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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 13:18:41 -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/t12599580125otdf9e2q0azfa9.htm/, Retrieved Sun, 28 Apr 2024 13:20:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64129, Retrieved Sun, 28 Apr 2024 13:20:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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]
-   PD      [Structural Time Series Models] [WS9] [2009-12-04 20:18:41] [b8ce264f75295a954feffaf60221d1b0] [Current]
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Dataseries X:
14,3
14,2
15,9
15,3
15,5
15,1
15
12,1
15,8
16,9
15,1
13,7
14,8
14,7
16
15,4
15
15,5
15,1
11,7
16,3
16,7
15
14,9
14,6
15,3
17,9
16,4
15,4
17,9
15,9
13,9
17,8
17,9
17,4
16,7
16
16,6
19,1
17,8
17,2
18,6
16,3
15,1
19,2
17,7
19,1
18
17,5
17,8
21,1
17,2
19,4
19,8
17,6
16,2
19,5
19,9
20
17,3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64129&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]4 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=64129&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64129&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
114.314.3000
214.214.29500774683440.0016661721550155-0.0780265031164082-0.135206814530930
315.914.71161310786780.04905815315998380.9919091412544421.81090677204527
415.315.07550087883080.08686003947674770.1181975208014801.11331515010707
515.515.31003185104660.1029130730841100.1335308145464560.589314249027969
615.115.35632750758300.0975760865211123-0.230424389718164-0.261606018765814
71515.31967470233970.0867515390937438-0.249454491585662-0.68982583395243
812.114.67505705026350.0360034328797377-2.15769139885873-4.02461137081414
915.814.67034397419720.03352912481363881.15418490005985-0.233744802012717
1016.915.10718565246210.05548306836771461.541570029852122.37637421446372
1115.115.28485089321900.061573077470008-0.2625711624293530.731624844678089
1213.715.0670354576870.0485841922323457-1.18693530095794-1.69016311865968
1314.815.04189263112700.0483022663226904-0.188765019917626-0.506042089126555
1414.715.10302785306810.0484603741658385-0.4118689384394420.0844505345182671
151615.17667781352910.04920833610939190.8078725972132150.150091107778446
1615.415.24824629142350.05012261341861810.139121677906730.125101567369744
171515.17832956320880.0446357383812894-0.112223283305054-0.662065917375712
1815.515.20701735128030.04389680340825720.301871067890596-0.0892618039525464
1915.115.16061797303180.0398709689973782-0.00892349253284508-0.518005837561388
2011.714.95224939247440.0295240195103816-3.10606746351644-1.45888694819494
2116.314.99464677877210.03001730152367851.297587230281160.0771677660447492
2216.715.04771895518460.03081876632780441.638104656171100.140291329473701
231515.06904574842970.0305249247535039-0.0631196386936098-0.0584266714262434
2414.915.25848896633500.0347497255643355-0.4590899322243380.98853549968283
2514.615.25907951414420.0340283265649276-0.63708080911465-0.215639816957508
2615.315.35058543516630.0352321631711392-0.08739365921308960.360837901617910
2717.915.69526802757380.04265833005393712.011183066933641.90381119753072
2816.415.90908596344080.04736603627144030.386473660393741.03256540511006
2915.415.91333132141780.0460712169734231-0.48739942557148-0.257517508509476
3017.916.20762293543610.05381921695450171.543489629299481.48215975139601
3115.916.24326913933720.053251914131949-0.332304975172251-0.109196623808679
3213.916.42490156542220.0571470498617714-2.603119324349090.778266781918136
