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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 07:55:50 -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/t125993858367lszch4f8p906k.htm/, Retrieved Fri, 03 May 2024 19:08:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63687, Retrieved Fri, 03 May 2024 19:08:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109
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] [] [2009-12-04 14:55:50] [6974478841a4d28b8cb590971bfdefb0] [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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63687&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63687&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1611611000
2594600.764715726819-10.7943516711373-6.76471572681921-1.79417466550545
3595590.130715219314-10.69220345062684.869284780686240.0135938238352938
4591587.228517321978-5.51620596190893.771482678022370.724337656566066
5589587.518362551015-1.662750317147061.481637448984650.499410966495785
6584584.81072081983-2.34298130283852-0.810720819829668-0.0887250814437027
7573575.314563085944-7.01140140169183-2.31456308594446-0.611703208028301
8567566.466885878727-8.212928323426080.533114121273051-0.157136433768578
9569565.563410628786-3.428857309587813.436589371214470.62555036552411
10621604.87453200195524.539152187486916.12546799804553.65735221006601
11629632.72573242986726.7059716998185-3.725732429866890.283344523304397
12628636.85854439927211.9394895598296-8.85854439927163-1.93093038824835
13612620.927066934544-6.2267084480038-8.92706693454376-2.38476752768488
14595603.453602299613-13.5910085716291-8.45360229961277-0.97884873548042
15597592.9638286582-11.61337644041104.036171341799690.256319207056960
16593589.204349054148-6.680867902958613.795650945851940.654112630564915
17590586.779954141924-3.973171858948163.220045858076040.356670496902642
18580578.073761001415-6.966654840593671.92623899858534-0.389762015972569
19574571.402373026475-6.780884075647282.597626973524570.024279516547364
20573572.149021127488-2.037097110908350.8509788725122510.620873163415635
21573582.4313692020745.73800974653048-9.431369202074161.01671498597325
22620602.60728369657814.851737643953717.39271630342161.19176013189274
23626622.02370878618317.73158913341073.976291213816590.37660135848543
24620625.0515421774088.46621582962653-5.05154217740801-1.21221175899113
25588603.571892883564-10.4094328635746-15.5718928835645-2.4762133653004
26566579.24200625601-19.1958760129663-13.2420062560106-1.15095315601659
27557556.703631738739-21.28884916516750.296368261261291-0.272843819168515
28561551.441627072655-11.31238350463699.558372927345491.30914873959252
29549542.533782437369-9.808193938162526.466217562631410.197665336189131
30532529.381383373013-11.90121791293702.61861662698734-0.273405424219038
31526522.632535179101-8.688314465850233.367464820898600.419478618704594
32511515.466459881514-7.73958239790163-4.466459881514170.124076269173489
33499514.019104682471-3.8133722778245-15.01910468247080.513498786688406
34555533.1601877418510.515898736094121.83981225815041.87377848599719
35565553.70483543853716.773890917338411.29516456146320.818423415510956
36542545.6488292382281.28521689417607-3.64882923822804-2.02704658924266
