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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 10:26:10 -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/t1259947598vemqzbzbl8j3arx.htm/, Retrieved Sun, 28 Apr 2024 00:01:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63942, Retrieved Sun, 28 Apr 2024 00:01:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
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] [] [2009-12-04 17:26:10] [d1856923bab8a0db5ebd860815c7444f] [Current]
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Dataseries X:
3.2
1.9
0
0.6
0.2
0.9
2.4
4.7
9.4
12.5
15.8
18.2
16.8
17.3
19.3
17.9
20.2
18.7
20.1
18.2
18.4
18.2
18.9
19.9
21.3
20
19.5
19.6
20.9
21
19.9
19.6
20.9
21.7
22.9
21.5
21.3
23.5
21.6
24.5
22.2
23.5
20.9
20.7
18.1
17.1
14.8
13.8
15.2
16
17.6
15
15
16.3
19.4
21.3
20.5
21.1
21.6
22.6




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=63942&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=63942&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63942&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
13.23.2000
21.91.99950470232471-0.096405474594391-0.0655584220717967-0.521277101224626
300.190832835847516-0.312752837852856-0.0731287351603493-1.15402486939756
40.60.624003640152058-0.187613814287069-0.07262144370824850.487860232628526
50.20.282341005224292-0.217751252623302-0.0726727838671348-0.0987391074671906
60.90.920061038393389-0.0354531864660894-0.07246273594402420.541297889146355
72.42.381830094896950.299377439675790-0.07218070261613550.939878724593047
84.74.653913316815910.752817337561874-0.07189640890717141.23249450630959
99.49.24322803772791.64857691774188-0.07147519743305152.39024652444412
1012.512.47730470984262.02209155310815-0.07134293899778990.98620025482177
1115.815.79457831079592.32877728922736-0.07126098019501460.80485584531452
1218.218.26299525119632.36193937902011-0.07125428309561550.0867276208980423
1316.817.55560525534131.66223837534188-0.581443129171793-2.2231483777695
1417.317.36643647682291.238673910525120.00980097712801869-0.961823882980048
1519.319.25163780174051.392595164322220.01015329672916940.4014731759223
1617.918.04123491327950.7719126731503670.0123316868001963-1.61384448220388
1720.220.11197307175041.081555733717770.01138951424968710.805372771542662
1818.718.82697110977520.5174300470098820.0127051224197373-1.46777307058046
1920.120.04616389730450.6847057843131050.01240943897138530.435315567603311
2018.218.32865202941390.1120989079828570.0131757604176496-1.49031665780677
2118.418.38981925655270.0999582760752240.0131880602802557-0.0316003855533143
2218.218.20365521835260.03175324535922520.0132403684392757-0.177535075757684
2318.918.85052041327580.1783856861650540.01315524028257840.381687008291549
2419.919.83908421883020.3715230898848060.01307036179115440.502746640559894
2521.321.18040506719480.5988648104395140.0634832051966240.652737531871778
262020.11021012282440.211804411274508-0.0309852413102971-0.930137403631389
2719.519.57533453063150.0338637291419047-0.0312242498911083-0.463518496215072
