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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 computationTue, 01 Dec 2009 14:20:48 -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/01/t12597025191munz8zvuete3fx.htm/, Retrieved Fri, 29 Mar 2024 13:35:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62264, Retrieved Fri, 29 Mar 2024 13:35:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160
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] [] [2009-12-01 21:20:48] [fc845972e0ebdb725d2fb9537c0c51aa] [Current]
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Dataseries X:
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3
91
93.2
103.1
94.1
91.8
102.7




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62264&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62264&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62264&T=0

As an alternative you can also use a QR Code:  

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

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







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1111.4111.4000
287.499.8959539316997-0.224183712393462-12.4959539316997-3.77471157315384
396.894.339309268493-0.5691313486212912.46069073150694-1.33271605733773
4114.1101.723564933706-0.098844172443867112.37643506629432.46831244139248
5110.3106.8598040531410.1168198116410483.440195946859471.83558024291616
6103.9107.0639709058570.119137784906014-3.163970905857390.0316988220915472
7101.6104.9481280284680.0773151079021414-3.348128028468-0.816544568468068
894.6100.2320158626470.0016220913592343-5.63201586264684-1.75189481224714
995.997.1668014595735-0.0452188529674338-1.26680145957346-1.11964241708753
10104.799.2958365156745-0.01148255395967515.40416348432550.792862361470975
11102.8101.2969362223310.02024592370175741.503063777669110.733381500474781
1298.1100.5911117035020.0087643243230906-2.49111170350176-0.264507486948939
13113.9102.814221941408-0.031859351006600111.08577805859170.849107270893794
1480.999.7935477949431-0.0117124586909016-18.8935477949431-1.13177845328493
1595.798.987425219725-0.024086084943073-3.28742521972501-0.273720806017243
16113.2100.8215939021150.025779952872058912.37840609788480.623623446985644
17105.9101.6098553417320.04701671455320734.290144658267970.263729185843117
18108.8104.7283030640010.1197651901511014.07169693599881.09584152464600
19102.3104.4854086960340.112983400460035-2.18540869603425-0.131478232683559
2099104.0421839232850.104658715053347-5.04218392328478-0.20288370454963
21100.7103.8462939456300.100876708529739-3.14629394563046-0.109821794033034
22115.5106.6512324741460.1296542557741778.848767525854270.987908168694762
23100.7104.4892103836210.111252194241004-3.78921038362127-0.83656361546845
24109.9107.2286680105390.1224076582332332.671331989460940.960554293568703
25114.6105.8840175598370.1213360147397358.71598244016299-0.539045021946316
2685.4105.1226144615170.118582981810548-19.7226144615167-0.321690397564941
27100.5105.1431535372530.117625156977526-4.64315353725302-0.0348528561178327
28114.8104.6203650456590.10749628911827710.1796349543411-0.223986585483039
29116.5108.1435116358260.1713371164204788.356488364173521.19974494886135
30112.9109.1049141310690.1859302987293793.795085868931280.281552001571018
31102107.4028683969620.154943985213279-5.40286839696195-0.68149192715483
32106108.6041715703260.169321676514080-2.604171570325710.380494672759458
33105.3109.3648089448870.175880164664117-4.064808944886550.215661624190514
