Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 23 Dec 2011 11:13:56 -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/2011/Dec/23/t1324656862do95uzu4fbkvbj0.htm/, Retrieved Mon, 29 Apr 2024 18:04:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160549, Retrieved Mon, 29 Apr 2024 18:04:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
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]
- R  D    [Structural Time Series Models] [paper- STSM] [2011-12-23 13:08:59] [c2267e575f67090c7e8d960bdccd246a]
-    D        [Structural Time Series Models] [paper- Loess] [2011-12-23 16:13:56] [fe2dc4bc83c881ccd49ef12feaba2b65] [Current]
Feedback Forum

Post a new message
Dataseries X:
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
558
575
580
575
563
552
537
545
601
604
586
564
549
551
556
548
540
531
521
519
572
581
563
548
539




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160549&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160549&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160549&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 time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1590590000
2580583.283865255502-2.9752149420575-3.2838652555019-0.888883210196761
3574574.59315734654-5.80851754828858-0.59315734653974-0.525786612507606
4573571.273116902043-4.448971473759911.726883097957280.226176933831935
5573571.623060001106-1.843918455476311.376939998893880.421510588493358
6620605.79539089859517.529682829461914.20460910140513.19282578103008
7626629.15334275854820.6752953885225-3.153342758548430.522069078189157
8620629.9655147162979.92344769740635-9.96551471629725-1.78431483101233
9588602.040894545007-10.5759351441779-14.0408945450073-3.40139455420698
10566570.228873222773-22.0754964766754-4.22887322277343-1.9081612249883
11557552.547532170963-19.69660981250374.452467829037120.394738448428917
12561553.320759973915-8.615891200677387.679240026085271.83866397556424
13549546.31875576528-7.756538330999192.681244234720480.149474022932546
14532532.834199972709-10.7952270630508-0.83419997270873-0.498120659844999
15526523.386800625702-10.11004260526062.613199374298510.112822778331973
16511512.648916237073-10.4345519738008-1.64891623707322-0.0548549324446512
17499509.979312712935-6.35806331578892-10.97931271293510.675608409796844
18555533.5085074347099.2546498696882821.49149256529092.57081722651233
19565558.22515235724617.3110458509286.774847642754281.33404669754017
20542550.5182875780034.24647268900382-8.51828757800251-2.16823512210897
21527537.686696744475-4.68900396036749-10.6866967444747-1.48272449404469
22510520.484708577501-11.2375471357242-10.4847085775012-1.08661954520033
23514513.865138664441-8.824302012747220.1348613355594450.400506558360965
24517508.304616881595-7.122104859594758.695383118404840.28338598181224
25508501.687272980594-6.858246263037596.312727019405560.04419121030494
26493492.593863579878-8.022407985072790.40613642012234-0.192530690425326
27490484.913465120857-7.846952086610935.086534879143380.0289903758229804
28469476.12991027015-8.32793889734134-7.12991027014965-0.0804268108971488
29478492.3763602929484.4070641897294-14.37636029294752.12098603734532
30528509.56121296518111.029999601173318.43878703481861.09516323492062
31534519.12684014789610.273207258039414.8731598521041-0.125216106363323
32518522.2294168181856.56726070506355-4.22941681818469-0.614568534847593
