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Author's title

Author*Unverified author*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 10:44:32 -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/t12599487238xt3el1nmk5nueg.htm/, Retrieved Sat, 27 Apr 2024 23:57:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63968, Retrieved Sat, 27 Apr 2024 23:57:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [] [2009-12-04 17:44:32] [1c886d75b2eec2d50a82160bb8104e3b] [Current]
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Dataseries X:
95.5
76.7
79.4
55.2
60
64.8
82.3
210.5
106
80.8
97.3
189.5
90
69.3
87.3
57.4
56.2
61.6
77.7
177.2
97.6
81.6
96.8
191.3
106
75.1
72
63.5
57.4
62.3
79.4
178.1
109.3
85.2
102.7
193.7
108.4
73.4
85.9
58.5
58.6
62.7
77.5
180.5
102.2
82.6
97.8
197.8
93.8
72.4
77.7
58.7
53.1
64.3
76.4
188.4
105.5
79.8
96.1
202.5




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=63968&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=63968&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63968&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
195.595.5000
276.789.96470508052380.140802314220120-13.2647050805238-0.696780031634498
379.481.4020306370853-0.459989953857529-2.00203063708529-0.570450228961114
455.269.276082076661-1.34143029846953-14.0760820766610-0.873003554194243
56062.9977864262546-1.65313671872270-2.99778642625465-0.443327130581926
664.861.5625630548383-1.642996413014553.237436945161660.021338459694077
782.367.517624480029-1.3874891299403814.7823755199710.766881091400265
8210.5114.294957731568-0.13627504741756396.20504226843244.90587187882422
9106128.0060986967860.173220851111600-22.00609869678601.41329663505164
1080.8117.988646174801-0.0413891978181363-37.1886461748011-1.03956183577007
1197.3107.980233377384-0.248780870902242-10.6802333773836-1.01551536307027
12189.5130.1151765055920.21762248766334159.38482349440822.2782280183025
1390123.3004164492560.282788228521408-33.300416449256-0.74533208925881
1469.3105.8092285443900.395814878048897-36.5092285443897-1.89677974926168
1587.391.26584139188310.21473444970559-3.96584139188315-1.47875155178157
1657.479.0071905643165-0.114384805333091-21.6071905643165-1.16712314912949
1756.271.2223562086958-0.361627020563912-15.0223562086957-0.719921421338709
1861.671.6932314966833-0.335202581178285-10.09323149668330.0805299232462565
1977.779.8802695412708-0.0967021341851601-2.180269541270760.847039748289495
20177.284.41332745273580.011732415347087892.78667254726410.46763406185264
2197.694.54906160121630.2064149775280783.050938398783661.03042337267892
2281.6105.3442559624890.370649313007317-23.74425596248941.08077871115699
2396.8112.6072889623150.45252056612842-15.80728896231530.704071993371611
24191.3116.7446132135420.48264856530934774.55538678645760.376919776507163
25106118.9930833868390.491994336235459-12.99308338683880.181072300912466
2675.1112.6996243130940.450329663638033-37.5996243130942-0.69186622464626
277296.13737615556840.271229061623737-24.1373761555684-1.70341721247977
2863.586.59989583945210.115866702122258-23.0998958394521-0.963850479955913
2957.480.92824190307770.00162877943130724-23.5282419030777-0.565211877415148
3062.379.7086746041847-0.0243913020803348-17.4086746041847-0.120100573239084
3179.482.86796534819680.0416008588485094-3.467965348196770.317000656207032
32178.188.51294668793790.14633963287081189.58705331206210.564034070584997
