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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, 11 Dec 2015 12:49:22 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/11/t1449838410c74rd9k7yon3dr6.htm/, Retrieved Thu, 16 May 2024 10:30:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285925, Retrieved Thu, 16 May 2024 10:30:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [structural tijdre...] [2015-12-11 12:49:22] [c64ec7a2d0db7c519901da97df98e10d] [Current]
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Dataseries X:
1.9
2
2
1.8
1.6
1.4
0.2
0.3
0.4
0.7
1
1.1
0.8
0.8
1
1.1
1
0.8
1.6
1.5
1.6
1.6
1.6
1.9
2
1.9
2
2.1
2.3
2.3
2.6
2.6
2.7
2.6
2.6
2.4
2.5
2.5
2.5
2.4
2.1
2.1
2.3
2.3
2.3
2.9
2.8
2.9
3
3
2.9
2.6
2.8
2.9
3.1
2.8
2.4
1.6
1.5
1.7
1.4
1.1
0.8
1.2
0.8
0.9
0.9
1
0.9
1.1
1
0.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285925&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285925&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285925&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.91.9000
221.99480000034570.005199999753874020.005199999654303880.20679711894732
321.994820717373530.005179282626471220.00517928262647123-0.0188542140144174
41.81.795634920826960.004365079173038340.00436507917303837-0.743947097598869
51.61.596442687889840.003557312110155470.00355731211015546-0.741000761060923
61.41.397244094582530.002755905417466290.00275590541746632-0.738077669916454
70.20.201960784126878-0.00196078412687808-0.00196078412687806-4.36110177976651
80.30.301562499837503-0.00156249983750288-0.001562499837502870.369704920437472
90.40.401167315036578-0.00116731503657785-0.001167315036577850.368263570316114
100.70.6999999999307096.9291086540399e-116.92911071402402e-111.09203486011827
1110.9988416988405730.001158301159426860.001158301159426831.08781033830193
121.11.098461538482620.00153846151738420.001538461517384210.358406077715329
130.80.9068241435475410.00971128581634354-0.106824143547541-0.849572826848629
140.80.7932727275719610.006727272595350960.00672727242803878-0.381744850526067
1510.9929219603125730.007078039687427380.007078039687427490.701532955948363
161.11.092753623436580.007246376563421550.007246376563421550.337284652703566
1710.9929475590081430.007052440991856980.007052440991857-0.389279521312834
180.80.7933212998572680.006678700142731890.0066787001427318-0.751553660671191
191.61.591891892182660.008108107817337690.008108107817337712.8795820781255
201.51.49208633121560.007913668784397270.00791366878439728-0.392409324134112
211.61.591921005674240.008078994325756380.008078994325756380.334254240223238
221.61.591935484157960.008064515842035910.00806451584203594-0.0293251172734933
231.61.591949910840310.008050089159685690.00805008915968565-0.0292726103096395
241.91.891428571740160.008571428259840780.008571428259840821.05972259364866
2522.059765050137380.00543318628897784-0.05976505013738180.640078556405937
261.91.897176469481050.002823529219211270.00282353051895123-0.5548748809131
2721.997062279915040.002937720084963720.002937720084963810.352839963545518
282.12.096948357058260.003051642941744690.003051642941744670.352425583389943
292.32.29671746802540.003282531974598540.003282531974598540.715104647282669
302.32.296721311739420.003278688260577150.00327868826057715-0.0119186344989741
312.62.596374269290710.003625730709292460.003625730709292481.07737420826004
322.62.596378504957170.00362149504283390.0036214950428339-0.0131647821356897
332.72.69626604463150.003733955368499880.003733955368499870.349944049311506
342.62.596386946670040.003613053329960140.00361305332996016-0.376651465200114
352.62.596391152785420.003608847214580930.00360884721458092-0.0131187782248808
362.42.396627907244580.003372092755424010.00337209275542402-0.739291916635661