33506516.9428937698890.433065417893171-10.9428937698892-1.0179067725843
34502514.817937559795-0.890979322125457-12.8179375597953-0.219735021776374
35516516.1135042872330.239852217410683-0.1135042872331480.187813428821908
36528518.1462269433981.16718870535129.853773056602290.15433208535223
37533522.8375474589592.9927380862980110.16245254104070.303745119249397
38536531.5961889218675.970975387839784.403811078133230.493016824981042
39537534.4447695693824.36929991169152.55523043061844-0.265196202061861
40524540.8688706782395.42271411911422-16.86887067823860.175432347463346
41536553.3830270103389.07566253382764-17.38302701033840.608276123054868
42587566.97942166162211.407934563551320.02057833837790.386665470633031
43597578.85940459458211.651013676130218.1405954054180.0402416333376524
44581583.3880578019337.98767881965705-2.38805780193285-0.607154863189412
45564578.7078935745471.47108239911033-14.707893574547-1.08118054091213
46558573.326821273653-2.05445435837996-15.3268212736526-0.585292883313566
47575574.00477587745-0.6484517571424190.9952241225495690.233620621622018
48580572.354998623477-1.164134020701167.6450013765231-0.0857470486819306
49575567.661867026551-2.982742438296267.33813297344923-0.301995026758463
50563559.043614379996-5.880252355168093.95638562000403-0.479881464049787
51552551.075091163591-6.949710653295230.924908836409229-0.177227829260922
52537553.336679533017-2.23506840951835-16.33667953301660.783980873955845
53545561.452292045173.07773492748901-16.45229204517030.884043996639682
54601576.8609352467819.4170252093523424.13906475321951.05214564893283
55604584.3784391248998.4415275619889419.6215608751011-0.161607284549462
56586585.8595485024554.872545372523760.140451497544846-0.591406821930943
57564580.529469156378-0.355570040660422-16.5294691563785-0.867265434159036
58549569.635123689856-5.75649833237995-20.6351236898564-0.896845136193524
59551553.289968303978-11.1864477931511-2.28996830397836-0.90237256212942
60556544.141545805495-10.140267411842411.85845419450530.173854118757816
61548536.385720401466-8.9160027431333811.61427959853360.203150990936535
62540532.738101465533-6.216082885018377.261898534466970.447300748702044
63531532.145980628222-3.34146890058393-1.145980628222320.476566178043485
64521538.9386494226861.83626458418406-17.93864942268570.860409114716508
65519541.7611017099552.34107813259782-22.76110170995540.0839528196284191
66572546.6604899055373.652462669275225.33951009446320.217739346922561
67581556.3948817354386.768124336604624.60511826456180.516422862745321
68563560.2573547403935.281909734596512.74264525960711-0.246285585789814
69548560.9740798160112.94920073474937-12.974079816011-0.386926733195902
70539557.462466543419-0.352195877765391-18.4624665434194-0.548248666619341

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 590 & 590 & 0 & 0 & 0 \tabularnewline
2 & 580 & 583.283865255502 & -2.9752149420575 & -3.2838652555019 & -0.888883210196761 \tabularnewline
3 & 574 & 574.59315734654 & -5.80851754828858 & -0.59315734653974 & -0.525786612507606 \tabularnewline
4 & 573 & 571.273116902043 & -4.44897147375991 & 1.72688309795728 & 0.226176933831935 \tabularnewline
5 & 573 & 571.623060001106 & -1.84391845547631 & 1.37693999889388 & 0.421510588493358 \tabularnewline
6 & 620 & 605.795390898595 & 17.5296828294619 & 14.2046091014051 & 3.19282578103008 \tabularnewline
7 & 626 & 629.153342758548 & 20.6752953885225 & -3.15334275854843 & 0.522069078189157 \tabularnewline
