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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 05:25:38 -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/t1259929615orktg1w753iyn6y.htm/, Retrieved Sat, 27 Apr 2024 22:07:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63402, Retrieved Sat, 27 Apr 2024 22:07:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
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]
-    D      [Structural Time Series Models] [] [2009-12-04 12:25:38] [1c773da0103d9327c2f1f790e2d74438] [Current]
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Dataseries X:
1,4816
1,4562
1,4268
1,4088
1,4016
1,3650
1,3190
1,3050
1,2785
1,3239
1,3449
1,2732
1,3322
1,4369
1,4975
1,5770
1,5553
1,5557
1,5750
1,5527
1,4748
1,4718
1,4570
1,4684
1,4227
1,3896
1,3622
1,3716
1,3419
1,3511
1,3516
1,3242
1,3074
1,2999
1,3213
1,2881
1,2611
1,2727
1,2811
1,2684
1,2650
1,2770
1,2271
1,2020
1,1938
1,2103
1,1856
1,1786
1,2015
1,2256
1,2292
1,2037
1,2165
1,2694
1,2938
1,3201
1,3014
1,3119
1,3408
1,2991




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63402&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]4 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=63402&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63402&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.48161.4816000
21.45621.4575207999565-0.00132079994551282-0.00132079995650077-0.426435187031089
31.42681.42823266926491-0.00143266926490572-0.00143266926490572-0.826541506682138
41.40881.41029841263809-0.00149841263808581-0.00149841263808581-0.487681100692778
51.40161.40312094855557-0.00152094855557244-0.00152094855557244-0.167835020489262
61.3651.36665905505158-0.00165905505158485-0.00165905505158485-1.03261406592776
71.3191.32083294110301-0.00183294110301073-0.00183294110301073-1.30526443616929
81.3051.30688046867487-0.00188046867486810-0.00188046867486810-0.358164477712999
91.27851.28047626451267-0.00197626451266697-0.00197626451266697-0.724736267722295
101.32391.32569263558796-0.00179263558795749-0.001792635587957491.39464706268916
111.34491.34660463313754-0.00170463313753521-0.001704633137535210.670967231770499
121.27321.27517384607608-0.0019738460760805-0.00197384607608049-2.06053167576784
131.33221.29864567021683-0.003050393617501680.03355432978317090.908524196673319
141.43691.436538363501620.0003616363426051570.0003616364983825013.54372588351020
151.49751.497029038129420.0004709618705824680.0004709618705824581.77511095853184
161.5771.576385869584260.0006141304157435340.0006141304157435282.32884052408926
171.55531.554726220633220.0005737793667789980.000573779366779008-0.657557496272557
181.55571.555126534314380.0005734656856162750.000573465685616264-0.00512097536522936
191.5751.574392792811620.0006072071883756470.000607207188375650.551839259784684
201.55271.552133992823930.000566007176068270.000566007176068271-0.67503770671021
211.47481.474374865366110.0004251346338872820.000425134633887268-2.31226787157924
221.47181.471381003600130.0004189963998686140.00041899639986861-0.100933408217086
231.4571.456608228995790.0003917710042128430.000391771004212837-0.448480952493273
241.46841.467988571444300.0004114285556966870.0004114285556966960.324396484186423
251.42271.434541556349210.00107650511441964-0.0118415563492138-1.10126877603234
