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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 computationThu, 03 Dec 2009 17:58:30 -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/t1259888367xq8abb03ib7sv4p.htm/, Retrieved Sat, 27 Apr 2024 20:52:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63161, Retrieved Sat, 27 Apr 2024 20:52:11 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [WS9] [2009-12-04 00:58:30] [557d56ec4b06cd0135c259898de8ce95] [Current]
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Dataseries X:
10284,5
12792
12823,61538
13845,66667
15335,63636
11188,5
13633,25
12298,46667
15353,63636
12696,15385
12213,93333
13683,72727
11214,14286
13950,23077
11179,13333
11801,875
11188,82353
16456,27273
11110,0625
16530,69231
10038,41176
11681,25
11148,88235
8631
9386,444444
9764,736842
12043,75
12948,06667
10987,125
11648,3125
10633,35294
10219,3
9037,6
10296,31579
11705,41176
10681,94444
9362,947368
11306,35294
10984,45
10062,61905
8118,583333
8867,48
8346,72
8529,307692
10697,18182
8591,84
8695,607143
8125,571429
7009,758621
7883,466667
7527,645161
6763,758621
6682,333333
7855,681818
6738,88
7895,434783
6361,884615
6935,956522
8344,454545
9107,944444




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=63161&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=63161&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63161&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
110284.510284.5000
21279211178.0006250137111.819530865139110.9736513652651.46556684007203
312823.6153811805.0209409104160.528437059664158.4458206328170.835148915654928
413845.6666712584.1131602931208.035118296676203.3246537388971.01898289532986
515335.6363613622.7695948084262.895643531683252.8786501795561.39540317460509
611188.512849.4169400771201.275522155952200.291851305065-1.76894259145977
713633.2513183.9928433147208.670161409690206.2015700045230.23011101837892
812298.4666712941.1903130704184.690066641970188.351835510623-0.784802390529522
915353.6363613832.4029717134221.277946690684213.651528038081.23278699680744
1012696.1538513509.6670727545193.479256126687195.806347026147-0.950668440091467
1112213.9333313119.4155542252163.82110360036178.121940889089-1.02009291472283
1213683.7272713360.3613724045167.735473111296180.2923639481450.134653735166101
1311214.1428613455.7892066749171.974989508829-2075.07799431662-0.177190418272952
1413950.2307713681.0193540784175.302355452837190.8095898749790.0773269028209501
1511179.1333312786.3646281729114.535443923396156.825381758847-1.69803891798577
1611801.87512446.836579744990.3485881705442145.350428825921-0.752090679267918
1711188.8235312005.266675281863.1082011666263134.389581136806-0.899678739742892
1816456.2727313572.3083687124138.462524192561160.3138294810772.56951575518179
1911110.062512737.475906029890.1947053719671145.938372551318-1.67045334700007
2016530.6923114080.4896888586152.136107488979162.1297833171062.15369348706394
2110038.4117612697.954519721776.1628835418008144.468654926932-2.63847458663471
2211681.2512337.881298780354.4875985658019139.935459703266-0.74947452681078
2311148.8823511904.118215192730.109050016494135.30395682661-0.837926575775196