34118.8109.5079228057530.1755987254688919.29207719424673-0.0119566864945872
35106.1110.1582442897590.178568838772146-4.058244289759290.173204522851695
36109.3108.5883506293170.1708177464502120.711649370683478-0.637986259099148
37117.2108.0491123178660.1680398041639929.15088768213435-0.258756733401266
3892.5109.6600933897870.175858398132718-17.16009338978670.522480712336001
39104.2110.0491798819870.177665422541725-5.849179881986590.0763843959498218
40112.5108.0430116230970.1521695233502194.45698837690309-0.776126929599112
41122.4109.9858556106960.17685838603247712.41414438930390.636497263966404
42113.3110.0230779762870.1748557519045143.27692202371337-0.0499614453383717
43100108.6456036491940.153848165145766-8.64560364919415-0.559863637242873
44110.7110.3051012370870.1717079411637540.3948987629128720.546220229590452
45112.8113.2170600170350.198633909638723-0.417060017034550.997129139653574
46109.8109.0302967985220.1644032779779210.769703201477491-1.59748156769750
47117.3112.3701389773430.1838201123201234.929861022656741.15692001427661
48109.1111.7068753139380.179516311244377-2.60687531393763-0.308471097693046
49115.9110.0118608558660.1701297008511685.88813914413376-0.681263823932619
5096110.7677334143400.173635599652390-14.76773341434010.211947954752422
5199.8108.7192737712450.156503024925510-8.91927377124538-0.799428676081407
52116.8110.0676929607090.1679110136486726.732307039290650.426988954180712
53115.7107.7289510547220.1404470582802787.97104894527806-0.897619062052119
5499.4102.6959742726640.0809280947449408-3.29597427266353-1.85854962906632
5594.3102.2569676926200.0751193400567024-7.95696769261977-0.187663958651076
569198.12062887928110.0323539467074631-7.12062887928114-1.52625463107751
5793.294.82116286780340.00315485374652326-1.62116286780336-1.21053837022496
58103.197.56718348138050.02325899388687395.53281651861950.997738158170022
5994.194.64541192962820.00513789010988046-0.545411929628159-1.07157936364149
6091.893.409164439378-0.00166425573088006-1.60916443937796-0.451452290412623
61102.794.3173880154580.003299044174055258.382611984541950.330353121774033

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 111.4 & 111.4 & 0 & 0 & 0 \tabularnewline
2 & 87.4 & 99.8959539316997 & -0.224183712393462 & -12.4959539316997 & -3.77471157315384 \tabularnewline
3 & 96.8 & 94.339309268493 & -0.569131348621291 & 2.46069073150694 & -1.33271605733773 \tabularnewline
4 & 114.1 & 101.723564933706 & -0.0988441724438671 & 12.3764350662943 & 2.46831244139248 \tabularnewline
5 & 110.3 & 106.859804053141 & 0.116819811641048 & 3.44019594685947 & 1.83558024291616 \tabularnewline
6 & 103.9 & 107.063970905857 & 0.119137784906014 & -3.16397090585739 & 0.0316988220915472 \tabularnewline
7 & 101.6 & 104.948128028468 & 0.0773151079021414 & -3.348128028468 & -0.816544568468068 \tabularnewline
8 & 94.6 & 100.232015862647 & 0.0016220913592343 & -5.63201586264684 & -1.75189481224714 \tabularnewline
9 & 95.9 & 97.1668014595735 & -0.0452188529674338 & -1.26680145957346 & -1.11964241708753 \tabularnewline
10 & 104.7 & 99.2958365156745 & -0.0114825539596751 & 5.4041634843255 & 0.792862361470975 \tabularnewline
11 & 102.8 & 101.296936222331 & 0.0202459237017574 & 1.50306377766911 & 0.733381500474781 \tabularnewline
12 & 98.1 & 100.591111703502 & 0.0087643243230906 & -2.49111170350176 & -0.264507486948939 \tabularnewline
13 & 113.9 & 102.814221941408 & -0.0318593510066001 & 11.0857780585917 & 0.849107270893794 \tabularnewline
14 & 80.9 & 99.7935477949431 & -0.0117124586909016 & -18.8935477949431 & -1.13177845328493 \tabularnewline
