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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationMon, 20 Dec 2010 13:31:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t1292851800p8bc7ytq7g2orr2.htm/, Retrieved Fri, 03 May 2024 15:15:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112935, Retrieved Fri, 03 May 2024 15:15:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact195
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [STSM - werkloosheid] [2010-12-20 13:31:59] [f3d6336ce664ba129edd250394d444d3] [Current]
-    D    [Structural Time Series Models] [Paper: Structural...] [2010-12-28 01:36:48] [8e42c8cdf50f15ce85eb45a67cf771d0]
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Dataseries X:
493
514
522
490
484
506
501
462
465
454
464
427
460
473
465
422
415
413
420
363
376
380
384
346
389
407
393
346
348
353
364
305
307
312
312
286
324
336
327
302
299
311
315
264
278
278
287
279
324
354
354
360
363
385
412
370
389
395
417
404




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112935&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]6 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=112935&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112935&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1493493000
2514504.5710333488493.142428062595599.428966651150851.76691629309249
3522520.0074469454547.48379075677221.992553054546301.23183215236965
4490506.574978572565-1.37237606501525-16.5749785725646-2.12227545364971
5484488.710568130579-8.639501661703-4.71056813057891-1.59055060027424
6506493.561262728594-2.7688071750238912.43873727140581.28279545481015
7501498.7739001126800.6893036306838842.226099887319530.768261258051414
8462477.46497312555-8.87297564973663-15.4649731255497-2.13631655926010
9465463.087216928793-11.27266691643391.91278307120683-0.536233728014627
10454452.173628671692-11.11598043482381.826371328307870.0350066531413511
11464454.930611568434-5.062274144244369.069388431565781.35251809408963
12427437.668444233094-10.3847382757294-10.6684442330937-1.18915900585750
13460448.793092570577-1.1866146156013211.20690742942292.15392038600191
14473461.8176423963124.9543706642895611.18235760368831.37767126090792
15465459.5187690993831.942167799331845.48123090061652-0.656853657086611
16422444.329408514776-5.13641618829899-22.3294085147756-1.60475087331948
17415431.084306116483-8.55739224066271-16.0843061164830-0.774225276626705
18413409.973112057597-13.87991504387363.02688794240324-1.18307750176295
19420401.826319177325-11.46101611360918.17368082267480.536540822031456
20363382.296320682616-14.8598651647699-19.2963206826162-0.757455201188811
21376371.011564421791-13.35149027478044.988435578209490.336916973855627
22380372.938482022992-6.897822806444217.061517977007941.44206767783673
23384371.408407265191-4.632322917698712.59159273480950.506534429325817
24346368.550652855417-3.88437736920351-22.55065285541660.16780449449934
25389376.5257201015991.1241894193265212.47427989840141.12969771546276
26407387.1029985929585.115056632120219.89700140704230.891201994671527
27393385.2185249273092.195088988610617.78147507269059-0.647287238808744
28346372.065083949765-4.16863561400948-26.0650839497649-1.42613513215525
29348360.851992985825-7.10806307933134-12.8519929858245-0.662430282001851
30353351.343652640777-8.115093289155231.65634735922331-0.225423644293794