24863110719.2761979301-30.8796445203961124.696470815857-2.08245901937169
259386.44444410708.1562127416-30.4556246854229-1358.830902649570.0384012644183710
269764.73684210285.0890834710-52.8032219148085114.403426594338-0.612334328272692
2712043.7510853.5701730321-18.8598556140306125.1041229088381.01494119789880
2812948.0666711551.293501955819.3090515251865134.3777461092821.19601161434594
2910987.12511313.68915695615.82610626724678131.827344407195-0.433050641794794
3011648.312511389.80186734189.48514991193954132.3757667847640.118992462234861
3110633.3529411079.0630876294-7.12139379590471130.360313367273-0.54306627724927
3210219.310721.5743573603-25.2678412304869128.535219370353-0.594467742916478
339037.610059.1184898258-58.2767509747441125.721162392824-1.08098750957680
3410296.3157910061.3880758473-55.1368779969586125.9527845208510.102677157673894
3511705.4117610567.6425124901-25.9802542193340127.8474884309560.951565876298631
3610681.9444410546.1016479528-25.7493030842043127.86090765780.00752094145496387
379362.94736810596.9244134358-22.5403641131402-1369.64920301450.138772584674055
3811306.3529410800.0992198359-10.1638710327746128.1216737857960.361810250659946
3910984.4510814.0658126330-8.86410050279724128.3949712303150.0398595063337
4010062.6190510491.656156089-25.5745824152579125.865688769123-0.524824269424504
418118.5833339579.1648766682-72.552063106758120.713545988986-1.49297462073966
428867.489234.52757090855-86.9138386962373119.557669602085-0.459042860257614
438346.728818.13448310904-104.273497245977118.514299141072-0.556291183632599
448529.3076928605.36816529494-109.985043195555118.252437963843-0.183198760812948
4510697.181829241.42609354124-70.7195220980523119.6589771734261.25957049786890
468591.848920.52456036795-83.8883289655605119.280931151903-0.422294456120336
478695.6071438743.3812810445-88.7983904712855119.165051341506-0.157371666958283
488125.5714298422.325585081-101.031880266892118.921878488315-0.391855629864747
497009.7586218320.21049948783-101.084565444588-1308.56374884483-0.00191117509007912
507883.4666678051.80064396142-110.088258464870116.90133903832-0.271848964717028
517527.6451617749.45571907578-120.351615085586115.272858768103-0.31937803883799
526763.7586217276.41844905973-139.094533635412113.222702850316-0.591423222978227
536682.3333336933.48060326773-149.899682273902112.409536046902-0.34311822312988
547855.6818187127.59917174161-131.690821488625113.3528896405260.579842258504568
556738.886863.14856403698-138.712084072729113.101906529589-0.223841077911853
567895.4347837103.69228851225-118.663030556368113.5982824364440.63941754856377
576361.8846156720.8495201476-132.625841091782113.357555753568-0.445327217279059
586935.9565226672.28241552737-128.183347540341113.4112808584940.141671873380362
598344.4545457149.22235035013-96.2022689670068113.6851785285271.01970608868122
609107.9444447749.45195552104-59.3933602652303113.9111715106881.17341899904332

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 10284.5 & 10284.5 & 0 & 0 & 0 \tabularnewline
2 & 12792 & 11178.0006250137 & 111.819530865139 & 110.973651365265 & 1.46556684007203 \tabularnewline
3 & 12823.61538 & 11805.0209409104 & 160.528437059664 & 158.445820632817 & 0.835148915654928 \tabularnewline
