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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 computationThu, 03 Dec 2009 06:30:37 -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/t1259847084469jh93unecrlla.htm/, Retrieved Fri, 19 Apr 2024 10:45:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62732, Retrieved Fri, 19 Apr 2024 10:45:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJSSHWWS9P9
Estimated Impact123
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]
- RMPD    [Structural Time Series Models] [Structural Time S...] [2009-12-03 13:30:37] [c8fd62404619100d8e91184019148412] [Current]
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Dataseries X:
11.1
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62732&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62732&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62732&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
111.111.1000
210.910.9023139605178-0.197877275849182-0.00231396051779417-0.519268296715333
31010.0192310725768-0.860783815387878-0.0192310725767628-1.75760425368065
49.29.19482653488939-0.825853972813150.00517346511060820.0910733826338701
59.29.17898470917852-0.04767953085340810.02101529082147492.03083000302882
69.59.496613457038420.3033172553812260.003386542961582030.91596087871694
79.69.608751817861680.119631753029153-0.00875181786168083-0.479345721896692
89.59.50570325546266-0.094319524744667-0.00570325546266232-0.558327255182972
99.19.10770352363868-0.386095916729550-0.00770352363868439-0.761419673784439
108.98.89407385611935-0.2203900612668250.005926143880652470.432426001083685
1198.99338280030020.08677724702616450.006617199699795580.801583808956906
1210.110.07587543507731.043462156930630.02412456492272052.49656494473471
1310.310.32724357285300.282609815447896-0.0272435728530215-1.98625483889208
1410.210.1930624323175-0.1180145917630280.0069375676824756-1.04965212704135
159.69.61906717509268-0.54608704578929-0.0190671750926754-1.12797678282709
169.29.20693966770739-0.420475494555885-0.006939667707392410.327427338297058
179.39.276230991980640.03856788491942140.02376900801936501.19806338758620
189.49.39514283055270.1138914189594750.004857169447307010.196563696564963
199.49.41378045759690.0245898482455449-0.0137804575968885-0.233041580372739
209.29.20218397826148-0.196833786966918-0.00218397826147587-0.577827100212828
2199.00686393673775-0.195414658049320-0.006863936737746410.00370335885872557
2298.97362998957414-0.0433707818961410.02637001042585860.396773703549157
2399.045622027702040.0647823568253091-0.04562202770204320.282236524362768
249.89.72811843223820.6438785721296550.07188156776178991.51121218099864
251010.0411966820570.333836916004252-0.0411966820570087-0.809447417810787
269.89.78715326990133-0.217578113459660.0128467300986743-1.44159878044086
279.39.31726595248606-0.451057299275273-0.0172659524860579-0.612360284747078
2899.02276410745842-0.305990234073916-0.02276410745842140.37832176822637
2998.97216330472896-0.06960175301448640.02783669527104320.616906127733932
309.19.090532378419460.1044391493265230.009467621580536770.454181547716595
319.19.106463258496210.0224874125032467-0.00646325849621339-0.213861223369617
329.19.10065244854538-0.00371398418596567-0.00065244854538207-0.068375180528351
339.29.206213995665440.0974637128296588-0.006213995665442260.264033327056016
348.88.78512717529671-0.3826626612529070.0148728247032879-1.25293782103530
358.38.39135189386244-0.392951970598074-0.0913518938624389-0.0268510404219564
