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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 computationSun, 12 Dec 2010 19:13:20 +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/2010/Dec/12/t1292181070ugmwoeqc9d0vwmg.htm/, Retrieved Tue, 07 May 2024 18:29:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108622, Retrieved Tue, 07 May 2024 18:29:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
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] [Unemployment] [2010-11-30 13:26:46] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [ws 8] [2010-12-12 19:13:20] [c1f1b5e209adb4577289f490325e36f2] [Current]
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Dataseries X:
 1.3031
 1.3241
 1.2961
 1.2865
 1.2305
 1.2101
 1.2125
 1.2350
 1.2014
 1.1992
 1.1791
 1.1832
 1.2159
 1.1922
 1.2114
 1.2614
 1.2812
 1.2786
 1.2772
 1.2815
 1.2679
 1.2765
 1.3247
 1.3191
 1.3029
 1.3234
 1.3354
 1.3651
 1.3453
 1.3534
 1.3706
 1.3638
 1.4268
 1.4485
 1.4635
 1.4587
 1.4876
 1.5189
 1.5783
 1.5633
 1.5554
 1.5757
 1.5593
 1.4660
 1.4065
 1.2759
 1.2705
 1.3954
 1.2793
 1.2694
 1.3282
 1.3230
 1.4135
 1.4042
 1.4253
 1.4322
 1.4632
 1.4713
 1.5016
 1.4318




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108622&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108622&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108622&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'RServer@AstonUniversity' @ vre.aston.ac.uk







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.30311.3031000
21.32411.323008000061590.001091999937057620.001091999938413780.284544909239405
31.29611.295123904439710.0009760955602891920.00097609556028919-0.691139502135368
41.28651.285565873070440.0009341269295550570.000934126929555057-0.251258608894471
51.23051.229790909132220.0007090908677748920.000709090867774893-1.35260712073784
61.21011.209474015784390.0006259842156119870.000625984215611988-0.501501141687857
71.21251.211867058860140.0006329411398566730.0006329411398566740.0421466676067974
81.2351.234281640666360.0007183593336373680.0007183593336373670.519516656049798
91.20141.200815175130860.0005848248691395620.00058482486913956-0.81534034466468
101.19921.198625969025090.000574030974911290.00057403097491129-0.0661627566561902
111.17911.178605791533990.0004942084660129810.000494208466012981-0.491183905484689
121.18321.182691923105790.0005080768942078540.0005080768942078540.0856688263444004
131.21591.20932887166569-0.000597375305090030.006571128334305430.75293148809392
141.19221.19317399995478-0.000973999974556624-0.000973999954777134-0.315695399333558
151.21141.21233738654445-0.000937386544454878-0.0009373865444548790.47979592591895
161.26141.26224510867252-0.000845108672518426-0.000845108672518431.21144001203051
171.28121.28200777574635-0.000807775746347255-0.0008077757463472540.491001863969149
181.27861.2794110108081-0.000811010808098767-0.000811010808098766-0.0426244735224776
191.27721.27801207204986-0.000812072049860374-0.000812072049860374-0.0140079541182102
201.28151.28230287767589-0.000802877675891391-0.0008028776758913920.121580816674373
211.26791.2687258527603-0.000825852760301605-0.000825852760301609-0.304355471633153
221.27651.27730896055146-0.000808960551459083-0.0008089605514590830.224176530719851
