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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 12:03:49 -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/t1259694259ybdss1uo14wfyj6.htm/, Retrieved Wed, 24 Apr 2024 20:29:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62191, Retrieved Wed, 24 Apr 2024 20:29:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140
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]
- R  D      [Structural Time Series Models] [] [2009-12-01 19:03:49] [508aab72d879399b4187e5fcd8f7c773] [Current]
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Dataseries X:
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8
8.2
8.1
8.1
8
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.4
6.1
6.5
7.7
7.9
7.5
6.9
6.6
6.9




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=62191&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=62191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62191&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
18.98.9000
28.88.80116511062432-0.0989311795123792-0.00116511062431548-0.282751620481151
38.38.3111650143321-0.477189480934895-0.0111650143321000-1.09234441260947
47.57.50640511938206-0.791631310115204-0.00640511938205747-0.892873240433318
57.27.18503514599877-0.3399646789600450.01496485400122711.28373061268300
67.47.388638292049520.1821805917986530.01136170795047951.48396631035935
78.88.771029592258231.335066580762450.02897040774177153.27656850899902
89.39.330217289642430.589784159016862-0.0302172896424309-2.11813549261455
99.39.311612596811230.00538604414095833-0.0116125968112328-1.6608930432081
108.78.71268521128293-0.575097457634592-0.0126852112829362-1.64976748750803
118.28.1944642782976-0.5204637522347790.005535721702392260.155272131963560
128.38.283878861341050.06536517031815950.01612113865894561.66495948069117
138.58.49947320465330.2096335424485090.0005267953467059180.410171625509456
148.68.603027242874490.10769334662923-0.00302724287448834-0.290885332876739
158.58.49939254551501-0.09063084823382430.000607454484992285-0.56915629633319
168.28.22114002512543-0.266503472689155-0.0211400251254290-0.499275050458061
178.18.06859506650381-0.1597294994553140.03140493349618820.303494460607542
187.97.9504542607466-0.120752516451051-0.05045426074659750.110774490408360
198.68.523016949399010.5290226621806140.07698305060098971.84670003104847
208.78.741716343712090.238192770378631-0.0417163437120898-0.826555246652197
218.78.69897726508716-0.02509181563243620.00102273491284020-0.748269937356021
228.58.50556690768398-0.182837382859991-0.00556690768397682-0.448321976087315
238.48.4088918022782-0.102086748566112-0.00889180227819240.229498022688034
248.58.484404808371690.06435450887900360.01559519162831080.47303647532219
258.78.69477211954640.2011588351894450.00522788045360090.388981265434598
268.78.707179413194440.0241738054931103-0.00717941319444131-0.503921089884762
278.68.59185891650277-0.1048633390658380.00814108349722883-0.368581482019345
288.58.52267464327635-0.0718135929079912-0.02267464327634770.0938686093731788
298.38.256898445496-0.2512827736983540.0431015545040007-0.510083273041614
3088.09768740595025-0.166063730839384-0.09768740595025240.242199854442588
318.28.10462034736826-0.00593775042036590.09537965263173950.455087165429725
328.18.130362002156470.0233841422091786-0.03036200215646760.0833345244824027
338.18.09014705982349-0.03548156382356870.0098529401765114-0.167299726482142
3488.00655886836528-0.0800081143337638-0.00655886836527993-0.126547020089523
