Free Statistics

of Irreproducible Research!

Author's title

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 02:37:55 -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/t1259919630zljwdzhpi6pe0mt.htm/, Retrieved Sun, 28 Apr 2024 16:59:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63212, Retrieved Sun, 28 Apr 2024 16:59:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws9.2st
Estimated Impact102
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]
- R  D      [Structural Time Series Models] [] [2009-12-04 09:37:55] [9ea4b07b6662a0f40f92decdf1e3b5d5] [Current]
Feedback Forum

Post a new message
Dataseries X:
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34
2197.82
2014.45
1862.83
1905.41
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27




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=63212&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=63212&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63212&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
12756.762756.76000
22849.272844.556143255116.572769511994324.713856744892530.334856114850539
32921.442916.6710789275613.75894559777004.76892107243610.397644653822629
42981.852977.0476895248520.63388371132084.802310475148860.276723980078269
53080.583075.731520114134.222955322484.848479885897330.455956242881725
63106.223101.3756211009632.5768688395724.84437889903677-0.0495684161884733
73119.313114.4730397261828.61644058056934.83696027381733-0.111740821245485
83061.263056.44909034310.37844032343884.810909657-0.494707706596145
93097.313092.4930321658615.89536591896404.816967834144680.14612296011944
103161.693156.8640806475726.44789097363994.825919352433790.275492913068759
113257.163252.3241322573441.58594989769094.835867742663470.3917681298115
123277.013272.1765748309336.79661922083814.83342516906696-0.123292234285078
133295.323336.9621758588242.7274960267156-41.64217585881830.185097043221914
143363.993360.632958087438.66116707367433.35704191260091-0.0909000087708877
153494.173490.8917890527158.97367656412143.278210947288940.518584323520462
163667.033663.8281162264484.24904896926483.201883773558240.645333722884836
173813.063809.8903330491197.95989198067883.16966695089370.350081949503154
183917.963914.7931489521199.50005204133033.166851047886750.0393262718754584
193895.513892.3046491486572.43707319847093.20535085134800-0.69103396764026
203801.063797.813654674435.40204648274253.24634532560158-0.945672202688552
213570.123566.82274866132-23.70339835856313.2972513386771-1.50923883675795
223701.613698.3358283372310.73632588840453.274171662770040.879410554312389
233862.273859.0131765212644.00655796916423.256823478736490.849549644226362
243970.13966.8489228637558.16988706002423.251077136249120.361658634020461
254138.524149.9673846995885.4874292632685-11.44738469958120.76665883299017
264199.754199.2324408707677.65711086422640.51755912923884-0.185278196338789
274290.894290.3805632852580.65305331702650.5094367147527250.0763872412502124
284443.914443.4344833125696.72496030198990.4755166874429930.410024742494637
294502.644502.1506281882688.28932292957740.489371811736392-0.215285665396504
304356.984356.4242541751136.35770993259290.555745824886518-1.32563082550640
314591.274590.7579458739180.289317102630.5120541260871651.12156519386773
324696.964696.4523084421685.92667775097780.5076915578437990.143932062087999
334621.44620.8707283505850.08824949952610.529271649422923-0.91506395027393
344562.844562.299431374825.97676295811730.54056862519734-0.615657321927685
