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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 10:59:51 -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/04/t12599496934a9me2ep0k0q8z2.htm/, Retrieved Sat, 27 Apr 2024 17:07:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63988, Retrieved Sat, 27 Apr 2024 17:07:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [] [2009-12-04 17:59:51] [0545e25c765ce26b196961216dc11e13] [Current]
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Dataseries X:
1.4
1.2
1
1.7
2.4
2
2.1
2
1.8
2.7
2.3
1.9
2
2.3
2.8
2.4
2.3
2.7
2.7
2.9
3
2.2
2.3
2.8
2.8
2.8
2.2
2.6
2.8
2.5
2.4
2.3
1.9
1.7
2
2.1
1.7
1.8
1.8
1.8
1.3
1.3
1.3
1.2
1.4
2.2
2.9
3.1
3.5
3.6
4.4
4.1
5.1
5.8
5.9
5.4
5.5
4.8
3.2
2.7




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=63988&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=63988&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63988&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.41.4000
21.21.21021463649639-0.0137793058873016-0.0102146364963925-0.267829279109299
311.01043086295789-0.0321499223549408-0.0104308629578906-0.419696617100316
41.71.709694609751830.0659163658073972-0.00969460975183211.61746170371269
52.42.409158758232750.167134886666108-0.009158758232751071.37988419523486
622.009553015721140.0665326347807482-0.00955301572114442-1.22108225604380
72.12.10953415263330.0728656639364329-0.009534152633302040.0715370596606255
822.009612395983640.0388213214879076-0.00961239598363637-0.367741955205151
91.81.80969866963719-0.00939271723135667-0.009698669637191-0.506485501705009
102.72.709437505675780.177050179052900-0.00943750567577751.92482183922168
112.32.309568922427420.0576030943148097-0.00956892242742046-1.21986561752445
121.91.90965143519821-0.0376869115642739-0.00965143519821298-0.966600937801458
1321.88741471682334-0.0345997354155350.1125852831766560.0381016776443702
142.32.300153473139780.0561466540338654-0.0001534731397790470.795823001790371
152.82.800492303795020.149403093529309-0.0004923037950228960.936449824744887
162.42.400161048524810.0339282693520079-0.000161048524806911-1.15908489643239
172.32.300097274362920.005772571716646-9.72743629246727e-05-0.282542240483822
182.72.700245527504990.088662521672981-0.0002455275049904820.83166771870982
192.72.700219196248340.0700187763253945-0.000219196248337363-0.187041519589758
202.92.900249680830650.0973524136888926-0.0002496808306499470.274205022819628
2133.000250171189410.0979091912553759-0.0002501711894069880.00558525417386132
222.22.20011884300216-0.0909217876818235-0.000118843002161278-1.89419202375357
232.32.30014089465854-0.0507702600785963-0.0001408946585421500.402759981721342
242.82.800191130703710.065059631657089-0.0001911307037063051.16187879105915
252.82.808967593027260.0533968710683159-0.00896759302726437-0.128662803824972
262.82.800085432818440.0406146265049172-8.54328184351252e-05-0.118724163697982
272.22.19973221676550-0.0942670606967160.000267783234502387-1.35120041722536
282.62.599947385918580.009755996398805265.26140814156697e-051.04258069269520
292.82.800012779506730.0497837810949955-1.27795067309705e-050.401305050926975
302.52.49991783951061-0.02379914491743508.2160489385259e-05-0.737858471396365
312.42.39990150722762-0.03982759708245149.84927723785144e-05-0.160745685189536
322.32.29989132296714-0.05248371844228960.000108677032861557-0.126934777289846
331.91.89984487602571-0.1255741760819340.000155123974291983-0.7330959007705
341.71.69983702080515-0.1412271948401710.000162979194849584-0.157004015417349
3521.99987379571457-0.04843107366746970.0001262042854341170.930786980775447
362.12.09988356516023-0.01721429572781590.0001164348397653610.313121890255179
371.71.74478650525524-0.08760710254893-0.0447865052552376-0.75259889458892
381.81.79446394239242-0.05925263495386080.005536057607575420.269399271041521
391.81.79448828045119-0.04678048813051010.00551171954880960.124981841330634
401.81.79450345341155-0.03693683622818040.005496546588446780.0986785923979794
411.31.29438485896281-0.1343557177263650.00561514103719068-0.976810310368645
