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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareStructural Time Series Models
Date of computationFri, 09 Dec 2011 16:13:09 -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/09/t13234651970a99kap1lmvuhub.htm/, Retrieved Thu, 02 May 2024 19:15:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153448, Retrieved Thu, 02 May 2024 19:15:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Classical Decomposition] [] [2011-11-28 14:43:30] [b4c8fd31b0af00c33711722ddf8d2c4c]
- RMP   [Structural Time Series Models] [] [2011-11-28 15:23:01] [b4c8fd31b0af00c33711722ddf8d2c4c]
- RM        [Structural Time Series Models] [] [2011-12-09 21:13:09] [c092f3a3bdd85c7279ddab6c8c6c9261] [Current]
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Dataseries X:
579
572
560
551
537
541
588
607
599
578
563
566
561
554
540
526
512
505
554
584
569
540
522
526
527
516
503
489
479
475
524
552
532
511
492
492
493
481
462
457
442
439
488
521
501
485
464
460
467
460
448
443
436
431
484
510
513
503
471
471




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153448&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153448&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153448&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'AstonUniversity' @ aston.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1579579000
2572573.782271187188-5.36502395237765-1.78227118718833-0.580403680234096
3560561.020891519266-10.4690351756307-1.02089151926592-0.507111229451857
4551550.40771496142-10.57462122187080.592285038580426-0.0102470824217607
5537537.624441689451-12.1601416721329-0.624441689451243-0.147838492446946
6541537.436001149507-3.619433235642163.563998850493190.80438806775658
7588576.82139570298727.162754192306811.17860429701322.89717647860301
8607609.31156456255130.9787212891968-2.311564562551480.358928301396519
9599608.6014184989328.28266571602788-9.60141849893185-2.13498528186871
10578584.546794269067-14.8732248252845-6.54679426906707-2.17820156501726
11563562.343732515803-20.12149622832510.656267484196975-0.493685757845808
12566560.278059929701-7.193072040010015.721940070299251.21613130067148
13561560.321470475971-2.025124601579070.6785295240289350.487630970310958
14554555.35879356875-4.1310015030549-1.35879356874966-0.201215976109549
15540542.119698607812-10.4307456762809-2.11969860781156-0.58927434680001
16526523.349176986219-16.18059738529752.65082301378137-0.549444814288722
17512512.309301949449-12.6115514272937-0.3093019494490960.33576708474758
18505512.378826191014-3.8778759534748-7.378826191013850.81948847225295
19554539.30904061451617.328110521753314.6909593854841.9962601419468
20584575.23410176697230.15033783372248.76589823302851.20641327167959
21569578.07127870934411.3106242712344-9.0712787093439-1.77205436859773
22540552.959184521607-13.8116136795661-12.9591845216068-2.36332264394069
23522527.861957595504-21.5928080903501-5.86195759550437-0.73195799531329
24526518.635124799091-13.0756859083087.364875200908650.801444897352559
25527521.592134062372-2.031831225472735.407865937628071.04201008287523
26516515.649929478392-4.72884425408390.350070521607658-0.254072064738837
27503502.546601420894-10.44632548213060.453398579106427-0.53652991086951
28489486.388132693295-14.33428886605032.61186730670483-0.367696779582512
29479479.951592616519-8.93140187594584-0.9515926165187210.509392536327634
