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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, 04 Dec 2009 12:09:52 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259953848783lfbjnx1b8rbw.htm/, Retrieved Sat, 04 May 2024 02:37:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64051, Retrieved Sat, 04 May 2024 02:37:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [workshop 9,10] [2009-12-04 19:09:52] [2210215221105fab636491031ce54076] [Current]
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Dataseries X:
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
561
549
532
526
511
499
555
565
542
527
510
514
517
508
493
490
469
478
528
534
518
506
502
516
528
533
536
537
524
536
587
597
581
564




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64051&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64051&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64051&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1611611000
2594600.890077038808-10.6793508169096-6.89007703880759-1.80337200823213
3595590.132927178394-10.72875391575754.86707282160587-0.00662790276498473
4591587.158585444541-5.601997864494563.841414555458520.726000183754626
5589587.457975751064-1.699433441100811.542024248935570.511064434697025
6584584.803314038063-2.31867121307166-0.803314038063031-0.0815642560340103
7573575.350598766063-6.95407530653793-2.35059876606268-0.61357948024576
8567566.489146759818-8.196765034859510.510853240182474-0.164174374038259
9569565.534716887839-3.476816365959683.465283112161050.623424007260352
10621604.69157236857724.302896177694116.30842763142273.66959521756889
11629632.64361096588426.6800781232648-3.643610965883640.314007345084002
12628636.94634750935112.1041838444222-8.94634750935132-1.92534927868203
13612621.097434092153-6.03681289967139-9.0974340921529-2.40571170016917
14595603.606889533705-13.5044740366100-8.60688953370532-1.00268674995515
15597593.01700511195-11.65303263479273.982994888049760.242380254418430
16593589.156891698458-6.780145545929053.843108301542090.652622413957651
17590586.671446486108-4.059732710653013.328553513892250.362119643211553
18580577.986920095679-6.973110724524292.01307990432117-0.383179200641831
19574571.375675431364-6.746396353318332.624324568636050.0299279349434804
20573572.198857688032-1.996518153947830.8011423119679440.627993866454083
21573582.4875946928085.72430331283231-9.487594692808241.01987751759528
22620602.53134675747314.725328585144417.46865324252721.18895941486567
23626621.87865376602417.62907938224014.121346233975980.383580524407892
24620625.0184124479348.53716456531325-5.01841244793429-1.20159962401871
25588603.737412064846-10.1792332084258-15.7374120648462-2.48023872115726
26566579.452771640999-19.0451485994616-13.4527716409988-1.17316184100839
27557556.82416849091-21.27983200971980.175831509090555-0.294269186980529
28561551.368612128642-11.47067627285399.631387871358471.30011571574711
29549542.393425817566-9.916227768352326.606574182434210.206362142630146
30532529.288254389853-11.90391953110712.71174561014708-0.262304976459346
31526522.618612806552-8.65331773114493.381387193447790.428682137665396
32511515.556765228615-7.665603441677-4.556765228615290.130480696135232
33499514.103692140889-3.80543566060573-15.10369214088870.50998956029736
34555533.07190538926310.351480317163521.92809461073711.87003509218902
35565553.51865214147616.624706560616611.48134785852390.828747654724153