37527538.058014998981-4.25600353352343-11.0580149989809-0.725774793671245
38510525.673371532448-9.32959979527289-15.6733715324482-0.66340214563967
39514518.610647186793-7.92043704187319-4.61064718679330.183994200324925
40517508.63311278329-9.195793456529748.36688721671029-0.167015905203778
41508499.139423178701-9.380900869530228.86057682129927-0.0242750247298982
42493490.185988416559-9.114942627838952.814011583440590.0347846894223537
43490484.442394824679-7.021113522556545.557605175321020.273474028940806
44469477.444652784926-7.00661571675337-8.444652784925750.0018949568782837
45478494.6336022518738.00675331544998-16.63360225187271.96336642792224
46528509.37326969805312.186068705467318.62673030194670.546590436834772
47534515.9847898093388.7258507208347218.0152101906616-0.452632450021529
48518519.6303232163975.57178393674495-1.63032321639718-0.412767216269535
49506517.2777191778390.649183786890601-11.2777191778392-0.644180662158148
50502518.0751576954880.741221283426512-16.07515769548810.0120297749557840
51516519.4942055509491.16096821078641-3.494205550949310.0548397257549317
52528519.307124723790.3273849457143458.69287527621017-0.109095347491193
53533521.9541588951631.7636125577595611.04584110483650.188159321498297
54536530.3307007427075.862761461428255.669299257292870.536318992376891
55537533.7050068914284.321537708699933.29499310857175-0.201389600791940
56524541.1089923424436.22830744562767-17.10899234244260.249182208264339
57536553.57779636422710.0872894480547-17.57779636422660.504581332978417
58587566.1260112026911.609315631871620.87398879731000.199082830889453
59597577.15428492701411.249865707914219.8457150729865-0.0470290540356877
60581582.0351880421457.30813064230398-1.03518804214537-0.515801062787485

\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 & 600.764715726819 & -10.7943516711373 & -6.76471572681921 & -1.79417466550545 \tabularnewline
3 & 595 & 590.130715219314 & -10.6922034506268 & 4.86928478068624 & 0.0135938238352938 \tabularnewline
4 & 591 & 587.228517321978 & -5.5162059619089 & 3.77148267802237 & 0.724337656566066 \tabularnewline
5 & 589 & 587.518362551015 & -1.66275031714706 & 1.48163744898465 & 0.499410966495785 \tabularnewline
6 & 584 & 584.81072081983 & -2.34298130283852 & -0.810720819829668 & -0.0887250814437027 \tabularnewline
7 & 573 & 575.314563085944 & -7.01140140169183 & -2.31456308594446 & -0.611703208028301 \tabularnewline
8 & 567 & 566.466885878727 & -8.21292832342608 & 0.533114121273051 & -0.157136433768578 \tabularnewline
9 & 569 & 565.563410628786 & -3.42885730958781 & 3.43658937121447 & 0.62555036552411 \tabularnewline
10 & 621 & 604.874532001955 & 24.5391521874869 & 16.1254679980455 & 3.65735221006601 \tabularnewline
11 & 629 & 632.725732429867 & 26.7059716998185 & -3.72573242986689 & 0.283344523304397 \tabularnewline
12 & 628 & 636.858544399272 & 11.9394895598296 & -8.85854439927163 & -1.93093038824835 \tabularnewline
13 & 612 & 620.927066934544 & -6.2267084480038 & -8.92706693454376 & -2.38476752768488 \tabularnewline
14 & 595 & 603.453602299613 & -13.5910085716291 & -8.45360229961277 & -0.97884873548042 \tabularnewline
15 & 597 & 592.9638286582 & -11.6133764404110 & 4.03617134179969 & 0.256319207056960 \tabularnewline
16 & 593 & 589.204349054148 & -6.68086790295861 & 3.79565094585194 & 0.654112630564915 \tabularnewline