4 & 13845.66667 & 12584.1131602931 & 208.035118296676 & 203.324653738897 & 1.01898289532986 \tabularnewline
5 & 15335.63636 & 13622.7695948084 & 262.895643531683 & 252.878650179556 & 1.39540317460509 \tabularnewline
6 & 11188.5 & 12849.4169400771 & 201.275522155952 & 200.291851305065 & -1.76894259145977 \tabularnewline
7 & 13633.25 & 13183.9928433147 & 208.670161409690 & 206.201570004523 & 0.23011101837892 \tabularnewline
8 & 12298.46667 & 12941.1903130704 & 184.690066641970 & 188.351835510623 & -0.784802390529522 \tabularnewline
9 & 15353.63636 & 13832.4029717134 & 221.277946690684 & 213.65152803808 & 1.23278699680744 \tabularnewline
10 & 12696.15385 & 13509.6670727545 & 193.479256126687 & 195.806347026147 & -0.950668440091467 \tabularnewline
11 & 12213.93333 & 13119.4155542252 & 163.82110360036 & 178.121940889089 & -1.02009291472283 \tabularnewline
12 & 13683.72727 & 13360.3613724045 & 167.735473111296 & 180.292363948145 & 0.134653735166101 \tabularnewline
13 & 11214.14286 & 13455.7892066749 & 171.974989508829 & -2075.07799431662 & -0.177190418272952 \tabularnewline
14 & 13950.23077 & 13681.0193540784 & 175.302355452837 & 190.809589874979 & 0.0773269028209501 \tabularnewline
15 & 11179.13333 & 12786.3646281729 & 114.535443923396 & 156.825381758847 & -1.69803891798577 \tabularnewline
16 & 11801.875 & 12446.8365797449 & 90.3485881705442 & 145.350428825921 & -0.752090679267918 \tabularnewline
17 & 11188.82353 & 12005.2666752818 & 63.1082011666263 & 134.389581136806 & -0.899678739742892 \tabularnewline
18 & 16456.27273 & 13572.3083687124 & 138.462524192561 & 160.313829481077 & 2.56951575518179 \tabularnewline
19 & 11110.0625 & 12737.4759060298 & 90.1947053719671 & 145.938372551318 & -1.67045334700007 \tabularnewline
20 & 16530.69231 & 14080.4896888586 & 152.136107488979 & 162.129783317106 & 2.15369348706394 \tabularnewline
21 & 10038.41176 & 12697.9545197217 & 76.1628835418008 & 144.468654926932 & -2.63847458663471 \tabularnewline
22 & 11681.25 & 12337.8812987803 & 54.4875985658019 & 139.935459703266 & -0.74947452681078 \tabularnewline
23 & 11148.88235 & 11904.1182151927 & 30.109050016494 & 135.30395682661 & -0.837926575775196 \tabularnewline
24 & 8631 & 10719.2761979301 & -30.8796445203961 & 124.696470815857 & -2.08245901937169 \tabularnewline
25 & 9386.444444 & 10708.1562127416 & -30.4556246854229 & -1358.83090264957 & 0.0384012644183710 \tabularnewline
26 & 9764.736842 & 10285.0890834710 & -52.8032219148085 & 114.403426594338 & -0.612334328272692 \tabularnewline
27 & 12043.75 & 10853.5701730321 & -18.8598556140306 & 125.104122908838 & 1.01494119789880 \tabularnewline
28 & 12948.06667 & 11551.2935019558 & 19.3090515251865 & 134.377746109282 & 1.19601161434594 \tabularnewline
29 & 10987.125 & 11313.6891569561 & 5.82610626724678 & 131.827344407195 & -0.433050641794794 \tabularnewline
30 & 11648.3125 & 11389.8018673418 & 9.48514991193954 & 132.375766784764 & 0.118992462234861 \tabularnewline
31 & 10633.35294 & 11079.0630876294 & -7.12139379590471 & 130.360313367273 & -0.54306627724927 \tabularnewline
32 & 10219.3 & 10721.5743573603 & -25.2678412304869 & 128.535219370353 & -0.594467742916478 \tabularnewline
