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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 10:40:16 -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/t1259948467jmfr4yarxhc3ppx.htm/, Retrieved Sat, 27 Apr 2024 17:49:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63962, Retrieved Sat, 27 Apr 2024 17:49:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact69
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2009-12-04 17:40:16] [477c9cb8e7bda18f2375c22a66069c90] [Current]
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Dataseries X:
92.9
107.7
103.5
91.1
79.8
71.9
82.9
90.1
100.7
90.7
108.8
44.1
93.6
107.4
96.5
93.6
76.5
76.7
84
103.3
88.5
99
105.9
44.7
94
107.1
104.8
102.5
77.7
85.2
91.3
106.5
92.4
97.5
107
51.1
98.6
102.2
114.3
99.4
72.5
92.3
99.4
85.9
109.4
97.6
104.7
56.9
86.7
108.5
103.4
86.2
71
75.9
87.1
102
88.5
87.8
100.8
50.6
85.9




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=63962&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=63962&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63962&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
192.992.9000
2107.792.6896588715088-0.25540822458787111.48620580950461.5666548067674
3103.595.95711302130810.338734071021193.933284815428451.79029325294979
491.196.83495220218840.423485059763639-6.053275170983920.181397495851078
579.893.2838337171227-0.122637281959-11.0959300621323-1.39024174152871
671.987.7384773189906-0.771104665389701-12.0145288848744-2.17351412427144
782.984.8266931604193-0.994915865053252-0.160115655525621-0.977188508428675
890.184.4284428943173-0.9399338159306765.11280299669930.302467009589666
9100.786.5977434347225-0.68490144539461310.89391132360011.70913723134016
1090.787.7990789197332-0.5457216215514450.8052315803078281.10332943982679
11108.891.1591633714338-0.2837589875181313.04837559893122.39676117711606
1244.185.6522090833307-0.605278927103812-35.1350798057273-3.32701502292999
1393.684.6350834063906-0.6159604430691269.5819190415182-0.32363542878073
14107.485.5008160701826-0.57064264019913619.80851677975051.10129764248051
1596.586.1859164339805-0.5193514285341648.742455271834330.842663491769534
1693.687.3065763966798-0.4425065554489334.386104109221421.03887150098209
1776.586.8948407551352-0.44099857588712-10.42987067551200.0192374399233076
1876.786.678979958388-0.430026620876359-10.23801293101810.142547550202579
198486.3939921811031-0.423195811427058-2.565102180082550.0940317144533676
20103.388.1654160327014-0.32499088496140512.46861315391891.46077392759870
2188.587.6582452075743-0.3326502455449441.06949323662941-0.124355187337928
229988.8199850810234-0.2742254150549778.261767506704941.04369166108818
23105.988.9565083929617-0.25952214084887316.40296793229930.293011771483107
2444.787.9509488890128-0.283202932912098-42.2434226261589-0.544123915405248
259487.4804606057772-0.2880773213905266.7812964543588-0.140928604769788
26107.187.4854477285908-0.28077650812043819.20461306974030.220557366360404
27104.888.6386829232844-0.24294830059182114.19611189750161.06015371103563