31364342.070796780096-8.600265120167521.9292032199044-0.108003468651413
32305329.320108629591-10.3347944485435-24.3201086295909-0.386215234726404
33307312.262069318856-13.1417350570144-5.26206931885617-0.626331221209646
34312303.310588476613-11.39234247633258.689411523386550.391010452837393
35312298.641333580546-8.585198446140413.3586664194540.628496124810071
36286306.485381197079-1.71850775064605-20.48538119707911.540178885019
37324313.0182124119791.7360537962365910.98178758802130.774202772509078
38336313.4351384557151.1845500325112822.5648615442849-0.12298495952208
39327312.657553719240.36884170411183714.3424462807603-0.181531695080490
40302320.2327484305453.35428699579593-18.23274843054480.66770852676506
41299316.672395519310.482764751821045-17.6723955193100-0.64492546775473
42311310.691349745327-2.211397979138970.308650254673188-0.603795074957681
43315296.117659991278-7.365535318736918.8823400087221-1.15011773889491
44264284.870189599966-8.98034151744165-20.8701895999657-0.359736754979694
45278281.277263731508-6.74398809596575-3.277263731508230.498782918050458
46278274.92418785064-6.581837817169563.075812149360030.0362453251890144
47287276.740988406644-3.0958555781191810.25901159335630.780775914220422
48279292.4703483222664.73039338193652-13.47034832226621.75396508851009
49324308.6351968290469.4893804778948915.36480317095381.06457432972693
50354327.9036578626213.553791724026826.09634213737980.906359214920433
51354343.36056217101814.342032506133510.63943782898240.175663553763780
52360368.09938978724318.6390606994307-8.099389787242680.960553163847438
53363381.15850305310816.3300326177234-18.1585030531083-0.517671939646797
54385384.79887826274111.06598375789060.201121737258704-1.17936373343546
55412391.4111940846669.2173331877030220.5888059153339-0.412931112201856
56370394.8813459701726.83571197043593-24.8813459701715-0.530939027598221
57389395.327283591294.19347876811356-6.32728359128969-0.589340664512528
58395397.5338882225963.37267569384741-2.53388822259636-0.183456442636117
59417410.2665169963047.242033315672546.733483003695940.866445181414434
60404422.4817438748379.30109927695227-18.48174387483710.461154741842772

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 493 & 493 & 0 & 0 & 0 \tabularnewline
2 & 514 & 504.571033348849 & 3.14242806259559 & 9.42896665115085 & 1.76691629309249 \tabularnewline
3 & 522 & 520.007446945454 & 7.4837907567722 & 1.99255305454630 & 1.23183215236965 \tabularnewline
4 & 490 & 506.574978572565 & -1.37237606501525 & -16.5749785725646 & -2.12227545364971 \tabularnewline
5 & 484 & 488.710568130579 & -8.639501661703 & -4.71056813057891 & -1.59055060027424 \tabularnewline
6 & 506 & 493.561262728594 & -2.76880717502389 & 12.4387372714058 & 1.28279545481015 \tabularnewline
7 & 501 & 498.773900112680 & 0.689303630683884 & 2.22609988731953 & 0.768261258051414 \tabularnewline
8 & 462 & 477.46497312555 & -8.87297564973663 & -15.4649731255497 & -2.13631655926010 \tabularnewline
9 & 465 & 463.087216928793 & -11.2726669164339 & 1.91278307120683 & -0.536233728014627 \tabularnewline
10 & 454 & 452.173628671692 & -11.1159804348238 & 1.82637132830787 & 0.0350066531413511 \tabularnewline
11 & 464 & 454.930611568434 & -5.06227414424436 & 9.06938843156578 & 1.35251809408963 \tabularnewline
12 & 427 & 437.668444233094 & -10.3847382757294 & -10.6684442330937 & -1.18915900585750 \tabularnewline