2819.619.63000818657380.0388272294697034-0.03123618038069370.0129052577683213
2920.920.86143866155130.323204407759945-0.03182530858620570.739681298319622
302121.04029198219430.288789391616408-0.0317706918445207-0.0895448210253374
3119.920.0093120443539-0.0258365401856819-0.0313922553916121-0.818787584294202
3219.619.6509542831304-0.105105869364801-0.0313200680875866-0.206314773930425
3320.920.85427175079130.206807187317848-0.0315350950332030.811869915781978
3421.721.69422427771880.35774310225708-0.03161386226523950.392881325796056
3522.922.88263622481410.555769244644719-0.03169209084410980.515466485840842
3621.521.63789711583850.126534101670960-0.0315637318933684-1.11732212761886
3721.321.0874129409769-0.03311034067384620.252036433976438-0.444018014837077
3823.523.37952359515280.5100004518719050.003133828514279091.33553387882659
3921.621.7252271262780-0.005828390349870480.00262167601830755-1.34344172557241
4024.524.34360766589070.6200172772832390.001502915209251711.62770716651404
4122.222.3517192044945-0.002767207285345850.00246220560321459-1.62022187255569
4223.523.43379447329440.2558718619703790.002157033863571820.67303407615885
4320.921.0529672724647-0.3727139022912740.00271914752275455-1.63595069442787
4420.720.6963338861552-0.3688803978663780.002716552108815030.00997786668329047
4518.118.2213963634988-0.8709520479255740.00297387390616317-1.30685708158697
4617.117.1111365101682-0.9280020021523450.00299600780130432-0.148501055439314
4714.814.8742138196691-1.240045971138260.00308765245231412-0.812263278846355
4813.813.7878445647641-1.203409415647100.003079507411151670.0953673139770658
4915.214.862178320486-0.6647912373969030.20463884536721.47401532683094
501615.9136831765800-0.262205278798605-0.003326302380847821.00262874894880
5117.617.49413446104350.176993521558041-0.002979689239059851.14374343479664
521515.1508974976493-0.424013708540985-0.0021252901261855-1.56338795149384
531514.9868534610410-0.362027982296926-0.002201218231635930.161279917995477
5416.316.20901844504830.0156616529840883-0.002555601386478350.982897284505976
5519.419.22584776905740.731143433389478-0.003064392691241331.8621795108229
5621.321.22816138801151.03418741599033-0.003227545370660560.788782569343357
5720.520.60116768682980.638166061605588-0.00306614471756598-1.03082912577177
5821.121.11065601011380.607489454022188-0.00305668050479512-0.079851821182747
5921.621.60946888754780.581580906788774-0.00305062976711532-0.0674413031176323
6022.622.58009056700740.674329107528364-0.003067026513755850.241430135224306

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 3.2 & 3.2 & 0 & 0 & 0 \tabularnewline
2 & 1.9 & 1.99950470232471 & -0.096405474594391 & -0.0655584220717967 & -0.521277101224626 \tabularnewline
3 & 0 & 0.190832835847516 & -0.312752837852856 & -0.0731287351603493 & -1.15402486939756 \tabularnewline
4 & 0.6 & 0.624003640152058 & -0.187613814287069 & -0.0726214437082485 & 0.487860232628526 \tabularnewline
5 & 0.2 & 0.282341005224292 & -0.217751252623302 & -0.0726727838671348 & -0.0987391074671906 \tabularnewline
