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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 14:24:03 -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/t125996194381pjgvqikxrgmaq.htm/, Retrieved Sun, 28 Apr 2024 05:01:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64162, Retrieved Sun, 28 Apr 2024 05:01:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
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   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [Structural Time S...] [2009-12-04 21:24:03] [d45d8d97b86162be82506c3c0ea6e4a6] [Current]
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Dataseries X:
1.4
1
-0.8
-2.9
-0.7
-0.7
1.5
3
3.2
3.1
3.9
1
1.3
0.8
1.2
2.9
3.9
4.5
4.5
3.3
2
1.5
1
2.1
3
4
5.1
4.5
4.2
3.3
2.7
1.8
1.4
0.5
-0.4
0.8
0.7
1.9
2
1.1
0.9
0.4
0.7
2.1
2.8
3.9
3.5
2
2
1.5
2.5
3.1
2.7
2.8
2.5
3
3.2
2.8
2.4
2
1.8
1.1
-1.5
-3.7




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=64162&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=64162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64162&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







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.41.4000
211.03801152041440-0.146211813609435-0.0135888218791398-0.335875849641164
3-0.8-0.684707016057503-1.194367359388082.59577598804486e-05-1.4949461493531
4-2.9-2.83707455819736-1.82152644555070.00598001305087772-0.901324825695947
5-0.7-0.967646060074840.590208981698554-0.002404555416362783.51088925074813
6-0.7-0.6758637474194440.394990702031841-0.00222061368429005-0.284650173938428
71.51.380356443265241.48220454135134-0.002431639711590791.58545073527241
832.992857765346041.56748846835726-0.002433731575496210.12436740293503
93.23.295402203230080.739522692556956-0.00243541792231519-1.20740163140865
103.13.166277945696580.170951909926667-0.00243690393067207-0.829132446275882
113.93.863740759749520.515578011620494-0.002436538804207360.502559561182696
1211.23362871262796-1.54342277949812-0.00243717108764966-3.00258897724739
131.30.851198743252725-0.803666806504330.3657389181643581.23223193553873
140.80.783492622644794-0.384627140056491-0.01808909838853010.560274657687857
151.21.164709228419890.120177805895604-0.02037425323059200.725678727985411
162.92.813334604285291.12072459477073-0.02458387249685661.44951968210288
173.93.925222582212381.11494524363903-0.0245744914912431-0.00842106371047601
184.54.559727527807820.800564637077947-0.0244311096454100-0.458427775498266
194.54.581606501613270.290911305701806-0.0243811320886669-0.743213015011021
203.33.43035824266764-0.653040654873944-0.0243681443153670-1.37654132925854
2122.07591751849108-1.11213972716115-0.0243683288706696-0.669492609947243
221.51.48598822506478-0.770328470176313-0.02436794385973940.498454743894787
2311.00323275817253-0.582098510835371-0.02436785178079740.274491007243181
242.12.007759189699230.456421371552545-0.02436769882059571.51444737902976
2532.684344971264150.5986464469769390.2996855137937540.222219738541756
2643.978801930187461.01508272177387-0.01781067255117590.579019443877103
275.15.109595993084781.09117234035992-0.01803834745816490.109917032909162
284.54.629894410818240.0628897987264758-0.0151793885574565-1.49303270751098
294.24.24754484153516-0.228386122252998-0.0148671672437711-0.424533414640648