3317.816.53958639292380.05880894372948891.224986050803230.35191211740369
3417.916.57567353382090.05819349282756641.33845175324218-0.140042297330697
3517.416.80877844778880.06259182831032380.4816069149198021.08478235226376
3616.717.00309530455430.0656582305251482-0.3861726793680810.820927596171908
371617.09021173539230.0661268667354803-1.103799138405980.134125794935218
3816.617.15638207835110.0661278025753503-0.5564095692173550.000271328430303025
3919.117.23981220838800.06651160688128481.849315553254800.107395871153424
4017.817.35248172261940.06758515966034640.4187383559867280.284717613202098
4117.217.53169465228210.0702922184361049-0.4008928403593180.685651408141149
4218.617.55813624220920.06920165036727481.06898720970759-0.269051621832577
4316.317.46949751284570.0652520213595037-1.07174745728954-0.97004230462107
4415.117.532854741320.0652051385182-2.43167721929837-0.0116829235977194
4519.217.68382359498860.06727456087947621.462640821898090.530821575783378
4617.717.57878073520370.06325801109049040.2292646579917-1.07041315944125
4719.117.78008399752380.06635314537315341.233022378537070.860146087810786
481817.99286784564360.0695181798509990-0.08531008036747190.91443624558341
4917.518.21114269188970.0726485578419924-0.8051995070716020.929980344330664
5017.818.35596787149860.0741523140731951-0.6015925229628330.451050971789584
5121.118.60926358462170.07790772501598752.377705516723871.11768105353519
5217.218.42136934259080.0722486600696717-1.05408961133033-1.65490189898993
5319.418.61436145654110.07486261932601830.7098146120910210.75054699514837
5419.818.71544420167360.07543700682538581.068107391953920.162888033306992
5517.618.78613700839150.0753326760074915-1.18316011444552-0.0294863541080765
5616.218.83322033021730.0747143352158588-2.61547145295293-0.175796358356960
5719.518.74606657365530.07121370088358890.855825510628639-1.00895861596640
5819.918.95908572965780.07422841718130150.8514697329359280.885385781614835
592019.07731909882470.07514622229758070.8948734045415860.275155117591189
6017.318.89415479157060.0698533142828176-1.43069437521422-1.61691627781329

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 14.3 & 14.3 & 0 & 0 & 0 \tabularnewline
2 & 14.2 & 14.2950077468344 & 0.0016661721550155 & -0.0780265031164082 & -0.135206814530930 \tabularnewline
3 & 15.9 & 14.7116131078678 & 0.0490581531599838 & 0.991909141254442 & 1.81090677204527 \tabularnewline
4 & 15.3 & 15.0755008788308 & 0.0868600394767477 & 0.118197520801480 & 1.11331515010707 \tabularnewline
5 & 15.5 & 15.3100318510466 & 0.102913073084110 & 0.133530814546456 & 0.589314249027969 \tabularnewline
6 & 15.1 & 15.3563275075830 & 0.0975760865211123 & -0.230424389718164 & -0.261606018765814 \tabularnewline
7 & 15 & 15.3196747023397 & 0.0867515390937438 & -0.249454491585662 & -0.68982583395243 \tabularnewline
8 & 12.1 & 14.6750570502635 & 0.0360034328797377 & -2.15769139885873 & -4.02461137081414 \tabularnewline
9 & 15.8 & 14.6703439741972 & 0.0335291248136388 & 1.15418490005985 & -0.233744802012717 \tabularnewline
10 & 16.9 & 15.1071856524621 & 0.0554830683677146 & 1.54157002985212 & 2.37637421446372 \tabularnewline
11 & 15.1 & 15.2848508932190 & 0.061573077470008 & -0.262571162429353 & 0.731624844678089 \tabularnewline
12 & 13.7 & 15.067035457687 & 0.0485841922323457 & -1.18693530095794 & -1.69016311865968 \tabularnewline
13 & 14.8 & 15.0418926311270 & 0.0483022663226904 & -0.188765019917626 & -0.506042089126555 \tabularnewline