28102.590.187212978114-0.1923548103149219.909237879949861.30167389162281
2977.790.5945439357755-0.174649392821282-13.68940632796460.431823045761138
3085.291.4780356024694-0.142886443893557-7.677404547510080.761526374812002
3191.392.3839021929191-0.111625447586399-2.477727208706550.758796171771787
32106.592.9663899001254-0.091459316388035312.60264522168700.506528146446527
3392.493.1821204289992-0.082890093477477-1.198736151612190.226414573086229
3497.592.8154175798178-0.090393121926985.07404912766853-0.211347910648492
3510792.3636135731259-0.099337100974398615.1382138548937-0.271884604166801
3651.192.4024274984616-0.096170759646488-41.49645413133140.104956503193787
3798.692.5912284062815-0.09012671000454835.604594553426210.218340692796258
38102.291.9542646572515-0.10130549707185911.0234045807122-0.419869473713256
39114.392.9807456784587-0.07818241205469119.72113508143060.863179902415187
4099.493.1534977447879-0.07292857211809365.892935413722070.191159228555848
4172.592.6504471578614-0.082119141796351-19.5470088167793-0.326595268933698
4292.393.468368769283-0.0626884545257687-2.429288315409830.682930848028511
4399.494.8012246862116-0.03264269173220242.640824566432651.06073296136916
4485.992.8497014467416-0.0733147859915818-4.24769499770760-1.46360157733441
45109.494.0773542276447-0.046479176409976713.48153570346550.996807334186805
4697.694.3046864070464-0.04103538271619182.905610209657970.210850861143600
47104.793.8986426555385-0.047980749639253211.3238851407079-0.282503396091259
4856.994.2988988694428-0.0398403959701915-38.04403923265140.348537428297218
4986.792.9879798645008-0.0619774904272617-4.45084026349198-0.99191234376584
50108.593.1703345405175-0.057833925168040614.97581840330500.190986099273943
51103.492.2021495834002-0.073123337010830412.5160834006845-0.711500130759697
5286.290.5149500256944-0.100267479626643-1.98051497421404-1.26028062105924
537190.0067661009445-0.107158792933552-18.4174689131130-0.318242879042410
5475.988.5346700753104-0.130259213455715-10.6634088344547-1.06482595409878
5587.187.2752388661739-0.1492858463325071.45682568825914-0.881688871590838
5610288.9819829185483-0.11837973131382410.33054139853501.45174519427050
5788.587.9447125816579-0.1333911629434771.88919545860889-0.720401031225008
5887.887.0453990008443-0.1455926094491861.86974513715388-0.602056475077186
59100.886.802390550314-0.14709998325094314.139875682295-0.0767794008640793
6050.686.5560765552507-0.148589947577902-35.8107748484401-0.078388589618882
6185.986.6456027697475-0.145108503481557-1.095116937946960.188500270170006

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 92.9 & 92.9 & 0 & 0 & 0 \tabularnewline
2 & 107.7 & 92.6896588715088 & -0.255408224587871 & 11.4862058095046 & 1.5666548067674 \tabularnewline
3 & 103.5 & 95.9571130213081 & 0.33873407102119 & 3.93328481542845 & 1.79029325294979 \tabularnewline
4 & 91.1 & 96.8349522021884 & 0.423485059763639 & -6.05327517098392 & 0.181397495851078 \tabularnewline
5 & 79.8 & 93.2838337171227 & -0.122637281959 & -11.0959300621323 & -1.39024174152871 \tabularnewline
