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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 computationWed, 09 Dec 2009 08:53:17 -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/09/t1260374045j7c5xd7r1u7cmhz.htm/, Retrieved Mon, 29 Apr 2024 13:34:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65008, Retrieved Mon, 29 Apr 2024 13:34:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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] [Shw9] [2009-12-09 15:53:17] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
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Dataseries X:
0.7461
0.7775
0.7790
0.7744
0.7905
0.7719
0.7811
0.7557
0.7637
0.7595
0.7471
0.7615
0.7487
0.7389
0.7337
0.7510
0.7382
0.7159
0.7542
0.7636
0.7433
0.7658
0.7627
0.7480
0.7692
0.7850
0.7913
0.7720
0.7880
0.8070
0.8268
0.8244
0.8487
0.8572
0.8214
0.8827
0.9216
0.8865
0.8816
0.8884
0.9466
0.9180
0.9337
0.9559
0.9626
0.9434
0.8639
0.7996
0.6680
0.6572
0.6928
0.6438
0.6454
0.6873
0.7265
0.7912
0.8114
0.8281
0.8393




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65008&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]4 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=65008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65008&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
10.74610.7461000
20.77750.775885529664350.002486998247070850.001614470335651040.618750517320381
30.7790.7774419711053640.002348382844518840.00155802889463608-0.0301541419735241
40.77440.772848345264960.001003468937972890.00155165473504048-0.218786311683760
50.79050.7888311088645760.004326666753306570.001668891135424300.463803478791201
60.77190.770449902061507-0.001089118954394760.00145009793849276-0.695521853286597
70.78110.7794307112525940.001408479438122980.001669288747406240.306495432974868
80.75570.754258136760836-0.005326435438195190.00144186323916443-0.806162779114748
90.76370.762009699569943-0.001974005551705350.001690300430057380.395850547580657
100.75950.75794341493708-0.002513770082598580.00155658506292036-0.0632609218882115
110.74710.745558139433298-0.005069307648495970.00154186056670222-0.298290731983266
120.76150.75980486981006-5.92090109411145e-050.001695130189939780.583489434363387
130.74870.7643419807344460.00109175681835272-0.01564198073444590.160974038159959
140.73890.738165422876601-0.005658476240777040.000734577123398706-0.690237273279185
150.73370.732803592450955-0.005581396947874260.0008964075490449650.00894276803303716
160.7510.7501503975513230.0003768583751465240.0008496024486771030.690735297915322
170.73820.737364550104456-0.003042088074146540.000835449895543552-0.39675690407421
180.71590.715474316239343-0.007937008845718330.000425683760656951-0.568267801972448
190.75420.752669851154130.003783415333339570.001530148845869391.36095496369854
200.76360.7630960887399250.005508474511254270.0005039112600745440.200334377177951
210.74330.742675162217303-0.001225176327126950.000624837782696836-0.782041889583183
220.76580.7646499742287840.004799804228665070.001150025771215690.699762163944851
230.76270.762197082991350.002916207070602650.000502917008650255-0.218773271919558
240.7480.747263424487018-0.001718852190154000.00073657551298153-0.538335725945388