372.52.520183485583280.00183486216241107-0.02018348558327620.466339313780357
382.52.498434780998730.001565217157959990.00156521900126671-0.0800395066715639
392.52.498436142715480.001563857284519550.00156385728451961-0.00568404234082562
402.42.398524305779410.0014756942205910.00147569422059104-0.368826880306221
412.12.098785776439920.001214223560083120.00121422356008315-1.09480266556742
422.12.098786828626630.001213171373368290.00121317137336832-0.00440942907050267
432.32.298614718831210.001385281168791890.00138528116879190.721890744748659
442.32.298615917171230.00138408282877030.0013840828287703-0.0050306251996081
452.32.298617113439790.001382886560209260.00138288656020926-0.00502627532864968
462.92.8981001729660.001899827034000060.001899827034000092.17386957620779
472.82.798188093431410.001811906568584890.00181190656858488-0.37004791783073
482.92.898103448529610.0018965514703930.0018965514703930.356568937476273
4933.009445744953720.000858703914201139-0.009445744953722190.417846152896537
5032.999241377624240.000758620456771880.000758622375756703-0.0381196990276031
512.92.899310820300360.0006891796996408270.000689179699640864-0.365935427218521
522.62.599517906493470.0004820935065343480.000482093506534345-1.09204404427736
532.82.79938059204860.0006194079513969860.0006194079513969980.724610022226101
542.92.89931224226660.0006877577334018850.0006877577334019090.360930962706682
553.13.099175257920070.0008247420799325250.0008247420799325230.723863432601133
562.82.799381868301130.0006181316988654710.00061813169886542-1.09253741449457
572.42.399656829245160.0003431707548442040.000343170754844295-1.45496809209132
581.61.60020576141139-0.000205761411387339-0.000205761411387347-2.90669332789536
591.51.50027416047209-0.000274160472092365-0.000274160472092356-0.36243367382254
601.71.70013698640197-0.000136986401967327-0.0001369864019672990.727357789665618
611.41.421568626971220.00196078416123581-0.0215686269712197-1.05263942597995
621.11.10045714125118-0.000457143069017675-0.000457141251182283-1.12397533195734
630.80.800628212496787-0.000628212496787171-0.00062821249678708-1.08794483252803
641.21.20039954345024-0.000399543450239442-0.000399543450239471.45508883312709
650.80.800627495768402-0.000627495768402115-0.000627495768402074-1.45135624156121
660.90.900570125480492-0.000570125480492209-0.0005701254804921640.365480983380003
670.90.900569800622703-0.00056980062270333-0.0005698006227033210.00207070695274448
6811.0005125285328-0.00051252853280201-0.0005125285328019920.365271552172007
690.90.900569152016486-0.000569152016486146-0.000569152016486141-0.36134056935641
701.11.10045506263617-0.00045506263617333-0.0004550626361733330.728471457305286
7111.00051165440805-0.000511654408052584-0.000511654408052551-0.361549403741867
720.70.700681818222617-0.000681818222617545-0.000681818222617503-1.08774843946663

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.9 & 1.9 & 0 & 0 & 0 \tabularnewline
2 & 2 & 1.9948000003457 & 0.00519999975387402 & 0.00519999965430388 & 0.20679711894732 \tabularnewline
3 & 2 & 1.99482071737353 & 0.00517928262647122 & 0.00517928262647123 & -0.0188542140144174 \tabularnewline
4 & 1.8 & 1.79563492082696 & 0.00436507917303834 & 0.00436507917303837 & -0.743947097598869 \tabularnewline
5 & 1.6 & 1.59644268788984 & 0.00355731211015547 & 0.00355731211015546 & -0.741000761060923 \tabularnewline
6 & 1.4 & 1.39724409458253 & 0.00275590541746629 & 0.00275590541746632 & -0.738077669916454 \tabularnewline
7 & 0.2 & 0.201960784126878 & -0.00196078412687808 & -0.00196078412687806 & -4.36110177976651 \tabularnewline
8 & 0.3 & 0.301562499837503 & -0.00156249983750288 & -0.00156249983750287 & 0.369704920437472 \tabularnewline