6 & 71.9 & 87.7384773189906 & -0.771104665389701 & -12.0145288848744 & -2.17351412427144 \tabularnewline
7 & 82.9 & 84.8266931604193 & -0.994915865053252 & -0.160115655525621 & -0.977188508428675 \tabularnewline
8 & 90.1 & 84.4284428943173 & -0.939933815930676 & 5.1128029966993 & 0.302467009589666 \tabularnewline
9 & 100.7 & 86.5977434347225 & -0.684901445394613 & 10.8939113236001 & 1.70913723134016 \tabularnewline
10 & 90.7 & 87.7990789197332 & -0.545721621551445 & 0.805231580307828 & 1.10332943982679 \tabularnewline
11 & 108.8 & 91.1591633714338 & -0.28375898751813 & 13.0483755989312 & 2.39676117711606 \tabularnewline
12 & 44.1 & 85.6522090833307 & -0.605278927103812 & -35.1350798057273 & -3.32701502292999 \tabularnewline
13 & 93.6 & 84.6350834063906 & -0.615960443069126 & 9.5819190415182 & -0.32363542878073 \tabularnewline
14 & 107.4 & 85.5008160701826 & -0.570642640199136 & 19.8085167797505 & 1.10129764248051 \tabularnewline
15 & 96.5 & 86.1859164339805 & -0.519351428534164 & 8.74245527183433 & 0.842663491769534 \tabularnewline
16 & 93.6 & 87.3065763966798 & -0.442506555448933 & 4.38610410922142 & 1.03887150098209 \tabularnewline
17 & 76.5 & 86.8948407551352 & -0.44099857588712 & -10.4298706755120 & 0.0192374399233076 \tabularnewline
18 & 76.7 & 86.678979958388 & -0.430026620876359 & -10.2380129310181 & 0.142547550202579 \tabularnewline
19 & 84 & 86.3939921811031 & -0.423195811427058 & -2.56510218008255 & 0.0940317144533676 \tabularnewline
20 & 103.3 & 88.1654160327014 & -0.324990884961405 & 12.4686131539189 & 1.46077392759870 \tabularnewline
21 & 88.5 & 87.6582452075743 & -0.332650245544944 & 1.06949323662941 & -0.124355187337928 \tabularnewline
22 & 99 & 88.8199850810234 & -0.274225415054977 & 8.26176750670494 & 1.04369166108818 \tabularnewline
23 & 105.9 & 88.9565083929617 & -0.259522140848873 & 16.4029679322993 & 0.293011771483107 \tabularnewline
24 & 44.7 & 87.9509488890128 & -0.283202932912098 & -42.2434226261589 & -0.544123915405248 \tabularnewline
25 & 94 & 87.4804606057772 & -0.288077321390526 & 6.7812964543588 & -0.140928604769788 \tabularnewline
26 & 107.1 & 87.4854477285908 & -0.280776508120438 & 19.2046130697403 & 0.220557366360404 \tabularnewline
27 & 104.8 & 88.6386829232844 & -0.242948300591821 & 14.1961118975016 & 1.06015371103563 \tabularnewline
28 & 102.5 & 90.187212978114 & -0.192354810314921 & 9.90923787994986 & 1.30167389162281 \tabularnewline
29 & 77.7 & 90.5945439357755 & -0.174649392821282 & -13.6894063279646 & 0.431823045761138 \tabularnewline
30 & 85.2 & 91.4780356024694 & -0.142886443893557 & -7.67740454751008 & 0.761526374812002 \tabularnewline
31 & 91.3 & 92.3839021929191 & -0.111625447586399 & -2.47772720870655 & 0.758796171771787 \tabularnewline
32 & 106.5 & 92.9663899001254 & -0.0914593163880353 & 12.6026452216870 & 0.506528146446527 \tabularnewline
33 & 92.4 & 93.1821204289992 & -0.082890093477477 & -1.19873615161219 & 0.226414573086229 \tabularnewline
34 & 97.5 & 92.8154175798178 & -0.09039312192698 & 5.07404912766853 & -0.211347910648492 \tabularnewline