17 & 590 & 586.779954141924 & -3.97317185894816 & 3.22004585807604 & 0.356670496902642 \tabularnewline
18 & 580 & 578.073761001415 & -6.96665484059367 & 1.92623899858534 & -0.389762015972569 \tabularnewline
19 & 574 & 571.402373026475 & -6.78088407564728 & 2.59762697352457 & 0.024279516547364 \tabularnewline
20 & 573 & 572.149021127488 & -2.03709711090835 & 0.850978872512251 & 0.620873163415635 \tabularnewline
21 & 573 & 582.431369202074 & 5.73800974653048 & -9.43136920207416 & 1.01671498597325 \tabularnewline
22 & 620 & 602.607283696578 & 14.8517376439537 & 17.3927163034216 & 1.19176013189274 \tabularnewline
23 & 626 & 622.023708786183 & 17.7315891334107 & 3.97629121381659 & 0.37660135848543 \tabularnewline
24 & 620 & 625.051542177408 & 8.46621582962653 & -5.05154217740801 & -1.21221175899113 \tabularnewline
25 & 588 & 603.571892883564 & -10.4094328635746 & -15.5718928835645 & -2.4762133653004 \tabularnewline
26 & 566 & 579.24200625601 & -19.1958760129663 & -13.2420062560106 & -1.15095315601659 \tabularnewline
27 & 557 & 556.703631738739 & -21.2888491651675 & 0.296368261261291 & -0.272843819168515 \tabularnewline
28 & 561 & 551.441627072655 & -11.3123835046369 & 9.55837292734549 & 1.30914873959252 \tabularnewline
29 & 549 & 542.533782437369 & -9.80819393816252 & 6.46621756263141 & 0.197665336189131 \tabularnewline
30 & 532 & 529.381383373013 & -11.9012179129370 & 2.61861662698734 & -0.273405424219038 \tabularnewline
31 & 526 & 522.632535179101 & -8.68831446585023 & 3.36746482089860 & 0.419478618704594 \tabularnewline
32 & 511 & 515.466459881514 & -7.73958239790163 & -4.46645988151417 & 0.124076269173489 \tabularnewline
33 & 499 & 514.019104682471 & -3.8133722778245 & -15.0191046824708 & 0.513498786688406 \tabularnewline
34 & 555 & 533.16018774185 & 10.5158987360941 & 21.8398122581504 & 1.87377848599719 \tabularnewline
35 & 565 & 553.704835438537 & 16.7738909173384 & 11.2951645614632 & 0.818423415510956 \tabularnewline
36 & 542 & 545.648829238228 & 1.28521689417607 & -3.64882923822804 & -2.02704658924266 \tabularnewline
37 & 527 & 538.058014998981 & -4.25600353352343 & -11.0580149989809 & -0.725774793671245 \tabularnewline
38 & 510 & 525.673371532448 & -9.32959979527289 & -15.6733715324482 & -0.66340214563967 \tabularnewline
39 & 514 & 518.610647186793 & -7.92043704187319 & -4.6106471867933 & 0.183994200324925 \tabularnewline
40 & 517 & 508.63311278329 & -9.19579345652974 & 8.36688721671029 & -0.167015905203778 \tabularnewline
41 & 508 & 499.139423178701 & -9.38090086953022 & 8.86057682129927 & -0.0242750247298982 \tabularnewline
42 & 493 & 490.185988416559 & -9.11494262783895 & 2.81401158344059 & 0.0347846894223537 \tabularnewline
43 & 490 & 484.442394824679 & -7.02111352255654 & 5.55760517532102 & 0.273474028940806 \tabularnewline
44 & 469 & 477.444652784926 & -7.00661571675337 & -8.44465278492575 & 0.0018949568782837 \tabularnewline
45 & 478 & 494.633602251873 & 8.00675331544998 & -16.6336022518727 & 1.96336642792224 \tabularnewline
46 & 528 & 509.373269698053 & 12.1860687054673 & 18.6267303019467 & 0.546590436834772 \tabularnewline
47 & 534 & 515.984789809338 & 8.72585072083472 & 18.0152101906616 & -0.452632450021529 \tabularnewline
48 & 518 & 519.630323216397 & 5.57178393674495 & -1.63032321639718 & -0.412767216269535 \tabularnewline