9 & 0.4 & 0.401167315036578 & -0.00116731503657785 & -0.00116731503657785 & 0.368263570316114 \tabularnewline
10 & 0.7 & 0.699999999930709 & 6.9291086540399e-11 & 6.92911071402402e-11 & 1.09203486011827 \tabularnewline
11 & 1 & 0.998841698840573 & 0.00115830115942686 & 0.00115830115942683 & 1.08781033830193 \tabularnewline
12 & 1.1 & 1.09846153848262 & 0.0015384615173842 & 0.00153846151738421 & 0.358406077715329 \tabularnewline
13 & 0.8 & 0.906824143547541 & 0.00971128581634354 & -0.106824143547541 & -0.849572826848629 \tabularnewline
14 & 0.8 & 0.793272727571961 & 0.00672727259535096 & 0.00672727242803878 & -0.381744850526067 \tabularnewline
15 & 1 & 0.992921960312573 & 0.00707803968742738 & 0.00707803968742749 & 0.701532955948363 \tabularnewline
16 & 1.1 & 1.09275362343658 & 0.00724637656342155 & 0.00724637656342155 & 0.337284652703566 \tabularnewline
17 & 1 & 0.992947559008143 & 0.00705244099185698 & 0.007052440991857 & -0.389279521312834 \tabularnewline
18 & 0.8 & 0.793321299857268 & 0.00667870014273189 & 0.0066787001427318 & -0.751553660671191 \tabularnewline
19 & 1.6 & 1.59189189218266 & 0.00810810781733769 & 0.00810810781733771 & 2.8795820781255 \tabularnewline
20 & 1.5 & 1.4920863312156 & 0.00791366878439727 & 0.00791366878439728 & -0.392409324134112 \tabularnewline
21 & 1.6 & 1.59192100567424 & 0.00807899432575638 & 0.00807899432575638 & 0.334254240223238 \tabularnewline
22 & 1.6 & 1.59193548415796 & 0.00806451584203591 & 0.00806451584203594 & -0.0293251172734933 \tabularnewline
23 & 1.6 & 1.59194991084031 & 0.00805008915968569 & 0.00805008915968565 & -0.0292726103096395 \tabularnewline
24 & 1.9 & 1.89142857174016 & 0.00857142825984078 & 0.00857142825984082 & 1.05972259364866 \tabularnewline
25 & 2 & 2.05976505013738 & 0.00543318628897784 & -0.0597650501373818 & 0.640078556405937 \tabularnewline
26 & 1.9 & 1.89717646948105 & 0.00282352921921127 & 0.00282353051895123 & -0.5548748809131 \tabularnewline
27 & 2 & 1.99706227991504 & 0.00293772008496372 & 0.00293772008496381 & 0.352839963545518 \tabularnewline
28 & 2.1 & 2.09694835705826 & 0.00305164294174469 & 0.00305164294174467 & 0.352425583389943 \tabularnewline
29 & 2.3 & 2.2967174680254 & 0.00328253197459854 & 0.00328253197459854 & 0.715104647282669 \tabularnewline
30 & 2.3 & 2.29672131173942 & 0.00327868826057715 & 0.00327868826057715 & -0.0119186344989741 \tabularnewline
31 & 2.6 & 2.59637426929071 & 0.00362573070929246 & 0.00362573070929248 & 1.07737420826004 \tabularnewline
32 & 2.6 & 2.59637850495717 & 0.0036214950428339 & 0.0036214950428339 & -0.0131647821356897 \tabularnewline
33 & 2.7 & 2.6962660446315 & 0.00373395536849988 & 0.00373395536849987 & 0.349944049311506 \tabularnewline
34 & 2.6 & 2.59638694667004 & 0.00361305332996014 & 0.00361305332996016 & -0.376651465200114 \tabularnewline
35 & 2.6 & 2.59639115278542 & 0.00360884721458093 & 0.00360884721458092 & -0.0131187782248808 \tabularnewline
36 & 2.4 & 2.39662790724458 & 0.00337209275542401 & 0.00337209275542402 & -0.739291916635661 \tabularnewline
37 & 2.5 & 2.52018348558328 & 0.00183486216241107 & -0.0201834855832762 & 0.466339313780357 \tabularnewline
38 & 2.5 & 2.49843478099873 & 0.00156521715795999 & 0.00156521900126671 & -0.0800395066715639 \tabularnewline
39 & 2.5 & 2.49843614271548 & 0.00156385728451955 & 0.00156385728451961 & -0.00568404234082562 \tabularnewline
40 & 2.4 & 2.39852430577941 & 0.001475694220591 & 0.00147569422059104 & -0.368826880306221 \tabularnewline
41 & 2.1 & 2.09878577643992 & 0.00121422356008312 & 0.00121422356008315 & -1.09480266556742 \tabularnewline
42 & 2.1 & 2.09878682862663 & 0.00121317137336829 & 0.00121317137336832 & -0.00440942907050267 \tabularnewline
43 & 2.3 & 2.29861471883121 & 0.00138528116879189 & 0.0013852811687919 & 0.721890744748659 \tabularnewline