8 & 620 & 629.965514716297 & 9.92344769740635 & -9.96551471629725 & -1.78431483101233 \tabularnewline
9 & 588 & 602.040894545007 & -10.5759351441779 & -14.0408945450073 & -3.40139455420698 \tabularnewline
10 & 566 & 570.228873222773 & -22.0754964766754 & -4.22887322277343 & -1.9081612249883 \tabularnewline
11 & 557 & 552.547532170963 & -19.6966098125037 & 4.45246782903712 & 0.394738448428917 \tabularnewline
12 & 561 & 553.320759973915 & -8.61589120067738 & 7.67924002608527 & 1.83866397556424 \tabularnewline
13 & 549 & 546.31875576528 & -7.75653833099919 & 2.68124423472048 & 0.149474022932546 \tabularnewline
14 & 532 & 532.834199972709 & -10.7952270630508 & -0.83419997270873 & -0.498120659844999 \tabularnewline
15 & 526 & 523.386800625702 & -10.1100426052606 & 2.61319937429851 & 0.112822778331973 \tabularnewline
16 & 511 & 512.648916237073 & -10.4345519738008 & -1.64891623707322 & -0.0548549324446512 \tabularnewline
17 & 499 & 509.979312712935 & -6.35806331578892 & -10.9793127129351 & 0.675608409796844 \tabularnewline
18 & 555 & 533.508507434709 & 9.25464986968828 & 21.4914925652909 & 2.57081722651233 \tabularnewline
19 & 565 & 558.225152357246 & 17.311045850928 & 6.77484764275428 & 1.33404669754017 \tabularnewline
20 & 542 & 550.518287578003 & 4.24647268900382 & -8.51828757800251 & -2.16823512210897 \tabularnewline
21 & 527 & 537.686696744475 & -4.68900396036749 & -10.6866967444747 & -1.48272449404469 \tabularnewline
22 & 510 & 520.484708577501 & -11.2375471357242 & -10.4847085775012 & -1.08661954520033 \tabularnewline
23 & 514 & 513.865138664441 & -8.82430201274722 & 0.134861335559445 & 0.400506558360965 \tabularnewline
24 & 517 & 508.304616881595 & -7.12210485959475 & 8.69538311840484 & 0.28338598181224 \tabularnewline
25 & 508 & 501.687272980594 & -6.85824626303759 & 6.31272701940556 & 0.04419121030494 \tabularnewline
26 & 493 & 492.593863579878 & -8.02240798507279 & 0.40613642012234 & -0.192530690425326 \tabularnewline
27 & 490 & 484.913465120857 & -7.84695208661093 & 5.08653487914338 & 0.0289903758229804 \tabularnewline
28 & 469 & 476.12991027015 & -8.32793889734134 & -7.12991027014965 & -0.0804268108971488 \tabularnewline
29 & 478 & 492.376360292948 & 4.4070641897294 & -14.3763602929475 & 2.12098603734532 \tabularnewline
30 & 528 & 509.561212965181 & 11.0299996011733 & 18.4387870348186 & 1.09516323492062 \tabularnewline
31 & 534 & 519.126840147896 & 10.2732072580394 & 14.8731598521041 & -0.125216106363323 \tabularnewline
32 & 518 & 522.229416818185 & 6.56726070506355 & -4.22941681818469 & -0.614568534847593 \tabularnewline
33 & 506 & 516.942893769889 & 0.433065417893171 & -10.9428937698892 & -1.0179067725843 \tabularnewline
34 & 502 & 514.817937559795 & -0.890979322125457 & -12.8179375597953 & -0.219735021776374 \tabularnewline
35 & 516 & 516.113504287233 & 0.239852217410683 & -0.113504287233148 & 0.187813428821908 \tabularnewline
36 & 528 & 518.146226943398 & 1.1671887053512 & 9.85377305660229 & 0.15433208535223 \tabularnewline
37 & 533 & 522.837547458959 & 2.99273808629801 & 10.1624525410407 & 0.303745119249397 \tabularnewline
38 & 536 & 531.596188921867 & 5.97097538783978 & 4.40381107813323 & 0.493016824981042 \tabularnewline
39 & 537 & 534.444769569382 & 4.3692999116915 & 2.55523043061844 & -0.265196202061861 \tabularnewline
40 & 524 & 540.868870678239 & 5.42271411911422 & -16.8688706782386 & 0.175432347463346 \tabularnewline