250.76920.7726786452321730.00522571134906362-0.003478645232172560.882313761775195
260.7850.7845066996108250.006888352813451530.000493300389175170.179755082805423
270.79130.7911272719006370.006818861629291910.000172728099362487-0.00807049813824836
280.7720.7717775237490972.43195478266522e-050.000222476250903224-0.787872467840286
290.7880.7868365522187380.00392628097134650.001163447781261800.452877667940988
300.8070.8072218838483450.00819724131223787-0.0002218838483446450.495875188494861
310.82680.825714314901950.01086858163388290.001085685098050570.310206681448403
320.82440.8241069575920990.007631443359000890.000293042407901490-0.375945130869039
330.84870.8484221402149290.01196039807863510.0002778597850711270.502769422439874
340.85720.8565132861870960.01095642375637040.000686713812903671-0.116606089115199
350.82140.821587886566007-0.0009494369835858-0.000187886566006950-1.38283996096273
360.88270.8815444472787350.01485001248749810.001155552721265021.83502479584584
370.92160.9228381330232470.0216506028565576-0.001238133023247430.837243790038453
380.88650.8875595132573910.0071961176569999-0.00105951325739133-1.59852617972725
390.88160.8819191284703480.00386926739285232-0.000319128470347547-0.386692495514300
400.88840.889097262315070.00472761964790711-0.000697262315069960.0995574782171932
410.94660.945659276894110.01816898281766350.0009407231058901261.56027693941272
420.9180.9203719607172690.00690157152643476-0.00237196071726866-1.30830420667872
430.93370.9329362439659480.008369739754853430.000763756034051820.170497684733044
440.95590.9565520674271080.0123225345743226-0.000652067427107970.45906983555905
450.96260.9635647622130180.0109458686324665-0.00096476221301819-0.159889713638003
460.94340.9431373130617340.002811687219012230.000262686938265963-0.944749038281431
470.86390.867100060639984-0.0176328114461369-0.00320006063998362-2.37460735984882
480.79960.80009862361249-0.0304276091756617-0.000498623612489193-1.48610844652289
490.6680.671794007468285-0.0556509632105046-0.00379400746828511-3.05519372172811
500.65720.656387762610567-0.04538517313205690.0008122373894332221.14963321012992
510.69280.690077083944275-0.02491308776629950.002722916055725392.38070923924700
520.64380.645467305163713-0.0300186687795213-0.00166730516371247-0.592277279318708
530.64540.641793879158155-0.02319192068231160.00360612084184520.792502912123001
540.68730.687254541144807-0.005404158764787614.54588551929221e-052.06550810899139
550.72650.7244602191090880.005635550732370560.002039780890911791.28207131466889
560.79120.7898937633958140.02112834614250670.001306236604185561.79932930521796
570.81140.8115171850820490.0212566133459178-0.0001171850820486080.0148974212061835
580.82810.8257389722396420.01943395535946670.00236102776035813-0.211694998477516
590.83930.840953891705430.0183407761174926-0.00165389170542903-0.126972340707844

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 0.7461 & 0.7461 & 0 & 0 & 0 \tabularnewline
2 & 0.7775 & 0.77588552966435 & 0.00248699824707085 & 0.00161447033565104 & 0.618750517320381 \tabularnewline
3 & 0.779 & 0.777441971105364 & 0.00234838284451884 & 0.00155802889463608 & -0.0301541419735241 \tabularnewline