261.38961.389243764664410.0003562352840395820.000356235335589112-1.24331648436759
271.36221.361876380741370.0003236192586264180.000323619258626414-0.818185982906271
281.37161.371265727712380.0003342722876238920.0003342722876238830.267549707947931
291.34191.341600937879050.0002990621209530770.000299062120953085-0.885338169794054
301.35111.35079051523520.0003094847647988490.0003094847647988380.262378438573609
311.35161.351290292410370.0003097075896344730.0003097075896344750.00561593900392191
321.32421.323922663563970.0002773364360343750.000277336436034372-0.816817268774445
331.30741.307142590444210.0002574095557898210.000257409555789808-0.503400217378331
341.29991.299651631714050.0002483682859464190.000248368285946414-0.228670573412785
351.32131.321027008161510.0002729918384865110.0002729918384865040.623501827896867
361.28811.287865930244900.0002340697551043130.000234069755104321-0.986708071547363
371.26111.266687461703990.000507951055583273-0.00558746170399208-0.674528374654215
381.27271.272135565136700.0005644347670280540.0005644348633044710.136116288107659
391.28111.280528757616650.000571242383345150.0005712423833451470.231008727017761
401.26841.267840277792380.0005597222076191650.000559722207619156-0.391263915138439
411.2651.264443712070110.0005562879298885350.000556287929888545-0.116740916394658
421.2771.276433795508490.0005662044915139470.0005662044915139330.337384762474245
431.22711.226577489192200.0005225108077975040.000522510807797505-1.48785068199346
441.2021.201499653994030.0005003460059724610.000500346005972458-0.755406213968388
451.19381.193307173739960.0004928262600428490.000492826260042838-0.256504834555903
461.21031.209793350619230.0005066493807712940.0005066493807712910.471925942101908
471.18561.185115099238280.0004849007617196910.000484900761719684-0.743146565720972
481.17861.178121551738960.000478448261037450.000478448261037457-0.220671152371540
491.20151.204102732203990.000236612009283087-0.002602732203988640.790458499377938
501.22561.225174896451230.0004251034324183370.0004251035487708220.582845713590406
511.22921.228772708492280.0004272915077169620.0004272915077169570.0936109569276325
521.20371.203290564753840.0004094352461584020.000409435246158393-0.764459252765785
531.21651.216082037179940.0004179628200643360.0004179628200643460.36533257307745
541.26941.268945942243390.0004540577566093530.0004540577566093271.54741955714384
551.29381.293329484550950.0004705154490472480.0004705154490472520.70604020149205
561.32011.319611744520170.0004882554798332860.0004882554798332830.761576161780302
571.30141.300924914222080.0004750857779232760.000475085777923262-0.565761247670541
581.31191.311418038423500.0004819615765022120.0004819615765022080.295582332635997
591.34081.340298560672490.0005014393275123620.0005014393275123550.837899644406836
601.29911.298627465768220.000472534231777310.000472534231777324-1.24430049621198

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.4816 & 1.4816 & 0 & 0 & 0 \tabularnewline
2 & 1.4562 & 1.4575207999565 & -0.00132079994551282 & -0.00132079995650077 & -0.426435187031089 \tabularnewline
3 & 1.4268 & 1.42823266926491 & -0.00143266926490572 & -0.00143266926490572 & -0.826541506682138 \tabularnewline