36542545.5981712443541.37722427140755-3.59817124435393-2.01574839720102
37527538.121887572847-4.12699430766737-11.1218875728465-0.728236534754263
38510525.831915535641-9.20089463108307-15.8319155356407-0.670173352040899
39514518.723581025135-7.9054367041488-4.723581025135450.170862476894353
40517508.634588800205-9.253585005881188.36541119979469-0.178331662818988
41508499.04894074898-9.4590582725888.95105925102045-0.0272199956210121
42493490.103188803812-9.141008790238552.896811196187880.0420227461999915
43490484.434930884789-6.993013070287325.56506911521070.283393626137543
44469477.544575803353-6.929596250581-8.544575803352580.00837278519233984
45478494.6621297822797.92914139846875-16.66212978227931.96284999813464
46528509.31042629512312.082450156913318.68957370487650.548708235659699
47534515.8610859675648.6630798040111318.1389140324358-0.451836215151636
48518519.5458685626845.58517408378212-1.54586856268413-0.406891413221724
49506517.3086780890810.74611671266553-11.3086780890814-0.639668806212862
50502518.163160392280.813111570403526-16.16316039227970.00884537791369302
51516519.5609226034321.17366170584363-3.56092260343250.0475832440995858
52528519.3098884318250.2963424920021578.69011156817484-0.115981796338448
53533521.8910027761051.7050595439903411.10899722389490.186430479481245
54536530.2625887801245.820286121506675.737411219875650.543911552792706
55537533.7440189371974.377676952267503.25598106280276-0.190418594400508
56524541.2105424312686.28048378321375-17.21054243126780.251181019527817
57536553.61935039614410.0542087786444-17.61935039614390.498433497753699
58587566.07086883202411.530655498127420.92913116797610.195082946548739
59597577.04760419973211.189418644187419.9523958002677-0.0451001588481676
60581581.9698892617197.3268804338397-0.969889261718967-0.510572477883051
61564578.8902990397760.911103941072681-14.8902990397762-0.847732019687734

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 611 & 611 & 0 & 0 & 0 \tabularnewline
2 & 594 & 600.890077038808 & -10.6793508169096 & -6.89007703880759 & -1.80337200823213 \tabularnewline
3 & 595 & 590.132927178394 & -10.7287539157575 & 4.86707282160587 & -0.00662790276498473 \tabularnewline
4 & 591 & 587.158585444541 & -5.60199786449456 & 3.84141455545852 & 0.726000183754626 \tabularnewline
5 & 589 & 587.457975751064 & -1.69943344110081 & 1.54202424893557 & 0.511064434697025 \tabularnewline
6 & 584 & 584.803314038063 & -2.31867121307166 & -0.803314038063031 & -0.0815642560340103 \tabularnewline
7 & 573 & 575.350598766063 & -6.95407530653793 & -2.35059876606268 & -0.61357948024576 \tabularnewline
8 & 567 & 566.489146759818 & -8.19676503485951 & 0.510853240182474 & -0.164174374038259 \tabularnewline
9 & 569 & 565.534716887839 & -3.47681636595968 & 3.46528311216105 & 0.623424007260352 \tabularnewline
10 & 621 & 604.691572368577 & 24.3028961776941 & 16.3084276314227 & 3.66959521756889 \tabularnewline
11 & 629 & 632.643610965884 & 26.6800781232648 & -3.64361096588364 & 0.314007345084002 \tabularnewline
12 & 628 & 636.946347509351 & 12.1041838444222 & -8.94634750935132 & -1.92534927868203 \tabularnewline
13 & 612 & 621.097434092153 & -6.03681289967139 & -9.0974340921529 & -2.40571170016917 \tabularnewline
14 & 595 & 603.606889533705 & -13.5044740366100 & -8.60688953370532 & -1.00268674995515 \tabularnewline
15 & 597 & 593.01700511195 & -11.6530326347927 & 3.98299488804976 & 0.242380254418430 \tabularnewline
