[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]8.93903032088975[/C][C]-0.296887263600064[/C][C]9.55785694271032[/C][C]-0.160969679110252[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.81747134188463[/C][C]-0.55114187515923[/C][C]9.5336705332746[/C][C]-0.0825286581153684[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.97744563718292[/C][C]-0.507956568051301[/C][C]9.53051093086838[/C][C]-0.0225543628170843[/C][/ROW]
[ROW][C]11[/C][C]10.1[/C][C]10.3374199978321[/C][C]0.335228673705751[/C][C]9.52735132846217[/C][C]0.237419997832076[/C][/ROW]
[ROW][C]12[/C][C]10.3[/C][C]10.5263371966483[/C][C]0.557916806990773[/C][C]9.5157459963609[/C][C]0.226337196648336[/C][/ROW]
[ROW][C]13[/C][C]10.2[/C][C]10.3703088213615[/C][C]0.525550514378846[/C][C]9.50414066425961[/C][C]0.170308821361543[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]9.59579814104068[/C][C]0.115948002866863[/C][C]9.48825385609246[/C][C]-0.00420185895932335[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]9.16128743457945[/C][C]-0.233654482504756[/C][C]9.4723670479253[/C][C]-0.0387125654205516[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]9.24680196828296[/C][C]-0.102884024192043[/C][C]9.45608205590908[/C][C]-0.053198031717038[/C][/ROW]
[ROW][C]17[/C][C]9.4[/C][C]9.25231663268814[/C][C]0.107886303419002[/C][C]9.43979706389286[/C][C]-0.147683367311862[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.2669514159799[/C][C]0.112626708057480[/C][C]9.42042187596261[/C][C]-0.133048584020093[/C][/ROW]
[ROW][C]19[/C][C]9.2[/C][C]9.06158612085063[/C][C]-0.0626328088830025[/C][C]9.40104668803237[/C][C]-0.138413879149367[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]8.91350407130043[/C][C]-0.296887263600064[/C][C]9.38338319229963[/C][C]-0.0864959286995681[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.18542217859233[/C][C]-0.55114187515923[/C][C]9.3657196965669[/C][C]0.185422178592333[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.1613657235499[/C][C]-0.507956568051301[/C][C]9.34659084450139[/C][C]0.161365723549906[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]9.93730933385836[/C][C]0.335228673705751[/C][C]9.32746199243589[/C][C]0.137309333858358[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.1355376595938[/C][C]0.557916806990773[/C][C]9.30654553341543[/C][C]0.135537659593799[/C][/ROW]
[ROW][C]25[/C][C]9.8[/C][C]9.7888204112262[/C][C]0.525550514378846[/C][C]9.28562907439496[/C][C]-0.0111795887738069[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]9.2202184135908[/C][C]0.115948002866863[/C][C]9.26383358354234[/C][C]-0.0797815864092026[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]8.99161638981504[/C][C]-0.233654482504756[/C][C]9.24203809268972[/C][C]-0.00838361018496236[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.91696059152913[/C][C]-0.102884024192043[/C][C]9.18592343266292[/C][C]-0.0830394084708743[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]8.96230492394488[/C][C]0.107886303419002[/C][C]9.12980877263612[/C][C]-0.137695076055120[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]9.07171069458403[/C][C]0.112626708057480[/C][C]9.01566259735849[/C][C]-0.0282893054159654[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]9.36111638680215[/C][C]-0.0626328088830025[/C][C]8.90151642208085[/C][C]0.261116386802151[/C][/ROW]
[ROW][C]32[/C][C]9.2[/C][C]9.9276759048647[/C][C]-0.296887263600064[/C][C]8.76921135873537[/C][C]0.727675904864698[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]9.51423557976935[/C][C]-0.55114187515923[/C][C]8.63690629538988[/C][C]0.71423557976935[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]8.59549649605017[/C][C]-0.507956568051301[/C][C]8.51246007200114[/C][C]0.295496496050164[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.07675747768186[/C][C]0.335228673705751[/C][C]8.38801384861239[/C][C]-0.323242522318143[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]7.3970301910989[/C][C]0.557916806990773[/C][C]8.24505300191033[/C][C]-0.702969808901102[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]6.77235733041289[/C][C]0.525550514378846[/C][C]8.10209215520826[/C][C]-0.92764266958711[/C][/ROW]