49 & 506 & 517.277719177839 & 0.649183786890601 & -11.2777191778392 & -0.644180662158148 \tabularnewline
50 & 502 & 518.075157695488 & 0.741221283426512 & -16.0751576954881 & 0.0120297749557840 \tabularnewline
51 & 516 & 519.494205550949 & 1.16096821078641 & -3.49420555094931 & 0.0548397257549317 \tabularnewline
52 & 528 & 519.30712472379 & 0.327384945714345 & 8.69287527621017 & -0.109095347491193 \tabularnewline
53 & 533 & 521.954158895163 & 1.76361255775956 & 11.0458411048365 & 0.188159321498297 \tabularnewline
54 & 536 & 530.330700742707 & 5.86276146142825 & 5.66929925729287 & 0.536318992376891 \tabularnewline
55 & 537 & 533.705006891428 & 4.32153770869993 & 3.29499310857175 & -0.201389600791940 \tabularnewline
56 & 524 & 541.108992342443 & 6.22830744562767 & -17.1089923424426 & 0.249182208264339 \tabularnewline
57 & 536 & 553.577796364227 & 10.0872894480547 & -17.5777963642266 & 0.504581332978417 \tabularnewline
58 & 587 & 566.12601120269 & 11.6093156318716 & 20.8739887973100 & 0.199082830889453 \tabularnewline
59 & 597 & 577.154284927014 & 11.2498657079142 & 19.8457150729865 & -0.0470290540356877 \tabularnewline
60 & 581 & 582.035188042145 & 7.30813064230398 & -1.03518804214537 & -0.515801062787485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63687&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]600.764715726819[/C][C]-10.7943516711373[/C][C]-6.76471572681921[/C][C]-1.79417466550545[/C][/ROW]
[ROW][C]3[/C][C]595[/C][C]590.130715219314[/C][C]-10.6922034506268[/C][C]4.86928478068624[/C][C]0.0135938238352938[/C][/ROW]
[ROW][C]4[/C][C]591[/C][C]587.228517321978[/C][C]-5.5162059619089[/C][C]3.77148267802237[/C][C]0.724337656566066[/C][/ROW]
[ROW][C]5[/C][C]589[/C][C]587.518362551015[/C][C]-1.66275031714706[/C][C]1.48163744898465[/C][C]0.499410966495785[/C][/ROW]
[ROW][C]6[/C][C]584[/C][C]584.81072081983[/C][C]-2.34298130283852[/C][C]-0.810720819829668[/C][C]-0.0887250814437027[/C][/ROW]
[ROW][C]7[/C][C]573[/C][C]575.314563085944[/C][C]-7.01140140169183[/C][C]-2.31456308594446[/C][C]-0.611703208028301[/C][/ROW]
[ROW][C]8[/C][C]567[/C][C]566.466885878727[/C][C]-8.21292832342608[/C][C]0.533114121273051[/C][C]-0.157136433768578[/C][/ROW]
[ROW][C]9[/C][C]569[/C][C]565.563410628786[/C][C]-3.42885730958781[/C][C]3.43658937121447[/C][C]0.62555036552411[/C][/ROW]
[ROW][C]10[/C][C]621[/C][C]604.874532001955[/C][C]24.5391521874869[/C][C]16.1254679980455[/C][C]3.65735221006601[/C][/ROW]
[ROW][C]11[/C][C]629[/C][C]632.725732429867[/C][C]26.7059716998185[/C][C]-3.72573242986689[/C][C]0.283344523304397[/C][/ROW]
[ROW][C]12[/C][C]628[/C][C]636.858544399272[/C][C]11.9394895598296[/C][C]-8.85854439927163[/C][C]-1.93093038824835[/C][/ROW]
[ROW][C]13[/C][C]612[/C][C]620.927066934544[/C][C]-6.2267084480038[/C][C]-8.92706693454376[/C][C]-2.38476752768488[/C][/ROW]
[ROW][C]14[/C][C]595[/C][C]603.453602299613[/C][C]-13.5910085716291[/C][C]-8.45360229961277[/C][C]-0.97884873548042[/C][/ROW]
[ROW][C]15[/C][C]597[/C][C]592.9638286582[/C][C]-11.6133764404110[/C][C]4.03617134179969[/C][C]0.256319207056960[/C][/ROW]
[ROW][C]16[/C][C]593[/C][C]589.204349054148[/C][C]-6.68086790295861[/C][C]3.79565094585194[/C][C]0.654112630564915[/C][/ROW]
[ROW][C]17[/C][C]590[/C][C]586.779954141924[/C][C]-3.97317185894816[/C][C]3.22004585807604[/C][C]0.356670496902642[/C][/ROW]
