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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, 11 Dec 2009 08:32:22 -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/11/t1260545614apcrmzb0lxk1gml.htm/, Retrieved Mon, 29 Apr 2024 03:16:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66368, Retrieved Mon, 29 Apr 2024 03:16:41 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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] [workshop 9 verbet...] [2009-12-11 15:32:22] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
8
8,1
7,7
7,5
7,6
7,8
7,8
7,8
7,5
7,5
7,1
7,5
7,5
7,6
7,7
7,7
7,9
8,1
8,2
8,2
8,2
7,9
7,3
6,9
6,6
6,7
6,9
7
7,1
7,2
7,1
6,9
7
6,8
6,4
6,7
6,6
6,4
6,3
6,2
6,5
6,8
6,8
6,4
6,1
5,8
6,1
7,2
7,3
6,9
6,1
5,8
6,2
7,1
7,7
7,9
7,7
7,4
7,5
8




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=66368&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=66368&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66368&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
188000
28.18.099304449198870.09936193272613850.0006955508011307090.308511249714436
37.77.70967023168305-0.379823096535826-0.00967023168305377-1.49769065662826
47.57.4954508121445-0.2184496016235050.004549187855506430.499193671101117
57.67.595752373383740.09229663378910540.00424762661626090.961635756904163
67.87.799986517266570.2014291937076021.34827334256824e-050.337716734691692
77.87.804405785082840.0093581382982604-0.00440578508283849-0.594374418874952
87.87.79989706675575-0.004161079449765050.000102933244253802-0.0418359609874697
97.57.50546242137795-0.287157700128211-0.00546242137795457-0.875748567568387
107.57.49448998041217-0.01789620282582410.005510019587830160.833244474791906
117.17.10808047676384-0.377171139797197-0.0080804767638441-1.11179600188506
127.57.485895703931070.3588882619472460.01410429606892542.27777619810589
137.57.50817152722320.030766744971273-0.00817152722320605-1.01562861635822
147.67.58577584302210.0764431868568890.01422415697789750.141721318024696
157.77.708084902549690.120444402834582-0.008084902549686730.137155056554520
167.77.705033960769090.00224290686069793-0.00503396076908836-0.365535498036017
177.97.89494398479970.1818778379552560.005056015200306150.555922000900198
188.18.095222322883090.1994928643845230.004777677116913320.054510592384948
198.28.211568636725210.119896450216909-0.0115686367252141-0.246315523744929
208.28.19136317121849-0.01422264373837790.00863682878151016-0.415038890572853
218.28.217620936181070.0245291499262838-0.01762093618107020.11991955150004
227.97.88334811969565-0.3189511367191680.0166518803043466-1.06291859284222
237.37.3366405515546-0.536982619866879-0.0366405515545952-0.674710439127613
246.96.87274831376788-0.4670137243020970.02725168623212080.216522756053243
256.66.60322880781594-0.277984677233863-0.003228807815936660.585118113270929
266.76.680778280804920.06248848515555740.01922171919508441.05523538936971
276.96.895538934429750.2067130949940310.004461065570246950.448346259232338
2877.016707986308370.125703228716512-0.0167079863083701-0.250537507528987
297.17.098777097004710.0843970806454650.00122290299528812-0.127832287785298
307.27.193594864226460.09426346627676990.006405135773542810.0305320301909731
317.17.1097016351423-0.0744170650141647-0.00970163514229267-0.521991434909512
326.96.90500525228746-0.197765369007872-0.00500525228745867-0.381708087264946
3376.996834894033540.0764225235792980.003165105966455330.848489486405636
346.86.78486847607902-0.1966240158552560.0151315239209758-0.844957502523326