41 & 536 & 553.383027010338 & 9.07566253382764 & -17.3830270103384 & 0.608276123054868 \tabularnewline
42 & 587 & 566.979421661622 & 11.4079345635513 & 20.0205783383779 & 0.386665470633031 \tabularnewline
43 & 597 & 578.859404594582 & 11.6510136761302 & 18.140595405418 & 0.0402416333376524 \tabularnewline
44 & 581 & 583.388057801933 & 7.98767881965705 & -2.38805780193285 & -0.607154863189412 \tabularnewline
45 & 564 & 578.707893574547 & 1.47108239911033 & -14.707893574547 & -1.08118054091213 \tabularnewline
46 & 558 & 573.326821273653 & -2.05445435837996 & -15.3268212736526 & -0.585292883313566 \tabularnewline
47 & 575 & 574.00477587745 & -0.648451757142419 & 0.995224122549569 & 0.233620621622018 \tabularnewline
48 & 580 & 572.354998623477 & -1.16413402070116 & 7.6450013765231 & -0.0857470486819306 \tabularnewline
49 & 575 & 567.661867026551 & -2.98274243829626 & 7.33813297344923 & -0.301995026758463 \tabularnewline
50 & 563 & 559.043614379996 & -5.88025235516809 & 3.95638562000403 & -0.479881464049787 \tabularnewline
51 & 552 & 551.075091163591 & -6.94971065329523 & 0.924908836409229 & -0.177227829260922 \tabularnewline
52 & 537 & 553.336679533017 & -2.23506840951835 & -16.3366795330166 & 0.783980873955845 \tabularnewline
53 & 545 & 561.45229204517 & 3.07773492748901 & -16.4522920451703 & 0.884043996639682 \tabularnewline
54 & 601 & 576.860935246781 & 9.41702520935234 & 24.1390647532195 & 1.05214564893283 \tabularnewline
55 & 604 & 584.378439124899 & 8.44152756198894 & 19.6215608751011 & -0.161607284549462 \tabularnewline
56 & 586 & 585.859548502455 & 4.87254537252376 & 0.140451497544846 & -0.591406821930943 \tabularnewline
57 & 564 & 580.529469156378 & -0.355570040660422 & -16.5294691563785 & -0.867265434159036 \tabularnewline
58 & 549 & 569.635123689856 & -5.75649833237995 & -20.6351236898564 & -0.896845136193524 \tabularnewline
59 & 551 & 553.289968303978 & -11.1864477931511 & -2.28996830397836 & -0.90237256212942 \tabularnewline
60 & 556 & 544.141545805495 & -10.1402674118424 & 11.8584541945053 & 0.173854118757816 \tabularnewline
61 & 548 & 536.385720401466 & -8.91600274313338 & 11.6142795985336 & 0.203150990936535 \tabularnewline
62 & 540 & 532.738101465533 & -6.21608288501837 & 7.26189853446697 & 0.447300748702044 \tabularnewline
63 & 531 & 532.145980628222 & -3.34146890058393 & -1.14598062822232 & 0.476566178043485 \tabularnewline
64 & 521 & 538.938649422686 & 1.83626458418406 & -17.9386494226857 & 0.860409114716508 \tabularnewline
65 & 519 & 541.761101709955 & 2.34107813259782 & -22.7611017099554 & 0.0839528196284191 \tabularnewline
66 & 572 & 546.660489905537 & 3.6524626692752 & 25.3395100944632 & 0.217739346922561 \tabularnewline
67 & 581 & 556.394881735438 & 6.7681243366046 & 24.6051182645618 & 0.516422862745321 \tabularnewline
68 & 563 & 560.257354740393 & 5.28190973459651 & 2.74264525960711 & -0.246285585789814 \tabularnewline
69 & 548 & 560.974079816011 & 2.94920073474937 & -12.974079816011 & -0.386926733195902 \tabularnewline
70 & 539 & 557.462466543419 & -0.352195877765391 & -18.4624665434194 & -0.548248666619341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160549&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]590[/C][C]590[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]580[/C][C]583.283865255502[/C][C]-2.9752149420575[/C][C]-3.2838652555019[/C][C]-0.888883210196761[/C][/ROW]