6 & 0.9 & 0.920061038393389 & -0.0354531864660894 & -0.0724627359440242 & 0.541297889146355 \tabularnewline
7 & 2.4 & 2.38183009489695 & 0.299377439675790 & -0.0721807026161355 & 0.939878724593047 \tabularnewline
8 & 4.7 & 4.65391331681591 & 0.752817337561874 & -0.0718964089071714 & 1.23249450630959 \tabularnewline
9 & 9.4 & 9.2432280377279 & 1.64857691774188 & -0.0714751974330515 & 2.39024652444412 \tabularnewline
10 & 12.5 & 12.4773047098426 & 2.02209155310815 & -0.0713429389977899 & 0.98620025482177 \tabularnewline
11 & 15.8 & 15.7945783107959 & 2.32877728922736 & -0.0712609801950146 & 0.80485584531452 \tabularnewline
12 & 18.2 & 18.2629952511963 & 2.36193937902011 & -0.0712542830956155 & 0.0867276208980423 \tabularnewline
13 & 16.8 & 17.5556052553413 & 1.66223837534188 & -0.581443129171793 & -2.2231483777695 \tabularnewline
14 & 17.3 & 17.3664364768229 & 1.23867391052512 & 0.00980097712801869 & -0.961823882980048 \tabularnewline
15 & 19.3 & 19.2516378017405 & 1.39259516432222 & 0.0101532967291694 & 0.4014731759223 \tabularnewline
16 & 17.9 & 18.0412349132795 & 0.771912673150367 & 0.0123316868001963 & -1.61384448220388 \tabularnewline
17 & 20.2 & 20.1119730717504 & 1.08155573371777 & 0.0113895142496871 & 0.805372771542662 \tabularnewline
18 & 18.7 & 18.8269711097752 & 0.517430047009882 & 0.0127051224197373 & -1.46777307058046 \tabularnewline
19 & 20.1 & 20.0461638973045 & 0.684705784313105 & 0.0124094389713853 & 0.435315567603311 \tabularnewline
20 & 18.2 & 18.3286520294139 & 0.112098907982857 & 0.0131757604176496 & -1.49031665780677 \tabularnewline
21 & 18.4 & 18.3898192565527 & 0.099958276075224 & 0.0131880602802557 & -0.0316003855533143 \tabularnewline
22 & 18.2 & 18.2036552183526 & 0.0317532453592252 & 0.0132403684392757 & -0.177535075757684 \tabularnewline
23 & 18.9 & 18.8505204132758 & 0.178385686165054 & 0.0131552402825784 & 0.381687008291549 \tabularnewline
24 & 19.9 & 19.8390842188302 & 0.371523089884806 & 0.0130703617911544 & 0.502746640559894 \tabularnewline
25 & 21.3 & 21.1804050671948 & 0.598864810439514 & 0.063483205196624 & 0.652737531871778 \tabularnewline
26 & 20 & 20.1102101228244 & 0.211804411274508 & -0.0309852413102971 & -0.930137403631389 \tabularnewline
27 & 19.5 & 19.5753345306315 & 0.0338637291419047 & -0.0312242498911083 & -0.463518496215072 \tabularnewline
28 & 19.6 & 19.6300081865738 & 0.0388272294697034 & -0.0312361803806937 & 0.0129052577683213 \tabularnewline
29 & 20.9 & 20.8614386615513 & 0.323204407759945 & -0.0318253085862057 & 0.739681298319622 \tabularnewline
30 & 21 & 21.0402919821943 & 0.288789391616408 & -0.0317706918445207 & -0.0895448210253374 \tabularnewline
31 & 19.9 & 20.0093120443539 & -0.0258365401856819 & -0.0313922553916121 & -0.818787584294202 \tabularnewline
32 & 19.6 & 19.6509542831304 & -0.105105869364801 & -0.0313200680875866 & -0.206314773930425 \tabularnewline
33 & 20.9 & 20.8542717507913 & 0.206807187317848 & -0.031535095033203 & 0.811869915781978 \tabularnewline
34 & 21.7 & 21.6942242777188 & 0.35774310225708 & -0.0316138622652395 & 0.392881325796056 \tabularnewline