357.97.9148843387258-0.090806451314017-0.0148843387257907-0.0306895626239415
367.97.8960375512163-0.02420533651832390.003962448783704230.189284883186909
3787.98135922802080.07714585924536210.01864077197920000.288205654434986
3888.000609647753320.0235476075564089-0.000609647753322567-0.152418334971885
397.97.90799502759682-0.0831752697023524-0.00799502759681478-0.304111733842731
4087.994797067208540.07326715575668580.005202932791458920.444540005722395
417.77.67793272093754-0.285370643212750.0220672790624626-1.01918476015459
427.27.31235165181671-0.359125510900463-0.112351651816708-0.209622129067310
437.57.385142156819450.03805528935802570.1148578431805491.12880724109671
447.37.3338869911354-0.0440714939602683-0.0338869911353984-0.233409192515345
4577.00332994115018-0.307510243260277-0.00332994115017805-0.748708086482017
4676.98591465417594-0.0407501063096030.01408534582405690.758147806048401
4777.012935557067170.0215699729508071-0.01293555706717170.177117933216027
487.27.199209106145850.1730108444706070.000790893854154660.430409039622866
497.37.284696874956220.09254188314712680.0153031250437844-0.228827733082672
507.17.1033193555692-0.159316911146325-0.00331935556920586-0.715843643918503
516.86.84155945681376-0.253084869175999-0.0415594568137613-0.266886673704591
526.46.3722661816598-0.4512924294415360.0277338183402029-0.563372996812719
536.16.04811626816333-0.334852336584520.05188373183667390.330873375589449
546.56.605799686063850.482676445456254-0.1057996860638492.32356290755763
557.77.564060665440860.918336944209580.1359393345591411.23816733332057
567.97.934299324249640.416252565729041-0.0342993242496419-1.42695310587975
577.57.56597644032848-0.302447461947194-0.0659764403284789-2.04258685254906
586.96.90093570241678-0.63459888729584-0.000935702416784052-0.943994372545665
596.66.60725402942677-0.322304655662228-0.007254029426769140.88756062858964
606.96.869279171806450.2128980580940260.03072082819355221.52112584256506

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8.9 & 8.9 & 0 & 0 & 0 \tabularnewline
2 & 8.8 & 8.80116511062432 & -0.0989311795123792 & -0.00116511062431548 & -0.282751620481151 \tabularnewline
3 & 8.3 & 8.3111650143321 & -0.477189480934895 & -0.0111650143321000 & -1.09234441260947 \tabularnewline
4 & 7.5 & 7.50640511938206 & -0.791631310115204 & -0.00640511938205747 & -0.892873240433318 \tabularnewline
5 & 7.2 & 7.18503514599877 & -0.339964678960045 & 0.0149648540012271 & 1.28373061268300 \tabularnewline
6 & 7.4 & 7.38863829204952 & 0.182180591798653 & 0.0113617079504795 & 1.48396631035935 \tabularnewline
7 & 8.8 & 8.77102959225823 & 1.33506658076245 & 0.0289704077417715 & 3.27656850899902 \tabularnewline
8 & 9.3 & 9.33021728964243 & 0.589784159016862 & -0.0302172896424309 & -2.11813549261455 \tabularnewline
9 & 9.3 & 9.31161259681123 & 0.00538604414095833 & -0.0116125968112328 & -1.6608930432081 \tabularnewline
10 & 8.7 & 8.71268521128293 & -0.575097457634592 & -0.0126852112829362 & -1.64976748750803 \tabularnewline
11 & 8.2 & 8.1944642782976 & -0.520463752234779 & 0.00553572170239226 & 0.155272131963560 \tabularnewline
12 & 8.3 & 8.28387886134105 & 0.0653651703181595 & 0.0161211386589456 & 1.66495948069117 \tabularnewline
13 & 8.5 & 8.4994732046533 & 0.209633542448509 & 0.000526795346705918 & 0.410171625509456 \tabularnewline
14 & 8.6 & 8.60302724287449 & 0.10769334662923 & -0.00302724287448834 & -0.290885332876739 \tabularnewline
