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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 computationTue, 29 Nov 2011 08:23:42 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322573124wu1s88dx3y8gn1i.htm/, Retrieved Fri, 29 Mar 2024 04:42:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148327, Retrieved Fri, 29 Mar 2024 04:42:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2011-11-29 13:23:42] [204816f6f70a8d342ddc2b9d4f4a80d3] [Current]
- RMPD    [Exponential Smoothing] [] [2011-11-29 13:28:23] [80bca13c5f9401fbb753952fd2952f4a]
-    D      [Exponential Smoothing] [] [2011-11-29 13:45:43] [80bca13c5f9401fbb753952fd2952f4a]
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Dataseries X:
1,36
1,4
1,4
1,43
1,42
1,44
1,44
1,44
1,42
1,41
1,39
1,41
1,42
1,44
1,43
1,44
1,41
1,43
1,44
1,5
1,59
1,64
1,69
1,8
1,88
1,96
2,04
2,05
2,06
2,04
2,04
2,05
2,01
2,05
2,01
2,03
2,02
2
1,98
1,99
1,99
1,99
1,83
1,76
1,71
1,67
1,65
1,62
1,61
1,6
1,59
1,58
1,58
1,58
1,58
1,58
1,58
1,58
1,58
1,58
1,56
1,62
1,67
1,64
1,63
1,63
1,62




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148327&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148327&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148327&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 time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.361.36000
21.41.398100608336170.00624044456968520.001871654097691970.877106517928616
31.41.398143264978240.003735848137045720.00186545958310043-0.193295709474642
41.431.428118661465730.01524400424846160.001846437607289670.796606457082359
51.421.418269444365770.003966572007343360.00176339960423482-0.756439591766377
61.441.438106601473580.01115441207786980.001872718582438660.477602632450304
71.441.438223644212030.00614278877951210.00179072247277258-0.332071499982514
81.441.438185749526140.003333864467421310.00182229409934262-0.185965794735244
91.421.41824592643673-0.007246937243763490.00178436306799304-0.700333472737808
101.411.4081603915726-0.00853762193407310.0018433029029807-0.0854230046319552
111.391.38820435439655-0.01372981587161660.00181050740345335-0.343634072969069
121.411.408037311986840.001532376716715150.001919002862223451.01008859352371
131.421.432496079139620.011644716846645-0.01252501008023950.784009408038322
141.441.438909034853990.009491271700363870.001095391264568-0.128209897618626
151.431.429169497779980.0007078217836957380.000855220785696509-0.575001354104742
161.441.438708023924940.004724221723749190.001280577791118860.264723820090549
171.411.40925008899152-0.01080395413031250.000794150879397316-1.02651835615028
181.431.428623175203780.002902719148099770.001337733701251920.90683304623529
191.441.438965800871090.006282180794553880.001024558033723990.223637982659693
201.51.498575942136320.03050732578210820.001354940890726931.6032266088256
211.591.588712920379830.05759651925912620.001209789123319261.79280740334231
221.641.638978660280710.05426610845369850.00103084207552609-0.220413429329969
231.691.689042580476870.0523569669201470.000962866718529406-0.126351201764327
241.81.798578658884880.07833221578086020.00134722834245261.71910055039755
251.881.889722362423070.0840736599431202-0.009738754687255490.410637821980415
261.961.959188489125530.07783470748249050.000826263427903162-0.389916692763692
272.042.039646015742780.07902894666439130.0003506128396975910.0785509619015399
282.052.04933015954930.04752839503505050.000759108474520307-2.07878062222047
292.062.059829806849750.0307216292917710.000217954736883625-1.11146449814314