33 & 9037.6 & 10059.1184898258 & -58.2767509747441 & 125.721162392824 & -1.08098750957680 \tabularnewline
34 & 10296.31579 & 10061.3880758473 & -55.1368779969586 & 125.952784520851 & 0.102677157673894 \tabularnewline
35 & 11705.41176 & 10567.6425124901 & -25.9802542193340 & 127.847488430956 & 0.951565876298631 \tabularnewline
36 & 10681.94444 & 10546.1016479528 & -25.7493030842043 & 127.8609076578 & 0.00752094145496387 \tabularnewline
37 & 9362.947368 & 10596.9244134358 & -22.5403641131402 & -1369.6492030145 & 0.138772584674055 \tabularnewline
38 & 11306.35294 & 10800.0992198359 & -10.1638710327746 & 128.121673785796 & 0.361810250659946 \tabularnewline
39 & 10984.45 & 10814.0658126330 & -8.86410050279724 & 128.394971230315 & 0.0398595063337 \tabularnewline
40 & 10062.61905 & 10491.656156089 & -25.5745824152579 & 125.865688769123 & -0.524824269424504 \tabularnewline
41 & 8118.583333 & 9579.1648766682 & -72.552063106758 & 120.713545988986 & -1.49297462073966 \tabularnewline
42 & 8867.48 & 9234.52757090855 & -86.9138386962373 & 119.557669602085 & -0.459042860257614 \tabularnewline
43 & 8346.72 & 8818.13448310904 & -104.273497245977 & 118.514299141072 & -0.556291183632599 \tabularnewline
44 & 8529.307692 & 8605.36816529494 & -109.985043195555 & 118.252437963843 & -0.183198760812948 \tabularnewline
45 & 10697.18182 & 9241.42609354124 & -70.7195220980523 & 119.658977173426 & 1.25957049786890 \tabularnewline
46 & 8591.84 & 8920.52456036795 & -83.8883289655605 & 119.280931151903 & -0.422294456120336 \tabularnewline
47 & 8695.607143 & 8743.3812810445 & -88.7983904712855 & 119.165051341506 & -0.157371666958283 \tabularnewline
48 & 8125.571429 & 8422.325585081 & -101.031880266892 & 118.921878488315 & -0.391855629864747 \tabularnewline
49 & 7009.758621 & 8320.21049948783 & -101.084565444588 & -1308.56374884483 & -0.00191117509007912 \tabularnewline
50 & 7883.466667 & 8051.80064396142 & -110.088258464870 & 116.90133903832 & -0.271848964717028 \tabularnewline
51 & 7527.645161 & 7749.45571907578 & -120.351615085586 & 115.272858768103 & -0.31937803883799 \tabularnewline
52 & 6763.758621 & 7276.41844905973 & -139.094533635412 & 113.222702850316 & -0.591423222978227 \tabularnewline
53 & 6682.333333 & 6933.48060326773 & -149.899682273902 & 112.409536046902 & -0.34311822312988 \tabularnewline
54 & 7855.681818 & 7127.59917174161 & -131.690821488625 & 113.352889640526 & 0.579842258504568 \tabularnewline
55 & 6738.88 & 6863.14856403698 & -138.712084072729 & 113.101906529589 & -0.223841077911853 \tabularnewline
56 & 7895.434783 & 7103.69228851225 & -118.663030556368 & 113.598282436444 & 0.63941754856377 \tabularnewline
57 & 6361.884615 & 6720.8495201476 & -132.625841091782 & 113.357555753568 & -0.445327217279059 \tabularnewline
58 & 6935.956522 & 6672.28241552737 & -128.183347540341 & 113.411280858494 & 0.141671873380362 \tabularnewline
59 & 8344.454545 & 7149.22235035013 & -96.2022689670068 & 113.685178528527 & 1.01970608868122 \tabularnewline
60 & 9107.944444 & 7749.45195552104 & -59.3933602652303 & 113.911171510688 & 1.17341899904332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63161&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]10284.5[/C][C]10284.5[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]12792[/C][C]11178.0006250137[/C][C]111.819530865139[/C][C]110.973651365265[/C][C]1.46556684007203[/C][/ROW]