354202.524201.94817883528-59.74984926552330.571821164718137-2.18896256318707
364296.494295.92785537591-25.63680247031550.5621446240902860.871059436870004
374435.234422.085979865767.727539426020113.14402013424180.907704831720602
384105.184109.78394232916-61.929438299715-4.60394232915507-1.68562661103423
394116.684121.31681963735-45.6185323795723-4.636819637353890.416033667024512
403844.493849.04789353429-95.9275693601094-4.55789353428539-1.28377041015625
413720.983725.53041818677-102.050680568908-4.55041818677158-0.156289252734622
423674.43678.96211488358-89.7383050460216-4.562114883576170.314317681527007
433857.623862.22689729918-29.1571448128794-4.606897299180871.54670297320979
443801.063805.66339925824-35.2386997959309-4.60339925823721-0.155277891589227
453504.373508.94743107151-93.2611460507691-4.57743107151422-1.48151671004765
463032.63037.14817938195-177.259938429254-4.54817938194637-2.14483044050196
473047.033051.58970594275-134.720540862608-4.559705942751141.08621788741837
482962.342966.90204673031-123.617953506867-4.562046730310380.283500128762141
492197.822207.76348024009-263.650756326525-9.94348024009174-3.75065326528572
502014.452012.96501405620-248.6036855592121.484985943802900.368587950599816
511862.831861.37949323273-227.0649873474001.450506767271450.549502112016056
521905.411904.03409089213-167.1989639981721.375909107865661.52785457454302
531810.991809.62975592235-151.0437186113701.360244077646410.412388934231326
541670.071668.71145132100-148.7967391786561.358548678997110.0573651508928167
551864.441863.12616616894-72.63536683870491.313833831056211.94454261168317
562052.022050.73254766379-14.88633896865011.287452336212471.47451012825262
572029.62028.31195337778-16.55822633454661.28804662222115-0.0426896344201635
582070.832069.54550031604-3.733923405341911.284499683958410.327458793443655
592293.412292.1363084694646.48896880572771.273691530535391.28241659775575
602443.272442.0001496588769.42862464558431.269850341130020.585756406119439

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 2756.76 & 2756.76 & 0 & 0 & 0 \tabularnewline
2 & 2849.27 & 2844.55614325511 & 6.57276951199432 & 4.71385674489253 & 0.334856114850539 \tabularnewline
3 & 2921.44 & 2916.67107892756 & 13.7589455977700 & 4.7689210724361 & 0.397644653822629 \tabularnewline
4 & 2981.85 & 2977.04768952485 & 20.6338837113208 & 4.80231047514886 & 0.276723980078269 \tabularnewline
5 & 3080.58 & 3075.7315201141 & 34.22295532248 & 4.84847988589733 & 0.455956242881725 \tabularnewline
6 & 3106.22 & 3101.37562110096 & 32.576868839572 & 4.84437889903677 & -0.0495684161884733 \tabularnewline
7 & 3119.31 & 3114.47303972618 & 28.6164405805693 & 4.83696027381733 & -0.111740821245485 \tabularnewline
8 & 3061.26 & 3056.449090343 & 10.3784403234388 & 4.810909657 & -0.494707706596145 \tabularnewline
9 & 3097.31 & 3092.49303216586 & 15.8953659189640 & 4.81696783414468 & 0.14612296011944 \tabularnewline
10 & 3161.69 & 3156.86408064757 & 26.4478909736399 & 4.82591935243379 & 0.275492913068759 \tabularnewline
11 & 3257.16 & 3252.32413225734 & 41.5859498976909 & 4.83586774266347 & 0.3917681298115 \tabularnewline
12 & 3277.01 & 3272.17657483093 & 36.7966192208381 & 4.83342516906696 & -0.123292234285078 \tabularnewline
13 & 3295.32 & 3336.96217585882 & 42.7274960267156 & -41.6421758588183 & 0.185097043221914 \tabularnewline
14 & 3363.99 & 3360.6329580874 & 38.6611670736743 & 3.35704191260091 & -0.0909000087708877 \tabularnewline