33109.398.99957815107750.31109237086785810.30042184892251.04807739110824
3485.2107.1133227328110.412662587479414-21.91332273281130.79367077968045
35102.7113.3824644912730.473388736254452-10.68246449127260.596690150580938
36193.7115.5643060671710.48774140327432378.1356939328290.174167614077608
37108.4114.0219848607060.472383848650871-5.6219848607058-0.206708842184021
3873.4107.7136338268230.416998348318029-34.3136338268231-0.68729249675441
3985.9103.9849784513670.375531961057078-18.0849784513668-0.416941887966221
4058.595.1702720787910.26198248768711-36.670272078791-0.917223770259407
4158.689.41897631640680.175409249426957-30.8189763164068-0.597998758929304
4262.787.2097138141540.138330827590241-24.5097138141541-0.237561568125511
4377.587.85507750851320.146247785104954-10.35507750851320.0507752682399754
44180.592.61492875021560.21417515676854187.88507124978440.464730078127725
45102.296.28811356774710.2598321573564455.911886432252870.350044524755977
4682.6101.0882310250670.311755016686575-18.48823102506730.460824319594567
4797.8104.387905951040.34107903160625-6.587905951040070.303736477858379
48197.8108.3179355392600.37214823296454989.48206446073960.364966150618770
4993.8104.2381078482910.33571003559806-10.4381078482907-0.452254869983743
5072.4101.4856524842550.309594627450412-29.0856524842555-0.312871264093216
5177.796.3276475215610.258590000148854-18.6276475215609-0.551815443431176
5258.793.28420952470470.223960192853091-34.5842095247047-0.332047063396327
5353.189.65271075678310.179384305212936-36.5527107567831-0.386954425432744
5464.389.49644788769110.175273917081454-25.1964478876911-0.0337021422069455
5576.490.52332026930120.185817809756085-14.12332026930120.085705306590993
56188.495.47925663802660.24292431385676292.92074336197340.481527644582842
57105.599.65493137589760.2867970502221415.845068624102430.398106562392282
5879.8101.1889637754630.299457044086982-21.38896377546340.126514393201487
5996.1102.0125845862490.304275260052465-5.912584586248580.0532325067792305
60202.5103.9210801119160.3178862053832898.5789198880840.162973171473983

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 95.5 & 95.5 & 0 & 0 & 0 \tabularnewline
2 & 76.7 & 89.9647050805238 & 0.140802314220120 & -13.2647050805238 & -0.696780031634498 \tabularnewline
3 & 79.4 & 81.4020306370853 & -0.459989953857529 & -2.00203063708529 & -0.570450228961114 \tabularnewline
4 & 55.2 & 69.276082076661 & -1.34143029846953 & -14.0760820766610 & -0.873003554194243 \tabularnewline
5 & 60 & 62.9977864262546 & -1.65313671872270 & -2.99778642625465 & -0.443327130581926 \tabularnewline
6 & 64.8 & 61.5625630548383 & -1.64299641301455 & 3.23743694516166 & 0.021338459694077 \tabularnewline
7 & 82.3 & 67.517624480029 & -1.38748912994038 & 14.782375519971 & 0.766881091400265 \tabularnewline
8 & 210.5 & 114.294957731568 & -0.136275047417563 & 96.2050422684324 & 4.90587187882422 \tabularnewline
9 & 106 & 128.006098696786 & 0.173220851111600 & -22.0060986967860 & 1.41329663505164 \tabularnewline
10 & 80.8 & 117.988646174801 & -0.0413891978181363 & -37.1886461748011 & -1.03956183577007 \tabularnewline
11 & 97.3 & 107.980233377384 & -0.248780870902242 & -10.6802333773836 & -1.01551536307027 \tabularnewline
12 & 189.5 & 130.115176505592 & 0.217622487663341 & 59.3848234944082 & 2.2782280183025 \tabularnewline
13 & 90 & 123.300416449256 & 0.282788228521408 & -33.300416449256 & -0.74533208925881 \tabularnewline
