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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 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, 01 Nov 2024 00:37:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148327, Retrieved Fri, 01 Nov 2024 00:37:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

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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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





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:  
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:  
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


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')