15 & 8.5 & 8.49939254551501 & -0.0906308482338243 & 0.000607454484992285 & -0.56915629633319 \tabularnewline
16 & 8.2 & 8.22114002512543 & -0.266503472689155 & -0.0211400251254290 & -0.499275050458061 \tabularnewline
17 & 8.1 & 8.06859506650381 & -0.159729499455314 & 0.0314049334961882 & 0.303494460607542 \tabularnewline
18 & 7.9 & 7.9504542607466 & -0.120752516451051 & -0.0504542607465975 & 0.110774490408360 \tabularnewline
19 & 8.6 & 8.52301694939901 & 0.529022662180614 & 0.0769830506009897 & 1.84670003104847 \tabularnewline
20 & 8.7 & 8.74171634371209 & 0.238192770378631 & -0.0417163437120898 & -0.826555246652197 \tabularnewline
21 & 8.7 & 8.69897726508716 & -0.0250918156324362 & 0.00102273491284020 & -0.748269937356021 \tabularnewline
22 & 8.5 & 8.50556690768398 & -0.182837382859991 & -0.00556690768397682 & -0.448321976087315 \tabularnewline
23 & 8.4 & 8.4088918022782 & -0.102086748566112 & -0.0088918022781924 & 0.229498022688034 \tabularnewline
24 & 8.5 & 8.48440480837169 & 0.0643545088790036 & 0.0155951916283108 & 0.47303647532219 \tabularnewline
25 & 8.7 & 8.6947721195464 & 0.201158835189445 & 0.0052278804536009 & 0.388981265434598 \tabularnewline
26 & 8.7 & 8.70717941319444 & 0.0241738054931103 & -0.00717941319444131 & -0.503921089884762 \tabularnewline
27 & 8.6 & 8.59185891650277 & -0.104863339065838 & 0.00814108349722883 & -0.368581482019345 \tabularnewline
28 & 8.5 & 8.52267464327635 & -0.0718135929079912 & -0.0226746432763477 & 0.0938686093731788 \tabularnewline
29 & 8.3 & 8.256898445496 & -0.251282773698354 & 0.0431015545040007 & -0.510083273041614 \tabularnewline
30 & 8 & 8.09768740595025 & -0.166063730839384 & -0.0976874059502524 & 0.242199854442588 \tabularnewline
31 & 8.2 & 8.10462034736826 & -0.0059377504203659 & 0.0953796526317395 & 0.455087165429725 \tabularnewline
32 & 8.1 & 8.13036200215647 & 0.0233841422091786 & -0.0303620021564676 & 0.0833345244824027 \tabularnewline
33 & 8.1 & 8.09014705982349 & -0.0354815638235687 & 0.0098529401765114 & -0.167299726482142 \tabularnewline
34 & 8 & 8.00655886836528 & -0.0800081143337638 & -0.00655886836527993 & -0.126547020089523 \tabularnewline
35 & 7.9 & 7.9148843387258 & -0.090806451314017 & -0.0148843387257907 & -0.0306895626239415 \tabularnewline
36 & 7.9 & 7.8960375512163 & -0.0242053365183239 & 0.00396244878370423 & 0.189284883186909 \tabularnewline
37 & 8 & 7.9813592280208 & 0.0771458592453621 & 0.0186407719792000 & 0.288205654434986 \tabularnewline
38 & 8 & 8.00060964775332 & 0.0235476075564089 & -0.000609647753322567 & -0.152418334971885 \tabularnewline
39 & 7.9 & 7.90799502759682 & -0.0831752697023524 & -0.00799502759681478 & -0.304111733842731 \tabularnewline
40 & 8 & 7.99479706720854 & 0.0732671557566858 & 0.00520293279145892 & 0.444540005722395 \tabularnewline
41 & 7.7 & 7.67793272093754 & -0.28537064321275 & 0.0220672790624626 & -1.01918476015459 \tabularnewline
42 & 7.2 & 7.31235165181671 & -0.359125510900463 & -0.112351651816708 & -0.209622129067310 \tabularnewline
43 & 7.5 & 7.38514215681945 & 0.0380552893580257 & 0.114857843180549 & 1.12880724109671 \tabularnewline
44 & 7.3 & 7.3338869911354 & -0.0440714939602683 & -0.0338869911353984 & -0.233409192515345 \tabularnewline
45 & 7 & 7.00332994115018 & -0.307510243260277 & -0.00332994115017805 & -0.748708086482017 \tabularnewline
46 & 7 & 6.98591465417594 & -0.040750106309603 & 0.0140853458240569 & 0.758147806048401 \tabularnewline
47 & 7 & 7.01293555706717 & 0.0215699729508071 & -0.0129355570671717 & 0.177117933216027 \tabularnewline