302.042.039200575018440.007416590407236860.000865699654678086-1.54203697001879
312.042.039365486523990.00412537115958480.000643874908598639-0.217805851950712
322.052.049194001531760.006713901436965330.0007986352981444640.171311344493377
332.012.00940473778957-0.01439356850217720.000655304258331109-1.39693123078028
342.052.048563374397550.009913945122797940.001367480378368141.60872265912755
352.012.01007375484606-0.0120574878161765-1.125466877386e-05-1.45411949774396
362.032.02865574611450.001847795459932640.001304699223421280.920290235363531
372.022.028935310973860.00114192412187991-0.00893330017833241-0.0491389829219518
3821.99978348136717-0.01201586456501920.000249432314937263-0.837748590649516
391.981.9794149060883-0.01581155040769110.000595812800112297-0.250266487575583
401.991.98910775056883-0.004237471006967440.0008595190745392860.764216081277499
411.991.9897021441658-0.00204617884481320.0002916446751321510.144939633339348
421.991.98936871349784-0.001269490284287910.0006290838000814520.0513943441753686
431.831.8314062464895-0.0723278330655473-0.00120469304407921-4.70256414767427
441.761.75857732940854-0.07255507669153190.00142331518600137-0.0150392655605177
451.711.70996704262761-0.06169573966741472.15358714078164e-060.718691918225985
461.671.66829945004129-0.05261249439326980.001674784157122910.601148673963686
471.651.65027771217577-0.0369240124173201-0.0003222147137889921.03830084353723
481.621.61901092527717-0.03435891919385470.0009817986635808160.169768111268073
491.611.6133036604805-0.0214392063235712-0.00334038453994530.887116562579553
501.61.59962462124861-0.0180363952298670.0003666062561479230.218802314055094
511.591.58971554240883-0.01434870234259290.0002740504952137230.243512924711575
521.581.57929943602274-0.01256559917129440.0006955324670844460.117770772217366
531.581.58034972940795-0.00639542140555435-0.0003671760575307380.408155110864177
541.581.57787734825687-0.004617761374833850.002117622933404650.117633398358729
551.581.58116579638985-0.00103511319863424-0.001175932577130860.237097958243877
561.581.5789185828483-0.001584384730183060.00108297123267807-0.0363515705110608
571.581.5801702062194-0.000299201602592665-0.0001738424913914050.0850559714010555
581.581.5788185509338-0.0007761466125926360.00118279853009764-0.0315652560949032
591.581.580383833072160.00028498266863307-0.0003868354307514950.070228149651161
601.581.57940911548639-0.0002857047676777450.000592499147251613-0.0377715341563189
611.561.56477104297988-0.00676307795273544-0.00475266899132322-0.441029176980186
621.621.61879746931130.0200556477558580.00113209151395481.73509545060754
631.671.668971302967910.03370461814105990.0009902180505894330.90222520246456
641.641.639901965307040.005263461775021080.000178125546740534-1.87884544803264
651.631.62989312424694-0.001652483612726630.000126387786036804-0.457511970733486
661.631.62743445312071-0.002017541606839560.00256657725819799-0.024157524977977
671.621.62070534055264-0.00415107821385725-0.000699317989519489-0.141197285730913

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.36 & 1.36 & 0 & 0 & 0 \tabularnewline
2 & 1.4 & 1.39810060833617 & 0.0062404445696852 & 0.00187165409769197 & 0.877106517928616 \tabularnewline
3 & 1.4 & 1.39814326497824 & 0.00373584813704572 & 0.00186545958310043 & -0.193295709474642 \tabularnewline
4 & 1.43 & 1.42811866146573 & 0.0152440042484616 & 0.00184643760728967 & 0.796606457082359 \tabularnewline
5 & 1.42 & 1.41826944436577 & 0.00396657200734336 & 0.00176339960423482 & -0.756439591766377 \tabularnewline