15 & 95.7 & 98.987425219725 & -0.024086084943073 & -3.28742521972501 & -0.273720806017243 \tabularnewline
16 & 113.2 & 100.821593902115 & 0.0257799528720589 & 12.3784060978848 & 0.623623446985644 \tabularnewline
17 & 105.9 & 101.609855341732 & 0.0470167145532073 & 4.29014465826797 & 0.263729185843117 \tabularnewline
18 & 108.8 & 104.728303064001 & 0.119765190151101 & 4.0716969359988 & 1.09584152464600 \tabularnewline
19 & 102.3 & 104.485408696034 & 0.112983400460035 & -2.18540869603425 & -0.131478232683559 \tabularnewline
20 & 99 & 104.042183923285 & 0.104658715053347 & -5.04218392328478 & -0.20288370454963 \tabularnewline
21 & 100.7 & 103.846293945630 & 0.100876708529739 & -3.14629394563046 & -0.109821794033034 \tabularnewline
22 & 115.5 & 106.651232474146 & 0.129654255774177 & 8.84876752585427 & 0.987908168694762 \tabularnewline
23 & 100.7 & 104.489210383621 & 0.111252194241004 & -3.78921038362127 & -0.83656361546845 \tabularnewline
24 & 109.9 & 107.228668010539 & 0.122407658233233 & 2.67133198946094 & 0.960554293568703 \tabularnewline
25 & 114.6 & 105.884017559837 & 0.121336014739735 & 8.71598244016299 & -0.539045021946316 \tabularnewline
26 & 85.4 & 105.122614461517 & 0.118582981810548 & -19.7226144615167 & -0.321690397564941 \tabularnewline
27 & 100.5 & 105.143153537253 & 0.117625156977526 & -4.64315353725302 & -0.0348528561178327 \tabularnewline
28 & 114.8 & 104.620365045659 & 0.107496289118277 & 10.1796349543411 & -0.223986585483039 \tabularnewline
29 & 116.5 & 108.143511635826 & 0.171337116420478 & 8.35648836417352 & 1.19974494886135 \tabularnewline
30 & 112.9 & 109.104914131069 & 0.185930298729379 & 3.79508586893128 & 0.281552001571018 \tabularnewline
31 & 102 & 107.402868396962 & 0.154943985213279 & -5.40286839696195 & -0.68149192715483 \tabularnewline
32 & 106 & 108.604171570326 & 0.169321676514080 & -2.60417157032571 & 0.380494672759458 \tabularnewline
33 & 105.3 & 109.364808944887 & 0.175880164664117 & -4.06480894488655 & 0.215661624190514 \tabularnewline
34 & 118.8 & 109.507922805753 & 0.175598725468891 & 9.29207719424673 & -0.0119566864945872 \tabularnewline
35 & 106.1 & 110.158244289759 & 0.178568838772146 & -4.05824428975929 & 0.173204522851695 \tabularnewline
36 & 109.3 & 108.588350629317 & 0.170817746450212 & 0.711649370683478 & -0.637986259099148 \tabularnewline
37 & 117.2 & 108.049112317866 & 0.168039804163992 & 9.15088768213435 & -0.258756733401266 \tabularnewline
38 & 92.5 & 109.660093389787 & 0.175858398132718 & -17.1600933897867 & 0.522480712336001 \tabularnewline
39 & 104.2 & 110.049179881987 & 0.177665422541725 & -5.84917988198659 & 0.0763843959498218 \tabularnewline
40 & 112.5 & 108.043011623097 & 0.152169523350219 & 4.45698837690309 & -0.776126929599112 \tabularnewline
41 & 122.4 & 109.985855610696 & 0.176858386032477 & 12.4141443893039 & 0.636497263966404 \tabularnewline
42 & 113.3 & 110.023077976287 & 0.174855751904514 & 3.27692202371337 & -0.0499614453383717 \tabularnewline
43 & 100 & 108.645603649194 & 0.153848165145766 & -8.64560364919415 & -0.559863637242873 \tabularnewline
44 & 110.7 & 110.305101237087 & 0.171707941163754 & 0.394898762912872 & 0.546220229590452 \tabularnewline
45 & 112.8 & 113.217060017035 & 0.198633909638723 & -0.41706001703455 & 0.997129139653574 \tabularnewline
46 & 109.8 & 109.030296798522 & 0.164403277977921 & 0.769703201477491 & -1.59748156769750 \tabularnewline
47 & 117.3 & 112.370138977343 & 0.183820112320123 & 4.92986102265674 & 1.15692001427661 \tabularnewline