[ROW][C]3[/C][C]574[/C][C]574.59315734654[/C][C]-5.80851754828858[/C][C]-0.59315734653974[/C][C]-0.525786612507606[/C][/ROW]
[ROW][C]4[/C][C]573[/C][C]571.273116902043[/C][C]-4.44897147375991[/C][C]1.72688309795728[/C][C]0.226176933831935[/C][/ROW]
[ROW][C]5[/C][C]573[/C][C]571.623060001106[/C][C]-1.84391845547631[/C][C]1.37693999889388[/C][C]0.421510588493358[/C][/ROW]
[ROW][C]6[/C][C]620[/C][C]605.795390898595[/C][C]17.5296828294619[/C][C]14.2046091014051[/C][C]3.19282578103008[/C][/ROW]
[ROW][C]7[/C][C]626[/C][C]629.153342758548[/C][C]20.6752953885225[/C][C]-3.15334275854843[/C][C]0.522069078189157[/C][/ROW]
[ROW][C]8[/C][C]620[/C][C]629.965514716297[/C][C]9.92344769740635[/C][C]-9.96551471629725[/C][C]-1.78431483101233[/C][/ROW]
[ROW][C]9[/C][C]588[/C][C]602.040894545007[/C][C]-10.5759351441779[/C][C]-14.0408945450073[/C][C]-3.40139455420698[/C][/ROW]
[ROW][C]10[/C][C]566[/C][C]570.228873222773[/C][C]-22.0754964766754[/C][C]-4.22887322277343[/C][C]-1.9081612249883[/C][/ROW]
[ROW][C]11[/C][C]557[/C][C]552.547532170963[/C][C]-19.6966098125037[/C][C]4.45246782903712[/C][C]0.394738448428917[/C][/ROW]
[ROW][C]12[/C][C]561[/C][C]553.320759973915[/C][C]-8.61589120067738[/C][C]7.67924002608527[/C][C]1.83866397556424[/C][/ROW]
[ROW][C]13[/C][C]549[/C][C]546.31875576528[/C][C]-7.75653833099919[/C][C]2.68124423472048[/C][C]0.149474022932546[/C][/ROW]
[ROW][C]14[/C][C]532[/C][C]532.834199972709[/C][C]-10.7952270630508[/C][C]-0.83419997270873[/C][C]-0.498120659844999[/C][/ROW]
[ROW][C]15[/C][C]526[/C][C]523.386800625702[/C][C]-10.1100426052606[/C][C]2.61319937429851[/C][C]0.112822778331973[/C][/ROW]
[ROW][C]16[/C][C]511[/C][C]512.648916237073[/C][C]-10.4345519738008[/C][C]-1.64891623707322[/C][C]-0.0548549324446512[/C][/ROW]
[ROW][C]17[/C][C]499[/C][C]509.979312712935[/C][C]-6.35806331578892[/C][C]-10.9793127129351[/C][C]0.675608409796844[/C][/ROW]
[ROW][C]18[/C][C]555[/C][C]533.508507434709[/C][C]9.25464986968828[/C][C]21.4914925652909[/C][C]2.57081722651233[/C][/ROW]
[ROW][C]19[/C][C]565[/C][C]558.225152357246[/C][C]17.311045850928[/C][C]6.77484764275428[/C][C]1.33404669754017[/C][/ROW]
[ROW][C]20[/C][C]542[/C][C]550.518287578003[/C][C]4.24647268900382[/C][C]-8.51828757800251[/C][C]-2.16823512210897[/C][/ROW]
[ROW][C]21[/C][C]527[/C][C]537.686696744475[/C][C]-4.68900396036749[/C][C]-10.6866967444747[/C][C]-1.48272449404469[/C][/ROW]
[ROW][C]22[/C][C]510[/C][C]520.484708577501[/C][C]-11.2375471357242[/C][C]-10.4847085775012[/C][C]-1.08661954520033[/C][/ROW]
[ROW][C]23[/C][C]514[/C][C]513.865138664441[/C][C]-8.82430201274722[/C][C]0.134861335559445[/C][C]0.400506558360965[/C][/ROW]
[ROW][C]24[/C][C]517[/C][C]508.304616881595[/C][C]-7.12210485959475[/C][C]8.69538311840484[/C][C]0.28338598181224[/C][/ROW]
[ROW][C]25[/C][C]508[/C][C]501.687272980594[/C][C]-6.85824626303759[/C][C]6.31272701940556[/C][C]0.04419121030494[/C][/ROW]
[ROW][C]26[/C][C]493[/C][C]492.593863579878[/C][C]-8.02240798507279[/C][C]0.40613642012234[/C][C]-0.192530690425326[/C][/ROW]
[ROW][C]27[/C][C]490[/C][C]484.913465120857[/C][C]-7.84695208661093[/C][C]5.08653487914338[/C][C]0.0289903758229804[/C][/ROW]
[ROW][C]28[/C][C]469[/C][C]476.12991027015[/C][C]-8.32793889734134[/C][C]-7.12991027014965[/C][C]-0.0804268108971488[/C][/ROW]
[ROW][C]29[/C][C]478[/C][C]492.376360292948[/C][C]4.4070641897294[/C][C]-14.3763602929475[/C][C]2.12098603734532[/C][/ROW]
[ROW][C]30[/C][C]528[/C][C]509.561212965181[/C][C]11.0299996011733[/C][C]18.4387870348186[/C][C]1.09516323492062[/C][/ROW]