6 & 1.44 & 1.43810660147358 & 0.0111544120778698 & 0.00187271858243866 & 0.477602632450304 \tabularnewline
7 & 1.44 & 1.43822364421203 & 0.0061427887795121 & 0.00179072247277258 & -0.332071499982514 \tabularnewline
8 & 1.44 & 1.43818574952614 & 0.00333386446742131 & 0.00182229409934262 & -0.185965794735244 \tabularnewline
9 & 1.42 & 1.41824592643673 & -0.00724693724376349 & 0.00178436306799304 & -0.700333472737808 \tabularnewline
10 & 1.41 & 1.4081603915726 & -0.0085376219340731 & 0.0018433029029807 & -0.0854230046319552 \tabularnewline
11 & 1.39 & 1.38820435439655 & -0.0137298158716166 & 0.00181050740345335 & -0.343634072969069 \tabularnewline
12 & 1.41 & 1.40803731198684 & 0.00153237671671515 & 0.00191900286222345 & 1.01008859352371 \tabularnewline
13 & 1.42 & 1.43249607913962 & 0.011644716846645 & -0.0125250100802395 & 0.784009408038322 \tabularnewline
14 & 1.44 & 1.43890903485399 & 0.00949127170036387 & 0.001095391264568 & -0.128209897618626 \tabularnewline
15 & 1.43 & 1.42916949777998 & 0.000707821783695738 & 0.000855220785696509 & -0.575001354104742 \tabularnewline
16 & 1.44 & 1.43870802392494 & 0.00472422172374919 & 0.00128057779111886 & 0.264723820090549 \tabularnewline
17 & 1.41 & 1.40925008899152 & -0.0108039541303125 & 0.000794150879397316 & -1.02651835615028 \tabularnewline
18 & 1.43 & 1.42862317520378 & 0.00290271914809977 & 0.00133773370125192 & 0.90683304623529 \tabularnewline
19 & 1.44 & 1.43896580087109 & 0.00628218079455388 & 0.00102455803372399 & 0.223637982659693 \tabularnewline
20 & 1.5 & 1.49857594213632 & 0.0305073257821082 & 0.00135494089072693 & 1.6032266088256 \tabularnewline
21 & 1.59 & 1.58871292037983 & 0.0575965192591262 & 0.00120978912331926 & 1.79280740334231 \tabularnewline
22 & 1.64 & 1.63897866028071 & 0.0542661084536985 & 0.00103084207552609 & -0.220413429329969 \tabularnewline
23 & 1.69 & 1.68904258047687 & 0.052356966920147 & 0.000962866718529406 & -0.126351201764327 \tabularnewline
24 & 1.8 & 1.79857865888488 & 0.0783322157808602 & 0.0013472283424526 & 1.71910055039755 \tabularnewline
25 & 1.88 & 1.88972236242307 & 0.0840736599431202 & -0.00973875468725549 & 0.410637821980415 \tabularnewline
26 & 1.96 & 1.95918848912553 & 0.0778347074824905 & 0.000826263427903162 & -0.389916692763692 \tabularnewline
27 & 2.04 & 2.03964601574278 & 0.0790289466643913 & 0.000350612839697591 & 0.0785509619015399 \tabularnewline
28 & 2.05 & 2.0493301595493 & 0.0475283950350505 & 0.000759108474520307 & -2.07878062222047 \tabularnewline
29 & 2.06 & 2.05982980684975 & 0.030721629291771 & 0.000217954736883625 & -1.11146449814314 \tabularnewline
30 & 2.04 & 2.03920057501844 & 0.00741659040723686 & 0.000865699654678086 & -1.54203697001879 \tabularnewline
31 & 2.04 & 2.03936548652399 & 0.0041253711595848 & 0.000643874908598639 & -0.217805851950712 \tabularnewline
32 & 2.05 & 2.04919400153176 & 0.00671390143696533 & 0.000798635298144464 & 0.171311344493377 \tabularnewline
33 & 2.01 & 2.00940473778957 & -0.0143935685021772 & 0.000655304258331109 & -1.39693123078028 \tabularnewline
34 & 2.05 & 2.04856337439755 & 0.00991394512279794 & 0.00136748037836814 & 1.60872265912755 \tabularnewline
35 & 2.01 & 2.01007375484606 & -0.0120574878161765 & -1.125466877386e-05 & -1.45411949774396 \tabularnewline
36 & 2.03 & 2.0286557461145 & 0.00184779545993264 & 0.00130469922342128 & 0.920290235363531 \tabularnewline
37 & 2.02 & 2.02893531097386 & 0.00114192412187991 & -0.00893330017833241 & -0.0491389829219518 \tabularnewline