35 & 107 & 92.3636135731259 & -0.0993371009743986 & 15.1382138548937 & -0.271884604166801 \tabularnewline
36 & 51.1 & 92.4024274984616 & -0.096170759646488 & -41.4964541313314 & 0.104956503193787 \tabularnewline
37 & 98.6 & 92.5912284062815 & -0.0901267100045483 & 5.60459455342621 & 0.218340692796258 \tabularnewline
38 & 102.2 & 91.9542646572515 & -0.101305497071859 & 11.0234045807122 & -0.419869473713256 \tabularnewline
39 & 114.3 & 92.9807456784587 & -0.078182412054691 & 19.7211350814306 & 0.863179902415187 \tabularnewline
40 & 99.4 & 93.1534977447879 & -0.0729285721180936 & 5.89293541372207 & 0.191159228555848 \tabularnewline
41 & 72.5 & 92.6504471578614 & -0.082119141796351 & -19.5470088167793 & -0.326595268933698 \tabularnewline
42 & 92.3 & 93.468368769283 & -0.0626884545257687 & -2.42928831540983 & 0.682930848028511 \tabularnewline
43 & 99.4 & 94.8012246862116 & -0.0326426917322024 & 2.64082456643265 & 1.06073296136916 \tabularnewline
44 & 85.9 & 92.8497014467416 & -0.0733147859915818 & -4.24769499770760 & -1.46360157733441 \tabularnewline
45 & 109.4 & 94.0773542276447 & -0.0464791764099767 & 13.4815357034655 & 0.996807334186805 \tabularnewline
46 & 97.6 & 94.3046864070464 & -0.0410353827161918 & 2.90561020965797 & 0.210850861143600 \tabularnewline
47 & 104.7 & 93.8986426555385 & -0.0479807496392532 & 11.3238851407079 & -0.282503396091259 \tabularnewline
48 & 56.9 & 94.2988988694428 & -0.0398403959701915 & -38.0440392326514 & 0.348537428297218 \tabularnewline
49 & 86.7 & 92.9879798645008 & -0.0619774904272617 & -4.45084026349198 & -0.99191234376584 \tabularnewline
50 & 108.5 & 93.1703345405175 & -0.0578339251680406 & 14.9758184033050 & 0.190986099273943 \tabularnewline
51 & 103.4 & 92.2021495834002 & -0.0731233370108304 & 12.5160834006845 & -0.711500130759697 \tabularnewline
52 & 86.2 & 90.5149500256944 & -0.100267479626643 & -1.98051497421404 & -1.26028062105924 \tabularnewline
53 & 71 & 90.0067661009445 & -0.107158792933552 & -18.4174689131130 & -0.318242879042410 \tabularnewline
54 & 75.9 & 88.5346700753104 & -0.130259213455715 & -10.6634088344547 & -1.06482595409878 \tabularnewline
55 & 87.1 & 87.2752388661739 & -0.149285846332507 & 1.45682568825914 & -0.881688871590838 \tabularnewline
56 & 102 & 88.9819829185483 & -0.118379731313824 & 10.3305413985350 & 1.45174519427050 \tabularnewline
57 & 88.5 & 87.9447125816579 & -0.133391162943477 & 1.88919545860889 & -0.720401031225008 \tabularnewline
58 & 87.8 & 87.0453990008443 & -0.145592609449186 & 1.86974513715388 & -0.602056475077186 \tabularnewline
59 & 100.8 & 86.802390550314 & -0.147099983250943 & 14.139875682295 & -0.0767794008640793 \tabularnewline
60 & 50.6 & 86.5560765552507 & -0.148589947577902 & -35.8107748484401 & -0.078388589618882 \tabularnewline
61 & 85.9 & 86.6456027697475 & -0.145108503481557 & -1.09511693794696 & 0.188500270170006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63962&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]92.9[/C][C]92.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]107.7[/C][C]92.6896588715088[/C][C]-0.255408224587871[/C][C]11.4862058095046[/C][C]1.5666548067674[/C][/ROW]