35 & 22.9 & 22.8826362248141 & 0.555769244644719 & -0.0316920908441098 & 0.515466485840842 \tabularnewline
36 & 21.5 & 21.6378971158385 & 0.126534101670960 & -0.0315637318933684 & -1.11732212761886 \tabularnewline
37 & 21.3 & 21.0874129409769 & -0.0331103406738462 & 0.252036433976438 & -0.444018014837077 \tabularnewline
38 & 23.5 & 23.3795235951528 & 0.510000451871905 & 0.00313382851427909 & 1.33553387882659 \tabularnewline
39 & 21.6 & 21.7252271262780 & -0.00582839034987048 & 0.00262167601830755 & -1.34344172557241 \tabularnewline
40 & 24.5 & 24.3436076658907 & 0.620017277283239 & 0.00150291520925171 & 1.62770716651404 \tabularnewline
41 & 22.2 & 22.3517192044945 & -0.00276720728534585 & 0.00246220560321459 & -1.62022187255569 \tabularnewline
42 & 23.5 & 23.4337944732944 & 0.255871861970379 & 0.00215703386357182 & 0.67303407615885 \tabularnewline
43 & 20.9 & 21.0529672724647 & -0.372713902291274 & 0.00271914752275455 & -1.63595069442787 \tabularnewline
44 & 20.7 & 20.6963338861552 & -0.368880397866378 & 0.00271655210881503 & 0.00997786668329047 \tabularnewline
45 & 18.1 & 18.2213963634988 & -0.870952047925574 & 0.00297387390616317 & -1.30685708158697 \tabularnewline
46 & 17.1 & 17.1111365101682 & -0.928002002152345 & 0.00299600780130432 & -0.148501055439314 \tabularnewline
47 & 14.8 & 14.8742138196691 & -1.24004597113826 & 0.00308765245231412 & -0.812263278846355 \tabularnewline
48 & 13.8 & 13.7878445647641 & -1.20340941564710 & 0.00307950741115167 & 0.0953673139770658 \tabularnewline
49 & 15.2 & 14.862178320486 & -0.664791237396903 & 0.2046388453672 & 1.47401532683094 \tabularnewline
50 & 16 & 15.9136831765800 & -0.262205278798605 & -0.00332630238084782 & 1.00262874894880 \tabularnewline
51 & 17.6 & 17.4941344610435 & 0.176993521558041 & -0.00297968923905985 & 1.14374343479664 \tabularnewline
52 & 15 & 15.1508974976493 & -0.424013708540985 & -0.0021252901261855 & -1.56338795149384 \tabularnewline
53 & 15 & 14.9868534610410 & -0.362027982296926 & -0.00220121823163593 & 0.161279917995477 \tabularnewline
54 & 16.3 & 16.2090184450483 & 0.0156616529840883 & -0.00255560138647835 & 0.982897284505976 \tabularnewline
55 & 19.4 & 19.2258477690574 & 0.731143433389478 & -0.00306439269124133 & 1.8621795108229 \tabularnewline
56 & 21.3 & 21.2281613880115 & 1.03418741599033 & -0.00322754537066056 & 0.788782569343357 \tabularnewline
57 & 20.5 & 20.6011676868298 & 0.638166061605588 & -0.00306614471756598 & -1.03082912577177 \tabularnewline
58 & 21.1 & 21.1106560101138 & 0.607489454022188 & -0.00305668050479512 & -0.079851821182747 \tabularnewline
59 & 21.6 & 21.6094688875478 & 0.581580906788774 & -0.00305062976711532 & -0.0674413031176323 \tabularnewline
60 & 22.6 & 22.5800905670074 & 0.674329107528364 & -0.00306702651375585 & 0.241430135224306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63942&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]3.2[/C][C]3.2[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.9[/C][C]1.99950470232471[/C][C]-0.096405474594391[/C][C]-0.0655584220717967[/C][C]-0.521277101224626[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.190832835847516[/C][C]-0.312752837852856[/C][C]-0.0731287351603493[/C][C]-1.15402486939756[/C][/ROW]