30475489.6575367072813.78416606063323-14.65753670728061.1938034640176
31524513.30933456432717.308663421282610.6906654356731.27189894427969
32552535.24110208746720.458753073655316.75889791253330.296415992120629
33532535.0877009461556.4031066534532-3.08770094615456-1.32213558583386
34511523.889974814709-5.59954162899469-12.8899748147091-1.12907812401902
35492505.408268072713-14.3799326534156-13.4082680727132-0.825957409108008
36492490.159387809756-14.9718357240721.84061219024365-0.0557150779156299
37493483.727982820794-9.150075144644049.272017179205820.548613452472556
38481476.27781552025-7.991669785810944.722184479750060.10895413082179
39462459.857730683498-13.70700307310392.1422693165024-0.536989024992038
40457452.930551602453-9.116922924059954.069448397546640.432817957981487
41442448.338888122149-6.04436953256173-6.338888122149290.289574272672244
42439457.6361157659814.36702249811191-18.63611576598090.978466942807857
43488476.82231282589514.405086143188511.17768717410530.943636888824368
44521497.36976475368218.5662000971223.63023524631780.391461334202804
45501502.538711795139.484067296738-1.53871179512957-0.854359678888976
46485497.234315622179-0.542456952291863-12.2343156221791-0.94316438589227
47464481.459492786306-10.8663945634314-17.4594927863063-0.971243532782676
48460462.985279004774-16.0220635966107-2.98527900477437-0.485298692529005
49467454.973248195123-10.590543992016512.02675180487720.511400129068983
50460450.089952213328-6.723174063644549.910047786671920.363646111791656
51448445.66118800311-5.172932989201112.338811996890170.145728527328167
52443439.458258251545-5.868234986339923.54174174845529-0.0654925513213083
53436444.2646957637281.35044100397761-8.264695763727720.679991718863281
54431454.0078670112057.02709314103533-23.00786701120540.53380792600914
55484473.15779464216315.216354532877410.84220535783680.76983841580621
56510484.75344677119612.771354364445625.2465532288042-0.229966114076365
57513507.02181554573719.18691722357165.978184454262550.603517030449634
58503513.90013024624810.8707553953576-10.9001302462481-0.782318111202336
59471494.067492201125-9.87112884777171-23.0674922011246-1.951537135627
60471477.296227029098-14.5332271307881-6.29622702909762-0.438795500395293

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 579 & 579 & 0 & 0 & 0 \tabularnewline
2 & 572 & 573.782271187188 & -5.36502395237765 & -1.78227118718833 & -0.580403680234096 \tabularnewline
3 & 560 & 561.020891519266 & -10.4690351756307 & -1.02089151926592 & -0.507111229451857 \tabularnewline
4 & 551 & 550.40771496142 & -10.5746212218708 & 0.592285038580426 & -0.0102470824217607 \tabularnewline
5 & 537 & 537.624441689451 & -12.1601416721329 & -0.624441689451243 & -0.147838492446946 \tabularnewline
6 & 541 & 537.436001149507 & -3.61943323564216 & 3.56399885049319 & 0.80438806775658 \tabularnewline
7 & 588 & 576.821395702987 & 27.1627541923068 & 11.1786042970132 & 2.89717647860301 \tabularnewline
8 & 607 & 609.311564562551 & 30.9787212891968 & -2.31156456255148 & 0.358928301396519 \tabularnewline
9 & 599 & 608.601418498932 & 8.28266571602788 & -9.60141849893185 & -2.13498528186871 \tabularnewline
10 & 578 & 584.546794269067 & -14.8732248252845 & -6.54679426906707 & -2.17820156501726 \tabularnewline
11 & 563 & 562.343732515803 & -20.1214962283251 & 0.656267484196975 & -0.493685757845808 \tabularnewline