[ROW][C]3[/C][C]103.5[/C][C]95.9571130213081[/C][C]0.33873407102119[/C][C]3.93328481542845[/C][C]1.79029325294979[/C][/ROW]
[ROW][C]4[/C][C]91.1[/C][C]96.8349522021884[/C][C]0.423485059763639[/C][C]-6.05327517098392[/C][C]0.181397495851078[/C][/ROW]
[ROW][C]5[/C][C]79.8[/C][C]93.2838337171227[/C][C]-0.122637281959[/C][C]-11.0959300621323[/C][C]-1.39024174152871[/C][/ROW]
[ROW][C]6[/C][C]71.9[/C][C]87.7384773189906[/C][C]-0.771104665389701[/C][C]-12.0145288848744[/C][C]-2.17351412427144[/C][/ROW]
[ROW][C]7[/C][C]82.9[/C][C]84.8266931604193[/C][C]-0.994915865053252[/C][C]-0.160115655525621[/C][C]-0.977188508428675[/C][/ROW]
[ROW][C]8[/C][C]90.1[/C][C]84.4284428943173[/C][C]-0.939933815930676[/C][C]5.1128029966993[/C][C]0.302467009589666[/C][/ROW]
[ROW][C]9[/C][C]100.7[/C][C]86.5977434347225[/C][C]-0.684901445394613[/C][C]10.8939113236001[/C][C]1.70913723134016[/C][/ROW]
[ROW][C]10[/C][C]90.7[/C][C]87.7990789197332[/C][C]-0.545721621551445[/C][C]0.805231580307828[/C][C]1.10332943982679[/C][/ROW]
[ROW][C]11[/C][C]108.8[/C][C]91.1591633714338[/C][C]-0.28375898751813[/C][C]13.0483755989312[/C][C]2.39676117711606[/C][/ROW]
[ROW][C]12[/C][C]44.1[/C][C]85.6522090833307[/C][C]-0.605278927103812[/C][C]-35.1350798057273[/C][C]-3.32701502292999[/C][/ROW]
[ROW][C]13[/C][C]93.6[/C][C]84.6350834063906[/C][C]-0.615960443069126[/C][C]9.5819190415182[/C][C]-0.32363542878073[/C][/ROW]
[ROW][C]14[/C][C]107.4[/C][C]85.5008160701826[/C][C]-0.570642640199136[/C][C]19.8085167797505[/C][C]1.10129764248051[/C][/ROW]
[ROW][C]15[/C][C]96.5[/C][C]86.1859164339805[/C][C]-0.519351428534164[/C][C]8.74245527183433[/C][C]0.842663491769534[/C][/ROW]
[ROW][C]16[/C][C]93.6[/C][C]87.3065763966798[/C][C]-0.442506555448933[/C][C]4.38610410922142[/C][C]1.03887150098209[/C][/ROW]
[ROW][C]17[/C][C]76.5[/C][C]86.8948407551352[/C][C]-0.44099857588712[/C][C]-10.4298706755120[/C][C]0.0192374399233076[/C][/ROW]
[ROW][C]18[/C][C]76.7[/C][C]86.678979958388[/C][C]-0.430026620876359[/C][C]-10.2380129310181[/C][C]0.142547550202579[/C][/ROW]
[ROW][C]19[/C][C]84[/C][C]86.3939921811031[/C][C]-0.423195811427058[/C][C]-2.56510218008255[/C][C]0.0940317144533676[/C][/ROW]
[ROW][C]20[/C][C]103.3[/C][C]88.1654160327014[/C][C]-0.324990884961405[/C][C]12.4686131539189[/C][C]1.46077392759870[/C][/ROW]
[ROW][C]21[/C][C]88.5[/C][C]87.6582452075743[/C][C]-0.332650245544944[/C][C]1.06949323662941[/C][C]-0.124355187337928[/C][/ROW]
[ROW][C]22[/C][C]99[/C][C]88.8199850810234[/C][C]-0.274225415054977[/C][C]8.26176750670494[/C][C]1.04369166108818[/C][/ROW]
[ROW][C]23[/C][C]105.9[/C][C]88.9565083929617[/C][C]-0.259522140848873[/C][C]16.4029679322993[/C][C]0.293011771483107[/C][/ROW]
[ROW][C]24[/C][C]44.7[/C][C]87.9509488890128[/C][C]-0.283202932912098[/C][C]-42.2434226261589[/C][C]-0.544123915405248[/C][/ROW]
[ROW][C]25[/C][C]94[/C][C]87.4804606057772[/C][C]-0.288077321390526[/C][C]6.7812964543588[/C][C]-0.140928604769788[/C][/ROW]
[ROW][C]26[/C][C]107.1[/C][C]87.4854477285908[/C][C]-0.280776508120438[/C][C]19.2046130697403[/C][C]0.220557366360404[/C][/ROW]