[ROW][C]31[/C][C]534[/C][C]519.126840147896[/C][C]10.2732072580394[/C][C]14.8731598521041[/C][C]-0.125216106363323[/C][/ROW]
[ROW][C]32[/C][C]518[/C][C]522.229416818185[/C][C]6.56726070506355[/C][C]-4.22941681818469[/C][C]-0.614568534847593[/C][/ROW]
[ROW][C]33[/C][C]506[/C][C]516.942893769889[/C][C]0.433065417893171[/C][C]-10.9428937698892[/C][C]-1.0179067725843[/C][/ROW]
[ROW][C]34[/C][C]502[/C][C]514.817937559795[/C][C]-0.890979322125457[/C][C]-12.8179375597953[/C][C]-0.219735021776374[/C][/ROW]
[ROW][C]35[/C][C]516[/C][C]516.113504287233[/C][C]0.239852217410683[/C][C]-0.113504287233148[/C][C]0.187813428821908[/C][/ROW]
[ROW][C]36[/C][C]528[/C][C]518.146226943398[/C][C]1.1671887053512[/C][C]9.85377305660229[/C][C]0.15433208535223[/C][/ROW]
[ROW][C]37[/C][C]533[/C][C]522.837547458959[/C][C]2.99273808629801[/C][C]10.1624525410407[/C][C]0.303745119249397[/C][/ROW]
[ROW][C]38[/C][C]536[/C][C]531.596188921867[/C][C]5.97097538783978[/C][C]4.40381107813323[/C][C]0.493016824981042[/C][/ROW]
[ROW][C]39[/C][C]537[/C][C]534.444769569382[/C][C]4.3692999116915[/C][C]2.55523043061844[/C][C]-0.265196202061861[/C][/ROW]
[ROW][C]40[/C][C]524[/C][C]540.868870678239[/C][C]5.42271411911422[/C][C]-16.8688706782386[/C][C]0.175432347463346[/C][/ROW]
[ROW][C]41[/C][C]536[/C][C]553.383027010338[/C][C]9.07566253382764[/C][C]-17.3830270103384[/C][C]0.608276123054868[/C][/ROW]
[ROW][C]42[/C][C]587[/C][C]566.979421661622[/C][C]11.4079345635513[/C][C]20.0205783383779[/C][C]0.386665470633031[/C][/ROW]
[ROW][C]43[/C][C]597[/C][C]578.859404594582[/C][C]11.6510136761302[/C][C]18.140595405418[/C][C]0.0402416333376524[/C][/ROW]
[ROW][C]44[/C][C]581[/C][C]583.388057801933[/C][C]7.98767881965705[/C][C]-2.38805780193285[/C][C]-0.607154863189412[/C][/ROW]
[ROW][C]45[/C][C]564[/C][C]578.707893574547[/C][C]1.47108239911033[/C][C]-14.707893574547[/C][C]-1.08118054091213[/C][/ROW]
[ROW][C]46[/C][C]558[/C][C]573.326821273653[/C][C]-2.05445435837996[/C][C]-15.3268212736526[/C][C]-0.585292883313566[/C][/ROW]
[ROW][C]47[/C][C]575[/C][C]574.00477587745[/C][C]-0.648451757142419[/C][C]0.995224122549569[/C][C]0.233620621622018[/C][/ROW]
[ROW][C]48[/C][C]580[/C][C]572.354998623477[/C][C]-1.16413402070116[/C][C]7.6450013765231[/C][C]-0.0857470486819306[/C][/ROW]
[ROW][C]49[/C][C]575[/C][C]567.661867026551[/C][C]-2.98274243829626[/C][C]7.33813297344923[/C][C]-0.301995026758463[/C][/ROW]
[ROW][C]50[/C][C]563[/C][C]559.043614379996[/C][C]-5.88025235516809[/C][C]3.95638562000403[/C][C]-0.479881464049787[/C][/ROW]
[ROW][C]51[/C][C]552[/C][C]551.075091163591[/C][C]-6.94971065329523[/C][C]0.924908836409229[/C][C]-0.177227829260922[/C][/ROW]
[ROW][C]52[/C][C]537[/C][C]553.336679533017[/C][C]-2.23506840951835[/C][C]-16.3366795330166[/C][C]0.783980873955845[/C][/ROW]
[ROW][C]53[/C][C]545[/C][C]561.45229204517[/C][C]3.07773492748901[/C][C]-16.4522920451703[/C][C]0.884043996639682[/C][/ROW]
[ROW][C]54[/C][C]601[/C][C]576.860935246781[/C][C]9.41702520935234[/C][C]24.1390647532195[/C][C]1.05214564893283[/C][/ROW]
[ROW][C]55[/C][C]604[/C][C]584.378439124899[/C][C]8.44152756198894[/C][C]19.6215608751011[/C][C]-0.161607284549462[/C][/ROW]
[ROW][C]56[/C][C]586[/C][C]585.859548502455[/C][C]4.87254537252376[/C][C]0.140451497544846[/C][C]-0.591406821930943[/C][/ROW]
[ROW][C]57[/C][C]564[/C][C]580.529469156378[/C][C]-0.355570040660422[/C][C]-16.5294691563785[/C][C]-0.867265434159036[/C][/ROW]