[ROW][C]3[/C][C]12823.61538[/C][C]11805.0209409104[/C][C]160.528437059664[/C][C]158.445820632817[/C][C]0.835148915654928[/C][/ROW]
[ROW][C]4[/C][C]13845.66667[/C][C]12584.1131602931[/C][C]208.035118296676[/C][C]203.324653738897[/C][C]1.01898289532986[/C][/ROW]
[ROW][C]5[/C][C]15335.63636[/C][C]13622.7695948084[/C][C]262.895643531683[/C][C]252.878650179556[/C][C]1.39540317460509[/C][/ROW]
[ROW][C]6[/C][C]11188.5[/C][C]12849.4169400771[/C][C]201.275522155952[/C][C]200.291851305065[/C][C]-1.76894259145977[/C][/ROW]
[ROW][C]7[/C][C]13633.25[/C][C]13183.9928433147[/C][C]208.670161409690[/C][C]206.201570004523[/C][C]0.23011101837892[/C][/ROW]
[ROW][C]8[/C][C]12298.46667[/C][C]12941.1903130704[/C][C]184.690066641970[/C][C]188.351835510623[/C][C]-0.784802390529522[/C][/ROW]
[ROW][C]9[/C][C]15353.63636[/C][C]13832.4029717134[/C][C]221.277946690684[/C][C]213.65152803808[/C][C]1.23278699680744[/C][/ROW]
[ROW][C]10[/C][C]12696.15385[/C][C]13509.6670727545[/C][C]193.479256126687[/C][C]195.806347026147[/C][C]-0.950668440091467[/C][/ROW]
[ROW][C]11[/C][C]12213.93333[/C][C]13119.4155542252[/C][C]163.82110360036[/C][C]178.121940889089[/C][C]-1.02009291472283[/C][/ROW]
[ROW][C]12[/C][C]13683.72727[/C][C]13360.3613724045[/C][C]167.735473111296[/C][C]180.292363948145[/C][C]0.134653735166101[/C][/ROW]
[ROW][C]13[/C][C]11214.14286[/C][C]13455.7892066749[/C][C]171.974989508829[/C][C]-2075.07799431662[/C][C]-0.177190418272952[/C][/ROW]
[ROW][C]14[/C][C]13950.23077[/C][C]13681.0193540784[/C][C]175.302355452837[/C][C]190.809589874979[/C][C]0.0773269028209501[/C][/ROW]
[ROW][C]15[/C][C]11179.13333[/C][C]12786.3646281729[/C][C]114.535443923396[/C][C]156.825381758847[/C][C]-1.69803891798577[/C][/ROW]
[ROW][C]16[/C][C]11801.875[/C][C]12446.8365797449[/C][C]90.3485881705442[/C][C]145.350428825921[/C][C]-0.752090679267918[/C][/ROW]
[ROW][C]17[/C][C]11188.82353[/C][C]12005.2666752818[/C][C]63.1082011666263[/C][C]134.389581136806[/C][C]-0.899678739742892[/C][/ROW]
[ROW][C]18[/C][C]16456.27273[/C][C]13572.3083687124[/C][C]138.462524192561[/C][C]160.313829481077[/C][C]2.56951575518179[/C][/ROW]
[ROW][C]19[/C][C]11110.0625[/C][C]12737.4759060298[/C][C]90.1947053719671[/C][C]145.938372551318[/C][C]-1.67045334700007[/C][/ROW]
[ROW][C]20[/C][C]16530.69231[/C][C]14080.4896888586[/C][C]152.136107488979[/C][C]162.129783317106[/C][C]2.15369348706394[/C][/ROW]
[ROW][C]21[/C][C]10038.41176[/C][C]12697.9545197217[/C][C]76.1628835418008[/C][C]144.468654926932[/C][C]-2.63847458663471[/C][/ROW]
[ROW][C]22[/C][C]11681.25[/C][C]12337.8812987803[/C][C]54.4875985658019[/C][C]139.935459703266[/C][C]-0.74947452681078[/C][/ROW]
[ROW][C]23[/C][C]11148.88235[/C][C]11904.1182151927[/C][C]30.109050016494[/C][C]135.30395682661[/C][C]-0.837926575775196[/C][/ROW]
[ROW][C]24[/C][C]8631[/C][C]10719.2761979301[/C][C]-30.8796445203961[/C][C]124.696470815857[/C][C]-2.08245901937169[/C][/ROW]
[ROW][C]25[/C][C]9386.444444[/C][C]10708.1562127416[/C][C]-30.4556246854229[/C][C]-1358.83090264957[/C][C]0.0384012644183710[/C][/ROW]