15 & 3494.17 & 3490.89178905271 & 58.9736765641214 & 3.27821094728894 & 0.518584323520462 \tabularnewline
16 & 3667.03 & 3663.82811622644 & 84.2490489692648 & 3.20188377355824 & 0.645333722884836 \tabularnewline
17 & 3813.06 & 3809.89033304911 & 97.9598919806788 & 3.1696669508937 & 0.350081949503154 \tabularnewline
18 & 3917.96 & 3914.79314895211 & 99.5000520413303 & 3.16685104788675 & 0.0393262718754584 \tabularnewline
19 & 3895.51 & 3892.30464914865 & 72.4370731984709 & 3.20535085134800 & -0.69103396764026 \tabularnewline
20 & 3801.06 & 3797.8136546744 & 35.4020464827425 & 3.24634532560158 & -0.945672202688552 \tabularnewline
21 & 3570.12 & 3566.82274866132 & -23.7033983585631 & 3.2972513386771 & -1.50923883675795 \tabularnewline
22 & 3701.61 & 3698.33582833723 & 10.7363258884045 & 3.27417166277004 & 0.879410554312389 \tabularnewline
23 & 3862.27 & 3859.01317652126 & 44.0065579691642 & 3.25682347873649 & 0.849549644226362 \tabularnewline
24 & 3970.1 & 3966.84892286375 & 58.1698870600242 & 3.25107713624912 & 0.361658634020461 \tabularnewline
25 & 4138.52 & 4149.96738469958 & 85.4874292632685 & -11.4473846995812 & 0.76665883299017 \tabularnewline
26 & 4199.75 & 4199.23244087076 & 77.6571108642264 & 0.51755912923884 & -0.185278196338789 \tabularnewline
27 & 4290.89 & 4290.38056328525 & 80.6530533170265 & 0.509436714752725 & 0.0763872412502124 \tabularnewline
28 & 4443.91 & 4443.43448331256 & 96.7249603019899 & 0.475516687442993 & 0.410024742494637 \tabularnewline
29 & 4502.64 & 4502.15062818826 & 88.2893229295774 & 0.489371811736392 & -0.215285665396504 \tabularnewline
30 & 4356.98 & 4356.42425417511 & 36.3577099325929 & 0.555745824886518 & -1.32563082550640 \tabularnewline
31 & 4591.27 & 4590.75794587391 & 80.28931710263 & 0.512054126087165 & 1.12156519386773 \tabularnewline
32 & 4696.96 & 4696.45230844216 & 85.9266777509778 & 0.507691557843799 & 0.143932062087999 \tabularnewline
33 & 4621.4 & 4620.87072835058 & 50.0882494995261 & 0.529271649422923 & -0.91506395027393 \tabularnewline
34 & 4562.84 & 4562.2994313748 & 25.9767629581173 & 0.54056862519734 & -0.615657321927685 \tabularnewline
35 & 4202.52 & 4201.94817883528 & -59.7498492655233 & 0.571821164718137 & -2.18896256318707 \tabularnewline
36 & 4296.49 & 4295.92785537591 & -25.6368024703155 & 0.562144624090286 & 0.871059436870004 \tabularnewline
37 & 4435.23 & 4422.08597986576 & 7.7275394260201 & 13.1440201342418 & 0.907704831720602 \tabularnewline
38 & 4105.18 & 4109.78394232916 & -61.929438299715 & -4.60394232915507 & -1.68562661103423 \tabularnewline
39 & 4116.68 & 4121.31681963735 & -45.6185323795723 & -4.63681963735389 & 0.416033667024512 \tabularnewline
40 & 3844.49 & 3849.04789353429 & -95.9275693601094 & -4.55789353428539 & -1.28377041015625 \tabularnewline
41 & 3720.98 & 3725.53041818677 & -102.050680568908 & -4.55041818677158 & -0.156289252734622 \tabularnewline
42 & 3674.4 & 3678.96211488358 & -89.7383050460216 & -4.56211488357617 & 0.314317681527007 \tabularnewline
43 & 3857.62 & 3862.22689729918 & -29.1571448128794 & -4.60689729918087 & 1.54670297320979 \tabularnewline
44 & 3801.06 & 3805.66339925824 & -35.2386997959309 & -4.60339925823721 & -0.155277891589227 \tabularnewline
45 & 3504.37 & 3508.94743107151 & -93.2611460507691 & -4.57743107151422 & -1.48151671004765 \tabularnewline
46 & 3032.6 & 3037.14817938195 & -177.259938429254 & -4.54817938194637 & -2.14483044050196 \tabularnewline
47 & 3047.03 & 3051.58970594275 & -134.720540862608 & -4.55970594275114 & 1.08621788741837 \tabularnewline