4 & 0.7744 & 0.77284834526496 & 0.00100346893797289 & 0.00155165473504048 & -0.218786311683760 \tabularnewline
5 & 0.7905 & 0.788831108864576 & 0.00432666675330657 & 0.00166889113542430 & 0.463803478791201 \tabularnewline
6 & 0.7719 & 0.770449902061507 & -0.00108911895439476 & 0.00145009793849276 & -0.695521853286597 \tabularnewline
7 & 0.7811 & 0.779430711252594 & 0.00140847943812298 & 0.00166928874740624 & 0.306495432974868 \tabularnewline
8 & 0.7557 & 0.754258136760836 & -0.00532643543819519 & 0.00144186323916443 & -0.806162779114748 \tabularnewline
9 & 0.7637 & 0.762009699569943 & -0.00197400555170535 & 0.00169030043005738 & 0.395850547580657 \tabularnewline
10 & 0.7595 & 0.75794341493708 & -0.00251377008259858 & 0.00155658506292036 & -0.0632609218882115 \tabularnewline
11 & 0.7471 & 0.745558139433298 & -0.00506930764849597 & 0.00154186056670222 & -0.298290731983266 \tabularnewline
12 & 0.7615 & 0.75980486981006 & -5.92090109411145e-05 & 0.00169513018993978 & 0.583489434363387 \tabularnewline
13 & 0.7487 & 0.764341980734446 & 0.00109175681835272 & -0.0156419807344459 & 0.160974038159959 \tabularnewline
14 & 0.7389 & 0.738165422876601 & -0.00565847624077704 & 0.000734577123398706 & -0.690237273279185 \tabularnewline
15 & 0.7337 & 0.732803592450955 & -0.00558139694787426 & 0.000896407549044965 & 0.00894276803303716 \tabularnewline
16 & 0.751 & 0.750150397551323 & 0.000376858375146524 & 0.000849602448677103 & 0.690735297915322 \tabularnewline
17 & 0.7382 & 0.737364550104456 & -0.00304208807414654 & 0.000835449895543552 & -0.39675690407421 \tabularnewline
18 & 0.7159 & 0.715474316239343 & -0.00793700884571833 & 0.000425683760656951 & -0.568267801972448 \tabularnewline
19 & 0.7542 & 0.75266985115413 & 0.00378341533333957 & 0.00153014884586939 & 1.36095496369854 \tabularnewline
20 & 0.7636 & 0.763096088739925 & 0.00550847451125427 & 0.000503911260074544 & 0.200334377177951 \tabularnewline
21 & 0.7433 & 0.742675162217303 & -0.00122517632712695 & 0.000624837782696836 & -0.782041889583183 \tabularnewline
22 & 0.7658 & 0.764649974228784 & 0.00479980422866507 & 0.00115002577121569 & 0.699762163944851 \tabularnewline
23 & 0.7627 & 0.76219708299135 & 0.00291620707060265 & 0.000502917008650255 & -0.218773271919558 \tabularnewline
24 & 0.748 & 0.747263424487018 & -0.00171885219015400 & 0.00073657551298153 & -0.538335725945388 \tabularnewline
25 & 0.7692 & 0.772678645232173 & 0.00522571134906362 & -0.00347864523217256 & 0.882313761775195 \tabularnewline
26 & 0.785 & 0.784506699610825 & 0.00688835281345153 & 0.00049330038917517 & 0.179755082805423 \tabularnewline
27 & 0.7913 & 0.791127271900637 & 0.00681886162929191 & 0.000172728099362487 & -0.00807049813824836 \tabularnewline
28 & 0.772 & 0.771777523749097 & 2.43195478266522e-05 & 0.000222476250903224 & -0.787872467840286 \tabularnewline
29 & 0.788 & 0.786836552218738 & 0.0039262809713465 & 0.00116344778126180 & 0.452877667940988 \tabularnewline
30 & 0.807 & 0.807221883848345 & 0.00819724131223787 & -0.000221883848344645 & 0.495875188494861 \tabularnewline
31 & 0.8268 & 0.82571431490195 & 0.0108685816338829 & 0.00108568509805057 & 0.310206681448403 \tabularnewline