[ROW][C]26[/C][C]9764.736842[/C][C]10285.0890834710[/C][C]-52.8032219148085[/C][C]114.403426594338[/C][C]-0.612334328272692[/C][/ROW]
[ROW][C]27[/C][C]12043.75[/C][C]10853.5701730321[/C][C]-18.8598556140306[/C][C]125.104122908838[/C][C]1.01494119789880[/C][/ROW]
[ROW][C]28[/C][C]12948.06667[/C][C]11551.2935019558[/C][C]19.3090515251865[/C][C]134.377746109282[/C][C]1.19601161434594[/C][/ROW]
[ROW][C]29[/C][C]10987.125[/C][C]11313.6891569561[/C][C]5.82610626724678[/C][C]131.827344407195[/C][C]-0.433050641794794[/C][/ROW]
[ROW][C]30[/C][C]11648.3125[/C][C]11389.8018673418[/C][C]9.48514991193954[/C][C]132.375766784764[/C][C]0.118992462234861[/C][/ROW]
[ROW][C]31[/C][C]10633.35294[/C][C]11079.0630876294[/C][C]-7.12139379590471[/C][C]130.360313367273[/C][C]-0.54306627724927[/C][/ROW]
[ROW][C]32[/C][C]10219.3[/C][C]10721.5743573603[/C][C]-25.2678412304869[/C][C]128.535219370353[/C][C]-0.594467742916478[/C][/ROW]
[ROW][C]33[/C][C]9037.6[/C][C]10059.1184898258[/C][C]-58.2767509747441[/C][C]125.721162392824[/C][C]-1.08098750957680[/C][/ROW]
[ROW][C]34[/C][C]10296.31579[/C][C]10061.3880758473[/C][C]-55.1368779969586[/C][C]125.952784520851[/C][C]0.102677157673894[/C][/ROW]
[ROW][C]35[/C][C]11705.41176[/C][C]10567.6425124901[/C][C]-25.9802542193340[/C][C]127.847488430956[/C][C]0.951565876298631[/C][/ROW]
[ROW][C]36[/C][C]10681.94444[/C][C]10546.1016479528[/C][C]-25.7493030842043[/C][C]127.8609076578[/C][C]0.00752094145496387[/C][/ROW]
[ROW][C]37[/C][C]9362.947368[/C][C]10596.9244134358[/C][C]-22.5403641131402[/C][C]-1369.6492030145[/C][C]0.138772584674055[/C][/ROW]
[ROW][C]38[/C][C]11306.35294[/C][C]10800.0992198359[/C][C]-10.1638710327746[/C][C]128.121673785796[/C][C]0.361810250659946[/C][/ROW]
[ROW][C]39[/C][C]10984.45[/C][C]10814.0658126330[/C][C]-8.86410050279724[/C][C]128.394971230315[/C][C]0.0398595063337[/C][/ROW]
[ROW][C]40[/C][C]10062.61905[/C][C]10491.656156089[/C][C]-25.5745824152579[/C][C]125.865688769123[/C][C]-0.524824269424504[/C][/ROW]
[ROW][C]41[/C][C]8118.583333[/C][C]9579.1648766682[/C][C]-72.552063106758[/C][C]120.713545988986[/C][C]-1.49297462073966[/C][/ROW]
[ROW][C]42[/C][C]8867.48[/C][C]9234.52757090855[/C][C]-86.9138386962373[/C][C]119.557669602085[/C][C]-0.459042860257614[/C][/ROW]
[ROW][C]43[/C][C]8346.72[/C][C]8818.13448310904[/C][C]-104.273497245977[/C][C]118.514299141072[/C][C]-0.556291183632599[/C][/ROW]
[ROW][C]44[/C][C]8529.307692[/C][C]8605.36816529494[/C][C]-109.985043195555[/C][C]118.252437963843[/C][C]-0.183198760812948[/C][/ROW]
[ROW][C]45[/C][C]10697.18182[/C][C]9241.42609354124[/C][C]-70.7195220980523[/C][C]119.658977173426[/C][C]1.25957049786890[/C][/ROW]
[ROW][C]46[/C][C]8591.84[/C][C]8920.52456036795[/C][C]-83.8883289655605[/C][C]119.280931151903[/C][C]-0.422294456120336[/C][/ROW]
[ROW][C]47[/C][C]8695.607143[/C][C]8743.3812810445[/C][C]-88.7983904712855[/C][C]119.165051341506[/C][C]-0.157371666958283[/C][/ROW]
[ROW][C]48[/C][C]8125.571429[/C][C]8422.325585081[/C][C]-101.031880266892[/C][C]118.921878488315[/C][C]-0.391855629864747[/C][/ROW]
[ROW][C]49[/C][C]7009.758621[/C][C]8320.21049948783[/C][C]-101.084565444588[/C][C]-1308.56374884483[/C][C]-0.00191117509007912[/C][/ROW]
