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Description of Statistical Computation

Author's title

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 05:50:35 -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/t1260535889mobetfc7xscshky.htm/, Retrieved Sun, 28 Apr 2024 20:35:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66136, Retrieved Sun, 28 Apr 2024 20:35:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

Tree of Dependent Computations

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] [ws 8 Ad hoc forec...] [2009-12-02 20:13:08] [616e2df490b611f6cb7080068870ecbd]
-   PD      [Structural Time Series Models] [Workshop 9] [2009-12-04 12:11:14] [4fe1472705bb0a32f118ba3ca90ffa8e]
-    D          [Structural Time Series Models] [WS9] [2009-12-11 12:50:35] [ee8fc1691ecec7724e0ca78f0c288737] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7.55
7.55
7.59
7.59
7.59
7.57
7.57
7.59
7.6
7.64
7.64
7.76
7.76
7.76
7.77
7.83
7.94
7.94
7.94
8.09
8.18
8.26
8.28
8.28
8.28
8.29
8.3
8.3
8.31
8.33
8.33
8.34
8.48
8.59
8.67
8.67
8.67
8.71
8.72
8.72
8.72
8.74
8.74
8.74
8.74
8.79
8.85
8.86
8.87
8.92
8.96
8.97
8.99
8.98
8.98
9.01
9.01
9.03
9.05
9.05

Tables (Output of Computation)





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=66136&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=66136&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66136&T=0

As an alternative you can also use a QR Code:  
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
17.557.55000
27.557.55000
37.597.589907713409920.00240071055502759.22865900841344e-051.06827691323131
47.597.589912787028050.002199074165853578.7212971953873e-05-0.0632927357523968
57.597.589916952693820.001971089964493228.30473061849324e-05-0.0573504975986414
67.577.56995361060425-0.0006481200671535684.63893957548886e-05-0.568004021155119
77.577.56995267101184-0.000563149097515274.73289881560325e-050.0166419211157233
87.597.589927033431130.002315490724445407.29665688688694e-050.52529751643838
97.67.599918856308960.003441370920738348.1143691039594e-050.195559371745820
107.647.639885837596320.008970427704349470.0001141624036819120.92778399049776
117.647.639892686657070.00758334445689940.000107313342932199-0.227196164587133
127.767.759820334213480.0252377562232370.0001796657865165092.84313202135591
137.767.768919389268940.0228611326116433-0.00891938926893606-0.479562959226583
147.767.759914519516490.01779953220032418.54804835116081e-05-0.682317175534645
157.777.769911959494420.01654503131091398.80405055766801e-05-0.196813177933444
167.837.829923919863150.02356028876876897.60801368530244e-051.09615309066543
177.947.939943860431610.03755099795878975.61395683930133e-052.17990158004761
187.947.939936602501330.03146216211983856.339749867429e-05-0.946823552635257
197.947.939931508720760.02635414454852716.84912792434782e-05-0.793200122860028
208.098.089948274228330.04644643893510565.17257716723804e-053.11699660771905
218.188.179953219541070.05352824861689874.67804589345699e-051.09787822891675
228.268.259955736342090.05783443579806844.42636579128188e-050.667259493580475
238.288.279952724558530.05167798520519124.72754414694701e-05-0.953644339934985