[ROW][C]27[/C][C]104.8[/C][C]88.6386829232844[/C][C]-0.242948300591821[/C][C]14.1961118975016[/C][C]1.06015371103563[/C][/ROW]
[ROW][C]28[/C][C]102.5[/C][C]90.187212978114[/C][C]-0.192354810314921[/C][C]9.90923787994986[/C][C]1.30167389162281[/C][/ROW]
[ROW][C]29[/C][C]77.7[/C][C]90.5945439357755[/C][C]-0.174649392821282[/C][C]-13.6894063279646[/C][C]0.431823045761138[/C][/ROW]
[ROW][C]30[/C][C]85.2[/C][C]91.4780356024694[/C][C]-0.142886443893557[/C][C]-7.67740454751008[/C][C]0.761526374812002[/C][/ROW]
[ROW][C]31[/C][C]91.3[/C][C]92.3839021929191[/C][C]-0.111625447586399[/C][C]-2.47772720870655[/C][C]0.758796171771787[/C][/ROW]
[ROW][C]32[/C][C]106.5[/C][C]92.9663899001254[/C][C]-0.0914593163880353[/C][C]12.6026452216870[/C][C]0.506528146446527[/C][/ROW]
[ROW][C]33[/C][C]92.4[/C][C]93.1821204289992[/C][C]-0.082890093477477[/C][C]-1.19873615161219[/C][C]0.226414573086229[/C][/ROW]
[ROW][C]34[/C][C]97.5[/C][C]92.8154175798178[/C][C]-0.09039312192698[/C][C]5.07404912766853[/C][C]-0.211347910648492[/C][/ROW]
[ROW][C]35[/C][C]107[/C][C]92.3636135731259[/C][C]-0.0993371009743986[/C][C]15.1382138548937[/C][C]-0.271884604166801[/C][/ROW]
[ROW][C]36[/C][C]51.1[/C][C]92.4024274984616[/C][C]-0.096170759646488[/C][C]-41.4964541313314[/C][C]0.104956503193787[/C][/ROW]
[ROW][C]37[/C][C]98.6[/C][C]92.5912284062815[/C][C]-0.0901267100045483[/C][C]5.60459455342621[/C][C]0.218340692796258[/C][/ROW]
[ROW][C]38[/C][C]102.2[/C][C]91.9542646572515[/C][C]-0.101305497071859[/C][C]11.0234045807122[/C][C]-0.419869473713256[/C][/ROW]
[ROW][C]39[/C][C]114.3[/C][C]92.9807456784587[/C][C]-0.078182412054691[/C][C]19.7211350814306[/C][C]0.863179902415187[/C][/ROW]
[ROW][C]40[/C][C]99.4[/C][C]93.1534977447879[/C][C]-0.0729285721180936[/C][C]5.89293541372207[/C][C]0.191159228555848[/C][/ROW]
[ROW][C]41[/C][C]72.5[/C][C]92.6504471578614[/C][C]-0.082119141796351[/C][C]-19.5470088167793[/C][C]-0.326595268933698[/C][/ROW]
[ROW][C]42[/C][C]92.3[/C][C]93.468368769283[/C][C]-0.0626884545257687[/C][C]-2.42928831540983[/C][C]0.682930848028511[/C][/ROW]
[ROW][C]43[/C][C]99.4[/C][C]94.8012246862116[/C][C]-0.0326426917322024[/C][C]2.64082456643265[/C][C]1.06073296136916[/C][/ROW]
[ROW][C]44[/C][C]85.9[/C][C]92.8497014467416[/C][C]-0.0733147859915818[/C][C]-4.24769499770760[/C][C]-1.46360157733441[/C][/ROW]
[ROW][C]45[/C][C]109.4[/C][C]94.0773542276447[/C][C]-0.0464791764099767[/C][C]13.4815357034655[/C][C]0.996807334186805[/C][/ROW]
[ROW][C]46[/C][C]97.6[/C][C]94.3046864070464[/C][C]-0.0410353827161918[/C][C]2.90561020965797[/C][C]0.210850861143600[/C][/ROW]
[ROW][C]47[/C][C]104.7[/C][C]93.8986426555385[/C][C]-0.0479807496392532[/C][C]11.3238851407079[/C][C]-0.282503396091259[/C][/ROW]
[ROW][C]48[/C][C]56.9[/C][C]94.2988988694428[/C][C]-0.0398403959701915[/C][C]-38.0440392326514[/C][C]0.348537428297218[/C][/ROW]
[ROW][C]49[/C][C]86.7[/C][C]92.9879798645008[/C][C]-0.0619774904272617[/C][C]-4.45084026349198[/C][C]-0.99191234376584[/C][/ROW]
[ROW][C]50[/C][C]108.5[/C][C]93.1703345405175[/C][C]-0.0578339251680406[/C][C]14.9758184033050[/C][C]0.190986099273943[/C][/ROW]