[ROW][C]58[/C][C]549[/C][C]569.635123689856[/C][C]-5.75649833237995[/C][C]-20.6351236898564[/C][C]-0.896845136193524[/C][/ROW]
[ROW][C]59[/C][C]551[/C][C]553.289968303978[/C][C]-11.1864477931511[/C][C]-2.28996830397836[/C][C]-0.90237256212942[/C][/ROW]
[ROW][C]60[/C][C]556[/C][C]544.141545805495[/C][C]-10.1402674118424[/C][C]11.8584541945053[/C][C]0.173854118757816[/C][/ROW]
[ROW][C]61[/C][C]548[/C][C]536.385720401466[/C][C]-8.91600274313338[/C][C]11.6142795985336[/C][C]0.203150990936535[/C][/ROW]
[ROW][C]62[/C][C]540[/C][C]532.738101465533[/C][C]-6.21608288501837[/C][C]7.26189853446697[/C][C]0.447300748702044[/C][/ROW]
[ROW][C]63[/C][C]531[/C][C]532.145980628222[/C][C]-3.34146890058393[/C][C]-1.14598062822232[/C][C]0.476566178043485[/C][/ROW]
[ROW][C]64[/C][C]521[/C][C]538.938649422686[/C][C]1.83626458418406[/C][C]-17.9386494226857[/C][C]0.860409114716508[/C][/ROW]
[ROW][C]65[/C][C]519[/C][C]541.761101709955[/C][C]2.34107813259782[/C][C]-22.7611017099554[/C][C]0.0839528196284191[/C][/ROW]
[ROW][C]66[/C][C]572[/C][C]546.660489905537[/C][C]3.6524626692752[/C][C]25.3395100944632[/C][C]0.217739346922561[/C][/ROW]
[ROW][C]67[/C][C]581[/C][C]556.394881735438[/C][C]6.7681243366046[/C][C]24.6051182645618[/C][C]0.516422862745321[/C][/ROW]
[ROW][C]68[/C][C]563[/C][C]560.257354740393[/C][C]5.28190973459651[/C][C]2.74264525960711[/C][C]-0.246285585789814[/C][/ROW]
[ROW][C]69[/C][C]548[/C][C]560.974079816011[/C][C]2.94920073474937[/C][C]-12.974079816011[/C][C]-0.386926733195902[/C][/ROW]
[ROW][C]70[/C][C]539[/C][C]557.462466543419[/C][C]-0.352195877765391[/C][C]-18.4624665434194[/C][C]-0.548248666619341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160549&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160549&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
1590590000
2580583.283865255502-2.9752149420575-3.2838652555019-0.888883210196761
3574574.59315734654-5.80851754828858-0.59315734653974-0.525786612507606
4573571.273116902043-4.448971473759911.726883097957280.226176933831935
5573571.623060001106-1.843918455476311.376939998893880.421510588493358
6620605.79539089859517.529682829461914.20460910140513.19282578103008
7626629.15334275854820.6752953885225-3.153342758548430.522069078189157
8620629.9655147162979.92344769740635-9.96551471629725-1.78431483101233
9588602.040894545007-10.5759351441779-14.0408945450073-3.40139455420698
10566570.228873222773-22.0754964766754-4.22887322277343-1.9081612249883
11557552.547532170963-19.69660981250374.452467829037120.394738448428917
12561553.320759973915-8.615891200677387.679240026085271.83866397556424
13549546.31875576528-7.756538330999192.681244234720480.149474022932546
14532532.834199972709-10.7952270630508-0.83419997270873-0.498120659844999
15526523.386800625702-10.11004260526062.613199374298510.112822778331973
16511512.648916237073-10.4345519738008-1.64891623707322-0.0548549324446512
17499509.979312712935-6.35806331578892-10.97931271293510.675608409796844
18555533.5085074347099.2546498696882821.49149256529092.57081722651233
19565558.22515235724617.3110458509286.774847642754281.33404669754017
20542550.5182875780034.24647268900382-8.51828757800251-2.16823512210897
21527537.686696744475-4.68900396036749-10.6866967444747-1.48272449404469
22510520.484708577501-11.2375471357242-10.4847085775012-1.08661954520033
23514513.865138664441-8.824302012747220.1348613355594450.400506558360965
24517508.304616881595-7.122104859594758.695383118404840.28338598181224