[ROW][C]4[/C][C]0.6[/C][C]0.624003640152058[/C][C]-0.187613814287069[/C][C]-0.0726214437082485[/C][C]0.487860232628526[/C][/ROW]
[ROW][C]5[/C][C]0.2[/C][C]0.282341005224292[/C][C]-0.217751252623302[/C][C]-0.0726727838671348[/C][C]-0.0987391074671906[/C][/ROW]
[ROW][C]6[/C][C]0.9[/C][C]0.920061038393389[/C][C]-0.0354531864660894[/C][C]-0.0724627359440242[/C][C]0.541297889146355[/C][/ROW]
[ROW][C]7[/C][C]2.4[/C][C]2.38183009489695[/C][C]0.299377439675790[/C][C]-0.0721807026161355[/C][C]0.939878724593047[/C][/ROW]
[ROW][C]8[/C][C]4.7[/C][C]4.65391331681591[/C][C]0.752817337561874[/C][C]-0.0718964089071714[/C][C]1.23249450630959[/C][/ROW]
[ROW][C]9[/C][C]9.4[/C][C]9.2432280377279[/C][C]1.64857691774188[/C][C]-0.0714751974330515[/C][C]2.39024652444412[/C][/ROW]
[ROW][C]10[/C][C]12.5[/C][C]12.4773047098426[/C][C]2.02209155310815[/C][C]-0.0713429389977899[/C][C]0.98620025482177[/C][/ROW]
[ROW][C]11[/C][C]15.8[/C][C]15.7945783107959[/C][C]2.32877728922736[/C][C]-0.0712609801950146[/C][C]0.80485584531452[/C][/ROW]
[ROW][C]12[/C][C]18.2[/C][C]18.2629952511963[/C][C]2.36193937902011[/C][C]-0.0712542830956155[/C][C]0.0867276208980423[/C][/ROW]
[ROW][C]13[/C][C]16.8[/C][C]17.5556052553413[/C][C]1.66223837534188[/C][C]-0.581443129171793[/C][C]-2.2231483777695[/C][/ROW]
[ROW][C]14[/C][C]17.3[/C][C]17.3664364768229[/C][C]1.23867391052512[/C][C]0.00980097712801869[/C][C]-0.961823882980048[/C][/ROW]
[ROW][C]15[/C][C]19.3[/C][C]19.2516378017405[/C][C]1.39259516432222[/C][C]0.0101532967291694[/C][C]0.4014731759223[/C][/ROW]
[ROW][C]16[/C][C]17.9[/C][C]18.0412349132795[/C][C]0.771912673150367[/C][C]0.0123316868001963[/C][C]-1.61384448220388[/C][/ROW]
[ROW][C]17[/C][C]20.2[/C][C]20.1119730717504[/C][C]1.08155573371777[/C][C]0.0113895142496871[/C][C]0.805372771542662[/C][/ROW]
[ROW][C]18[/C][C]18.7[/C][C]18.8269711097752[/C][C]0.517430047009882[/C][C]0.0127051224197373[/C][C]-1.46777307058046[/C][/ROW]
[ROW][C]19[/C][C]20.1[/C][C]20.0461638973045[/C][C]0.684705784313105[/C][C]0.0124094389713853[/C][C]0.435315567603311[/C][/ROW]
[ROW][C]20[/C][C]18.2[/C][C]18.3286520294139[/C][C]0.112098907982857[/C][C]0.0131757604176496[/C][C]-1.49031665780677[/C][/ROW]
[ROW][C]21[/C][C]18.4[/C][C]18.3898192565527[/C][C]0.099958276075224[/C][C]0.0131880602802557[/C][C]-0.0316003855533143[/C][/ROW]
[ROW][C]22[/C][C]18.2[/C][C]18.2036552183526[/C][C]0.0317532453592252[/C][C]0.0132403684392757[/C][C]-0.177535075757684[/C][/ROW]
[ROW][C]23[/C][C]18.9[/C][C]18.8505204132758[/C][C]0.178385686165054[/C][C]0.0131552402825784[/C][C]0.381687008291549[/C][/ROW]
[ROW][C]24[/C][C]19.9[/C][C]19.8390842188302[/C][C]0.371523089884806[/C][C]0.0130703617911544[/C][C]0.502746640559894[/C][/ROW]
[ROW][C]25[/C][C]21.3[/C][C]21.1804050671948[/C][C]0.598864810439514[/C][C]0.063483205196624[/C][C]0.652737531871778[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]20.1102101228244[/C][C]0.211804411274508[/C][C]-0.0309852413102971[/C][C]-0.930137403631389[/C][/ROW]