13 & 460 & 448.793092570577 & -1.18661461560132 & 11.2069074294229 & 2.15392038600191 \tabularnewline
14 & 473 & 461.817642396312 & 4.95437066428956 & 11.1823576036883 & 1.37767126090792 \tabularnewline
15 & 465 & 459.518769099383 & 1.94216779933184 & 5.48123090061652 & -0.656853657086611 \tabularnewline
16 & 422 & 444.329408514776 & -5.13641618829899 & -22.3294085147756 & -1.60475087331948 \tabularnewline
17 & 415 & 431.084306116483 & -8.55739224066271 & -16.0843061164830 & -0.774225276626705 \tabularnewline
18 & 413 & 409.973112057597 & -13.8799150438736 & 3.02688794240324 & -1.18307750176295 \tabularnewline
19 & 420 & 401.826319177325 & -11.461016113609 & 18.1736808226748 & 0.536540822031456 \tabularnewline
20 & 363 & 382.296320682616 & -14.8598651647699 & -19.2963206826162 & -0.757455201188811 \tabularnewline
21 & 376 & 371.011564421791 & -13.3514902747804 & 4.98843557820949 & 0.336916973855627 \tabularnewline
22 & 380 & 372.938482022992 & -6.89782280644421 & 7.06151797700794 & 1.44206767783673 \tabularnewline
23 & 384 & 371.408407265191 & -4.6323229176987 & 12.5915927348095 & 0.506534429325817 \tabularnewline
24 & 346 & 368.550652855417 & -3.88437736920351 & -22.5506528554166 & 0.16780449449934 \tabularnewline
25 & 389 & 376.525720101599 & 1.12418941932652 & 12.4742798984014 & 1.12969771546276 \tabularnewline
26 & 407 & 387.102998592958 & 5.1150566321202 & 19.8970014070423 & 0.891201994671527 \tabularnewline
27 & 393 & 385.218524927309 & 2.19508898861061 & 7.78147507269059 & -0.647287238808744 \tabularnewline
28 & 346 & 372.065083949765 & -4.16863561400948 & -26.0650839497649 & -1.42613513215525 \tabularnewline
29 & 348 & 360.851992985825 & -7.10806307933134 & -12.8519929858245 & -0.662430282001851 \tabularnewline
30 & 353 & 351.343652640777 & -8.11509328915523 & 1.65634735922331 & -0.225423644293794 \tabularnewline
31 & 364 & 342.070796780096 & -8.6002651201675 & 21.9292032199044 & -0.108003468651413 \tabularnewline
32 & 305 & 329.320108629591 & -10.3347944485435 & -24.3201086295909 & -0.386215234726404 \tabularnewline
33 & 307 & 312.262069318856 & -13.1417350570144 & -5.26206931885617 & -0.626331221209646 \tabularnewline
34 & 312 & 303.310588476613 & -11.3923424763325 & 8.68941152338655 & 0.391010452837393 \tabularnewline
35 & 312 & 298.641333580546 & -8.5851984461404 & 13.358666419454 & 0.628496124810071 \tabularnewline
36 & 286 & 306.485381197079 & -1.71850775064605 & -20.4853811970791 & 1.540178885019 \tabularnewline
37 & 324 & 313.018212411979 & 1.73605379623659 & 10.9817875880213 & 0.774202772509078 \tabularnewline
38 & 336 & 313.435138455715 & 1.18455003251128 & 22.5648615442849 & -0.12298495952208 \tabularnewline
39 & 327 & 312.65755371924 & 0.368841704111837 & 14.3424462807603 & -0.181531695080490 \tabularnewline
40 & 302 & 320.232748430545 & 3.35428699579593 & -18.2327484305448 & 0.66770852676506 \tabularnewline
41 & 299 & 316.67239551931 & 0.482764751821045 & -17.6723955193100 & -0.64492546775473 \tabularnewline
42 & 311 & 310.691349745327 & -2.21139797913897 & 0.308650254673188 & -0.603795074957681 \tabularnewline
43 & 315 & 296.117659991278 & -7.3655353187369 & 18.8823400087221 & -1.15011773889491 \tabularnewline
44 & 264 & 284.870189599966 & -8.98034151744165 & -20.8701895999657 & -0.359736754979694 \tabularnewline
45 & 278 & 281.277263731508 & -6.74398809596575 & -3.27726373150823 & 0.498782918050458 \tabularnewline