356.46.43998676398004-0.336995346712752-0.0399867639800414-0.434387071684056
366.76.651503306542550.1823258811300790.04849669345745371.60707073403234
376.66.62560915364917-0.0147828240076449-0.0256091536491688-0.61016753382485
386.46.39911093706961-0.2152853249086930.00088906293038985-0.620897814428474
396.36.28569187521146-0.1194870064512530.01430812478853720.297268925891285
406.26.21185284272088-0.0765268697523408-0.01185284272087870.13289122419191
416.56.491816169881960.2587653209561350.00818383011803811.0376043736334
426.86.787134501511950.2931512568634130.01286549848805420.106409678177342
436.86.806088275997420.0352052074693835-0.00608827599741574-0.798228167377785
446.46.44443838014493-0.338123841981909-0.0444383801449323-1.15528735084653
456.16.08321905330234-0.359850099215210.0167809466976619-0.0672330974337153
465.85.75831892912312-0.3269720003536920.04168107087687860.101743081994066
476.16.147582261202330.346809114373828-0.04758226120233372.08505650834347
487.27.125677002658760.9406481544460280.07432299734123841.83767299461869
497.37.345755389712940.262895392745059-0.0457553897129378-2.09813128064469
506.96.92888049085359-0.37661523213112-0.0288804908535856-1.97945586579179
516.16.09037159746416-0.8092106158748720.00962840253584268-1.34095569420808
525.85.80630513631187-0.316903597736691-0.00630513631186751.52319813244369
536.26.17541031405940.325773629474970.02458968594060471.98873885773683
547.17.064526955465950.8536231628801050.03547304453404641.63348204105457
557.77.699526355700560.6487668777603030.000473644299443214-0.633938179025671
567.97.959281110007140.284256019334568-0.0592811100071382-1.12799918071472
577.77.68073704880277-0.2430934212024120.0192629511972343-1.63191183021578
587.47.38031644492034-0.2968099095679340.0196835550796612-0.166228641584115
597.57.58480114684650.172913430160318-0.08480114684650081.45358846577102
6087.901772118934040.3078893353042790.09822788106596240.417693835744486

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8 & 8 & 0 & 0 & 0 \tabularnewline
2 & 8.1 & 8.09930444919887 & 0.0993619327261385 & 0.000695550801130709 & 0.308511249714436 \tabularnewline
3 & 7.7 & 7.70967023168305 & -0.379823096535826 & -0.00967023168305377 & -1.49769065662826 \tabularnewline
4 & 7.5 & 7.4954508121445 & -0.218449601623505 & 0.00454918785550643 & 0.499193671101117 \tabularnewline
5 & 7.6 & 7.59575237338374 & 0.0922966337891054 & 0.0042476266162609 & 0.961635756904163 \tabularnewline
6 & 7.8 & 7.79998651726657 & 0.201429193707602 & 1.34827334256824e-05 & 0.337716734691692 \tabularnewline
7 & 7.8 & 7.80440578508284 & 0.0093581382982604 & -0.00440578508283849 & -0.594374418874952 \tabularnewline
8 & 7.8 & 7.79989706675575 & -0.00416107944976505 & 0.000102933244253802 & -0.0418359609874697 \tabularnewline
9 & 7.5 & 7.50546242137795 & -0.287157700128211 & -0.00546242137795457 & -0.875748567568387 \tabularnewline
10 & 7.5 & 7.49448998041217 & -0.0178962028258241 & 0.00551001958783016 & 0.833244474791906 \tabularnewline
11 & 7.1 & 7.10808047676384 & -0.377171139797197 & -0.0080804767638441 & -1.11179600188506 \tabularnewline
12 & 7.5 & 7.48589570393107 & 0.358888261947246 & 0.0141042960689254 & 2.27777619810589 \tabularnewline
13 & 7.5 & 7.5081715272232 & 0.030766744971273 & -0.00817152722320605 & -1.01562861635822 \tabularnewline
14 & 7.6 & 7.5857758430221 & 0.076443186856889 & 0.0142241569778975 & 0.141721318024696 \tabularnewline
15 & 7.7 & 7.70808490254969 & 0.120444402834582 & -0.00808490254968673 & 0.137155056554520 \tabularnewline