248.288.27994928031180.04326709710551875.07196881949043e-05-1.30255387275861
258.288.287818190768960.0375929514288642-0.00781819076896485-0.969939021343275
268.298.289806829944190.03188995663225850.000193170055808733-0.81457050795472
278.38.299799105104040.02832386650653060.000200894895957535-0.551689911682417
288.38.299790738021170.02371024209800770.000209261978827828-0.713852183622498
298.318.309787347566250.02147721305352430.000212652433754479-0.345545158482727
308.338.329787041755440.021236630551990.000212958244557618-0.0372310755894764
318.338.329783361347160.01777813960264530.000216638652835544-0.53524176071904
328.348.339782232878430.01651147148025380.000217767121566926-0.196038543351975
338.488.479797231305620.03662109612036990.0002027686943759983.11238488854689
348.598.589804692304270.0485703758878710.0001953076957256991.84943285114186
358.678.669807367604330.053688430350510.0001926323956699200.792149223483837
368.678.669803541804510.0449457809283370.000196458195488437-1.35315905324315
378.678.677215580175370.0388864245590012-0.00721558017537371-1.00113850041526
388.718.7094157223470.03781170092902670.000584277653006517-0.157265106796134
398.728.719408109769310.03328063696630360.000591890230692181-0.700930959637714
408.728.719400484648380.02785939234252370.000599515351616428-0.838775358579526
418.728.719395141273380.02332171849653520.00060485872662086-0.702150360107278
428.748.739394607936050.02278072454780650.000605392063954994-0.0837190760111358
438.748.739391545928830.01907071822392260.000608454071174561-0.574157866944926
448.748.739389400034620.01596502595019730.000610599965376414-0.4806537862901
458.748.739387896146220.01336516592509150.00061210385378419-0.402379661059021
468.798.78939078513890.01933094041888760.0006092148610994620.923339410461482
478.858.849393470011160.02595358295460150.0006065299888386931.02501872946052
488.868.859392588301180.02335568779656970.000607411698818161-0.402092780351392
498.878.8772340068160.022463385363615-0.00723400681599721-0.145071746740841
508.928.919176842665020.02560261775727760.000823157334982050.465227470300943
518.968.959179988853460.02794800192423070.0008200111465392780.362856825217533
528.978.969176704745630.02502456359000040.000823295254371052-0.452348355170663
538.998.989175935096850.02420621322955840.00082406490315245-0.126636484180673
548.988.979171548808210.01863538113131040.000828451191791742-0.862120579036046
558.988.979169548338480.01560054514019880.00083045166151505-0.469681068759545
569.019.009170842366620.01794547736564670.000829157633376380.362920969542036
579.019.009169492290110.01502313995682510.00083050770989142-0.452295110641569
589.039.029169805738250.01583358679244070.0008301942617446370.125436206750590
599.059.049170025413130.01651205120046700.0008299745868737630.105009900970198
609.059.049169296581140.01382322524033070.000830703418861457-0.416168527928599

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 7.55 & 7.55 & 0 & 0 & 0 \tabularnewline
2 & 7.55 & 7.55 & 0 & 0 & 0 \tabularnewline
3 & 7.59 & 7.58990771340992 & 0.0024007105550275 & 9.22865900841344e-05 & 1.06827691323131 \tabularnewline