48 & 2962.34 & 2966.90204673031 & -123.617953506867 & -4.56204673031038 & 0.283500128762141 \tabularnewline
49 & 2197.82 & 2207.76348024009 & -263.650756326525 & -9.94348024009174 & -3.75065326528572 \tabularnewline
50 & 2014.45 & 2012.96501405620 & -248.603685559212 & 1.48498594380290 & 0.368587950599816 \tabularnewline
51 & 1862.83 & 1861.37949323273 & -227.064987347400 & 1.45050676727145 & 0.549502112016056 \tabularnewline
52 & 1905.41 & 1904.03409089213 & -167.198963998172 & 1.37590910786566 & 1.52785457454302 \tabularnewline
53 & 1810.99 & 1809.62975592235 & -151.043718611370 & 1.36024407764641 & 0.412388934231326 \tabularnewline
54 & 1670.07 & 1668.71145132100 & -148.796739178656 & 1.35854867899711 & 0.0573651508928167 \tabularnewline
55 & 1864.44 & 1863.12616616894 & -72.6353668387049 & 1.31383383105621 & 1.94454261168317 \tabularnewline
56 & 2052.02 & 2050.73254766379 & -14.8863389686501 & 1.28745233621247 & 1.47451012825262 \tabularnewline
57 & 2029.6 & 2028.31195337778 & -16.5582263345466 & 1.28804662222115 & -0.0426896344201635 \tabularnewline
58 & 2070.83 & 2069.54550031604 & -3.73392340534191 & 1.28449968395841 & 0.327458793443655 \tabularnewline
59 & 2293.41 & 2292.13630846946 & 46.4889688057277 & 1.27369153053539 & 1.28241659775575 \tabularnewline
60 & 2443.27 & 2442.00014965887 & 69.4286246455843 & 1.26985034113002 & 0.585756406119439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63212&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]2756.76[/C][C]2756.76[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2849.27[/C][C]2844.55614325511[/C][C]6.57276951199432[/C][C]4.71385674489253[/C][C]0.334856114850539[/C][/ROW]
[ROW][C]3[/C][C]2921.44[/C][C]2916.67107892756[/C][C]13.7589455977700[/C][C]4.7689210724361[/C][C]0.397644653822629[/C][/ROW]
[ROW][C]4[/C][C]2981.85[/C][C]2977.04768952485[/C][C]20.6338837113208[/C][C]4.80231047514886[/C][C]0.276723980078269[/C][/ROW]
[ROW][C]5[/C][C]3080.58[/C][C]3075.7315201141[/C][C]34.22295532248[/C][C]4.84847988589733[/C][C]0.455956242881725[/C][/ROW]
[ROW][C]6[/C][C]3106.22[/C][C]3101.37562110096[/C][C]32.576868839572[/C][C]4.84437889903677[/C][C]-0.0495684161884733[/C][/ROW]
[ROW][C]7[/C][C]3119.31[/C][C]3114.47303972618[/C][C]28.6164405805693[/C][C]4.83696027381733[/C][C]-0.111740821245485[/C][/ROW]
[ROW][C]8[/C][C]3061.26[/C][C]3056.449090343[/C][C]10.3784403234388[/C][C]4.810909657[/C][C]-0.494707706596145[/C][/ROW]
[ROW][C]9[/C][C]3097.31[/C][C]3092.49303216586[/C][C]15.8953659189640[/C][C]4.81696783414468[/C][C]0.14612296011944[/C][/ROW]
[ROW][C]10[/C][C]3161.69[/C][C]3156.86408064757[/C][C]26.4478909736399[/C][C]4.82591935243379[/C][C]0.275492913068759[/C][/ROW]
[ROW][C]11[/C][C]3257.16[/C][C]3252.32413225734[/C][C]41.5859498976909[/C][C]4.83586774266347[/C][C]0.3917681298115[/C][/ROW]
[ROW][C]12[/C][C]3277.01[/C][C]3272.17657483093[/C][C]36.7966192208381[/C][C]4.83342516906696[/C][C]-0.123292234285078[/C][/ROW]
[ROW][C]13[/C][C]3295.32[/C][C]3336.96217585882[/C][C]42.7274960267156[/C][C]-41.6421758588183[/C][C]0.185097043221914[/C][/ROW]
[ROW][C]14[/C][C]3363.99[/C][C]3360.6329580874[/C][C]38.6611670736743[/C][C]3.35704191260091[/C][C]-0.0909000087708877[/C][/ROW]
[ROW][C]15[/C][C]3494.17[/C][C]3490.89178905271[/C][C]58.9736765641214[/C][C]3.27821094728894[/C][C]0.518584323520462[/C][/ROW]
[ROW][C]16[/C][C]3667.03[/C][C]3663.82811622644[/C][C]84.2490489692648[/C][C]3.20188377355824[/C][C]0.645333722884836[/C][/ROW]
