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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, 21 Dec 2012 06:00:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356087648m34cvcgymy834r7.htm/, Retrieved Thu, 25 Apr 2024 00:30:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203455, Retrieved Thu, 25 Apr 2024 00:30:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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- RM D  [Decomposition by Loess] [] [2012-11-24 21:14:59] [0883bf8f4217d775edf6393676d58a73]
- RMPD      [Structural Time Series Models] [] [2012-12-21 11:00:33] [b650a28572edc4a1d205c228043a3295] [Current]
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Dataseries X:
1,4761
1,4721
1,487
1,5167
1,5812
1,554
1,5508
1,5764
1,5611
1,4735
1,4303
1,2757
1,2727
1,3917
1,2816
1,2644
1,3308
1,3275
1,4098
1,4134
1,4138
1,4272
1,4643
1,48
1,5023
1,4406
1,3966
1,357
1,3479
1,3315
1,2307
1,2271
1,3028
1,268
1,3648
1,3857
1,2998
1,3362
1,3692
1,3834
1,4207
1,486
1,4385
1,4453
1,426
1,445
1,3503
1,4001
1,3418
1,2939
1,3176
1,3443
1,3356
1,3214
1,2403
1,259
1,2284
1,2611
1,293
1,2993
1,2986




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203455&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203455&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'Herman Ole Andreas Wold' @ wold.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.47611.4761000
21.47211.4724045410594-0.000206739420129031-0.00020673941968499-0.0466025083844365
31.4871.48609280062215-9.31209884382086e-05-9.31209884382082e-050.288702645608972
41.51671.51458062547184.89270661498418e-054.89270661498418e-050.594932165727773
51.58121.576383986424620.0003412785846188030.0003412785846188061.28549672018168
61.5541.555311023357010.0002406836030766870.000240683603076687-0.445761283141193
71.55081.550916833380450.000219017175623170.00021901717562317-0.096479166349625
81.57641.574387909564870.0003272052470801160.0003272052470801150.484008568984297
91.56111.561770719143820.0002672560569109010.000267256056910902-0.269444567407082
101.47351.47958737066018-0.000112835882574426-0.000112835882574429-1.71623814783716
111.43031.43392264882751-0.000321863517736719-0.000321863517736718-0.948169890073508
121.27571.2872920312921-0.000990175630650501-0.000990175630650504-3.04541326760041
131.27271.27084098293654-0.0002712267887968470.00298349467568372-0.391029578536177
141.39171.383278202446270.002576971028426380.0025769709926131.98474180607014
151.28161.286569027946780.002205536259123910.00220553625912391-2.06278975577745
161.26441.264043278343640.002148891361017710.00214889136101771-0.514249767960543
171.33081.324311758517750.002276072823358380.002276072823358381.20852713701535
181.32751.325318672065980.002273309916621660.00227330991662166-0.0263906167836692
191.40981.401974303741760.002434852596686870.002434852596686871.54668649973825
201.41341.410509785029710.002448072979052410.00244807297905240.126854619633883
211.41381.411463464395880.002444841555375920.00244484155537591-0.0310739046836975
221.42721.424001851201750.002466620384029420.002466620384029420.209881276314507
231.46431.459377449994870.002537475157765850.002537475157765840.684295213566435
241.481.476382877759580.002568558502014970.002568558502014970.300839821284495