421.31.29441203045880-0.1060936581149300.005587969541196860.283421970463178
431.31.29442897341819-0.08377836353505520.005571026581807390.223805818398663
441.21.19442692771472-0.0871901856343430.00557307228527782-0.0342199581975331
451.41.39445552793331-0.02678865997113490.00554447206669120.605837552048657
462.22.194520548123480.1470972808161080.005479451876523051.74414351975505
472.92.894554884764150.2633795432299890.005445115235849171.16637211201761
483.13.094551776525170.2500501431812120.00544822347482587-0.133702010837959
493.53.541124570858710.29109269001546-0.04112457085871350.431964445297103
503.63.598239061596720.242584469172300.00176093840328068-0.466574855304682
514.44.398420950375450.3598944991569320.001579049624552541.17578700212252
524.14.098250925585340.221053513401720.00174907441466244-1.39199970347889
535.15.09840940466550.3849163221222790.001590595334498981.64316674544985
545.85.798460025406920.4511920902442370.001539974593082620.664669527999577
555.95.898415470771780.3773258137318510.00158452922821826-0.740846503152483
565.45.398327576779420.1928050202541420.00167242322058052-1.85074545419029
575.55.498320234638630.1732865747403760.00167976536136757-0.195775648573712
584.84.79826567609804-0.01037736656274930.00173432390195885-1.84223432328944
593.23.19818725086379-0.3446932515364860.00181274913620764-3.35337926816011
602.72.6981812001219-0.3773558490676030.00181879987810306-0.327626715890255

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.4 & 1.4 & 0 & 0 & 0 \tabularnewline
2 & 1.2 & 1.21021463649639 & -0.0137793058873016 & -0.0102146364963925 & -0.267829279109299 \tabularnewline
3 & 1 & 1.01043086295789 & -0.0321499223549408 & -0.0104308629578906 & -0.419696617100316 \tabularnewline
4 & 1.7 & 1.70969460975183 & 0.0659163658073972 & -0.0096946097518321 & 1.61746170371269 \tabularnewline
5 & 2.4 & 2.40915875823275 & 0.167134886666108 & -0.00915875823275107 & 1.37988419523486 \tabularnewline
6 & 2 & 2.00955301572114 & 0.0665326347807482 & -0.00955301572114442 & -1.22108225604380 \tabularnewline
7 & 2.1 & 2.1095341526333 & 0.0728656639364329 & -0.00953415263330204 & 0.0715370596606255 \tabularnewline
8 & 2 & 2.00961239598364 & 0.0388213214879076 & -0.00961239598363637 & -0.367741955205151 \tabularnewline
9 & 1.8 & 1.80969866963719 & -0.00939271723135667 & -0.009698669637191 & -0.506485501705009 \tabularnewline
10 & 2.7 & 2.70943750567578 & 0.177050179052900 & -0.0094375056757775 & 1.92482183922168 \tabularnewline
11 & 2.3 & 2.30956892242742 & 0.0576030943148097 & -0.00956892242742046 & -1.21986561752445 \tabularnewline
12 & 1.9 & 1.90965143519821 & -0.0376869115642739 & -0.00965143519821298 & -0.966600937801458 \tabularnewline
13 & 2 & 1.88741471682334 & -0.034599735415535 & 0.112585283176656 & 0.0381016776443702 \tabularnewline
14 & 2.3 & 2.30015347313978 & 0.0561466540338654 & -0.000153473139779047 & 0.795823001790371 \tabularnewline
15 & 2.8 & 2.80049230379502 & 0.149403093529309 & -0.000492303795022896 & 0.936449824744887 \tabularnewline
16 & 2.4 & 2.40016104852481 & 0.0339282693520079 & -0.000161048524806911 & -1.15908489643239 \tabularnewline
17 & 2.3 & 2.30009727436292 & 0.005772571716646 & -9.72743629246727e-05 & -0.282542240483822 \tabularnewline
18 & 2.7 & 2.70024552750499 & 0.088662521672981 & -0.000245527504990482 & 0.83166771870982 \tabularnewline
19 & 2.7 & 2.70021919624834 & 0.0700187763253945 & -0.000219196248337363 & -0.187041519589758 \tabularnewline
20 & 2.9 & 2.90024968083065 & 0.0973524136888926 & -0.000249680830649947 & 0.274205022819628 \tabularnewline
21 & 3 & 3.00025017118941 & 0.0979091912553759 & -0.000250171189406988 & 0.00558525417386132 \tabularnewline
22 & 2.2 & 2.20011884300216 & -0.0909217876818235 & -0.000118843002161278 & -1.89419202375357 \tabularnewline
23 & 2.3 & 2.30014089465854 & -0.0507702600785963 & -0.000140894658542150 & 0.402759981721342 \tabularnewline
24 & 2.8 & 2.80019113070371 & 0.065059631657089 & -0.000191130703706305 & 1.16187879105915 \tabularnewline