[ROW][C]18[/C][C]580[/C][C]578.073761001415[/C][C]-6.96665484059367[/C][C]1.92623899858534[/C][C]-0.389762015972569[/C][/ROW]
[ROW][C]19[/C][C]574[/C][C]571.402373026475[/C][C]-6.78088407564728[/C][C]2.59762697352457[/C][C]0.024279516547364[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]572.149021127488[/C][C]-2.03709711090835[/C][C]0.850978872512251[/C][C]0.620873163415635[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]582.431369202074[/C][C]5.73800974653048[/C][C]-9.43136920207416[/C][C]1.01671498597325[/C][/ROW]
[ROW][C]22[/C][C]620[/C][C]602.607283696578[/C][C]14.8517376439537[/C][C]17.3927163034216[/C][C]1.19176013189274[/C][/ROW]
[ROW][C]23[/C][C]626[/C][C]622.023708786183[/C][C]17.7315891334107[/C][C]3.97629121381659[/C][C]0.37660135848543[/C][/ROW]
[ROW][C]24[/C][C]620[/C][C]625.051542177408[/C][C]8.46621582962653[/C][C]-5.05154217740801[/C][C]-1.21221175899113[/C][/ROW]
[ROW][C]25[/C][C]588[/C][C]603.571892883564[/C][C]-10.4094328635746[/C][C]-15.5718928835645[/C][C]-2.4762133653004[/C][/ROW]
[ROW][C]26[/C][C]566[/C][C]579.24200625601[/C][C]-19.1958760129663[/C][C]-13.2420062560106[/C][C]-1.15095315601659[/C][/ROW]
[ROW][C]27[/C][C]557[/C][C]556.703631738739[/C][C]-21.2888491651675[/C][C]0.296368261261291[/C][C]-0.272843819168515[/C][/ROW]
[ROW][C]28[/C][C]561[/C][C]551.441627072655[/C][C]-11.3123835046369[/C][C]9.55837292734549[/C][C]1.30914873959252[/C][/ROW]
[ROW][C]29[/C][C]549[/C][C]542.533782437369[/C][C]-9.80819393816252[/C][C]6.46621756263141[/C][C]0.197665336189131[/C][/ROW]
[ROW][C]30[/C][C]532[/C][C]529.381383373013[/C][C]-11.9012179129370[/C][C]2.61861662698734[/C][C]-0.273405424219038[/C][/ROW]
[ROW][C]31[/C][C]526[/C][C]522.632535179101[/C][C]-8.68831446585023[/C][C]3.36746482089860[/C][C]0.419478618704594[/C][/ROW]
[ROW][C]32[/C][C]511[/C][C]515.466459881514[/C][C]-7.73958239790163[/C][C]-4.46645988151417[/C][C]0.124076269173489[/C][/ROW]
[ROW][C]33[/C][C]499[/C][C]514.019104682471[/C][C]-3.8133722778245[/C][C]-15.0191046824708[/C][C]0.513498786688406[/C][/ROW]
[ROW][C]34[/C][C]555[/C][C]533.16018774185[/C][C]10.5158987360941[/C][C]21.8398122581504[/C][C]1.87377848599719[/C][/ROW]
[ROW][C]35[/C][C]565[/C][C]553.704835438537[/C][C]16.7738909173384[/C][C]11.2951645614632[/C][C]0.818423415510956[/C][/ROW]
[ROW][C]36[/C][C]542[/C][C]545.648829238228[/C][C]1.28521689417607[/C][C]-3.64882923822804[/C][C]-2.02704658924266[/C][/ROW]
[ROW][C]37[/C][C]527[/C][C]538.058014998981[/C][C]-4.25600353352343[/C][C]-11.0580149989809[/C][C]-0.725774793671245[/C][/ROW]
[ROW][C]38[/C][C]510[/C][C]525.673371532448[/C][C]-9.32959979527289[/C][C]-15.6733715324482[/C][C]-0.66340214563967[/C][/ROW]
[ROW][C]39[/C][C]514[/C][C]518.610647186793[/C][C]-7.92043704187319[/C][C]-4.6106471867933[/C][C]0.183994200324925[/C][/ROW]
[ROW][C]40[/C][C]517[/C][C]508.63311278329[/C][C]-9.19579345652974[/C][C]8.36688721671029[/C][C]-0.167015905203778[/C][/ROW]
[ROW][C]41[/C][C]508[/C][C]499.139423178701[/C][C]-9.38090086953022[/C][C]8.86057682129927[/C][C]-0.0242750247298982[/C][/ROW]
[ROW][C]42[/C][C]493[/C][C]490.185988416559[/C][C]-9.11494262783895[/C][C]2.81401158344059[/C][C]0.0347846894223537[/C][/ROW]
[ROW][C]43[/C][C]490[/C][C]484.442394824679[/C][C]-7.02111352255654[/C][C]5.55760517532102[/C][C]0.273474028940806[/C][/ROW]