12 & 566 & 560.278059929701 & -7.19307204001001 & 5.72194007029925 & 1.21613130067148 \tabularnewline
13 & 561 & 560.321470475971 & -2.02512460157907 & 0.678529524028935 & 0.487630970310958 \tabularnewline
14 & 554 & 555.35879356875 & -4.1310015030549 & -1.35879356874966 & -0.201215976109549 \tabularnewline
15 & 540 & 542.119698607812 & -10.4307456762809 & -2.11969860781156 & -0.58927434680001 \tabularnewline
16 & 526 & 523.349176986219 & -16.1805973852975 & 2.65082301378137 & -0.549444814288722 \tabularnewline
17 & 512 & 512.309301949449 & -12.6115514272937 & -0.309301949449096 & 0.33576708474758 \tabularnewline
18 & 505 & 512.378826191014 & -3.8778759534748 & -7.37882619101385 & 0.81948847225295 \tabularnewline
19 & 554 & 539.309040614516 & 17.3281105217533 & 14.690959385484 & 1.9962601419468 \tabularnewline
20 & 584 & 575.234101766972 & 30.1503378337224 & 8.7658982330285 & 1.20641327167959 \tabularnewline
21 & 569 & 578.071278709344 & 11.3106242712344 & -9.0712787093439 & -1.77205436859773 \tabularnewline
22 & 540 & 552.959184521607 & -13.8116136795661 & -12.9591845216068 & -2.36332264394069 \tabularnewline
23 & 522 & 527.861957595504 & -21.5928080903501 & -5.86195759550437 & -0.73195799531329 \tabularnewline
24 & 526 & 518.635124799091 & -13.075685908308 & 7.36487520090865 & 0.801444897352559 \tabularnewline
25 & 527 & 521.592134062372 & -2.03183122547273 & 5.40786593762807 & 1.04201008287523 \tabularnewline
26 & 516 & 515.649929478392 & -4.7288442540839 & 0.350070521607658 & -0.254072064738837 \tabularnewline
27 & 503 & 502.546601420894 & -10.4463254821306 & 0.453398579106427 & -0.53652991086951 \tabularnewline
28 & 489 & 486.388132693295 & -14.3342888660503 & 2.61186730670483 & -0.367696779582512 \tabularnewline
29 & 479 & 479.951592616519 & -8.93140187594584 & -0.951592616518721 & 0.509392536327634 \tabularnewline
30 & 475 & 489.657536707281 & 3.78416606063323 & -14.6575367072806 & 1.1938034640176 \tabularnewline
31 & 524 & 513.309334564327 & 17.3086634212826 & 10.690665435673 & 1.27189894427969 \tabularnewline
32 & 552 & 535.241102087467 & 20.4587530736553 & 16.7588979125333 & 0.296415992120629 \tabularnewline
33 & 532 & 535.087700946155 & 6.4031066534532 & -3.08770094615456 & -1.32213558583386 \tabularnewline
34 & 511 & 523.889974814709 & -5.59954162899469 & -12.8899748147091 & -1.12907812401902 \tabularnewline
35 & 492 & 505.408268072713 & -14.3799326534156 & -13.4082680727132 & -0.825957409108008 \tabularnewline
36 & 492 & 490.159387809756 & -14.971835724072 & 1.84061219024365 & -0.0557150779156299 \tabularnewline
37 & 493 & 483.727982820794 & -9.15007514464404 & 9.27201717920582 & 0.548613452472556 \tabularnewline
38 & 481 & 476.27781552025 & -7.99166978581094 & 4.72218447975006 & 0.10895413082179 \tabularnewline
39 & 462 & 459.857730683498 & -13.7070030731039 & 2.1422693165024 & -0.536989024992038 \tabularnewline
40 & 457 & 452.930551602453 & -9.11692292405995 & 4.06944839754664 & 0.432817957981487 \tabularnewline
41 & 442 & 448.338888122149 & -6.04436953256173 & -6.33888812214929 & 0.289574272672244 \tabularnewline
42 & 439 & 457.636115765981 & 4.36702249811191 & -18.6361157659809 & 0.978466942807857 \tabularnewline
43 & 488 & 476.822312825895 & 14.4050861431885 & 11.1776871741053 & 0.943636888824368 \tabularnewline
44 & 521 & 497.369764753682 & 18.56620009712 & 23.6302352463178 & 0.391461334202804 \tabularnewline
45 & 501 & 502.53871179513 & 9.484067296738 & -1.53871179512957 & -0.854359678888976 \tabularnewline