303.33.36295336788593-0.657821939668136-0.0147378402774864-0.626212394534033
312.72.71408053712136-0.651964579528715-0.01473821955089380.00854162859263727
321.81.83167307440080-0.80279926062265-0.0147368491858344-0.219958411788296
331.41.38831943965671-0.567525348005809-0.01473678673415240.343094018990734
340.50.535690531618139-0.754138972545623-0.0147369255312250-0.272133946856378
35-0.4-0.373842408727871-0.855851569782623-0.0147369583858546-0.148324917900332
360.80.6747689296769610.390708015816816-0.01473683715115611.81782643667692
370.70.613848536077260.09765783298413140.119056131414941-0.447739860982381
381.91.831727870624670.784097079883410.0004951663000472460.968857689294647
3922.040700128192510.4063491541334750.00133930556396310-0.54699525506795
401.11.18897281955767-0.4171637524353370.00304904641813670-1.19702993108501
410.90.888448454385456-0.3408479595189550.002987982077507030.111245017560968
420.40.407300736060819-0.432667731706090.00300862280273498-0.133894783442055
430.70.6475579176141620.007779005616975090.002987334514352370.642291699564307
442.11.998317161027260.8868180935504940.00298137322630651.28188053989151
452.82.803051367498030.8330903666454030.00298136258081404-0.0783497904675206
463.93.879158129570.9921559523859850.002981450890847990.231961335979678
473.53.591107167885050.1542008818454500.00298124884988678-1.22196876709453
4822.11671198635657-0.9117910008903890.00298117146348577-1.55450910498620
4921.92837927258412-0.4413236258780610.01879505897600310.710650440006949
501.51.50073478148677-0.432825428180175-0.001598493185697790.0120892562646396
512.52.405297810855640.444971777512692-0.003163777820468621.27290297424715
523.13.086149473912740.599372617274491-0.003419557115990840.224578718068195
532.72.77024975361620.000472815860059872-0.00303726580152022-0.873082652524495
542.82.800828503070180.020176333452032-0.003040799171971250.0287325138405989
552.52.52480207111156-0.173697264998156-0.00303332398486367-0.282720745054261
5632.958402483232270.223805886076751-0.003035474430787570.579668824839837
573.23.20160958599720.236504885619324-0.003035472423577510.0185186310802656
582.82.84651506745081-0.150724538399919-0.00303564392124261-0.564686912712573
592.42.42307856057176-0.329227221815416-0.00303567825485336-0.260305965852387
6022.00925319545875-0.384600641419640-0.00303568146162566-0.0807496626785948
611.81.75026829958601-0.3028050449579220.04054741439542930.122701515793977
621.11.12665043511292-0.503913421085249-0.0058334774787796-0.287474333163091
63-1.5-1.35151274916247-1.79911525964438-0.00391205454895118-1.87996337096643
64-3.7-3.65928154941211-2.13206428879176-0.00345321823164634-0.48449054227055

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.4 & 1.4 & 0 & 0 & 0 \tabularnewline
2 & 1 & 1.03801152041440 & -0.146211813609435 & -0.0135888218791398 & -0.335875849641164 \tabularnewline
3 & -0.8 & -0.684707016057503 & -1.19436735938808 & 2.59577598804486e-05 & -1.4949461493531 \tabularnewline
4 & -2.9 & -2.83707455819736 & -1.8215264455507 & 0.00598001305087772 & -0.901324825695947 \tabularnewline
5 & -0.7 & -0.96764606007484 & 0.590208981698554 & -0.00240455541636278 & 3.51088925074813 \tabularnewline
6 & -0.7 & -0.675863747419444 & 0.394990702031841 & -0.00222061368429005 & -0.284650173938428 \tabularnewline
7 & 1.5 & 1.38035644326524 & 1.48220454135134 & -0.00243163971159079 & 1.58545073527241 \tabularnewline