368.48.31038932075387-0.1040950188629240.0896106792461310.753803795253862
378.18.11910483601663-0.184804100431604-0.0191048360166275-0.210734473624561
387.77.67597026841-0.42404355090580.0240297315899946-0.624685550084273
397.97.88294297515470.1559005678482700.01705702484529851.51741587970476
407.97.941964717189820.0667057965112374-0.0419647171898167-0.232720465944251
4187.991337252220270.05076626021481350.00866274777973165-0.0415924660434171
427.97.89005066671207-0.08909731227702870.00994933328792953-0.364999188609893
437.67.61133181373123-0.263529391031732-0.0113318137312261-0.455196598605809
447.17.13751958385879-0.456964842218547-0.0375195838587898-0.504789479440009
456.86.75989621165295-0.3839809613709710.04010378834705020.190458739553836
466.56.46750805438957-0.2997267005243450.03249194561043390.219869964197356
476.96.978625888113840.446157526532614-0.0786258881138351.94646696161207
488.28.080070356041981.048886108524190.1199296439580151.57289977650452
498.78.708367648205170.662062153029747-0.00836764820516827-1.01002821703670
508.38.38313990557518-0.246030202978635-0.0831399055751773-2.36991742040616
517.97.88394172020598-0.4778398286998880.0160582797940225-0.605824340497071
527.57.53419178862317-0.360374393246054-0.03419178862317320.306566532113275
537.87.75509850943280.1721590582387680.04490149056719631.38946734900905
548.38.267970597485810.4843467064840050.03202940251418780.814719274910062
558.48.417286277520010.177335316925176-0.0172862775200103-0.80117379363102
568.28.25656920572426-0.132443127222985-0.0565692057242573-0.808398084804553
577.77.65360277760855-0.5636076538553690.0463972223914529-1.12516697671528
587.27.1987688812449-0.4639319179872390.001231118755096910.260114049663913
597.37.414412706403270.158801166112815-0.1144127064032731.62508881005397
608.17.965756951448620.5184652249372820.1342430485513830.938609053421418

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 11.1 & 11.1 & 0 & 0 & 0 \tabularnewline
2 & 10.9 & 10.9023139605178 & -0.197877275849182 & -0.00231396051779417 & -0.519268296715333 \tabularnewline
3 & 10 & 10.0192310725768 & -0.860783815387878 & -0.0192310725767628 & -1.75760425368065 \tabularnewline
4 & 9.2 & 9.19482653488939 & -0.82585397281315 & 0.0051734651106082 & 0.0910733826338701 \tabularnewline
5 & 9.2 & 9.17898470917852 & -0.0476795308534081 & 0.0210152908214749 & 2.03083000302882 \tabularnewline
6 & 9.5 & 9.49661345703842 & 0.303317255381226 & 0.00338654296158203 & 0.91596087871694 \tabularnewline
7 & 9.6 & 9.60875181786168 & 0.119631753029153 & -0.00875181786168083 & -0.479345721896692 \tabularnewline
8 & 9.5 & 9.50570325546266 & -0.094319524744667 & -0.00570325546266232 & -0.558327255182972 \tabularnewline
9 & 9.1 & 9.10770352363868 & -0.386095916729550 & -0.00770352363868439 & -0.761419673784439 \tabularnewline
10 & 8.9 & 8.89407385611935 & -0.220390061266825 & 0.00592614388065247 & 0.432426001083685 \tabularnewline
11 & 9 & 8.9933828003002 & 0.0867772470261645 & 0.00661719969979558 & 0.801583808956906 \tabularnewline
12 & 10.1 & 10.0758754350773 & 1.04346215693063 & 0.0241245649227205 & 2.49656494473471 \tabularnewline
13 & 10.3 & 10.3272435728530 & 0.282609815447896 & -0.0272435728530215 & -1.98625483889208 \tabularnewline
14 & 10.2 & 10.1930624323175 & -0.118014591763028 & 0.0069375676824756 & -1.04965212704135 \tabularnewline
15 & 9.6 & 9.61906717509268 & -0.54608704578929 & -0.0190671750926754 & -1.12797678282709 \tabularnewline