44 & 2.3 & 2.29861591717123 & 0.0013840828287703 & 0.0013840828287703 & -0.0050306251996081 \tabularnewline
45 & 2.3 & 2.29861711343979 & 0.00138288656020926 & 0.00138288656020926 & -0.00502627532864968 \tabularnewline
46 & 2.9 & 2.898100172966 & 0.00189982703400006 & 0.00189982703400009 & 2.17386957620779 \tabularnewline
47 & 2.8 & 2.79818809343141 & 0.00181190656858489 & 0.00181190656858488 & -0.37004791783073 \tabularnewline
48 & 2.9 & 2.89810344852961 & 0.001896551470393 & 0.001896551470393 & 0.356568937476273 \tabularnewline
49 & 3 & 3.00944574495372 & 0.000858703914201139 & -0.00944574495372219 & 0.417846152896537 \tabularnewline
50 & 3 & 2.99924137762424 & 0.00075862045677188 & 0.000758622375756703 & -0.0381196990276031 \tabularnewline
51 & 2.9 & 2.89931082030036 & 0.000689179699640827 & 0.000689179699640864 & -0.365935427218521 \tabularnewline
52 & 2.6 & 2.59951790649347 & 0.000482093506534348 & 0.000482093506534345 & -1.09204404427736 \tabularnewline
53 & 2.8 & 2.7993805920486 & 0.000619407951396986 & 0.000619407951396998 & 0.724610022226101 \tabularnewline
54 & 2.9 & 2.8993122422666 & 0.000687757733401885 & 0.000687757733401909 & 0.360930962706682 \tabularnewline
55 & 3.1 & 3.09917525792007 & 0.000824742079932525 & 0.000824742079932523 & 0.723863432601133 \tabularnewline
56 & 2.8 & 2.79938186830113 & 0.000618131698865471 & 0.00061813169886542 & -1.09253741449457 \tabularnewline
57 & 2.4 & 2.39965682924516 & 0.000343170754844204 & 0.000343170754844295 & -1.45496809209132 \tabularnewline
58 & 1.6 & 1.60020576141139 & -0.000205761411387339 & -0.000205761411387347 & -2.90669332789536 \tabularnewline
59 & 1.5 & 1.50027416047209 & -0.000274160472092365 & -0.000274160472092356 & -0.36243367382254 \tabularnewline
60 & 1.7 & 1.70013698640197 & -0.000136986401967327 & -0.000136986401967299 & 0.727357789665618 \tabularnewline
61 & 1.4 & 1.42156862697122 & 0.00196078416123581 & -0.0215686269712197 & -1.05263942597995 \tabularnewline
62 & 1.1 & 1.10045714125118 & -0.000457143069017675 & -0.000457141251182283 & -1.12397533195734 \tabularnewline
63 & 0.8 & 0.800628212496787 & -0.000628212496787171 & -0.00062821249678708 & -1.08794483252803 \tabularnewline
64 & 1.2 & 1.20039954345024 & -0.000399543450239442 & -0.00039954345023947 & 1.45508883312709 \tabularnewline
65 & 0.8 & 0.800627495768402 & -0.000627495768402115 & -0.000627495768402074 & -1.45135624156121 \tabularnewline
66 & 0.9 & 0.900570125480492 & -0.000570125480492209 & -0.000570125480492164 & 0.365480983380003 \tabularnewline
67 & 0.9 & 0.900569800622703 & -0.00056980062270333 & -0.000569800622703321 & 0.00207070695274448 \tabularnewline
68 & 1 & 1.0005125285328 & -0.00051252853280201 & -0.000512528532801992 & 0.365271552172007 \tabularnewline
69 & 0.9 & 0.900569152016486 & -0.000569152016486146 & -0.000569152016486141 & -0.36134056935641 \tabularnewline
70 & 1.1 & 1.10045506263617 & -0.00045506263617333 & -0.000455062636173333 & 0.728471457305286 \tabularnewline
71 & 1 & 1.00051165440805 & -0.000511654408052584 & -0.000511654408052551 & -0.361549403741867 \tabularnewline
72 & 0.7 & 0.700681818222617 & -0.000681818222617545 & -0.000681818222617503 & -1.08774843946663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285925&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]1.9[/C][C]1.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]1.9948000003457[/C][C]0.00519999975387402[/C][C]0.00519999965430388[/C][C]0.20679711894732[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]1.99482071737353[/C][C]0.00517928262647122[/C][C]0.00517928262647123[/C][C]-0.0188542140144174[/C][/ROW]
[ROW][C]4[/C][C]1.8[/C][C]1.79563492082696[/C][C]0.00436507917303834[/C][C]0.00436507917303837[/C][C]-0.743947097598869[/C][/ROW]