32 & 0.8244 & 0.824106957592099 & 0.00763144335900089 & 0.000293042407901490 & -0.375945130869039 \tabularnewline
33 & 0.8487 & 0.848422140214929 & 0.0119603980786351 & 0.000277859785071127 & 0.502769422439874 \tabularnewline
34 & 0.8572 & 0.856513286187096 & 0.0109564237563704 & 0.000686713812903671 & -0.116606089115199 \tabularnewline
35 & 0.8214 & 0.821587886566007 & -0.0009494369835858 & -0.000187886566006950 & -1.38283996096273 \tabularnewline
36 & 0.8827 & 0.881544447278735 & 0.0148500124874981 & 0.00115555272126502 & 1.83502479584584 \tabularnewline
37 & 0.9216 & 0.922838133023247 & 0.0216506028565576 & -0.00123813302324743 & 0.837243790038453 \tabularnewline
38 & 0.8865 & 0.887559513257391 & 0.0071961176569999 & -0.00105951325739133 & -1.59852617972725 \tabularnewline
39 & 0.8816 & 0.881919128470348 & 0.00386926739285232 & -0.000319128470347547 & -0.386692495514300 \tabularnewline
40 & 0.8884 & 0.88909726231507 & 0.00472761964790711 & -0.00069726231506996 & 0.0995574782171932 \tabularnewline
41 & 0.9466 & 0.94565927689411 & 0.0181689828176635 & 0.000940723105890126 & 1.56027693941272 \tabularnewline
42 & 0.918 & 0.920371960717269 & 0.00690157152643476 & -0.00237196071726866 & -1.30830420667872 \tabularnewline
43 & 0.9337 & 0.932936243965948 & 0.00836973975485343 & 0.00076375603405182 & 0.170497684733044 \tabularnewline
44 & 0.9559 & 0.956552067427108 & 0.0123225345743226 & -0.00065206742710797 & 0.45906983555905 \tabularnewline
45 & 0.9626 & 0.963564762213018 & 0.0109458686324665 & -0.00096476221301819 & -0.159889713638003 \tabularnewline
46 & 0.9434 & 0.943137313061734 & 0.00281168721901223 & 0.000262686938265963 & -0.944749038281431 \tabularnewline
47 & 0.8639 & 0.867100060639984 & -0.0176328114461369 & -0.00320006063998362 & -2.37460735984882 \tabularnewline
48 & 0.7996 & 0.80009862361249 & -0.0304276091756617 & -0.000498623612489193 & -1.48610844652289 \tabularnewline
49 & 0.668 & 0.671794007468285 & -0.0556509632105046 & -0.00379400746828511 & -3.05519372172811 \tabularnewline
50 & 0.6572 & 0.656387762610567 & -0.0453851731320569 & 0.000812237389433222 & 1.14963321012992 \tabularnewline
51 & 0.6928 & 0.690077083944275 & -0.0249130877662995 & 0.00272291605572539 & 2.38070923924700 \tabularnewline
52 & 0.6438 & 0.645467305163713 & -0.0300186687795213 & -0.00166730516371247 & -0.592277279318708 \tabularnewline
53 & 0.6454 & 0.641793879158155 & -0.0231919206823116 & 0.0036061208418452 & 0.792502912123001 \tabularnewline
54 & 0.6873 & 0.687254541144807 & -0.00540415876478761 & 4.54588551929221e-05 & 2.06550810899139 \tabularnewline
55 & 0.7265 & 0.724460219109088 & 0.00563555073237056 & 0.00203978089091179 & 1.28207131466889 \tabularnewline
56 & 0.7912 & 0.789893763395814 & 0.0211283461425067 & 0.00130623660418556 & 1.79932930521796 \tabularnewline
57 & 0.8114 & 0.811517185082049 & 0.0212566133459178 & -0.000117185082048608 & 0.0148974212061835 \tabularnewline
58 & 0.8281 & 0.825738972239642 & 0.0194339553594667 & 0.00236102776035813 & -0.211694998477516 \tabularnewline