[ROW][C]38[/C][C]7.9[/C][C]7.73787332799567[/C][C]0.115948002866863[/C][C]7.94617866913747[/C][C]-0.162126672004335[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]8.24338929943808[/C][C]-0.233654482504756[/C][C]7.79026518306668[/C][C]0.343389299438078[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.40429447349083[/C][C]-0.102884024192043[/C][C]7.69858955070122[/C][C]0.404294473490825[/C][/ROW]
[ROW][C]41[/C][C]7.9[/C][C]8.08519977824524[/C][C]0.107886303419002[/C][C]7.60691391833576[/C][C]0.185199778245240[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]7.49121718881979[/C][C]0.112626708057480[/C][C]7.59615610312273[/C][C]-0.108782811180209[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]6.6772345209733[/C][C]-0.0626328088830025[/C][C]7.5853982879097[/C][C]-0.422765479026698[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]6.3064196494693[/C][C]-0.296887263600064[/C][C]7.59046761413077[/C][C]-0.493580350530706[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]5.95560493480739[/C][C]-0.55114187515923[/C][C]7.59553694035184[/C][C]-0.544395065192608[/C][/ROW]
[ROW][C]46[/C][C]6.9[/C][C]6.6945638032315[/C][C]-0.507956568051301[/C][C]7.6133927648198[/C][C]-0.205436196768494[/C][/ROW]
[ROW][C]47[/C][C]8.2[/C][C]8.4335227370065[/C][C]0.335228673705751[/C][C]7.63124858928775[/C][C]0.233522737006498[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]9.1472730321374[/C][C]0.557916806990773[/C][C]7.69481016087183[/C][C]0.447273032137394[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]8.31607775316524[/C][C]0.525550514378846[/C][C]7.75837173245591[/C][C]0.0160777531652405[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]7.8565720729939[/C][C]0.115948002866863[/C][C]7.82747992413924[/C][C]-0.0434279270061042[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.33706636668219[/C][C]-0.233654482504756[/C][C]7.89658811582257[/C][C]-0.162933633317812[/C][/ROW]
[ROW][C]52[/C][C]7.8[/C][C]7.79549219800881[/C][C]-0.102884024192043[/C][C]7.90739182618323[/C][C]-0.00450780199118661[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]8.5739181600371[/C][C]0.107886303419002[/C][C]7.91819553654389[/C][C]0.273918160037107[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]8.7592963903526[/C][C]0.112626708057480[/C][C]7.92807690158993[/C][C]0.359296390352594[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]8.52467454224704[/C][C]-0.0626328088830025[/C][C]7.93795826663596[/C][C]0.32467454224704[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.75117223170349[/C][C]-0.296887263600064[/C][C]7.94571503189657[/C][C]0.0511722317034895[/C][/ROW]
[ROW][C]57[/C][C]7.2[/C][C]6.99767007800204[/C][C]-0.55114187515923[/C][C]7.95347179715719[/C][C]-0.202329921997956[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]7.15179431584823[/C][C]-0.507956568051301[/C][C]7.95616225220307[/C][C]-0.148205684151768[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]7.9059186190453[/C][C]0.335228673705751[/C][C]7.95885270724895[/C][C]-0.194081380954702[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]8.48459215610687[/C][C]0.557916806990773[/C][C]7.95749103690236[/C][C]-0.0154078438931329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63041&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63041&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
110.911.52483561717620.5255505143788469.749613868444960.624835617176194
21010.16470918009450.1159480028668639.719342817038640.164709180094496
39.28.94458271687243-0.2336544825047569.68907176563232-0.255417283127565
49.28.84070085167392-0.1028840241920439.66218317251812-0.359299148326079
59.59.256819117177080.1078863034190029.63529457940392-0.243180882822925
69.69.478704326167540.1126267080574809.60866896577498-0.121295673832458