38 & 2 & 1.99978348136717 & -0.0120158645650192 & 0.000249432314937263 & -0.837748590649516 \tabularnewline
39 & 1.98 & 1.9794149060883 & -0.0158115504076911 & 0.000595812800112297 & -0.250266487575583 \tabularnewline
40 & 1.99 & 1.98910775056883 & -0.00423747100696744 & 0.000859519074539286 & 0.764216081277499 \tabularnewline
41 & 1.99 & 1.9897021441658 & -0.0020461788448132 & 0.000291644675132151 & 0.144939633339348 \tabularnewline
42 & 1.99 & 1.98936871349784 & -0.00126949028428791 & 0.000629083800081452 & 0.0513943441753686 \tabularnewline
43 & 1.83 & 1.8314062464895 & -0.0723278330655473 & -0.00120469304407921 & -4.70256414767427 \tabularnewline
44 & 1.76 & 1.75857732940854 & -0.0725550766915319 & 0.00142331518600137 & -0.0150392655605177 \tabularnewline
45 & 1.71 & 1.70996704262761 & -0.0616957396674147 & 2.15358714078164e-06 & 0.718691918225985 \tabularnewline
46 & 1.67 & 1.66829945004129 & -0.0526124943932698 & 0.00167478415712291 & 0.601148673963686 \tabularnewline
47 & 1.65 & 1.65027771217577 & -0.0369240124173201 & -0.000322214713788992 & 1.03830084353723 \tabularnewline
48 & 1.62 & 1.61901092527717 & -0.0343589191938547 & 0.000981798663580816 & 0.169768111268073 \tabularnewline
49 & 1.61 & 1.6133036604805 & -0.0214392063235712 & -0.0033403845399453 & 0.887116562579553 \tabularnewline
50 & 1.6 & 1.59962462124861 & -0.018036395229867 & 0.000366606256147923 & 0.218802314055094 \tabularnewline
51 & 1.59 & 1.58971554240883 & -0.0143487023425929 & 0.000274050495213723 & 0.243512924711575 \tabularnewline
52 & 1.58 & 1.57929943602274 & -0.0125655991712944 & 0.000695532467084446 & 0.117770772217366 \tabularnewline
53 & 1.58 & 1.58034972940795 & -0.00639542140555435 & -0.000367176057530738 & 0.408155110864177 \tabularnewline
54 & 1.58 & 1.57787734825687 & -0.00461776137483385 & 0.00211762293340465 & 0.117633398358729 \tabularnewline
55 & 1.58 & 1.58116579638985 & -0.00103511319863424 & -0.00117593257713086 & 0.237097958243877 \tabularnewline
56 & 1.58 & 1.5789185828483 & -0.00158438473018306 & 0.00108297123267807 & -0.0363515705110608 \tabularnewline
57 & 1.58 & 1.5801702062194 & -0.000299201602592665 & -0.000173842491391405 & 0.0850559714010555 \tabularnewline
58 & 1.58 & 1.5788185509338 & -0.000776146612592636 & 0.00118279853009764 & -0.0315652560949032 \tabularnewline
59 & 1.58 & 1.58038383307216 & 0.00028498266863307 & -0.000386835430751495 & 0.070228149651161 \tabularnewline
60 & 1.58 & 1.57940911548639 & -0.000285704767677745 & 0.000592499147251613 & -0.0377715341563189 \tabularnewline
61 & 1.56 & 1.56477104297988 & -0.00676307795273544 & -0.00475266899132322 & -0.441029176980186 \tabularnewline
62 & 1.62 & 1.6187974693113 & 0.020055647755858 & 0.0011320915139548 & 1.73509545060754 \tabularnewline
63 & 1.67 & 1.66897130296791 & 0.0337046181410599 & 0.000990218050589433 & 0.90222520246456 \tabularnewline
64 & 1.64 & 1.63990196530704 & 0.00526346177502108 & 0.000178125546740534 & -1.87884544803264 \tabularnewline
65 & 1.63 & 1.62989312424694 & -0.00165248361272663 & 0.000126387786036804 & -0.457511970733486 \tabularnewline
66 & 1.63 & 1.62743445312071 & -0.00201754160683956 & 0.00256657725819799 & -0.024157524977977 \tabularnewline
67 & 1.62 & 1.62070534055264 & -0.00415107821385725 & -0.000699317989519489 & -0.141197285730913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148327&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]1.36[/C][C]1.36[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.4[/C][C]1.39810060833617[/C][C]0.0062404445696852[/C][C]0.00187165409769197[/C][C]0.877106517928616[/C][/ROW]