16 & 9.2 & 9.20693966770739 & -0.420475494555885 & -0.00693966770739241 & 0.327427338297058 \tabularnewline
17 & 9.3 & 9.27623099198064 & 0.0385678849194214 & 0.0237690080193650 & 1.19806338758620 \tabularnewline
18 & 9.4 & 9.3951428305527 & 0.113891418959475 & 0.00485716944730701 & 0.196563696564963 \tabularnewline
19 & 9.4 & 9.4137804575969 & 0.0245898482455449 & -0.0137804575968885 & -0.233041580372739 \tabularnewline
20 & 9.2 & 9.20218397826148 & -0.196833786966918 & -0.00218397826147587 & -0.577827100212828 \tabularnewline
21 & 9 & 9.00686393673775 & -0.195414658049320 & -0.00686393673774641 & 0.00370335885872557 \tabularnewline
22 & 9 & 8.97362998957414 & -0.043370781896141 & 0.0263700104258586 & 0.396773703549157 \tabularnewline
23 & 9 & 9.04562202770204 & 0.0647823568253091 & -0.0456220277020432 & 0.282236524362768 \tabularnewline
24 & 9.8 & 9.7281184322382 & 0.643878572129655 & 0.0718815677617899 & 1.51121218099864 \tabularnewline
25 & 10 & 10.041196682057 & 0.333836916004252 & -0.0411966820570087 & -0.809447417810787 \tabularnewline
26 & 9.8 & 9.78715326990133 & -0.21757811345966 & 0.0128467300986743 & -1.44159878044086 \tabularnewline
27 & 9.3 & 9.31726595248606 & -0.451057299275273 & -0.0172659524860579 & -0.612360284747078 \tabularnewline
28 & 9 & 9.02276410745842 & -0.305990234073916 & -0.0227641074584214 & 0.37832176822637 \tabularnewline
29 & 9 & 8.97216330472896 & -0.0696017530144864 & 0.0278366952710432 & 0.616906127733932 \tabularnewline
30 & 9.1 & 9.09053237841946 & 0.104439149326523 & 0.00946762158053677 & 0.454181547716595 \tabularnewline
31 & 9.1 & 9.10646325849621 & 0.0224874125032467 & -0.00646325849621339 & -0.213861223369617 \tabularnewline
32 & 9.1 & 9.10065244854538 & -0.00371398418596567 & -0.00065244854538207 & -0.068375180528351 \tabularnewline
33 & 9.2 & 9.20621399566544 & 0.0974637128296588 & -0.00621399566544226 & 0.264033327056016 \tabularnewline
34 & 8.8 & 8.78512717529671 & -0.382662661252907 & 0.0148728247032879 & -1.25293782103530 \tabularnewline
35 & 8.3 & 8.39135189386244 & -0.392951970598074 & -0.0913518938624389 & -0.0268510404219564 \tabularnewline
36 & 8.4 & 8.31038932075387 & -0.104095018862924 & 0.089610679246131 & 0.753803795253862 \tabularnewline
37 & 8.1 & 8.11910483601663 & -0.184804100431604 & -0.0191048360166275 & -0.210734473624561 \tabularnewline
38 & 7.7 & 7.67597026841 & -0.4240435509058 & 0.0240297315899946 & -0.624685550084273 \tabularnewline
39 & 7.9 & 7.8829429751547 & 0.155900567848270 & 0.0170570248452985 & 1.51741587970476 \tabularnewline
40 & 7.9 & 7.94196471718982 & 0.0667057965112374 & -0.0419647171898167 & -0.232720465944251 \tabularnewline
41 & 8 & 7.99133725222027 & 0.0507662602148135 & 0.00866274777973165 & -0.0415924660434171 \tabularnewline
42 & 7.9 & 7.89005066671207 & -0.0890973122770287 & 0.00994933328792953 & -0.364999188609893 \tabularnewline
43 & 7.6 & 7.61133181373123 & -0.263529391031732 & -0.0113318137312261 & -0.455196598605809 \tabularnewline
44 & 7.1 & 7.13751958385879 & -0.456964842218547 & -0.0375195838587898 & -0.504789479440009 \tabularnewline
45 & 6.8 & 6.75989621165295 & -0.383980961370971 & 0.0401037883470502 & 0.190458739553836 \tabularnewline
46 & 6.5 & 6.46750805438957 & -0.299726700524345 & 0.0324919456104339 & 0.219869964197356 \tabularnewline
47 & 6.9 & 6.97862588811384 & 0.446157526532614 & -0.078625888113835 & 1.94646696161207 \tabularnewline
48 & 8.2 & 8.08007035604198 & 1.04888610852419 & 0.119929643958015 & 1.57289977650452 \tabularnewline