48 & 7.2 & 7.19920910614585 & 0.173010844470607 & 0.00079089385415466 & 0.430409039622866 \tabularnewline
49 & 7.3 & 7.28469687495622 & 0.0925418831471268 & 0.0153031250437844 & -0.228827733082672 \tabularnewline
50 & 7.1 & 7.1033193555692 & -0.159316911146325 & -0.00331935556920586 & -0.715843643918503 \tabularnewline
51 & 6.8 & 6.84155945681376 & -0.253084869175999 & -0.0415594568137613 & -0.266886673704591 \tabularnewline
52 & 6.4 & 6.3722661816598 & -0.451292429441536 & 0.0277338183402029 & -0.563372996812719 \tabularnewline
53 & 6.1 & 6.04811626816333 & -0.33485233658452 & 0.0518837318366739 & 0.330873375589449 \tabularnewline
54 & 6.5 & 6.60579968606385 & 0.482676445456254 & -0.105799686063849 & 2.32356290755763 \tabularnewline
55 & 7.7 & 7.56406066544086 & 0.91833694420958 & 0.135939334559141 & 1.23816733332057 \tabularnewline
56 & 7.9 & 7.93429932424964 & 0.416252565729041 & -0.0342993242496419 & -1.42695310587975 \tabularnewline
57 & 7.5 & 7.56597644032848 & -0.302447461947194 & -0.0659764403284789 & -2.04258685254906 \tabularnewline
58 & 6.9 & 6.90093570241678 & -0.63459888729584 & -0.000935702416784052 & -0.943994372545665 \tabularnewline
59 & 6.6 & 6.60725402942677 & -0.322304655662228 & -0.00725402942676914 & 0.88756062858964 \tabularnewline
60 & 6.9 & 6.86927917180645 & 0.212898058094026 & 0.0307208281935522 & 1.52112584256506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62191&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]8.9[/C][C]8.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.8[/C][C]8.80116511062432[/C][C]-0.0989311795123792[/C][C]-0.00116511062431548[/C][C]-0.282751620481151[/C][/ROW]
[ROW][C]3[/C][C]8.3[/C][C]8.3111650143321[/C][C]-0.477189480934895[/C][C]-0.0111650143321000[/C][C]-1.09234441260947[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]7.50640511938206[/C][C]-0.791631310115204[/C][C]-0.00640511938205747[/C][C]-0.892873240433318[/C][/ROW]
[ROW][C]5[/C][C]7.2[/C][C]7.18503514599877[/C][C]-0.339964678960045[/C][C]0.0149648540012271[/C][C]1.28373061268300[/C][/ROW]
[ROW][C]6[/C][C]7.4[/C][C]7.38863829204952[/C][C]0.182180591798653[/C][C]0.0113617079504795[/C][C]1.48396631035935[/C][/ROW]
[ROW][C]7[/C][C]8.8[/C][C]8.77102959225823[/C][C]1.33506658076245[/C][C]0.0289704077417715[/C][C]3.27656850899902[/C][/ROW]
[ROW][C]8[/C][C]9.3[/C][C]9.33021728964243[/C][C]0.589784159016862[/C][C]-0.0302172896424309[/C][C]-2.11813549261455[/C][/ROW]
[ROW][C]9[/C][C]9.3[/C][C]9.31161259681123[/C][C]0.00538604414095833[/C][C]-0.0116125968112328[/C][C]-1.6608930432081[/C][/ROW]
[ROW][C]10[/C][C]8.7[/C][C]8.71268521128293[/C][C]-0.575097457634592[/C][C]-0.0126852112829362[/C][C]-1.64976748750803[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]8.1944642782976[/C][C]-0.520463752234779[/C][C]0.00553572170239226[/C][C]0.155272131963560[/C][/ROW]
[ROW][C]12[/C][C]8.3[/C][C]8.28387886134105[/C][C]0.0653651703181595[/C][C]0.0161211386589456[/C][C]1.66495948069117[/C][/ROW]
[ROW][C]13[/C][C]8.5[/C][C]8.4994732046533[/C][C]0.209633542448509[/C][C]0.000526795346705918[/C][C]0.410171625509456[/C][/ROW]
[ROW][C]14[/C][C]8.6[/C][C]8.60302724287449[/C][C]0.10769334662923[/C][C]-0.00302724287448834[/C][C]-0.290885332876739[/C][/ROW]
[ROW][C]15[/C][C]8.5[/C][C]8.49939254551501[/C][C]-0.0906308482338243[/C][C]0.000607454484992285[/C][C]-0.56915629633319[/C][/ROW]
[ROW][C]16[/C][C]8.2[/C][C]8.22114002512543[/C][C]-0.266503472689155[/C][C]-0.0211400251254290[/C][C]-0.499275050458061[/C][/ROW]