[ROW][C]17[/C][C]3813.06[/C][C]3809.89033304911[/C][C]97.9598919806788[/C][C]3.1696669508937[/C][C]0.350081949503154[/C][/ROW]
[ROW][C]18[/C][C]3917.96[/C][C]3914.79314895211[/C][C]99.5000520413303[/C][C]3.16685104788675[/C][C]0.0393262718754584[/C][/ROW]
[ROW][C]19[/C][C]3895.51[/C][C]3892.30464914865[/C][C]72.4370731984709[/C][C]3.20535085134800[/C][C]-0.69103396764026[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3797.8136546744[/C][C]35.4020464827425[/C][C]3.24634532560158[/C][C]-0.945672202688552[/C][/ROW]
[ROW][C]21[/C][C]3570.12[/C][C]3566.82274866132[/C][C]-23.7033983585631[/C][C]3.2972513386771[/C][C]-1.50923883675795[/C][/ROW]
[ROW][C]22[/C][C]3701.61[/C][C]3698.33582833723[/C][C]10.7363258884045[/C][C]3.27417166277004[/C][C]0.879410554312389[/C][/ROW]
[ROW][C]23[/C][C]3862.27[/C][C]3859.01317652126[/C][C]44.0065579691642[/C][C]3.25682347873649[/C][C]0.849549644226362[/C][/ROW]
[ROW][C]24[/C][C]3970.1[/C][C]3966.84892286375[/C][C]58.1698870600242[/C][C]3.25107713624912[/C][C]0.361658634020461[/C][/ROW]
[ROW][C]25[/C][C]4138.52[/C][C]4149.96738469958[/C][C]85.4874292632685[/C][C]-11.4473846995812[/C][C]0.76665883299017[/C][/ROW]
[ROW][C]26[/C][C]4199.75[/C][C]4199.23244087076[/C][C]77.6571108642264[/C][C]0.51755912923884[/C][C]-0.185278196338789[/C][/ROW]
[ROW][C]27[/C][C]4290.89[/C][C]4290.38056328525[/C][C]80.6530533170265[/C][C]0.509436714752725[/C][C]0.0763872412502124[/C][/ROW]
[ROW][C]28[/C][C]4443.91[/C][C]4443.43448331256[/C][C]96.7249603019899[/C][C]0.475516687442993[/C][C]0.410024742494637[/C][/ROW]
[ROW][C]29[/C][C]4502.64[/C][C]4502.15062818826[/C][C]88.2893229295774[/C][C]0.489371811736392[/C][C]-0.215285665396504[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]4356.42425417511[/C][C]36.3577099325929[/C][C]0.555745824886518[/C][C]-1.32563082550640[/C][/ROW]
[ROW][C]31[/C][C]4591.27[/C][C]4590.75794587391[/C][C]80.28931710263[/C][C]0.512054126087165[/C][C]1.12156519386773[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]4696.45230844216[/C][C]85.9266777509778[/C][C]0.507691557843799[/C][C]0.143932062087999[/C][/ROW]
[ROW][C]33[/C][C]4621.4[/C][C]4620.87072835058[/C][C]50.0882494995261[/C][C]0.529271649422923[/C][C]-0.91506395027393[/C][/ROW]
[ROW][C]34[/C][C]4562.84[/C][C]4562.2994313748[/C][C]25.9767629581173[/C][C]0.54056862519734[/C][C]-0.615657321927685[/C][/ROW]
[ROW][C]35[/C][C]4202.52[/C][C]4201.94817883528[/C][C]-59.7498492655233[/C][C]0.571821164718137[/C][C]-2.18896256318707[/C][/ROW]
[ROW][C]36[/C][C]4296.49[/C][C]4295.92785537591[/C][C]-25.6368024703155[/C][C]0.562144624090286[/C][C]0.871059436870004[/C][/ROW]
[ROW][C]37[/C][C]4435.23[/C][C]4422.08597986576[/C][C]7.7275394260201[/C][C]13.1440201342418[/C][C]0.907704831720602[/C][/ROW]
[ROW][C]38[/C][C]4105.18[/C][C]4109.78394232916[/C][C]-61.929438299715[/C][C]-4.60394232915507[/C][C]-1.68562661103423[/C][/ROW]
[ROW][C]39[/C][C]4116.68[/C][C]4121.31681963735[/C][C]-45.6185323795723[/C][C]-4.63681963735389[/C][C]0.416033667024512[/C][/ROW]
[ROW][C]40[/C][C]3844.49[/C][C]3849.04789353429[/C][C]-95.9275693601094[/C][C]-4.55789353428539[/C][C]-1.28377041015625[/C][/ROW]
[ROW][C]41[/C][C]3720.98[/C][C]3725.53041818677[/C][C]-102.050680568908[/C][C]-4.55041818677158[/C][C]-0.156289252734622[/C][/ROW]
[ROW][C]42[/C][C]3674.4[/C][C]3678.96211488358[/C][C]-89.7383050460216[/C][C]-4.56211488357617[/C][C]0.314317681527007[/C][/ROW]