4 & 1.4088 & 1.41029841263809 & -0.00149841263808581 & -0.00149841263808581 & -0.487681100692778 \tabularnewline
5 & 1.4016 & 1.40312094855557 & -0.00152094855557244 & -0.00152094855557244 & -0.167835020489262 \tabularnewline
6 & 1.365 & 1.36665905505158 & -0.00165905505158485 & -0.00165905505158485 & -1.03261406592776 \tabularnewline
7 & 1.319 & 1.32083294110301 & -0.00183294110301073 & -0.00183294110301073 & -1.30526443616929 \tabularnewline
8 & 1.305 & 1.30688046867487 & -0.00188046867486810 & -0.00188046867486810 & -0.358164477712999 \tabularnewline
9 & 1.2785 & 1.28047626451267 & -0.00197626451266697 & -0.00197626451266697 & -0.724736267722295 \tabularnewline
10 & 1.3239 & 1.32569263558796 & -0.00179263558795749 & -0.00179263558795749 & 1.39464706268916 \tabularnewline
11 & 1.3449 & 1.34660463313754 & -0.00170463313753521 & -0.00170463313753521 & 0.670967231770499 \tabularnewline
12 & 1.2732 & 1.27517384607608 & -0.0019738460760805 & -0.00197384607608049 & -2.06053167576784 \tabularnewline
13 & 1.3322 & 1.29864567021683 & -0.00305039361750168 & 0.0335543297831709 & 0.908524196673319 \tabularnewline
14 & 1.4369 & 1.43653836350162 & 0.000361636342605157 & 0.000361636498382501 & 3.54372588351020 \tabularnewline
15 & 1.4975 & 1.49702903812942 & 0.000470961870582468 & 0.000470961870582458 & 1.77511095853184 \tabularnewline
16 & 1.577 & 1.57638586958426 & 0.000614130415743534 & 0.000614130415743528 & 2.32884052408926 \tabularnewline
17 & 1.5553 & 1.55472622063322 & 0.000573779366778998 & 0.000573779366779008 & -0.657557496272557 \tabularnewline
18 & 1.5557 & 1.55512653431438 & 0.000573465685616275 & 0.000573465685616264 & -0.00512097536522936 \tabularnewline
19 & 1.575 & 1.57439279281162 & 0.000607207188375647 & 0.00060720718837565 & 0.551839259784684 \tabularnewline
20 & 1.5527 & 1.55213399282393 & 0.00056600717606827 & 0.000566007176068271 & -0.67503770671021 \tabularnewline
21 & 1.4748 & 1.47437486536611 & 0.000425134633887282 & 0.000425134633887268 & -2.31226787157924 \tabularnewline
22 & 1.4718 & 1.47138100360013 & 0.000418996399868614 & 0.00041899639986861 & -0.100933408217086 \tabularnewline
23 & 1.457 & 1.45660822899579 & 0.000391771004212843 & 0.000391771004212837 & -0.448480952493273 \tabularnewline
24 & 1.4684 & 1.46798857144430 & 0.000411428555696687 & 0.000411428555696696 & 0.324396484186423 \tabularnewline
25 & 1.4227 & 1.43454155634921 & 0.00107650511441964 & -0.0118415563492138 & -1.10126877603234 \tabularnewline
26 & 1.3896 & 1.38924376466441 & 0.000356235284039582 & 0.000356235335589112 & -1.24331648436759 \tabularnewline
27 & 1.3622 & 1.36187638074137 & 0.000323619258626418 & 0.000323619258626414 & -0.818185982906271 \tabularnewline
28 & 1.3716 & 1.37126572771238 & 0.000334272287623892 & 0.000334272287623883 & 0.267549707947931 \tabularnewline
29 & 1.3419 & 1.34160093787905 & 0.000299062120953077 & 0.000299062120953085 & -0.885338169794054 \tabularnewline
30 & 1.3511 & 1.3507905152352 & 0.000309484764798849 & 0.000309484764798838 & 0.262378438573609 \tabularnewline
31 & 1.3516 & 1.35129029241037 & 0.000309707589634473 & 0.000309707589634475 & 0.00561593900392191 \tabularnewline
32 & 1.3242 & 1.32392266356397 & 0.000277336436034375 & 0.000277336436034372 & -0.816817268774445 \tabularnewline