46 & 278 & 274.92418785064 & -6.58183781716956 & 3.07581214936003 & 0.0362453251890144 \tabularnewline
47 & 287 & 276.740988406644 & -3.09585557811918 & 10.2590115933563 & 0.780775914220422 \tabularnewline
48 & 279 & 292.470348322266 & 4.73039338193652 & -13.4703483222662 & 1.75396508851009 \tabularnewline
49 & 324 & 308.635196829046 & 9.48938047789489 & 15.3648031709538 & 1.06457432972693 \tabularnewline
50 & 354 & 327.90365786262 & 13.5537917240268 & 26.0963421373798 & 0.906359214920433 \tabularnewline
51 & 354 & 343.360562171018 & 14.3420325061335 & 10.6394378289824 & 0.175663553763780 \tabularnewline
52 & 360 & 368.099389787243 & 18.6390606994307 & -8.09938978724268 & 0.960553163847438 \tabularnewline
53 & 363 & 381.158503053108 & 16.3300326177234 & -18.1585030531083 & -0.517671939646797 \tabularnewline
54 & 385 & 384.798878262741 & 11.0659837578906 & 0.201121737258704 & -1.17936373343546 \tabularnewline
55 & 412 & 391.411194084666 & 9.21733318770302 & 20.5888059153339 & -0.412931112201856 \tabularnewline
56 & 370 & 394.881345970172 & 6.83571197043593 & -24.8813459701715 & -0.530939027598221 \tabularnewline
57 & 389 & 395.32728359129 & 4.19347876811356 & -6.32728359128969 & -0.589340664512528 \tabularnewline
58 & 395 & 397.533888222596 & 3.37267569384741 & -2.53388822259636 & -0.183456442636117 \tabularnewline
59 & 417 & 410.266516996304 & 7.24203331567254 & 6.73348300369594 & 0.866445181414434 \tabularnewline
60 & 404 & 422.481743874837 & 9.30109927695227 & -18.4817438748371 & 0.461154741842772 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112935&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]493[/C][C]493[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]514[/C][C]504.571033348849[/C][C]3.14242806259559[/C][C]9.42896665115085[/C][C]1.76691629309249[/C][/ROW]
[ROW][C]3[/C][C]522[/C][C]520.007446945454[/C][C]7.4837907567722[/C][C]1.99255305454630[/C][C]1.23183215236965[/C][/ROW]
[ROW][C]4[/C][C]490[/C][C]506.574978572565[/C][C]-1.37237606501525[/C][C]-16.5749785725646[/C][C]-2.12227545364971[/C][/ROW]
[ROW][C]5[/C][C]484[/C][C]488.710568130579[/C][C]-8.639501661703[/C][C]-4.71056813057891[/C][C]-1.59055060027424[/C][/ROW]
[ROW][C]6[/C][C]506[/C][C]493.561262728594[/C][C]-2.76880717502389[/C][C]12.4387372714058[/C][C]1.28279545481015[/C][/ROW]
[ROW][C]7[/C][C]501[/C][C]498.773900112680[/C][C]0.689303630683884[/C][C]2.22609988731953[/C][C]0.768261258051414[/C][/ROW]
[ROW][C]8[/C][C]462[/C][C]477.46497312555[/C][C]-8.87297564973663[/C][C]-15.4649731255497[/C][C]-2.13631655926010[/C][/ROW]
[ROW][C]9[/C][C]465[/C][C]463.087216928793[/C][C]-11.2726669164339[/C][C]1.91278307120683[/C][C]-0.536233728014627[/C][/ROW]
[ROW][C]10[/C][C]454[/C][C]452.173628671692[/C][C]-11.1159804348238[/C][C]1.82637132830787[/C][C]0.0350066531413511[/C][/ROW]
[ROW][C]11[/C][C]464[/C][C]454.930611568434[/C][C]-5.06227414424436[/C][C]9.06938843156578[/C][C]1.35251809408963[/C][/ROW]
[ROW][C]12[/C][C]427[/C][C]437.668444233094[/C][C]-10.3847382757294[/C][C]-10.6684442330937[/C][C]-1.18915900585750[/C][/ROW]
[ROW][C]13[/C][C]460[/C][C]448.793092570577[/C][C]-1.18661461560132[/C][C]11.2069074294229[/C][C]2.15392038600191[/C][/ROW]
[ROW][C]14[/C][C]473[/C][C]461.817642396312[/C][C]4.95437066428956[/C][C]11.1823576036883[/C][C]1.37767126090792[/C][/ROW]
[ROW][C]15[/C][C]465[/C][C]459.518769099383[/C][C]1.94216779933184[/C][C]5.48123090061652[/C][C]-0.656853657086611[/C][/ROW]