25508501.687272980594-6.858246263037596.312727019405560.04419121030494
26493492.593863579878-8.022407985072790.40613642012234-0.192530690425326
27490484.913465120857-7.846952086610935.086534879143380.0289903758229804
28469476.12991027015-8.32793889734134-7.12991027014965-0.0804268108971488
29478492.3763602929484.4070641897294-14.37636029294752.12098603734532
30528509.56121296518111.029999601173318.43878703481861.09516323492062
31534519.12684014789610.273207258039414.8731598521041-0.125216106363323
32518522.2294168181856.56726070506355-4.22941681818469-0.614568534847593
33506516.9428937698890.433065417893171-10.9428937698892-1.0179067725843
34502514.817937559795-0.890979322125457-12.8179375597953-0.219735021776374
35516516.1135042872330.239852217410683-0.1135042872331480.187813428821908
36528518.1462269433981.16718870535129.853773056602290.15433208535223
37533522.8375474589592.9927380862980110.16245254104070.303745119249397
38536531.5961889218675.970975387839784.403811078133230.493016824981042
39537534.4447695693824.36929991169152.55523043061844-0.265196202061861
40524540.8688706782395.42271411911422-16.86887067823860.175432347463346
41536553.3830270103389.07566253382764-17.38302701033840.608276123054868
42587566.97942166162211.407934563551320.02057833837790.386665470633031
43597578.85940459458211.651013676130218.1405954054180.0402416333376524
44581583.3880578019337.98767881965705-2.38805780193285-0.607154863189412
45564578.7078935745471.47108239911033-14.707893574547-1.08118054091213
46558573.326821273653-2.05445435837996-15.3268212736526-0.585292883313566
47575574.00477587745-0.6484517571424190.9952241225495690.233620621622018
48580572.354998623477-1.164134020701167.6450013765231-0.0857470486819306
49575567.661867026551-2.982742438296267.33813297344923-0.301995026758463
50563559.043614379996-5.880252355168093.95638562000403-0.479881464049787
51552551.075091163591-6.949710653295230.924908836409229-0.177227829260922
52537553.336679533017-2.23506840951835-16.33667953301660.783980873955845
53545561.452292045173.07773492748901-16.45229204517030.884043996639682
54601576.8609352467819.4170252093523424.13906475321951.05214564893283
55604584.3784391248998.4415275619889419.6215608751011-0.161607284549462
56586585.8595485024554.872545372523760.140451497544846-0.591406821930943
57564580.529469156378-0.355570040660422-16.5294691563785-0.867265434159036
58549569.635123689856-5.75649833237995-20.6351236898564-0.896845136193524
59551553.289968303978-11.1864477931511-2.28996830397836-0.90237256212942
60556544.141545805495-10.140267411842411.85845419450530.173854118757816
61548536.385720401466-8.9160027431333811.61427959853360.203150990936535
62540532.738101465533-6.216082885018377.261898534466970.447300748702044
63531532.145980628222-3.34146890058393-1.145980628222320.476566178043485
64521538.9386494226861.83626458418406-17.93864942268570.860409114716508
65519541.7611017099552.34107813259782-22.76110170995540.0839528196284191
66572546.6604899055373.652462669275225.33951009446320.217739346922561
67581556.3948817354386.768124336604624.60511826456180.516422862745321
68563560.2573547403935.281909734596512.74264525960711-0.246285585789814
69548560.9740798160112.94920073474937-12.974079816011-0.386926733195902
70539557.462466543419-0.352195877765391-18.4624665434194-0.548248666619341



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')