48 & 109.1 & 111.706875313938 & 0.179516311244377 & -2.60687531393763 & -0.308471097693046 \tabularnewline
49 & 115.9 & 110.011860855866 & 0.170129700851168 & 5.88813914413376 & -0.681263823932619 \tabularnewline
50 & 96 & 110.767733414340 & 0.173635599652390 & -14.7677334143401 & 0.211947954752422 \tabularnewline
51 & 99.8 & 108.719273771245 & 0.156503024925510 & -8.91927377124538 & -0.799428676081407 \tabularnewline
52 & 116.8 & 110.067692960709 & 0.167911013648672 & 6.73230703929065 & 0.426988954180712 \tabularnewline
53 & 115.7 & 107.728951054722 & 0.140447058280278 & 7.97104894527806 & -0.897619062052119 \tabularnewline
54 & 99.4 & 102.695974272664 & 0.0809280947449408 & -3.29597427266353 & -1.85854962906632 \tabularnewline
55 & 94.3 & 102.256967692620 & 0.0751193400567024 & -7.95696769261977 & -0.187663958651076 \tabularnewline
56 & 91 & 98.1206288792811 & 0.0323539467074631 & -7.12062887928114 & -1.52625463107751 \tabularnewline
57 & 93.2 & 94.8211628678034 & 0.00315485374652326 & -1.62116286780336 & -1.21053837022496 \tabularnewline
58 & 103.1 & 97.5671834813805 & 0.0232589938868739 & 5.5328165186195 & 0.997738158170022 \tabularnewline
59 & 94.1 & 94.6454119296282 & 0.00513789010988046 & -0.545411929628159 & -1.07157936364149 \tabularnewline
60 & 91.8 & 93.409164439378 & -0.00166425573088006 & -1.60916443937796 & -0.451452290412623 \tabularnewline
61 & 102.7 & 94.317388015458 & 0.00329904417405525 & 8.38261198454195 & 0.330353121774033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62264&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]111.4[/C][C]111.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]87.4[/C][C]99.8959539316997[/C][C]-0.224183712393462[/C][C]-12.4959539316997[/C][C]-3.77471157315384[/C][/ROW]
[ROW][C]3[/C][C]96.8[/C][C]94.339309268493[/C][C]-0.569131348621291[/C][C]2.46069073150694[/C][C]-1.33271605733773[/C][/ROW]
[ROW][C]4[/C][C]114.1[/C][C]101.723564933706[/C][C]-0.0988441724438671[/C][C]12.3764350662943[/C][C]2.46831244139248[/C][/ROW]
[ROW][C]5[/C][C]110.3[/C][C]106.859804053141[/C][C]0.116819811641048[/C][C]3.44019594685947[/C][C]1.83558024291616[/C][/ROW]
[ROW][C]6[/C][C]103.9[/C][C]107.063970905857[/C][C]0.119137784906014[/C][C]-3.16397090585739[/C][C]0.0316988220915472[/C][/ROW]
[ROW][C]7[/C][C]101.6[/C][C]104.948128028468[/C][C]0.0773151079021414[/C][C]-3.348128028468[/C][C]-0.816544568468068[/C][/ROW]
[ROW][C]8[/C][C]94.6[/C][C]100.232015862647[/C][C]0.0016220913592343[/C][C]-5.63201586264684[/C][C]-1.75189481224714[/C][/ROW]
[ROW][C]9[/C][C]95.9[/C][C]97.1668014595735[/C][C]-0.0452188529674338[/C][C]-1.26680145957346[/C][C]-1.11964241708753[/C][/ROW]
[ROW][C]10[/C][C]104.7[/C][C]99.2958365156745[/C][C]-0.0114825539596751[/C][C]5.4041634843255[/C][C]0.792862361470975[/C][/ROW]
[ROW][C]11[/C][C]102.8[/C][C]101.296936222331[/C][C]0.0202459237017574[/C][C]1.50306377766911[/C][C]0.733381500474781[/C][/ROW]
[ROW][C]12[/C][C]98.1[/C][C]100.591111703502[/C][C]0.0087643243230906[/C][C]-2.49111170350176[/C][C]-0.264507486948939[/C][/ROW]
[ROW][C]13[/C][C]113.9[/C][C]102.814221941408[/C][C]-0.0318593510066001[/C][C]11.0857780585917[/C][C]0.849107270893794[/C][/ROW]
[ROW][C]14[/C][C]80.9[/C][C]99.7935477949431[/C][C]-0.0117124586909016[/C][C]-18.8935477949431[/C][C]-1.13177845328493[/C][/ROW]
[ROW][C]15[/C][C]95.7[/C][C]98.987425219725[/C][C]-0.024086084943073[/C][C]-3.28742521972501[/C][C]-0.273720806017243[/C][/ROW]
[ROW][C]16[/C][C]113.2[/C][C]100.821593902115[/C][C]0.0257799528720589[/C][C]12.3784060978848[/C][C]0.623623446985644[/C][/ROW]