231.32471.32542128799443-0.000721287994432987-0.000721287994432991.16558964720041
241.31911.31982999997995-0.000729999979948916-0.000729999979948914-0.116031542813216
251.30291.29917518364635-0.0003386196689241490.00372481635365155-0.523037542810113
261.32341.323357764698964.22352932610852e-054.22353010408676e-050.530589056506489
271.33541.335343713279025.62867209832501e-055.6286720983248e-050.284481271817941
281.36511.365008920188779.10798112281102e-059.10798112281042e-050.705239422293093
291.34531.345232239156596.77608434091449e-056.77608434091454e-05-0.473219504090004
301.35341.353322833724457.716627555324e-057.716627555324e-050.191091427839903
311.37061.37050280701869.71929814008855e-059.71929814008849e-050.407361997632161
321.36381.363710864486938.91355130702872e-058.91355130702862e-05-0.164088274049579
331.42681.426637456244620.0001625437553844670.0001625437553844651.49668745598011
341.44851.448312354314590.0001876456854112250.0001876456854112260.512389438658394
351.46351.463295110596170.0002048894038292760.0002048894038292730.352395333095917
361.45871.458500930234940.0001990697650635440.000199069765063544-0.119069583823409
371.48761.48595504590463-0.0001495412815044540.001644954095373560.692932912639914
381.51891.518673739127740.0002262608665366720.0002262608722555750.730900620931029
391.57831.578022328410950.0002776715890463750.0002776715890463741.40798901159727
401.56331.563035590278560.0002644097214414230.000264409721441418-0.363519394857206
411.55541.555142671293010.0002573287069889510.000257328706988951-0.194265361851859
421.57571.575425303293750.0002746967062535840.0002746967062535850.476898929435036
431.55931.559039740260490.0002602597395102690.000260259739510268-0.396760884678923
441.4661.465820674740660.0001793252593376540.000179325259337662-2.22619133601994
451.40651.406372255833870.0001277441661321860.000127744166132181-1.4200222072003
461.27591.275885146803861.48531961446394e-051.485319614464e-05-3.11056414166282
471.27051.270489818808311.01811916942101e-051.01811916942064e-05-0.128842232932366
481.39541.395282155172160.0001178448278410880.0001178448278410852.97165750065224
491.27931.291440358964820.00110366899876624-0.0121403589648204-2.60057471451443
501.26941.268513724168550.0008862758564512740.000886275831450488-0.542525372398785
511.32821.327273811168010.0009261888319855960.0009261888319855961.37813188896661
521.3231.322078030306320.0009219696936837330.000921969693683727-0.145780612842954
531.41351.412516379907110.0009836200928908570.000983620092890862.13162597667844
541.40421.403223452548140.0009765474518569840.000976547451856987-0.244712200534846
551.42531.42430962199660.0009903780033966280.0009903780033966270.478864006604029
561.43221.43120556319030.0009944368097012940.0009944368097012950.140627257097516
571.46321.462184969118160.001015030881838560.001015030881838560.714022156876081
581.47131.470280109742920.00101989025707710.00101989025707710.168596272983934
591.50161.500560041127670.001039958872333320.001039958872333320.696759358298355
601.43181.43080856164730.000991438352697020.000991438352697016-1.68573192903972

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.3031 & 1.3031 & 0 & 0 & 0 \tabularnewline
2 & 1.3241 & 1.32300800006159 & 0.00109199993705762 & 0.00109199993841378 & 0.284544909239405 \tabularnewline