[ROW][C]17[/C][C]8.1[/C][C]8.06859506650381[/C][C]-0.159729499455314[/C][C]0.0314049334961882[/C][C]0.303494460607542[/C][/ROW]
[ROW][C]18[/C][C]7.9[/C][C]7.9504542607466[/C][C]-0.120752516451051[/C][C]-0.0504542607465975[/C][C]0.110774490408360[/C][/ROW]
[ROW][C]19[/C][C]8.6[/C][C]8.52301694939901[/C][C]0.529022662180614[/C][C]0.0769830506009897[/C][C]1.84670003104847[/C][/ROW]
[ROW][C]20[/C][C]8.7[/C][C]8.74171634371209[/C][C]0.238192770378631[/C][C]-0.0417163437120898[/C][C]-0.826555246652197[/C][/ROW]
[ROW][C]21[/C][C]8.7[/C][C]8.69897726508716[/C][C]-0.0250918156324362[/C][C]0.00102273491284020[/C][C]-0.748269937356021[/C][/ROW]
[ROW][C]22[/C][C]8.5[/C][C]8.50556690768398[/C][C]-0.182837382859991[/C][C]-0.00556690768397682[/C][C]-0.448321976087315[/C][/ROW]
[ROW][C]23[/C][C]8.4[/C][C]8.4088918022782[/C][C]-0.102086748566112[/C][C]-0.0088918022781924[/C][C]0.229498022688034[/C][/ROW]
[ROW][C]24[/C][C]8.5[/C][C]8.48440480837169[/C][C]0.0643545088790036[/C][C]0.0155951916283108[/C][C]0.47303647532219[/C][/ROW]
[ROW][C]25[/C][C]8.7[/C][C]8.6947721195464[/C][C]0.201158835189445[/C][C]0.0052278804536009[/C][C]0.388981265434598[/C][/ROW]
[ROW][C]26[/C][C]8.7[/C][C]8.70717941319444[/C][C]0.0241738054931103[/C][C]-0.00717941319444131[/C][C]-0.503921089884762[/C][/ROW]
[ROW][C]27[/C][C]8.6[/C][C]8.59185891650277[/C][C]-0.104863339065838[/C][C]0.00814108349722883[/C][C]-0.368581482019345[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.52267464327635[/C][C]-0.0718135929079912[/C][C]-0.0226746432763477[/C][C]0.0938686093731788[/C][/ROW]
[ROW][C]29[/C][C]8.3[/C][C]8.256898445496[/C][C]-0.251282773698354[/C][C]0.0431015545040007[/C][C]-0.510083273041614[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]8.09768740595025[/C][C]-0.166063730839384[/C][C]-0.0976874059502524[/C][C]0.242199854442588[/C][/ROW]
[ROW][C]31[/C][C]8.2[/C][C]8.10462034736826[/C][C]-0.0059377504203659[/C][C]0.0953796526317395[/C][C]0.455087165429725[/C][/ROW]
[ROW][C]32[/C][C]8.1[/C][C]8.13036200215647[/C][C]0.0233841422091786[/C][C]-0.0303620021564676[/C][C]0.0833345244824027[/C][/ROW]
[ROW][C]33[/C][C]8.1[/C][C]8.09014705982349[/C][C]-0.0354815638235687[/C][C]0.0098529401765114[/C][C]-0.167299726482142[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.00655886836528[/C][C]-0.0800081143337638[/C][C]-0.00655886836527993[/C][C]-0.126547020089523[/C][/ROW]
[ROW][C]35[/C][C]7.9[/C][C]7.9148843387258[/C][C]-0.090806451314017[/C][C]-0.0148843387257907[/C][C]-0.0306895626239415[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]7.8960375512163[/C][C]-0.0242053365183239[/C][C]0.00396244878370423[/C][C]0.189284883186909[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]7.9813592280208[/C][C]0.0771458592453621[/C][C]0.0186407719792000[/C][C]0.288205654434986[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]8.00060964775332[/C][C]0.0235476075564089[/C][C]-0.000609647753322567[/C][C]-0.152418334971885[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.90799502759682[/C][C]-0.0831752697023524[/C][C]-0.00799502759681478[/C][C]-0.304111733842731[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]7.99479706720854[/C][C]0.0732671557566858[/C][C]0.00520293279145892[/C][C]0.444540005722395[/C][/ROW]
[ROW][C]41[/C][C]7.7[/C][C]7.67793272093754[/C][C]-0.28537064321275[/C][C]0.0220672790624626[/C][C]-1.01918476015459[/C][/ROW]
[ROW][C]42[/C][C]7.2[/C][C]7.31235165181671[/C][C]-0.359125510900463[/C][C]-0.112351651816708[/C][C]-0.209622129067310[/C][/ROW]