[ROW][C]17[/C][C]105.9[/C][C]101.609855341732[/C][C]0.0470167145532073[/C][C]4.29014465826797[/C][C]0.263729185843117[/C][/ROW]
[ROW][C]18[/C][C]108.8[/C][C]104.728303064001[/C][C]0.119765190151101[/C][C]4.0716969359988[/C][C]1.09584152464600[/C][/ROW]
[ROW][C]19[/C][C]102.3[/C][C]104.485408696034[/C][C]0.112983400460035[/C][C]-2.18540869603425[/C][C]-0.131478232683559[/C][/ROW]
[ROW][C]20[/C][C]99[/C][C]104.042183923285[/C][C]0.104658715053347[/C][C]-5.04218392328478[/C][C]-0.20288370454963[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]103.846293945630[/C][C]0.100876708529739[/C][C]-3.14629394563046[/C][C]-0.109821794033034[/C][/ROW]
[ROW][C]22[/C][C]115.5[/C][C]106.651232474146[/C][C]0.129654255774177[/C][C]8.84876752585427[/C][C]0.987908168694762[/C][/ROW]
[ROW][C]23[/C][C]100.7[/C][C]104.489210383621[/C][C]0.111252194241004[/C][C]-3.78921038362127[/C][C]-0.83656361546845[/C][/ROW]
[ROW][C]24[/C][C]109.9[/C][C]107.228668010539[/C][C]0.122407658233233[/C][C]2.67133198946094[/C][C]0.960554293568703[/C][/ROW]
[ROW][C]25[/C][C]114.6[/C][C]105.884017559837[/C][C]0.121336014739735[/C][C]8.71598244016299[/C][C]-0.539045021946316[/C][/ROW]
[ROW][C]26[/C][C]85.4[/C][C]105.122614461517[/C][C]0.118582981810548[/C][C]-19.7226144615167[/C][C]-0.321690397564941[/C][/ROW]
[ROW][C]27[/C][C]100.5[/C][C]105.143153537253[/C][C]0.117625156977526[/C][C]-4.64315353725302[/C][C]-0.0348528561178327[/C][/ROW]
[ROW][C]28[/C][C]114.8[/C][C]104.620365045659[/C][C]0.107496289118277[/C][C]10.1796349543411[/C][C]-0.223986585483039[/C][/ROW]
[ROW][C]29[/C][C]116.5[/C][C]108.143511635826[/C][C]0.171337116420478[/C][C]8.35648836417352[/C][C]1.19974494886135[/C][/ROW]
[ROW][C]30[/C][C]112.9[/C][C]109.104914131069[/C][C]0.185930298729379[/C][C]3.79508586893128[/C][C]0.281552001571018[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]107.402868396962[/C][C]0.154943985213279[/C][C]-5.40286839696195[/C][C]-0.68149192715483[/C][/ROW]
[ROW][C]32[/C][C]106[/C][C]108.604171570326[/C][C]0.169321676514080[/C][C]-2.60417157032571[/C][C]0.380494672759458[/C][/ROW]
[ROW][C]33[/C][C]105.3[/C][C]109.364808944887[/C][C]0.175880164664117[/C][C]-4.06480894488655[/C][C]0.215661624190514[/C][/ROW]
[ROW][C]34[/C][C]118.8[/C][C]109.507922805753[/C][C]0.175598725468891[/C][C]9.29207719424673[/C][C]-0.0119566864945872[/C][/ROW]
[ROW][C]35[/C][C]106.1[/C][C]110.158244289759[/C][C]0.178568838772146[/C][C]-4.05824428975929[/C][C]0.173204522851695[/C][/ROW]
[ROW][C]36[/C][C]109.3[/C][C]108.588350629317[/C][C]0.170817746450212[/C][C]0.711649370683478[/C][C]-0.637986259099148[/C][/ROW]
[ROW][C]37[/C][C]117.2[/C][C]108.049112317866[/C][C]0.168039804163992[/C][C]9.15088768213435[/C][C]-0.258756733401266[/C][/ROW]
[ROW][C]38[/C][C]92.5[/C][C]109.660093389787[/C][C]0.175858398132718[/C][C]-17.1600933897867[/C][C]0.522480712336001[/C][/ROW]
[ROW][C]39[/C][C]104.2[/C][C]110.049179881987[/C][C]0.177665422541725[/C][C]-5.84917988198659[/C][C]0.0763843959498218[/C][/ROW]
[ROW][C]40[/C][C]112.5[/C][C]108.043011623097[/C][C]0.152169523350219[/C][C]4.45698837690309[/C][C]-0.776126929599112[/C][/ROW]
[ROW][C]41[/C][C]122.4[/C][C]109.985855610696[/C][C]0.176858386032477[/C][C]12.4141443893039[/C][C]0.636497263966404[/C][/ROW]
[ROW][C]42[/C][C]113.3[/C][C]110.023077976287[/C][C]0.174855751904514[/C][C]3.27692202371337[/C][C]-0.0499614453383717[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]108.645603649194[/C][C]0.153848165145766[/C][C]-8.64560364919415[/C][C]-0.559863637242873[/C][/ROW]