[ROW][C]50[/C][C]7883.466667[/C][C]8051.80064396142[/C][C]-110.088258464870[/C][C]116.90133903832[/C][C]-0.271848964717028[/C][/ROW]
[ROW][C]51[/C][C]7527.645161[/C][C]7749.45571907578[/C][C]-120.351615085586[/C][C]115.272858768103[/C][C]-0.31937803883799[/C][/ROW]
[ROW][C]52[/C][C]6763.758621[/C][C]7276.41844905973[/C][C]-139.094533635412[/C][C]113.222702850316[/C][C]-0.591423222978227[/C][/ROW]
[ROW][C]53[/C][C]6682.333333[/C][C]6933.48060326773[/C][C]-149.899682273902[/C][C]112.409536046902[/C][C]-0.34311822312988[/C][/ROW]
[ROW][C]54[/C][C]7855.681818[/C][C]7127.59917174161[/C][C]-131.690821488625[/C][C]113.352889640526[/C][C]0.579842258504568[/C][/ROW]
[ROW][C]55[/C][C]6738.88[/C][C]6863.14856403698[/C][C]-138.712084072729[/C][C]113.101906529589[/C][C]-0.223841077911853[/C][/ROW]
[ROW][C]56[/C][C]7895.434783[/C][C]7103.69228851225[/C][C]-118.663030556368[/C][C]113.598282436444[/C][C]0.63941754856377[/C][/ROW]
[ROW][C]57[/C][C]6361.884615[/C][C]6720.8495201476[/C][C]-132.625841091782[/C][C]113.357555753568[/C][C]-0.445327217279059[/C][/ROW]
[ROW][C]58[/C][C]6935.956522[/C][C]6672.28241552737[/C][C]-128.183347540341[/C][C]113.411280858494[/C][C]0.141671873380362[/C][/ROW]
[ROW][C]59[/C][C]8344.454545[/C][C]7149.22235035013[/C][C]-96.2022689670068[/C][C]113.685178528527[/C][C]1.01970608868122[/C][/ROW]
[ROW][C]60[/C][C]9107.944444[/C][C]7749.45195552104[/C][C]-59.3933602652303[/C][C]113.911171510688[/C][C]1.17341899904332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63161&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63161&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
110284.510284.5000
21279211178.0006250137111.819530865139110.9736513652651.46556684007203
312823.6153811805.0209409104160.528437059664158.4458206328170.835148915654928
413845.6666712584.1131602931208.035118296676203.3246537388971.01898289532986
515335.6363613622.7695948084262.895643531683252.8786501795561.39540317460509
611188.512849.4169400771201.275522155952200.291851305065-1.76894259145977
713633.2513183.9928433147208.670161409690206.2015700045230.23011101837892
812298.4666712941.1903130704184.690066641970188.351835510623-0.784802390529522
915353.6363613832.4029717134221.277946690684213.651528038081.23278699680744
1012696.1538513509.6670727545193.479256126687195.806347026147-0.950668440091467
1112213.9333313119.4155542252163.82110360036178.121940889089-1.02009291472283
1213683.7272713360.3613724045167.735473111296180.2923639481450.134653735166101
1311214.1428613455.7892066749171.974989508829-2075.07799431662-0.177190418272952
1413950.2307713681.0193540784175.302355452837190.8095898749790.0773269028209501
1511179.1333312786.3646281729114.535443923396156.825381758847-1.69803891798577
1611801.87512446.836579744990.3485881705442145.350428825921-0.752090679267918
1711188.8235312005.266675281863.1082011666263134.389581136806-0.899678739742892
1816456.2727313572.3083687124138.462524192561160.3138294810772.56951575518179
1911110.062512737.475906029890.1947053719671145.938372551318-1.67045334700007
2016530.6923114080.4896888586152.136107488979162.1297833171062.15369348706394
2110038.4117612697.954519721776.1628835418008144.468654926932-2.63847458663471