16 & 593 & 589.156891698458 & -6.78014554592905 & 3.84310830154209 & 0.652622413957651 \tabularnewline
17 & 590 & 586.671446486108 & -4.05973271065301 & 3.32855351389225 & 0.362119643211553 \tabularnewline
18 & 580 & 577.986920095679 & -6.97311072452429 & 2.01307990432117 & -0.383179200641831 \tabularnewline
19 & 574 & 571.375675431364 & -6.74639635331833 & 2.62432456863605 & 0.0299279349434804 \tabularnewline
20 & 573 & 572.198857688032 & -1.99651815394783 & 0.801142311967944 & 0.627993866454083 \tabularnewline
21 & 573 & 582.487594692808 & 5.72430331283231 & -9.48759469280824 & 1.01987751759528 \tabularnewline
22 & 620 & 602.531346757473 & 14.7253285851444 & 17.4686532425272 & 1.18895941486567 \tabularnewline
23 & 626 & 621.878653766024 & 17.6290793822401 & 4.12134623397598 & 0.383580524407892 \tabularnewline
24 & 620 & 625.018412447934 & 8.53716456531325 & -5.01841244793429 & -1.20159962401871 \tabularnewline
25 & 588 & 603.737412064846 & -10.1792332084258 & -15.7374120648462 & -2.48023872115726 \tabularnewline
26 & 566 & 579.452771640999 & -19.0451485994616 & -13.4527716409988 & -1.17316184100839 \tabularnewline
27 & 557 & 556.82416849091 & -21.2798320097198 & 0.175831509090555 & -0.294269186980529 \tabularnewline
28 & 561 & 551.368612128642 & -11.4706762728539 & 9.63138787135847 & 1.30011571574711 \tabularnewline
29 & 549 & 542.393425817566 & -9.91622776835232 & 6.60657418243421 & 0.206362142630146 \tabularnewline
30 & 532 & 529.288254389853 & -11.9039195311071 & 2.71174561014708 & -0.262304976459346 \tabularnewline
31 & 526 & 522.618612806552 & -8.6533177311449 & 3.38138719344779 & 0.428682137665396 \tabularnewline
32 & 511 & 515.556765228615 & -7.665603441677 & -4.55676522861529 & 0.130480696135232 \tabularnewline
33 & 499 & 514.103692140889 & -3.80543566060573 & -15.1036921408887 & 0.50998956029736 \tabularnewline
34 & 555 & 533.071905389263 & 10.3514803171635 & 21.9280946107371 & 1.87003509218902 \tabularnewline
35 & 565 & 553.518652141476 & 16.6247065606166 & 11.4813478585239 & 0.828747654724153 \tabularnewline
36 & 542 & 545.598171244354 & 1.37722427140755 & -3.59817124435393 & -2.01574839720102 \tabularnewline
37 & 527 & 538.121887572847 & -4.12699430766737 & -11.1218875728465 & -0.728236534754263 \tabularnewline
38 & 510 & 525.831915535641 & -9.20089463108307 & -15.8319155356407 & -0.670173352040899 \tabularnewline
39 & 514 & 518.723581025135 & -7.9054367041488 & -4.72358102513545 & 0.170862476894353 \tabularnewline
40 & 517 & 508.634588800205 & -9.25358500588118 & 8.36541119979469 & -0.178331662818988 \tabularnewline
41 & 508 & 499.04894074898 & -9.459058272588 & 8.95105925102045 & -0.0272199956210121 \tabularnewline
42 & 493 & 490.103188803812 & -9.14100879023855 & 2.89681119618788 & 0.0420227461999915 \tabularnewline
43 & 490 & 484.434930884789 & -6.99301307028732 & 5.5650691152107 & 0.283393626137543 \tabularnewline
44 & 469 & 477.544575803353 & -6.929596250581 & -8.54457580335258 & 0.00837278519233984 \tabularnewline
45 & 478 & 494.662129782279 & 7.92914139846875 & -16.6621297822793 & 1.96284999813464 \tabularnewline
46 & 528 & 509.310426295123 & 12.0824501569133 & 18.6895737048765 & 0.548708235659699 \tabularnewline
47 & 534 & 515.861085967564 & 8.66307980401113 & 18.1389140324358 & -0.451836215151636 \tabularnewline
48 & 518 & 519.545868562684 & 5.58517408378212 & -1.54586856268413 & -0.406891413221724 \tabularnewline