4 & 7.59 & 7.58991278702805 & 0.00219907416585357 & 8.7212971953873e-05 & -0.0632927357523968 \tabularnewline
5 & 7.59 & 7.58991695269382 & 0.00197108996449322 & 8.30473061849324e-05 & -0.0573504975986414 \tabularnewline
6 & 7.57 & 7.56995361060425 & -0.000648120067153568 & 4.63893957548886e-05 & -0.568004021155119 \tabularnewline
7 & 7.57 & 7.56995267101184 & -0.00056314909751527 & 4.73289881560325e-05 & 0.0166419211157233 \tabularnewline
8 & 7.59 & 7.58992703343113 & 0.00231549072444540 & 7.29665688688694e-05 & 0.52529751643838 \tabularnewline
9 & 7.6 & 7.59991885630896 & 0.00344137092073834 & 8.1143691039594e-05 & 0.195559371745820 \tabularnewline
10 & 7.64 & 7.63988583759632 & 0.00897042770434947 & 0.000114162403681912 & 0.92778399049776 \tabularnewline
11 & 7.64 & 7.63989268665707 & 0.0075833444568994 & 0.000107313342932199 & -0.227196164587133 \tabularnewline
12 & 7.76 & 7.75982033421348 & 0.025237756223237 & 0.000179665786516509 & 2.84313202135591 \tabularnewline
13 & 7.76 & 7.76891938926894 & 0.0228611326116433 & -0.00891938926893606 & -0.479562959226583 \tabularnewline
14 & 7.76 & 7.75991451951649 & 0.0177995322003241 & 8.54804835116081e-05 & -0.682317175534645 \tabularnewline
15 & 7.77 & 7.76991195949442 & 0.0165450313109139 & 8.80405055766801e-05 & -0.196813177933444 \tabularnewline
16 & 7.83 & 7.82992391986315 & 0.0235602887687689 & 7.60801368530244e-05 & 1.09615309066543 \tabularnewline
17 & 7.94 & 7.93994386043161 & 0.0375509979587897 & 5.61395683930133e-05 & 2.17990158004761 \tabularnewline
18 & 7.94 & 7.93993660250133 & 0.0314621621198385 & 6.339749867429e-05 & -0.946823552635257 \tabularnewline
19 & 7.94 & 7.93993150872076 & 0.0263541445485271 & 6.84912792434782e-05 & -0.793200122860028 \tabularnewline
20 & 8.09 & 8.08994827422833 & 0.0464464389351056 & 5.17257716723804e-05 & 3.11699660771905 \tabularnewline
21 & 8.18 & 8.17995321954107 & 0.0535282486168987 & 4.67804589345699e-05 & 1.09787822891675 \tabularnewline
22 & 8.26 & 8.25995573634209 & 0.0578344357980684 & 4.42636579128188e-05 & 0.667259493580475 \tabularnewline
23 & 8.28 & 8.27995272455853 & 0.0516779852051912 & 4.72754414694701e-05 & -0.953644339934985 \tabularnewline
24 & 8.28 & 8.2799492803118 & 0.0432670971055187 & 5.07196881949043e-05 & -1.30255387275861 \tabularnewline
25 & 8.28 & 8.28781819076896 & 0.0375929514288642 & -0.00781819076896485 & -0.969939021343275 \tabularnewline
26 & 8.29 & 8.28980682994419 & 0.0318899566322585 & 0.000193170055808733 & -0.81457050795472 \tabularnewline
27 & 8.3 & 8.29979910510404 & 0.0283238665065306 & 0.000200894895957535 & -0.551689911682417 \tabularnewline
28 & 8.3 & 8.29979073802117 & 0.0237102420980077 & 0.000209261978827828 & -0.713852183622498 \tabularnewline
29 & 8.31 & 8.30978734756625 & 0.0214772130535243 & 0.000212652433754479 & -0.345545158482727 \tabularnewline
30 & 8.33 & 8.32978704175544 & 0.02123663055199 & 0.000212958244557618 & -0.0372310755894764 \tabularnewline
31 & 8.33 & 8.32978336134716 & 0.0177781396026453 & 0.000216638652835544 & -0.53524176071904 \tabularnewline
32 & 8.34 & 8.33978223287843 & 0.0165114714802538 & 0.000217767121566926 & -0.196038543351975 \tabularnewline