[ROW][C]51[/C][C]103.4[/C][C]92.2021495834002[/C][C]-0.0731233370108304[/C][C]12.5160834006845[/C][C]-0.711500130759697[/C][/ROW]
[ROW][C]52[/C][C]86.2[/C][C]90.5149500256944[/C][C]-0.100267479626643[/C][C]-1.98051497421404[/C][C]-1.26028062105924[/C][/ROW]
[ROW][C]53[/C][C]71[/C][C]90.0067661009445[/C][C]-0.107158792933552[/C][C]-18.4174689131130[/C][C]-0.318242879042410[/C][/ROW]
[ROW][C]54[/C][C]75.9[/C][C]88.5346700753104[/C][C]-0.130259213455715[/C][C]-10.6634088344547[/C][C]-1.06482595409878[/C][/ROW]
[ROW][C]55[/C][C]87.1[/C][C]87.2752388661739[/C][C]-0.149285846332507[/C][C]1.45682568825914[/C][C]-0.881688871590838[/C][/ROW]
[ROW][C]56[/C][C]102[/C][C]88.9819829185483[/C][C]-0.118379731313824[/C][C]10.3305413985350[/C][C]1.45174519427050[/C][/ROW]
[ROW][C]57[/C][C]88.5[/C][C]87.9447125816579[/C][C]-0.133391162943477[/C][C]1.88919545860889[/C][C]-0.720401031225008[/C][/ROW]
[ROW][C]58[/C][C]87.8[/C][C]87.0453990008443[/C][C]-0.145592609449186[/C][C]1.86974513715388[/C][C]-0.602056475077186[/C][/ROW]
[ROW][C]59[/C][C]100.8[/C][C]86.802390550314[/C][C]-0.147099983250943[/C][C]14.139875682295[/C][C]-0.0767794008640793[/C][/ROW]
[ROW][C]60[/C][C]50.6[/C][C]86.5560765552507[/C][C]-0.148589947577902[/C][C]-35.8107748484401[/C][C]-0.078388589618882[/C][/ROW]
[ROW][C]61[/C][C]85.9[/C][C]86.6456027697475[/C][C]-0.145108503481557[/C][C]-1.09511693794696[/C][C]0.188500270170006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63962&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63962&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
192.992.9000
2107.792.6896588715088-0.25540822458787111.48620580950461.5666548067674
3103.595.95711302130810.338734071021193.933284815428451.79029325294979
491.196.83495220218840.423485059763639-6.053275170983920.181397495851078
579.893.2838337171227-0.122637281959-11.0959300621323-1.39024174152871
671.987.7384773189906-0.771104665389701-12.0145288848744-2.17351412427144
782.984.8266931604193-0.994915865053252-0.160115655525621-0.977188508428675
890.184.4284428943173-0.9399338159306765.11280299669930.302467009589666
9100.786.5977434347225-0.68490144539461310.89391132360011.70913723134016
1090.787.7990789197332-0.5457216215514450.8052315803078281.10332943982679
11108.891.1591633714338-0.2837589875181313.04837559893122.39676117711606
1244.185.6522090833307-0.605278927103812-35.1350798057273-3.32701502292999
1393.684.6350834063906-0.6159604430691269.5819190415182-0.32363542878073
14107.485.5008160701826-0.57064264019913619.80851677975051.10129764248051
1596.586.1859164339805-0.5193514285341648.742455271834330.842663491769534
1693.687.3065763966798-0.4425065554489334.386104109221421.03887150098209
1776.586.8948407551352-0.44099857588712-10.42987067551200.0192374399233076
1876.786.678979958388-0.430026620876359-10.23801293101810.142547550202579
198486.3939921811031-0.423195811427058-2.565102180082550.0940317144533676
20103.388.1654160327014-0.32499088496140512.46861315391891.46077392759870
2188.587.6582452075743-0.3326502455449441.06949323662941-0.124355187337928