251.50231.518243954962090.00170878529559331-0.0187966382074420.904001702632701
261.44061.444581096481050.0004759277603357620.00047592773453355-1.41623757476315
271.39661.399528182134790.0003653911166446940.000365391116644691-0.9458899283601
281.3571.359613750713570.000305443907650490.000305443907650481-0.837289825188133
291.34791.348442686253060.0002891124492618680.000289112449261871-0.238562646565834
301.33151.332416513701950.0002659976386516250.000265997638651623-0.339146414820465
311.23071.237469593668820.0001313221504664220.00013132215046642-1.9791927264113
321.22711.227700250285960.0001173379624095740.000117337962409571-0.205805147369037
331.30281.297531549360910.0002156664224079930.0002156664224079751.44914296848944
341.2681.269845886856930.0001763682622971350.000176368262297131-0.579983975805906
351.36481.358114880899320.0003002694935441250.0003002694935441151.83117686850439
361.38571.383543187135860.0003355621398980780.0003355621398980750.522334763240957
371.29981.318519566687920.00127063764909711-0.0139770140999771-1.45481156943281
381.33621.333876974814420.001440974248036710.001440974205100330.272272597522506
391.36921.36551886406950.001495246185398980.001495246185398980.627433908536554
401.38341.380884211484140.001510534407517760.001510534407517750.288270958319391
411.42071.416669109188980.001546671017948750.001546671017948750.712350917201053
421.4861.479916433903450.001611461044624030.001611461044624021.28237296633739
431.43851.43994603076110.00156785181538210.0015678518153821-0.864228041441891
441.44531.443580214230210.001570016593035150.001570016593035140.0429461487309452
451.4261.425849139698040.001549817122843040.00154981712284302-0.401149128521285
461.4451.442350829000050.00156544857748020.00156544857748020.310755886961475
471.35031.355253490194170.001472852753418560.00147285275341855-1.84274383545704
481.40011.395757425945510.001513572709353290.001513572709353280.811211289894281
491.34181.364632558906760.00186044541602547-0.0204648995368184-0.71433693938531
501.29391.297185504796450.00119571131515680.00119571128804556-1.36096383589569
511.31761.315165179998740.0012196522006360.0012196522006360.348689421370156
521.34431.341255961048560.001241420091483460.001241420091483450.516865258215939
531.33561.334914545638650.001235070418915540.00123507041891555-0.15758546475578
541.32141.321256810272540.001222647298006220.00122264729800621-0.309500303345598
551.24031.244774315744710.001157895224451650.00115789522445166-1.61485745301208
561.2591.25702858069440.001167134057835970.001167134057835960.23060314479037
571.22841.229347813628120.001143135309504930.00114313530950491-0.599512716056618
581.26111.25794418407710.001165954841389190.001165954841389180.570528810081816
591.2931.289598845769510.001191276454097850.001191276454097850.633611318406749
601.29931.297608832409510.001196934859334510.00119693485933450.14170527795524
611.29861.309925684401140.00110292900084819-0.01213221898541470.240895604567086

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.4761 & 1.4761 & 0 & 0 & 0 \tabularnewline
2 & 1.4721 & 1.4724045410594 & -0.000206739420129031 & -0.00020673941968499 & -0.0466025083844365 \tabularnewline
3 & 1.487 & 1.48609280062215 & -9.31209884382086e-05 & -9.31209884382082e-05 & 0.288702645608972 \tabularnewline
