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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 11 Dec 2009 09:03:21 -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/11/t126054746818e0rf5ajsih91c.htm/, Retrieved Mon, 29 Apr 2024 00:37:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66439, Retrieved Mon, 29 Apr 2024 00:37:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

Tree of Dependent Computations

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] [Structurele tijdr...] [2009-12-01 19:54:37] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-    D      [Structural Time Series Models] [structurele tijdr...] [2009-12-04 19:23:56] [4f1a20f787b3465111b61213cdeef1a9]
-    D          [Structural Time Series Models] [Structurele tijdr...] [2009-12-11 16:03:21] [d1818fb1d9a1b0f34f8553ada228d3d5] [Current]
-   PD            [Structural Time Series Models] [Structurele tijdr...] [2009-12-11 16:46:43] [4f1a20f787b3465111b61213cdeef1a9]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.3
8.2
8
7.9
7.6
7.6
8.3
8.4
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8
8.2
8.1
8.1
8
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.4
6.1
6.5
7.7
7.9
7.5
6.9
6.6
6.9

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66439&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66439&T=0

As an alternative you can also use a QR Code:  
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'Gwilym Jenkins' @ 72.249.127.135






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
18.38.3000
28.28.20150794324155-0.0986166801562142-0.00150794324155027-0.309040537073739
388.00283057750846-0.194512078765493-0.00283057750846062-0.304377034984027
47.97.89619813771343-0.1109922219244770.003801862286571220.259490908910541
57.67.60710290313238-0.280349045317253-0.00710290313238139-0.526922252594266
67.67.5894352782938-0.03051547187117830.01056472170620590.777236677085165
78.38.277563500338230.6529629345688760.02243649966176692.12631693214153
88.48.424114650872860.171335733221709-0.0241146508728594-1.49835286505826
98.48.4022979407693-0.0123638970675732-0.00229794076929965-0.571493612201239
108.48.398118670039-0.004579819594935950.001881329961004930.0242164371391531
118.48.399979649598710.001545771431250552.03504012858244e-050.0190568491318921
128.68.59353025279410.1841540191876590.006469747205907530.568098296613493
138.98.897237211188910.2978197216615050.002762788811087510.353780641744862
148.88.81254857887911-0.0661567648095394-0.0125485788791144-1.13787364407034
158.38.33210551744682-0.449554428603814-0.03210551744682-1.20575176823243
167.57.49654751038298-0.8068027690146520.00345248961702379-1.10993647895224
177.27.16825468178275-0.3643792685715750.03174531821724761.37659020782180
187.47.411106370729320.197272316466894-0.01110637072932081.74730949268202
198.88.707786854756341.214171158303650.09221314524366543.16359846568683
209.39.358289572978840.692818917212127-0.0582895729788386-1.62193836112218
219.39.33724579139320.0325503854806996-0.0372457913932058-2.05411002973329
228.78.71867517045162-0.569688907524338-0.0186751704516244-1.87357979531947
238.28.1937878995701-0.5282501754803310.006212100429894750.128916898298164
248.38.275650128164730.03604488635970180.02434987183527051.75553885567322
258.58.486810746204860.1979604095138430.01318925379514030.504014491173652
268.68.606593221340470.125609684135195-0.00659322134047034-0.225515294382770
278.58.50279066740803-0.0836346857588167-0.00279066740803013-0.654241362504792
288.28.21530980336555-0.269995115243943-0.0153098033655529-0.579478181871975
298.18.06084726067879-0.1645435401714580.03915273932121410.328054018511747
307.98.00679015783698-0.0636579506010582-0.1067901578369760.313866338946917