[ROW][C]27[/C][C]19.5[/C][C]19.5753345306315[/C][C]0.0338637291419047[/C][C]-0.0312242498911083[/C][C]-0.463518496215072[/C][/ROW]
[ROW][C]28[/C][C]19.6[/C][C]19.6300081865738[/C][C]0.0388272294697034[/C][C]-0.0312361803806937[/C][C]0.0129052577683213[/C][/ROW]
[ROW][C]29[/C][C]20.9[/C][C]20.8614386615513[/C][C]0.323204407759945[/C][C]-0.0318253085862057[/C][C]0.739681298319622[/C][/ROW]
[ROW][C]30[/C][C]21[/C][C]21.0402919821943[/C][C]0.288789391616408[/C][C]-0.0317706918445207[/C][C]-0.0895448210253374[/C][/ROW]
[ROW][C]31[/C][C]19.9[/C][C]20.0093120443539[/C][C]-0.0258365401856819[/C][C]-0.0313922553916121[/C][C]-0.818787584294202[/C][/ROW]
[ROW][C]32[/C][C]19.6[/C][C]19.6509542831304[/C][C]-0.105105869364801[/C][C]-0.0313200680875866[/C][C]-0.206314773930425[/C][/ROW]
[ROW][C]33[/C][C]20.9[/C][C]20.8542717507913[/C][C]0.206807187317848[/C][C]-0.031535095033203[/C][C]0.811869915781978[/C][/ROW]
[ROW][C]34[/C][C]21.7[/C][C]21.6942242777188[/C][C]0.35774310225708[/C][C]-0.0316138622652395[/C][C]0.392881325796056[/C][/ROW]
[ROW][C]35[/C][C]22.9[/C][C]22.8826362248141[/C][C]0.555769244644719[/C][C]-0.0316920908441098[/C][C]0.515466485840842[/C][/ROW]
[ROW][C]36[/C][C]21.5[/C][C]21.6378971158385[/C][C]0.126534101670960[/C][C]-0.0315637318933684[/C][C]-1.11732212761886[/C][/ROW]
[ROW][C]37[/C][C]21.3[/C][C]21.0874129409769[/C][C]-0.0331103406738462[/C][C]0.252036433976438[/C][C]-0.444018014837077[/C][/ROW]
[ROW][C]38[/C][C]23.5[/C][C]23.3795235951528[/C][C]0.510000451871905[/C][C]0.00313382851427909[/C][C]1.33553387882659[/C][/ROW]
[ROW][C]39[/C][C]21.6[/C][C]21.7252271262780[/C][C]-0.00582839034987048[/C][C]0.00262167601830755[/C][C]-1.34344172557241[/C][/ROW]
[ROW][C]40[/C][C]24.5[/C][C]24.3436076658907[/C][C]0.620017277283239[/C][C]0.00150291520925171[/C][C]1.62770716651404[/C][/ROW]
[ROW][C]41[/C][C]22.2[/C][C]22.3517192044945[/C][C]-0.00276720728534585[/C][C]0.00246220560321459[/C][C]-1.62022187255569[/C][/ROW]
[ROW][C]42[/C][C]23.5[/C][C]23.4337944732944[/C][C]0.255871861970379[/C][C]0.00215703386357182[/C][C]0.67303407615885[/C][/ROW]
[ROW][C]43[/C][C]20.9[/C][C]21.0529672724647[/C][C]-0.372713902291274[/C][C]0.00271914752275455[/C][C]-1.63595069442787[/C][/ROW]
[ROW][C]44[/C][C]20.7[/C][C]20.6963338861552[/C][C]-0.368880397866378[/C][C]0.00271655210881503[/C][C]0.00997786668329047[/C][/ROW]
[ROW][C]45[/C][C]18.1[/C][C]18.2213963634988[/C][C]-0.870952047925574[/C][C]0.00297387390616317[/C][C]-1.30685708158697[/C][/ROW]
[ROW][C]46[/C][C]17.1[/C][C]17.1111365101682[/C][C]-0.928002002152345[/C][C]0.00299600780130432[/C][C]-0.148501055439314[/C][/ROW]
[ROW][C]47[/C][C]14.8[/C][C]14.8742138196691[/C][C]-1.24004597113826[/C][C]0.00308765245231412[/C][C]-0.812263278846355[/C][/ROW]
[ROW][C]48[/C][C]13.8[/C][C]13.7878445647641[/C][C]-1.20340941564710[/C][C]0.00307950741115167[/C][C]0.0953673139770658[/C][/ROW]
[ROW][C]49[/C][C]15.2[/C][C]14.862178320486[/C][C]-0.664791237396903[/C][C]0.2046388453672[/C][C]1.47401532683094[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]15.9136831765800[/C][C]-0.262205278798605[/C][C]-0.00332630238084782[/C][C]1.00262874894880[/C][/ROW]