[ROW][C]3[/C][C]1.4[/C][C]1.39814326497824[/C][C]0.00373584813704572[/C][C]0.00186545958310043[/C][C]-0.193295709474642[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.42811866146573[/C][C]0.0152440042484616[/C][C]0.00184643760728967[/C][C]0.796606457082359[/C][/ROW]
[ROW][C]5[/C][C]1.42[/C][C]1.41826944436577[/C][C]0.00396657200734336[/C][C]0.00176339960423482[/C][C]-0.756439591766377[/C][/ROW]
[ROW][C]6[/C][C]1.44[/C][C]1.43810660147358[/C][C]0.0111544120778698[/C][C]0.00187271858243866[/C][C]0.477602632450304[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.43822364421203[/C][C]0.0061427887795121[/C][C]0.00179072247277258[/C][C]-0.332071499982514[/C][/ROW]
[ROW][C]8[/C][C]1.44[/C][C]1.43818574952614[/C][C]0.00333386446742131[/C][C]0.00182229409934262[/C][C]-0.185965794735244[/C][/ROW]
[ROW][C]9[/C][C]1.42[/C][C]1.41824592643673[/C][C]-0.00724693724376349[/C][C]0.00178436306799304[/C][C]-0.700333472737808[/C][/ROW]
[ROW][C]10[/C][C]1.41[/C][C]1.4081603915726[/C][C]-0.0085376219340731[/C][C]0.0018433029029807[/C][C]-0.0854230046319552[/C][/ROW]
[ROW][C]11[/C][C]1.39[/C][C]1.38820435439655[/C][C]-0.0137298158716166[/C][C]0.00181050740345335[/C][C]-0.343634072969069[/C][/ROW]
[ROW][C]12[/C][C]1.41[/C][C]1.40803731198684[/C][C]0.00153237671671515[/C][C]0.00191900286222345[/C][C]1.01008859352371[/C][/ROW]
[ROW][C]13[/C][C]1.42[/C][C]1.43249607913962[/C][C]0.011644716846645[/C][C]-0.0125250100802395[/C][C]0.784009408038322[/C][/ROW]
[ROW][C]14[/C][C]1.44[/C][C]1.43890903485399[/C][C]0.00949127170036387[/C][C]0.001095391264568[/C][C]-0.128209897618626[/C][/ROW]
[ROW][C]15[/C][C]1.43[/C][C]1.42916949777998[/C][C]0.000707821783695738[/C][C]0.000855220785696509[/C][C]-0.575001354104742[/C][/ROW]
[ROW][C]16[/C][C]1.44[/C][C]1.43870802392494[/C][C]0.00472422172374919[/C][C]0.00128057779111886[/C][C]0.264723820090549[/C][/ROW]
[ROW][C]17[/C][C]1.41[/C][C]1.40925008899152[/C][C]-0.0108039541303125[/C][C]0.000794150879397316[/C][C]-1.02651835615028[/C][/ROW]
[ROW][C]18[/C][C]1.43[/C][C]1.42862317520378[/C][C]0.00290271914809977[/C][C]0.00133773370125192[/C][C]0.90683304623529[/C][/ROW]
[ROW][C]19[/C][C]1.44[/C][C]1.43896580087109[/C][C]0.00628218079455388[/C][C]0.00102455803372399[/C][C]0.223637982659693[/C][/ROW]
[ROW][C]20[/C][C]1.5[/C][C]1.49857594213632[/C][C]0.0305073257821082[/C][C]0.00135494089072693[/C][C]1.6032266088256[/C][/ROW]
[ROW][C]21[/C][C]1.59[/C][C]1.58871292037983[/C][C]0.0575965192591262[/C][C]0.00120978912331926[/C][C]1.79280740334231[/C][/ROW]
[ROW][C]22[/C][C]1.64[/C][C]1.63897866028071[/C][C]0.0542661084536985[/C][C]0.00103084207552609[/C][C]-0.220413429329969[/C][/ROW]
[ROW][C]23[/C][C]1.69[/C][C]1.68904258047687[/C][C]0.052356966920147[/C][C]0.000962866718529406[/C][C]-0.126351201764327[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]1.79857865888488[/C][C]0.0783322157808602[/C][C]0.0013472283424526[/C][C]1.71910055039755[/C][/ROW]
[ROW][C]25[/C][C]1.88[/C][C]1.88972236242307[/C][C]0.0840736599431202[/C][C]-0.00973875468725549[/C][C]0.410637821980415[/C][/ROW]
[ROW][C]26[/C][C]1.96[/C][C]1.95918848912553[/C][C]0.0778347074824905[/C][C]0.000826263427903162[/C][C]-0.389916692763692[/C][/ROW]
[ROW][C]27[/C][C]2.04[/C][C]2.03964601574278[/C][C]0.0790289466643913[/C][C]0.000350612839697591[/C][C]0.0785509619015399[/C][/ROW]
[ROW][C]28[/C][C]2.05[/C][C]2.0493301595493[/C][C]0.0475283950350505[/C][C]0.000759108474520307[/C][C]-2.07878062222047[/C][/ROW]