3 & 1.2961 & 1.29512390443971 & 0.000976095560289192 & 0.00097609556028919 & -0.691139502135368 \tabularnewline
4 & 1.2865 & 1.28556587307044 & 0.000934126929555057 & 0.000934126929555057 & -0.251258608894471 \tabularnewline
5 & 1.2305 & 1.22979090913222 & 0.000709090867774892 & 0.000709090867774893 & -1.35260712073784 \tabularnewline
6 & 1.2101 & 1.20947401578439 & 0.000625984215611987 & 0.000625984215611988 & -0.501501141687857 \tabularnewline
7 & 1.2125 & 1.21186705886014 & 0.000632941139856673 & 0.000632941139856674 & 0.0421466676067974 \tabularnewline
8 & 1.235 & 1.23428164066636 & 0.000718359333637368 & 0.000718359333637367 & 0.519516656049798 \tabularnewline
9 & 1.2014 & 1.20081517513086 & 0.000584824869139562 & 0.00058482486913956 & -0.81534034466468 \tabularnewline
10 & 1.1992 & 1.19862596902509 & 0.00057403097491129 & 0.00057403097491129 & -0.0661627566561902 \tabularnewline
11 & 1.1791 & 1.17860579153399 & 0.000494208466012981 & 0.000494208466012981 & -0.491183905484689 \tabularnewline
12 & 1.1832 & 1.18269192310579 & 0.000508076894207854 & 0.000508076894207854 & 0.0856688263444004 \tabularnewline
13 & 1.2159 & 1.20932887166569 & -0.00059737530509003 & 0.00657112833430543 & 0.75293148809392 \tabularnewline
14 & 1.1922 & 1.19317399995478 & -0.000973999974556624 & -0.000973999954777134 & -0.315695399333558 \tabularnewline
15 & 1.2114 & 1.21233738654445 & -0.000937386544454878 & -0.000937386544454879 & 0.47979592591895 \tabularnewline
16 & 1.2614 & 1.26224510867252 & -0.000845108672518426 & -0.00084510867251843 & 1.21144001203051 \tabularnewline
17 & 1.2812 & 1.28200777574635 & -0.000807775746347255 & -0.000807775746347254 & 0.491001863969149 \tabularnewline
18 & 1.2786 & 1.2794110108081 & -0.000811010808098767 & -0.000811010808098766 & -0.0426244735224776 \tabularnewline
19 & 1.2772 & 1.27801207204986 & -0.000812072049860374 & -0.000812072049860374 & -0.0140079541182102 \tabularnewline
20 & 1.2815 & 1.28230287767589 & -0.000802877675891391 & -0.000802877675891392 & 0.121580816674373 \tabularnewline
21 & 1.2679 & 1.2687258527603 & -0.000825852760301605 & -0.000825852760301609 & -0.304355471633153 \tabularnewline
22 & 1.2765 & 1.27730896055146 & -0.000808960551459083 & -0.000808960551459083 & 0.224176530719851 \tabularnewline
23 & 1.3247 & 1.32542128799443 & -0.000721287994432987 & -0.00072128799443299 & 1.16558964720041 \tabularnewline
24 & 1.3191 & 1.31982999997995 & -0.000729999979948916 & -0.000729999979948914 & -0.116031542813216 \tabularnewline
25 & 1.3029 & 1.29917518364635 & -0.000338619668924149 & 0.00372481635365155 & -0.523037542810113 \tabularnewline
26 & 1.3234 & 1.32335776469896 & 4.22352932610852e-05 & 4.22353010408676e-05 & 0.530589056506489 \tabularnewline
27 & 1.3354 & 1.33534371327902 & 5.62867209832501e-05 & 5.6286720983248e-05 & 0.284481271817941 \tabularnewline
28 & 1.3651 & 1.36500892018877 & 9.10798112281102e-05 & 9.10798112281042e-05 & 0.705239422293093 \tabularnewline
29 & 1.3453 & 1.34523223915659 & 6.77608434091449e-05 & 6.77608434091454e-05 & -0.473219504090004 \tabularnewline
30 & 1.3534 & 1.35332283372445 & 7.716627555324e-05 & 7.716627555324e-05 & 0.191091427839903 \tabularnewline
31 & 1.3706 & 1.3705028070186 & 9.71929814008855e-05 & 9.71929814008849e-05 & 0.407361997632161 \tabularnewline