46 & 485 & 497.234315622179 & -0.542456952291863 & -12.2343156221791 & -0.94316438589227 \tabularnewline
47 & 464 & 481.459492786306 & -10.8663945634314 & -17.4594927863063 & -0.971243532782676 \tabularnewline
48 & 460 & 462.985279004774 & -16.0220635966107 & -2.98527900477437 & -0.485298692529005 \tabularnewline
49 & 467 & 454.973248195123 & -10.5905439920165 & 12.0267518048772 & 0.511400129068983 \tabularnewline
50 & 460 & 450.089952213328 & -6.72317406364454 & 9.91004778667192 & 0.363646111791656 \tabularnewline
51 & 448 & 445.66118800311 & -5.17293298920111 & 2.33881199689017 & 0.145728527328167 \tabularnewline
52 & 443 & 439.458258251545 & -5.86823498633992 & 3.54174174845529 & -0.0654925513213083 \tabularnewline
53 & 436 & 444.264695763728 & 1.35044100397761 & -8.26469576372772 & 0.679991718863281 \tabularnewline
54 & 431 & 454.007867011205 & 7.02709314103533 & -23.0078670112054 & 0.53380792600914 \tabularnewline
55 & 484 & 473.157794642163 & 15.2163545328774 & 10.8422053578368 & 0.76983841580621 \tabularnewline
56 & 510 & 484.753446771196 & 12.7713543644456 & 25.2465532288042 & -0.229966114076365 \tabularnewline
57 & 513 & 507.021815545737 & 19.1869172235716 & 5.97818445426255 & 0.603517030449634 \tabularnewline
58 & 503 & 513.900130246248 & 10.8707553953576 & -10.9001302462481 & -0.782318111202336 \tabularnewline
59 & 471 & 494.067492201125 & -9.87112884777171 & -23.0674922011246 & -1.951537135627 \tabularnewline
60 & 471 & 477.296227029098 & -14.5332271307881 & -6.29622702909762 & -0.438795500395293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153448&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]579[/C][C]579[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]572[/C][C]573.782271187188[/C][C]-5.36502395237765[/C][C]-1.78227118718833[/C][C]-0.580403680234096[/C][/ROW]
[ROW][C]3[/C][C]560[/C][C]561.020891519266[/C][C]-10.4690351756307[/C][C]-1.02089151926592[/C][C]-0.507111229451857[/C][/ROW]
[ROW][C]4[/C][C]551[/C][C]550.40771496142[/C][C]-10.5746212218708[/C][C]0.592285038580426[/C][C]-0.0102470824217607[/C][/ROW]
[ROW][C]5[/C][C]537[/C][C]537.624441689451[/C][C]-12.1601416721329[/C][C]-0.624441689451243[/C][C]-0.147838492446946[/C][/ROW]
[ROW][C]6[/C][C]541[/C][C]537.436001149507[/C][C]-3.61943323564216[/C][C]3.56399885049319[/C][C]0.80438806775658[/C][/ROW]
[ROW][C]7[/C][C]588[/C][C]576.821395702987[/C][C]27.1627541923068[/C][C]11.1786042970132[/C][C]2.89717647860301[/C][/ROW]
[ROW][C]8[/C][C]607[/C][C]609.311564562551[/C][C]30.9787212891968[/C][C]-2.31156456255148[/C][C]0.358928301396519[/C][/ROW]
[ROW][C]9[/C][C]599[/C][C]608.601418498932[/C][C]8.28266571602788[/C][C]-9.60141849893185[/C][C]-2.13498528186871[/C][/ROW]
[ROW][C]10[/C][C]578[/C][C]584.546794269067[/C][C]-14.8732248252845[/C][C]-6.54679426906707[/C][C]-2.17820156501726[/C][/ROW]
[ROW][C]11[/C][C]563[/C][C]562.343732515803[/C][C]-20.1214962283251[/C][C]0.656267484196975[/C][C]-0.493685757845808[/C][/ROW]
[ROW][C]12[/C][C]566[/C][C]560.278059929701[/C][C]-7.19307204001001[/C][C]5.72194007029925[/C][C]1.21613130067148[/C][/ROW]
[ROW][C]13[/C][C]561[/C][C]560.321470475971[/C][C]-2.02512460157907[/C][C]0.678529524028935[/C][C]0.487630970310958[/C][/ROW]
[ROW][C]14[/C][C]554[/C][C]555.35879356875[/C][C]-4.1310015030549[/C][C]-1.35879356874966[/C][C]-0.201215976109549[/C][/ROW]