318.68.464588886211650.4125164875166750.1354111137883501.48138602867508
328.78.744667650724230.291582226359057-0.0446676507242307-0.376229419108265
338.78.70697815706679-0.00908476219982657-0.00697815706678867-0.935381666329714
348.58.50706404644173-0.183337772839505-0.00706404644173238-0.542105007729224
358.48.42349551328738-0.0922336519816608-0.02349551328738160.283427940059473
368.58.482545288530280.04589768743891690.0174547114697240.429732441604963
378.78.682090576285630.186159733477110.01790942371436970.436660402369008
388.78.706676338129320.038598811591521-0.00667633812932111-0.459289685022563
398.68.59138376215762-0.1008622988516710.0086162378423773-0.434927081496445
408.58.51385208004961-0.0796716143642026-0.01385208004961180.0659285991504954
418.38.25890553924152-0.2386379134148010.0410944607584801-0.494452744334046
4288.14993461344097-0.121006421414725-0.1499346134409690.365974353648347
438.28.06582710542257-0.08752855448510580.1341728945774310.104149836040597
448.18.116175409899930.0375638170478271-0.01617540989992750.389165448216263
458.18.09088508707781-0.01946124567316150.00911491292219052-0.177406231052720
4688.01186461406146-0.0734975032603589-0.0118646140614547-0.168108093818893
477.97.93121085797819-0.0799901770160099-0.0312108579781895-0.0201989406438855
487.97.89963440230783-0.03607189853782370.0003655976921668850.136634123441552
4987.96676472579530.05754415660344150.03323527420469410.291437152539457
5087.996012962318870.03187779674301050.00398703768113005-0.0798466092202058
517.97.90736032468487-0.0769665872681344-0.00736032468486671-0.339061405531233
5287.983135960919080.06120941543164950.01686403908092080.430003205562517
537.77.68911191307443-0.2598014482566190.0108880869255737-0.998402009993685
547.27.352599876354-0.329126626078210-0.152599876353997-0.215684513089949
557.57.36012550557213-0.02483946188275520.1398744944278660.946641438144736
567.37.3141494166909-0.0439441485933053-0.0141494166908954-0.059435064635252
5777.00270763400846-0.285723114504312-0.00270763400845993-0.752179878351502
5876.9915471819148-0.03755679289086240.008452818085192240.772052556840845
5977.026932709808180.0283719342405384-0.02693270980817530.205106003491748
607.27.202391377522130.161294440603718-0.002391377522129990.4135480595682
617.37.27288683197940.07923186691649840.0271131680205903-0.2554448349319
627.17.09985318516729-0.1486861333618200.000146814832710113-0.70892621824615
636.86.84291559765728-0.24621877452675-0.0429155976572829-0.303662639828411
646.46.36032599414731-0.4594655317249170.0396740058526884-0.663676077715483
656.16.05897452667951-0.3169226189164310.04102547332049240.44332999844042
666.56.635227080602320.488227690252844-0.1352270806023172.50494243221914
677.77.540674076570170.8643920926352540.1593259234298281.17026054465987
687.97.910565741672310.418541277665396-0.0105657416723083-1.38704799283956
697.57.5732063271842-0.262988181540286-0.073206327184208-2.12025519382940
706.96.91661774716365-0.617863481089187-0.0166177471636463-1.10402931056326
716.66.62313661158964-0.325406663450460-0.02313661158963570.909837401592211
726.96.867074242605150.1878229737691780.03292575739485421.59680928356641

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8.3 & 8.3 & 0 & 0 & 0 \tabularnewline
2 & 8.2 & 8.20150794324155 & -0.0986166801562142 & -0.00150794324155027 & -0.309040537073739 \tabularnewline
3 & 8 & 8.00283057750846 & -0.194512078765493 & -0.00283057750846062 & -0.304377034984027 \tabularnewline
4 & 7.9 & 7.89619813771343 & -0.110992221924477 & 0.00380186228657122 & 0.259490908910541 \tabularnewline
5 & 7.6 & 7.60710290313238 & -0.280349045317253 & -0.00710290313238139 & -0.526922252594266 \tabularnewline