14 & 69.3 & 105.809228544390 & 0.395814878048897 & -36.5092285443897 & -1.89677974926168 \tabularnewline
15 & 87.3 & 91.2658413918831 & 0.21473444970559 & -3.96584139188315 & -1.47875155178157 \tabularnewline
16 & 57.4 & 79.0071905643165 & -0.114384805333091 & -21.6071905643165 & -1.16712314912949 \tabularnewline
17 & 56.2 & 71.2223562086958 & -0.361627020563912 & -15.0223562086957 & -0.719921421338709 \tabularnewline
18 & 61.6 & 71.6932314966833 & -0.335202581178285 & -10.0932314966833 & 0.0805299232462565 \tabularnewline
19 & 77.7 & 79.8802695412708 & -0.0967021341851601 & -2.18026954127076 & 0.847039748289495 \tabularnewline
20 & 177.2 & 84.4133274527358 & 0.0117324153470878 & 92.7866725472641 & 0.46763406185264 \tabularnewline
21 & 97.6 & 94.5490616012163 & 0.206414977528078 & 3.05093839878366 & 1.03042337267892 \tabularnewline
22 & 81.6 & 105.344255962489 & 0.370649313007317 & -23.7442559624894 & 1.08077871115699 \tabularnewline
23 & 96.8 & 112.607288962315 & 0.45252056612842 & -15.8072889623153 & 0.704071993371611 \tabularnewline
24 & 191.3 & 116.744613213542 & 0.482648565309347 & 74.5553867864576 & 0.376919776507163 \tabularnewline
25 & 106 & 118.993083386839 & 0.491994336235459 & -12.9930833868388 & 0.181072300912466 \tabularnewline
26 & 75.1 & 112.699624313094 & 0.450329663638033 & -37.5996243130942 & -0.69186622464626 \tabularnewline
27 & 72 & 96.1373761555684 & 0.271229061623737 & -24.1373761555684 & -1.70341721247977 \tabularnewline
28 & 63.5 & 86.5998958394521 & 0.115866702122258 & -23.0998958394521 & -0.963850479955913 \tabularnewline
29 & 57.4 & 80.9282419030777 & 0.00162877943130724 & -23.5282419030777 & -0.565211877415148 \tabularnewline
30 & 62.3 & 79.7086746041847 & -0.0243913020803348 & -17.4086746041847 & -0.120100573239084 \tabularnewline
31 & 79.4 & 82.8679653481968 & 0.0416008588485094 & -3.46796534819677 & 0.317000656207032 \tabularnewline
32 & 178.1 & 88.5129466879379 & 0.146339632870811 & 89.5870533120621 & 0.564034070584997 \tabularnewline
33 & 109.3 & 98.9995781510775 & 0.311092370867858 & 10.3004218489225 & 1.04807739110824 \tabularnewline
34 & 85.2 & 107.113322732811 & 0.412662587479414 & -21.9133227328113 & 0.79367077968045 \tabularnewline
35 & 102.7 & 113.382464491273 & 0.473388736254452 & -10.6824644912726 & 0.596690150580938 \tabularnewline
36 & 193.7 & 115.564306067171 & 0.487741403274323 & 78.135693932829 & 0.174167614077608 \tabularnewline
37 & 108.4 & 114.021984860706 & 0.472383848650871 & -5.6219848607058 & -0.206708842184021 \tabularnewline
38 & 73.4 & 107.713633826823 & 0.416998348318029 & -34.3136338268231 & -0.68729249675441 \tabularnewline
39 & 85.9 & 103.984978451367 & 0.375531961057078 & -18.0849784513668 & -0.416941887966221 \tabularnewline
40 & 58.5 & 95.170272078791 & 0.26198248768711 & -36.670272078791 & -0.917223770259407 \tabularnewline
41 & 58.6 & 89.4189763164068 & 0.175409249426957 & -30.8189763164068 & -0.597998758929304 \tabularnewline
42 & 62.7 & 87.209713814154 & 0.138330827590241 & -24.5097138141541 & -0.237561568125511 \tabularnewline
43 & 77.5 & 87.8550775085132 & 0.146247785104954 & -10.3550775085132 & 0.0507752682399754 \tabularnewline
44 & 180.5 & 92.6149287502156 & 0.214175156768541 & 87.8850712497844 & 0.464730078127725 \tabularnewline
45 & 102.2 & 96.2881135677471 & 0.259832157356445 & 5.91188643225287 & 0.350044524755977 \tabularnewline
46 & 82.6 & 101.088231025067 & 0.311755016686575 & -18.4882310250673 & 0.460824319594567 \tabularnewline