33 & 1.3074 & 1.30714259044421 & 0.000257409555789821 & 0.000257409555789808 & -0.503400217378331 \tabularnewline
34 & 1.2999 & 1.29965163171405 & 0.000248368285946419 & 0.000248368285946414 & -0.228670573412785 \tabularnewline
35 & 1.3213 & 1.32102700816151 & 0.000272991838486511 & 0.000272991838486504 & 0.623501827896867 \tabularnewline
36 & 1.2881 & 1.28786593024490 & 0.000234069755104313 & 0.000234069755104321 & -0.986708071547363 \tabularnewline
37 & 1.2611 & 1.26668746170399 & 0.000507951055583273 & -0.00558746170399208 & -0.674528374654215 \tabularnewline
38 & 1.2727 & 1.27213556513670 & 0.000564434767028054 & 0.000564434863304471 & 0.136116288107659 \tabularnewline
39 & 1.2811 & 1.28052875761665 & 0.00057124238334515 & 0.000571242383345147 & 0.231008727017761 \tabularnewline
40 & 1.2684 & 1.26784027779238 & 0.000559722207619165 & 0.000559722207619156 & -0.391263915138439 \tabularnewline
41 & 1.265 & 1.26444371207011 & 0.000556287929888535 & 0.000556287929888545 & -0.116740916394658 \tabularnewline
42 & 1.277 & 1.27643379550849 & 0.000566204491513947 & 0.000566204491513933 & 0.337384762474245 \tabularnewline
43 & 1.2271 & 1.22657748919220 & 0.000522510807797504 & 0.000522510807797505 & -1.48785068199346 \tabularnewline
44 & 1.202 & 1.20149965399403 & 0.000500346005972461 & 0.000500346005972458 & -0.755406213968388 \tabularnewline
45 & 1.1938 & 1.19330717373996 & 0.000492826260042849 & 0.000492826260042838 & -0.256504834555903 \tabularnewline
46 & 1.2103 & 1.20979335061923 & 0.000506649380771294 & 0.000506649380771291 & 0.471925942101908 \tabularnewline
47 & 1.1856 & 1.18511509923828 & 0.000484900761719691 & 0.000484900761719684 & -0.743146565720972 \tabularnewline
48 & 1.1786 & 1.17812155173896 & 0.00047844826103745 & 0.000478448261037457 & -0.220671152371540 \tabularnewline
49 & 1.2015 & 1.20410273220399 & 0.000236612009283087 & -0.00260273220398864 & 0.790458499377938 \tabularnewline
50 & 1.2256 & 1.22517489645123 & 0.000425103432418337 & 0.000425103548770822 & 0.582845713590406 \tabularnewline
51 & 1.2292 & 1.22877270849228 & 0.000427291507716962 & 0.000427291507716957 & 0.0936109569276325 \tabularnewline
52 & 1.2037 & 1.20329056475384 & 0.000409435246158402 & 0.000409435246158393 & -0.764459252765785 \tabularnewline
53 & 1.2165 & 1.21608203717994 & 0.000417962820064336 & 0.000417962820064346 & 0.36533257307745 \tabularnewline
54 & 1.2694 & 1.26894594224339 & 0.000454057756609353 & 0.000454057756609327 & 1.54741955714384 \tabularnewline
55 & 1.2938 & 1.29332948455095 & 0.000470515449047248 & 0.000470515449047252 & 0.70604020149205 \tabularnewline
56 & 1.3201 & 1.31961174452017 & 0.000488255479833286 & 0.000488255479833283 & 0.761576161780302 \tabularnewline
57 & 1.3014 & 1.30092491422208 & 0.000475085777923276 & 0.000475085777923262 & -0.565761247670541 \tabularnewline
58 & 1.3119 & 1.31141803842350 & 0.000481961576502212 & 0.000481961576502208 & 0.295582332635997 \tabularnewline
59 & 1.3408 & 1.34029856067249 & 0.000501439327512362 & 0.000501439327512355 & 0.837899644406836 \tabularnewline
60 & 1.2991 & 1.29862746576822 & 0.00047253423177731 & 0.000472534231777324 & -1.24430049621198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63402&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]1.4816[/C][C]1.4816[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.4562[/C][C]1.4575207999565[/C][C]-0.00132079994551282[/C][C]-0.00132079995650077[/C][C]-0.426435187031089[/C][/ROW]