[ROW][C]15[/C][C]540[/C][C]542.119698607812[/C][C]-10.4307456762809[/C][C]-2.11969860781156[/C][C]-0.58927434680001[/C][/ROW]
[ROW][C]16[/C][C]526[/C][C]523.349176986219[/C][C]-16.1805973852975[/C][C]2.65082301378137[/C][C]-0.549444814288722[/C][/ROW]
[ROW][C]17[/C][C]512[/C][C]512.309301949449[/C][C]-12.6115514272937[/C][C]-0.309301949449096[/C][C]0.33576708474758[/C][/ROW]
[ROW][C]18[/C][C]505[/C][C]512.378826191014[/C][C]-3.8778759534748[/C][C]-7.37882619101385[/C][C]0.81948847225295[/C][/ROW]
[ROW][C]19[/C][C]554[/C][C]539.309040614516[/C][C]17.3281105217533[/C][C]14.690959385484[/C][C]1.9962601419468[/C][/ROW]
[ROW][C]20[/C][C]584[/C][C]575.234101766972[/C][C]30.1503378337224[/C][C]8.7658982330285[/C][C]1.20641327167959[/C][/ROW]
[ROW][C]21[/C][C]569[/C][C]578.071278709344[/C][C]11.3106242712344[/C][C]-9.0712787093439[/C][C]-1.77205436859773[/C][/ROW]
[ROW][C]22[/C][C]540[/C][C]552.959184521607[/C][C]-13.8116136795661[/C][C]-12.9591845216068[/C][C]-2.36332264394069[/C][/ROW]
[ROW][C]23[/C][C]522[/C][C]527.861957595504[/C][C]-21.5928080903501[/C][C]-5.86195759550437[/C][C]-0.73195799531329[/C][/ROW]
[ROW][C]24[/C][C]526[/C][C]518.635124799091[/C][C]-13.075685908308[/C][C]7.36487520090865[/C][C]0.801444897352559[/C][/ROW]
[ROW][C]25[/C][C]527[/C][C]521.592134062372[/C][C]-2.03183122547273[/C][C]5.40786593762807[/C][C]1.04201008287523[/C][/ROW]
[ROW][C]26[/C][C]516[/C][C]515.649929478392[/C][C]-4.7288442540839[/C][C]0.350070521607658[/C][C]-0.254072064738837[/C][/ROW]
[ROW][C]27[/C][C]503[/C][C]502.546601420894[/C][C]-10.4463254821306[/C][C]0.453398579106427[/C][C]-0.53652991086951[/C][/ROW]
[ROW][C]28[/C][C]489[/C][C]486.388132693295[/C][C]-14.3342888660503[/C][C]2.61186730670483[/C][C]-0.367696779582512[/C][/ROW]
[ROW][C]29[/C][C]479[/C][C]479.951592616519[/C][C]-8.93140187594584[/C][C]-0.951592616518721[/C][C]0.509392536327634[/C][/ROW]
[ROW][C]30[/C][C]475[/C][C]489.657536707281[/C][C]3.78416606063323[/C][C]-14.6575367072806[/C][C]1.1938034640176[/C][/ROW]
[ROW][C]31[/C][C]524[/C][C]513.309334564327[/C][C]17.3086634212826[/C][C]10.690665435673[/C][C]1.27189894427969[/C][/ROW]
[ROW][C]32[/C][C]552[/C][C]535.241102087467[/C][C]20.4587530736553[/C][C]16.7588979125333[/C][C]0.296415992120629[/C][/ROW]
[ROW][C]33[/C][C]532[/C][C]535.087700946155[/C][C]6.4031066534532[/C][C]-3.08770094615456[/C][C]-1.32213558583386[/C][/ROW]
[ROW][C]34[/C][C]511[/C][C]523.889974814709[/C][C]-5.59954162899469[/C][C]-12.8899748147091[/C][C]-1.12907812401902[/C][/ROW]
[ROW][C]35[/C][C]492[/C][C]505.408268072713[/C][C]-14.3799326534156[/C][C]-13.4082680727132[/C][C]-0.825957409108008[/C][/ROW]
[ROW][C]36[/C][C]492[/C][C]490.159387809756[/C][C]-14.971835724072[/C][C]1.84061219024365[/C][C]-0.0557150779156299[/C][/ROW]
[ROW][C]37[/C][C]493[/C][C]483.727982820794[/C][C]-9.15007514464404[/C][C]9.27201717920582[/C][C]0.548613452472556[/C][/ROW]
[ROW][C]38[/C][C]481[/C][C]476.27781552025[/C][C]-7.99166978581094[/C][C]4.72218447975006[/C][C]0.10895413082179[/C][/ROW]
[ROW][C]39[/C][C]462[/C][C]459.857730683498[/C][C]-13.7070030731039[/C][C]2.1422693165024[/C][C]-0.536989024992038[/C][/ROW]
[ROW][C]40[/C][C]457[/C][C]452.930551602453[/C][C]-9.11692292405995[/C][C]4.06944839754664[/C][C]0.432817957981487[/C][/ROW]
[ROW][C]41[/C][C]442[/C][C]448.338888122149[/C][C]-6.04436953256173[/C][C]-6.33888812214929[/C][C]0.289574272672244[/C][/ROW]