32 & 1.3638 & 1.36371086448693 & 8.91355130702872e-05 & 8.91355130702862e-05 & -0.164088274049579 \tabularnewline
33 & 1.4268 & 1.42663745624462 & 0.000162543755384467 & 0.000162543755384465 & 1.49668745598011 \tabularnewline
34 & 1.4485 & 1.44831235431459 & 0.000187645685411225 & 0.000187645685411226 & 0.512389438658394 \tabularnewline
35 & 1.4635 & 1.46329511059617 & 0.000204889403829276 & 0.000204889403829273 & 0.352395333095917 \tabularnewline
36 & 1.4587 & 1.45850093023494 & 0.000199069765063544 & 0.000199069765063544 & -0.119069583823409 \tabularnewline
37 & 1.4876 & 1.48595504590463 & -0.000149541281504454 & 0.00164495409537356 & 0.692932912639914 \tabularnewline
38 & 1.5189 & 1.51867373912774 & 0.000226260866536672 & 0.000226260872255575 & 0.730900620931029 \tabularnewline
39 & 1.5783 & 1.57802232841095 & 0.000277671589046375 & 0.000277671589046374 & 1.40798901159727 \tabularnewline
40 & 1.5633 & 1.56303559027856 & 0.000264409721441423 & 0.000264409721441418 & -0.363519394857206 \tabularnewline
41 & 1.5554 & 1.55514267129301 & 0.000257328706988951 & 0.000257328706988951 & -0.194265361851859 \tabularnewline
42 & 1.5757 & 1.57542530329375 & 0.000274696706253584 & 0.000274696706253585 & 0.476898929435036 \tabularnewline
43 & 1.5593 & 1.55903974026049 & 0.000260259739510269 & 0.000260259739510268 & -0.396760884678923 \tabularnewline
44 & 1.466 & 1.46582067474066 & 0.000179325259337654 & 0.000179325259337662 & -2.22619133601994 \tabularnewline
45 & 1.4065 & 1.40637225583387 & 0.000127744166132186 & 0.000127744166132181 & -1.4200222072003 \tabularnewline
46 & 1.2759 & 1.27588514680386 & 1.48531961446394e-05 & 1.485319614464e-05 & -3.11056414166282 \tabularnewline
47 & 1.2705 & 1.27048981880831 & 1.01811916942101e-05 & 1.01811916942064e-05 & -0.128842232932366 \tabularnewline
48 & 1.3954 & 1.39528215517216 & 0.000117844827841088 & 0.000117844827841085 & 2.97165750065224 \tabularnewline
49 & 1.2793 & 1.29144035896482 & 0.00110366899876624 & -0.0121403589648204 & -2.60057471451443 \tabularnewline
50 & 1.2694 & 1.26851372416855 & 0.000886275856451274 & 0.000886275831450488 & -0.542525372398785 \tabularnewline
51 & 1.3282 & 1.32727381116801 & 0.000926188831985596 & 0.000926188831985596 & 1.37813188896661 \tabularnewline
52 & 1.323 & 1.32207803030632 & 0.000921969693683733 & 0.000921969693683727 & -0.145780612842954 \tabularnewline
53 & 1.4135 & 1.41251637990711 & 0.000983620092890857 & 0.00098362009289086 & 2.13162597667844 \tabularnewline
54 & 1.4042 & 1.40322345254814 & 0.000976547451856984 & 0.000976547451856987 & -0.244712200534846 \tabularnewline
55 & 1.4253 & 1.4243096219966 & 0.000990378003396628 & 0.000990378003396627 & 0.478864006604029 \tabularnewline
56 & 1.4322 & 1.4312055631903 & 0.000994436809701294 & 0.000994436809701295 & 0.140627257097516 \tabularnewline
57 & 1.4632 & 1.46218496911816 & 0.00101503088183856 & 0.00101503088183856 & 0.714022156876081 \tabularnewline
58 & 1.4713 & 1.47028010974292 & 0.0010198902570771 & 0.0010198902570771 & 0.168596272983934 \tabularnewline
59 & 1.5016 & 1.50056004112767 & 0.00103995887233332 & 0.00103995887233332 & 0.696759358298355 \tabularnewline