6 & 7.6 & 7.5894352782938 & -0.0305154718711783 & 0.0105647217062059 & 0.777236677085165 \tabularnewline
7 & 8.3 & 8.27756350033823 & 0.652962934568876 & 0.0224364996617669 & 2.12631693214153 \tabularnewline
8 & 8.4 & 8.42411465087286 & 0.171335733221709 & -0.0241146508728594 & -1.49835286505826 \tabularnewline
9 & 8.4 & 8.4022979407693 & -0.0123638970675732 & -0.00229794076929965 & -0.571493612201239 \tabularnewline
10 & 8.4 & 8.398118670039 & -0.00457981959493595 & 0.00188132996100493 & 0.0242164371391531 \tabularnewline
11 & 8.4 & 8.39997964959871 & 0.00154577143125055 & 2.03504012858244e-05 & 0.0190568491318921 \tabularnewline
12 & 8.6 & 8.5935302527941 & 0.184154019187659 & 0.00646974720590753 & 0.568098296613493 \tabularnewline
13 & 8.9 & 8.89723721118891 & 0.297819721661505 & 0.00276278881108751 & 0.353780641744862 \tabularnewline
14 & 8.8 & 8.81254857887911 & -0.0661567648095394 & -0.0125485788791144 & -1.13787364407034 \tabularnewline
15 & 8.3 & 8.33210551744682 & -0.449554428603814 & -0.03210551744682 & -1.20575176823243 \tabularnewline
16 & 7.5 & 7.49654751038298 & -0.806802769014652 & 0.00345248961702379 & -1.10993647895224 \tabularnewline
17 & 7.2 & 7.16825468178275 & -0.364379268571575 & 0.0317453182172476 & 1.37659020782180 \tabularnewline
18 & 7.4 & 7.41110637072932 & 0.197272316466894 & -0.0111063707293208 & 1.74730949268202 \tabularnewline
19 & 8.8 & 8.70778685475634 & 1.21417115830365 & 0.0922131452436654 & 3.16359846568683 \tabularnewline
20 & 9.3 & 9.35828957297884 & 0.692818917212127 & -0.0582895729788386 & -1.62193836112218 \tabularnewline
21 & 9.3 & 9.3372457913932 & 0.0325503854806996 & -0.0372457913932058 & -2.05411002973329 \tabularnewline
22 & 8.7 & 8.71867517045162 & -0.569688907524338 & -0.0186751704516244 & -1.87357979531947 \tabularnewline
23 & 8.2 & 8.1937878995701 & -0.528250175480331 & 0.00621210042989475 & 0.128916898298164 \tabularnewline
24 & 8.3 & 8.27565012816473 & 0.0360448863597018 & 0.0243498718352705 & 1.75553885567322 \tabularnewline
25 & 8.5 & 8.48681074620486 & 0.197960409513843 & 0.0131892537951403 & 0.504014491173652 \tabularnewline
26 & 8.6 & 8.60659322134047 & 0.125609684135195 & -0.00659322134047034 & -0.225515294382770 \tabularnewline
27 & 8.5 & 8.50279066740803 & -0.0836346857588167 & -0.00279066740803013 & -0.654241362504792 \tabularnewline
28 & 8.2 & 8.21530980336555 & -0.269995115243943 & -0.0153098033655529 & -0.579478181871975 \tabularnewline
29 & 8.1 & 8.06084726067879 & -0.164543540171458 & 0.0391527393212141 & 0.328054018511747 \tabularnewline
30 & 7.9 & 8.00679015783698 & -0.0636579506010582 & -0.106790157836976 & 0.313866338946917 \tabularnewline
31 & 8.6 & 8.46458888621165 & 0.412516487516675 & 0.135411113788350 & 1.48138602867508 \tabularnewline
32 & 8.7 & 8.74466765072423 & 0.291582226359057 & -0.0446676507242307 & -0.376229419108265 \tabularnewline
33 & 8.7 & 8.70697815706679 & -0.00908476219982657 & -0.00697815706678867 & -0.935381666329714 \tabularnewline
34 & 8.5 & 8.50706404644173 & -0.183337772839505 & -0.00706404644173238 & -0.542105007729224 \tabularnewline
35 & 8.4 & 8.42349551328738 & -0.0922336519816608 & -0.0234955132873816 & 0.283427940059473 \tabularnewline
36 & 8.5 & 8.48254528853028 & 0.0458976874389169 & 0.017454711469724 & 0.429732441604963 \tabularnewline
37 & 8.7 & 8.68209057628563 & 0.18615973347711 & 0.0179094237143697 & 0.436660402369008 \tabularnewline
38 & 8.7 & 8.70667633812932 & 0.038598811591521 & -0.00667633812932111 & -0.459289685022563 \tabularnewline
39 & 8.6 & 8.59138376215762 & -0.100862298851671 & 0.0086162378423773 & -0.434927081496445 \tabularnewline
40 & 8.5 & 8.51385208004961 & -0.0796716143642026 & -0.0138520800496118 & 0.0659285991504954 \tabularnewline