47 & 97.8 & 104.38790595104 & 0.34107903160625 & -6.58790595104007 & 0.303736477858379 \tabularnewline
48 & 197.8 & 108.317935539260 & 0.372148232964549 & 89.4820644607396 & 0.364966150618770 \tabularnewline
49 & 93.8 & 104.238107848291 & 0.33571003559806 & -10.4381078482907 & -0.452254869983743 \tabularnewline
50 & 72.4 & 101.485652484255 & 0.309594627450412 & -29.0856524842555 & -0.312871264093216 \tabularnewline
51 & 77.7 & 96.327647521561 & 0.258590000148854 & -18.6276475215609 & -0.551815443431176 \tabularnewline
52 & 58.7 & 93.2842095247047 & 0.223960192853091 & -34.5842095247047 & -0.332047063396327 \tabularnewline
53 & 53.1 & 89.6527107567831 & 0.179384305212936 & -36.5527107567831 & -0.386954425432744 \tabularnewline
54 & 64.3 & 89.4964478876911 & 0.175273917081454 & -25.1964478876911 & -0.0337021422069455 \tabularnewline
55 & 76.4 & 90.5233202693012 & 0.185817809756085 & -14.1233202693012 & 0.085705306590993 \tabularnewline
56 & 188.4 & 95.4792566380266 & 0.242924313856762 & 92.9207433619734 & 0.481527644582842 \tabularnewline
57 & 105.5 & 99.6549313758976 & 0.286797050222141 & 5.84506862410243 & 0.398106562392282 \tabularnewline
58 & 79.8 & 101.188963775463 & 0.299457044086982 & -21.3889637754634 & 0.126514393201487 \tabularnewline
59 & 96.1 & 102.012584586249 & 0.304275260052465 & -5.91258458624858 & 0.0532325067792305 \tabularnewline
60 & 202.5 & 103.921080111916 & 0.31788620538328 & 98.578919888084 & 0.162973171473983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63968&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]95.5[/C][C]95.5[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]76.7[/C][C]89.9647050805238[/C][C]0.140802314220120[/C][C]-13.2647050805238[/C][C]-0.696780031634498[/C][/ROW]
[ROW][C]3[/C][C]79.4[/C][C]81.4020306370853[/C][C]-0.459989953857529[/C][C]-2.00203063708529[/C][C]-0.570450228961114[/C][/ROW]
[ROW][C]4[/C][C]55.2[/C][C]69.276082076661[/C][C]-1.34143029846953[/C][C]-14.0760820766610[/C][C]-0.873003554194243[/C][/ROW]
[ROW][C]5[/C][C]60[/C][C]62.9977864262546[/C][C]-1.65313671872270[/C][C]-2.99778642625465[/C][C]-0.443327130581926[/C][/ROW]
[ROW][C]6[/C][C]64.8[/C][C]61.5625630548383[/C][C]-1.64299641301455[/C][C]3.23743694516166[/C][C]0.021338459694077[/C][/ROW]
[ROW][C]7[/C][C]82.3[/C][C]67.517624480029[/C][C]-1.38748912994038[/C][C]14.782375519971[/C][C]0.766881091400265[/C][/ROW]
[ROW][C]8[/C][C]210.5[/C][C]114.294957731568[/C][C]-0.136275047417563[/C][C]96.2050422684324[/C][C]4.90587187882422[/C][/ROW]
[ROW][C]9[/C][C]106[/C][C]128.006098696786[/C][C]0.173220851111600[/C][C]-22.0060986967860[/C][C]1.41329663505164[/C][/ROW]
[ROW][C]10[/C][C]80.8[/C][C]117.988646174801[/C][C]-0.0413891978181363[/C][C]-37.1886461748011[/C][C]-1.03956183577007[/C][/ROW]
[ROW][C]11[/C][C]97.3[/C][C]107.980233377384[/C][C]-0.248780870902242[/C][C]-10.6802333773836[/C][C]-1.01551536307027[/C][/ROW]
[ROW][C]12[/C][C]189.5[/C][C]130.115176505592[/C][C]0.217622487663341[/C][C]59.3848234944082[/C][C]2.2782280183025[/C][/ROW]
[ROW][C]13[/C][C]90[/C][C]123.300416449256[/C][C]0.282788228521408[/C][C]-33.300416449256[/C][C]-0.74533208925881[/C][/ROW]
[ROW][C]14[/C][C]69.3[/C][C]105.809228544390[/C][C]0.395814878048897[/C][C]-36.5092285443897[/C][C]-1.89677974926168[/C][/ROW]
[ROW][C]15[/C][C]87.3[/C][C]91.2658413918831[/C][C]0.21473444970559[/C][C]-3.96584139188315[/C][C]-1.47875155178157[/C][/ROW]