59 & 0.8393 & 0.84095389170543 & 0.0183407761174926 & -0.00165389170542903 & -0.126972340707844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65008&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]0.7461[/C][C]0.7461[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.7775[/C][C]0.77588552966435[/C][C]0.00248699824707085[/C][C]0.00161447033565104[/C][C]0.618750517320381[/C][/ROW]
[ROW][C]3[/C][C]0.779[/C][C]0.777441971105364[/C][C]0.00234838284451884[/C][C]0.00155802889463608[/C][C]-0.0301541419735241[/C][/ROW]
[ROW][C]4[/C][C]0.7744[/C][C]0.77284834526496[/C][C]0.00100346893797289[/C][C]0.00155165473504048[/C][C]-0.218786311683760[/C][/ROW]
[ROW][C]5[/C][C]0.7905[/C][C]0.788831108864576[/C][C]0.00432666675330657[/C][C]0.00166889113542430[/C][C]0.463803478791201[/C][/ROW]
[ROW][C]6[/C][C]0.7719[/C][C]0.770449902061507[/C][C]-0.00108911895439476[/C][C]0.00145009793849276[/C][C]-0.695521853286597[/C][/ROW]
[ROW][C]7[/C][C]0.7811[/C][C]0.779430711252594[/C][C]0.00140847943812298[/C][C]0.00166928874740624[/C][C]0.306495432974868[/C][/ROW]
[ROW][C]8[/C][C]0.7557[/C][C]0.754258136760836[/C][C]-0.00532643543819519[/C][C]0.00144186323916443[/C][C]-0.806162779114748[/C][/ROW]
[ROW][C]9[/C][C]0.7637[/C][C]0.762009699569943[/C][C]-0.00197400555170535[/C][C]0.00169030043005738[/C][C]0.395850547580657[/C][/ROW]
[ROW][C]10[/C][C]0.7595[/C][C]0.75794341493708[/C][C]-0.00251377008259858[/C][C]0.00155658506292036[/C][C]-0.0632609218882115[/C][/ROW]
[ROW][C]11[/C][C]0.7471[/C][C]0.745558139433298[/C][C]-0.00506930764849597[/C][C]0.00154186056670222[/C][C]-0.298290731983266[/C][/ROW]
[ROW][C]12[/C][C]0.7615[/C][C]0.75980486981006[/C][C]-5.92090109411145e-05[/C][C]0.00169513018993978[/C][C]0.583489434363387[/C][/ROW]
[ROW][C]13[/C][C]0.7487[/C][C]0.764341980734446[/C][C]0.00109175681835272[/C][C]-0.0156419807344459[/C][C]0.160974038159959[/C][/ROW]
[ROW][C]14[/C][C]0.7389[/C][C]0.738165422876601[/C][C]-0.00565847624077704[/C][C]0.000734577123398706[/C][C]-0.690237273279185[/C][/ROW]
[ROW][C]15[/C][C]0.7337[/C][C]0.732803592450955[/C][C]-0.00558139694787426[/C][C]0.000896407549044965[/C][C]0.00894276803303716[/C][/ROW]
[ROW][C]16[/C][C]0.751[/C][C]0.750150397551323[/C][C]0.000376858375146524[/C][C]0.000849602448677103[/C][C]0.690735297915322[/C][/ROW]
[ROW][C]17[/C][C]0.7382[/C][C]0.737364550104456[/C][C]-0.00304208807414654[/C][C]0.000835449895543552[/C][C]-0.39675690407421[/C][/ROW]
[ROW][C]18[/C][C]0.7159[/C][C]0.715474316239343[/C][C]-0.00793700884571833[/C][C]0.000425683760656951[/C][C]-0.568267801972448[/C][/ROW]
[ROW][C]19[/C][C]0.7542[/C][C]0.75266985115413[/C][C]0.00378341533333957[/C][C]0.00153014884586939[/C][C]1.36095496369854[/C][/ROW]
[ROW][C]20[/C][C]0.7636[/C][C]0.763096088739925[/C][C]0.00550847451125427[/C][C]0.000503911260074544[/C][C]0.200334377177951[/C][/ROW]
[ROW][C]21[/C][C]0.7433[/C][C]0.742675162217303[/C][C]-0.00122517632712695[/C][C]0.000624837782696836[/C][C]-0.782041889583183[/C][/ROW]
[ROW][C]22[/C][C]0.7658[/C][C]0.764649974228784[/C][C]0.00479980422866507[/C][C]0.00115002577121569[/C][C]0.699762163944851[/C][/ROW]
[ROW][C]23[/C][C]0.7627[/C][C]0.76219708299135[/C][C]0.00291620707060265[/C][C]0.000502917008650255[/C][C]-0.218773271919558[/C][/ROW]
[ROW][C]24[/C][C]0.748[/C][C]0.747263424487018[/C][C]-0.00171885219015400[/C][C]0.00073657551298153[/C][C]-0.538335725945388[/C][/ROW]