[ROW][C]16[/C][C]422[/C][C]444.329408514776[/C][C]-5.13641618829899[/C][C]-22.3294085147756[/C][C]-1.60475087331948[/C][/ROW]
[ROW][C]17[/C][C]415[/C][C]431.084306116483[/C][C]-8.55739224066271[/C][C]-16.0843061164830[/C][C]-0.774225276626705[/C][/ROW]
[ROW][C]18[/C][C]413[/C][C]409.973112057597[/C][C]-13.8799150438736[/C][C]3.02688794240324[/C][C]-1.18307750176295[/C][/ROW]
[ROW][C]19[/C][C]420[/C][C]401.826319177325[/C][C]-11.461016113609[/C][C]18.1736808226748[/C][C]0.536540822031456[/C][/ROW]
[ROW][C]20[/C][C]363[/C][C]382.296320682616[/C][C]-14.8598651647699[/C][C]-19.2963206826162[/C][C]-0.757455201188811[/C][/ROW]
[ROW][C]21[/C][C]376[/C][C]371.011564421791[/C][C]-13.3514902747804[/C][C]4.98843557820949[/C][C]0.336916973855627[/C][/ROW]
[ROW][C]22[/C][C]380[/C][C]372.938482022992[/C][C]-6.89782280644421[/C][C]7.06151797700794[/C][C]1.44206767783673[/C][/ROW]
[ROW][C]23[/C][C]384[/C][C]371.408407265191[/C][C]-4.6323229176987[/C][C]12.5915927348095[/C][C]0.506534429325817[/C][/ROW]
[ROW][C]24[/C][C]346[/C][C]368.550652855417[/C][C]-3.88437736920351[/C][C]-22.5506528554166[/C][C]0.16780449449934[/C][/ROW]
[ROW][C]25[/C][C]389[/C][C]376.525720101599[/C][C]1.12418941932652[/C][C]12.4742798984014[/C][C]1.12969771546276[/C][/ROW]
[ROW][C]26[/C][C]407[/C][C]387.102998592958[/C][C]5.1150566321202[/C][C]19.8970014070423[/C][C]0.891201994671527[/C][/ROW]
[ROW][C]27[/C][C]393[/C][C]385.218524927309[/C][C]2.19508898861061[/C][C]7.78147507269059[/C][C]-0.647287238808744[/C][/ROW]
[ROW][C]28[/C][C]346[/C][C]372.065083949765[/C][C]-4.16863561400948[/C][C]-26.0650839497649[/C][C]-1.42613513215525[/C][/ROW]
[ROW][C]29[/C][C]348[/C][C]360.851992985825[/C][C]-7.10806307933134[/C][C]-12.8519929858245[/C][C]-0.662430282001851[/C][/ROW]
[ROW][C]30[/C][C]353[/C][C]351.343652640777[/C][C]-8.11509328915523[/C][C]1.65634735922331[/C][C]-0.225423644293794[/C][/ROW]
[ROW][C]31[/C][C]364[/C][C]342.070796780096[/C][C]-8.6002651201675[/C][C]21.9292032199044[/C][C]-0.108003468651413[/C][/ROW]
[ROW][C]32[/C][C]305[/C][C]329.320108629591[/C][C]-10.3347944485435[/C][C]-24.3201086295909[/C][C]-0.386215234726404[/C][/ROW]
[ROW][C]33[/C][C]307[/C][C]312.262069318856[/C][C]-13.1417350570144[/C][C]-5.26206931885617[/C][C]-0.626331221209646[/C][/ROW]
[ROW][C]34[/C][C]312[/C][C]303.310588476613[/C][C]-11.3923424763325[/C][C]8.68941152338655[/C][C]0.391010452837393[/C][/ROW]
[ROW][C]35[/C][C]312[/C][C]298.641333580546[/C][C]-8.5851984461404[/C][C]13.358666419454[/C][C]0.628496124810071[/C][/ROW]
[ROW][C]36[/C][C]286[/C][C]306.485381197079[/C][C]-1.71850775064605[/C][C]-20.4853811970791[/C][C]1.540178885019[/C][/ROW]
[ROW][C]37[/C][C]324[/C][C]313.018212411979[/C][C]1.73605379623659[/C][C]10.9817875880213[/C][C]0.774202772509078[/C][/ROW]
[ROW][C]38[/C][C]336[/C][C]313.435138455715[/C][C]1.18455003251128[/C][C]22.5648615442849[/C][C]-0.12298495952208[/C][/ROW]
[ROW][C]39[/C][C]327[/C][C]312.65755371924[/C][C]0.368841704111837[/C][C]14.3424462807603[/C][C]-0.181531695080490[/C][/ROW]
[ROW][C]40[/C][C]302[/C][C]320.232748430545[/C][C]3.35428699579593[/C][C]-18.2327484305448[/C][C]0.66770852676506[/C][/ROW]
[ROW][C]41[/C][C]299[/C][C]316.67239551931[/C][C]0.482764751821045[/C][C]-17.6723955193100[/C][C]-0.64492546775473[/C][/ROW]
[ROW][C]42[/C][C]311[/C][C]310.691349745327[/C][C]-2.21139797913897[/C][C]0.308650254673188[/C][C]-0.603795074957681[/C][/ROW]