33 & 8.48 & 8.47979723130562 & 0.0366210961203699 & 0.000202768694375998 & 3.11238488854689 \tabularnewline
34 & 8.59 & 8.58980469230427 & 0.048570375887871 & 0.000195307695725699 & 1.84943285114186 \tabularnewline
35 & 8.67 & 8.66980736760433 & 0.05368843035051 & 0.000192632395669920 & 0.792149223483837 \tabularnewline
36 & 8.67 & 8.66980354180451 & 0.044945780928337 & 0.000196458195488437 & -1.35315905324315 \tabularnewline
37 & 8.67 & 8.67721558017537 & 0.0388864245590012 & -0.00721558017537371 & -1.00113850041526 \tabularnewline
38 & 8.71 & 8.709415722347 & 0.0378117009290267 & 0.000584277653006517 & -0.157265106796134 \tabularnewline
39 & 8.72 & 8.71940810976931 & 0.0332806369663036 & 0.000591890230692181 & -0.700930959637714 \tabularnewline
40 & 8.72 & 8.71940048464838 & 0.0278593923425237 & 0.000599515351616428 & -0.838775358579526 \tabularnewline
41 & 8.72 & 8.71939514127338 & 0.0233217184965352 & 0.00060485872662086 & -0.702150360107278 \tabularnewline
42 & 8.74 & 8.73939460793605 & 0.0227807245478065 & 0.000605392063954994 & -0.0837190760111358 \tabularnewline
43 & 8.74 & 8.73939154592883 & 0.0190707182239226 & 0.000608454071174561 & -0.574157866944926 \tabularnewline
44 & 8.74 & 8.73938940003462 & 0.0159650259501973 & 0.000610599965376414 & -0.4806537862901 \tabularnewline
45 & 8.74 & 8.73938789614622 & 0.0133651659250915 & 0.00061210385378419 & -0.402379661059021 \tabularnewline
46 & 8.79 & 8.7893907851389 & 0.0193309404188876 & 0.000609214861099462 & 0.923339410461482 \tabularnewline
47 & 8.85 & 8.84939347001116 & 0.0259535829546015 & 0.000606529988838693 & 1.02501872946052 \tabularnewline
48 & 8.86 & 8.85939258830118 & 0.0233556877965697 & 0.000607411698818161 & -0.402092780351392 \tabularnewline
49 & 8.87 & 8.877234006816 & 0.022463385363615 & -0.00723400681599721 & -0.145071746740841 \tabularnewline
50 & 8.92 & 8.91917684266502 & 0.0256026177572776 & 0.00082315733498205 & 0.465227470300943 \tabularnewline
51 & 8.96 & 8.95917998885346 & 0.0279480019242307 & 0.000820011146539278 & 0.362856825217533 \tabularnewline
52 & 8.97 & 8.96917670474563 & 0.0250245635900004 & 0.000823295254371052 & -0.452348355170663 \tabularnewline
53 & 8.99 & 8.98917593509685 & 0.0242062132295584 & 0.00082406490315245 & -0.126636484180673 \tabularnewline
54 & 8.98 & 8.97917154880821 & 0.0186353811313104 & 0.000828451191791742 & -0.862120579036046 \tabularnewline
55 & 8.98 & 8.97916954833848 & 0.0156005451401988 & 0.00083045166151505 & -0.469681068759545 \tabularnewline
56 & 9.01 & 9.00917084236662 & 0.0179454773656467 & 0.00082915763337638 & 0.362920969542036 \tabularnewline
57 & 9.01 & 9.00916949229011 & 0.0150231399568251 & 0.00083050770989142 & -0.452295110641569 \tabularnewline
58 & 9.03 & 9.02916980573825 & 0.0158335867924407 & 0.000830194261744637 & 0.125436206750590 \tabularnewline
59 & 9.05 & 9.04917002541313 & 0.0165120512004670 & 0.000829974586873763 & 0.105009900970198 \tabularnewline