49 & 506 & 517.308678089081 & 0.74611671266553 & -11.3086780890814 & -0.639668806212862 \tabularnewline
50 & 502 & 518.16316039228 & 0.813111570403526 & -16.1631603922797 & 0.00884537791369302 \tabularnewline
51 & 516 & 519.560922603432 & 1.17366170584363 & -3.5609226034325 & 0.0475832440995858 \tabularnewline
52 & 528 & 519.309888431825 & 0.296342492002157 & 8.69011156817484 & -0.115981796338448 \tabularnewline
53 & 533 & 521.891002776105 & 1.70505954399034 & 11.1089972238949 & 0.186430479481245 \tabularnewline
54 & 536 & 530.262588780124 & 5.82028612150667 & 5.73741121987565 & 0.543911552792706 \tabularnewline
55 & 537 & 533.744018937197 & 4.37767695226750 & 3.25598106280276 & -0.190418594400508 \tabularnewline
56 & 524 & 541.210542431268 & 6.28048378321375 & -17.2105424312678 & 0.251181019527817 \tabularnewline
57 & 536 & 553.619350396144 & 10.0542087786444 & -17.6193503961439 & 0.498433497753699 \tabularnewline
58 & 587 & 566.070868832024 & 11.5306554981274 & 20.9291311679761 & 0.195082946548739 \tabularnewline
59 & 597 & 577.047604199732 & 11.1894186441874 & 19.9523958002677 & -0.0451001588481676 \tabularnewline
60 & 581 & 581.969889261719 & 7.3268804338397 & -0.969889261718967 & -0.510572477883051 \tabularnewline
61 & 564 & 578.890299039776 & 0.911103941072681 & -14.8902990397762 & -0.847732019687734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64051&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]611[/C][C]611[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]594[/C][C]600.890077038808[/C][C]-10.6793508169096[/C][C]-6.89007703880759[/C][C]-1.80337200823213[/C][/ROW]
[ROW][C]3[/C][C]595[/C][C]590.132927178394[/C][C]-10.7287539157575[/C][C]4.86707282160587[/C][C]-0.00662790276498473[/C][/ROW]
[ROW][C]4[/C][C]591[/C][C]587.158585444541[/C][C]-5.60199786449456[/C][C]3.84141455545852[/C][C]0.726000183754626[/C][/ROW]
[ROW][C]5[/C][C]589[/C][C]587.457975751064[/C][C]-1.69943344110081[/C][C]1.54202424893557[/C][C]0.511064434697025[/C][/ROW]
[ROW][C]6[/C][C]584[/C][C]584.803314038063[/C][C]-2.31867121307166[/C][C]-0.803314038063031[/C][C]-0.0815642560340103[/C][/ROW]
[ROW][C]7[/C][C]573[/C][C]575.350598766063[/C][C]-6.95407530653793[/C][C]-2.35059876606268[/C][C]-0.61357948024576[/C][/ROW]
[ROW][C]8[/C][C]567[/C][C]566.489146759818[/C][C]-8.19676503485951[/C][C]0.510853240182474[/C][C]-0.164174374038259[/C][/ROW]
[ROW][C]9[/C][C]569[/C][C]565.534716887839[/C][C]-3.47681636595968[/C][C]3.46528311216105[/C][C]0.623424007260352[/C][/ROW]
[ROW][C]10[/C][C]621[/C][C]604.691572368577[/C][C]24.3028961776941[/C][C]16.3084276314227[/C][C]3.66959521756889[/C][/ROW]
[ROW][C]11[/C][C]629[/C][C]632.643610965884[/C][C]26.6800781232648[/C][C]-3.64361096588364[/C][C]0.314007345084002[/C][/ROW]
[ROW][C]12[/C][C]628[/C][C]636.946347509351[/C][C]12.1041838444222[/C][C]-8.94634750935132[/C][C]-1.92534927868203[/C][/ROW]
[ROW][C]13[/C][C]612[/C][C]621.097434092153[/C][C]-6.03681289967139[/C][C]-9.0974340921529[/C][C]-2.40571170016917[/C][/ROW]
[ROW][C]14[/C][C]595[/C][C]603.606889533705[/C][C]-13.5044740366100[/C][C]-8.60688953370532[/C][C]-1.00268674995515[/C][/ROW]
[ROW][C]15[/C][C]597[/C][C]593.01700511195[/C][C]-11.6530326347927[/C][C]3.98299488804976[/C][C]0.242380254418430[/C][/ROW]
[ROW][C]16[/C][C]593[/C][C]589.156891698458[/C][C]-6.78014554592905[/C][C]3.84310830154209[/C][C]0.652622413957651[/C][/ROW]