4 & 1.5167 & 1.5145806254718 & 4.89270661498418e-05 & 4.89270661498418e-05 & 0.594932165727773 \tabularnewline
5 & 1.5812 & 1.57638398642462 & 0.000341278584618803 & 0.000341278584618806 & 1.28549672018168 \tabularnewline
6 & 1.554 & 1.55531102335701 & 0.000240683603076687 & 0.000240683603076687 & -0.445761283141193 \tabularnewline
7 & 1.5508 & 1.55091683338045 & 0.00021901717562317 & 0.00021901717562317 & -0.096479166349625 \tabularnewline
8 & 1.5764 & 1.57438790956487 & 0.000327205247080116 & 0.000327205247080115 & 0.484008568984297 \tabularnewline
9 & 1.5611 & 1.56177071914382 & 0.000267256056910901 & 0.000267256056910902 & -0.269444567407082 \tabularnewline
10 & 1.4735 & 1.47958737066018 & -0.000112835882574426 & -0.000112835882574429 & -1.71623814783716 \tabularnewline
11 & 1.4303 & 1.43392264882751 & -0.000321863517736719 & -0.000321863517736718 & -0.948169890073508 \tabularnewline
12 & 1.2757 & 1.2872920312921 & -0.000990175630650501 & -0.000990175630650504 & -3.04541326760041 \tabularnewline
13 & 1.2727 & 1.27084098293654 & -0.000271226788796847 & 0.00298349467568372 & -0.391029578536177 \tabularnewline
14 & 1.3917 & 1.38327820244627 & 0.00257697102842638 & 0.002576970992613 & 1.98474180607014 \tabularnewline
15 & 1.2816 & 1.28656902794678 & 0.00220553625912391 & 0.00220553625912391 & -2.06278975577745 \tabularnewline
16 & 1.2644 & 1.26404327834364 & 0.00214889136101771 & 0.00214889136101771 & -0.514249767960543 \tabularnewline
17 & 1.3308 & 1.32431175851775 & 0.00227607282335838 & 0.00227607282335838 & 1.20852713701535 \tabularnewline
18 & 1.3275 & 1.32531867206598 & 0.00227330991662166 & 0.00227330991662166 & -0.0263906167836692 \tabularnewline
19 & 1.4098 & 1.40197430374176 & 0.00243485259668687 & 0.00243485259668687 & 1.54668649973825 \tabularnewline
20 & 1.4134 & 1.41050978502971 & 0.00244807297905241 & 0.0024480729790524 & 0.126854619633883 \tabularnewline
21 & 1.4138 & 1.41146346439588 & 0.00244484155537592 & 0.00244484155537591 & -0.0310739046836975 \tabularnewline
22 & 1.4272 & 1.42400185120175 & 0.00246662038402942 & 0.00246662038402942 & 0.209881276314507 \tabularnewline
23 & 1.4643 & 1.45937744999487 & 0.00253747515776585 & 0.00253747515776584 & 0.684295213566435 \tabularnewline
24 & 1.48 & 1.47638287775958 & 0.00256855850201497 & 0.00256855850201497 & 0.300839821284495 \tabularnewline
25 & 1.5023 & 1.51824395496209 & 0.00170878529559331 & -0.018796638207442 & 0.904001702632701 \tabularnewline
26 & 1.4406 & 1.44458109648105 & 0.000475927760335762 & 0.00047592773453355 & -1.41623757476315 \tabularnewline
27 & 1.3966 & 1.39952818213479 & 0.000365391116644694 & 0.000365391116644691 & -0.9458899283601 \tabularnewline
28 & 1.357 & 1.35961375071357 & 0.00030544390765049 & 0.000305443907650481 & -0.837289825188133 \tabularnewline
29 & 1.3479 & 1.34844268625306 & 0.000289112449261868 & 0.000289112449261871 & -0.238562646565834 \tabularnewline
30 & 1.3315 & 1.33241651370195 & 0.000265997638651625 & 0.000265997638651623 & -0.339146414820465 \tabularnewline
31 & 1.2307 & 1.23746959366882 & 0.000131322150466422 & 0.00013132215046642 & -1.9791927264113 \tabularnewline
32 & 1.2271 & 1.22770025028596 & 0.000117337962409574 & 0.000117337962409571 & -0.205805147369037 \tabularnewline