14 & 14.7 & 15.1030278530681 & 0.0484603741658385 & -0.411868938439442 & 0.0844505345182671 \tabularnewline
15 & 16 & 15.1766778135291 & 0.0492083361093919 & 0.807872597213215 & 0.150091107778446 \tabularnewline
16 & 15.4 & 15.2482462914235 & 0.0501226134186181 & 0.13912167790673 & 0.125101567369744 \tabularnewline
17 & 15 & 15.1783295632088 & 0.0446357383812894 & -0.112223283305054 & -0.662065917375712 \tabularnewline
18 & 15.5 & 15.2070173512803 & 0.0438968034082572 & 0.301871067890596 & -0.0892618039525464 \tabularnewline
19 & 15.1 & 15.1606179730318 & 0.0398709689973782 & -0.00892349253284508 & -0.518005837561388 \tabularnewline
20 & 11.7 & 14.9522493924744 & 0.0295240195103816 & -3.10606746351644 & -1.45888694819494 \tabularnewline
21 & 16.3 & 14.9946467787721 & 0.0300173015236785 & 1.29758723028116 & 0.0771677660447492 \tabularnewline
22 & 16.7 & 15.0477189551846 & 0.0308187663278044 & 1.63810465617110 & 0.140291329473701 \tabularnewline
23 & 15 & 15.0690457484297 & 0.0305249247535039 & -0.0631196386936098 & -0.0584266714262434 \tabularnewline
24 & 14.9 & 15.2584889663350 & 0.0347497255643355 & -0.459089932224338 & 0.98853549968283 \tabularnewline
25 & 14.6 & 15.2590795141442 & 0.0340283265649276 & -0.63708080911465 & -0.215639816957508 \tabularnewline
26 & 15.3 & 15.3505854351663 & 0.0352321631711392 & -0.0873936592130896 & 0.360837901617910 \tabularnewline
27 & 17.9 & 15.6952680275738 & 0.0426583300539371 & 2.01118306693364 & 1.90381119753072 \tabularnewline
28 & 16.4 & 15.9090859634408 & 0.0473660362714403 & 0.38647366039374 & 1.03256540511006 \tabularnewline
29 & 15.4 & 15.9133313214178 & 0.0460712169734231 & -0.48739942557148 & -0.257517508509476 \tabularnewline
30 & 17.9 & 16.2076229354361 & 0.0538192169545017 & 1.54348962929948 & 1.48215975139601 \tabularnewline
31 & 15.9 & 16.2432691393372 & 0.053251914131949 & -0.332304975172251 & -0.109196623808679 \tabularnewline
32 & 13.9 & 16.4249015654222 & 0.0571470498617714 & -2.60311932434909 & 0.778266781918136 \tabularnewline
33 & 17.8 & 16.5395863929238 & 0.0588089437294889 & 1.22498605080323 & 0.35191211740369 \tabularnewline
34 & 17.9 & 16.5756735338209 & 0.0581934928275664 & 1.33845175324218 & -0.140042297330697 \tabularnewline
35 & 17.4 & 16.8087784477888 & 0.0625918283103238 & 0.481606914919802 & 1.08478235226376 \tabularnewline
36 & 16.7 & 17.0030953045543 & 0.0656582305251482 & -0.386172679368081 & 0.820927596171908 \tabularnewline
37 & 16 & 17.0902117353923 & 0.0661268667354803 & -1.10379913840598 & 0.134125794935218 \tabularnewline
38 & 16.6 & 17.1563820783511 & 0.0661278025753503 & -0.556409569217355 & 0.000271328430303025 \tabularnewline
39 & 19.1 & 17.2398122083880 & 0.0665116068812848 & 1.84931555325480 & 0.107395871153424 \tabularnewline
40 & 17.8 & 17.3524817226194 & 0.0675851596603464 & 0.418738355986728 & 0.284717613202098 \tabularnewline
41 & 17.2 & 17.5316946522821 & 0.0702922184361049 & -0.400892840359318 & 0.685651408141149 \tabularnewline
42 & 18.6 & 17.5581362422092 & 0.0692016503672748 & 1.06898720970759 & -0.269051621832577 \tabularnewline
43 & 16.3 & 17.4694975128457 & 0.0652520213595037 & -1.07174745728954 & -0.97004230462107 \tabularnewline
44 & 15.1 & 17.53285474132 & 0.0652051385182 & -2.43167721929837 & -0.0116829235977194 \tabularnewline
45 & 19.2 & 17.6838235949886 & 0.0672745608794762 & 1.46264082189809 & 0.530821575783378 \tabularnewline
46 & 17.7 & 17.5787807352037 & 0.0632580110904904 & 0.2292646579917 & -1.07041315944125 \tabularnewline