16 & 7.7 & 7.70503396076909 & 0.00224290686069793 & -0.00503396076908836 & -0.365535498036017 \tabularnewline
17 & 7.9 & 7.8949439847997 & 0.181877837955256 & 0.00505601520030615 & 0.555922000900198 \tabularnewline
18 & 8.1 & 8.09522232288309 & 0.199492864384523 & 0.00477767711691332 & 0.054510592384948 \tabularnewline
19 & 8.2 & 8.21156863672521 & 0.119896450216909 & -0.0115686367252141 & -0.246315523744929 \tabularnewline
20 & 8.2 & 8.19136317121849 & -0.0142226437383779 & 0.00863682878151016 & -0.415038890572853 \tabularnewline
21 & 8.2 & 8.21762093618107 & 0.0245291499262838 & -0.0176209361810702 & 0.11991955150004 \tabularnewline
22 & 7.9 & 7.88334811969565 & -0.318951136719168 & 0.0166518803043466 & -1.06291859284222 \tabularnewline
23 & 7.3 & 7.3366405515546 & -0.536982619866879 & -0.0366405515545952 & -0.674710439127613 \tabularnewline
24 & 6.9 & 6.87274831376788 & -0.467013724302097 & 0.0272516862321208 & 0.216522756053243 \tabularnewline
25 & 6.6 & 6.60322880781594 & -0.277984677233863 & -0.00322880781593666 & 0.585118113270929 \tabularnewline
26 & 6.7 & 6.68077828080492 & 0.0624884851555574 & 0.0192217191950844 & 1.05523538936971 \tabularnewline
27 & 6.9 & 6.89553893442975 & 0.206713094994031 & 0.00446106557024695 & 0.448346259232338 \tabularnewline
28 & 7 & 7.01670798630837 & 0.125703228716512 & -0.0167079863083701 & -0.250537507528987 \tabularnewline
29 & 7.1 & 7.09877709700471 & 0.084397080645465 & 0.00122290299528812 & -0.127832287785298 \tabularnewline
30 & 7.2 & 7.19359486422646 & 0.0942634662767699 & 0.00640513577354281 & 0.0305320301909731 \tabularnewline
31 & 7.1 & 7.1097016351423 & -0.0744170650141647 & -0.00970163514229267 & -0.521991434909512 \tabularnewline
32 & 6.9 & 6.90500525228746 & -0.197765369007872 & -0.00500525228745867 & -0.381708087264946 \tabularnewline
33 & 7 & 6.99683489403354 & 0.076422523579298 & 0.00316510596645533 & 0.848489486405636 \tabularnewline
34 & 6.8 & 6.78486847607902 & -0.196624015855256 & 0.0151315239209758 & -0.844957502523326 \tabularnewline
35 & 6.4 & 6.43998676398004 & -0.336995346712752 & -0.0399867639800414 & -0.434387071684056 \tabularnewline
36 & 6.7 & 6.65150330654255 & 0.182325881130079 & 0.0484966934574537 & 1.60707073403234 \tabularnewline
37 & 6.6 & 6.62560915364917 & -0.0147828240076449 & -0.0256091536491688 & -0.61016753382485 \tabularnewline
38 & 6.4 & 6.39911093706961 & -0.215285324908693 & 0.00088906293038985 & -0.620897814428474 \tabularnewline
39 & 6.3 & 6.28569187521146 & -0.119487006451253 & 0.0143081247885372 & 0.297268925891285 \tabularnewline
40 & 6.2 & 6.21185284272088 & -0.0765268697523408 & -0.0118528427208787 & 0.13289122419191 \tabularnewline
41 & 6.5 & 6.49181616988196 & 0.258765320956135 & 0.0081838301180381 & 1.0376043736334 \tabularnewline
42 & 6.8 & 6.78713450151195 & 0.293151256863413 & 0.0128654984880542 & 0.106409678177342 \tabularnewline
43 & 6.8 & 6.80608827599742 & 0.0352052074693835 & -0.00608827599741574 & -0.798228167377785 \tabularnewline
44 & 6.4 & 6.44443838014493 & -0.338123841981909 & -0.0444383801449323 & -1.15528735084653 \tabularnewline
45 & 6.1 & 6.08321905330234 & -0.35985009921521 & 0.0167809466976619 & -0.0672330974337153 \tabularnewline
46 & 5.8 & 5.75831892912312 & -0.326972000353692 & 0.0416810708768786 & 0.101743081994066 \tabularnewline
47 & 6.1 & 6.14758226120233 & 0.346809114373828 & -0.0475822612023337 & 2.08505650834347 \tabularnewline
48 & 7.2 & 7.12567700265876 & 0.940648154446028 & 0.0743229973412384 & 1.83767299461869 \tabularnewline