2211681.2512337.881298780354.4875985658019139.935459703266-0.74947452681078
2311148.8823511904.118215192730.109050016494135.30395682661-0.837926575775196
24863110719.2761979301-30.8796445203961124.696470815857-2.08245901937169
259386.44444410708.1562127416-30.4556246854229-1358.830902649570.0384012644183710
269764.73684210285.0890834710-52.8032219148085114.403426594338-0.612334328272692
2712043.7510853.5701730321-18.8598556140306125.1041229088381.01494119789880
2812948.0666711551.293501955819.3090515251865134.3777461092821.19601161434594
2910987.12511313.68915695615.82610626724678131.827344407195-0.433050641794794
3011648.312511389.80186734189.48514991193954132.3757667847640.118992462234861
3110633.3529411079.0630876294-7.12139379590471130.360313367273-0.54306627724927
3210219.310721.5743573603-25.2678412304869128.535219370353-0.594467742916478
339037.610059.1184898258-58.2767509747441125.721162392824-1.08098750957680
3410296.3157910061.3880758473-55.1368779969586125.9527845208510.102677157673894
3511705.4117610567.6425124901-25.9802542193340127.8474884309560.951565876298631
3610681.9444410546.1016479528-25.7493030842043127.86090765780.00752094145496387
379362.94736810596.9244134358-22.5403641131402-1369.64920301450.138772584674055
3811306.3529410800.0992198359-10.1638710327746128.1216737857960.361810250659946
3910984.4510814.0658126330-8.86410050279724128.3949712303150.0398595063337
4010062.6190510491.656156089-25.5745824152579125.865688769123-0.524824269424504
418118.5833339579.1648766682-72.552063106758120.713545988986-1.49297462073966
428867.489234.52757090855-86.9138386962373119.557669602085-0.459042860257614
438346.728818.13448310904-104.273497245977118.514299141072-0.556291183632599
448529.3076928605.36816529494-109.985043195555118.252437963843-0.183198760812948
4510697.181829241.42609354124-70.7195220980523119.6589771734261.25957049786890
468591.848920.52456036795-83.8883289655605119.280931151903-0.422294456120336
478695.6071438743.3812810445-88.7983904712855119.165051341506-0.157371666958283
488125.5714298422.325585081-101.031880266892118.921878488315-0.391855629864747
497009.7586218320.21049948783-101.084565444588-1308.56374884483-0.00191117509007912
507883.4666678051.80064396142-110.088258464870116.90133903832-0.271848964717028
517527.6451617749.45571907578-120.351615085586115.272858768103-0.31937803883799
526763.7586217276.41844905973-139.094533635412113.222702850316-0.591423222978227
536682.3333336933.48060326773-149.899682273902112.409536046902-0.34311822312988
547855.6818187127.59917174161-131.690821488625113.3528896405260.579842258504568
556738.886863.14856403698-138.712084072729113.101906529589-0.223841077911853
567895.4347837103.69228851225-118.663030556368113.5982824364440.63941754856377
576361.8846156720.8495201476-132.625841091782113.357555753568-0.445327217279059
586935.9565226672.28241552737-128.183347540341113.4112808584940.141671873380362
598344.4545457149.22235035013-96.2022689670068113.6851785285271.01970608868122
609107.9444447749.45195552104-59.3933602652303113.9111715106881.17341899904332



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
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')