47 & 19.1 & 17.7800839975238 & 0.0663531453731534 & 1.23302237853707 & 0.860146087810786 \tabularnewline
48 & 18 & 17.9928678456436 & 0.0695181798509990 & -0.0853100803674719 & 0.91443624558341 \tabularnewline
49 & 17.5 & 18.2111426918897 & 0.0726485578419924 & -0.805199507071602 & 0.929980344330664 \tabularnewline
50 & 17.8 & 18.3559678714986 & 0.0741523140731951 & -0.601592522962833 & 0.451050971789584 \tabularnewline
51 & 21.1 & 18.6092635846217 & 0.0779077250159875 & 2.37770551672387 & 1.11768105353519 \tabularnewline
52 & 17.2 & 18.4213693425908 & 0.0722486600696717 & -1.05408961133033 & -1.65490189898993 \tabularnewline
53 & 19.4 & 18.6143614565411 & 0.0748626193260183 & 0.709814612091021 & 0.75054699514837 \tabularnewline
54 & 19.8 & 18.7154442016736 & 0.0754370068253858 & 1.06810739195392 & 0.162888033306992 \tabularnewline
55 & 17.6 & 18.7861370083915 & 0.0753326760074915 & -1.18316011444552 & -0.0294863541080765 \tabularnewline
56 & 16.2 & 18.8332203302173 & 0.0747143352158588 & -2.61547145295293 & -0.175796358356960 \tabularnewline
57 & 19.5 & 18.7460665736553 & 0.0712137008835889 & 0.855825510628639 & -1.00895861596640 \tabularnewline
58 & 19.9 & 18.9590857296578 & 0.0742284171813015 & 0.851469732935928 & 0.885385781614835 \tabularnewline
59 & 20 & 19.0773190988247 & 0.0751462222975807 & 0.894873404541586 & 0.275155117591189 \tabularnewline
60 & 17.3 & 18.8941547915706 & 0.0698533142828176 & -1.43069437521422 & -1.61691627781329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64129&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]14.3[/C][C]14.3[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]14.2[/C][C]14.2950077468344[/C][C]0.0016661721550155[/C][C]-0.0780265031164082[/C][C]-0.135206814530930[/C][/ROW]
[ROW][C]3[/C][C]15.9[/C][C]14.7116131078678[/C][C]0.0490581531599838[/C][C]0.991909141254442[/C][C]1.81090677204527[/C][/ROW]
[ROW][C]4[/C][C]15.3[/C][C]15.0755008788308[/C][C]0.0868600394767477[/C][C]0.118197520801480[/C][C]1.11331515010707[/C][/ROW]
[ROW][C]5[/C][C]15.5[/C][C]15.3100318510466[/C][C]0.102913073084110[/C][C]0.133530814546456[/C][C]0.589314249027969[/C][/ROW]
[ROW][C]6[/C][C]15.1[/C][C]15.3563275075830[/C][C]0.0975760865211123[/C][C]-0.230424389718164[/C][C]-0.261606018765814[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]15.3196747023397[/C][C]0.0867515390937438[/C][C]-0.249454491585662[/C][C]-0.68982583395243[/C][/ROW]
[ROW][C]8[/C][C]12.1[/C][C]14.6750570502635[/C][C]0.0360034328797377[/C][C]-2.15769139885873[/C][C]-4.02461137081414[/C][/ROW]
[ROW][C]9[/C][C]15.8[/C][C]14.6703439741972[/C][C]0.0335291248136388[/C][C]1.15418490005985[/C][C]-0.233744802012717[/C][/ROW]
[ROW][C]10[/C][C]16.9[/C][C]15.1071856524621[/C][C]0.0554830683677146[/C][C]1.54157002985212[/C][C]2.37637421446372[/C][/ROW]
[ROW][C]11[/C][C]15.1[/C][C]15.2848508932190[/C][C]0.061573077470008[/C][C]-0.262571162429353[/C][C]0.731624844678089[/C][/ROW]
[ROW][C]12[/C][C]13.7[/C][C]15.067035457687[/C][C]0.0485841922323457[/C][C]-1.18693530095794[/C][C]-1.69016311865968[/C][/ROW]
[ROW][C]13[/C][C]14.8[/C][C]15.0418926311270[/C][C]0.0483022663226904[/C][C]-0.188765019917626[/C][C]-0.506042089126555[/C][/ROW]
[ROW][C]14[/C][C]14.7[/C][C]15.1030278530681[/C][C]0.0484603741658385[/C][C]-0.411868938439442[/C][C]0.0844505345182671[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]15.1766778135291[/C][C]0.0492083361093919[/C][C]0.807872597213215[/C][C]0.150091107778446[/C][/ROW]