[ROW][C]25[/C][C]0.7692[/C][C]0.772678645232173[/C][C]0.00522571134906362[/C][C]-0.00347864523217256[/C][C]0.882313761775195[/C][/ROW]
[ROW][C]26[/C][C]0.785[/C][C]0.784506699610825[/C][C]0.00688835281345153[/C][C]0.00049330038917517[/C][C]0.179755082805423[/C][/ROW]
[ROW][C]27[/C][C]0.7913[/C][C]0.791127271900637[/C][C]0.00681886162929191[/C][C]0.000172728099362487[/C][C]-0.00807049813824836[/C][/ROW]
[ROW][C]28[/C][C]0.772[/C][C]0.771777523749097[/C][C]2.43195478266522e-05[/C][C]0.000222476250903224[/C][C]-0.787872467840286[/C][/ROW]
[ROW][C]29[/C][C]0.788[/C][C]0.786836552218738[/C][C]0.0039262809713465[/C][C]0.00116344778126180[/C][C]0.452877667940988[/C][/ROW]
[ROW][C]30[/C][C]0.807[/C][C]0.807221883848345[/C][C]0.00819724131223787[/C][C]-0.000221883848344645[/C][C]0.495875188494861[/C][/ROW]
[ROW][C]31[/C][C]0.8268[/C][C]0.82571431490195[/C][C]0.0108685816338829[/C][C]0.00108568509805057[/C][C]0.310206681448403[/C][/ROW]
[ROW][C]32[/C][C]0.8244[/C][C]0.824106957592099[/C][C]0.00763144335900089[/C][C]0.000293042407901490[/C][C]-0.375945130869039[/C][/ROW]
[ROW][C]33[/C][C]0.8487[/C][C]0.848422140214929[/C][C]0.0119603980786351[/C][C]0.000277859785071127[/C][C]0.502769422439874[/C][/ROW]
[ROW][C]34[/C][C]0.8572[/C][C]0.856513286187096[/C][C]0.0109564237563704[/C][C]0.000686713812903671[/C][C]-0.116606089115199[/C][/ROW]
[ROW][C]35[/C][C]0.8214[/C][C]0.821587886566007[/C][C]-0.0009494369835858[/C][C]-0.000187886566006950[/C][C]-1.38283996096273[/C][/ROW]
[ROW][C]36[/C][C]0.8827[/C][C]0.881544447278735[/C][C]0.0148500124874981[/C][C]0.00115555272126502[/C][C]1.83502479584584[/C][/ROW]
[ROW][C]37[/C][C]0.9216[/C][C]0.922838133023247[/C][C]0.0216506028565576[/C][C]-0.00123813302324743[/C][C]0.837243790038453[/C][/ROW]
[ROW][C]38[/C][C]0.8865[/C][C]0.887559513257391[/C][C]0.0071961176569999[/C][C]-0.00105951325739133[/C][C]-1.59852617972725[/C][/ROW]
[ROW][C]39[/C][C]0.8816[/C][C]0.881919128470348[/C][C]0.00386926739285232[/C][C]-0.000319128470347547[/C][C]-0.386692495514300[/C][/ROW]
[ROW][C]40[/C][C]0.8884[/C][C]0.88909726231507[/C][C]0.00472761964790711[/C][C]-0.00069726231506996[/C][C]0.0995574782171932[/C][/ROW]
[ROW][C]41[/C][C]0.9466[/C][C]0.94565927689411[/C][C]0.0181689828176635[/C][C]0.000940723105890126[/C][C]1.56027693941272[/C][/ROW]
[ROW][C]42[/C][C]0.918[/C][C]0.920371960717269[/C][C]0.00690157152643476[/C][C]-0.00237196071726866[/C][C]-1.30830420667872[/C][/ROW]
[ROW][C]43[/C][C]0.9337[/C][C]0.932936243965948[/C][C]0.00836973975485343[/C][C]0.00076375603405182[/C][C]0.170497684733044[/C][/ROW]
[ROW][C]44[/C][C]0.9559[/C][C]0.956552067427108[/C][C]0.0123225345743226[/C][C]-0.00065206742710797[/C][C]0.45906983555905[/C][/ROW]
[ROW][C]45[/C][C]0.9626[/C][C]0.963564762213018[/C][C]0.0109458686324665[/C][C]-0.00096476221301819[/C][C]-0.159889713638003[/C][/ROW]
[ROW][C]46[/C][C]0.9434[/C][C]0.943137313061734[/C][C]0.00281168721901223[/C][C]0.000262686938265963[/C][C]-0.944749038281431[/C][/ROW]
[ROW][C]47[/C][C]0.8639[/C][C]0.867100060639984[/C][C]-0.0176328114461369[/C][C]-0.00320006063998362[/C][C]-2.37460735984882[/C][/ROW]
[ROW][C]48[/C][C]0.7996[/C][C]0.80009862361249[/C][C]-0.0304276091756617[/C][C]-0.000498623612489193[/C][C]-1.48610844652289[/C][/ROW]