49 & 8.7 & 8.70836764820517 & 0.662062153029747 & -0.00836764820516827 & -1.01002821703670 \tabularnewline
50 & 8.3 & 8.38313990557518 & -0.246030202978635 & -0.0831399055751773 & -2.36991742040616 \tabularnewline
51 & 7.9 & 7.88394172020598 & -0.477839828699888 & 0.0160582797940225 & -0.605824340497071 \tabularnewline
52 & 7.5 & 7.53419178862317 & -0.360374393246054 & -0.0341917886231732 & 0.306566532113275 \tabularnewline
53 & 7.8 & 7.7550985094328 & 0.172159058238768 & 0.0449014905671963 & 1.38946734900905 \tabularnewline
54 & 8.3 & 8.26797059748581 & 0.484346706484005 & 0.0320294025141878 & 0.814719274910062 \tabularnewline
55 & 8.4 & 8.41728627752001 & 0.177335316925176 & -0.0172862775200103 & -0.80117379363102 \tabularnewline
56 & 8.2 & 8.25656920572426 & -0.132443127222985 & -0.0565692057242573 & -0.808398084804553 \tabularnewline
57 & 7.7 & 7.65360277760855 & -0.563607653855369 & 0.0463972223914529 & -1.12516697671528 \tabularnewline
58 & 7.2 & 7.1987688812449 & -0.463931917987239 & 0.00123111875509691 & 0.260114049663913 \tabularnewline
59 & 7.3 & 7.41441270640327 & 0.158801166112815 & -0.114412706403273 & 1.62508881005397 \tabularnewline
60 & 8.1 & 7.96575695144862 & 0.518465224937282 & 0.134243048551383 & 0.938609053421418 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62732&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]11.1[/C][C]11.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]10.9[/C][C]10.9023139605178[/C][C]-0.197877275849182[/C][C]-0.00231396051779417[/C][C]-0.519268296715333[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]10.0192310725768[/C][C]-0.860783815387878[/C][C]-0.0192310725767628[/C][C]-1.75760425368065[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]9.19482653488939[/C][C]-0.82585397281315[/C][C]0.0051734651106082[/C][C]0.0910733826338701[/C][/ROW]
[ROW][C]5[/C][C]9.2[/C][C]9.17898470917852[/C][C]-0.0476795308534081[/C][C]0.0210152908214749[/C][C]2.03083000302882[/C][/ROW]
[ROW][C]6[/C][C]9.5[/C][C]9.49661345703842[/C][C]0.303317255381226[/C][C]0.00338654296158203[/C][C]0.91596087871694[/C][/ROW]
[ROW][C]7[/C][C]9.6[/C][C]9.60875181786168[/C][C]0.119631753029153[/C][C]-0.00875181786168083[/C][C]-0.479345721896692[/C][/ROW]
[ROW][C]8[/C][C]9.5[/C][C]9.50570325546266[/C][C]-0.094319524744667[/C][C]-0.00570325546266232[/C][C]-0.558327255182972[/C][/ROW]
[ROW][C]9[/C][C]9.1[/C][C]9.10770352363868[/C][C]-0.386095916729550[/C][C]-0.00770352363868439[/C][C]-0.761419673784439[/C][/ROW]
[ROW][C]10[/C][C]8.9[/C][C]8.89407385611935[/C][C]-0.220390061266825[/C][C]0.00592614388065247[/C][C]0.432426001083685[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]8.9933828003002[/C][C]0.0867772470261645[/C][C]0.00661719969979558[/C][C]0.801583808956906[/C][/ROW]
[ROW][C]12[/C][C]10.1[/C][C]10.0758754350773[/C][C]1.04346215693063[/C][C]0.0241245649227205[/C][C]2.49656494473471[/C][/ROW]
[ROW][C]13[/C][C]10.3[/C][C]10.3272435728530[/C][C]0.282609815447896[/C][C]-0.0272435728530215[/C][C]-1.98625483889208[/C][/ROW]
[ROW][C]14[/C][C]10.2[/C][C]10.1930624323175[/C][C]-0.118014591763028[/C][C]0.0069375676824756[/C][C]-1.04965212704135[/C][/ROW]
[ROW][C]15[/C][C]9.6[/C][C]9.61906717509268[/C][C]-0.54608704578929[/C][C]-0.0190671750926754[/C][C]-1.12797678282709[/C][/ROW]
[ROW][C]16[/C][C]9.2[/C][C]9.20693966770739[/C][C]-0.420475494555885[/C][C]-0.00693966770739241[/C][C]0.327427338297058[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]9.27623099198064[/C][C]0.0385678849194214[/C][C]0.0237690080193650[/C][C]1.19806338758620[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.3951428305527[/C][C]0.113891418959475[/C][C]0.00485716944730701[/C][C]0.196563696564963[/C][/ROW]