60 & 1.4318 & 1.4308085616473 & 0.00099143835269702 & 0.000991438352697016 & -1.68573192903972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108622&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]1.3031[/C][C]1.3031[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.3241[/C][C]1.32300800006159[/C][C]0.00109199993705762[/C][C]0.00109199993841378[/C][C]0.284544909239405[/C][/ROW]
[ROW][C]3[/C][C]1.2961[/C][C]1.29512390443971[/C][C]0.000976095560289192[/C][C]0.00097609556028919[/C][C]-0.691139502135368[/C][/ROW]
[ROW][C]4[/C][C]1.2865[/C][C]1.28556587307044[/C][C]0.000934126929555057[/C][C]0.000934126929555057[/C][C]-0.251258608894471[/C][/ROW]
[ROW][C]5[/C][C]1.2305[/C][C]1.22979090913222[/C][C]0.000709090867774892[/C][C]0.000709090867774893[/C][C]-1.35260712073784[/C][/ROW]
[ROW][C]6[/C][C]1.2101[/C][C]1.20947401578439[/C][C]0.000625984215611987[/C][C]0.000625984215611988[/C][C]-0.501501141687857[/C][/ROW]
[ROW][C]7[/C][C]1.2125[/C][C]1.21186705886014[/C][C]0.000632941139856673[/C][C]0.000632941139856674[/C][C]0.0421466676067974[/C][/ROW]
[ROW][C]8[/C][C]1.235[/C][C]1.23428164066636[/C][C]0.000718359333637368[/C][C]0.000718359333637367[/C][C]0.519516656049798[/C][/ROW]
[ROW][C]9[/C][C]1.2014[/C][C]1.20081517513086[/C][C]0.000584824869139562[/C][C]0.00058482486913956[/C][C]-0.81534034466468[/C][/ROW]
[ROW][C]10[/C][C]1.1992[/C][C]1.19862596902509[/C][C]0.00057403097491129[/C][C]0.00057403097491129[/C][C]-0.0661627566561902[/C][/ROW]
[ROW][C]11[/C][C]1.1791[/C][C]1.17860579153399[/C][C]0.000494208466012981[/C][C]0.000494208466012981[/C][C]-0.491183905484689[/C][/ROW]
[ROW][C]12[/C][C]1.1832[/C][C]1.18269192310579[/C][C]0.000508076894207854[/C][C]0.000508076894207854[/C][C]0.0856688263444004[/C][/ROW]
[ROW][C]13[/C][C]1.2159[/C][C]1.20932887166569[/C][C]-0.00059737530509003[/C][C]0.00657112833430543[/C][C]0.75293148809392[/C][/ROW]
[ROW][C]14[/C][C]1.1922[/C][C]1.19317399995478[/C][C]-0.000973999974556624[/C][C]-0.000973999954777134[/C][C]-0.315695399333558[/C][/ROW]
[ROW][C]15[/C][C]1.2114[/C][C]1.21233738654445[/C][C]-0.000937386544454878[/C][C]-0.000937386544454879[/C][C]0.47979592591895[/C][/ROW]
[ROW][C]16[/C][C]1.2614[/C][C]1.26224510867252[/C][C]-0.000845108672518426[/C][C]-0.00084510867251843[/C][C]1.21144001203051[/C][/ROW]
[ROW][C]17[/C][C]1.2812[/C][C]1.28200777574635[/C][C]-0.000807775746347255[/C][C]-0.000807775746347254[/C][C]0.491001863969149[/C][/ROW]
[ROW][C]18[/C][C]1.2786[/C][C]1.2794110108081[/C][C]-0.000811010808098767[/C][C]-0.000811010808098766[/C][C]-0.0426244735224776[/C][/ROW]
[ROW][C]19[/C][C]1.2772[/C][C]1.27801207204986[/C][C]-0.000812072049860374[/C][C]-0.000812072049860374[/C][C]-0.0140079541182102[/C][/ROW]
[ROW][C]20[/C][C]1.2815[/C][C]1.28230287767589[/C][C]-0.000802877675891391[/C][C]-0.000802877675891392[/C][C]0.121580816674373[/C][/ROW]
[ROW][C]21[/C][C]1.2679[/C][C]1.2687258527603[/C][C]-0.000825852760301605[/C][C]-0.000825852760301609[/C][C]-0.304355471633153[/C][/ROW]
[ROW][C]22[/C][C]1.2765[/C][C]1.27730896055146[/C][C]-0.000808960551459083[/C][C]-0.000808960551459083[/C][C]0.224176530719851[/C][/ROW]
[ROW][C]23[/C][C]1.3247[/C][C]1.32542128799443[/C][C]-0.000721287994432987[/C][C]-0.00072128799443299[/C][C]1.16558964720041[/C][/ROW]
[ROW][C]24[/C][C]1.3191[/C][C]1.31982999997995[/C][C]-0.000729999979948916[/C][C]-0.000729999979948914[/C][C]-0.116031542813216[/C][/ROW]
[ROW][C]25[/C][C]1.3029[/C][C]1.29917518364635[/C][C]-0.000338619668924149[/C][C]0.00372481635365155[/C][C]-0.523037542810113[/C][/ROW]