[ROW][C]16[/C][C]57.4[/C][C]79.0071905643165[/C][C]-0.114384805333091[/C][C]-21.6071905643165[/C][C]-1.16712314912949[/C][/ROW]
[ROW][C]17[/C][C]56.2[/C][C]71.2223562086958[/C][C]-0.361627020563912[/C][C]-15.0223562086957[/C][C]-0.719921421338709[/C][/ROW]
[ROW][C]18[/C][C]61.6[/C][C]71.6932314966833[/C][C]-0.335202581178285[/C][C]-10.0932314966833[/C][C]0.0805299232462565[/C][/ROW]
[ROW][C]19[/C][C]77.7[/C][C]79.8802695412708[/C][C]-0.0967021341851601[/C][C]-2.18026954127076[/C][C]0.847039748289495[/C][/ROW]
[ROW][C]20[/C][C]177.2[/C][C]84.4133274527358[/C][C]0.0117324153470878[/C][C]92.7866725472641[/C][C]0.46763406185264[/C][/ROW]
[ROW][C]21[/C][C]97.6[/C][C]94.5490616012163[/C][C]0.206414977528078[/C][C]3.05093839878366[/C][C]1.03042337267892[/C][/ROW]
[ROW][C]22[/C][C]81.6[/C][C]105.344255962489[/C][C]0.370649313007317[/C][C]-23.7442559624894[/C][C]1.08077871115699[/C][/ROW]
[ROW][C]23[/C][C]96.8[/C][C]112.607288962315[/C][C]0.45252056612842[/C][C]-15.8072889623153[/C][C]0.704071993371611[/C][/ROW]
[ROW][C]24[/C][C]191.3[/C][C]116.744613213542[/C][C]0.482648565309347[/C][C]74.5553867864576[/C][C]0.376919776507163[/C][/ROW]
[ROW][C]25[/C][C]106[/C][C]118.993083386839[/C][C]0.491994336235459[/C][C]-12.9930833868388[/C][C]0.181072300912466[/C][/ROW]
[ROW][C]26[/C][C]75.1[/C][C]112.699624313094[/C][C]0.450329663638033[/C][C]-37.5996243130942[/C][C]-0.69186622464626[/C][/ROW]
[ROW][C]27[/C][C]72[/C][C]96.1373761555684[/C][C]0.271229061623737[/C][C]-24.1373761555684[/C][C]-1.70341721247977[/C][/ROW]
[ROW][C]28[/C][C]63.5[/C][C]86.5998958394521[/C][C]0.115866702122258[/C][C]-23.0998958394521[/C][C]-0.963850479955913[/C][/ROW]
[ROW][C]29[/C][C]57.4[/C][C]80.9282419030777[/C][C]0.00162877943130724[/C][C]-23.5282419030777[/C][C]-0.565211877415148[/C][/ROW]
[ROW][C]30[/C][C]62.3[/C][C]79.7086746041847[/C][C]-0.0243913020803348[/C][C]-17.4086746041847[/C][C]-0.120100573239084[/C][/ROW]
[ROW][C]31[/C][C]79.4[/C][C]82.8679653481968[/C][C]0.0416008588485094[/C][C]-3.46796534819677[/C][C]0.317000656207032[/C][/ROW]
[ROW][C]32[/C][C]178.1[/C][C]88.5129466879379[/C][C]0.146339632870811[/C][C]89.5870533120621[/C][C]0.564034070584997[/C][/ROW]
[ROW][C]33[/C][C]109.3[/C][C]98.9995781510775[/C][C]0.311092370867858[/C][C]10.3004218489225[/C][C]1.04807739110824[/C][/ROW]
[ROW][C]34[/C][C]85.2[/C][C]107.113322732811[/C][C]0.412662587479414[/C][C]-21.9133227328113[/C][C]0.79367077968045[/C][/ROW]
[ROW][C]35[/C][C]102.7[/C][C]113.382464491273[/C][C]0.473388736254452[/C][C]-10.6824644912726[/C][C]0.596690150580938[/C][/ROW]
[ROW][C]36[/C][C]193.7[/C][C]115.564306067171[/C][C]0.487741403274323[/C][C]78.135693932829[/C][C]0.174167614077608[/C][/ROW]
[ROW][C]37[/C][C]108.4[/C][C]114.021984860706[/C][C]0.472383848650871[/C][C]-5.6219848607058[/C][C]-0.206708842184021[/C][/ROW]
[ROW][C]38[/C][C]73.4[/C][C]107.713633826823[/C][C]0.416998348318029[/C][C]-34.3136338268231[/C][C]-0.68729249675441[/C][/ROW]
[ROW][C]39[/C][C]85.9[/C][C]103.984978451367[/C][C]0.375531961057078[/C][C]-18.0849784513668[/C][C]-0.416941887966221[/C][/ROW]
[ROW][C]40[/C][C]58.5[/C][C]95.170272078791[/C][C]0.26198248768711[/C][C]-36.670272078791[/C][C]-0.917223770259407[/C][/ROW]
[ROW][C]41[/C][C]58.6[/C][C]89.4189763164068[/C][C]0.175409249426957[/C][C]-30.8189763164068[/C][C]-0.597998758929304[/C][/ROW]
[ROW][C]42[/C][C]62.7[/C][C]87.209713814154[/C][C]0.138330827590241[/C][C]-24.5097138141541[/C][C]-0.237561568125511[/C][/ROW]