[ROW][C]29[/C][C]2.06[/C][C]2.05982980684975[/C][C]0.030721629291771[/C][C]0.000217954736883625[/C][C]-1.11146449814314[/C][/ROW]
[ROW][C]30[/C][C]2.04[/C][C]2.03920057501844[/C][C]0.00741659040723686[/C][C]0.000865699654678086[/C][C]-1.54203697001879[/C][/ROW]
[ROW][C]31[/C][C]2.04[/C][C]2.03936548652399[/C][C]0.0041253711595848[/C][C]0.000643874908598639[/C][C]-0.217805851950712[/C][/ROW]
[ROW][C]32[/C][C]2.05[/C][C]2.04919400153176[/C][C]0.00671390143696533[/C][C]0.000798635298144464[/C][C]0.171311344493377[/C][/ROW]
[ROW][C]33[/C][C]2.01[/C][C]2.00940473778957[/C][C]-0.0143935685021772[/C][C]0.000655304258331109[/C][C]-1.39693123078028[/C][/ROW]
[ROW][C]34[/C][C]2.05[/C][C]2.04856337439755[/C][C]0.00991394512279794[/C][C]0.00136748037836814[/C][C]1.60872265912755[/C][/ROW]
[ROW][C]35[/C][C]2.01[/C][C]2.01007375484606[/C][C]-0.0120574878161765[/C][C]-1.125466877386e-05[/C][C]-1.45411949774396[/C][/ROW]
[ROW][C]36[/C][C]2.03[/C][C]2.0286557461145[/C][C]0.00184779545993264[/C][C]0.00130469922342128[/C][C]0.920290235363531[/C][/ROW]
[ROW][C]37[/C][C]2.02[/C][C]2.02893531097386[/C][C]0.00114192412187991[/C][C]-0.00893330017833241[/C][C]-0.0491389829219518[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]1.99978348136717[/C][C]-0.0120158645650192[/C][C]0.000249432314937263[/C][C]-0.837748590649516[/C][/ROW]
[ROW][C]39[/C][C]1.98[/C][C]1.9794149060883[/C][C]-0.0158115504076911[/C][C]0.000595812800112297[/C][C]-0.250266487575583[/C][/ROW]
[ROW][C]40[/C][C]1.99[/C][C]1.98910775056883[/C][C]-0.00423747100696744[/C][C]0.000859519074539286[/C][C]0.764216081277499[/C][/ROW]
[ROW][C]41[/C][C]1.99[/C][C]1.9897021441658[/C][C]-0.0020461788448132[/C][C]0.000291644675132151[/C][C]0.144939633339348[/C][/ROW]
[ROW][C]42[/C][C]1.99[/C][C]1.98936871349784[/C][C]-0.00126949028428791[/C][C]0.000629083800081452[/C][C]0.0513943441753686[/C][/ROW]
[ROW][C]43[/C][C]1.83[/C][C]1.8314062464895[/C][C]-0.0723278330655473[/C][C]-0.00120469304407921[/C][C]-4.70256414767427[/C][/ROW]
[ROW][C]44[/C][C]1.76[/C][C]1.75857732940854[/C][C]-0.0725550766915319[/C][C]0.00142331518600137[/C][C]-0.0150392655605177[/C][/ROW]
[ROW][C]45[/C][C]1.71[/C][C]1.70996704262761[/C][C]-0.0616957396674147[/C][C]2.15358714078164e-06[/C][C]0.718691918225985[/C][/ROW]
[ROW][C]46[/C][C]1.67[/C][C]1.66829945004129[/C][C]-0.0526124943932698[/C][C]0.00167478415712291[/C][C]0.601148673963686[/C][/ROW]
[ROW][C]47[/C][C]1.65[/C][C]1.65027771217577[/C][C]-0.0369240124173201[/C][C]-0.000322214713788992[/C][C]1.03830084353723[/C][/ROW]
[ROW][C]48[/C][C]1.62[/C][C]1.61901092527717[/C][C]-0.0343589191938547[/C][C]0.000981798663580816[/C][C]0.169768111268073[/C][/ROW]
[ROW][C]49[/C][C]1.61[/C][C]1.6133036604805[/C][C]-0.0214392063235712[/C][C]-0.0033403845399453[/C][C]0.887116562579553[/C][/ROW]
[ROW][C]50[/C][C]1.6[/C][C]1.59962462124861[/C][C]-0.018036395229867[/C][C]0.000366606256147923[/C][C]0.218802314055094[/C][/ROW]
[ROW][C]51[/C][C]1.59[/C][C]1.58971554240883[/C][C]-0.0143487023425929[/C][C]0.000274050495213723[/C][C]0.243512924711575[/C][/ROW]
[ROW][C]52[/C][C]1.58[/C][C]1.57929943602274[/C][C]-0.0125655991712944[/C][C]0.000695532467084446[/C][C]0.117770772217366[/C][/ROW]
[ROW][C]53[/C][C]1.58[/C][C]1.58034972940795[/C][C]-0.00639542140555435[/C][C]-0.000367176057530738[/C][C]0.408155110864177[/C][/ROW]
[ROW][C]54[/C][C]1.58[/C][C]1.57787734825687[/C][C]-0.00461776137483385[/C][C]0.00211762293340465[/C][C]0.117633398358729[/C][/ROW]
[ROW][C]55[/C][C]1.58[/C][C]1.58116579638985[/C][C]-0.00103511319863424[/C][C]-0.00117593257713086[/C][C]0.237097958243877[/C][/ROW]