[ROW][C]3[/C][C]1.4268[/C][C]1.42823266926491[/C][C]-0.00143266926490572[/C][C]-0.00143266926490572[/C][C]-0.826541506682138[/C][/ROW]
[ROW][C]4[/C][C]1.4088[/C][C]1.41029841263809[/C][C]-0.00149841263808581[/C][C]-0.00149841263808581[/C][C]-0.487681100692778[/C][/ROW]
[ROW][C]5[/C][C]1.4016[/C][C]1.40312094855557[/C][C]-0.00152094855557244[/C][C]-0.00152094855557244[/C][C]-0.167835020489262[/C][/ROW]
[ROW][C]6[/C][C]1.365[/C][C]1.36665905505158[/C][C]-0.00165905505158485[/C][C]-0.00165905505158485[/C][C]-1.03261406592776[/C][/ROW]
[ROW][C]7[/C][C]1.319[/C][C]1.32083294110301[/C][C]-0.00183294110301073[/C][C]-0.00183294110301073[/C][C]-1.30526443616929[/C][/ROW]
[ROW][C]8[/C][C]1.305[/C][C]1.30688046867487[/C][C]-0.00188046867486810[/C][C]-0.00188046867486810[/C][C]-0.358164477712999[/C][/ROW]
[ROW][C]9[/C][C]1.2785[/C][C]1.28047626451267[/C][C]-0.00197626451266697[/C][C]-0.00197626451266697[/C][C]-0.724736267722295[/C][/ROW]
[ROW][C]10[/C][C]1.3239[/C][C]1.32569263558796[/C][C]-0.00179263558795749[/C][C]-0.00179263558795749[/C][C]1.39464706268916[/C][/ROW]
[ROW][C]11[/C][C]1.3449[/C][C]1.34660463313754[/C][C]-0.00170463313753521[/C][C]-0.00170463313753521[/C][C]0.670967231770499[/C][/ROW]
[ROW][C]12[/C][C]1.2732[/C][C]1.27517384607608[/C][C]-0.0019738460760805[/C][C]-0.00197384607608049[/C][C]-2.06053167576784[/C][/ROW]
[ROW][C]13[/C][C]1.3322[/C][C]1.29864567021683[/C][C]-0.00305039361750168[/C][C]0.0335543297831709[/C][C]0.908524196673319[/C][/ROW]
[ROW][C]14[/C][C]1.4369[/C][C]1.43653836350162[/C][C]0.000361636342605157[/C][C]0.000361636498382501[/C][C]3.54372588351020[/C][/ROW]
[ROW][C]15[/C][C]1.4975[/C][C]1.49702903812942[/C][C]0.000470961870582468[/C][C]0.000470961870582458[/C][C]1.77511095853184[/C][/ROW]
[ROW][C]16[/C][C]1.577[/C][C]1.57638586958426[/C][C]0.000614130415743534[/C][C]0.000614130415743528[/C][C]2.32884052408926[/C][/ROW]
[ROW][C]17[/C][C]1.5553[/C][C]1.55472622063322[/C][C]0.000573779366778998[/C][C]0.000573779366779008[/C][C]-0.657557496272557[/C][/ROW]
[ROW][C]18[/C][C]1.5557[/C][C]1.55512653431438[/C][C]0.000573465685616275[/C][C]0.000573465685616264[/C][C]-0.00512097536522936[/C][/ROW]
[ROW][C]19[/C][C]1.575[/C][C]1.57439279281162[/C][C]0.000607207188375647[/C][C]0.00060720718837565[/C][C]0.551839259784684[/C][/ROW]
[ROW][C]20[/C][C]1.5527[/C][C]1.55213399282393[/C][C]0.00056600717606827[/C][C]0.000566007176068271[/C][C]-0.67503770671021[/C][/ROW]
[ROW][C]21[/C][C]1.4748[/C][C]1.47437486536611[/C][C]0.000425134633887282[/C][C]0.000425134633887268[/C][C]-2.31226787157924[/C][/ROW]
[ROW][C]22[/C][C]1.4718[/C][C]1.47138100360013[/C][C]0.000418996399868614[/C][C]0.00041899639986861[/C][C]-0.100933408217086[/C][/ROW]
[ROW][C]23[/C][C]1.457[/C][C]1.45660822899579[/C][C]0.000391771004212843[/C][C]0.000391771004212837[/C][C]-0.448480952493273[/C][/ROW]
[ROW][C]24[/C][C]1.4684[/C][C]1.46798857144430[/C][C]0.000411428555696687[/C][C]0.000411428555696696[/C][C]0.324396484186423[/C][/ROW]
[ROW][C]25[/C][C]1.4227[/C][C]1.43454155634921[/C][C]0.00107650511441964[/C][C]-0.0118415563492138[/C][C]-1.10126877603234[/C][/ROW]