49 & 7.3 & 7.34575538971294 & 0.262895392745059 & -0.0457553897129378 & -2.09813128064469 \tabularnewline
50 & 6.9 & 6.92888049085359 & -0.37661523213112 & -0.0288804908535856 & -1.97945586579179 \tabularnewline
51 & 6.1 & 6.09037159746416 & -0.809210615874872 & 0.00962840253584268 & -1.34095569420808 \tabularnewline
52 & 5.8 & 5.80630513631187 & -0.316903597736691 & -0.0063051363118675 & 1.52319813244369 \tabularnewline
53 & 6.2 & 6.1754103140594 & 0.32577362947497 & 0.0245896859406047 & 1.98873885773683 \tabularnewline
54 & 7.1 & 7.06452695546595 & 0.853623162880105 & 0.0354730445340464 & 1.63348204105457 \tabularnewline
55 & 7.7 & 7.69952635570056 & 0.648766877760303 & 0.000473644299443214 & -0.633938179025671 \tabularnewline
56 & 7.9 & 7.95928111000714 & 0.284256019334568 & -0.0592811100071382 & -1.12799918071472 \tabularnewline
57 & 7.7 & 7.68073704880277 & -0.243093421202412 & 0.0192629511972343 & -1.63191183021578 \tabularnewline
58 & 7.4 & 7.38031644492034 & -0.296809909567934 & 0.0196835550796612 & -0.166228641584115 \tabularnewline
59 & 7.5 & 7.5848011468465 & 0.172913430160318 & -0.0848011468465008 & 1.45358846577102 \tabularnewline
60 & 8 & 7.90177211893404 & 0.307889335304279 & 0.0982278810659624 & 0.417693835744486 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66368&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[/C][C]8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.1[/C][C]8.09930444919887[/C][C]0.0993619327261385[/C][C]0.000695550801130709[/C][C]0.308511249714436[/C][/ROW]
[ROW][C]3[/C][C]7.7[/C][C]7.70967023168305[/C][C]-0.379823096535826[/C][C]-0.00967023168305377[/C][C]-1.49769065662826[/C][/ROW]
[ROW][C]4[/C][C]7.5[/C][C]7.4954508121445[/C][C]-0.218449601623505[/C][C]0.00454918785550643[/C][C]0.499193671101117[/C][/ROW]
[ROW][C]5[/C][C]7.6[/C][C]7.59575237338374[/C][C]0.0922966337891054[/C][C]0.0042476266162609[/C][C]0.961635756904163[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.79998651726657[/C][C]0.201429193707602[/C][C]1.34827334256824e-05[/C][C]0.337716734691692[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.80440578508284[/C][C]0.0093581382982604[/C][C]-0.00440578508283849[/C][C]-0.594374418874952[/C][/ROW]
[ROW][C]8[/C][C]7.8[/C][C]7.79989706675575[/C][C]-0.00416107944976505[/C][C]0.000102933244253802[/C][C]-0.0418359609874697[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.50546242137795[/C][C]-0.287157700128211[/C][C]-0.00546242137795457[/C][C]-0.875748567568387[/C][/ROW]
[ROW][C]10[/C][C]7.5[/C][C]7.49448998041217[/C][C]-0.0178962028258241[/C][C]0.00551001958783016[/C][C]0.833244474791906[/C][/ROW]
[ROW][C]11[/C][C]7.1[/C][C]7.10808047676384[/C][C]-0.377171139797197[/C][C]-0.0080804767638441[/C][C]-1.11179600188506[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.48589570393107[/C][C]0.358888261947246[/C][C]0.0141042960689254[/C][C]2.27777619810589[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.5081715272232[/C][C]0.030766744971273[/C][C]-0.00817152722320605[/C][C]-1.01562861635822[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]7.5857758430221[/C][C]0.076443186856889[/C][C]0.0142241569778975[/C][C]0.141721318024696[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.70808490254969[/C][C]0.120444402834582[/C][C]-0.00808490254968673[/C][C]0.137155056554520[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]7.70503396076909[/C][C]0.00224290686069793[/C][C]-0.00503396076908836[/C][C]-0.365535498036017[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]7.8949439847997[/C][C]0.181877837955256[/C][C]0.00505601520030615[/C][C]0.555922000900198[/C][/ROW]