[ROW][C]16[/C][C]15.4[/C][C]15.2482462914235[/C][C]0.0501226134186181[/C][C]0.13912167790673[/C][C]0.125101567369744[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]15.1783295632088[/C][C]0.0446357383812894[/C][C]-0.112223283305054[/C][C]-0.662065917375712[/C][/ROW]
[ROW][C]18[/C][C]15.5[/C][C]15.2070173512803[/C][C]0.0438968034082572[/C][C]0.301871067890596[/C][C]-0.0892618039525464[/C][/ROW]
[ROW][C]19[/C][C]15.1[/C][C]15.1606179730318[/C][C]0.0398709689973782[/C][C]-0.00892349253284508[/C][C]-0.518005837561388[/C][/ROW]
[ROW][C]20[/C][C]11.7[/C][C]14.9522493924744[/C][C]0.0295240195103816[/C][C]-3.10606746351644[/C][C]-1.45888694819494[/C][/ROW]
[ROW][C]21[/C][C]16.3[/C][C]14.9946467787721[/C][C]0.0300173015236785[/C][C]1.29758723028116[/C][C]0.0771677660447492[/C][/ROW]
[ROW][C]22[/C][C]16.7[/C][C]15.0477189551846[/C][C]0.0308187663278044[/C][C]1.63810465617110[/C][C]0.140291329473701[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]15.0690457484297[/C][C]0.0305249247535039[/C][C]-0.0631196386936098[/C][C]-0.0584266714262434[/C][/ROW]
[ROW][C]24[/C][C]14.9[/C][C]15.2584889663350[/C][C]0.0347497255643355[/C][C]-0.459089932224338[/C][C]0.98853549968283[/C][/ROW]
[ROW][C]25[/C][C]14.6[/C][C]15.2590795141442[/C][C]0.0340283265649276[/C][C]-0.63708080911465[/C][C]-0.215639816957508[/C][/ROW]
[ROW][C]26[/C][C]15.3[/C][C]15.3505854351663[/C][C]0.0352321631711392[/C][C]-0.0873936592130896[/C][C]0.360837901617910[/C][/ROW]
[ROW][C]27[/C][C]17.9[/C][C]15.6952680275738[/C][C]0.0426583300539371[/C][C]2.01118306693364[/C][C]1.90381119753072[/C][/ROW]
[ROW][C]28[/C][C]16.4[/C][C]15.9090859634408[/C][C]0.0473660362714403[/C][C]0.38647366039374[/C][C]1.03256540511006[/C][/ROW]
[ROW][C]29[/C][C]15.4[/C][C]15.9133313214178[/C][C]0.0460712169734231[/C][C]-0.48739942557148[/C][C]-0.257517508509476[/C][/ROW]
[ROW][C]30[/C][C]17.9[/C][C]16.2076229354361[/C][C]0.0538192169545017[/C][C]1.54348962929948[/C][C]1.48215975139601[/C][/ROW]
[ROW][C]31[/C][C]15.9[/C][C]16.2432691393372[/C][C]0.053251914131949[/C][C]-0.332304975172251[/C][C]-0.109196623808679[/C][/ROW]
[ROW][C]32[/C][C]13.9[/C][C]16.4249015654222[/C][C]0.0571470498617714[/C][C]-2.60311932434909[/C][C]0.778266781918136[/C][/ROW]
[ROW][C]33[/C][C]17.8[/C][C]16.5395863929238[/C][C]0.0588089437294889[/C][C]1.22498605080323[/C][C]0.35191211740369[/C][/ROW]
[ROW][C]34[/C][C]17.9[/C][C]16.5756735338209[/C][C]0.0581934928275664[/C][C]1.33845175324218[/C][C]-0.140042297330697[/C][/ROW]
[ROW][C]35[/C][C]17.4[/C][C]16.8087784477888[/C][C]0.0625918283103238[/C][C]0.481606914919802[/C][C]1.08478235226376[/C][/ROW]
[ROW][C]36[/C][C]16.7[/C][C]17.0030953045543[/C][C]0.0656582305251482[/C][C]-0.386172679368081[/C][C]0.820927596171908[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]17.0902117353923[/C][C]0.0661268667354803[/C][C]-1.10379913840598[/C][C]0.134125794935218[/C][/ROW]
[ROW][C]38[/C][C]16.6[/C][C]17.1563820783511[/C][C]0.0661278025753503[/C][C]-0.556409569217355[/C][C]0.000271328430303025[/C][/ROW]
[ROW][C]39[/C][C]19.1[/C][C]17.2398122083880[/C][C]0.0665116068812848[/C][C]1.84931555325480[/C][C]0.107395871153424[/C][/ROW]
[ROW][C]40[/C][C]17.8[/C][C]17.3524817226194[/C][C]0.0675851596603464[/C][C]0.418738355986728[/C][C]0.284717613202098[/C][/ROW]
[ROW][C]41[/C][C]17.2[/C][C]17.5316946522821[/C][C]0.0702922184361049[/C][C]-0.400892840359318[/C][C]0.685651408141149[/C][/ROW]
[ROW][C]42[/C][C]18.6[/C][C]17.5581362422092[/C][C]0.0692016503672748[/C][C]1.06898720970759[/C][C]-0.269051621832577[/C][/ROW]