[ROW][C]19[/C][C]9.4[/C][C]9.4137804575969[/C][C]0.0245898482455449[/C][C]-0.0137804575968885[/C][C]-0.233041580372739[/C][/ROW]
[ROW][C]20[/C][C]9.2[/C][C]9.20218397826148[/C][C]-0.196833786966918[/C][C]-0.00218397826147587[/C][C]-0.577827100212828[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]9.00686393673775[/C][C]-0.195414658049320[/C][C]-0.00686393673774641[/C][C]0.00370335885872557[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]8.97362998957414[/C][C]-0.043370781896141[/C][C]0.0263700104258586[/C][C]0.396773703549157[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.04562202770204[/C][C]0.0647823568253091[/C][C]-0.0456220277020432[/C][C]0.282236524362768[/C][/ROW]
[ROW][C]24[/C][C]9.8[/C][C]9.7281184322382[/C][C]0.643878572129655[/C][C]0.0718815677617899[/C][C]1.51121218099864[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]10.041196682057[/C][C]0.333836916004252[/C][C]-0.0411966820570087[/C][C]-0.809447417810787[/C][/ROW]
[ROW][C]26[/C][C]9.8[/C][C]9.78715326990133[/C][C]-0.21757811345966[/C][C]0.0128467300986743[/C][C]-1.44159878044086[/C][/ROW]
[ROW][C]27[/C][C]9.3[/C][C]9.31726595248606[/C][C]-0.451057299275273[/C][C]-0.0172659524860579[/C][C]-0.612360284747078[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.02276410745842[/C][C]-0.305990234073916[/C][C]-0.0227641074584214[/C][C]0.37832176822637[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.97216330472896[/C][C]-0.0696017530144864[/C][C]0.0278366952710432[/C][C]0.616906127733932[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]9.09053237841946[/C][C]0.104439149326523[/C][C]0.00946762158053677[/C][C]0.454181547716595[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]9.10646325849621[/C][C]0.0224874125032467[/C][C]-0.00646325849621339[/C][C]-0.213861223369617[/C][/ROW]
[ROW][C]32[/C][C]9.1[/C][C]9.10065244854538[/C][C]-0.00371398418596567[/C][C]-0.00065244854538207[/C][C]-0.068375180528351[/C][/ROW]
[ROW][C]33[/C][C]9.2[/C][C]9.20621399566544[/C][C]0.0974637128296588[/C][C]-0.00621399566544226[/C][C]0.264033327056016[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]8.78512717529671[/C][C]-0.382662661252907[/C][C]0.0148728247032879[/C][C]-1.25293782103530[/C][/ROW]
[ROW][C]35[/C][C]8.3[/C][C]8.39135189386244[/C][C]-0.392951970598074[/C][C]-0.0913518938624389[/C][C]-0.0268510404219564[/C][/ROW]
[ROW][C]36[/C][C]8.4[/C][C]8.31038932075387[/C][C]-0.104095018862924[/C][C]0.089610679246131[/C][C]0.753803795253862[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]8.11910483601663[/C][C]-0.184804100431604[/C][C]-0.0191048360166275[/C][C]-0.210734473624561[/C][/ROW]
[ROW][C]38[/C][C]7.7[/C][C]7.67597026841[/C][C]-0.4240435509058[/C][C]0.0240297315899946[/C][C]-0.624685550084273[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.8829429751547[/C][C]0.155900567848270[/C][C]0.0170570248452985[/C][C]1.51741587970476[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]7.94196471718982[/C][C]0.0667057965112374[/C][C]-0.0419647171898167[/C][C]-0.232720465944251[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]7.99133725222027[/C][C]0.0507662602148135[/C][C]0.00866274777973165[/C][C]-0.0415924660434171[/C][/ROW]
[ROW][C]42[/C][C]7.9[/C][C]7.89005066671207[/C][C]-0.0890973122770287[/C][C]0.00994933328792953[/C][C]-0.364999188609893[/C][/ROW]