60 & 9.05 & 9.04916929658114 & 0.0138232252403307 & 0.000830703418861457 & -0.416168527928599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66136&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]7.55[/C][C]7.55[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]7.55[/C][C]7.55[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]7.59[/C][C]7.58990771340992[/C][C]0.0024007105550275[/C][C]9.22865900841344e-05[/C][C]1.06827691323131[/C][/ROW]
[ROW][C]4[/C][C]7.59[/C][C]7.58991278702805[/C][C]0.00219907416585357[/C][C]8.7212971953873e-05[/C][C]-0.0632927357523968[/C][/ROW]
[ROW][C]5[/C][C]7.59[/C][C]7.58991695269382[/C][C]0.00197108996449322[/C][C]8.30473061849324e-05[/C][C]-0.0573504975986414[/C][/ROW]
[ROW][C]6[/C][C]7.57[/C][C]7.56995361060425[/C][C]-0.000648120067153568[/C][C]4.63893957548886e-05[/C][C]-0.568004021155119[/C][/ROW]
[ROW][C]7[/C][C]7.57[/C][C]7.56995267101184[/C][C]-0.00056314909751527[/C][C]4.73289881560325e-05[/C][C]0.0166419211157233[/C][/ROW]
[ROW][C]8[/C][C]7.59[/C][C]7.58992703343113[/C][C]0.00231549072444540[/C][C]7.29665688688694e-05[/C][C]0.52529751643838[/C][/ROW]
[ROW][C]9[/C][C]7.6[/C][C]7.59991885630896[/C][C]0.00344137092073834[/C][C]8.1143691039594e-05[/C][C]0.195559371745820[/C][/ROW]
[ROW][C]10[/C][C]7.64[/C][C]7.63988583759632[/C][C]0.00897042770434947[/C][C]0.000114162403681912[/C][C]0.92778399049776[/C][/ROW]
[ROW][C]11[/C][C]7.64[/C][C]7.63989268665707[/C][C]0.0075833444568994[/C][C]0.000107313342932199[/C][C]-0.227196164587133[/C][/ROW]
[ROW][C]12[/C][C]7.76[/C][C]7.75982033421348[/C][C]0.025237756223237[/C][C]0.000179665786516509[/C][C]2.84313202135591[/C][/ROW]
[ROW][C]13[/C][C]7.76[/C][C]7.76891938926894[/C][C]0.0228611326116433[/C][C]-0.00891938926893606[/C][C]-0.479562959226583[/C][/ROW]
[ROW][C]14[/C][C]7.76[/C][C]7.75991451951649[/C][C]0.0177995322003241[/C][C]8.54804835116081e-05[/C][C]-0.682317175534645[/C][/ROW]
[ROW][C]15[/C][C]7.77[/C][C]7.76991195949442[/C][C]0.0165450313109139[/C][C]8.80405055766801e-05[/C][C]-0.196813177933444[/C][/ROW]
[ROW][C]16[/C][C]7.83[/C][C]7.82992391986315[/C][C]0.0235602887687689[/C][C]7.60801368530244e-05[/C][C]1.09615309066543[/C][/ROW]
[ROW][C]17[/C][C]7.94[/C][C]7.93994386043161[/C][C]0.0375509979587897[/C][C]5.61395683930133e-05[/C][C]2.17990158004761[/C][/ROW]
[ROW][C]18[/C][C]7.94[/C][C]7.93993660250133[/C][C]0.0314621621198385[/C][C]6.339749867429e-05[/C][C]-0.946823552635257[/C][/ROW]
[ROW][C]19[/C][C]7.94[/C][C]7.93993150872076[/C][C]0.0263541445485271[/C][C]6.84912792434782e-05[/C][C]-0.793200122860028[/C][/ROW]
[ROW][C]20[/C][C]8.09[/C][C]8.08994827422833[/C][C]0.0464464389351056[/C][C]5.17257716723804e-05[/C][C]3.11699660771905[/C][/ROW]
[ROW][C]21[/C][C]8.18[/C][C]8.17995321954107[/C][C]0.0535282486168987[/C][C]4.67804589345699e-05[/C][C]1.09787822891675[/C][/ROW]
[ROW][C]22[/C][C]8.26[/C][C]8.25995573634209[/C][C]0.0578344357980684[/C][C]4.42636579128188e-05[/C][C]0.667259493580475[/C][/ROW]
[ROW][C]23[/C][C]8.28[/C][C]8.27995272455853[/C][C]0.0516779852051912[/C][C]4.72754414694701e-05[/C][C]-0.953644339934985[/C][/ROW]
[ROW][C]24[/C][C]8.28[/C][C]8.2799492803118[/C][C]0.0432670971055187[/C][C]5.07196881949043e-05[/C][C]-1.30255387275861[/C][/ROW]
[ROW][C]25[/C][C]8.28[/C][C]8.28781819076896[/C][C]0.0375929514288642[/C][C]-0.00781819076896485[/C][C]-0.969939021343275[/C][/ROW]