[ROW][C]43[/C][C]77.5[/C][C]87.8550775085132[/C][C]0.146247785104954[/C][C]-10.3550775085132[/C][C]0.0507752682399754[/C][/ROW]
[ROW][C]44[/C][C]180.5[/C][C]92.6149287502156[/C][C]0.214175156768541[/C][C]87.8850712497844[/C][C]0.464730078127725[/C][/ROW]
[ROW][C]45[/C][C]102.2[/C][C]96.2881135677471[/C][C]0.259832157356445[/C][C]5.91188643225287[/C][C]0.350044524755977[/C][/ROW]
[ROW][C]46[/C][C]82.6[/C][C]101.088231025067[/C][C]0.311755016686575[/C][C]-18.4882310250673[/C][C]0.460824319594567[/C][/ROW]
[ROW][C]47[/C][C]97.8[/C][C]104.38790595104[/C][C]0.34107903160625[/C][C]-6.58790595104007[/C][C]0.303736477858379[/C][/ROW]
[ROW][C]48[/C][C]197.8[/C][C]108.317935539260[/C][C]0.372148232964549[/C][C]89.4820644607396[/C][C]0.364966150618770[/C][/ROW]
[ROW][C]49[/C][C]93.8[/C][C]104.238107848291[/C][C]0.33571003559806[/C][C]-10.4381078482907[/C][C]-0.452254869983743[/C][/ROW]
[ROW][C]50[/C][C]72.4[/C][C]101.485652484255[/C][C]0.309594627450412[/C][C]-29.0856524842555[/C][C]-0.312871264093216[/C][/ROW]
[ROW][C]51[/C][C]77.7[/C][C]96.327647521561[/C][C]0.258590000148854[/C][C]-18.6276475215609[/C][C]-0.551815443431176[/C][/ROW]
[ROW][C]52[/C][C]58.7[/C][C]93.2842095247047[/C][C]0.223960192853091[/C][C]-34.5842095247047[/C][C]-0.332047063396327[/C][/ROW]
[ROW][C]53[/C][C]53.1[/C][C]89.6527107567831[/C][C]0.179384305212936[/C][C]-36.5527107567831[/C][C]-0.386954425432744[/C][/ROW]
[ROW][C]54[/C][C]64.3[/C][C]89.4964478876911[/C][C]0.175273917081454[/C][C]-25.1964478876911[/C][C]-0.0337021422069455[/C][/ROW]
[ROW][C]55[/C][C]76.4[/C][C]90.5233202693012[/C][C]0.185817809756085[/C][C]-14.1233202693012[/C][C]0.085705306590993[/C][/ROW]
[ROW][C]56[/C][C]188.4[/C][C]95.4792566380266[/C][C]0.242924313856762[/C][C]92.9207433619734[/C][C]0.481527644582842[/C][/ROW]
[ROW][C]57[/C][C]105.5[/C][C]99.6549313758976[/C][C]0.286797050222141[/C][C]5.84506862410243[/C][C]0.398106562392282[/C][/ROW]
[ROW][C]58[/C][C]79.8[/C][C]101.188963775463[/C][C]0.299457044086982[/C][C]-21.3889637754634[/C][C]0.126514393201487[/C][/ROW]
[ROW][C]59[/C][C]96.1[/C][C]102.012584586249[/C][C]0.304275260052465[/C][C]-5.91258458624858[/C][C]0.0532325067792305[/C][/ROW]
[ROW][C]60[/C][C]202.5[/C][C]103.921080111916[/C][C]0.31788620538328[/C][C]98.578919888084[/C][C]0.162973171473983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63968&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63968&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
195.595.5000
276.789.96470508052380.140802314220120-13.2647050805238-0.696780031634498
379.481.4020306370853-0.459989953857529-2.00203063708529-0.570450228961114
455.269.276082076661-1.34143029846953-14.0760820766610-0.873003554194243
56062.9977864262546-1.65313671872270-2.99778642625465-0.443327130581926
664.861.5625630548383-1.642996413014553.237436945161660.021338459694077
782.367.517624480029-1.3874891299403814.7823755199710.766881091400265
8210.5114.294957731568-0.13627504741756396.20504226843244.90587187882422
9106128.0060986967860.173220851111600-22.00609869678601.41329663505164
1080.8117.988646174801-0.0413891978181363-37.1886461748011-1.03956183577007
1197.3107.980233377384-0.248780870902242-10.6802333773836-1.01551536307027
12189.5130.1151765055920.21762248766334159.38482349440822.2782280183025
1390123.3004164492560.282788228521408-33.300416449256-0.74533208925881
1469.3105.8092285443900.395814878048897-36.5092285443897-1.89677974926168
1587.391.26584139188310.21473444970559-3.96584139188315-1.47875155178157