[ROW][C]43[/C][C]3857.62[/C][C]3862.22689729918[/C][C]-29.1571448128794[/C][C]-4.60689729918087[/C][C]1.54670297320979[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3805.66339925824[/C][C]-35.2386997959309[/C][C]-4.60339925823721[/C][C]-0.155277891589227[/C][/ROW]
[ROW][C]45[/C][C]3504.37[/C][C]3508.94743107151[/C][C]-93.2611460507691[/C][C]-4.57743107151422[/C][C]-1.48151671004765[/C][/ROW]
[ROW][C]46[/C][C]3032.6[/C][C]3037.14817938195[/C][C]-177.259938429254[/C][C]-4.54817938194637[/C][C]-2.14483044050196[/C][/ROW]
[ROW][C]47[/C][C]3047.03[/C][C]3051.58970594275[/C][C]-134.720540862608[/C][C]-4.55970594275114[/C][C]1.08621788741837[/C][/ROW]
[ROW][C]48[/C][C]2962.34[/C][C]2966.90204673031[/C][C]-123.617953506867[/C][C]-4.56204673031038[/C][C]0.283500128762141[/C][/ROW]
[ROW][C]49[/C][C]2197.82[/C][C]2207.76348024009[/C][C]-263.650756326525[/C][C]-9.94348024009174[/C][C]-3.75065326528572[/C][/ROW]
[ROW][C]50[/C][C]2014.45[/C][C]2012.96501405620[/C][C]-248.603685559212[/C][C]1.48498594380290[/C][C]0.368587950599816[/C][/ROW]
[ROW][C]51[/C][C]1862.83[/C][C]1861.37949323273[/C][C]-227.064987347400[/C][C]1.45050676727145[/C][C]0.549502112016056[/C][/ROW]
[ROW][C]52[/C][C]1905.41[/C][C]1904.03409089213[/C][C]-167.198963998172[/C][C]1.37590910786566[/C][C]1.52785457454302[/C][/ROW]
[ROW][C]53[/C][C]1810.99[/C][C]1809.62975592235[/C][C]-151.043718611370[/C][C]1.36024407764641[/C][C]0.412388934231326[/C][/ROW]
[ROW][C]54[/C][C]1670.07[/C][C]1668.71145132100[/C][C]-148.796739178656[/C][C]1.35854867899711[/C][C]0.0573651508928167[/C][/ROW]
[ROW][C]55[/C][C]1864.44[/C][C]1863.12616616894[/C][C]-72.6353668387049[/C][C]1.31383383105621[/C][C]1.94454261168317[/C][/ROW]
[ROW][C]56[/C][C]2052.02[/C][C]2050.73254766379[/C][C]-14.8863389686501[/C][C]1.28745233621247[/C][C]1.47451012825262[/C][/ROW]
[ROW][C]57[/C][C]2029.6[/C][C]2028.31195337778[/C][C]-16.5582263345466[/C][C]1.28804662222115[/C][C]-0.0426896344201635[/C][/ROW]
[ROW][C]58[/C][C]2070.83[/C][C]2069.54550031604[/C][C]-3.73392340534191[/C][C]1.28449968395841[/C][C]0.327458793443655[/C][/ROW]
[ROW][C]59[/C][C]2293.41[/C][C]2292.13630846946[/C][C]46.4889688057277[/C][C]1.27369153053539[/C][C]1.28241659775575[/C][/ROW]
[ROW][C]60[/C][C]2443.27[/C][C]2442.00014965887[/C][C]69.4286246455843[/C][C]1.26985034113002[/C][C]0.585756406119439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63212&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63212&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
12756.762756.76000
22849.272844.556143255116.572769511994324.713856744892530.334856114850539
32921.442916.6710789275613.75894559777004.76892107243610.397644653822629
42981.852977.0476895248520.63388371132084.802310475148860.276723980078269
53080.583075.731520114134.222955322484.848479885897330.455956242881725
63106.223101.3756211009632.5768688395724.84437889903677-0.0495684161884733
73119.313114.4730397261828.61644058056934.83696027381733-0.111740821245485
83061.263056.44909034310.37844032343884.810909657-0.494707706596145
93097.313092.4930321658615.89536591896404.816967834144680.14612296011944
103161.693156.8640806475726.44789097363994.825919352433790.275492913068759
113257.163252.3241322573441.58594989769094.835867742663470.3917681298115
123277.013272.1765748309336.79661922083814.83342516906696-0.123292234285078
133295.323336.9621758588242.7274960267156-41.64217585881830.185097043221914
143363.993360.632958087438.66116707367433.35704191260091-0.0909000087708877
153494.173490.8917890527158.97367656412143.278210947288940.518584323520462