[ROW][C]26[/C][C]1.3234[/C][C]1.32335776469896[/C][C]4.22352932610852e-05[/C][C]4.22353010408676e-05[/C][C]0.530589056506489[/C][/ROW]
[ROW][C]27[/C][C]1.3354[/C][C]1.33534371327902[/C][C]5.62867209832501e-05[/C][C]5.6286720983248e-05[/C][C]0.284481271817941[/C][/ROW]
[ROW][C]28[/C][C]1.3651[/C][C]1.36500892018877[/C][C]9.10798112281102e-05[/C][C]9.10798112281042e-05[/C][C]0.705239422293093[/C][/ROW]
[ROW][C]29[/C][C]1.3453[/C][C]1.34523223915659[/C][C]6.77608434091449e-05[/C][C]6.77608434091454e-05[/C][C]-0.473219504090004[/C][/ROW]
[ROW][C]30[/C][C]1.3534[/C][C]1.35332283372445[/C][C]7.716627555324e-05[/C][C]7.716627555324e-05[/C][C]0.191091427839903[/C][/ROW]
[ROW][C]31[/C][C]1.3706[/C][C]1.3705028070186[/C][C]9.71929814008855e-05[/C][C]9.71929814008849e-05[/C][C]0.407361997632161[/C][/ROW]
[ROW][C]32[/C][C]1.3638[/C][C]1.36371086448693[/C][C]8.91355130702872e-05[/C][C]8.91355130702862e-05[/C][C]-0.164088274049579[/C][/ROW]
[ROW][C]33[/C][C]1.4268[/C][C]1.42663745624462[/C][C]0.000162543755384467[/C][C]0.000162543755384465[/C][C]1.49668745598011[/C][/ROW]
[ROW][C]34[/C][C]1.4485[/C][C]1.44831235431459[/C][C]0.000187645685411225[/C][C]0.000187645685411226[/C][C]0.512389438658394[/C][/ROW]
[ROW][C]35[/C][C]1.4635[/C][C]1.46329511059617[/C][C]0.000204889403829276[/C][C]0.000204889403829273[/C][C]0.352395333095917[/C][/ROW]
[ROW][C]36[/C][C]1.4587[/C][C]1.45850093023494[/C][C]0.000199069765063544[/C][C]0.000199069765063544[/C][C]-0.119069583823409[/C][/ROW]
[ROW][C]37[/C][C]1.4876[/C][C]1.48595504590463[/C][C]-0.000149541281504454[/C][C]0.00164495409537356[/C][C]0.692932912639914[/C][/ROW]
[ROW][C]38[/C][C]1.5189[/C][C]1.51867373912774[/C][C]0.000226260866536672[/C][C]0.000226260872255575[/C][C]0.730900620931029[/C][/ROW]
[ROW][C]39[/C][C]1.5783[/C][C]1.57802232841095[/C][C]0.000277671589046375[/C][C]0.000277671589046374[/C][C]1.40798901159727[/C][/ROW]
[ROW][C]40[/C][C]1.5633[/C][C]1.56303559027856[/C][C]0.000264409721441423[/C][C]0.000264409721441418[/C][C]-0.363519394857206[/C][/ROW]
[ROW][C]41[/C][C]1.5554[/C][C]1.55514267129301[/C][C]0.000257328706988951[/C][C]0.000257328706988951[/C][C]-0.194265361851859[/C][/ROW]
[ROW][C]42[/C][C]1.5757[/C][C]1.57542530329375[/C][C]0.000274696706253584[/C][C]0.000274696706253585[/C][C]0.476898929435036[/C][/ROW]
[ROW][C]43[/C][C]1.5593[/C][C]1.55903974026049[/C][C]0.000260259739510269[/C][C]0.000260259739510268[/C][C]-0.396760884678923[/C][/ROW]
[ROW][C]44[/C][C]1.466[/C][C]1.46582067474066[/C][C]0.000179325259337654[/C][C]0.000179325259337662[/C][C]-2.22619133601994[/C][/ROW]
[ROW][C]45[/C][C]1.4065[/C][C]1.40637225583387[/C][C]0.000127744166132186[/C][C]0.000127744166132181[/C][C]-1.4200222072003[/C][/ROW]
[ROW][C]46[/C][C]1.2759[/C][C]1.27588514680386[/C][C]1.48531961446394e-05[/C][C]1.485319614464e-05[/C][C]-3.11056414166282[/C][/ROW]
[ROW][C]47[/C][C]1.2705[/C][C]1.27048981880831[/C][C]1.01811916942101e-05[/C][C]1.01811916942064e-05[/C][C]-0.128842232932366[/C][/ROW]
[ROW][C]48[/C][C]1.3954[/C][C]1.39528215517216[/C][C]0.000117844827841088[/C][C]0.000117844827841085[/C][C]2.97165750065224[/C][/ROW]
[ROW][C]49[/C][C]1.2793[/C][C]1.29144035896482[/C][C]0.00110366899876624[/C][C]-0.0121403589648204[/C][C]-2.60057471451443[/C][/ROW]