[ROW][C]26[/C][C]1.3896[/C][C]1.38924376466441[/C][C]0.000356235284039582[/C][C]0.000356235335589112[/C][C]-1.24331648436759[/C][/ROW]
[ROW][C]27[/C][C]1.3622[/C][C]1.36187638074137[/C][C]0.000323619258626418[/C][C]0.000323619258626414[/C][C]-0.818185982906271[/C][/ROW]
[ROW][C]28[/C][C]1.3716[/C][C]1.37126572771238[/C][C]0.000334272287623892[/C][C]0.000334272287623883[/C][C]0.267549707947931[/C][/ROW]
[ROW][C]29[/C][C]1.3419[/C][C]1.34160093787905[/C][C]0.000299062120953077[/C][C]0.000299062120953085[/C][C]-0.885338169794054[/C][/ROW]
[ROW][C]30[/C][C]1.3511[/C][C]1.3507905152352[/C][C]0.000309484764798849[/C][C]0.000309484764798838[/C][C]0.262378438573609[/C][/ROW]
[ROW][C]31[/C][C]1.3516[/C][C]1.35129029241037[/C][C]0.000309707589634473[/C][C]0.000309707589634475[/C][C]0.00561593900392191[/C][/ROW]
[ROW][C]32[/C][C]1.3242[/C][C]1.32392266356397[/C][C]0.000277336436034375[/C][C]0.000277336436034372[/C][C]-0.816817268774445[/C][/ROW]
[ROW][C]33[/C][C]1.3074[/C][C]1.30714259044421[/C][C]0.000257409555789821[/C][C]0.000257409555789808[/C][C]-0.503400217378331[/C][/ROW]
[ROW][C]34[/C][C]1.2999[/C][C]1.29965163171405[/C][C]0.000248368285946419[/C][C]0.000248368285946414[/C][C]-0.228670573412785[/C][/ROW]
[ROW][C]35[/C][C]1.3213[/C][C]1.32102700816151[/C][C]0.000272991838486511[/C][C]0.000272991838486504[/C][C]0.623501827896867[/C][/ROW]
[ROW][C]36[/C][C]1.2881[/C][C]1.28786593024490[/C][C]0.000234069755104313[/C][C]0.000234069755104321[/C][C]-0.986708071547363[/C][/ROW]
[ROW][C]37[/C][C]1.2611[/C][C]1.26668746170399[/C][C]0.000507951055583273[/C][C]-0.00558746170399208[/C][C]-0.674528374654215[/C][/ROW]
[ROW][C]38[/C][C]1.2727[/C][C]1.27213556513670[/C][C]0.000564434767028054[/C][C]0.000564434863304471[/C][C]0.136116288107659[/C][/ROW]
[ROW][C]39[/C][C]1.2811[/C][C]1.28052875761665[/C][C]0.00057124238334515[/C][C]0.000571242383345147[/C][C]0.231008727017761[/C][/ROW]
[ROW][C]40[/C][C]1.2684[/C][C]1.26784027779238[/C][C]0.000559722207619165[/C][C]0.000559722207619156[/C][C]-0.391263915138439[/C][/ROW]
[ROW][C]41[/C][C]1.265[/C][C]1.26444371207011[/C][C]0.000556287929888535[/C][C]0.000556287929888545[/C][C]-0.116740916394658[/C][/ROW]
[ROW][C]42[/C][C]1.277[/C][C]1.27643379550849[/C][C]0.000566204491513947[/C][C]0.000566204491513933[/C][C]0.337384762474245[/C][/ROW]
[ROW][C]43[/C][C]1.2271[/C][C]1.22657748919220[/C][C]0.000522510807797504[/C][C]0.000522510807797505[/C][C]-1.48785068199346[/C][/ROW]
[ROW][C]44[/C][C]1.202[/C][C]1.20149965399403[/C][C]0.000500346005972461[/C][C]0.000500346005972458[/C][C]-0.755406213968388[/C][/ROW]
[ROW][C]45[/C][C]1.1938[/C][C]1.19330717373996[/C][C]0.000492826260042849[/C][C]0.000492826260042838[/C][C]-0.256504834555903[/C][/ROW]
[ROW][C]46[/C][C]1.2103[/C][C]1.20979335061923[/C][C]0.000506649380771294[/C][C]0.000506649380771291[/C][C]0.471925942101908[/C][/ROW]
[ROW][C]47[/C][C]1.1856[/C][C]1.18511509923828[/C][C]0.000484900761719691[/C][C]0.000484900761719684[/C][C]-0.743146565720972[/C][/ROW]
[ROW][C]48[/C][C]1.1786[/C][C]1.17812155173896[/C][C]0.00047844826103745[/C][C]0.000478448261037457[/C][C]-0.220671152371540[/C][/ROW]
[ROW][C]49[/C][C]1.2015[/C][C]1.20410273220399[/C][C]0.000236612009283087[/C][C]-0.00260273220398864[/C][C]0.790458499377938[/C][/ROW]