[ROW][C]18[/C][C]8.1[/C][C]8.09522232288309[/C][C]0.199492864384523[/C][C]0.00477767711691332[/C][C]0.054510592384948[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.21156863672521[/C][C]0.119896450216909[/C][C]-0.0115686367252141[/C][C]-0.246315523744929[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.19136317121849[/C][C]-0.0142226437383779[/C][C]0.00863682878151016[/C][C]-0.415038890572853[/C][/ROW]
[ROW][C]21[/C][C]8.2[/C][C]8.21762093618107[/C][C]0.0245291499262838[/C][C]-0.0176209361810702[/C][C]0.11991955150004[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.88334811969565[/C][C]-0.318951136719168[/C][C]0.0166518803043466[/C][C]-1.06291859284222[/C][/ROW]
[ROW][C]23[/C][C]7.3[/C][C]7.3366405515546[/C][C]-0.536982619866879[/C][C]-0.0366405515545952[/C][C]-0.674710439127613[/C][/ROW]
[ROW][C]24[/C][C]6.9[/C][C]6.87274831376788[/C][C]-0.467013724302097[/C][C]0.0272516862321208[/C][C]0.216522756053243[/C][/ROW]
[ROW][C]25[/C][C]6.6[/C][C]6.60322880781594[/C][C]-0.277984677233863[/C][C]-0.00322880781593666[/C][C]0.585118113270929[/C][/ROW]
[ROW][C]26[/C][C]6.7[/C][C]6.68077828080492[/C][C]0.0624884851555574[/C][C]0.0192217191950844[/C][C]1.05523538936971[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]6.89553893442975[/C][C]0.206713094994031[/C][C]0.00446106557024695[/C][C]0.448346259232338[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]7.01670798630837[/C][C]0.125703228716512[/C][C]-0.0167079863083701[/C][C]-0.250537507528987[/C][/ROW]
[ROW][C]29[/C][C]7.1[/C][C]7.09877709700471[/C][C]0.084397080645465[/C][C]0.00122290299528812[/C][C]-0.127832287785298[/C][/ROW]
[ROW][C]30[/C][C]7.2[/C][C]7.19359486422646[/C][C]0.0942634662767699[/C][C]0.00640513577354281[/C][C]0.0305320301909731[/C][/ROW]
[ROW][C]31[/C][C]7.1[/C][C]7.1097016351423[/C][C]-0.0744170650141647[/C][C]-0.00970163514229267[/C][C]-0.521991434909512[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]6.90500525228746[/C][C]-0.197765369007872[/C][C]-0.00500525228745867[/C][C]-0.381708087264946[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]6.99683489403354[/C][C]0.076422523579298[/C][C]0.00316510596645533[/C][C]0.848489486405636[/C][/ROW]
[ROW][C]34[/C][C]6.8[/C][C]6.78486847607902[/C][C]-0.196624015855256[/C][C]0.0151315239209758[/C][C]-0.844957502523326[/C][/ROW]
[ROW][C]35[/C][C]6.4[/C][C]6.43998676398004[/C][C]-0.336995346712752[/C][C]-0.0399867639800414[/C][C]-0.434387071684056[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]6.65150330654255[/C][C]0.182325881130079[/C][C]0.0484966934574537[/C][C]1.60707073403234[/C][/ROW]
[ROW][C]37[/C][C]6.6[/C][C]6.62560915364917[/C][C]-0.0147828240076449[/C][C]-0.0256091536491688[/C][C]-0.61016753382485[/C][/ROW]
[ROW][C]38[/C][C]6.4[/C][C]6.39911093706961[/C][C]-0.215285324908693[/C][C]0.00088906293038985[/C][C]-0.620897814428474[/C][/ROW]
[ROW][C]39[/C][C]6.3[/C][C]6.28569187521146[/C][C]-0.119487006451253[/C][C]0.0143081247885372[/C][C]0.297268925891285[/C][/ROW]
[ROW][C]40[/C][C]6.2[/C][C]6.21185284272088[/C][C]-0.0765268697523408[/C][C]-0.0118528427208787[/C][C]0.13289122419191[/C][/ROW]
[ROW][C]41[/C][C]6.5[/C][C]6.49181616988196[/C][C]0.258765320956135[/C][C]0.0081838301180381[/C][C]1.0376043736334[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]6.78713450151195[/C][C]0.293151256863413[/C][C]0.0128654984880542[/C][C]0.106409678177342[/C][/ROW]