[ROW][C]43[/C][C]16.3[/C][C]17.4694975128457[/C][C]0.0652520213595037[/C][C]-1.07174745728954[/C][C]-0.97004230462107[/C][/ROW]
[ROW][C]44[/C][C]15.1[/C][C]17.53285474132[/C][C]0.0652051385182[/C][C]-2.43167721929837[/C][C]-0.0116829235977194[/C][/ROW]
[ROW][C]45[/C][C]19.2[/C][C]17.6838235949886[/C][C]0.0672745608794762[/C][C]1.46264082189809[/C][C]0.530821575783378[/C][/ROW]
[ROW][C]46[/C][C]17.7[/C][C]17.5787807352037[/C][C]0.0632580110904904[/C][C]0.2292646579917[/C][C]-1.07041315944125[/C][/ROW]
[ROW][C]47[/C][C]19.1[/C][C]17.7800839975238[/C][C]0.0663531453731534[/C][C]1.23302237853707[/C][C]0.860146087810786[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]17.9928678456436[/C][C]0.0695181798509990[/C][C]-0.0853100803674719[/C][C]0.91443624558341[/C][/ROW]
[ROW][C]49[/C][C]17.5[/C][C]18.2111426918897[/C][C]0.0726485578419924[/C][C]-0.805199507071602[/C][C]0.929980344330664[/C][/ROW]
[ROW][C]50[/C][C]17.8[/C][C]18.3559678714986[/C][C]0.0741523140731951[/C][C]-0.601592522962833[/C][C]0.451050971789584[/C][/ROW]
[ROW][C]51[/C][C]21.1[/C][C]18.6092635846217[/C][C]0.0779077250159875[/C][C]2.37770551672387[/C][C]1.11768105353519[/C][/ROW]
[ROW][C]52[/C][C]17.2[/C][C]18.4213693425908[/C][C]0.0722486600696717[/C][C]-1.05408961133033[/C][C]-1.65490189898993[/C][/ROW]
[ROW][C]53[/C][C]19.4[/C][C]18.6143614565411[/C][C]0.0748626193260183[/C][C]0.709814612091021[/C][C]0.75054699514837[/C][/ROW]
[ROW][C]54[/C][C]19.8[/C][C]18.7154442016736[/C][C]0.0754370068253858[/C][C]1.06810739195392[/C][C]0.162888033306992[/C][/ROW]
[ROW][C]55[/C][C]17.6[/C][C]18.7861370083915[/C][C]0.0753326760074915[/C][C]-1.18316011444552[/C][C]-0.0294863541080765[/C][/ROW]
[ROW][C]56[/C][C]16.2[/C][C]18.8332203302173[/C][C]0.0747143352158588[/C][C]-2.61547145295293[/C][C]-0.175796358356960[/C][/ROW]
[ROW][C]57[/C][C]19.5[/C][C]18.7460665736553[/C][C]0.0712137008835889[/C][C]0.855825510628639[/C][C]-1.00895861596640[/C][/ROW]
[ROW][C]58[/C][C]19.9[/C][C]18.9590857296578[/C][C]0.0742284171813015[/C][C]0.851469732935928[/C][C]0.885385781614835[/C][/ROW]
[ROW][C]59[/C][C]20[/C][C]19.0773190988247[/C][C]0.0751462222975807[/C][C]0.894873404541586[/C][C]0.275155117591189[/C][/ROW]
[ROW][C]60[/C][C]17.3[/C][C]18.8941547915706[/C][C]0.0698533142828176[/C][C]-1.43069437521422[/C][C]-1.61691627781329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64129&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64129&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
114.314.3000
214.214.29500774683440.0016661721550155-0.0780265031164082-0.135206814530930
315.914.71161310786780.04905815315998380.9919091412544421.81090677204527
415.315.07550087883080.08686003947674770.1181975208014801.11331515010707
515.515.31003185104660.1029130730841100.1335308145464560.589314249027969
615.115.35632750758300.0975760865211123-0.230424389718164-0.261606018765814
71515.31967470233970.0867515390937438-0.249454491585662-0.68982583395243
812.114.67505705026350.0360034328797377-2.15769139885873-4.02461137081414
915.814.67034397419720.03352912481363881.15418490005985-0.233744802012717
1016.915.10718565246210.05548306836771461.541570029852122.37637421446372
1115.115.28485089321900.061573077470008-0.2625711624293530.731624844678089
1213.715.0670354576870.0485841922323457-1.18693530095794-1.69016311865968
1314.815.04189263112700.0483022663226904-0.188765019917626-0.506042089126555
1414.715.10302785306810.0484603741658385-0.4118689384394420.0844505345182671
151615.17667781352910.04920833610939190.8078725972132150.150091107778446