33 & 1.3028 & 1.29753154936091 & 0.000215666422407993 & 0.000215666422407975 & 1.44914296848944 \tabularnewline
34 & 1.268 & 1.26984588685693 & 0.000176368262297135 & 0.000176368262297131 & -0.579983975805906 \tabularnewline
35 & 1.3648 & 1.35811488089932 & 0.000300269493544125 & 0.000300269493544115 & 1.83117686850439 \tabularnewline
36 & 1.3857 & 1.38354318713586 & 0.000335562139898078 & 0.000335562139898075 & 0.522334763240957 \tabularnewline
37 & 1.2998 & 1.31851956668792 & 0.00127063764909711 & -0.0139770140999771 & -1.45481156943281 \tabularnewline
38 & 1.3362 & 1.33387697481442 & 0.00144097424803671 & 0.00144097420510033 & 0.272272597522506 \tabularnewline
39 & 1.3692 & 1.3655188640695 & 0.00149524618539898 & 0.00149524618539898 & 0.627433908536554 \tabularnewline
40 & 1.3834 & 1.38088421148414 & 0.00151053440751776 & 0.00151053440751775 & 0.288270958319391 \tabularnewline
41 & 1.4207 & 1.41666910918898 & 0.00154667101794875 & 0.00154667101794875 & 0.712350917201053 \tabularnewline
42 & 1.486 & 1.47991643390345 & 0.00161146104462403 & 0.00161146104462402 & 1.28237296633739 \tabularnewline
43 & 1.4385 & 1.4399460307611 & 0.0015678518153821 & 0.0015678518153821 & -0.864228041441891 \tabularnewline
44 & 1.4453 & 1.44358021423021 & 0.00157001659303515 & 0.00157001659303514 & 0.0429461487309452 \tabularnewline
45 & 1.426 & 1.42584913969804 & 0.00154981712284304 & 0.00154981712284302 & -0.401149128521285 \tabularnewline
46 & 1.445 & 1.44235082900005 & 0.0015654485774802 & 0.0015654485774802 & 0.310755886961475 \tabularnewline
47 & 1.3503 & 1.35525349019417 & 0.00147285275341856 & 0.00147285275341855 & -1.84274383545704 \tabularnewline
48 & 1.4001 & 1.39575742594551 & 0.00151357270935329 & 0.00151357270935328 & 0.811211289894281 \tabularnewline
49 & 1.3418 & 1.36463255890676 & 0.00186044541602547 & -0.0204648995368184 & -0.71433693938531 \tabularnewline
50 & 1.2939 & 1.29718550479645 & 0.0011957113151568 & 0.00119571128804556 & -1.36096383589569 \tabularnewline
51 & 1.3176 & 1.31516517999874 & 0.001219652200636 & 0.001219652200636 & 0.348689421370156 \tabularnewline
52 & 1.3443 & 1.34125596104856 & 0.00124142009148346 & 0.00124142009148345 & 0.516865258215939 \tabularnewline
53 & 1.3356 & 1.33491454563865 & 0.00123507041891554 & 0.00123507041891555 & -0.15758546475578 \tabularnewline
54 & 1.3214 & 1.32125681027254 & 0.00122264729800622 & 0.00122264729800621 & -0.309500303345598 \tabularnewline
55 & 1.2403 & 1.24477431574471 & 0.00115789522445165 & 0.00115789522445166 & -1.61485745301208 \tabularnewline
56 & 1.259 & 1.2570285806944 & 0.00116713405783597 & 0.00116713405783596 & 0.23060314479037 \tabularnewline
57 & 1.2284 & 1.22934781362812 & 0.00114313530950493 & 0.00114313530950491 & -0.599512716056618 \tabularnewline
58 & 1.2611 & 1.2579441840771 & 0.00116595484138919 & 0.00116595484138918 & 0.570528810081816 \tabularnewline
59 & 1.293 & 1.28959884576951 & 0.00119127645409785 & 0.00119127645409785 & 0.633611318406749 \tabularnewline
60 & 1.2993 & 1.29760883240951 & 0.00119693485933451 & 0.0011969348593345 & 0.14170527795524 \tabularnewline