[ROW][C]51[/C][C]17.6[/C][C]17.4941344610435[/C][C]0.176993521558041[/C][C]-0.00297968923905985[/C][C]1.14374343479664[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]15.1508974976493[/C][C]-0.424013708540985[/C][C]-0.0021252901261855[/C][C]-1.56338795149384[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]14.9868534610410[/C][C]-0.362027982296926[/C][C]-0.00220121823163593[/C][C]0.161279917995477[/C][/ROW]
[ROW][C]54[/C][C]16.3[/C][C]16.2090184450483[/C][C]0.0156616529840883[/C][C]-0.00255560138647835[/C][C]0.982897284505976[/C][/ROW]
[ROW][C]55[/C][C]19.4[/C][C]19.2258477690574[/C][C]0.731143433389478[/C][C]-0.00306439269124133[/C][C]1.8621795108229[/C][/ROW]
[ROW][C]56[/C][C]21.3[/C][C]21.2281613880115[/C][C]1.03418741599033[/C][C]-0.00322754537066056[/C][C]0.788782569343357[/C][/ROW]
[ROW][C]57[/C][C]20.5[/C][C]20.6011676868298[/C][C]0.638166061605588[/C][C]-0.00306614471756598[/C][C]-1.03082912577177[/C][/ROW]
[ROW][C]58[/C][C]21.1[/C][C]21.1106560101138[/C][C]0.607489454022188[/C][C]-0.00305668050479512[/C][C]-0.079851821182747[/C][/ROW]
[ROW][C]59[/C][C]21.6[/C][C]21.6094688875478[/C][C]0.581580906788774[/C][C]-0.00305062976711532[/C][C]-0.0674413031176323[/C][/ROW]
[ROW][C]60[/C][C]22.6[/C][C]22.5800905670074[/C][C]0.674329107528364[/C][C]-0.00306702651375585[/C][C]0.241430135224306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63942&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63942&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
13.23.2000
21.91.99950470232471-0.096405474594391-0.0655584220717967-0.521277101224626
300.190832835847516-0.312752837852856-0.0731287351603493-1.15402486939756
40.60.624003640152058-0.187613814287069-0.07262144370824850.487860232628526
50.20.282341005224292-0.217751252623302-0.0726727838671348-0.0987391074671906
60.90.920061038393389-0.0354531864660894-0.07246273594402420.541297889146355
72.42.381830094896950.299377439675790-0.07218070261613550.939878724593047
84.74.653913316815910.752817337561874-0.07189640890717141.23249450630959
99.49.24322803772791.64857691774188-0.07147519743305152.39024652444412
1012.512.47730470984262.02209155310815-0.07134293899778990.98620025482177
1115.815.79457831079592.32877728922736-0.07126098019501460.80485584531452
1218.218.26299525119632.36193937902011-0.07125428309561550.0867276208980423
1316.817.55560525534131.66223837534188-0.581443129171793-2.2231483777695
1417.317.36643647682291.238673910525120.00980097712801869-0.961823882980048
1519.319.25163780174051.392595164322220.01015329672916940.4014731759223
1617.918.04123491327950.7719126731503670.0123316868001963-1.61384448220388
1720.220.11197307175041.081555733717770.01138951424968710.805372771542662
1818.718.82697110977520.5174300470098820.0127051224197373-1.46777307058046
1920.120.04616389730450.6847057843131050.01240943897138530.435315567603311
2018.218.32865202941390.1120989079828570.0131757604176496-1.49031665780677
2118.418.38981925655270.0999582760752240.0131880602802557-0.0316003855533143