[ROW][C]26[/C][C]8.29[/C][C]8.28980682994419[/C][C]0.0318899566322585[/C][C]0.000193170055808733[/C][C]-0.81457050795472[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]8.29979910510404[/C][C]0.0283238665065306[/C][C]0.000200894895957535[/C][C]-0.551689911682417[/C][/ROW]
[ROW][C]28[/C][C]8.3[/C][C]8.29979073802117[/C][C]0.0237102420980077[/C][C]0.000209261978827828[/C][C]-0.713852183622498[/C][/ROW]
[ROW][C]29[/C][C]8.31[/C][C]8.30978734756625[/C][C]0.0214772130535243[/C][C]0.000212652433754479[/C][C]-0.345545158482727[/C][/ROW]
[ROW][C]30[/C][C]8.33[/C][C]8.32978704175544[/C][C]0.02123663055199[/C][C]0.000212958244557618[/C][C]-0.0372310755894764[/C][/ROW]
[ROW][C]31[/C][C]8.33[/C][C]8.32978336134716[/C][C]0.0177781396026453[/C][C]0.000216638652835544[/C][C]-0.53524176071904[/C][/ROW]
[ROW][C]32[/C][C]8.34[/C][C]8.33978223287843[/C][C]0.0165114714802538[/C][C]0.000217767121566926[/C][C]-0.196038543351975[/C][/ROW]
[ROW][C]33[/C][C]8.48[/C][C]8.47979723130562[/C][C]0.0366210961203699[/C][C]0.000202768694375998[/C][C]3.11238488854689[/C][/ROW]
[ROW][C]34[/C][C]8.59[/C][C]8.58980469230427[/C][C]0.048570375887871[/C][C]0.000195307695725699[/C][C]1.84943285114186[/C][/ROW]
[ROW][C]35[/C][C]8.67[/C][C]8.66980736760433[/C][C]0.05368843035051[/C][C]0.000192632395669920[/C][C]0.792149223483837[/C][/ROW]
[ROW][C]36[/C][C]8.67[/C][C]8.66980354180451[/C][C]0.044945780928337[/C][C]0.000196458195488437[/C][C]-1.35315905324315[/C][/ROW]
[ROW][C]37[/C][C]8.67[/C][C]8.67721558017537[/C][C]0.0388864245590012[/C][C]-0.00721558017537371[/C][C]-1.00113850041526[/C][/ROW]
[ROW][C]38[/C][C]8.71[/C][C]8.709415722347[/C][C]0.0378117009290267[/C][C]0.000584277653006517[/C][C]-0.157265106796134[/C][/ROW]
[ROW][C]39[/C][C]8.72[/C][C]8.71940810976931[/C][C]0.0332806369663036[/C][C]0.000591890230692181[/C][C]-0.700930959637714[/C][/ROW]
[ROW][C]40[/C][C]8.72[/C][C]8.71940048464838[/C][C]0.0278593923425237[/C][C]0.000599515351616428[/C][C]-0.838775358579526[/C][/ROW]
[ROW][C]41[/C][C]8.72[/C][C]8.71939514127338[/C][C]0.0233217184965352[/C][C]0.00060485872662086[/C][C]-0.702150360107278[/C][/ROW]
[ROW][C]42[/C][C]8.74[/C][C]8.73939460793605[/C][C]0.0227807245478065[/C][C]0.000605392063954994[/C][C]-0.0837190760111358[/C][/ROW]
[ROW][C]43[/C][C]8.74[/C][C]8.73939154592883[/C][C]0.0190707182239226[/C][C]0.000608454071174561[/C][C]-0.574157866944926[/C][/ROW]
[ROW][C]44[/C][C]8.74[/C][C]8.73938940003462[/C][C]0.0159650259501973[/C][C]0.000610599965376414[/C][C]-0.4806537862901[/C][/ROW]
[ROW][C]45[/C][C]8.74[/C][C]8.73938789614622[/C][C]0.0133651659250915[/C][C]0.00061210385378419[/C][C]-0.402379661059021[/C][/ROW]
[ROW][C]46[/C][C]8.79[/C][C]8.7893907851389[/C][C]0.0193309404188876[/C][C]0.000609214861099462[/C][C]0.923339410461482[/C][/ROW]
[ROW][C]47[/C][C]8.85[/C][C]8.84939347001116[/C][C]0.0259535829546015[/C][C]0.000606529988838693[/C][C]1.02501872946052[/C][/ROW]
[ROW][C]48[/C][C]8.86[/C][C]8.85939258830118[/C][C]0.0233556877965697[/C][C]0.000607411698818161[/C][C]-0.402092780351392[/C][/ROW]
[ROW][C]49[/C][C]8.87[/C][C]8.877234006816[/C][C]0.022463385363615[/C][C]-0.00723400681599721[/C][C]-0.145071746740841[/C][/ROW]