61 & 1.2986 & 1.30992568440114 & 0.00110292900084819 & -0.0121322189854147 & 0.240895604567086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203455&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]1.4761[/C][C]1.4761[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.4721[/C][C]1.4724045410594[/C][C]-0.000206739420129031[/C][C]-0.00020673941968499[/C][C]-0.0466025083844365[/C][/ROW]
[ROW][C]3[/C][C]1.487[/C][C]1.48609280062215[/C][C]-9.31209884382086e-05[/C][C]-9.31209884382082e-05[/C][C]0.288702645608972[/C][/ROW]
[ROW][C]4[/C][C]1.5167[/C][C]1.5145806254718[/C][C]4.89270661498418e-05[/C][C]4.89270661498418e-05[/C][C]0.594932165727773[/C][/ROW]
[ROW][C]5[/C][C]1.5812[/C][C]1.57638398642462[/C][C]0.000341278584618803[/C][C]0.000341278584618806[/C][C]1.28549672018168[/C][/ROW]
[ROW][C]6[/C][C]1.554[/C][C]1.55531102335701[/C][C]0.000240683603076687[/C][C]0.000240683603076687[/C][C]-0.445761283141193[/C][/ROW]
[ROW][C]7[/C][C]1.5508[/C][C]1.55091683338045[/C][C]0.00021901717562317[/C][C]0.00021901717562317[/C][C]-0.096479166349625[/C][/ROW]
[ROW][C]8[/C][C]1.5764[/C][C]1.57438790956487[/C][C]0.000327205247080116[/C][C]0.000327205247080115[/C][C]0.484008568984297[/C][/ROW]
[ROW][C]9[/C][C]1.5611[/C][C]1.56177071914382[/C][C]0.000267256056910901[/C][C]0.000267256056910902[/C][C]-0.269444567407082[/C][/ROW]
[ROW][C]10[/C][C]1.4735[/C][C]1.47958737066018[/C][C]-0.000112835882574426[/C][C]-0.000112835882574429[/C][C]-1.71623814783716[/C][/ROW]
[ROW][C]11[/C][C]1.4303[/C][C]1.43392264882751[/C][C]-0.000321863517736719[/C][C]-0.000321863517736718[/C][C]-0.948169890073508[/C][/ROW]
[ROW][C]12[/C][C]1.2757[/C][C]1.2872920312921[/C][C]-0.000990175630650501[/C][C]-0.000990175630650504[/C][C]-3.04541326760041[/C][/ROW]
[ROW][C]13[/C][C]1.2727[/C][C]1.27084098293654[/C][C]-0.000271226788796847[/C][C]0.00298349467568372[/C][C]-0.391029578536177[/C][/ROW]
[ROW][C]14[/C][C]1.3917[/C][C]1.38327820244627[/C][C]0.00257697102842638[/C][C]0.002576970992613[/C][C]1.98474180607014[/C][/ROW]
[ROW][C]15[/C][C]1.2816[/C][C]1.28656902794678[/C][C]0.00220553625912391[/C][C]0.00220553625912391[/C][C]-2.06278975577745[/C][/ROW]
[ROW][C]16[/C][C]1.2644[/C][C]1.26404327834364[/C][C]0.00214889136101771[/C][C]0.00214889136101771[/C][C]-0.514249767960543[/C][/ROW]
[ROW][C]17[/C][C]1.3308[/C][C]1.32431175851775[/C][C]0.00227607282335838[/C][C]0.00227607282335838[/C][C]1.20852713701535[/C][/ROW]
[ROW][C]18[/C][C]1.3275[/C][C]1.32531867206598[/C][C]0.00227330991662166[/C][C]0.00227330991662166[/C][C]-0.0263906167836692[/C][/ROW]
[ROW][C]19[/C][C]1.4098[/C][C]1.40197430374176[/C][C]0.00243485259668687[/C][C]0.00243485259668687[/C][C]1.54668649973825[/C][/ROW]
[ROW][C]20[/C][C]1.4134[/C][C]1.41050978502971[/C][C]0.00244807297905241[/C][C]0.0024480729790524[/C][C]0.126854619633883[/C][/ROW]
[ROW][C]21[/C][C]1.4138[/C][C]1.41146346439588[/C][C]0.00244484155537592[/C][C]0.00244484155537591[/C][C]-0.0310739046836975[/C][/ROW]
[ROW][C]22[/C][C]1.4272[/C][C]1.42400185120175[/C][C]0.00246662038402942[/C][C]0.00246662038402942[/C][C]0.209881276314507[/C][/ROW]
[ROW][C]23[/C][C]1.4643[/C][C]1.45937744999487[/C][C]0.00253747515776585[/C][C]0.00253747515776584[/C][C]0.684295213566435[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.47638287775958[/C][C]0.00256855850201497[/C][C]0.00256855850201497[/C][C]0.300839821284495[/C][/ROW]
[ROW][C]25[/C][C]1.5023[/C][C]1.51824395496209[/C][C]0.00170878529559331[/C][C]-0.018796638207442[/C][C]0.904001702632701[/C][/ROW]