163667.033663.8281162264484.24904896926483.201883773558240.645333722884836
173813.063809.8903330491197.95989198067883.16966695089370.350081949503154
183917.963914.7931489521199.50005204133033.166851047886750.0393262718754584
193895.513892.3046491486572.43707319847093.20535085134800-0.69103396764026
203801.063797.813654674435.40204648274253.24634532560158-0.945672202688552
213570.123566.82274866132-23.70339835856313.2972513386771-1.50923883675795
223701.613698.3358283372310.73632588840453.274171662770040.879410554312389
233862.273859.0131765212644.00655796916423.256823478736490.849549644226362
243970.13966.8489228637558.16988706002423.251077136249120.361658634020461
254138.524149.9673846995885.4874292632685-11.44738469958120.76665883299017
264199.754199.2324408707677.65711086422640.51755912923884-0.185278196338789
274290.894290.3805632852580.65305331702650.5094367147527250.0763872412502124
284443.914443.4344833125696.72496030198990.4755166874429930.410024742494637
294502.644502.1506281882688.28932292957740.489371811736392-0.215285665396504
304356.984356.4242541751136.35770993259290.555745824886518-1.32563082550640
314591.274590.7579458739180.289317102630.5120541260871651.12156519386773
324696.964696.4523084421685.92667775097780.5076915578437990.143932062087999
334621.44620.8707283505850.08824949952610.529271649422923-0.91506395027393
344562.844562.299431374825.97676295811730.54056862519734-0.615657321927685
354202.524201.94817883528-59.74984926552330.571821164718137-2.18896256318707
364296.494295.92785537591-25.63680247031550.5621446240902860.871059436870004
374435.234422.085979865767.727539426020113.14402013424180.907704831720602
384105.184109.78394232916-61.929438299715-4.60394232915507-1.68562661103423
394116.684121.31681963735-45.6185323795723-4.636819637353890.416033667024512
403844.493849.04789353429-95.9275693601094-4.55789353428539-1.28377041015625
413720.983725.53041818677-102.050680568908-4.55041818677158-0.156289252734622
423674.43678.96211488358-89.7383050460216-4.562114883576170.314317681527007
433857.623862.22689729918-29.1571448128794-4.606897299180871.54670297320979
443801.063805.66339925824-35.2386997959309-4.60339925823721-0.155277891589227
453504.373508.94743107151-93.2611460507691-4.57743107151422-1.48151671004765
463032.63037.14817938195-177.259938429254-4.54817938194637-2.14483044050196
473047.033051.58970594275-134.720540862608-4.559705942751141.08621788741837
482962.342966.90204673031-123.617953506867-4.562046730310380.283500128762141
492197.822207.76348024009-263.650756326525-9.94348024009174-3.75065326528572
502014.452012.96501405620-248.6036855592121.484985943802900.368587950599816
511862.831861.37949323273-227.0649873474001.450506767271450.549502112016056
521905.411904.03409089213-167.1989639981721.375909107865661.52785457454302
531810.991809.62975592235-151.0437186113701.360244077646410.412388934231326
541670.071668.71145132100-148.7967391786561.358548678997110.0573651508928167
551864.441863.12616616894-72.63536683870491.313833831056211.94454261168317
562052.022050.73254766379-14.88633896865011.287452336212471.47451012825262
572029.62028.31195337778-16.55822633454661.28804662222115-0.0426896344201635
582070.832069.54550031604-3.733923405341911.284499683958410.327458793443655
592293.412292.1363084694646.48896880572771.273691530535391.28241659775575
602443.272442.0001496588769.42862464558431.269850341130020.585756406119439



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')