[ROW][C]5[/C][C]1.6[/C][C]1.59644268788984[/C][C]0.00355731211015547[/C][C]0.00355731211015546[/C][C]-0.741000761060923[/C][/ROW]
[ROW][C]6[/C][C]1.4[/C][C]1.39724409458253[/C][C]0.00275590541746629[/C][C]0.00275590541746632[/C][C]-0.738077669916454[/C][/ROW]
[ROW][C]7[/C][C]0.2[/C][C]0.201960784126878[/C][C]-0.00196078412687808[/C][C]-0.00196078412687806[/C][C]-4.36110177976651[/C][/ROW]
[ROW][C]8[/C][C]0.3[/C][C]0.301562499837503[/C][C]-0.00156249983750288[/C][C]-0.00156249983750287[/C][C]0.369704920437472[/C][/ROW]
[ROW][C]9[/C][C]0.4[/C][C]0.401167315036578[/C][C]-0.00116731503657785[/C][C]-0.00116731503657785[/C][C]0.368263570316114[/C][/ROW]
[ROW][C]10[/C][C]0.7[/C][C]0.699999999930709[/C][C]6.9291086540399e-11[/C][C]6.92911071402402e-11[/C][C]1.09203486011827[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.998841698840573[/C][C]0.00115830115942686[/C][C]0.00115830115942683[/C][C]1.08781033830193[/C][/ROW]
[ROW][C]12[/C][C]1.1[/C][C]1.09846153848262[/C][C]0.0015384615173842[/C][C]0.00153846151738421[/C][C]0.358406077715329[/C][/ROW]
[ROW][C]13[/C][C]0.8[/C][C]0.906824143547541[/C][C]0.00971128581634354[/C][C]-0.106824143547541[/C][C]-0.849572826848629[/C][/ROW]
[ROW][C]14[/C][C]0.8[/C][C]0.793272727571961[/C][C]0.00672727259535096[/C][C]0.00672727242803878[/C][C]-0.381744850526067[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.992921960312573[/C][C]0.00707803968742738[/C][C]0.00707803968742749[/C][C]0.701532955948363[/C][/ROW]
[ROW][C]16[/C][C]1.1[/C][C]1.09275362343658[/C][C]0.00724637656342155[/C][C]0.00724637656342155[/C][C]0.337284652703566[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.992947559008143[/C][C]0.00705244099185698[/C][C]0.007052440991857[/C][C]-0.389279521312834[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.793321299857268[/C][C]0.00667870014273189[/C][C]0.0066787001427318[/C][C]-0.751553660671191[/C][/ROW]
[ROW][C]19[/C][C]1.6[/C][C]1.59189189218266[/C][C]0.00810810781733769[/C][C]0.00810810781733771[/C][C]2.8795820781255[/C][/ROW]
[ROW][C]20[/C][C]1.5[/C][C]1.4920863312156[/C][C]0.00791366878439727[/C][C]0.00791366878439728[/C][C]-0.392409324134112[/C][/ROW]
[ROW][C]21[/C][C]1.6[/C][C]1.59192100567424[/C][C]0.00807899432575638[/C][C]0.00807899432575638[/C][C]0.334254240223238[/C][/ROW]
[ROW][C]22[/C][C]1.6[/C][C]1.59193548415796[/C][C]0.00806451584203591[/C][C]0.00806451584203594[/C][C]-0.0293251172734933[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.59194991084031[/C][C]0.00805008915968569[/C][C]0.00805008915968565[/C][C]-0.0292726103096395[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]1.89142857174016[/C][C]0.00857142825984078[/C][C]0.00857142825984082[/C][C]1.05972259364866[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]2.05976505013738[/C][C]0.00543318628897784[/C][C]-0.0597650501373818[/C][C]0.640078556405937[/C][/ROW]
[ROW][C]26[/C][C]1.9[/C][C]1.89717646948105[/C][C]0.00282352921921127[/C][C]0.00282353051895123[/C][C]-0.5548748809131[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.99706227991504[/C][C]0.00293772008496372[/C][C]0.00293772008496381[/C][C]0.352839963545518[/C][/ROW]
[ROW][C]28[/C][C]2.1[/C][C]2.09694835705826[/C][C]0.00305164294174469[/C][C]0.00305164294174467[/C][C]0.352425583389943[/C][/ROW]
[ROW][C]29[/C][C]2.3[/C][C]2.2967174680254[/C][C]0.00328253197459854[/C][C]0.00328253197459854[/C][C]0.715104647282669[/C][/ROW]
[ROW][C]30[/C][C]2.3[/C][C]2.29672131173942[/C][C]0.00327868826057715[/C][C]0.00327868826057715[/C][C]-0.0119186344989741[/C][/ROW]
[ROW][C]31[/C][C]2.6[/C][C]2.59637426929071[/C][C]0.00362573070929246[/C][C]0.00362573070929248[/C][C]1.07737420826004[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]2.59637850495717[/C][C]0.0036214950428339[/C][C]0.0036214950428339[/C][C]-0.0131647821356897[/C][/ROW]