[ROW][C]42[/C][C]439[/C][C]457.636115765981[/C][C]4.36702249811191[/C][C]-18.6361157659809[/C][C]0.978466942807857[/C][/ROW]
[ROW][C]43[/C][C]488[/C][C]476.822312825895[/C][C]14.4050861431885[/C][C]11.1776871741053[/C][C]0.943636888824368[/C][/ROW]
[ROW][C]44[/C][C]521[/C][C]497.369764753682[/C][C]18.56620009712[/C][C]23.6302352463178[/C][C]0.391461334202804[/C][/ROW]
[ROW][C]45[/C][C]501[/C][C]502.53871179513[/C][C]9.484067296738[/C][C]-1.53871179512957[/C][C]-0.854359678888976[/C][/ROW]
[ROW][C]46[/C][C]485[/C][C]497.234315622179[/C][C]-0.542456952291863[/C][C]-12.2343156221791[/C][C]-0.94316438589227[/C][/ROW]
[ROW][C]47[/C][C]464[/C][C]481.459492786306[/C][C]-10.8663945634314[/C][C]-17.4594927863063[/C][C]-0.971243532782676[/C][/ROW]
[ROW][C]48[/C][C]460[/C][C]462.985279004774[/C][C]-16.0220635966107[/C][C]-2.98527900477437[/C][C]-0.485298692529005[/C][/ROW]
[ROW][C]49[/C][C]467[/C][C]454.973248195123[/C][C]-10.5905439920165[/C][C]12.0267518048772[/C][C]0.511400129068983[/C][/ROW]
[ROW][C]50[/C][C]460[/C][C]450.089952213328[/C][C]-6.72317406364454[/C][C]9.91004778667192[/C][C]0.363646111791656[/C][/ROW]
[ROW][C]51[/C][C]448[/C][C]445.66118800311[/C][C]-5.17293298920111[/C][C]2.33881199689017[/C][C]0.145728527328167[/C][/ROW]
[ROW][C]52[/C][C]443[/C][C]439.458258251545[/C][C]-5.86823498633992[/C][C]3.54174174845529[/C][C]-0.0654925513213083[/C][/ROW]
[ROW][C]53[/C][C]436[/C][C]444.264695763728[/C][C]1.35044100397761[/C][C]-8.26469576372772[/C][C]0.679991718863281[/C][/ROW]
[ROW][C]54[/C][C]431[/C][C]454.007867011205[/C][C]7.02709314103533[/C][C]-23.0078670112054[/C][C]0.53380792600914[/C][/ROW]
[ROW][C]55[/C][C]484[/C][C]473.157794642163[/C][C]15.2163545328774[/C][C]10.8422053578368[/C][C]0.76983841580621[/C][/ROW]
[ROW][C]56[/C][C]510[/C][C]484.753446771196[/C][C]12.7713543644456[/C][C]25.2465532288042[/C][C]-0.229966114076365[/C][/ROW]
[ROW][C]57[/C][C]513[/C][C]507.021815545737[/C][C]19.1869172235716[/C][C]5.97818445426255[/C][C]0.603517030449634[/C][/ROW]
[ROW][C]58[/C][C]503[/C][C]513.900130246248[/C][C]10.8707553953576[/C][C]-10.9001302462481[/C][C]-0.782318111202336[/C][/ROW]
[ROW][C]59[/C][C]471[/C][C]494.067492201125[/C][C]-9.87112884777171[/C][C]-23.0674922011246[/C][C]-1.951537135627[/C][/ROW]
[ROW][C]60[/C][C]471[/C][C]477.296227029098[/C][C]-14.5332271307881[/C][C]-6.29622702909762[/C][C]-0.438795500395293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153448&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153448&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
1579579000
2572573.782271187188-5.36502395237765-1.78227118718833-0.580403680234096
3560561.020891519266-10.4690351756307-1.02089151926592-0.507111229451857
4551550.40771496142-10.57462122187080.592285038580426-0.0102470824217607
5537537.624441689451-12.1601416721329-0.624441689451243-0.147838492446946
6541537.436001149507-3.619433235642163.563998850493190.80438806775658
7588576.82139570298727.162754192306811.17860429701322.89717647860301
8607609.31156456255130.9787212891968-2.311564562551480.358928301396519
9599608.6014184989328.28266571602788-9.60141849893185-2.13498528186871
10578584.546794269067-14.8732248252845-6.54679426906707-2.17820156501726
11563562.343732515803-20.12149622832510.656267484196975-0.493685757845808
12566560.278059929701-7.193072040010015.721940070299251.21613130067148
13561560.321470475971-2.025124601579070.6785295240289350.487630970310958
14554555.35879356875-4.1310015030549-1.35879356874966-0.201215976109549