1615.415.24824629142350.05012261341861810.139121677906730.125101567369744
171515.17832956320880.0446357383812894-0.112223283305054-0.662065917375712
1815.515.20701735128030.04389680340825720.301871067890596-0.0892618039525464
1915.115.16061797303180.0398709689973782-0.00892349253284508-0.518005837561388
2011.714.95224939247440.0295240195103816-3.10606746351644-1.45888694819494
2116.314.99464677877210.03001730152367851.297587230281160.0771677660447492
2216.715.04771895518460.03081876632780441.638104656171100.140291329473701
231515.06904574842970.0305249247535039-0.0631196386936098-0.0584266714262434
2414.915.25848896633500.0347497255643355-0.4590899322243380.98853549968283
2514.615.25907951414420.0340283265649276-0.63708080911465-0.215639816957508
2615.315.35058543516630.0352321631711392-0.08739365921308960.360837901617910
2717.915.69526802757380.04265833005393712.011183066933641.90381119753072
2816.415.90908596344080.04736603627144030.386473660393741.03256540511006
2915.415.91333132141780.0460712169734231-0.48739942557148-0.257517508509476
3017.916.20762293543610.05381921695450171.543489629299481.48215975139601
3115.916.24326913933720.053251914131949-0.332304975172251-0.109196623808679
3213.916.42490156542220.0571470498617714-2.603119324349090.778266781918136
3317.816.53958639292380.05880894372948891.224986050803230.35191211740369
3417.916.57567353382090.05819349282756641.33845175324218-0.140042297330697
3517.416.80877844778880.06259182831032380.4816069149198021.08478235226376
3616.717.00309530455430.0656582305251482-0.3861726793680810.820927596171908
371617.09021173539230.0661268667354803-1.103799138405980.134125794935218
3816.617.15638207835110.0661278025753503-0.5564095692173550.000271328430303025
3919.117.23981220838800.06651160688128481.849315553254800.107395871153424
4017.817.35248172261940.06758515966034640.4187383559867280.284717613202098
4117.217.53169465228210.0702922184361049-0.4008928403593180.685651408141149
4218.617.55813624220920.06920165036727481.06898720970759-0.269051621832577
4316.317.46949751284570.0652520213595037-1.07174745728954-0.97004230462107
4415.117.532854741320.0652051385182-2.43167721929837-0.0116829235977194
4519.217.68382359498860.06727456087947621.462640821898090.530821575783378
4617.717.57878073520370.06325801109049040.2292646579917-1.07041315944125
4719.117.78008399752380.06635314537315341.233022378537070.860146087810786
481817.99286784564360.0695181798509990-0.08531008036747190.91443624558341
4917.518.21114269188970.0726485578419924-0.8051995070716020.929980344330664
5017.818.35596787149860.0741523140731951-0.6015925229628330.451050971789584
5121.118.60926358462170.07790772501598752.377705516723871.11768105353519
5217.218.42136934259080.0722486600696717-1.05408961133033-1.65490189898993
5319.418.61436145654110.07486261932601830.7098146120910210.75054699514837
5419.818.71544420167360.07543700682538581.068107391953920.162888033306992
5517.618.78613700839150.0753326760074915-1.18316011444552-0.0294863541080765
5616.218.83322033021730.0747143352158588-2.61547145295293-0.175796358356960
5719.518.74606657365530.07121370088358890.855825510628639-1.00895861596640
5819.918.95908572965780.07422841718130150.8514697329359280.885385781614835
592019.07731909882470.07514622229758070.8948734045415860.275155117591189
6017.318.89415479157060.0698533142828176-1.43069437521422-1.61691627781329



Parameters (Session):
par1 = 0.2 ; par2 = 1 ; par3 = 1 ; par4 = 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')