[ROW][C]50[/C][C]1.2256[/C][C]1.22517489645123[/C][C]0.000425103432418337[/C][C]0.000425103548770822[/C][C]0.582845713590406[/C][/ROW]
[ROW][C]51[/C][C]1.2292[/C][C]1.22877270849228[/C][C]0.000427291507716962[/C][C]0.000427291507716957[/C][C]0.0936109569276325[/C][/ROW]
[ROW][C]52[/C][C]1.2037[/C][C]1.20329056475384[/C][C]0.000409435246158402[/C][C]0.000409435246158393[/C][C]-0.764459252765785[/C][/ROW]
[ROW][C]53[/C][C]1.2165[/C][C]1.21608203717994[/C][C]0.000417962820064336[/C][C]0.000417962820064346[/C][C]0.36533257307745[/C][/ROW]
[ROW][C]54[/C][C]1.2694[/C][C]1.26894594224339[/C][C]0.000454057756609353[/C][C]0.000454057756609327[/C][C]1.54741955714384[/C][/ROW]
[ROW][C]55[/C][C]1.2938[/C][C]1.29332948455095[/C][C]0.000470515449047248[/C][C]0.000470515449047252[/C][C]0.70604020149205[/C][/ROW]
[ROW][C]56[/C][C]1.3201[/C][C]1.31961174452017[/C][C]0.000488255479833286[/C][C]0.000488255479833283[/C][C]0.761576161780302[/C][/ROW]
[ROW][C]57[/C][C]1.3014[/C][C]1.30092491422208[/C][C]0.000475085777923276[/C][C]0.000475085777923262[/C][C]-0.565761247670541[/C][/ROW]
[ROW][C]58[/C][C]1.3119[/C][C]1.31141803842350[/C][C]0.000481961576502212[/C][C]0.000481961576502208[/C][C]0.295582332635997[/C][/ROW]
[ROW][C]59[/C][C]1.3408[/C][C]1.34029856067249[/C][C]0.000501439327512362[/C][C]0.000501439327512355[/C][C]0.837899644406836[/C][/ROW]
[ROW][C]60[/C][C]1.2991[/C][C]1.29862746576822[/C][C]0.00047253423177731[/C][C]0.000472534231777324[/C][C]-1.24430049621198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63402&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63402&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
11.48161.4816000
21.45621.4575207999565-0.00132079994551282-0.00132079995650077-0.426435187031089
31.42681.42823266926491-0.00143266926490572-0.00143266926490572-0.826541506682138
41.40881.41029841263809-0.00149841263808581-0.00149841263808581-0.487681100692778
51.40161.40312094855557-0.00152094855557244-0.00152094855557244-0.167835020489262
61.3651.36665905505158-0.00165905505158485-0.00165905505158485-1.03261406592776
71.3191.32083294110301-0.00183294110301073-0.00183294110301073-1.30526443616929
81.3051.30688046867487-0.00188046867486810-0.00188046867486810-0.358164477712999
91.27851.28047626451267-0.00197626451266697-0.00197626451266697-0.724736267722295
101.32391.32569263558796-0.00179263558795749-0.001792635587957491.39464706268916
111.34491.34660463313754-0.00170463313753521-0.001704633137535210.670967231770499
121.27321.27517384607608-0.0019738460760805-0.00197384607608049-2.06053167576784
131.33221.29864567021683-0.003050393617501680.03355432978317090.908524196673319
141.43691.436538363501620.0003616363426051570.0003616364983825013.54372588351020
151.49751.497029038129420.0004709618705824680.0004709618705824581.77511095853184
161.5771.576385869584260.0006141304157435340.0006141304157435282.32884052408926
171.55531.554726220633220.0005737793667789980.000573779366779008-0.657557496272557
181.55571.555126534314380.0005734656856162750.000573465685616264-0.00512097536522936
191.5751.574392792811620.0006072071883756470.000607207188375650.551839259784684
201.55271.552133992823930.000566007176068270.000566007176068271-0.67503770671021
211.47481.474374865366110.0004251346338872820.000425134633887268-2.31226787157924