[ROW][C]50[/C][C]1.2694[/C][C]1.26851372416855[/C][C]0.000886275856451274[/C][C]0.000886275831450488[/C][C]-0.542525372398785[/C][/ROW]
[ROW][C]51[/C][C]1.3282[/C][C]1.32727381116801[/C][C]0.000926188831985596[/C][C]0.000926188831985596[/C][C]1.37813188896661[/C][/ROW]
[ROW][C]52[/C][C]1.323[/C][C]1.32207803030632[/C][C]0.000921969693683733[/C][C]0.000921969693683727[/C][C]-0.145780612842954[/C][/ROW]
[ROW][C]53[/C][C]1.4135[/C][C]1.41251637990711[/C][C]0.000983620092890857[/C][C]0.00098362009289086[/C][C]2.13162597667844[/C][/ROW]
[ROW][C]54[/C][C]1.4042[/C][C]1.40322345254814[/C][C]0.000976547451856984[/C][C]0.000976547451856987[/C][C]-0.244712200534846[/C][/ROW]
[ROW][C]55[/C][C]1.4253[/C][C]1.4243096219966[/C][C]0.000990378003396628[/C][C]0.000990378003396627[/C][C]0.478864006604029[/C][/ROW]
[ROW][C]56[/C][C]1.4322[/C][C]1.4312055631903[/C][C]0.000994436809701294[/C][C]0.000994436809701295[/C][C]0.140627257097516[/C][/ROW]
[ROW][C]57[/C][C]1.4632[/C][C]1.46218496911816[/C][C]0.00101503088183856[/C][C]0.00101503088183856[/C][C]0.714022156876081[/C][/ROW]
[ROW][C]58[/C][C]1.4713[/C][C]1.47028010974292[/C][C]0.0010198902570771[/C][C]0.0010198902570771[/C][C]0.168596272983934[/C][/ROW]
[ROW][C]59[/C][C]1.5016[/C][C]1.50056004112767[/C][C]0.00103995887233332[/C][C]0.00103995887233332[/C][C]0.696759358298355[/C][/ROW]
[ROW][C]60[/C][C]1.4318[/C][C]1.4308085616473[/C][C]0.00099143835269702[/C][C]0.000991438352697016[/C][C]-1.68573192903972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108622&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108622&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
11.30311.3031000
21.32411.323008000061590.001091999937057620.001091999938413780.284544909239405
31.29611.295123904439710.0009760955602891920.00097609556028919-0.691139502135368
41.28651.285565873070440.0009341269295550570.000934126929555057-0.251258608894471
51.23051.229790909132220.0007090908677748920.000709090867774893-1.35260712073784
61.21011.209474015784390.0006259842156119870.000625984215611988-0.501501141687857
71.21251.211867058860140.0006329411398566730.0006329411398566740.0421466676067974
81.2351.234281640666360.0007183593336373680.0007183593336373670.519516656049798
91.20141.200815175130860.0005848248691395620.00058482486913956-0.81534034466468
101.19921.198625969025090.000574030974911290.00057403097491129-0.0661627566561902
111.17911.178605791533990.0004942084660129810.000494208466012981-0.491183905484689
121.18321.182691923105790.0005080768942078540.0005080768942078540.0856688263444004
131.21591.20932887166569-0.000597375305090030.006571128334305430.75293148809392
141.19221.19317399995478-0.000973999974556624-0.000973999954777134-0.315695399333558
151.21141.21233738654445-0.000937386544454878-0.0009373865444548790.47979592591895
161.26141.26224510867252-0.000845108672518426-0.000845108672518431.21144001203051
171.28121.28200777574635-0.000807775746347255-0.0008077757463472540.491001863969149
181.27861.2794110108081-0.000811010808098767-0.000811010808098766-0.0426244735224776
191.27721.27801207204986-0.000812072049860374-0.000812072049860374-0.0140079541182102
201.28151.28230287767589-0.000802877675891391-0.0008028776758913920.121580816674373
211.26791.2687258527603-0.000825852760301605-0.000825852760301609-0.304355471633153