79.59.48058945673697-0.06263280888300259.58204335214603-0.0194105432630316
89.18.93903032088975-0.2968872636000649.55785694271032-0.160969679110252
98.98.81747134188463-0.551141875159239.5336705332746-0.0825286581153684
1098.97744563718292-0.5079565680513019.53051093086838-0.0225543628170843
1110.110.33741999783210.3352286737057519.527351328462170.237419997832076
1210.310.52633719664830.5579168069907739.51574599636090.226337196648336
1310.210.37030882136150.5255505143788469.504140664259610.170308821361543
149.69.595798141040680.1159480028668639.48825385609246-0.00420185895932335
159.29.16128743457945-0.2336544825047569.4723670479253-0.0387125654205516
169.39.24680196828296-0.1028840241920439.45608205590908-0.053198031717038
179.49.252316632688140.1078863034190029.43979706389286-0.147683367311862
189.49.26695141597990.1126267080574809.42042187596261-0.133048584020093
199.29.06158612085063-0.06263280888300259.40104668803237-0.138413879149367
2098.91350407130043-0.2968872636000649.38338319229963-0.0864959286995681
2199.18542217859233-0.551141875159239.36571969656690.185422178592333
2299.1613657235499-0.5079565680513019.346590844501390.161365723549906
239.89.937309333858360.3352286737057519.327461992435890.137309333858358
241010.13553765959380.5579168069907739.306545533415430.135537659593799
259.89.78882041122620.5255505143788469.28562907439496-0.0111795887738069
269.39.22021841359080.1159480028668639.26383358354234-0.0797815864092026
2798.99161638981504-0.2336544825047569.24203809268972-0.00838361018496236
2898.91696059152913-0.1028840241920439.18592343266292-0.0830394084708743
299.18.962304923944880.1078863034190029.12980877263612-0.137695076055120
309.19.071710694584030.1126267080574809.01566259735849-0.0282893054159654
319.19.36111638680215-0.06263280888300258.901516422080850.261116386802151
329.29.9276759048647-0.2968872636000648.769211358735370.727675904864698
338.89.51423557976935-0.551141875159238.636906295389880.71423557976935
348.38.59549649605017-0.5079565680513018.512460072001140.295496496050164
358.48.076757477681860.3352286737057518.38801384861239-0.323242522318143
368.17.39703019109890.5579168069907738.24505300191033-0.702969808901102
377.76.772357330412890.5255505143788468.10209215520826-0.92764266958711
387.97.737873327995670.1159480028668637.94617866913747-0.162126672004335
397.98.24338929943808-0.2336544825047567.790265183066680.343389299438078
4088.40429447349083-0.1028840241920437.698589550701220.404294473490825
417.98.085199778245240.1078863034190027.606913918335760.185199778245240
427.67.491217188819790.1126267080574807.59615610312273-0.108782811180209
437.16.6772345209733-0.06263280888300257.5853982879097-0.422765479026698
446.86.3064196494693-0.2968872636000647.59046761413077-0.493580350530706
456.55.95560493480739-0.551141875159237.59553694035184-0.544395065192608
466.96.6945638032315-0.5079565680513017.6133927648198-0.205436196768494
478.28.43352273700650.3352286737057517.631248589287750.233522737006498
488.79.14727303213740.5579168069907737.694810160871830.447273032137394
498.38.316077753165240.5255505143788467.758371732455910.0160777531652405
507.97.85657207299390.1159480028668637.82747992413924-0.0434279270061042
517.57.33706636668219-0.2336544825047567.89658811582257-0.162933633317812
527.87.79549219800881-0.1028840241920437.90739182618323-0.00450780199118661
538.38.57391816003710.1078863034190027.918195536543890.273918160037107
548.48.75929639035260.1126267080574807.928076901589930.359296390352594
558.28.52467454224704-0.06263280888300257.937958266635960.32467454224704
567.77.75117223170349-0.2968872636000647.945715031896570.0511722317034895
577.26.99767007800204-0.551141875159237.95347179715719-0.202329921997956
587.37.15179431584823-0.5079565680513017.95616225220307-0.148205684151768
598.17.90591861904530.3352286737057517.95885270724895-0.194081380954702
608.58.484592156106870.5579168069907737.95749103690236-0.0154078438931329



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')