[ROW][C]33[/C][C]2.7[/C][C]2.6962660446315[/C][C]0.00373395536849988[/C][C]0.00373395536849987[/C][C]0.349944049311506[/C][/ROW]
[ROW][C]34[/C][C]2.6[/C][C]2.59638694667004[/C][C]0.00361305332996014[/C][C]0.00361305332996016[/C][C]-0.376651465200114[/C][/ROW]
[ROW][C]35[/C][C]2.6[/C][C]2.59639115278542[/C][C]0.00360884721458093[/C][C]0.00360884721458092[/C][C]-0.0131187782248808[/C][/ROW]
[ROW][C]36[/C][C]2.4[/C][C]2.39662790724458[/C][C]0.00337209275542401[/C][C]0.00337209275542402[/C][C]-0.739291916635661[/C][/ROW]
[ROW][C]37[/C][C]2.5[/C][C]2.52018348558328[/C][C]0.00183486216241107[/C][C]-0.0201834855832762[/C][C]0.466339313780357[/C][/ROW]
[ROW][C]38[/C][C]2.5[/C][C]2.49843478099873[/C][C]0.00156521715795999[/C][C]0.00156521900126671[/C][C]-0.0800395066715639[/C][/ROW]
[ROW][C]39[/C][C]2.5[/C][C]2.49843614271548[/C][C]0.00156385728451955[/C][C]0.00156385728451961[/C][C]-0.00568404234082562[/C][/ROW]
[ROW][C]40[/C][C]2.4[/C][C]2.39852430577941[/C][C]0.001475694220591[/C][C]0.00147569422059104[/C][C]-0.368826880306221[/C][/ROW]
[ROW][C]41[/C][C]2.1[/C][C]2.09878577643992[/C][C]0.00121422356008312[/C][C]0.00121422356008315[/C][C]-1.09480266556742[/C][/ROW]
[ROW][C]42[/C][C]2.1[/C][C]2.09878682862663[/C][C]0.00121317137336829[/C][C]0.00121317137336832[/C][C]-0.00440942907050267[/C][/ROW]
[ROW][C]43[/C][C]2.3[/C][C]2.29861471883121[/C][C]0.00138528116879189[/C][C]0.0013852811687919[/C][C]0.721890744748659[/C][/ROW]
[ROW][C]44[/C][C]2.3[/C][C]2.29861591717123[/C][C]0.0013840828287703[/C][C]0.0013840828287703[/C][C]-0.0050306251996081[/C][/ROW]
[ROW][C]45[/C][C]2.3[/C][C]2.29861711343979[/C][C]0.00138288656020926[/C][C]0.00138288656020926[/C][C]-0.00502627532864968[/C][/ROW]
[ROW][C]46[/C][C]2.9[/C][C]2.898100172966[/C][C]0.00189982703400006[/C][C]0.00189982703400009[/C][C]2.17386957620779[/C][/ROW]
[ROW][C]47[/C][C]2.8[/C][C]2.79818809343141[/C][C]0.00181190656858489[/C][C]0.00181190656858488[/C][C]-0.37004791783073[/C][/ROW]
[ROW][C]48[/C][C]2.9[/C][C]2.89810344852961[/C][C]0.001896551470393[/C][C]0.001896551470393[/C][C]0.356568937476273[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]3.00944574495372[/C][C]0.000858703914201139[/C][C]-0.00944574495372219[/C][C]0.417846152896537[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.99924137762424[/C][C]0.00075862045677188[/C][C]0.000758622375756703[/C][C]-0.0381196990276031[/C][/ROW]
[ROW][C]51[/C][C]2.9[/C][C]2.89931082030036[/C][C]0.000689179699640827[/C][C]0.000689179699640864[/C][C]-0.365935427218521[/C][/ROW]
[ROW][C]52[/C][C]2.6[/C][C]2.59951790649347[/C][C]0.000482093506534348[/C][C]0.000482093506534345[/C][C]-1.09204404427736[/C][/ROW]
[ROW][C]53[/C][C]2.8[/C][C]2.7993805920486[/C][C]0.000619407951396986[/C][C]0.000619407951396998[/C][C]0.724610022226101[/C][/ROW]
[ROW][C]54[/C][C]2.9[/C][C]2.8993122422666[/C][C]0.000687757733401885[/C][C]0.000687757733401909[/C][C]0.360930962706682[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]3.09917525792007[/C][C]0.000824742079932525[/C][C]0.000824742079932523[/C][C]0.723863432601133[/C][/ROW]
[ROW][C]56[/C][C]2.8[/C][C]2.79938186830113[/C][C]0.000618131698865471[/C][C]0.00061813169886542[/C][C]-1.09253741449457[/C][/ROW]
[ROW][C]57[/C][C]2.4[/C][C]2.39965682924516[/C][C]0.000343170754844204[/C][C]0.000343170754844295[/C][C]-1.45496809209132[/C][/ROW]
[ROW][C]58[/C][C]1.6[/C][C]1.60020576141139[/C][C]-0.000205761411387339[/C][C]-0.000205761411387347[/C][C]-2.90669332789536[/C][/ROW]
[ROW][C]59[/C][C]1.5[/C][C]1.50027416047209[/C][C]-0.000274160472092365[/C][C]-0.000274160472092356[/C][C]-0.36243367382254[/C][/ROW]