[ROW][C]43[/C][C]315[/C][C]296.117659991278[/C][C]-7.3655353187369[/C][C]18.8823400087221[/C][C]-1.15011773889491[/C][/ROW]
[ROW][C]44[/C][C]264[/C][C]284.870189599966[/C][C]-8.98034151744165[/C][C]-20.8701895999657[/C][C]-0.359736754979694[/C][/ROW]
[ROW][C]45[/C][C]278[/C][C]281.277263731508[/C][C]-6.74398809596575[/C][C]-3.27726373150823[/C][C]0.498782918050458[/C][/ROW]
[ROW][C]46[/C][C]278[/C][C]274.92418785064[/C][C]-6.58183781716956[/C][C]3.07581214936003[/C][C]0.0362453251890144[/C][/ROW]
[ROW][C]47[/C][C]287[/C][C]276.740988406644[/C][C]-3.09585557811918[/C][C]10.2590115933563[/C][C]0.780775914220422[/C][/ROW]
[ROW][C]48[/C][C]279[/C][C]292.470348322266[/C][C]4.73039338193652[/C][C]-13.4703483222662[/C][C]1.75396508851009[/C][/ROW]
[ROW][C]49[/C][C]324[/C][C]308.635196829046[/C][C]9.48938047789489[/C][C]15.3648031709538[/C][C]1.06457432972693[/C][/ROW]
[ROW][C]50[/C][C]354[/C][C]327.90365786262[/C][C]13.5537917240268[/C][C]26.0963421373798[/C][C]0.906359214920433[/C][/ROW]
[ROW][C]51[/C][C]354[/C][C]343.360562171018[/C][C]14.3420325061335[/C][C]10.6394378289824[/C][C]0.175663553763780[/C][/ROW]
[ROW][C]52[/C][C]360[/C][C]368.099389787243[/C][C]18.6390606994307[/C][C]-8.09938978724268[/C][C]0.960553163847438[/C][/ROW]
[ROW][C]53[/C][C]363[/C][C]381.158503053108[/C][C]16.3300326177234[/C][C]-18.1585030531083[/C][C]-0.517671939646797[/C][/ROW]
[ROW][C]54[/C][C]385[/C][C]384.798878262741[/C][C]11.0659837578906[/C][C]0.201121737258704[/C][C]-1.17936373343546[/C][/ROW]
[ROW][C]55[/C][C]412[/C][C]391.411194084666[/C][C]9.21733318770302[/C][C]20.5888059153339[/C][C]-0.412931112201856[/C][/ROW]
[ROW][C]56[/C][C]370[/C][C]394.881345970172[/C][C]6.83571197043593[/C][C]-24.8813459701715[/C][C]-0.530939027598221[/C][/ROW]
[ROW][C]57[/C][C]389[/C][C]395.32728359129[/C][C]4.19347876811356[/C][C]-6.32728359128969[/C][C]-0.589340664512528[/C][/ROW]
[ROW][C]58[/C][C]395[/C][C]397.533888222596[/C][C]3.37267569384741[/C][C]-2.53388822259636[/C][C]-0.183456442636117[/C][/ROW]
[ROW][C]59[/C][C]417[/C][C]410.266516996304[/C][C]7.24203331567254[/C][C]6.73348300369594[/C][C]0.866445181414434[/C][/ROW]
[ROW][C]60[/C][C]404[/C][C]422.481743874837[/C][C]9.30109927695227[/C][C]-18.4817438748371[/C][C]0.461154741842772[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112935&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112935&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
1493493000
2514504.5710333488493.142428062595599.428966651150851.76691629309249
3522520.0074469454547.48379075677221.992553054546301.23183215236965
4490506.574978572565-1.37237606501525-16.5749785725646-2.12227545364971
5484488.710568130579-8.639501661703-4.71056813057891-1.59055060027424
6506493.561262728594-2.7688071750238912.43873727140581.28279545481015
7501498.7739001126800.6893036306838842.226099887319530.768261258051414
8462477.46497312555-8.87297564973663-15.4649731255497-2.13631655926010
9465463.087216928793-11.27266691643391.91278307120683-0.536233728014627
10454452.173628671692-11.11598043482381.826371328307870.0350066531413511
11464454.930611568434-5.062274144244369.069388431565781.35251809408963
12427437.668444233094-10.3847382757294-10.6684442330937-1.18915900585750
13460448.793092570577-1.1866146156013211.20690742942292.15392038600191
14473461.8176423963124.9543706642895611.18235760368831.37767126090792