[ROW][C]56[/C][C]1.58[/C][C]1.5789185828483[/C][C]-0.00158438473018306[/C][C]0.00108297123267807[/C][C]-0.0363515705110608[/C][/ROW]
[ROW][C]57[/C][C]1.58[/C][C]1.5801702062194[/C][C]-0.000299201602592665[/C][C]-0.000173842491391405[/C][C]0.0850559714010555[/C][/ROW]
[ROW][C]58[/C][C]1.58[/C][C]1.5788185509338[/C][C]-0.000776146612592636[/C][C]0.00118279853009764[/C][C]-0.0315652560949032[/C][/ROW]
[ROW][C]59[/C][C]1.58[/C][C]1.58038383307216[/C][C]0.00028498266863307[/C][C]-0.000386835430751495[/C][C]0.070228149651161[/C][/ROW]
[ROW][C]60[/C][C]1.58[/C][C]1.57940911548639[/C][C]-0.000285704767677745[/C][C]0.000592499147251613[/C][C]-0.0377715341563189[/C][/ROW]
[ROW][C]61[/C][C]1.56[/C][C]1.56477104297988[/C][C]-0.00676307795273544[/C][C]-0.00475266899132322[/C][C]-0.441029176980186[/C][/ROW]
[ROW][C]62[/C][C]1.62[/C][C]1.6187974693113[/C][C]0.020055647755858[/C][C]0.0011320915139548[/C][C]1.73509545060754[/C][/ROW]
[ROW][C]63[/C][C]1.67[/C][C]1.66897130296791[/C][C]0.0337046181410599[/C][C]0.000990218050589433[/C][C]0.90222520246456[/C][/ROW]
[ROW][C]64[/C][C]1.64[/C][C]1.63990196530704[/C][C]0.00526346177502108[/C][C]0.000178125546740534[/C][C]-1.87884544803264[/C][/ROW]
[ROW][C]65[/C][C]1.63[/C][C]1.62989312424694[/C][C]-0.00165248361272663[/C][C]0.000126387786036804[/C][C]-0.457511970733486[/C][/ROW]
[ROW][C]66[/C][C]1.63[/C][C]1.62743445312071[/C][C]-0.00201754160683956[/C][C]0.00256657725819799[/C][C]-0.024157524977977[/C][/ROW]
[ROW][C]67[/C][C]1.62[/C][C]1.62070534055264[/C][C]-0.00415107821385725[/C][C]-0.000699317989519489[/C][C]-0.141197285730913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148327&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148327&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
11.361.36000
21.41.398100608336170.00624044456968520.001871654097691970.877106517928616
31.41.398143264978240.003735848137045720.00186545958310043-0.193295709474642
41.431.428118661465730.01524400424846160.001846437607289670.796606457082359
51.421.418269444365770.003966572007343360.00176339960423482-0.756439591766377
61.441.438106601473580.01115441207786980.001872718582438660.477602632450304
71.441.438223644212030.00614278877951210.00179072247277258-0.332071499982514
81.441.438185749526140.003333864467421310.00182229409934262-0.185965794735244
91.421.41824592643673-0.007246937243763490.00178436306799304-0.700333472737808
101.411.4081603915726-0.00853762193407310.0018433029029807-0.0854230046319552
111.391.38820435439655-0.01372981587161660.00181050740345335-0.343634072969069
121.411.408037311986840.001532376716715150.001919002862223451.01008859352371
131.421.432496079139620.011644716846645-0.01252501008023950.784009408038322
141.441.438909034853990.009491271700363870.001095391264568-0.128209897618626
151.431.429169497779980.0007078217836957380.000855220785696509-0.575001354104742
161.441.438708023924940.004724221723749190.001280577791118860.264723820090549
171.411.40925008899152-0.01080395413031250.000794150879397316-1.02651835615028
181.431.428623175203780.002902719148099770.001337733701251920.90683304623529
191.441.438965800871090.006282180794553880.001024558033723990.223637982659693
201.51.498575942136320.03050732578210820.001354940890726931.6032266088256
211.591.588712920379830.05759651925912620.001209789123319261.79280740334231
221.641.638978660280710.05426610845369850.00103084207552609-0.220413429329969
231.691.689042580476870.0523569669201470.000962866718529406-0.126351201764327