[ROW][C]17[/C][C]590[/C][C]586.671446486108[/C][C]-4.05973271065301[/C][C]3.32855351389225[/C][C]0.362119643211553[/C][/ROW]
[ROW][C]18[/C][C]580[/C][C]577.986920095679[/C][C]-6.97311072452429[/C][C]2.01307990432117[/C][C]-0.383179200641831[/C][/ROW]
[ROW][C]19[/C][C]574[/C][C]571.375675431364[/C][C]-6.74639635331833[/C][C]2.62432456863605[/C][C]0.0299279349434804[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]572.198857688032[/C][C]-1.99651815394783[/C][C]0.801142311967944[/C][C]0.627993866454083[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]582.487594692808[/C][C]5.72430331283231[/C][C]-9.48759469280824[/C][C]1.01987751759528[/C][/ROW]
[ROW][C]22[/C][C]620[/C][C]602.531346757473[/C][C]14.7253285851444[/C][C]17.4686532425272[/C][C]1.18895941486567[/C][/ROW]
[ROW][C]23[/C][C]626[/C][C]621.878653766024[/C][C]17.6290793822401[/C][C]4.12134623397598[/C][C]0.383580524407892[/C][/ROW]
[ROW][C]24[/C][C]620[/C][C]625.018412447934[/C][C]8.53716456531325[/C][C]-5.01841244793429[/C][C]-1.20159962401871[/C][/ROW]
[ROW][C]25[/C][C]588[/C][C]603.737412064846[/C][C]-10.1792332084258[/C][C]-15.7374120648462[/C][C]-2.48023872115726[/C][/ROW]
[ROW][C]26[/C][C]566[/C][C]579.452771640999[/C][C]-19.0451485994616[/C][C]-13.4527716409988[/C][C]-1.17316184100839[/C][/ROW]
[ROW][C]27[/C][C]557[/C][C]556.82416849091[/C][C]-21.2798320097198[/C][C]0.175831509090555[/C][C]-0.294269186980529[/C][/ROW]
[ROW][C]28[/C][C]561[/C][C]551.368612128642[/C][C]-11.4706762728539[/C][C]9.63138787135847[/C][C]1.30011571574711[/C][/ROW]
[ROW][C]29[/C][C]549[/C][C]542.393425817566[/C][C]-9.91622776835232[/C][C]6.60657418243421[/C][C]0.206362142630146[/C][/ROW]
[ROW][C]30[/C][C]532[/C][C]529.288254389853[/C][C]-11.9039195311071[/C][C]2.71174561014708[/C][C]-0.262304976459346[/C][/ROW]
[ROW][C]31[/C][C]526[/C][C]522.618612806552[/C][C]-8.6533177311449[/C][C]3.38138719344779[/C][C]0.428682137665396[/C][/ROW]
[ROW][C]32[/C][C]511[/C][C]515.556765228615[/C][C]-7.665603441677[/C][C]-4.55676522861529[/C][C]0.130480696135232[/C][/ROW]
[ROW][C]33[/C][C]499[/C][C]514.103692140889[/C][C]-3.80543566060573[/C][C]-15.1036921408887[/C][C]0.50998956029736[/C][/ROW]
[ROW][C]34[/C][C]555[/C][C]533.071905389263[/C][C]10.3514803171635[/C][C]21.9280946107371[/C][C]1.87003509218902[/C][/ROW]
[ROW][C]35[/C][C]565[/C][C]553.518652141476[/C][C]16.6247065606166[/C][C]11.4813478585239[/C][C]0.828747654724153[/C][/ROW]
[ROW][C]36[/C][C]542[/C][C]545.598171244354[/C][C]1.37722427140755[/C][C]-3.59817124435393[/C][C]-2.01574839720102[/C][/ROW]
[ROW][C]37[/C][C]527[/C][C]538.121887572847[/C][C]-4.12699430766737[/C][C]-11.1218875728465[/C][C]-0.728236534754263[/C][/ROW]
[ROW][C]38[/C][C]510[/C][C]525.831915535641[/C][C]-9.20089463108307[/C][C]-15.8319155356407[/C][C]-0.670173352040899[/C][/ROW]
[ROW][C]39[/C][C]514[/C][C]518.723581025135[/C][C]-7.9054367041488[/C][C]-4.72358102513545[/C][C]0.170862476894353[/C][/ROW]
[ROW][C]40[/C][C]517[/C][C]508.634588800205[/C][C]-9.25358500588118[/C][C]8.36541119979469[/C][C]-0.178331662818988[/C][/ROW]
[ROW][C]41[/C][C]508[/C][C]499.04894074898[/C][C]-9.459058272588[/C][C]8.95105925102045[/C][C]-0.0272199956210121[/C][/ROW]
[ROW][C]42[/C][C]493[/C][C]490.103188803812[/C][C]-9.14100879023855[/C][C]2.89681119618788[/C][C]0.0420227461999915[/C][/ROW]
[ROW][C]43[/C][C]490[/C][C]484.434930884789[/C][C]-6.99301307028732[/C][C]5.5650691152107[/C][C]0.283393626137543[/C][/ROW]