[ROW][C]60[/C][C]1.7[/C][C]1.70013698640197[/C][C]-0.000136986401967327[/C][C]-0.000136986401967299[/C][C]0.727357789665618[/C][/ROW]
[ROW][C]61[/C][C]1.4[/C][C]1.42156862697122[/C][C]0.00196078416123581[/C][C]-0.0215686269712197[/C][C]-1.05263942597995[/C][/ROW]
[ROW][C]62[/C][C]1.1[/C][C]1.10045714125118[/C][C]-0.000457143069017675[/C][C]-0.000457141251182283[/C][C]-1.12397533195734[/C][/ROW]
[ROW][C]63[/C][C]0.8[/C][C]0.800628212496787[/C][C]-0.000628212496787171[/C][C]-0.00062821249678708[/C][C]-1.08794483252803[/C][/ROW]
[ROW][C]64[/C][C]1.2[/C][C]1.20039954345024[/C][C]-0.000399543450239442[/C][C]-0.00039954345023947[/C][C]1.45508883312709[/C][/ROW]
[ROW][C]65[/C][C]0.8[/C][C]0.800627495768402[/C][C]-0.000627495768402115[/C][C]-0.000627495768402074[/C][C]-1.45135624156121[/C][/ROW]
[ROW][C]66[/C][C]0.9[/C][C]0.900570125480492[/C][C]-0.000570125480492209[/C][C]-0.000570125480492164[/C][C]0.365480983380003[/C][/ROW]
[ROW][C]67[/C][C]0.9[/C][C]0.900569800622703[/C][C]-0.00056980062270333[/C][C]-0.000569800622703321[/C][C]0.00207070695274448[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.0005125285328[/C][C]-0.00051252853280201[/C][C]-0.000512528532801992[/C][C]0.365271552172007[/C][/ROW]
[ROW][C]69[/C][C]0.9[/C][C]0.900569152016486[/C][C]-0.000569152016486146[/C][C]-0.000569152016486141[/C][C]-0.36134056935641[/C][/ROW]
[ROW][C]70[/C][C]1.1[/C][C]1.10045506263617[/C][C]-0.00045506263617333[/C][C]-0.000455062636173333[/C][C]0.728471457305286[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.00051165440805[/C][C]-0.000511654408052584[/C][C]-0.000511654408052551[/C][C]-0.361549403741867[/C][/ROW]
[ROW][C]72[/C][C]0.7[/C][C]0.700681818222617[/C][C]-0.000681818222617545[/C][C]-0.000681818222617503[/C][C]-1.08774843946663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285925&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285925&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
11.91.9000
221.99480000034570.005199999753874020.005199999654303880.20679711894732
321.994820717373530.005179282626471220.00517928262647123-0.0188542140144174
41.81.795634920826960.004365079173038340.00436507917303837-0.743947097598869
51.61.596442687889840.003557312110155470.00355731211015546-0.741000761060923
61.41.397244094582530.002755905417466290.00275590541746632-0.738077669916454
70.20.201960784126878-0.00196078412687808-0.00196078412687806-4.36110177976651
80.30.301562499837503-0.00156249983750288-0.001562499837502870.369704920437472
90.40.401167315036578-0.00116731503657785-0.001167315036577850.368263570316114
100.70.6999999999307096.9291086540399e-116.92911071402402e-111.09203486011827
1110.9988416988405730.001158301159426860.001158301159426831.08781033830193
121.11.098461538482620.00153846151738420.001538461517384210.358406077715329
130.80.9068241435475410.00971128581634354-0.106824143547541-0.849572826848629
140.80.7932727275719610.006727272595350960.00672727242803878-0.381744850526067
1510.9929219603125730.007078039687427380.007078039687427490.701532955948363
161.11.092753623436580.007246376563421550.007246376563421550.337284652703566
1710.9929475590081430.007052440991856980.007052440991857-0.389279521312834
180.80.7933212998572680.006678700142731890.0066787001427318-0.751553660671191
191.61.591891892182660.008108107817337690.008108107817337712.8795820781255
201.51.49208633121560.007913668784397270.00791366878439728-0.392409324134112
211.61.591921005674240.008078994325756380.008078994325756380.334254240223238
221.61.591935484157960.008064515842035910.00806451584203594-0.0293251172734933
231.61.591949910840310.008050089159685690.00805008915968565-0.0292726103096395
241.91.891428571740160.008571428259840780.008571428259840821.05972259364866
2522.059765050137380.00543318628897784-0.05976505013738180.640078556405937