241.81.798578658884880.07833221578086020.00134722834245261.71910055039755
251.881.889722362423070.0840736599431202-0.009738754687255490.410637821980415
261.961.959188489125530.07783470748249050.000826263427903162-0.389916692763692
272.042.039646015742780.07902894666439130.0003506128396975910.0785509619015399
282.052.04933015954930.04752839503505050.000759108474520307-2.07878062222047
292.062.059829806849750.0307216292917710.000217954736883625-1.11146449814314
302.042.039200575018440.007416590407236860.000865699654678086-1.54203697001879
312.042.039365486523990.00412537115958480.000643874908598639-0.217805851950712
322.052.049194001531760.006713901436965330.0007986352981444640.171311344493377
332.012.00940473778957-0.01439356850217720.000655304258331109-1.39693123078028
342.052.048563374397550.009913945122797940.001367480378368141.60872265912755
352.012.01007375484606-0.0120574878161765-1.125466877386e-05-1.45411949774396
362.032.02865574611450.001847795459932640.001304699223421280.920290235363531
372.022.028935310973860.00114192412187991-0.00893330017833241-0.0491389829219518
3821.99978348136717-0.01201586456501920.000249432314937263-0.837748590649516
391.981.9794149060883-0.01581155040769110.000595812800112297-0.250266487575583
401.991.98910775056883-0.004237471006967440.0008595190745392860.764216081277499
411.991.9897021441658-0.00204617884481320.0002916446751321510.144939633339348
421.991.98936871349784-0.001269490284287910.0006290838000814520.0513943441753686
431.831.8314062464895-0.0723278330655473-0.00120469304407921-4.70256414767427
441.761.75857732940854-0.07255507669153190.00142331518600137-0.0150392655605177
451.711.70996704262761-0.06169573966741472.15358714078164e-060.718691918225985
461.671.66829945004129-0.05261249439326980.001674784157122910.601148673963686
471.651.65027771217577-0.0369240124173201-0.0003222147137889921.03830084353723
481.621.61901092527717-0.03435891919385470.0009817986635808160.169768111268073
491.611.6133036604805-0.0214392063235712-0.00334038453994530.887116562579553
501.61.59962462124861-0.0180363952298670.0003666062561479230.218802314055094
511.591.58971554240883-0.01434870234259290.0002740504952137230.243512924711575
521.581.57929943602274-0.01256559917129440.0006955324670844460.117770772217366
531.581.58034972940795-0.00639542140555435-0.0003671760575307380.408155110864177
541.581.57787734825687-0.004617761374833850.002117622933404650.117633398358729
551.581.58116579638985-0.00103511319863424-0.001175932577130860.237097958243877
561.581.5789185828483-0.001584384730183060.00108297123267807-0.0363515705110608
571.581.5801702062194-0.000299201602592665-0.0001738424913914050.0850559714010555
581.581.5788185509338-0.0007761466125926360.00118279853009764-0.0315652560949032
591.581.580383833072160.00028498266863307-0.0003868354307514950.070228149651161
601.581.57940911548639-0.0002857047676777450.000592499147251613-0.0377715341563189
611.561.56477104297988-0.00676307795273544-0.00475266899132322-0.441029176980186
621.621.61879746931130.0200556477558580.00113209151395481.73509545060754
631.671.668971302967910.03370461814105990.0009902180505894330.90222520246456
641.641.639901965307040.005263461775021080.000178125546740534-1.87884544803264
651.631.62989312424694-0.001652483612726630.000126387786036804-0.457511970733486
661.631.62743445312071-0.002017541606839560.00256657725819799-0.024157524977977
671.621.62070534055264-0.00415107821385725-0.000699317989519489-0.141197285730913



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')