15540542.119698607812-10.4307456762809-2.11969860781156-0.58927434680001
16526523.349176986219-16.18059738529752.65082301378137-0.549444814288722
17512512.309301949449-12.6115514272937-0.3093019494490960.33576708474758
18505512.378826191014-3.8778759534748-7.378826191013850.81948847225295
19554539.30904061451617.328110521753314.6909593854841.9962601419468
20584575.23410176697230.15033783372248.76589823302851.20641327167959
21569578.07127870934411.3106242712344-9.0712787093439-1.77205436859773
22540552.959184521607-13.8116136795661-12.9591845216068-2.36332264394069
23522527.861957595504-21.5928080903501-5.86195759550437-0.73195799531329
24526518.635124799091-13.0756859083087.364875200908650.801444897352559
25527521.592134062372-2.031831225472735.407865937628071.04201008287523
26516515.649929478392-4.72884425408390.350070521607658-0.254072064738837
27503502.546601420894-10.44632548213060.453398579106427-0.53652991086951
28489486.388132693295-14.33428886605032.61186730670483-0.367696779582512
29479479.951592616519-8.93140187594584-0.9515926165187210.509392536327634
30475489.6575367072813.78416606063323-14.65753670728061.1938034640176
31524513.30933456432717.308663421282610.6906654356731.27189894427969
32552535.24110208746720.458753073655316.75889791253330.296415992120629
33532535.0877009461556.4031066534532-3.08770094615456-1.32213558583386
34511523.889974814709-5.59954162899469-12.8899748147091-1.12907812401902
35492505.408268072713-14.3799326534156-13.4082680727132-0.825957409108008
36492490.159387809756-14.9718357240721.84061219024365-0.0557150779156299
37493483.727982820794-9.150075144644049.272017179205820.548613452472556
38481476.27781552025-7.991669785810944.722184479750060.10895413082179
39462459.857730683498-13.70700307310392.1422693165024-0.536989024992038
40457452.930551602453-9.116922924059954.069448397546640.432817957981487
41442448.338888122149-6.04436953256173-6.338888122149290.289574272672244
42439457.6361157659814.36702249811191-18.63611576598090.978466942807857
43488476.82231282589514.405086143188511.17768717410530.943636888824368
44521497.36976475368218.5662000971223.63023524631780.391461334202804
45501502.538711795139.484067296738-1.53871179512957-0.854359678888976
46485497.234315622179-0.542456952291863-12.2343156221791-0.94316438589227
47464481.459492786306-10.8663945634314-17.4594927863063-0.971243532782676
48460462.985279004774-16.0220635966107-2.98527900477437-0.485298692529005
49467454.973248195123-10.590543992016512.02675180487720.511400129068983
50460450.089952213328-6.723174063644549.910047786671920.363646111791656
51448445.66118800311-5.172932989201112.338811996890170.145728527328167
52443439.458258251545-5.868234986339923.54174174845529-0.0654925513213083
53436444.2646957637281.35044100397761-8.264695763727720.679991718863281
54431454.0078670112057.02709314103533-23.00786701120540.53380792600914
55484473.15779464216315.216354532877410.84220535783680.76983841580621
56510484.75344677119612.771354364445625.2465532288042-0.229966114076365
57513507.02181554573719.18691722357165.978184454262550.603517030449634
58503513.90013024624810.8707553953576-10.9001302462481-0.782318111202336
59471494.067492201125-9.87112884777171-23.0674922011246-1.951537135627
60471477.296227029098-14.5332271307881-6.29622702909762-0.438795500395293



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')