[ROW][C]43[/C][C]7.6[/C][C]7.61133181373123[/C][C]-0.263529391031732[/C][C]-0.0113318137312261[/C][C]-0.455196598605809[/C][/ROW]
[ROW][C]44[/C][C]7.1[/C][C]7.13751958385879[/C][C]-0.456964842218547[/C][C]-0.0375195838587898[/C][C]-0.504789479440009[/C][/ROW]
[ROW][C]45[/C][C]6.8[/C][C]6.75989621165295[/C][C]-0.383980961370971[/C][C]0.0401037883470502[/C][C]0.190458739553836[/C][/ROW]
[ROW][C]46[/C][C]6.5[/C][C]6.46750805438957[/C][C]-0.299726700524345[/C][C]0.0324919456104339[/C][C]0.219869964197356[/C][/ROW]
[ROW][C]47[/C][C]6.9[/C][C]6.97862588811384[/C][C]0.446157526532614[/C][C]-0.078625888113835[/C][C]1.94646696161207[/C][/ROW]
[ROW][C]48[/C][C]8.2[/C][C]8.08007035604198[/C][C]1.04888610852419[/C][C]0.119929643958015[/C][C]1.57289977650452[/C][/ROW]
[ROW][C]49[/C][C]8.7[/C][C]8.70836764820517[/C][C]0.662062153029747[/C][C]-0.00836764820516827[/C][C]-1.01002821703670[/C][/ROW]
[ROW][C]50[/C][C]8.3[/C][C]8.38313990557518[/C][C]-0.246030202978635[/C][C]-0.0831399055751773[/C][C]-2.36991742040616[/C][/ROW]
[ROW][C]51[/C][C]7.9[/C][C]7.88394172020598[/C][C]-0.477839828699888[/C][C]0.0160582797940225[/C][C]-0.605824340497071[/C][/ROW]
[ROW][C]52[/C][C]7.5[/C][C]7.53419178862317[/C][C]-0.360374393246054[/C][C]-0.0341917886231732[/C][C]0.306566532113275[/C][/ROW]
[ROW][C]53[/C][C]7.8[/C][C]7.7550985094328[/C][C]0.172159058238768[/C][C]0.0449014905671963[/C][C]1.38946734900905[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]8.26797059748581[/C][C]0.484346706484005[/C][C]0.0320294025141878[/C][C]0.814719274910062[/C][/ROW]
[ROW][C]55[/C][C]8.4[/C][C]8.41728627752001[/C][C]0.177335316925176[/C][C]-0.0172862775200103[/C][C]-0.80117379363102[/C][/ROW]
[ROW][C]56[/C][C]8.2[/C][C]8.25656920572426[/C][C]-0.132443127222985[/C][C]-0.0565692057242573[/C][C]-0.808398084804553[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.65360277760855[/C][C]-0.563607653855369[/C][C]0.0463972223914529[/C][C]-1.12516697671528[/C][/ROW]
[ROW][C]58[/C][C]7.2[/C][C]7.1987688812449[/C][C]-0.463931917987239[/C][C]0.00123111875509691[/C][C]0.260114049663913[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]7.41441270640327[/C][C]0.158801166112815[/C][C]-0.114412706403273[/C][C]1.62508881005397[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]7.96575695144862[/C][C]0.518465224937282[/C][C]0.134243048551383[/C][C]0.938609053421418[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62732&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62732&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
111.111.1000
210.910.9023139605178-0.197877275849182-0.00231396051779417-0.519268296715333
31010.0192310725768-0.860783815387878-0.0192310725767628-1.75760425368065
49.29.19482653488939-0.825853972813150.00517346511060820.0910733826338701
59.29.17898470917852-0.04767953085340810.02101529082147492.03083000302882
69.59.496613457038420.3033172553812260.003386542961582030.91596087871694
79.69.608751817861680.119631753029153-0.00875181786168083-0.479345721896692
89.59.50570325546266-0.094319524744667-0.00570325546266232-0.558327255182972
99.19.10770352363868-0.386095916729550-0.00770352363868439-0.761419673784439
108.98.89407385611935-0.2203900612668250.005926143880652470.432426001083685
1198.99338280030020.08677724702616450.006617199699795580.801583808956906
1210.110.07587543507731.043462156930630.02412456492272052.49656494473471
1310.310.32724357285300.282609815447896-0.0272435728530215-1.98625483889208
1410.210.1930624323175-0.1180145917630280.0069375676824756-1.04965212704135