221.47181.471381003600130.0004189963998686140.00041899639986861-0.100933408217086
231.4571.456608228995790.0003917710042128430.000391771004212837-0.448480952493273
241.46841.467988571444300.0004114285556966870.0004114285556966960.324396484186423
251.42271.434541556349210.00107650511441964-0.0118415563492138-1.10126877603234
261.38961.389243764664410.0003562352840395820.000356235335589112-1.24331648436759
271.36221.361876380741370.0003236192586264180.000323619258626414-0.818185982906271
281.37161.371265727712380.0003342722876238920.0003342722876238830.267549707947931
291.34191.341600937879050.0002990621209530770.000299062120953085-0.885338169794054
301.35111.35079051523520.0003094847647988490.0003094847647988380.262378438573609
311.35161.351290292410370.0003097075896344730.0003097075896344750.00561593900392191
321.32421.323922663563970.0002773364360343750.000277336436034372-0.816817268774445
331.30741.307142590444210.0002574095557898210.000257409555789808-0.503400217378331
341.29991.299651631714050.0002483682859464190.000248368285946414-0.228670573412785
351.32131.321027008161510.0002729918384865110.0002729918384865040.623501827896867
361.28811.287865930244900.0002340697551043130.000234069755104321-0.986708071547363
371.26111.266687461703990.000507951055583273-0.00558746170399208-0.674528374654215
381.27271.272135565136700.0005644347670280540.0005644348633044710.136116288107659
391.28111.280528757616650.000571242383345150.0005712423833451470.231008727017761
401.26841.267840277792380.0005597222076191650.000559722207619156-0.391263915138439
411.2651.264443712070110.0005562879298885350.000556287929888545-0.116740916394658
421.2771.276433795508490.0005662044915139470.0005662044915139330.337384762474245
431.22711.226577489192200.0005225108077975040.000522510807797505-1.48785068199346
441.2021.201499653994030.0005003460059724610.000500346005972458-0.755406213968388
451.19381.193307173739960.0004928262600428490.000492826260042838-0.256504834555903
461.21031.209793350619230.0005066493807712940.0005066493807712910.471925942101908
471.18561.185115099238280.0004849007617196910.000484900761719684-0.743146565720972
481.17861.178121551738960.000478448261037450.000478448261037457-0.220671152371540
491.20151.204102732203990.000236612009283087-0.002602732203988640.790458499377938
501.22561.225174896451230.0004251034324183370.0004251035487708220.582845713590406
511.22921.228772708492280.0004272915077169620.0004272915077169570.0936109569276325
521.20371.203290564753840.0004094352461584020.000409435246158393-0.764459252765785
531.21651.216082037179940.0004179628200643360.0004179628200643460.36533257307745
541.26941.268945942243390.0004540577566093530.0004540577566093271.54741955714384
551.29381.293329484550950.0004705154490472480.0004705154490472520.70604020149205
561.32011.319611744520170.0004882554798332860.0004882554798332830.761576161780302
571.30141.300924914222080.0004750857779232760.000475085777923262-0.565761247670541
581.31191.311418038423500.0004819615765022120.0004819615765022080.295582332635997
591.34081.340298560672490.0005014393275123620.0005014393275123550.837899644406836
601.29911.298627465768220.000472534231777310.000472534231777324-1.24430049621198



Parameters (Session):
par1 = 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')