8 & 3 & 2.99285776534604 & 1.56748846835726 & -0.00243373157549621 & 0.12436740293503 \tabularnewline
9 & 3.2 & 3.29540220323008 & 0.739522692556956 & -0.00243541792231519 & -1.20740163140865 \tabularnewline
10 & 3.1 & 3.16627794569658 & 0.170951909926667 & -0.00243690393067207 & -0.829132446275882 \tabularnewline
11 & 3.9 & 3.86374075974952 & 0.515578011620494 & -0.00243653880420736 & 0.502559561182696 \tabularnewline
12 & 1 & 1.23362871262796 & -1.54342277949812 & -0.00243717108764966 & -3.00258897724739 \tabularnewline
13 & 1.3 & 0.851198743252725 & -0.80366680650433 & 0.365738918164358 & 1.23223193553873 \tabularnewline
14 & 0.8 & 0.783492622644794 & -0.384627140056491 & -0.0180890983885301 & 0.560274657687857 \tabularnewline
15 & 1.2 & 1.16470922841989 & 0.120177805895604 & -0.0203742532305920 & 0.725678727985411 \tabularnewline
16 & 2.9 & 2.81333460428529 & 1.12072459477073 & -0.0245838724968566 & 1.44951968210288 \tabularnewline
17 & 3.9 & 3.92522258221238 & 1.11494524363903 & -0.0245744914912431 & -0.00842106371047601 \tabularnewline
18 & 4.5 & 4.55972752780782 & 0.800564637077947 & -0.0244311096454100 & -0.458427775498266 \tabularnewline
19 & 4.5 & 4.58160650161327 & 0.290911305701806 & -0.0243811320886669 & -0.743213015011021 \tabularnewline
20 & 3.3 & 3.43035824266764 & -0.653040654873944 & -0.0243681443153670 & -1.37654132925854 \tabularnewline
21 & 2 & 2.07591751849108 & -1.11213972716115 & -0.0243683288706696 & -0.669492609947243 \tabularnewline
22 & 1.5 & 1.48598822506478 & -0.770328470176313 & -0.0243679438597394 & 0.498454743894787 \tabularnewline
23 & 1 & 1.00323275817253 & -0.582098510835371 & -0.0243678517807974 & 0.274491007243181 \tabularnewline
24 & 2.1 & 2.00775918969923 & 0.456421371552545 & -0.0243676988205957 & 1.51444737902976 \tabularnewline
25 & 3 & 2.68434497126415 & 0.598646446976939 & 0.299685513793754 & 0.222219738541756 \tabularnewline
26 & 4 & 3.97880193018746 & 1.01508272177387 & -0.0178106725511759 & 0.579019443877103 \tabularnewline
27 & 5.1 & 5.10959599308478 & 1.09117234035992 & -0.0180383474581649 & 0.109917032909162 \tabularnewline
28 & 4.5 & 4.62989441081824 & 0.0628897987264758 & -0.0151793885574565 & -1.49303270751098 \tabularnewline
29 & 4.2 & 4.24754484153516 & -0.228386122252998 & -0.0148671672437711 & -0.424533414640648 \tabularnewline
30 & 3.3 & 3.36295336788593 & -0.657821939668136 & -0.0147378402774864 & -0.626212394534033 \tabularnewline
31 & 2.7 & 2.71408053712136 & -0.651964579528715 & -0.0147382195508938 & 0.00854162859263727 \tabularnewline
32 & 1.8 & 1.83167307440080 & -0.80279926062265 & -0.0147368491858344 & -0.219958411788296 \tabularnewline
33 & 1.4 & 1.38831943965671 & -0.567525348005809 & -0.0147367867341524 & 0.343094018990734 \tabularnewline
34 & 0.5 & 0.535690531618139 & -0.754138972545623 & -0.0147369255312250 & -0.272133946856378 \tabularnewline
35 & -0.4 & -0.373842408727871 & -0.855851569782623 & -0.0147369583858546 & -0.148324917900332 \tabularnewline
36 & 0.8 & 0.674768929676961 & 0.390708015816816 & -0.0147368371511561 & 1.81782643667692 \tabularnewline
37 & 0.7 & 0.61384853607726 & 0.0976578329841314 & 0.119056131414941 & -0.447739860982381 \tabularnewline
38 & 1.9 & 1.83172787062467 & 0.78409707988341 & 0.000495166300047246 & 0.968857689294647 \tabularnewline