159.69.61906717509268-0.54608704578929-0.0190671750926754-1.12797678282709
169.29.20693966770739-0.420475494555885-0.006939667707392410.327427338297058
179.39.276230991980640.03856788491942140.02376900801936501.19806338758620
189.49.39514283055270.1138914189594750.004857169447307010.196563696564963
199.49.41378045759690.0245898482455449-0.0137804575968885-0.233041580372739
209.29.20218397826148-0.196833786966918-0.00218397826147587-0.577827100212828
2199.00686393673775-0.195414658049320-0.006863936737746410.00370335885872557
2298.97362998957414-0.0433707818961410.02637001042585860.396773703549157
2399.045622027702040.0647823568253091-0.04562202770204320.282236524362768
249.89.72811843223820.6438785721296550.07188156776178991.51121218099864
251010.0411966820570.333836916004252-0.0411966820570087-0.809447417810787
269.89.78715326990133-0.217578113459660.0128467300986743-1.44159878044086
279.39.31726595248606-0.451057299275273-0.0172659524860579-0.612360284747078
2899.02276410745842-0.305990234073916-0.02276410745842140.37832176822637
2998.97216330472896-0.06960175301448640.02783669527104320.616906127733932
309.19.090532378419460.1044391493265230.009467621580536770.454181547716595
319.19.106463258496210.0224874125032467-0.00646325849621339-0.213861223369617
329.19.10065244854538-0.00371398418596567-0.00065244854538207-0.068375180528351
339.29.206213995665440.0974637128296588-0.006213995665442260.264033327056016
348.88.78512717529671-0.3826626612529070.0148728247032879-1.25293782103530
358.38.39135189386244-0.392951970598074-0.0913518938624389-0.0268510404219564
368.48.31038932075387-0.1040950188629240.0896106792461310.753803795253862
378.18.11910483601663-0.184804100431604-0.0191048360166275-0.210734473624561
387.77.67597026841-0.42404355090580.0240297315899946-0.624685550084273
397.97.88294297515470.1559005678482700.01705702484529851.51741587970476
407.97.941964717189820.0667057965112374-0.0419647171898167-0.232720465944251
4187.991337252220270.05076626021481350.00866274777973165-0.0415924660434171
427.97.89005066671207-0.08909731227702870.00994933328792953-0.364999188609893
437.67.61133181373123-0.263529391031732-0.0113318137312261-0.455196598605809
447.17.13751958385879-0.456964842218547-0.0375195838587898-0.504789479440009
456.86.75989621165295-0.3839809613709710.04010378834705020.190458739553836
466.56.46750805438957-0.2997267005243450.03249194561043390.219869964197356
476.96.978625888113840.446157526532614-0.0786258881138351.94646696161207
488.28.080070356041981.048886108524190.1199296439580151.57289977650452
498.78.708367648205170.662062153029747-0.00836764820516827-1.01002821703670
508.38.38313990557518-0.246030202978635-0.0831399055751773-2.36991742040616
517.97.88394172020598-0.4778398286998880.0160582797940225-0.605824340497071
527.57.53419178862317-0.360374393246054-0.03419178862317320.306566532113275
537.87.75509850943280.1721590582387680.04490149056719631.38946734900905
548.38.267970597485810.4843467064840050.03202940251418780.814719274910062
558.48.417286277520010.177335316925176-0.0172862775200103-0.80117379363102
568.28.25656920572426-0.132443127222985-0.0565692057242573-0.808398084804553
577.77.65360277760855-0.5636076538553690.0463972223914529-1.12516697671528
587.27.1987688812449-0.4639319179872390.001231118755096910.260114049663913
597.37.414412706403270.158801166112815-0.1144127064032731.62508881005397
608.17.965756951448620.5184652249372820.1342430485513830.938609053421418



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')