41 & 8.3 & 8.25890553924152 & -0.238637913414801 & 0.0410944607584801 & -0.494452744334046 \tabularnewline
42 & 8 & 8.14993461344097 & -0.121006421414725 & -0.149934613440969 & 0.365974353648347 \tabularnewline
43 & 8.2 & 8.06582710542257 & -0.0875285544851058 & 0.134172894577431 & 0.104149836040597 \tabularnewline
44 & 8.1 & 8.11617540989993 & 0.0375638170478271 & -0.0161754098999275 & 0.389165448216263 \tabularnewline
45 & 8.1 & 8.09088508707781 & -0.0194612456731615 & 0.00911491292219052 & -0.177406231052720 \tabularnewline
46 & 8 & 8.01186461406146 & -0.0734975032603589 & -0.0118646140614547 & -0.168108093818893 \tabularnewline
47 & 7.9 & 7.93121085797819 & -0.0799901770160099 & -0.0312108579781895 & -0.0201989406438855 \tabularnewline
48 & 7.9 & 7.89963440230783 & -0.0360718985378237 & 0.000365597692166885 & 0.136634123441552 \tabularnewline
49 & 8 & 7.9667647257953 & 0.0575441566034415 & 0.0332352742046941 & 0.291437152539457 \tabularnewline
50 & 8 & 7.99601296231887 & 0.0318777967430105 & 0.00398703768113005 & -0.0798466092202058 \tabularnewline
51 & 7.9 & 7.90736032468487 & -0.0769665872681344 & -0.00736032468486671 & -0.339061405531233 \tabularnewline
52 & 8 & 7.98313596091908 & 0.0612094154316495 & 0.0168640390809208 & 0.430003205562517 \tabularnewline
53 & 7.7 & 7.68911191307443 & -0.259801448256619 & 0.0108880869255737 & -0.998402009993685 \tabularnewline
54 & 7.2 & 7.352599876354 & -0.329126626078210 & -0.152599876353997 & -0.215684513089949 \tabularnewline
55 & 7.5 & 7.36012550557213 & -0.0248394618827552 & 0.139874494427866 & 0.946641438144736 \tabularnewline
56 & 7.3 & 7.3141494166909 & -0.0439441485933053 & -0.0141494166908954 & -0.059435064635252 \tabularnewline
57 & 7 & 7.00270763400846 & -0.285723114504312 & -0.00270763400845993 & -0.752179878351502 \tabularnewline
58 & 7 & 6.9915471819148 & -0.0375567928908624 & 0.00845281808519224 & 0.772052556840845 \tabularnewline
59 & 7 & 7.02693270980818 & 0.0283719342405384 & -0.0269327098081753 & 0.205106003491748 \tabularnewline
60 & 7.2 & 7.20239137752213 & 0.161294440603718 & -0.00239137752212999 & 0.4135480595682 \tabularnewline
61 & 7.3 & 7.2728868319794 & 0.0792318669164984 & 0.0271131680205903 & -0.2554448349319 \tabularnewline
62 & 7.1 & 7.09985318516729 & -0.148686133361820 & 0.000146814832710113 & -0.70892621824615 \tabularnewline
63 & 6.8 & 6.84291559765728 & -0.24621877452675 & -0.0429155976572829 & -0.303662639828411 \tabularnewline
64 & 6.4 & 6.36032599414731 & -0.459465531724917 & 0.0396740058526884 & -0.663676077715483 \tabularnewline
65 & 6.1 & 6.05897452667951 & -0.316922618916431 & 0.0410254733204924 & 0.44332999844042 \tabularnewline
66 & 6.5 & 6.63522708060232 & 0.488227690252844 & -0.135227080602317 & 2.50494243221914 \tabularnewline
67 & 7.7 & 7.54067407657017 & 0.864392092635254 & 0.159325923429828 & 1.17026054465987 \tabularnewline
68 & 7.9 & 7.91056574167231 & 0.418541277665396 & -0.0105657416723083 & -1.38704799283956 \tabularnewline
69 & 7.5 & 7.5732063271842 & -0.262988181540286 & -0.073206327184208 & -2.12025519382940 \tabularnewline
70 & 6.9 & 6.91661774716365 & -0.617863481089187 & -0.0166177471636463 & -1.10402931056326 \tabularnewline
71 & 6.6 & 6.62313661158964 & -0.325406663450460 & -0.0231366115896357 & 0.909837401592211 \tabularnewline
72 & 6.9 & 6.86707424260515 & 0.187822973769178 & 0.0329257573948542 & 1.59680928356641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66439&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]8.3[/C][C]8.3[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.2[/C][C]8.20150794324155[/C][C]-0.0986166801562142[/C][C]-0.00150794324155027[/C][C]-0.309040537073739[/C][/ROW]