[ROW][C]44[/C][C]110.7[/C][C]110.305101237087[/C][C]0.171707941163754[/C][C]0.394898762912872[/C][C]0.546220229590452[/C][/ROW]
[ROW][C]45[/C][C]112.8[/C][C]113.217060017035[/C][C]0.198633909638723[/C][C]-0.41706001703455[/C][C]0.997129139653574[/C][/ROW]
[ROW][C]46[/C][C]109.8[/C][C]109.030296798522[/C][C]0.164403277977921[/C][C]0.769703201477491[/C][C]-1.59748156769750[/C][/ROW]
[ROW][C]47[/C][C]117.3[/C][C]112.370138977343[/C][C]0.183820112320123[/C][C]4.92986102265674[/C][C]1.15692001427661[/C][/ROW]
[ROW][C]48[/C][C]109.1[/C][C]111.706875313938[/C][C]0.179516311244377[/C][C]-2.60687531393763[/C][C]-0.308471097693046[/C][/ROW]
[ROW][C]49[/C][C]115.9[/C][C]110.011860855866[/C][C]0.170129700851168[/C][C]5.88813914413376[/C][C]-0.681263823932619[/C][/ROW]
[ROW][C]50[/C][C]96[/C][C]110.767733414340[/C][C]0.173635599652390[/C][C]-14.7677334143401[/C][C]0.211947954752422[/C][/ROW]
[ROW][C]51[/C][C]99.8[/C][C]108.719273771245[/C][C]0.156503024925510[/C][C]-8.91927377124538[/C][C]-0.799428676081407[/C][/ROW]
[ROW][C]52[/C][C]116.8[/C][C]110.067692960709[/C][C]0.167911013648672[/C][C]6.73230703929065[/C][C]0.426988954180712[/C][/ROW]
[ROW][C]53[/C][C]115.7[/C][C]107.728951054722[/C][C]0.140447058280278[/C][C]7.97104894527806[/C][C]-0.897619062052119[/C][/ROW]
[ROW][C]54[/C][C]99.4[/C][C]102.695974272664[/C][C]0.0809280947449408[/C][C]-3.29597427266353[/C][C]-1.85854962906632[/C][/ROW]
[ROW][C]55[/C][C]94.3[/C][C]102.256967692620[/C][C]0.0751193400567024[/C][C]-7.95696769261977[/C][C]-0.187663958651076[/C][/ROW]
[ROW][C]56[/C][C]91[/C][C]98.1206288792811[/C][C]0.0323539467074631[/C][C]-7.12062887928114[/C][C]-1.52625463107751[/C][/ROW]
[ROW][C]57[/C][C]93.2[/C][C]94.8211628678034[/C][C]0.00315485374652326[/C][C]-1.62116286780336[/C][C]-1.21053837022496[/C][/ROW]
[ROW][C]58[/C][C]103.1[/C][C]97.5671834813805[/C][C]0.0232589938868739[/C][C]5.5328165186195[/C][C]0.997738158170022[/C][/ROW]
[ROW][C]59[/C][C]94.1[/C][C]94.6454119296282[/C][C]0.00513789010988046[/C][C]-0.545411929628159[/C][C]-1.07157936364149[/C][/ROW]
[ROW][C]60[/C][C]91.8[/C][C]93.409164439378[/C][C]-0.00166425573088006[/C][C]-1.60916443937796[/C][C]-0.451452290412623[/C][/ROW]
[ROW][C]61[/C][C]102.7[/C][C]94.317388015458[/C][C]0.00329904417405525[/C][C]8.38261198454195[/C][C]0.330353121774033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62264&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62264&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
1111.4111.4000
287.499.8959539316997-0.224183712393462-12.4959539316997-3.77471157315384
396.894.339309268493-0.5691313486212912.46069073150694-1.33271605733773
4114.1101.723564933706-0.098844172443867112.37643506629432.46831244139248
5110.3106.8598040531410.1168198116410483.440195946859471.83558024291616
6103.9107.0639709058570.119137784906014-3.163970905857390.0316988220915472
7101.6104.9481280284680.0773151079021414-3.348128028468-0.816544568468068
894.6100.2320158626470.0016220913592343-5.63201586264684-1.75189481224714
995.997.1668014595735-0.0452188529674338-1.26680145957346-1.11964241708753
10104.799.2958365156745-0.01148255395967515.40416348432550.792862361470975
11102.8101.2969362223310.02024592370175741.503063777669110.733381500474781
1298.1100.5911117035020.0087643243230906-2.49111170350176-0.264507486948939
13113.9102.814221941408-0.031859351006600111.08577805859170.849107270893794
1480.999.7935477949431-0.0117124586909016-18.8935477949431-1.13177845328493
1595.798.987425219725-0.024086084943073-3.28742521972501-0.273720806017243