[ROW][C]49[/C][C]0.668[/C][C]0.671794007468285[/C][C]-0.0556509632105046[/C][C]-0.00379400746828511[/C][C]-3.05519372172811[/C][/ROW]
[ROW][C]50[/C][C]0.6572[/C][C]0.656387762610567[/C][C]-0.0453851731320569[/C][C]0.000812237389433222[/C][C]1.14963321012992[/C][/ROW]
[ROW][C]51[/C][C]0.6928[/C][C]0.690077083944275[/C][C]-0.0249130877662995[/C][C]0.00272291605572539[/C][C]2.38070923924700[/C][/ROW]
[ROW][C]52[/C][C]0.6438[/C][C]0.645467305163713[/C][C]-0.0300186687795213[/C][C]-0.00166730516371247[/C][C]-0.592277279318708[/C][/ROW]
[ROW][C]53[/C][C]0.6454[/C][C]0.641793879158155[/C][C]-0.0231919206823116[/C][C]0.0036061208418452[/C][C]0.792502912123001[/C][/ROW]
[ROW][C]54[/C][C]0.6873[/C][C]0.687254541144807[/C][C]-0.00540415876478761[/C][C]4.54588551929221e-05[/C][C]2.06550810899139[/C][/ROW]
[ROW][C]55[/C][C]0.7265[/C][C]0.724460219109088[/C][C]0.00563555073237056[/C][C]0.00203978089091179[/C][C]1.28207131466889[/C][/ROW]
[ROW][C]56[/C][C]0.7912[/C][C]0.789893763395814[/C][C]0.0211283461425067[/C][C]0.00130623660418556[/C][C]1.79932930521796[/C][/ROW]
[ROW][C]57[/C][C]0.8114[/C][C]0.811517185082049[/C][C]0.0212566133459178[/C][C]-0.000117185082048608[/C][C]0.0148974212061835[/C][/ROW]
[ROW][C]58[/C][C]0.8281[/C][C]0.825738972239642[/C][C]0.0194339553594667[/C][C]0.00236102776035813[/C][C]-0.211694998477516[/C][/ROW]
[ROW][C]59[/C][C]0.8393[/C][C]0.84095389170543[/C][C]0.0183407761174926[/C][C]-0.00165389170542903[/C][C]-0.126972340707844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65008&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
10.74610.7461000
20.77750.775885529664350.002486998247070850.001614470335651040.618750517320381
30.7790.7774419711053640.002348382844518840.00155802889463608-0.0301541419735241
40.77440.772848345264960.001003468937972890.00155165473504048-0.218786311683760
50.79050.7888311088645760.004326666753306570.001668891135424300.463803478791201
60.77190.770449902061507-0.001089118954394760.00145009793849276-0.695521853286597
70.78110.7794307112525940.001408479438122980.001669288747406240.306495432974868
80.75570.754258136760836-0.005326435438195190.00144186323916443-0.806162779114748
90.76370.762009699569943-0.001974005551705350.001690300430057380.395850547580657
100.75950.75794341493708-0.002513770082598580.00155658506292036-0.0632609218882115
110.74710.745558139433298-0.005069307648495970.00154186056670222-0.298290731983266
120.76150.75980486981006-5.92090109411145e-050.001695130189939780.583489434363387
130.74870.7643419807344460.00109175681835272-0.01564198073444590.160974038159959
140.73890.738165422876601-0.005658476240777040.000734577123398706-0.690237273279185
150.73370.732803592450955-0.005581396947874260.0008964075490449650.00894276803303716
160.7510.7501503975513230.0003768583751465240.0008496024486771030.690735297915322
170.73820.737364550104456-0.003042088074146540.000835449895543552-0.39675690407421
180.71590.715474316239343-0.007937008845718330.000425683760656951-0.568267801972448
190.75420.752669851154130.003783415333339570.001530148845869391.36095496369854
200.76360.7630960887399250.005508474511254270.0005039112600745440.200334377177951