[ROW][C]3[/C][C]8[/C][C]8.00283057750846[/C][C]-0.194512078765493[/C][C]-0.00283057750846062[/C][C]-0.304377034984027[/C][/ROW]
[ROW][C]4[/C][C]7.9[/C][C]7.89619813771343[/C][C]-0.110992221924477[/C][C]0.00380186228657122[/C][C]0.259490908910541[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.60710290313238[/C][C]-0.280349045317253[/C][C]-0.00710290313238139[/C][C]-0.526922252594266[/C][/ROW]
[ROW][C]6[/C][C]7.6[/C][C]7.5894352782938[/C][C]-0.0305154718711783[/C][C]0.0105647217062059[/C][C]0.777236677085165[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.27756350033823[/C][C]0.652962934568876[/C][C]0.0224364996617669[/C][C]2.12631693214153[/C][/ROW]
[ROW][C]8[/C][C]8.4[/C][C]8.42411465087286[/C][C]0.171335733221709[/C][C]-0.0241146508728594[/C][C]-1.49835286505826[/C][/ROW]
[ROW][C]9[/C][C]8.4[/C][C]8.4022979407693[/C][C]-0.0123638970675732[/C][C]-0.00229794076929965[/C][C]-0.571493612201239[/C][/ROW]
[ROW][C]10[/C][C]8.4[/C][C]8.398118670039[/C][C]-0.00457981959493595[/C][C]0.00188132996100493[/C][C]0.0242164371391531[/C][/ROW]
[ROW][C]11[/C][C]8.4[/C][C]8.39997964959871[/C][C]0.00154577143125055[/C][C]2.03504012858244e-05[/C][C]0.0190568491318921[/C][/ROW]
[ROW][C]12[/C][C]8.6[/C][C]8.5935302527941[/C][C]0.184154019187659[/C][C]0.00646974720590753[/C][C]0.568098296613493[/C][/ROW]
[ROW][C]13[/C][C]8.9[/C][C]8.89723721118891[/C][C]0.297819721661505[/C][C]0.00276278881108751[/C][C]0.353780641744862[/C][/ROW]
[ROW][C]14[/C][C]8.8[/C][C]8.81254857887911[/C][C]-0.0661567648095394[/C][C]-0.0125485788791144[/C][C]-1.13787364407034[/C][/ROW]
[ROW][C]15[/C][C]8.3[/C][C]8.33210551744682[/C][C]-0.449554428603814[/C][C]-0.03210551744682[/C][C]-1.20575176823243[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]7.49654751038298[/C][C]-0.806802769014652[/C][C]0.00345248961702379[/C][C]-1.10993647895224[/C][/ROW]
[ROW][C]17[/C][C]7.2[/C][C]7.16825468178275[/C][C]-0.364379268571575[/C][C]0.0317453182172476[/C][C]1.37659020782180[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]7.41110637072932[/C][C]0.197272316466894[/C][C]-0.0111063707293208[/C][C]1.74730949268202[/C][/ROW]
[ROW][C]19[/C][C]8.8[/C][C]8.70778685475634[/C][C]1.21417115830365[/C][C]0.0922131452436654[/C][C]3.16359846568683[/C][/ROW]
[ROW][C]20[/C][C]9.3[/C][C]9.35828957297884[/C][C]0.692818917212127[/C][C]-0.0582895729788386[/C][C]-1.62193836112218[/C][/ROW]
[ROW][C]21[/C][C]9.3[/C][C]9.3372457913932[/C][C]0.0325503854806996[/C][C]-0.0372457913932058[/C][C]-2.05411002973329[/C][/ROW]
[ROW][C]22[/C][C]8.7[/C][C]8.71867517045162[/C][C]-0.569688907524338[/C][C]-0.0186751704516244[/C][C]-1.87357979531947[/C][/ROW]
[ROW][C]23[/C][C]8.2[/C][C]8.1937878995701[/C][C]-0.528250175480331[/C][C]0.00621210042989475[/C][C]0.128916898298164[/C][/ROW]
[ROW][C]24[/C][C]8.3[/C][C]8.27565012816473[/C][C]0.0360448863597018[/C][C]0.0243498718352705[/C][C]1.75553885567322[/C][/ROW]
[ROW][C]25[/C][C]8.5[/C][C]8.48681074620486[/C][C]0.197960409513843[/C][C]0.0131892537951403[/C][C]0.504014491173652[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]8.60659322134047[/C][C]0.125609684135195[/C][C]-0.00659322134047034[/C][C]-0.225515294382770[/C][/ROW]
[ROW][C]27[/C][C]8.5[/C][C]8.50279066740803[/C][C]-0.0836346857588167[/C][C]-0.00279066740803013[/C][C]-0.654241362504792[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]8.21530980336555[/C][C]-0.269995115243943[/C][C]-0.0153098033655529[/C][C]-0.579478181871975[/C][/ROW]
[ROW][C]29[/C][C]8.1[/C][C]8.06084726067879[/C][C]-0.164543540171458[/C][C]0.0391527393212141[/C][C]0.328054018511747[/C][/ROW]
[ROW][C]30[/C][C]7.9[/C][C]8.00679015783698[/C][C]-0.0636579506010582[/C][C]-0.106790157836976[/C][C]0.313866338946917[/C][/ROW]