[ROW][C]44[/C][C]469[/C][C]477.444652784926[/C][C]-7.00661571675337[/C][C]-8.44465278492575[/C][C]0.0018949568782837[/C][/ROW]
[ROW][C]45[/C][C]478[/C][C]494.633602251873[/C][C]8.00675331544998[/C][C]-16.6336022518727[/C][C]1.96336642792224[/C][/ROW]
[ROW][C]46[/C][C]528[/C][C]509.373269698053[/C][C]12.1860687054673[/C][C]18.6267303019467[/C][C]0.546590436834772[/C][/ROW]
[ROW][C]47[/C][C]534[/C][C]515.984789809338[/C][C]8.72585072083472[/C][C]18.0152101906616[/C][C]-0.452632450021529[/C][/ROW]
[ROW][C]48[/C][C]518[/C][C]519.630323216397[/C][C]5.57178393674495[/C][C]-1.63032321639718[/C][C]-0.412767216269535[/C][/ROW]
[ROW][C]49[/C][C]506[/C][C]517.277719177839[/C][C]0.649183786890601[/C][C]-11.2777191778392[/C][C]-0.644180662158148[/C][/ROW]
[ROW][C]50[/C][C]502[/C][C]518.075157695488[/C][C]0.741221283426512[/C][C]-16.0751576954881[/C][C]0.0120297749557840[/C][/ROW]
[ROW][C]51[/C][C]516[/C][C]519.494205550949[/C][C]1.16096821078641[/C][C]-3.49420555094931[/C][C]0.0548397257549317[/C][/ROW]
[ROW][C]52[/C][C]528[/C][C]519.30712472379[/C][C]0.327384945714345[/C][C]8.69287527621017[/C][C]-0.109095347491193[/C][/ROW]
[ROW][C]53[/C][C]533[/C][C]521.954158895163[/C][C]1.76361255775956[/C][C]11.0458411048365[/C][C]0.188159321498297[/C][/ROW]
[ROW][C]54[/C][C]536[/C][C]530.330700742707[/C][C]5.86276146142825[/C][C]5.66929925729287[/C][C]0.536318992376891[/C][/ROW]
[ROW][C]55[/C][C]537[/C][C]533.705006891428[/C][C]4.32153770869993[/C][C]3.29499310857175[/C][C]-0.201389600791940[/C][/ROW]
[ROW][C]56[/C][C]524[/C][C]541.108992342443[/C][C]6.22830744562767[/C][C]-17.1089923424426[/C][C]0.249182208264339[/C][/ROW]
[ROW][C]57[/C][C]536[/C][C]553.577796364227[/C][C]10.0872894480547[/C][C]-17.5777963642266[/C][C]0.504581332978417[/C][/ROW]
[ROW][C]58[/C][C]587[/C][C]566.12601120269[/C][C]11.6093156318716[/C][C]20.8739887973100[/C][C]0.199082830889453[/C][/ROW]
[ROW][C]59[/C][C]597[/C][C]577.154284927014[/C][C]11.2498657079142[/C][C]19.8457150729865[/C][C]-0.0470290540356877[/C][/ROW]
[ROW][C]60[/C][C]581[/C][C]582.035188042145[/C][C]7.30813064230398[/C][C]-1.03518804214537[/C][C]-0.515801062787485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63687&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
2594600.764715726819-10.7943516711373-6.76471572681921-1.79417466550545
3595590.130715219314-10.69220345062684.869284780686240.0135938238352938
4591587.228517321978-5.51620596190893.771482678022370.724337656566066
5589587.518362551015-1.662750317147061.481637448984650.499410966495785
6584584.81072081983-2.34298130283852-0.810720819829668-0.0887250814437027
7573575.314563085944-7.01140140169183-2.31456308594446-0.611703208028301
8567566.466885878727-8.212928323426080.533114121273051-0.157136433768578
9569565.563410628786-3.428857309587813.436589371214470.62555036552411
10621604.87453200195524.539152187486916.12546799804553.65735221006601
11629632.72573242986726.7059716998185-3.725732429866890.283344523304397
12628636.85854439927211.9394895598296-8.85854439927163-1.93093038824835
13612620.927066934544-6.2267084480038-8.92706693454376-2.38476752768488
14595603.453602299613-13.5910085716291-8.45360229961277-0.97884873548042
15597592.9638286582-11.61337644041104.036171341799690.256319207056960