2218.218.20365521835260.03175324535922520.0132403684392757-0.177535075757684
2318.918.85052041327580.1783856861650540.01315524028257840.381687008291549
2419.919.83908421883020.3715230898848060.01307036179115440.502746640559894
2521.321.18040506719480.5988648104395140.0634832051966240.652737531871778
262020.11021012282440.211804411274508-0.0309852413102971-0.930137403631389
2719.519.57533453063150.0338637291419047-0.0312242498911083-0.463518496215072
2819.619.63000818657380.0388272294697034-0.03123618038069370.0129052577683213
2920.920.86143866155130.323204407759945-0.03182530858620570.739681298319622
302121.04029198219430.288789391616408-0.0317706918445207-0.0895448210253374
3119.920.0093120443539-0.0258365401856819-0.0313922553916121-0.818787584294202
3219.619.6509542831304-0.105105869364801-0.0313200680875866-0.206314773930425
3320.920.85427175079130.206807187317848-0.0315350950332030.811869915781978
3421.721.69422427771880.35774310225708-0.03161386226523950.392881325796056
3522.922.88263622481410.555769244644719-0.03169209084410980.515466485840842
3621.521.63789711583850.126534101670960-0.0315637318933684-1.11732212761886
3721.321.0874129409769-0.03311034067384620.252036433976438-0.444018014837077
3823.523.37952359515280.5100004518719050.003133828514279091.33553387882659
3921.621.7252271262780-0.005828390349870480.00262167601830755-1.34344172557241
4024.524.34360766589070.6200172772832390.001502915209251711.62770716651404
4122.222.3517192044945-0.002767207285345850.00246220560321459-1.62022187255569
4223.523.43379447329440.2558718619703790.002157033863571820.67303407615885
4320.921.0529672724647-0.3727139022912740.00271914752275455-1.63595069442787
4420.720.6963338861552-0.3688803978663780.002716552108815030.00997786668329047
4518.118.2213963634988-0.8709520479255740.00297387390616317-1.30685708158697
4617.117.1111365101682-0.9280020021523450.00299600780130432-0.148501055439314
4714.814.8742138196691-1.240045971138260.00308765245231412-0.812263278846355
4813.813.7878445647641-1.203409415647100.003079507411151670.0953673139770658
4915.214.862178320486-0.6647912373969030.20463884536721.47401532683094
501615.9136831765800-0.262205278798605-0.003326302380847821.00262874894880
5117.617.49413446104350.176993521558041-0.002979689239059851.14374343479664
521515.1508974976493-0.424013708540985-0.0021252901261855-1.56338795149384
531514.9868534610410-0.362027982296926-0.002201218231635930.161279917995477
5416.316.20901844504830.0156616529840883-0.002555601386478350.982897284505976
5519.419.22584776905740.731143433389478-0.003064392691241331.8621795108229
5621.321.22816138801151.03418741599033-0.003227545370660560.788782569343357
5720.520.60116768682980.638166061605588-0.00306614471756598-1.03082912577177
5821.121.11065601011380.607489454022188-0.00305668050479512-0.079851821182747
5921.621.60946888754780.581580906788774-0.00305062976711532-0.0674413031176323
6022.622.58009056700740.674329107528364-0.003067026513755850.241430135224306



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')