1657.479.0071905643165-0.114384805333091-21.6071905643165-1.16712314912949
1756.271.2223562086958-0.361627020563912-15.0223562086957-0.719921421338709
1861.671.6932314966833-0.335202581178285-10.09323149668330.0805299232462565
1977.779.8802695412708-0.0967021341851601-2.180269541270760.847039748289495
20177.284.41332745273580.011732415347087892.78667254726410.46763406185264
2197.694.54906160121630.2064149775280783.050938398783661.03042337267892
2281.6105.3442559624890.370649313007317-23.74425596248941.08077871115699
2396.8112.6072889623150.45252056612842-15.80728896231530.704071993371611
24191.3116.7446132135420.48264856530934774.55538678645760.376919776507163
25106118.9930833868390.491994336235459-12.99308338683880.181072300912466
2675.1112.6996243130940.450329663638033-37.5996243130942-0.69186622464626
277296.13737615556840.271229061623737-24.1373761555684-1.70341721247977
2863.586.59989583945210.115866702122258-23.0998958394521-0.963850479955913
2957.480.92824190307770.00162877943130724-23.5282419030777-0.565211877415148
3062.379.7086746041847-0.0243913020803348-17.4086746041847-0.120100573239084
3179.482.86796534819680.0416008588485094-3.467965348196770.317000656207032
32178.188.51294668793790.14633963287081189.58705331206210.564034070584997
33109.398.99957815107750.31109237086785810.30042184892251.04807739110824
3485.2107.1133227328110.412662587479414-21.91332273281130.79367077968045
35102.7113.3824644912730.473388736254452-10.68246449127260.596690150580938
36193.7115.5643060671710.48774140327432378.1356939328290.174167614077608
37108.4114.0219848607060.472383848650871-5.6219848607058-0.206708842184021
3873.4107.7136338268230.416998348318029-34.3136338268231-0.68729249675441
3985.9103.9849784513670.375531961057078-18.0849784513668-0.416941887966221
4058.595.1702720787910.26198248768711-36.670272078791-0.917223770259407
4158.689.41897631640680.175409249426957-30.8189763164068-0.597998758929304
4262.787.2097138141540.138330827590241-24.5097138141541-0.237561568125511
4377.587.85507750851320.146247785104954-10.35507750851320.0507752682399754
44180.592.61492875021560.21417515676854187.88507124978440.464730078127725
45102.296.28811356774710.2598321573564455.911886432252870.350044524755977
4682.6101.0882310250670.311755016686575-18.48823102506730.460824319594567
4797.8104.387905951040.34107903160625-6.587905951040070.303736477858379
48197.8108.3179355392600.37214823296454989.48206446073960.364966150618770
4993.8104.2381078482910.33571003559806-10.4381078482907-0.452254869983743
5072.4101.4856524842550.309594627450412-29.0856524842555-0.312871264093216
5177.796.3276475215610.258590000148854-18.6276475215609-0.551815443431176
5258.793.28420952470470.223960192853091-34.5842095247047-0.332047063396327
5353.189.65271075678310.179384305212936-36.5527107567831-0.386954425432744
5464.389.49644788769110.175273917081454-25.1964478876911-0.0337021422069455
5576.490.52332026930120.185817809756085-14.12332026930120.085705306590993
56188.495.47925663802660.24292431385676292.92074336197340.481527644582842
57105.599.65493137589760.2867970502221415.845068624102430.398106562392282
5879.8101.1889637754630.299457044086982-21.38896377546340.126514393201487
5996.1102.0125845862490.304275260052465-5.912584586248580.0532325067792305
60202.5103.9210801119160.3178862053832898.5789198880840.162973171473983



Parameters (Session):
par1 = FALSE ; par2 = -0.4 ; 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')