[ROW][C]31[/C][C]8.6[/C][C]8.46458888621165[/C][C]0.412516487516675[/C][C]0.135411113788350[/C][C]1.48138602867508[/C][/ROW]
[ROW][C]32[/C][C]8.7[/C][C]8.74466765072423[/C][C]0.291582226359057[/C][C]-0.0446676507242307[/C][C]-0.376229419108265[/C][/ROW]
[ROW][C]33[/C][C]8.7[/C][C]8.70697815706679[/C][C]-0.00908476219982657[/C][C]-0.00697815706678867[/C][C]-0.935381666329714[/C][/ROW]
[ROW][C]34[/C][C]8.5[/C][C]8.50706404644173[/C][C]-0.183337772839505[/C][C]-0.00706404644173238[/C][C]-0.542105007729224[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.42349551328738[/C][C]-0.0922336519816608[/C][C]-0.0234955132873816[/C][C]0.283427940059473[/C][/ROW]
[ROW][C]36[/C][C]8.5[/C][C]8.48254528853028[/C][C]0.0458976874389169[/C][C]0.017454711469724[/C][C]0.429732441604963[/C][/ROW]
[ROW][C]37[/C][C]8.7[/C][C]8.68209057628563[/C][C]0.18615973347711[/C][C]0.0179094237143697[/C][C]0.436660402369008[/C][/ROW]
[ROW][C]38[/C][C]8.7[/C][C]8.70667633812932[/C][C]0.038598811591521[/C][C]-0.00667633812932111[/C][C]-0.459289685022563[/C][/ROW]
[ROW][C]39[/C][C]8.6[/C][C]8.59138376215762[/C][C]-0.100862298851671[/C][C]0.0086162378423773[/C][C]-0.434927081496445[/C][/ROW]
[ROW][C]40[/C][C]8.5[/C][C]8.51385208004961[/C][C]-0.0796716143642026[/C][C]-0.0138520800496118[/C][C]0.0659285991504954[/C][/ROW]
[ROW][C]41[/C][C]8.3[/C][C]8.25890553924152[/C][C]-0.238637913414801[/C][C]0.0410944607584801[/C][C]-0.494452744334046[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]8.14993461344097[/C][C]-0.121006421414725[/C][C]-0.149934613440969[/C][C]0.365974353648347[/C][/ROW]
[ROW][C]43[/C][C]8.2[/C][C]8.06582710542257[/C][C]-0.0875285544851058[/C][C]0.134172894577431[/C][C]0.104149836040597[/C][/ROW]
[ROW][C]44[/C][C]8.1[/C][C]8.11617540989993[/C][C]0.0375638170478271[/C][C]-0.0161754098999275[/C][C]0.389165448216263[/C][/ROW]
[ROW][C]45[/C][C]8.1[/C][C]8.09088508707781[/C][C]-0.0194612456731615[/C][C]0.00911491292219052[/C][C]-0.177406231052720[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]8.01186461406146[/C][C]-0.0734975032603589[/C][C]-0.0118646140614547[/C][C]-0.168108093818893[/C][/ROW]
[ROW][C]47[/C][C]7.9[/C][C]7.93121085797819[/C][C]-0.0799901770160099[/C][C]-0.0312108579781895[/C][C]-0.0201989406438855[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]7.89963440230783[/C][C]-0.0360718985378237[/C][C]0.000365597692166885[/C][C]0.136634123441552[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.9667647257953[/C][C]0.0575441566034415[/C][C]0.0332352742046941[/C][C]0.291437152539457[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]7.99601296231887[/C][C]0.0318777967430105[/C][C]0.00398703768113005[/C][C]-0.0798466092202058[/C][/ROW]
[ROW][C]51[/C][C]7.9[/C][C]7.90736032468487[/C][C]-0.0769665872681344[/C][C]-0.00736032468486671[/C][C]-0.339061405531233[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.98313596091908[/C][C]0.0612094154316495[/C][C]0.0168640390809208[/C][C]0.430003205562517[/C][/ROW]
[ROW][C]53[/C][C]7.7[/C][C]7.68911191307443[/C][C]-0.259801448256619[/C][C]0.0108880869255737[/C][C]-0.998402009993685[/C][/ROW]
[ROW][C]54[/C][C]7.2[/C][C]7.352599876354[/C][C]-0.329126626078210[/C][C]-0.152599876353997[/C][C]-0.215684513089949[/C][/ROW]
[ROW][C]55[/C][C]7.5[/C][C]7.36012550557213[/C][C]-0.0248394618827552[/C][C]0.139874494427866[/C][C]0.946641438144736[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]7.3141494166909[/C][C]-0.0439441485933053[/C][C]-0.0141494166908954[/C][C]-0.059435064635252[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]7.00270763400846[/C][C]-0.285723114504312[/C][C]-0.00270763400845993[/C][C]-0.752179878351502[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]6.9915471819148[/C][C]-0.0375567928908624[/C][C]0.00845281808519224[/C][C]0.772052556840845[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]7.02693270980818[/C][C]0.0283719342405384[/C][C]-0.0269327098081753[/C][C]0.205106003491748[/C][/ROW]