229988.8199850810234-0.2742254150549778.261767506704941.04369166108818
23105.988.9565083929617-0.25952214084887316.40296793229930.293011771483107
2444.787.9509488890128-0.283202932912098-42.2434226261589-0.544123915405248
259487.4804606057772-0.2880773213905266.7812964543588-0.140928604769788
26107.187.4854477285908-0.28077650812043819.20461306974030.220557366360404
27104.888.6386829232844-0.24294830059182114.19611189750161.06015371103563
28102.590.187212978114-0.1923548103149219.909237879949861.30167389162281
2977.790.5945439357755-0.174649392821282-13.68940632796460.431823045761138
3085.291.4780356024694-0.142886443893557-7.677404547510080.761526374812002
3191.392.3839021929191-0.111625447586399-2.477727208706550.758796171771787
32106.592.9663899001254-0.091459316388035312.60264522168700.506528146446527
3392.493.1821204289992-0.082890093477477-1.198736151612190.226414573086229
3497.592.8154175798178-0.090393121926985.07404912766853-0.211347910648492
3510792.3636135731259-0.099337100974398615.1382138548937-0.271884604166801
3651.192.4024274984616-0.096170759646488-41.49645413133140.104956503193787
3798.692.5912284062815-0.09012671000454835.604594553426210.218340692796258
38102.291.9542646572515-0.10130549707185911.0234045807122-0.419869473713256
39114.392.9807456784587-0.07818241205469119.72113508143060.863179902415187
4099.493.1534977447879-0.07292857211809365.892935413722070.191159228555848
4172.592.6504471578614-0.082119141796351-19.5470088167793-0.326595268933698
4292.393.468368769283-0.0626884545257687-2.429288315409830.682930848028511
4399.494.8012246862116-0.03264269173220242.640824566432651.06073296136916
4485.992.8497014467416-0.0733147859915818-4.24769499770760-1.46360157733441
45109.494.0773542276447-0.046479176409976713.48153570346550.996807334186805
4697.694.3046864070464-0.04103538271619182.905610209657970.210850861143600
47104.793.8986426555385-0.047980749639253211.3238851407079-0.282503396091259
4856.994.2988988694428-0.0398403959701915-38.04403923265140.348537428297218
4986.792.9879798645008-0.0619774904272617-4.45084026349198-0.99191234376584
50108.593.1703345405175-0.057833925168040614.97581840330500.190986099273943
51103.492.2021495834002-0.073123337010830412.5160834006845-0.711500130759697
5286.290.5149500256944-0.100267479626643-1.98051497421404-1.26028062105924
537190.0067661009445-0.107158792933552-18.4174689131130-0.318242879042410
5475.988.5346700753104-0.130259213455715-10.6634088344547-1.06482595409878
5587.187.2752388661739-0.1492858463325071.45682568825914-0.881688871590838
5610288.9819829185483-0.11837973131382410.33054139853501.45174519427050
5788.587.9447125816579-0.1333911629434771.88919545860889-0.720401031225008
5887.887.0453990008443-0.1455926094491861.86974513715388-0.602056475077186
59100.886.802390550314-0.14709998325094314.139875682295-0.0767794008640793
6050.686.5560765552507-0.148589947577902-35.8107748484401-0.078388589618882
6185.986.6456027697475-0.145108503481557-1.095116937946960.188500270170006



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')