[ROW][C]44[/C][C]469[/C][C]477.544575803353[/C][C]-6.929596250581[/C][C]-8.54457580335258[/C][C]0.00837278519233984[/C][/ROW]
[ROW][C]45[/C][C]478[/C][C]494.662129782279[/C][C]7.92914139846875[/C][C]-16.6621297822793[/C][C]1.96284999813464[/C][/ROW]
[ROW][C]46[/C][C]528[/C][C]509.310426295123[/C][C]12.0824501569133[/C][C]18.6895737048765[/C][C]0.548708235659699[/C][/ROW]
[ROW][C]47[/C][C]534[/C][C]515.861085967564[/C][C]8.66307980401113[/C][C]18.1389140324358[/C][C]-0.451836215151636[/C][/ROW]
[ROW][C]48[/C][C]518[/C][C]519.545868562684[/C][C]5.58517408378212[/C][C]-1.54586856268413[/C][C]-0.406891413221724[/C][/ROW]
[ROW][C]49[/C][C]506[/C][C]517.308678089081[/C][C]0.74611671266553[/C][C]-11.3086780890814[/C][C]-0.639668806212862[/C][/ROW]
[ROW][C]50[/C][C]502[/C][C]518.16316039228[/C][C]0.813111570403526[/C][C]-16.1631603922797[/C][C]0.00884537791369302[/C][/ROW]
[ROW][C]51[/C][C]516[/C][C]519.560922603432[/C][C]1.17366170584363[/C][C]-3.5609226034325[/C][C]0.0475832440995858[/C][/ROW]
[ROW][C]52[/C][C]528[/C][C]519.309888431825[/C][C]0.296342492002157[/C][C]8.69011156817484[/C][C]-0.115981796338448[/C][/ROW]
[ROW][C]53[/C][C]533[/C][C]521.891002776105[/C][C]1.70505954399034[/C][C]11.1089972238949[/C][C]0.186430479481245[/C][/ROW]
[ROW][C]54[/C][C]536[/C][C]530.262588780124[/C][C]5.82028612150667[/C][C]5.73741121987565[/C][C]0.543911552792706[/C][/ROW]
[ROW][C]55[/C][C]537[/C][C]533.744018937197[/C][C]4.37767695226750[/C][C]3.25598106280276[/C][C]-0.190418594400508[/C][/ROW]
[ROW][C]56[/C][C]524[/C][C]541.210542431268[/C][C]6.28048378321375[/C][C]-17.2105424312678[/C][C]0.251181019527817[/C][/ROW]
[ROW][C]57[/C][C]536[/C][C]553.619350396144[/C][C]10.0542087786444[/C][C]-17.6193503961439[/C][C]0.498433497753699[/C][/ROW]
[ROW][C]58[/C][C]587[/C][C]566.070868832024[/C][C]11.5306554981274[/C][C]20.9291311679761[/C][C]0.195082946548739[/C][/ROW]
[ROW][C]59[/C][C]597[/C][C]577.047604199732[/C][C]11.1894186441874[/C][C]19.9523958002677[/C][C]-0.0451001588481676[/C][/ROW]
[ROW][C]60[/C][C]581[/C][C]581.969889261719[/C][C]7.3268804338397[/C][C]-0.969889261718967[/C][C]-0.510572477883051[/C][/ROW]
[ROW][C]61[/C][C]564[/C][C]578.890299039776[/C][C]0.911103941072681[/C][C]-14.8902990397762[/C][C]-0.847732019687734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64051&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64051&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
1611611000
2594600.890077038808-10.6793508169096-6.89007703880759-1.80337200823213
3595590.132927178394-10.72875391575754.86707282160587-0.00662790276498473
4591587.158585444541-5.601997864494563.841414555458520.726000183754626
5589587.457975751064-1.699433441100811.542024248935570.511064434697025
6584584.803314038063-2.31867121307166-0.803314038063031-0.0815642560340103
7573575.350598766063-6.95407530653793-2.35059876606268-0.61357948024576
8567566.489146759818-8.196765034859510.510853240182474-0.164174374038259
9569565.534716887839-3.476816365959683.465283112161050.623424007260352
10621604.69157236857724.302896177694116.30842763142273.66959521756889
11629632.64361096588426.6800781232648-3.643610965883640.314007345084002
12628636.94634750935112.1041838444222-8.94634750935132-1.92534927868203
13612621.097434092153-6.03681289967139-9.0974340921529-2.40571170016917
14595603.606889533705-13.5044740366100-8.60688953370532-1.00268674995515
15597593.01700511195-11.65303263479273.982994888049760.242380254418430