16113.2100.8215939021150.025779952872058912.37840609788480.623623446985644
17105.9101.6098553417320.04701671455320734.290144658267970.263729185843117
18108.8104.7283030640010.1197651901511014.07169693599881.09584152464600
19102.3104.4854086960340.112983400460035-2.18540869603425-0.131478232683559
2099104.0421839232850.104658715053347-5.04218392328478-0.20288370454963
21100.7103.8462939456300.100876708529739-3.14629394563046-0.109821794033034
22115.5106.6512324741460.1296542557741778.848767525854270.987908168694762
23100.7104.4892103836210.111252194241004-3.78921038362127-0.83656361546845
24109.9107.2286680105390.1224076582332332.671331989460940.960554293568703
25114.6105.8840175598370.1213360147397358.71598244016299-0.539045021946316
2685.4105.1226144615170.118582981810548-19.7226144615167-0.321690397564941
27100.5105.1431535372530.117625156977526-4.64315353725302-0.0348528561178327
28114.8104.6203650456590.10749628911827710.1796349543411-0.223986585483039
29116.5108.1435116358260.1713371164204788.356488364173521.19974494886135
30112.9109.1049141310690.1859302987293793.795085868931280.281552001571018
31102107.4028683969620.154943985213279-5.40286839696195-0.68149192715483
32106108.6041715703260.169321676514080-2.604171570325710.380494672759458
33105.3109.3648089448870.175880164664117-4.064808944886550.215661624190514
34118.8109.5079228057530.1755987254688919.29207719424673-0.0119566864945872
35106.1110.1582442897590.178568838772146-4.058244289759290.173204522851695
36109.3108.5883506293170.1708177464502120.711649370683478-0.637986259099148
37117.2108.0491123178660.1680398041639929.15088768213435-0.258756733401266
3892.5109.6600933897870.175858398132718-17.16009338978670.522480712336001
39104.2110.0491798819870.177665422541725-5.849179881986590.0763843959498218
40112.5108.0430116230970.1521695233502194.45698837690309-0.776126929599112
41122.4109.9858556106960.17685838603247712.41414438930390.636497263966404
42113.3110.0230779762870.1748557519045143.27692202371337-0.0499614453383717
43100108.6456036491940.153848165145766-8.64560364919415-0.559863637242873
44110.7110.3051012370870.1717079411637540.3948987629128720.546220229590452
45112.8113.2170600170350.198633909638723-0.417060017034550.997129139653574
46109.8109.0302967985220.1644032779779210.769703201477491-1.59748156769750
47117.3112.3701389773430.1838201123201234.929861022656741.15692001427661
48109.1111.7068753139380.179516311244377-2.60687531393763-0.308471097693046
49115.9110.0118608558660.1701297008511685.88813914413376-0.681263823932619
5096110.7677334143400.173635599652390-14.76773341434010.211947954752422
5199.8108.7192737712450.156503024925510-8.91927377124538-0.799428676081407
52116.8110.0676929607090.1679110136486726.732307039290650.426988954180712
53115.7107.7289510547220.1404470582802787.97104894527806-0.897619062052119
5499.4102.6959742726640.0809280947449408-3.29597427266353-1.85854962906632
5594.3102.2569676926200.0751193400567024-7.95696769261977-0.187663958651076
569198.12062887928110.0323539467074631-7.12062887928114-1.52625463107751
5793.294.82116286780340.00315485374652326-1.62116286780336-1.21053837022496
58103.197.56718348138050.02325899388687395.53281651861950.997738158170022
5994.194.64541192962820.00513789010988046-0.545411929628159-1.07157936364149
6091.893.409164439378-0.00166425573088006-1.60916443937796-0.451452290412623
61102.794.3173880154580.003299044174055258.382611984541950.330353121774033



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
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')