39 & 2 & 2.04070012819251 & 0.406349154133475 & 0.00133930556396310 & -0.54699525506795 \tabularnewline
40 & 1.1 & 1.18897281955767 & -0.417163752435337 & 0.00304904641813670 & -1.19702993108501 \tabularnewline
41 & 0.9 & 0.888448454385456 & -0.340847959518955 & 0.00298798207750703 & 0.111245017560968 \tabularnewline
42 & 0.4 & 0.407300736060819 & -0.43266773170609 & 0.00300862280273498 & -0.133894783442055 \tabularnewline
43 & 0.7 & 0.647557917614162 & 0.00777900561697509 & 0.00298733451435237 & 0.642291699564307 \tabularnewline
44 & 2.1 & 1.99831716102726 & 0.886818093550494 & 0.0029813732263065 & 1.28188053989151 \tabularnewline
45 & 2.8 & 2.80305136749803 & 0.833090366645403 & 0.00298136258081404 & -0.0783497904675206 \tabularnewline
46 & 3.9 & 3.87915812957 & 0.992155952385985 & 0.00298145089084799 & 0.231961335979678 \tabularnewline
47 & 3.5 & 3.59110716788505 & 0.154200881845450 & 0.00298124884988678 & -1.22196876709453 \tabularnewline
48 & 2 & 2.11671198635657 & -0.911791000890389 & 0.00298117146348577 & -1.55450910498620 \tabularnewline
49 & 2 & 1.92837927258412 & -0.441323625878061 & 0.0187950589760031 & 0.710650440006949 \tabularnewline
50 & 1.5 & 1.50073478148677 & -0.432825428180175 & -0.00159849318569779 & 0.0120892562646396 \tabularnewline
51 & 2.5 & 2.40529781085564 & 0.444971777512692 & -0.00316377782046862 & 1.27290297424715 \tabularnewline
52 & 3.1 & 3.08614947391274 & 0.599372617274491 & -0.00341955711599084 & 0.224578718068195 \tabularnewline
53 & 2.7 & 2.7702497536162 & 0.000472815860059872 & -0.00303726580152022 & -0.873082652524495 \tabularnewline
54 & 2.8 & 2.80082850307018 & 0.020176333452032 & -0.00304079917197125 & 0.0287325138405989 \tabularnewline
55 & 2.5 & 2.52480207111156 & -0.173697264998156 & -0.00303332398486367 & -0.282720745054261 \tabularnewline
56 & 3 & 2.95840248323227 & 0.223805886076751 & -0.00303547443078757 & 0.579668824839837 \tabularnewline
57 & 3.2 & 3.2016095859972 & 0.236504885619324 & -0.00303547242357751 & 0.0185186310802656 \tabularnewline
58 & 2.8 & 2.84651506745081 & -0.150724538399919 & -0.00303564392124261 & -0.564686912712573 \tabularnewline
59 & 2.4 & 2.42307856057176 & -0.329227221815416 & -0.00303567825485336 & -0.260305965852387 \tabularnewline
60 & 2 & 2.00925319545875 & -0.384600641419640 & -0.00303568146162566 & -0.0807496626785948 \tabularnewline
61 & 1.8 & 1.75026829958601 & -0.302805044957922 & 0.0405474143954293 & 0.122701515793977 \tabularnewline
62 & 1.1 & 1.12665043511292 & -0.503913421085249 & -0.0058334774787796 & -0.287474333163091 \tabularnewline
63 & -1.5 & -1.35151274916247 & -1.79911525964438 & -0.00391205454895118 & -1.87996337096643 \tabularnewline
64 & -3.7 & -3.65928154941211 & -2.13206428879176 & -0.00345321823164634 & -0.48449054227055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64162&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]1.4[/C][C]1.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.03801152041440[/C][C]-0.146211813609435[/C][C]-0.0135888218791398[/C][C]-0.335875849641164[/C][/ROW]
[ROW][C]3[/C][C]-0.8[/C][C]-0.684707016057503[/C][C]-1.19436735938808[/C][C]2.59577598804486e-05[/C][C]-1.4949461493531[/C][/ROW]
[ROW][C]4[/C][C]-2.9[/C][C]-2.83707455819736[/C][C]-1.8215264455507[/C][C]0.00598001305087772[/C][C]-0.901324825695947[/C][/ROW]