[ROW][C]60[/C][C]7.2[/C][C]7.20239137752213[/C][C]0.161294440603718[/C][C]-0.00239137752212999[/C][C]0.4135480595682[/C][/ROW]
[ROW][C]61[/C][C]7.3[/C][C]7.2728868319794[/C][C]0.0792318669164984[/C][C]0.0271131680205903[/C][C]-0.2554448349319[/C][/ROW]
[ROW][C]62[/C][C]7.1[/C][C]7.09985318516729[/C][C]-0.148686133361820[/C][C]0.000146814832710113[/C][C]-0.70892621824615[/C][/ROW]
[ROW][C]63[/C][C]6.8[/C][C]6.84291559765728[/C][C]-0.24621877452675[/C][C]-0.0429155976572829[/C][C]-0.303662639828411[/C][/ROW]
[ROW][C]64[/C][C]6.4[/C][C]6.36032599414731[/C][C]-0.459465531724917[/C][C]0.0396740058526884[/C][C]-0.663676077715483[/C][/ROW]
[ROW][C]65[/C][C]6.1[/C][C]6.05897452667951[/C][C]-0.316922618916431[/C][C]0.0410254733204924[/C][C]0.44332999844042[/C][/ROW]
[ROW][C]66[/C][C]6.5[/C][C]6.63522708060232[/C][C]0.488227690252844[/C][C]-0.135227080602317[/C][C]2.50494243221914[/C][/ROW]
[ROW][C]67[/C][C]7.7[/C][C]7.54067407657017[/C][C]0.864392092635254[/C][C]0.159325923429828[/C][C]1.17026054465987[/C][/ROW]
[ROW][C]68[/C][C]7.9[/C][C]7.91056574167231[/C][C]0.418541277665396[/C][C]-0.0105657416723083[/C][C]-1.38704799283956[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]7.5732063271842[/C][C]-0.262988181540286[/C][C]-0.073206327184208[/C][C]-2.12025519382940[/C][/ROW]
[ROW][C]70[/C][C]6.9[/C][C]6.91661774716365[/C][C]-0.617863481089187[/C][C]-0.0166177471636463[/C][C]-1.10402931056326[/C][/ROW]
[ROW][C]71[/C][C]6.6[/C][C]6.62313661158964[/C][C]-0.325406663450460[/C][C]-0.0231366115896357[/C][C]0.909837401592211[/C][/ROW]
[ROW][C]72[/C][C]6.9[/C][C]6.86707424260515[/C][C]0.187822973769178[/C][C]0.0329257573948542[/C][C]1.59680928356641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66439&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
18.38.3000
28.28.20150794324155-0.0986166801562142-0.00150794324155027-0.309040537073739
388.00283057750846-0.194512078765493-0.00283057750846062-0.304377034984027
47.97.89619813771343-0.1109922219244770.003801862286571220.259490908910541
57.67.60710290313238-0.280349045317253-0.00710290313238139-0.526922252594266
67.67.5894352782938-0.03051547187117830.01056472170620590.777236677085165
78.38.277563500338230.6529629345688760.02243649966176692.12631693214153
88.48.424114650872860.171335733221709-0.0241146508728594-1.49835286505826
98.48.4022979407693-0.0123638970675732-0.00229794076929965-0.571493612201239
108.48.398118670039-0.004579819594935950.001881329961004930.0242164371391531
118.48.399979649598710.001545771431250552.03504012858244e-050.0190568491318921
128.68.59353025279410.1841540191876590.006469747205907530.568098296613493
138.98.897237211188910.2978197216615050.002762788811087510.353780641744862
148.88.81254857887911-0.0661567648095394-0.0125485788791144-1.13787364407034
158.38.33210551744682-0.449554428603814-0.03210551744682-1.20575176823243
167.57.49654751038298-0.8068027690146520.00345248961702379-1.10993647895224
177.27.16825468178275-0.3643792685715750.03174531821724761.37659020782180
187.47.411106370729320.197272316466894-0.01110637072932081.74730949268202
198.88.707786854756341.214171158303650.09221314524366543.16359846568683
209.39.358289572978840.692818917212127-0.0582895729788386-1.62193836112218
219.39.33724579139320.0325503854806996-0.0372457913932058-2.05411002973329
228.78.71867517045162-0.569688907524338-0.0186751704516244-1.87357979531947
238.28.1937878995701-0.5282501754803310.006212100429894750.128916898298164
248.38.275650128164730.03604488635970180.02434987183527051.75553885567322