[ROW][C]50[/C][C]8.92[/C][C]8.91917684266502[/C][C]0.0256026177572776[/C][C]0.00082315733498205[/C][C]0.465227470300943[/C][/ROW]
[ROW][C]51[/C][C]8.96[/C][C]8.95917998885346[/C][C]0.0279480019242307[/C][C]0.000820011146539278[/C][C]0.362856825217533[/C][/ROW]
[ROW][C]52[/C][C]8.97[/C][C]8.96917670474563[/C][C]0.0250245635900004[/C][C]0.000823295254371052[/C][C]-0.452348355170663[/C][/ROW]
[ROW][C]53[/C][C]8.99[/C][C]8.98917593509685[/C][C]0.0242062132295584[/C][C]0.00082406490315245[/C][C]-0.126636484180673[/C][/ROW]
[ROW][C]54[/C][C]8.98[/C][C]8.97917154880821[/C][C]0.0186353811313104[/C][C]0.000828451191791742[/C][C]-0.862120579036046[/C][/ROW]
[ROW][C]55[/C][C]8.98[/C][C]8.97916954833848[/C][C]0.0156005451401988[/C][C]0.00083045166151505[/C][C]-0.469681068759545[/C][/ROW]
[ROW][C]56[/C][C]9.01[/C][C]9.00917084236662[/C][C]0.0179454773656467[/C][C]0.00082915763337638[/C][C]0.362920969542036[/C][/ROW]
[ROW][C]57[/C][C]9.01[/C][C]9.00916949229011[/C][C]0.0150231399568251[/C][C]0.00083050770989142[/C][C]-0.452295110641569[/C][/ROW]
[ROW][C]58[/C][C]9.03[/C][C]9.02916980573825[/C][C]0.0158335867924407[/C][C]0.000830194261744637[/C][C]0.125436206750590[/C][/ROW]
[ROW][C]59[/C][C]9.05[/C][C]9.04917002541313[/C][C]0.0165120512004670[/C][C]0.000829974586873763[/C][C]0.105009900970198[/C][/ROW]
[ROW][C]60[/C][C]9.05[/C][C]9.04916929658114[/C][C]0.0138232252403307[/C][C]0.000830703418861457[/C][C]-0.416168527928599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66136&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66136&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
17.557.55000
27.557.55000
37.597.589907713409920.00240071055502759.22865900841344e-051.06827691323131
47.597.589912787028050.002199074165853578.7212971953873e-05-0.0632927357523968
57.597.589916952693820.001971089964493228.30473061849324e-05-0.0573504975986414
67.577.56995361060425-0.0006481200671535684.63893957548886e-05-0.568004021155119
77.577.56995267101184-0.000563149097515274.73289881560325e-050.0166419211157233
87.597.589927033431130.002315490724445407.29665688688694e-050.52529751643838
97.67.599918856308960.003441370920738348.1143691039594e-050.195559371745820
107.647.639885837596320.008970427704349470.0001141624036819120.92778399049776
117.647.639892686657070.00758334445689940.000107313342932199-0.227196164587133
127.767.759820334213480.0252377562232370.0001796657865165092.84313202135591
137.767.768919389268940.0228611326116433-0.00891938926893606-0.479562959226583
147.767.759914519516490.01779953220032418.54804835116081e-05-0.682317175534645
157.777.769911959494420.01654503131091398.80405055766801e-05-0.196813177933444
167.837.829923919863150.02356028876876897.60801368530244e-051.09615309066543
177.947.939943860431610.03755099795878975.61395683930133e-052.17990158004761
187.947.939936602501330.03146216211983856.339749867429e-05-0.946823552635257
197.947.939931508720760.02635414454852716.84912792434782e-05-0.793200122860028
208.098.089948274228330.04644643893510565.17257716723804e-053.11699660771905
218.188.179953219541070.05352824861689874.67804589345699e-051.09787822891675