[ROW][C]5[/C][C]-0.7[/C][C]-0.96764606007484[/C][C]0.590208981698554[/C][C]-0.00240455541636278[/C][C]3.51088925074813[/C][/ROW]
[ROW][C]6[/C][C]-0.7[/C][C]-0.675863747419444[/C][C]0.394990702031841[/C][C]-0.00222061368429005[/C][C]-0.284650173938428[/C][/ROW]
[ROW][C]7[/C][C]1.5[/C][C]1.38035644326524[/C][C]1.48220454135134[/C][C]-0.00243163971159079[/C][C]1.58545073527241[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]2.99285776534604[/C][C]1.56748846835726[/C][C]-0.00243373157549621[/C][C]0.12436740293503[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.29540220323008[/C][C]0.739522692556956[/C][C]-0.00243541792231519[/C][C]-1.20740163140865[/C][/ROW]
[ROW][C]10[/C][C]3.1[/C][C]3.16627794569658[/C][C]0.170951909926667[/C][C]-0.00243690393067207[/C][C]-0.829132446275882[/C][/ROW]
[ROW][C]11[/C][C]3.9[/C][C]3.86374075974952[/C][C]0.515578011620494[/C][C]-0.00243653880420736[/C][C]0.502559561182696[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.23362871262796[/C][C]-1.54342277949812[/C][C]-0.00243717108764966[/C][C]-3.00258897724739[/C][/ROW]
[ROW][C]13[/C][C]1.3[/C][C]0.851198743252725[/C][C]-0.80366680650433[/C][C]0.365738918164358[/C][C]1.23223193553873[/C][/ROW]
[ROW][C]14[/C][C]0.8[/C][C]0.783492622644794[/C][C]-0.384627140056491[/C][C]-0.0180890983885301[/C][C]0.560274657687857[/C][/ROW]
[ROW][C]15[/C][C]1.2[/C][C]1.16470922841989[/C][C]0.120177805895604[/C][C]-0.0203742532305920[/C][C]0.725678727985411[/C][/ROW]
[ROW][C]16[/C][C]2.9[/C][C]2.81333460428529[/C][C]1.12072459477073[/C][C]-0.0245838724968566[/C][C]1.44951968210288[/C][/ROW]
[ROW][C]17[/C][C]3.9[/C][C]3.92522258221238[/C][C]1.11494524363903[/C][C]-0.0245744914912431[/C][C]-0.00842106371047601[/C][/ROW]
[ROW][C]18[/C][C]4.5[/C][C]4.55972752780782[/C][C]0.800564637077947[/C][C]-0.0244311096454100[/C][C]-0.458427775498266[/C][/ROW]
[ROW][C]19[/C][C]4.5[/C][C]4.58160650161327[/C][C]0.290911305701806[/C][C]-0.0243811320886669[/C][C]-0.743213015011021[/C][/ROW]
[ROW][C]20[/C][C]3.3[/C][C]3.43035824266764[/C][C]-0.653040654873944[/C][C]-0.0243681443153670[/C][C]-1.37654132925854[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.07591751849108[/C][C]-1.11213972716115[/C][C]-0.0243683288706696[/C][C]-0.669492609947243[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]1.48598822506478[/C][C]-0.770328470176313[/C][C]-0.0243679438597394[/C][C]0.498454743894787[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.00323275817253[/C][C]-0.582098510835371[/C][C]-0.0243678517807974[/C][C]0.274491007243181[/C][/ROW]
[ROW][C]24[/C][C]2.1[/C][C]2.00775918969923[/C][C]0.456421371552545[/C][C]-0.0243676988205957[/C][C]1.51444737902976[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]2.68434497126415[/C][C]0.598646446976939[/C][C]0.299685513793754[/C][C]0.222219738541756[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.97880193018746[/C][C]1.01508272177387[/C][C]-0.0178106725511759[/C][C]0.579019443877103[/C][/ROW]
[ROW][C]27[/C][C]5.1[/C][C]5.10959599308478[/C][C]1.09117234035992[/C][C]-0.0180383474581649[/C][C]0.109917032909162[/C][/ROW]
[ROW][C]28[/C][C]4.5[/C][C]4.62989441081824[/C][C]0.0628897987264758[/C][C]-0.0151793885574565[/C][C]-1.49303270751098[/C][/ROW]
[ROW][C]29[/C][C]4.2[/C][C]4.24754484153516[/C][C]-0.228386122252998[/C][C]-0.0148671672437711[/C][C]-0.424533414640648[/C][/ROW]