16593589.204349054148-6.680867902958613.795650945851940.654112630564915
17590586.779954141924-3.973171858948163.220045858076040.356670496902642
18580578.073761001415-6.966654840593671.92623899858534-0.389762015972569
19574571.402373026475-6.780884075647282.597626973524570.024279516547364
20573572.149021127488-2.037097110908350.8509788725122510.620873163415635
21573582.4313692020745.73800974653048-9.431369202074161.01671498597325
22620602.60728369657814.851737643953717.39271630342161.19176013189274
23626622.02370878618317.73158913341073.976291213816590.37660135848543
24620625.0515421774088.46621582962653-5.05154217740801-1.21221175899113
25588603.571892883564-10.4094328635746-15.5718928835645-2.4762133653004
26566579.24200625601-19.1958760129663-13.2420062560106-1.15095315601659
27557556.703631738739-21.28884916516750.296368261261291-0.272843819168515
28561551.441627072655-11.31238350463699.558372927345491.30914873959252
29549542.533782437369-9.808193938162526.466217562631410.197665336189131
30532529.381383373013-11.90121791293702.61861662698734-0.273405424219038
31526522.632535179101-8.688314465850233.367464820898600.419478618704594
32511515.466459881514-7.73958239790163-4.466459881514170.124076269173489
33499514.019104682471-3.8133722778245-15.01910468247080.513498786688406
34555533.1601877418510.515898736094121.83981225815041.87377848599719
35565553.70483543853716.773890917338411.29516456146320.818423415510956
36542545.6488292382281.28521689417607-3.64882923822804-2.02704658924266
37527538.058014998981-4.25600353352343-11.0580149989809-0.725774793671245
38510525.673371532448-9.32959979527289-15.6733715324482-0.66340214563967
39514518.610647186793-7.92043704187319-4.61064718679330.183994200324925
40517508.63311278329-9.195793456529748.36688721671029-0.167015905203778
41508499.139423178701-9.380900869530228.86057682129927-0.0242750247298982
42493490.185988416559-9.114942627838952.814011583440590.0347846894223537
43490484.442394824679-7.021113522556545.557605175321020.273474028940806
44469477.444652784926-7.00661571675337-8.444652784925750.0018949568782837
45478494.6336022518738.00675331544998-16.63360225187271.96336642792224
46528509.37326969805312.186068705467318.62673030194670.546590436834772
47534515.9847898093388.7258507208347218.0152101906616-0.452632450021529
48518519.6303232163975.57178393674495-1.63032321639718-0.412767216269535
49506517.2777191778390.649183786890601-11.2777191778392-0.644180662158148
50502518.0751576954880.741221283426512-16.07515769548810.0120297749557840
51516519.4942055509491.16096821078641-3.494205550949310.0548397257549317
52528519.307124723790.3273849457143458.69287527621017-0.109095347491193
53533521.9541588951631.7636125577595611.04584110483650.188159321498297
54536530.3307007427075.862761461428255.669299257292870.536318992376891
55537533.7050068914284.321537708699933.29499310857175-0.201389600791940
56524541.1089923424436.22830744562767-17.10899234244260.249182208264339
57536553.57779636422710.0872894480547-17.57779636422660.504581332978417
58587566.1260112026911.609315631871620.87398879731000.199082830889453
59597577.15428492701411.249865707914219.8457150729865-0.0470290540356877
60581582.0351880421457.30813064230398-1.03518804214537-0.515801062787485



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')