261.91.897176469481050.002823529219211270.00282353051895123-0.5548748809131
2721.997062279915040.002937720084963720.002937720084963810.352839963545518
282.12.096948357058260.003051642941744690.003051642941744670.352425583389943
292.32.29671746802540.003282531974598540.003282531974598540.715104647282669
302.32.296721311739420.003278688260577150.00327868826057715-0.0119186344989741
312.62.596374269290710.003625730709292460.003625730709292481.07737420826004
322.62.596378504957170.00362149504283390.0036214950428339-0.0131647821356897
332.72.69626604463150.003733955368499880.003733955368499870.349944049311506
342.62.596386946670040.003613053329960140.00361305332996016-0.376651465200114
352.62.596391152785420.003608847214580930.00360884721458092-0.0131187782248808
362.42.396627907244580.003372092755424010.00337209275542402-0.739291916635661
372.52.520183485583280.00183486216241107-0.02018348558327620.466339313780357
382.52.498434780998730.001565217157959990.00156521900126671-0.0800395066715639
392.52.498436142715480.001563857284519550.00156385728451961-0.00568404234082562
402.42.398524305779410.0014756942205910.00147569422059104-0.368826880306221
412.12.098785776439920.001214223560083120.00121422356008315-1.09480266556742
422.12.098786828626630.001213171373368290.00121317137336832-0.00440942907050267
432.32.298614718831210.001385281168791890.00138528116879190.721890744748659
442.32.298615917171230.00138408282877030.0013840828287703-0.0050306251996081
452.32.298617113439790.001382886560209260.00138288656020926-0.00502627532864968
462.92.8981001729660.001899827034000060.001899827034000092.17386957620779
472.82.798188093431410.001811906568584890.00181190656858488-0.37004791783073
482.92.898103448529610.0018965514703930.0018965514703930.356568937476273
4933.009445744953720.000858703914201139-0.009445744953722190.417846152896537
5032.999241377624240.000758620456771880.000758622375756703-0.0381196990276031
512.92.899310820300360.0006891796996408270.000689179699640864-0.365935427218521
522.62.599517906493470.0004820935065343480.000482093506534345-1.09204404427736
532.82.79938059204860.0006194079513969860.0006194079513969980.724610022226101
542.92.89931224226660.0006877577334018850.0006877577334019090.360930962706682
553.13.099175257920070.0008247420799325250.0008247420799325230.723863432601133
562.82.799381868301130.0006181316988654710.00061813169886542-1.09253741449457
572.42.399656829245160.0003431707548442040.000343170754844295-1.45496809209132
581.61.60020576141139-0.000205761411387339-0.000205761411387347-2.90669332789536
591.51.50027416047209-0.000274160472092365-0.000274160472092356-0.36243367382254
601.71.70013698640197-0.000136986401967327-0.0001369864019672990.727357789665618
611.41.421568626971220.00196078416123581-0.0215686269712197-1.05263942597995
621.11.10045714125118-0.000457143069017675-0.000457141251182283-1.12397533195734
630.80.800628212496787-0.000628212496787171-0.00062821249678708-1.08794483252803
641.21.20039954345024-0.000399543450239442-0.000399543450239471.45508883312709
650.80.800627495768402-0.000627495768402115-0.000627495768402074-1.45135624156121
660.90.900570125480492-0.000570125480492209-0.0005701254804921640.365480983380003
670.90.900569800622703-0.00056980062270333-0.0005698006227033210.00207070695274448
6811.0005125285328-0.00051252853280201-0.0005125285328019920.365271552172007
690.90.900569152016486-0.000569152016486146-0.000569152016486141-0.36134056935641
701.11.10045506263617-0.00045506263617333-0.0004550626361733330.728471457305286
7111.00051165440805-0.000511654408052584-0.000511654408052551-0.361549403741867
720.70.700681818222617-0.000681818222617545-0.000681818222617503-1.08774843946663



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')