221.27651.27730896055146-0.000808960551459083-0.0008089605514590830.224176530719851
231.32471.32542128799443-0.000721287994432987-0.000721287994432991.16558964720041
241.31911.31982999997995-0.000729999979948916-0.000729999979948914-0.116031542813216
251.30291.29917518364635-0.0003386196689241490.00372481635365155-0.523037542810113
261.32341.323357764698964.22352932610852e-054.22353010408676e-050.530589056506489
271.33541.335343713279025.62867209832501e-055.6286720983248e-050.284481271817941
281.36511.365008920188779.10798112281102e-059.10798112281042e-050.705239422293093
291.34531.345232239156596.77608434091449e-056.77608434091454e-05-0.473219504090004
301.35341.353322833724457.716627555324e-057.716627555324e-050.191091427839903
311.37061.37050280701869.71929814008855e-059.71929814008849e-050.407361997632161
321.36381.363710864486938.91355130702872e-058.91355130702862e-05-0.164088274049579
331.42681.426637456244620.0001625437553844670.0001625437553844651.49668745598011
341.44851.448312354314590.0001876456854112250.0001876456854112260.512389438658394
351.46351.463295110596170.0002048894038292760.0002048894038292730.352395333095917
361.45871.458500930234940.0001990697650635440.000199069765063544-0.119069583823409
371.48761.48595504590463-0.0001495412815044540.001644954095373560.692932912639914
381.51891.518673739127740.0002262608665366720.0002262608722555750.730900620931029
391.57831.578022328410950.0002776715890463750.0002776715890463741.40798901159727
401.56331.563035590278560.0002644097214414230.000264409721441418-0.363519394857206
411.55541.555142671293010.0002573287069889510.000257328706988951-0.194265361851859
421.57571.575425303293750.0002746967062535840.0002746967062535850.476898929435036
431.55931.559039740260490.0002602597395102690.000260259739510268-0.396760884678923
441.4661.465820674740660.0001793252593376540.000179325259337662-2.22619133601994
451.40651.406372255833870.0001277441661321860.000127744166132181-1.4200222072003
461.27591.275885146803861.48531961446394e-051.485319614464e-05-3.11056414166282
471.27051.270489818808311.01811916942101e-051.01811916942064e-05-0.128842232932366
481.39541.395282155172160.0001178448278410880.0001178448278410852.97165750065224
491.27931.291440358964820.00110366899876624-0.0121403589648204-2.60057471451443
501.26941.268513724168550.0008862758564512740.000886275831450488-0.542525372398785
511.32821.327273811168010.0009261888319855960.0009261888319855961.37813188896661
521.3231.322078030306320.0009219696936837330.000921969693683727-0.145780612842954
531.41351.412516379907110.0009836200928908570.000983620092890862.13162597667844
541.40421.403223452548140.0009765474518569840.000976547451856987-0.244712200534846
551.42531.42430962199660.0009903780033966280.0009903780033966270.478864006604029
561.43221.43120556319030.0009944368097012940.0009944368097012950.140627257097516
571.46321.462184969118160.001015030881838560.001015030881838560.714022156876081
581.47131.470280109742920.00101989025707710.00101989025707710.168596272983934
591.50161.500560041127670.001039958872333320.001039958872333320.696759358298355
601.43181.43080856164730.000991438352697020.000991438352697016-1.68573192903972



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 1 ;
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')