16593589.156891698458-6.780145545929053.843108301542090.652622413957651
17590586.671446486108-4.059732710653013.328553513892250.362119643211553
18580577.986920095679-6.973110724524292.01307990432117-0.383179200641831
19574571.375675431364-6.746396353318332.624324568636050.0299279349434804
20573572.198857688032-1.996518153947830.8011423119679440.627993866454083
21573582.4875946928085.72430331283231-9.487594692808241.01987751759528
22620602.53134675747314.725328585144417.46865324252721.18895941486567
23626621.87865376602417.62907938224014.121346233975980.383580524407892
24620625.0184124479348.53716456531325-5.01841244793429-1.20159962401871
25588603.737412064846-10.1792332084258-15.7374120648462-2.48023872115726
26566579.452771640999-19.0451485994616-13.4527716409988-1.17316184100839
27557556.82416849091-21.27983200971980.175831509090555-0.294269186980529
28561551.368612128642-11.47067627285399.631387871358471.30011571574711
29549542.393425817566-9.916227768352326.606574182434210.206362142630146
30532529.288254389853-11.90391953110712.71174561014708-0.262304976459346
31526522.618612806552-8.65331773114493.381387193447790.428682137665396
32511515.556765228615-7.665603441677-4.556765228615290.130480696135232
33499514.103692140889-3.80543566060573-15.10369214088870.50998956029736
34555533.07190538926310.351480317163521.92809461073711.87003509218902
35565553.51865214147616.624706560616611.48134785852390.828747654724153
36542545.5981712443541.37722427140755-3.59817124435393-2.01574839720102
37527538.121887572847-4.12699430766737-11.1218875728465-0.728236534754263
38510525.831915535641-9.20089463108307-15.8319155356407-0.670173352040899
39514518.723581025135-7.9054367041488-4.723581025135450.170862476894353
40517508.634588800205-9.253585005881188.36541119979469-0.178331662818988
41508499.04894074898-9.4590582725888.95105925102045-0.0272199956210121
42493490.103188803812-9.141008790238552.896811196187880.0420227461999915
43490484.434930884789-6.993013070287325.56506911521070.283393626137543
44469477.544575803353-6.929596250581-8.544575803352580.00837278519233984
45478494.6621297822797.92914139846875-16.66212978227931.96284999813464
46528509.31042629512312.082450156913318.68957370487650.548708235659699
47534515.8610859675648.6630798040111318.1389140324358-0.451836215151636
48518519.5458685626845.58517408378212-1.54586856268413-0.406891413221724
49506517.3086780890810.74611671266553-11.3086780890814-0.639668806212862
50502518.163160392280.813111570403526-16.16316039227970.00884537791369302
51516519.5609226034321.17366170584363-3.56092260343250.0475832440995858
52528519.3098884318250.2963424920021578.69011156817484-0.115981796338448
53533521.8910027761051.7050595439903411.10899722389490.186430479481245
54536530.2625887801245.820286121506675.737411219875650.543911552792706
55537533.7440189371974.377676952267503.25598106280276-0.190418594400508
56524541.2105424312686.28048378321375-17.21054243126780.251181019527817
57536553.61935039614410.0542087786444-17.61935039614390.498433497753699
58587566.07086883202411.530655498127420.92913116797610.195082946548739
59597577.04760419973211.189418644187419.9523958002677-0.0451001588481676
60581581.9698892617197.3268804338397-0.969889261718967-0.510572477883051
61564578.8902990397760.911103941072681-14.8902990397762-0.847732019687734



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')