[ROW][C]30[/C][C]3.3[/C][C]3.36295336788593[/C][C]-0.657821939668136[/C][C]-0.0147378402774864[/C][C]-0.626212394534033[/C][/ROW]
[ROW][C]31[/C][C]2.7[/C][C]2.71408053712136[/C][C]-0.651964579528715[/C][C]-0.0147382195508938[/C][C]0.00854162859263727[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]1.83167307440080[/C][C]-0.80279926062265[/C][C]-0.0147368491858344[/C][C]-0.219958411788296[/C][/ROW]
[ROW][C]33[/C][C]1.4[/C][C]1.38831943965671[/C][C]-0.567525348005809[/C][C]-0.0147367867341524[/C][C]0.343094018990734[/C][/ROW]
[ROW][C]34[/C][C]0.5[/C][C]0.535690531618139[/C][C]-0.754138972545623[/C][C]-0.0147369255312250[/C][C]-0.272133946856378[/C][/ROW]
[ROW][C]35[/C][C]-0.4[/C][C]-0.373842408727871[/C][C]-0.855851569782623[/C][C]-0.0147369583858546[/C][C]-0.148324917900332[/C][/ROW]
[ROW][C]36[/C][C]0.8[/C][C]0.674768929676961[/C][C]0.390708015816816[/C][C]-0.0147368371511561[/C][C]1.81782643667692[/C][/ROW]
[ROW][C]37[/C][C]0.7[/C][C]0.61384853607726[/C][C]0.0976578329841314[/C][C]0.119056131414941[/C][C]-0.447739860982381[/C][/ROW]
[ROW][C]38[/C][C]1.9[/C][C]1.83172787062467[/C][C]0.78409707988341[/C][C]0.000495166300047246[/C][C]0.968857689294647[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.04070012819251[/C][C]0.406349154133475[/C][C]0.00133930556396310[/C][C]-0.54699525506795[/C][/ROW]
[ROW][C]40[/C][C]1.1[/C][C]1.18897281955767[/C][C]-0.417163752435337[/C][C]0.00304904641813670[/C][C]-1.19702993108501[/C][/ROW]
[ROW][C]41[/C][C]0.9[/C][C]0.888448454385456[/C][C]-0.340847959518955[/C][C]0.00298798207750703[/C][C]0.111245017560968[/C][/ROW]
[ROW][C]42[/C][C]0.4[/C][C]0.407300736060819[/C][C]-0.43266773170609[/C][C]0.00300862280273498[/C][C]-0.133894783442055[/C][/ROW]
[ROW][C]43[/C][C]0.7[/C][C]0.647557917614162[/C][C]0.00777900561697509[/C][C]0.00298733451435237[/C][C]0.642291699564307[/C][/ROW]
[ROW][C]44[/C][C]2.1[/C][C]1.99831716102726[/C][C]0.886818093550494[/C][C]0.0029813732263065[/C][C]1.28188053989151[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.80305136749803[/C][C]0.833090366645403[/C][C]0.00298136258081404[/C][C]-0.0783497904675206[/C][/ROW]
[ROW][C]46[/C][C]3.9[/C][C]3.87915812957[/C][C]0.992155952385985[/C][C]0.00298145089084799[/C][C]0.231961335979678[/C][/ROW]
[ROW][C]47[/C][C]3.5[/C][C]3.59110716788505[/C][C]0.154200881845450[/C][C]0.00298124884988678[/C][C]-1.22196876709453[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.11671198635657[/C][C]-0.911791000890389[/C][C]0.00298117146348577[/C][C]-1.55450910498620[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.92837927258412[/C][C]-0.441323625878061[/C][C]0.0187950589760031[/C][C]0.710650440006949[/C][/ROW]
[ROW][C]50[/C][C]1.5[/C][C]1.50073478148677[/C][C]-0.432825428180175[/C][C]-0.00159849318569779[/C][C]0.0120892562646396[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.40529781085564[/C][C]0.444971777512692[/C][C]-0.00316377782046862[/C][C]1.27290297424715[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]3.08614947391274[/C][C]0.599372617274491[/C][C]-0.00341955711599084[/C][C]0.224578718068195[/C][/ROW]
[ROW][C]53[/C][C]2.7[/C][C]2.7702497536162[/C][C]0.000472815860059872[/C][C]-0.00303726580152022[/C][C]-0.873082652524495[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]2.80082850307018[/C][C]0.020176333452032[/C][C]-0.00304079917197125[/C][C]0.0287325138405989[/C][/ROW]