258.58.486810746204860.1979604095138430.01318925379514030.504014491173652
268.68.606593221340470.125609684135195-0.00659322134047034-0.225515294382770
278.58.50279066740803-0.0836346857588167-0.00279066740803013-0.654241362504792
288.28.21530980336555-0.269995115243943-0.0153098033655529-0.579478181871975
298.18.06084726067879-0.1645435401714580.03915273932121410.328054018511747
307.98.00679015783698-0.0636579506010582-0.1067901578369760.313866338946917
318.68.464588886211650.4125164875166750.1354111137883501.48138602867508
328.78.744667650724230.291582226359057-0.0446676507242307-0.376229419108265
338.78.70697815706679-0.00908476219982657-0.00697815706678867-0.935381666329714
348.58.50706404644173-0.183337772839505-0.00706404644173238-0.542105007729224
358.48.42349551328738-0.0922336519816608-0.02349551328738160.283427940059473
368.58.482545288530280.04589768743891690.0174547114697240.429732441604963
378.78.682090576285630.186159733477110.01790942371436970.436660402369008
388.78.706676338129320.038598811591521-0.00667633812932111-0.459289685022563
398.68.59138376215762-0.1008622988516710.0086162378423773-0.434927081496445
408.58.51385208004961-0.0796716143642026-0.01385208004961180.0659285991504954
418.38.25890553924152-0.2386379134148010.0410944607584801-0.494452744334046
4288.14993461344097-0.121006421414725-0.1499346134409690.365974353648347
438.28.06582710542257-0.08752855448510580.1341728945774310.104149836040597
448.18.116175409899930.0375638170478271-0.01617540989992750.389165448216263
458.18.09088508707781-0.01946124567316150.00911491292219052-0.177406231052720
4688.01186461406146-0.0734975032603589-0.0118646140614547-0.168108093818893
477.97.93121085797819-0.0799901770160099-0.0312108579781895-0.0201989406438855
487.97.89963440230783-0.03607189853782370.0003655976921668850.136634123441552
4987.96676472579530.05754415660344150.03323527420469410.291437152539457
5087.996012962318870.03187779674301050.00398703768113005-0.0798466092202058
517.97.90736032468487-0.0769665872681344-0.00736032468486671-0.339061405531233
5287.983135960919080.06120941543164950.01686403908092080.430003205562517
537.77.68911191307443-0.2598014482566190.0108880869255737-0.998402009993685
547.27.352599876354-0.329126626078210-0.152599876353997-0.215684513089949
557.57.36012550557213-0.02483946188275520.1398744944278660.946641438144736
567.37.3141494166909-0.0439441485933053-0.0141494166908954-0.059435064635252
5777.00270763400846-0.285723114504312-0.00270763400845993-0.752179878351502
5876.9915471819148-0.03755679289086240.008452818085192240.772052556840845
5977.026932709808180.0283719342405384-0.02693270980817530.205106003491748
607.27.202391377522130.161294440603718-0.002391377522129990.4135480595682
617.37.27288683197940.07923186691649840.0271131680205903-0.2554448349319
627.17.09985318516729-0.1486861333618200.000146814832710113-0.70892621824615
636.86.84291559765728-0.24621877452675-0.0429155976572829-0.303662639828411
646.46.36032599414731-0.4594655317249170.0396740058526884-0.663676077715483
656.16.05897452667951-0.3169226189164310.04102547332049240.44332999844042
666.56.635227080602320.488227690252844-0.1352270806023172.50494243221914
677.77.540674076570170.8643920926352540.1593259234298281.17026054465987
687.97.910565741672310.418541277665396-0.0105657416723083-1.38704799283956
697.57.5732063271842-0.262988181540286-0.073206327184208-2.12025519382940
706.96.91661774716365-0.617863481089187-0.0166177471636463-1.10402931056326
716.66.62313661158964-0.325406663450460-0.02313661158963570.909837401592211
726.96.867074242605150.1878229737691780.03292575739485421.59680928356641


Figures (Output of Computation)

Input Parameters & R Code

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')