[ROW][C]26[/C][C]1.4406[/C][C]1.44458109648105[/C][C]0.000475927760335762[/C][C]0.00047592773453355[/C][C]-1.41623757476315[/C][/ROW]
[ROW][C]27[/C][C]1.3966[/C][C]1.39952818213479[/C][C]0.000365391116644694[/C][C]0.000365391116644691[/C][C]-0.9458899283601[/C][/ROW]
[ROW][C]28[/C][C]1.357[/C][C]1.35961375071357[/C][C]0.00030544390765049[/C][C]0.000305443907650481[/C][C]-0.837289825188133[/C][/ROW]
[ROW][C]29[/C][C]1.3479[/C][C]1.34844268625306[/C][C]0.000289112449261868[/C][C]0.000289112449261871[/C][C]-0.238562646565834[/C][/ROW]
[ROW][C]30[/C][C]1.3315[/C][C]1.33241651370195[/C][C]0.000265997638651625[/C][C]0.000265997638651623[/C][C]-0.339146414820465[/C][/ROW]
[ROW][C]31[/C][C]1.2307[/C][C]1.23746959366882[/C][C]0.000131322150466422[/C][C]0.00013132215046642[/C][C]-1.9791927264113[/C][/ROW]
[ROW][C]32[/C][C]1.2271[/C][C]1.22770025028596[/C][C]0.000117337962409574[/C][C]0.000117337962409571[/C][C]-0.205805147369037[/C][/ROW]
[ROW][C]33[/C][C]1.3028[/C][C]1.29753154936091[/C][C]0.000215666422407993[/C][C]0.000215666422407975[/C][C]1.44914296848944[/C][/ROW]
[ROW][C]34[/C][C]1.268[/C][C]1.26984588685693[/C][C]0.000176368262297135[/C][C]0.000176368262297131[/C][C]-0.579983975805906[/C][/ROW]
[ROW][C]35[/C][C]1.3648[/C][C]1.35811488089932[/C][C]0.000300269493544125[/C][C]0.000300269493544115[/C][C]1.83117686850439[/C][/ROW]
[ROW][C]36[/C][C]1.3857[/C][C]1.38354318713586[/C][C]0.000335562139898078[/C][C]0.000335562139898075[/C][C]0.522334763240957[/C][/ROW]
[ROW][C]37[/C][C]1.2998[/C][C]1.31851956668792[/C][C]0.00127063764909711[/C][C]-0.0139770140999771[/C][C]-1.45481156943281[/C][/ROW]
[ROW][C]38[/C][C]1.3362[/C][C]1.33387697481442[/C][C]0.00144097424803671[/C][C]0.00144097420510033[/C][C]0.272272597522506[/C][/ROW]
[ROW][C]39[/C][C]1.3692[/C][C]1.3655188640695[/C][C]0.00149524618539898[/C][C]0.00149524618539898[/C][C]0.627433908536554[/C][/ROW]
[ROW][C]40[/C][C]1.3834[/C][C]1.38088421148414[/C][C]0.00151053440751776[/C][C]0.00151053440751775[/C][C]0.288270958319391[/C][/ROW]
[ROW][C]41[/C][C]1.4207[/C][C]1.41666910918898[/C][C]0.00154667101794875[/C][C]0.00154667101794875[/C][C]0.712350917201053[/C][/ROW]
[ROW][C]42[/C][C]1.486[/C][C]1.47991643390345[/C][C]0.00161146104462403[/C][C]0.00161146104462402[/C][C]1.28237296633739[/C][/ROW]
[ROW][C]43[/C][C]1.4385[/C][C]1.4399460307611[/C][C]0.0015678518153821[/C][C]0.0015678518153821[/C][C]-0.864228041441891[/C][/ROW]
[ROW][C]44[/C][C]1.4453[/C][C]1.44358021423021[/C][C]0.00157001659303515[/C][C]0.00157001659303514[/C][C]0.0429461487309452[/C][/ROW]
[ROW][C]45[/C][C]1.426[/C][C]1.42584913969804[/C][C]0.00154981712284304[/C][C]0.00154981712284302[/C][C]-0.401149128521285[/C][/ROW]
[ROW][C]46[/C][C]1.445[/C][C]1.44235082900005[/C][C]0.0015654485774802[/C][C]0.0015654485774802[/C][C]0.310755886961475[/C][/ROW]
[ROW][C]47[/C][C]1.3503[/C][C]1.35525349019417[/C][C]0.00147285275341856[/C][C]0.00147285275341855[/C][C]-1.84274383545704[/C][/ROW]
[ROW][C]48[/C][C]1.4001[/C][C]1.39575742594551[/C][C]0.00151357270935329[/C][C]0.00151357270935328[/C][C]0.811211289894281[/C][/ROW]
[ROW][C]49[/C][C]1.3418[/C][C]1.36463255890676[/C][C]0.00186044541602547[/C][C]-0.0204648995368184[/C][C]-0.71433693938531[/C][/ROW]