[ROW][C]43[/C][C]6.8[/C][C]6.80608827599742[/C][C]0.0352052074693835[/C][C]-0.00608827599741574[/C][C]-0.798228167377785[/C][/ROW]
[ROW][C]44[/C][C]6.4[/C][C]6.44443838014493[/C][C]-0.338123841981909[/C][C]-0.0444383801449323[/C][C]-1.15528735084653[/C][/ROW]
[ROW][C]45[/C][C]6.1[/C][C]6.08321905330234[/C][C]-0.35985009921521[/C][C]0.0167809466976619[/C][C]-0.0672330974337153[/C][/ROW]
[ROW][C]46[/C][C]5.8[/C][C]5.75831892912312[/C][C]-0.326972000353692[/C][C]0.0416810708768786[/C][C]0.101743081994066[/C][/ROW]
[ROW][C]47[/C][C]6.1[/C][C]6.14758226120233[/C][C]0.346809114373828[/C][C]-0.0475822612023337[/C][C]2.08505650834347[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.12567700265876[/C][C]0.940648154446028[/C][C]0.0743229973412384[/C][C]1.83767299461869[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.34575538971294[/C][C]0.262895392745059[/C][C]-0.0457553897129378[/C][C]-2.09813128064469[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]6.92888049085359[/C][C]-0.37661523213112[/C][C]-0.0288804908535856[/C][C]-1.97945586579179[/C][/ROW]
[ROW][C]51[/C][C]6.1[/C][C]6.09037159746416[/C][C]-0.809210615874872[/C][C]0.00962840253584268[/C][C]-1.34095569420808[/C][/ROW]
[ROW][C]52[/C][C]5.8[/C][C]5.80630513631187[/C][C]-0.316903597736691[/C][C]-0.0063051363118675[/C][C]1.52319813244369[/C][/ROW]
[ROW][C]53[/C][C]6.2[/C][C]6.1754103140594[/C][C]0.32577362947497[/C][C]0.0245896859406047[/C][C]1.98873885773683[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.06452695546595[/C][C]0.853623162880105[/C][C]0.0354730445340464[/C][C]1.63348204105457[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.69952635570056[/C][C]0.648766877760303[/C][C]0.000473644299443214[/C][C]-0.633938179025671[/C][/ROW]
[ROW][C]56[/C][C]7.9[/C][C]7.95928111000714[/C][C]0.284256019334568[/C][C]-0.0592811100071382[/C][C]-1.12799918071472[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]7.68073704880277[/C][C]-0.243093421202412[/C][C]0.0192629511972343[/C][C]-1.63191183021578[/C][/ROW]
[ROW][C]58[/C][C]7.4[/C][C]7.38031644492034[/C][C]-0.296809909567934[/C][C]0.0196835550796612[/C][C]-0.166228641584115[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.5848011468465[/C][C]0.172913430160318[/C][C]-0.0848011468465008[/C][C]1.45358846577102[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]7.90177211893404[/C][C]0.307889335304279[/C][C]0.0982278810659624[/C][C]0.417693835744486[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66368&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66368&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
188000
28.18.099304449198870.09936193272613850.0006955508011307090.308511249714436
37.77.70967023168305-0.379823096535826-0.00967023168305377-1.49769065662826
47.57.4954508121445-0.2184496016235050.004549187855506430.499193671101117
57.67.595752373383740.09229663378910540.00424762661626090.961635756904163
67.87.799986517266570.2014291937076021.34827334256824e-050.337716734691692
77.87.804405785082840.0093581382982604-0.00440578508283849-0.594374418874952
87.87.79989706675575-0.004161079449765050.000102933244253802-0.0418359609874697
97.57.50546242137795-0.287157700128211-0.00546242137795457-0.875748567568387
107.57.49448998041217-0.01789620282582410.005510019587830160.833244474791906
117.17.10808047676384-0.377171139797197-0.0080804767638441-1.11179600188506
127.57.485895703931070.3588882619472460.01410429606892542.27777619810589
137.57.50817152722320.030766744971273-0.00817152722320605-1.01562861635822
147.67.58577584302210.0764431868568890.01422415697789750.141721318024696