[ROW][C]43[/C][C]7.5[/C][C]7.38514215681945[/C][C]0.0380552893580257[/C][C]0.114857843180549[/C][C]1.12880724109671[/C][/ROW]
[ROW][C]44[/C][C]7.3[/C][C]7.3338869911354[/C][C]-0.0440714939602683[/C][C]-0.0338869911353984[/C][C]-0.233409192515345[/C][/ROW]
[ROW][C]45[/C][C]7[/C][C]7.00332994115018[/C][C]-0.307510243260277[/C][C]-0.00332994115017805[/C][C]-0.748708086482017[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.98591465417594[/C][C]-0.040750106309603[/C][C]0.0140853458240569[/C][C]0.758147806048401[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]7.01293555706717[/C][C]0.0215699729508071[/C][C]-0.0129355570671717[/C][C]0.177117933216027[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.19920910614585[/C][C]0.173010844470607[/C][C]0.00079089385415466[/C][C]0.430409039622866[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.28469687495622[/C][C]0.0925418831471268[/C][C]0.0153031250437844[/C][C]-0.228827733082672[/C][/ROW]
[ROW][C]50[/C][C]7.1[/C][C]7.1033193555692[/C][C]-0.159316911146325[/C][C]-0.00331935556920586[/C][C]-0.715843643918503[/C][/ROW]
[ROW][C]51[/C][C]6.8[/C][C]6.84155945681376[/C][C]-0.253084869175999[/C][C]-0.0415594568137613[/C][C]-0.266886673704591[/C][/ROW]
[ROW][C]52[/C][C]6.4[/C][C]6.3722661816598[/C][C]-0.451292429441536[/C][C]0.0277338183402029[/C][C]-0.563372996812719[/C][/ROW]
[ROW][C]53[/C][C]6.1[/C][C]6.04811626816333[/C][C]-0.33485233658452[/C][C]0.0518837318366739[/C][C]0.330873375589449[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]6.60579968606385[/C][C]0.482676445456254[/C][C]-0.105799686063849[/C][C]2.32356290755763[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.56406066544086[/C][C]0.91833694420958[/C][C]0.135939334559141[/C][C]1.23816733332057[/C][/ROW]
[ROW][C]56[/C][C]7.9[/C][C]7.93429932424964[/C][C]0.416252565729041[/C][C]-0.0342993242496419[/C][C]-1.42695310587975[/C][/ROW]
[ROW][C]57[/C][C]7.5[/C][C]7.56597644032848[/C][C]-0.302447461947194[/C][C]-0.0659764403284789[/C][C]-2.04258685254906[/C][/ROW]
[ROW][C]58[/C][C]6.9[/C][C]6.90093570241678[/C][C]-0.63459888729584[/C][C]-0.000935702416784052[/C][C]-0.943994372545665[/C][/ROW]
[ROW][C]59[/C][C]6.6[/C][C]6.60725402942677[/C][C]-0.322304655662228[/C][C]-0.00725402942676914[/C][C]0.88756062858964[/C][/ROW]
[ROW][C]60[/C][C]6.9[/C][C]6.86927917180645[/C][C]0.212898058094026[/C][C]0.0307208281935522[/C][C]1.52112584256506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62191&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
18.98.9000
28.88.80116511062432-0.0989311795123792-0.00116511062431548-0.282751620481151
38.38.3111650143321-0.477189480934895-0.0111650143321000-1.09234441260947
47.57.50640511938206-0.791631310115204-0.00640511938205747-0.892873240433318
57.27.18503514599877-0.3399646789600450.01496485400122711.28373061268300
67.47.388638292049520.1821805917986530.01136170795047951.48396631035935
78.88.771029592258231.335066580762450.02897040774177153.27656850899902
89.39.330217289642430.589784159016862-0.0302172896424309-2.11813549261455
99.39.311612596811230.00538604414095833-0.0116125968112328-1.6608930432081
108.78.71268521128293-0.575097457634592-0.0126852112829362-1.64976748750803
118.28.1944642782976-0.5204637522347790.005535721702392260.155272131963560
128.38.283878861341050.06536517031815950.01612113865894561.66495948069117
138.58.49947320465330.2096335424485090.0005267953467059180.410171625509456
148.68.603027242874490.10769334662923-0.00302724287448834-0.290885332876739
158.58.49939254551501-0.09063084823382430.000607454484992285-0.56915629633319