[ROW][C]55[/C][C]2.5[/C][C]2.52480207111156[/C][C]-0.173697264998156[/C][C]-0.00303332398486367[/C][C]-0.282720745054261[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]2.95840248323227[/C][C]0.223805886076751[/C][C]-0.00303547443078757[/C][C]0.579668824839837[/C][/ROW]
[ROW][C]57[/C][C]3.2[/C][C]3.2016095859972[/C][C]0.236504885619324[/C][C]-0.00303547242357751[/C][C]0.0185186310802656[/C][/ROW]
[ROW][C]58[/C][C]2.8[/C][C]2.84651506745081[/C][C]-0.150724538399919[/C][C]-0.00303564392124261[/C][C]-0.564686912712573[/C][/ROW]
[ROW][C]59[/C][C]2.4[/C][C]2.42307856057176[/C][C]-0.329227221815416[/C][C]-0.00303567825485336[/C][C]-0.260305965852387[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]2.00925319545875[/C][C]-0.384600641419640[/C][C]-0.00303568146162566[/C][C]-0.0807496626785948[/C][/ROW]
[ROW][C]61[/C][C]1.8[/C][C]1.75026829958601[/C][C]-0.302805044957922[/C][C]0.0405474143954293[/C][C]0.122701515793977[/C][/ROW]
[ROW][C]62[/C][C]1.1[/C][C]1.12665043511292[/C][C]-0.503913421085249[/C][C]-0.0058334774787796[/C][C]-0.287474333163091[/C][/ROW]
[ROW][C]63[/C][C]-1.5[/C][C]-1.35151274916247[/C][C]-1.79911525964438[/C][C]-0.00391205454895118[/C][C]-1.87996337096643[/C][/ROW]
[ROW][C]64[/C][C]-3.7[/C][C]-3.65928154941211[/C][C]-2.13206428879176[/C][C]-0.00345321823164634[/C][C]-0.48449054227055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64162&T=1

As an alternative you can also use a QR Code:  

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

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.41.4000
211.03801152041440-0.146211813609435-0.0135888218791398-0.335875849641164
3-0.8-0.684707016057503-1.194367359388082.59577598804486e-05-1.4949461493531
4-2.9-2.83707455819736-1.82152644555070.00598001305087772-0.901324825695947
5-0.7-0.967646060074840.590208981698554-0.002404555416362783.51088925074813
6-0.7-0.6758637474194440.394990702031841-0.00222061368429005-0.284650173938428
71.51.380356443265241.48220454135134-0.002431639711590791.58545073527241
832.992857765346041.56748846835726-0.002433731575496210.12436740293503
93.23.295402203230080.739522692556956-0.00243541792231519-1.20740163140865
103.13.166277945696580.170951909926667-0.00243690393067207-0.829132446275882
113.93.863740759749520.515578011620494-0.002436538804207360.502559561182696
1211.23362871262796-1.54342277949812-0.00243717108764966-3.00258897724739
131.30.851198743252725-0.803666806504330.3657389181643581.23223193553873
140.80.783492622644794-0.384627140056491-0.01808909838853010.560274657687857
151.21.164709228419890.120177805895604-0.02037425323059200.725678727985411
162.92.813334604285291.12072459477073-0.02458387249685661.44951968210288
173.93.925222582212381.11494524363903-0.0245744914912431-0.00842106371047601
184.54.559727527807820.800564637077947-0.0244311096454100-0.458427775498266
194.54.581606501613270.290911305701806-0.0243811320886669-0.743213015011021
203.33.43035824266764-0.653040654873944-0.0243681443153670-1.37654132925854
2122.07591751849108-1.11213972716115-0.0243683288706696-0.669492609947243
221.51.48598822506478-0.770328470176313-0.02436794385973940.498454743894787
2311.00323275817253-0.582098510835371-0.02436785178079740.274491007243181
242.12.007759189699230.456421371552545-0.02436769882059571.51444737902976