210.74330.742675162217303-0.001225176327126950.000624837782696836-0.782041889583183
220.76580.7646499742287840.004799804228665070.001150025771215690.699762163944851
230.76270.762197082991350.002916207070602650.000502917008650255-0.218773271919558
240.7480.747263424487018-0.001718852190154000.00073657551298153-0.538335725945388
250.76920.7726786452321730.00522571134906362-0.003478645232172560.882313761775195
260.7850.7845066996108250.006888352813451530.000493300389175170.179755082805423
270.79130.7911272719006370.006818861629291910.000172728099362487-0.00807049813824836
280.7720.7717775237490972.43195478266522e-050.000222476250903224-0.787872467840286
290.7880.7868365522187380.00392628097134650.001163447781261800.452877667940988
300.8070.8072218838483450.00819724131223787-0.0002218838483446450.495875188494861
310.82680.825714314901950.01086858163388290.001085685098050570.310206681448403
320.82440.8241069575920990.007631443359000890.000293042407901490-0.375945130869039
330.84870.8484221402149290.01196039807863510.0002778597850711270.502769422439874
340.85720.8565132861870960.01095642375637040.000686713812903671-0.116606089115199
350.82140.821587886566007-0.0009494369835858-0.000187886566006950-1.38283996096273
360.88270.8815444472787350.01485001248749810.001155552721265021.83502479584584
370.92160.9228381330232470.0216506028565576-0.001238133023247430.837243790038453
380.88650.8875595132573910.0071961176569999-0.00105951325739133-1.59852617972725
390.88160.8819191284703480.00386926739285232-0.000319128470347547-0.386692495514300
400.88840.889097262315070.00472761964790711-0.000697262315069960.0995574782171932
410.94660.945659276894110.01816898281766350.0009407231058901261.56027693941272
420.9180.9203719607172690.00690157152643476-0.00237196071726866-1.30830420667872
430.93370.9329362439659480.008369739754853430.000763756034051820.170497684733044
440.95590.9565520674271080.0123225345743226-0.000652067427107970.45906983555905
450.96260.9635647622130180.0109458686324665-0.00096476221301819-0.159889713638003
460.94340.9431373130617340.002811687219012230.000262686938265963-0.944749038281431
470.86390.867100060639984-0.0176328114461369-0.00320006063998362-2.37460735984882
480.79960.80009862361249-0.0304276091756617-0.000498623612489193-1.48610844652289
490.6680.671794007468285-0.0556509632105046-0.00379400746828511-3.05519372172811
500.65720.656387762610567-0.04538517313205690.0008122373894332221.14963321012992
510.69280.690077083944275-0.02491308776629950.002722916055725392.38070923924700
520.64380.645467305163713-0.0300186687795213-0.00166730516371247-0.592277279318708
530.64540.641793879158155-0.02319192068231160.00360612084184520.792502912123001
540.68730.687254541144807-0.005404158764787614.54588551929221e-052.06550810899139
550.72650.7244602191090880.005635550732370560.002039780890911791.28207131466889
560.79120.7898937633958140.02112834614250670.001306236604185561.79932930521796
570.81140.8115171850820490.0212566133459178-0.0001171850820486080.0148974212061835
580.82810.8257389722396420.01943395535946670.00236102776035813-0.211694998477516
590.83930.840953891705430.0183407761174926-0.00165389170542903-0.126972340707844



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')