[ROW][C]50[/C][C]1.2939[/C][C]1.29718550479645[/C][C]0.0011957113151568[/C][C]0.00119571128804556[/C][C]-1.36096383589569[/C][/ROW]
[ROW][C]51[/C][C]1.3176[/C][C]1.31516517999874[/C][C]0.001219652200636[/C][C]0.001219652200636[/C][C]0.348689421370156[/C][/ROW]
[ROW][C]52[/C][C]1.3443[/C][C]1.34125596104856[/C][C]0.00124142009148346[/C][C]0.00124142009148345[/C][C]0.516865258215939[/C][/ROW]
[ROW][C]53[/C][C]1.3356[/C][C]1.33491454563865[/C][C]0.00123507041891554[/C][C]0.00123507041891555[/C][C]-0.15758546475578[/C][/ROW]
[ROW][C]54[/C][C]1.3214[/C][C]1.32125681027254[/C][C]0.00122264729800622[/C][C]0.00122264729800621[/C][C]-0.309500303345598[/C][/ROW]
[ROW][C]55[/C][C]1.2403[/C][C]1.24477431574471[/C][C]0.00115789522445165[/C][C]0.00115789522445166[/C][C]-1.61485745301208[/C][/ROW]
[ROW][C]56[/C][C]1.259[/C][C]1.2570285806944[/C][C]0.00116713405783597[/C][C]0.00116713405783596[/C][C]0.23060314479037[/C][/ROW]
[ROW][C]57[/C][C]1.2284[/C][C]1.22934781362812[/C][C]0.00114313530950493[/C][C]0.00114313530950491[/C][C]-0.599512716056618[/C][/ROW]
[ROW][C]58[/C][C]1.2611[/C][C]1.2579441840771[/C][C]0.00116595484138919[/C][C]0.00116595484138918[/C][C]0.570528810081816[/C][/ROW]
[ROW][C]59[/C][C]1.293[/C][C]1.28959884576951[/C][C]0.00119127645409785[/C][C]0.00119127645409785[/C][C]0.633611318406749[/C][/ROW]
[ROW][C]60[/C][C]1.2993[/C][C]1.29760883240951[/C][C]0.00119693485933451[/C][C]0.0011969348593345[/C][C]0.14170527795524[/C][/ROW]
[ROW][C]61[/C][C]1.2986[/C][C]1.30992568440114[/C][C]0.00110292900084819[/C][C]-0.0121322189854147[/C][C]0.240895604567086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203455&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
11.47611.4761000
21.47211.4724045410594-0.000206739420129031-0.00020673941968499-0.0466025083844365
31.4871.48609280062215-9.31209884382086e-05-9.31209884382082e-050.288702645608972
41.51671.51458062547184.89270661498418e-054.89270661498418e-050.594932165727773
51.58121.576383986424620.0003412785846188030.0003412785846188061.28549672018168
61.5541.555311023357010.0002406836030766870.000240683603076687-0.445761283141193
71.55081.550916833380450.000219017175623170.00021901717562317-0.096479166349625
81.57641.574387909564870.0003272052470801160.0003272052470801150.484008568984297
91.56111.561770719143820.0002672560569109010.000267256056910902-0.269444567407082
101.47351.47958737066018-0.000112835882574426-0.000112835882574429-1.71623814783716
111.43031.43392264882751-0.000321863517736719-0.000321863517736718-0.948169890073508
121.27571.2872920312921-0.000990175630650501-0.000990175630650504-3.04541326760041
131.27271.27084098293654-0.0002712267887968470.00298349467568372-0.391029578536177
141.39171.383278202446270.002576971028426380.0025769709926131.98474180607014
151.28161.286569027946780.002205536259123910.00220553625912391-2.06278975577745
161.26441.264043278343640.002148891361017710.00214889136101771-0.514249767960543
171.33081.324311758517750.002276072823358380.002276072823358381.20852713701535
181.32751.325318672065980.002273309916621660.00227330991662166-0.0263906167836692
191.40981.401974303741760.002434852596686870.002434852596686871.54668649973825
201.41341.410509785029710.002448072979052410.00244807297905240.126854619633883
211.41381.411463464395880.002444841555375920.00244484155537591-0.0310739046836975