2532.684344971264150.5986464469769390.2996855137937540.222219738541756
2643.978801930187461.01508272177387-0.01781067255117590.579019443877103
275.15.109595993084781.09117234035992-0.01803834745816490.109917032909162
284.54.629894410818240.0628897987264758-0.0151793885574565-1.49303270751098
294.24.24754484153516-0.228386122252998-0.0148671672437711-0.424533414640648
303.33.36295336788593-0.657821939668136-0.0147378402774864-0.626212394534033
312.72.71408053712136-0.651964579528715-0.01473821955089380.00854162859263727
321.81.83167307440080-0.80279926062265-0.0147368491858344-0.219958411788296
331.41.38831943965671-0.567525348005809-0.01473678673415240.343094018990734
340.50.535690531618139-0.754138972545623-0.0147369255312250-0.272133946856378
35-0.4-0.373842408727871-0.855851569782623-0.0147369583858546-0.148324917900332
360.80.6747689296769610.390708015816816-0.01473683715115611.81782643667692
370.70.613848536077260.09765783298413140.119056131414941-0.447739860982381
381.91.831727870624670.784097079883410.0004951663000472460.968857689294647
3922.040700128192510.4063491541334750.00133930556396310-0.54699525506795
401.11.18897281955767-0.4171637524353370.00304904641813670-1.19702993108501
410.90.888448454385456-0.3408479595189550.002987982077507030.111245017560968
420.40.407300736060819-0.432667731706090.00300862280273498-0.133894783442055
430.70.6475579176141620.007779005616975090.002987334514352370.642291699564307
442.11.998317161027260.8868180935504940.00298137322630651.28188053989151
452.82.803051367498030.8330903666454030.00298136258081404-0.0783497904675206
463.93.879158129570.9921559523859850.002981450890847990.231961335979678
473.53.591107167885050.1542008818454500.00298124884988678-1.22196876709453
4822.11671198635657-0.9117910008903890.00298117146348577-1.55450910498620
4921.92837927258412-0.4413236258780610.01879505897600310.710650440006949
501.51.50073478148677-0.432825428180175-0.001598493185697790.0120892562646396
512.52.405297810855640.444971777512692-0.003163777820468621.27290297424715
523.13.086149473912740.599372617274491-0.003419557115990840.224578718068195
532.72.77024975361620.000472815860059872-0.00303726580152022-0.873082652524495
542.82.800828503070180.020176333452032-0.003040799171971250.0287325138405989
552.52.52480207111156-0.173697264998156-0.00303332398486367-0.282720745054261
5632.958402483232270.223805886076751-0.003035474430787570.579668824839837
573.23.20160958599720.236504885619324-0.003035472423577510.0185186310802656
582.82.84651506745081-0.150724538399919-0.00303564392124261-0.564686912712573
592.42.42307856057176-0.329227221815416-0.00303567825485336-0.260305965852387
6022.00925319545875-0.384600641419640-0.00303568146162566-0.0807496626785948
611.81.75026829958601-0.3028050449579220.04054741439542930.122701515793977
621.11.12665043511292-0.503913421085249-0.0058334774787796-0.287474333163091
63-1.5-1.35151274916247-1.79911525964438-0.00391205454895118-1.87996337096643
64-3.7-3.65928154941211-2.13206428879176-0.00345321823164634-0.48449054227055



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',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,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',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,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')