228.268.259955736342090.05783443579806844.42636579128188e-050.667259493580475
238.288.279952724558530.05167798520519124.72754414694701e-05-0.953644339934985
248.288.27994928031180.04326709710551875.07196881949043e-05-1.30255387275861
258.288.287818190768960.0375929514288642-0.00781819076896485-0.969939021343275
268.298.289806829944190.03188995663225850.000193170055808733-0.81457050795472
278.38.299799105104040.02832386650653060.000200894895957535-0.551689911682417
288.38.299790738021170.02371024209800770.000209261978827828-0.713852183622498
298.318.309787347566250.02147721305352430.000212652433754479-0.345545158482727
308.338.329787041755440.021236630551990.000212958244557618-0.0372310755894764
318.338.329783361347160.01777813960264530.000216638652835544-0.53524176071904
328.348.339782232878430.01651147148025380.000217767121566926-0.196038543351975
338.488.479797231305620.03662109612036990.0002027686943759983.11238488854689
348.598.589804692304270.0485703758878710.0001953076957256991.84943285114186
358.678.669807367604330.053688430350510.0001926323956699200.792149223483837
368.678.669803541804510.0449457809283370.000196458195488437-1.35315905324315
378.678.677215580175370.0388864245590012-0.00721558017537371-1.00113850041526
388.718.7094157223470.03781170092902670.000584277653006517-0.157265106796134
398.728.719408109769310.03328063696630360.000591890230692181-0.700930959637714
408.728.719400484648380.02785939234252370.000599515351616428-0.838775358579526
418.728.719395141273380.02332171849653520.00060485872662086-0.702150360107278
428.748.739394607936050.02278072454780650.000605392063954994-0.0837190760111358
438.748.739391545928830.01907071822392260.000608454071174561-0.574157866944926
448.748.739389400034620.01596502595019730.000610599965376414-0.4806537862901
458.748.739387896146220.01336516592509150.00061210385378419-0.402379661059021
468.798.78939078513890.01933094041888760.0006092148610994620.923339410461482
478.858.849393470011160.02595358295460150.0006065299888386931.02501872946052
488.868.859392588301180.02335568779656970.000607411698818161-0.402092780351392
498.878.8772340068160.022463385363615-0.00723400681599721-0.145071746740841
508.928.919176842665020.02560261775727760.000823157334982050.465227470300943
518.968.959179988853460.02794800192423070.0008200111465392780.362856825217533
528.978.969176704745630.02502456359000040.000823295254371052-0.452348355170663
538.998.989175935096850.02420621322955840.00082406490315245-0.126636484180673
548.988.979171548808210.01863538113131040.000828451191791742-0.862120579036046
558.988.979169548338480.01560054514019880.00083045166151505-0.469681068759545
569.019.009170842366620.01794547736564670.000829157633376380.362920969542036
579.019.009169492290110.01502313995682510.00083050770989142-0.452295110641569
589.039.029169805738250.01583358679244070.0008301942617446370.125436206750590
599.059.049170025413130.01651205120046700.0008299745868737630.105009900970198
609.059.049169296581140.01382322524033070.000830703418861457-0.416168527928599


Figures (Output of Computation)

Input Parameters & R Code

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')