168.28.22114002512543-0.266503472689155-0.0211400251254290-0.499275050458061
178.18.06859506650381-0.1597294994553140.03140493349618820.303494460607542
187.97.9504542607466-0.120752516451051-0.05045426074659750.110774490408360
198.68.523016949399010.5290226621806140.07698305060098971.84670003104847
208.78.741716343712090.238192770378631-0.0417163437120898-0.826555246652197
218.78.69897726508716-0.02509181563243620.00102273491284020-0.748269937356021
228.58.50556690768398-0.182837382859991-0.00556690768397682-0.448321976087315
238.48.4088918022782-0.102086748566112-0.00889180227819240.229498022688034
248.58.484404808371690.06435450887900360.01559519162831080.47303647532219
258.78.69477211954640.2011588351894450.00522788045360090.388981265434598
268.78.707179413194440.0241738054931103-0.00717941319444131-0.503921089884762
278.68.59185891650277-0.1048633390658380.00814108349722883-0.368581482019345
288.58.52267464327635-0.0718135929079912-0.02267464327634770.0938686093731788
298.38.256898445496-0.2512827736983540.0431015545040007-0.510083273041614
3088.09768740595025-0.166063730839384-0.09768740595025240.242199854442588
318.28.10462034736826-0.00593775042036590.09537965263173950.455087165429725
328.18.130362002156470.0233841422091786-0.03036200215646760.0833345244824027
338.18.09014705982349-0.03548156382356870.0098529401765114-0.167299726482142
3488.00655886836528-0.0800081143337638-0.00655886836527993-0.126547020089523
357.97.9148843387258-0.090806451314017-0.0148843387257907-0.0306895626239415
367.97.8960375512163-0.02420533651832390.003962448783704230.189284883186909
3787.98135922802080.07714585924536210.01864077197920000.288205654434986
3888.000609647753320.0235476075564089-0.000609647753322567-0.152418334971885
397.97.90799502759682-0.0831752697023524-0.00799502759681478-0.304111733842731
4087.994797067208540.07326715575668580.005202932791458920.444540005722395
417.77.67793272093754-0.285370643212750.0220672790624626-1.01918476015459
427.27.31235165181671-0.359125510900463-0.112351651816708-0.209622129067310
437.57.385142156819450.03805528935802570.1148578431805491.12880724109671
447.37.3338869911354-0.0440714939602683-0.0338869911353984-0.233409192515345
4577.00332994115018-0.307510243260277-0.00332994115017805-0.748708086482017
4676.98591465417594-0.0407501063096030.01408534582405690.758147806048401
4777.012935557067170.0215699729508071-0.01293555706717170.177117933216027
487.27.199209106145850.1730108444706070.000790893854154660.430409039622866
497.37.284696874956220.09254188314712680.0153031250437844-0.228827733082672
507.17.1033193555692-0.159316911146325-0.00331935556920586-0.715843643918503
516.86.84155945681376-0.253084869175999-0.0415594568137613-0.266886673704591
526.46.3722661816598-0.4512924294415360.0277338183402029-0.563372996812719
536.16.04811626816333-0.334852336584520.05188373183667390.330873375589449
546.56.605799686063850.482676445456254-0.1057996860638492.32356290755763
557.77.564060665440860.918336944209580.1359393345591411.23816733332057
567.97.934299324249640.416252565729041-0.0342993242496419-1.42695310587975
577.57.56597644032848-0.302447461947194-0.0659764403284789-2.04258685254906
586.96.90093570241678-0.63459888729584-0.000935702416784052-0.943994372545665
596.66.60725402942677-0.322304655662228-0.007254029426769140.88756062858964
606.96.869279171806450.2128980580940260.03072082819355221.52112584256506



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')