15465459.5187690993831.942167799331845.48123090061652-0.656853657086611
16422444.329408514776-5.13641618829899-22.3294085147756-1.60475087331948
17415431.084306116483-8.55739224066271-16.0843061164830-0.774225276626705
18413409.973112057597-13.87991504387363.02688794240324-1.18307750176295
19420401.826319177325-11.46101611360918.17368082267480.536540822031456
20363382.296320682616-14.8598651647699-19.2963206826162-0.757455201188811
21376371.011564421791-13.35149027478044.988435578209490.336916973855627
22380372.938482022992-6.897822806444217.061517977007941.44206767783673
23384371.408407265191-4.632322917698712.59159273480950.506534429325817
24346368.550652855417-3.88437736920351-22.55065285541660.16780449449934
25389376.5257201015991.1241894193265212.47427989840141.12969771546276
26407387.1029985929585.115056632120219.89700140704230.891201994671527
27393385.2185249273092.195088988610617.78147507269059-0.647287238808744
28346372.065083949765-4.16863561400948-26.0650839497649-1.42613513215525
29348360.851992985825-7.10806307933134-12.8519929858245-0.662430282001851
30353351.343652640777-8.115093289155231.65634735922331-0.225423644293794
31364342.070796780096-8.600265120167521.9292032199044-0.108003468651413
32305329.320108629591-10.3347944485435-24.3201086295909-0.386215234726404
33307312.262069318856-13.1417350570144-5.26206931885617-0.626331221209646
34312303.310588476613-11.39234247633258.689411523386550.391010452837393
35312298.641333580546-8.585198446140413.3586664194540.628496124810071
36286306.485381197079-1.71850775064605-20.48538119707911.540178885019
37324313.0182124119791.7360537962365910.98178758802130.774202772509078
38336313.4351384557151.1845500325112822.5648615442849-0.12298495952208
39327312.657553719240.36884170411183714.3424462807603-0.181531695080490
40302320.2327484305453.35428699579593-18.23274843054480.66770852676506
41299316.672395519310.482764751821045-17.6723955193100-0.64492546775473
42311310.691349745327-2.211397979138970.308650254673188-0.603795074957681
43315296.117659991278-7.365535318736918.8823400087221-1.15011773889491
44264284.870189599966-8.98034151744165-20.8701895999657-0.359736754979694
45278281.277263731508-6.74398809596575-3.277263731508230.498782918050458
46278274.92418785064-6.581837817169563.075812149360030.0362453251890144
47287276.740988406644-3.0958555781191810.25901159335630.780775914220422
48279292.4703483222664.73039338193652-13.47034832226621.75396508851009
49324308.6351968290469.4893804778948915.36480317095381.06457432972693
50354327.9036578626213.553791724026826.09634213737980.906359214920433
51354343.36056217101814.342032506133510.63943782898240.175663553763780
52360368.09938978724318.6390606994307-8.099389787242680.960553163847438
53363381.15850305310816.3300326177234-18.1585030531083-0.517671939646797
54385384.79887826274111.06598375789060.201121737258704-1.17936373343546
55412391.4111940846669.2173331877030220.5888059153339-0.412931112201856
56370394.8813459701726.83571197043593-24.8813459701715-0.530939027598221
57389395.327283591294.19347876811356-6.32728359128969-0.589340664512528
58395397.5338882225963.37267569384741-2.53388822259636-0.183456442636117
59417410.2665169963047.242033315672546.733483003695940.866445181414434
60404422.4817438748379.30109927695227-18.48174387483710.461154741842772



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')