221.42721.424001851201750.002466620384029420.002466620384029420.209881276314507
231.46431.459377449994870.002537475157765850.002537475157765840.684295213566435
241.481.476382877759580.002568558502014970.002568558502014970.300839821284495
251.50231.518243954962090.00170878529559331-0.0187966382074420.904001702632701
261.44061.444581096481050.0004759277603357620.00047592773453355-1.41623757476315
271.39661.399528182134790.0003653911166446940.000365391116644691-0.9458899283601
281.3571.359613750713570.000305443907650490.000305443907650481-0.837289825188133
291.34791.348442686253060.0002891124492618680.000289112449261871-0.238562646565834
301.33151.332416513701950.0002659976386516250.000265997638651623-0.339146414820465
311.23071.237469593668820.0001313221504664220.00013132215046642-1.9791927264113
321.22711.227700250285960.0001173379624095740.000117337962409571-0.205805147369037
331.30281.297531549360910.0002156664224079930.0002156664224079751.44914296848944
341.2681.269845886856930.0001763682622971350.000176368262297131-0.579983975805906
351.36481.358114880899320.0003002694935441250.0003002694935441151.83117686850439
361.38571.383543187135860.0003355621398980780.0003355621398980750.522334763240957
371.29981.318519566687920.00127063764909711-0.0139770140999771-1.45481156943281
381.33621.333876974814420.001440974248036710.001440974205100330.272272597522506
391.36921.36551886406950.001495246185398980.001495246185398980.627433908536554
401.38341.380884211484140.001510534407517760.001510534407517750.288270958319391
411.42071.416669109188980.001546671017948750.001546671017948750.712350917201053
421.4861.479916433903450.001611461044624030.001611461044624021.28237296633739
431.43851.43994603076110.00156785181538210.0015678518153821-0.864228041441891
441.44531.443580214230210.001570016593035150.001570016593035140.0429461487309452
451.4261.425849139698040.001549817122843040.00154981712284302-0.401149128521285
461.4451.442350829000050.00156544857748020.00156544857748020.310755886961475
471.35031.355253490194170.001472852753418560.00147285275341855-1.84274383545704
481.40011.395757425945510.001513572709353290.001513572709353280.811211289894281
491.34181.364632558906760.00186044541602547-0.0204648995368184-0.71433693938531
501.29391.297185504796450.00119571131515680.00119571128804556-1.36096383589569
511.31761.315165179998740.0012196522006360.0012196522006360.348689421370156
521.34431.341255961048560.001241420091483460.001241420091483450.516865258215939
531.33561.334914545638650.001235070418915540.00123507041891555-0.15758546475578
541.32141.321256810272540.001222647298006220.00122264729800621-0.309500303345598
551.24031.244774315744710.001157895224451650.00115789522445166-1.61485745301208
561.2591.25702858069440.001167134057835970.001167134057835960.23060314479037
571.22841.229347813628120.001143135309504930.00114313530950491-0.599512716056618
581.26111.25794418407710.001165954841389190.001165954841389180.570528810081816
591.2931.289598845769510.001191276454097850.001191276454097850.633611318406749
601.29931.297608832409510.001196934859334510.00119693485933450.14170527795524
611.29861.309925684401140.00110292900084819-0.01213221898541470.240895604567086



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')