157.77.708084902549690.120444402834582-0.008084902549686730.137155056554520
167.77.705033960769090.00224290686069793-0.00503396076908836-0.365535498036017
177.97.89494398479970.1818778379552560.005056015200306150.555922000900198
188.18.095222322883090.1994928643845230.004777677116913320.054510592384948
198.28.211568636725210.119896450216909-0.0115686367252141-0.246315523744929
208.28.19136317121849-0.01422264373837790.00863682878151016-0.415038890572853
218.28.217620936181070.0245291499262838-0.01762093618107020.11991955150004
227.97.88334811969565-0.3189511367191680.0166518803043466-1.06291859284222
237.37.3366405515546-0.536982619866879-0.0366405515545952-0.674710439127613
246.96.87274831376788-0.4670137243020970.02725168623212080.216522756053243
256.66.60322880781594-0.277984677233863-0.003228807815936660.585118113270929
266.76.680778280804920.06248848515555740.01922171919508441.05523538936971
276.96.895538934429750.2067130949940310.004461065570246950.448346259232338
2877.016707986308370.125703228716512-0.0167079863083701-0.250537507528987
297.17.098777097004710.0843970806454650.00122290299528812-0.127832287785298
307.27.193594864226460.09426346627676990.006405135773542810.0305320301909731
317.17.1097016351423-0.0744170650141647-0.00970163514229267-0.521991434909512
326.96.90500525228746-0.197765369007872-0.00500525228745867-0.381708087264946
3376.996834894033540.0764225235792980.003165105966455330.848489486405636
346.86.78486847607902-0.1966240158552560.0151315239209758-0.844957502523326
356.46.43998676398004-0.336995346712752-0.0399867639800414-0.434387071684056
366.76.651503306542550.1823258811300790.04849669345745371.60707073403234
376.66.62560915364917-0.0147828240076449-0.0256091536491688-0.61016753382485
386.46.39911093706961-0.2152853249086930.00088906293038985-0.620897814428474
396.36.28569187521146-0.1194870064512530.01430812478853720.297268925891285
406.26.21185284272088-0.0765268697523408-0.01185284272087870.13289122419191
416.56.491816169881960.2587653209561350.00818383011803811.0376043736334
426.86.787134501511950.2931512568634130.01286549848805420.106409678177342
436.86.806088275997420.0352052074693835-0.00608827599741574-0.798228167377785
446.46.44443838014493-0.338123841981909-0.0444383801449323-1.15528735084653
456.16.08321905330234-0.359850099215210.0167809466976619-0.0672330974337153
465.85.75831892912312-0.3269720003536920.04168107087687860.101743081994066
476.16.147582261202330.346809114373828-0.04758226120233372.08505650834347
487.27.125677002658760.9406481544460280.07432299734123841.83767299461869
497.37.345755389712940.262895392745059-0.0457553897129378-2.09813128064469
506.96.92888049085359-0.37661523213112-0.0288804908535856-1.97945586579179
516.16.09037159746416-0.8092106158748720.00962840253584268-1.34095569420808
525.85.80630513631187-0.316903597736691-0.00630513631186751.52319813244369
536.26.17541031405940.325773629474970.02458968594060471.98873885773683
547.17.064526955465950.8536231628801050.03547304453404641.63348204105457
557.77.699526355700560.6487668777603030.000473644299443214-0.633938179025671
567.97.959281110007140.284256019334568-0.0592811100071382-1.12799918071472
577.77.68073704880277-0.2430934212024120.0192629511972343-1.63191183021578
587.47.38031644492034-0.2968099095679340.0196835550796612-0.166228641584115
597.57.58480114684650.172913430160318-0.08480114684650081.45358846577102
6087.901772118934040.3078893353042790.09822788106596240.417693835744486



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')