25 & 2.8 & 2.80896759302726 & 0.0533968710683159 & -0.00896759302726437 & -0.128662803824972 \tabularnewline
26 & 2.8 & 2.80008543281844 & 0.0406146265049172 & -8.54328184351252e-05 & -0.118724163697982 \tabularnewline
27 & 2.2 & 2.19973221676550 & -0.094267060696716 & 0.000267783234502387 & -1.35120041722536 \tabularnewline
28 & 2.6 & 2.59994738591858 & 0.00975599639880526 & 5.26140814156697e-05 & 1.04258069269520 \tabularnewline
29 & 2.8 & 2.80001277950673 & 0.0497837810949955 & -1.27795067309705e-05 & 0.401305050926975 \tabularnewline
30 & 2.5 & 2.49991783951061 & -0.0237991449174350 & 8.2160489385259e-05 & -0.737858471396365 \tabularnewline
31 & 2.4 & 2.39990150722762 & -0.0398275970824514 & 9.84927723785144e-05 & -0.160745685189536 \tabularnewline
32 & 2.3 & 2.29989132296714 & -0.0524837184422896 & 0.000108677032861557 & -0.126934777289846 \tabularnewline
33 & 1.9 & 1.89984487602571 & -0.125574176081934 & 0.000155123974291983 & -0.7330959007705 \tabularnewline
34 & 1.7 & 1.69983702080515 & -0.141227194840171 & 0.000162979194849584 & -0.157004015417349 \tabularnewline
35 & 2 & 1.99987379571457 & -0.0484310736674697 & 0.000126204285434117 & 0.930786980775447 \tabularnewline
36 & 2.1 & 2.09988356516023 & -0.0172142957278159 & 0.000116434839765361 & 0.313121890255179 \tabularnewline
37 & 1.7 & 1.74478650525524 & -0.08760710254893 & -0.0447865052552376 & -0.75259889458892 \tabularnewline
38 & 1.8 & 1.79446394239242 & -0.0592526349538608 & 0.00553605760757542 & 0.269399271041521 \tabularnewline
39 & 1.8 & 1.79448828045119 & -0.0467804881305101 & 0.0055117195488096 & 0.124981841330634 \tabularnewline
40 & 1.8 & 1.79450345341155 & -0.0369368362281804 & 0.00549654658844678 & 0.0986785923979794 \tabularnewline
41 & 1.3 & 1.29438485896281 & -0.134355717726365 & 0.00561514103719068 & -0.976810310368645 \tabularnewline
42 & 1.3 & 1.29441203045880 & -0.106093658114930 & 0.00558796954119686 & 0.283421970463178 \tabularnewline
43 & 1.3 & 1.29442897341819 & -0.0837783635350552 & 0.00557102658180739 & 0.223805818398663 \tabularnewline
44 & 1.2 & 1.19442692771472 & -0.087190185634343 & 0.00557307228527782 & -0.0342199581975331 \tabularnewline
45 & 1.4 & 1.39445552793331 & -0.0267886599711349 & 0.0055444720666912 & 0.605837552048657 \tabularnewline
46 & 2.2 & 2.19452054812348 & 0.147097280816108 & 0.00547945187652305 & 1.74414351975505 \tabularnewline
47 & 2.9 & 2.89455488476415 & 0.263379543229989 & 0.00544511523584917 & 1.16637211201761 \tabularnewline
48 & 3.1 & 3.09455177652517 & 0.250050143181212 & 0.00544822347482587 & -0.133702010837959 \tabularnewline
49 & 3.5 & 3.54112457085871 & 0.29109269001546 & -0.0411245708587135 & 0.431964445297103 \tabularnewline
50 & 3.6 & 3.59823906159672 & 0.24258446917230 & 0.00176093840328068 & -0.466574855304682 \tabularnewline
51 & 4.4 & 4.39842095037545 & 0.359894499156932 & 0.00157904962455254 & 1.17578700212252 \tabularnewline
52 & 4.1 & 4.09825092558534 & 0.22105351340172 & 0.00174907441466244 & -1.39199970347889 \tabularnewline
53 & 5.1 & 5.0984094046655 & 0.384916322122279 & 0.00159059533449898 & 1.64316674544985 \tabularnewline
54 & 5.8 & 5.79846002540692 & 0.451192090244237 & 0.00153997459308262 & 0.664669527999577 \tabularnewline
55 & 5.9 & 5.89841547077178 & 0.377325813731851 & 0.00158452922821826 & -0.740846503152483 \tabularnewline
56 & 5.4 & 5.39832757677942 & 0.192805020254142 & 0.00167242322058052 & -1.85074545419029 \tabularnewline
57 & 5.5 & 5.49832023463863 & 0.173286574740376 & 0.00167976536136757 & -0.195775648573712 \tabularnewline
58 & 4.8 & 4.79826567609804 & -0.0103773665627493 & 0.00173432390195885 & -1.84223432328944 \tabularnewline
59 & 3.2 & 3.19818725086379 & -0.344693251536486 & 0.00181274913620764 & -3.35337926816011 \tabularnewline
60 & 2.7 & 2.6981812001219 & -0.377355849067603 & 0.00181879987810306 & -0.327626715890255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63988&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]1.4[/C][C]1.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.21021463649639[/C][C]-0.0137793058873016[/C][C]-0.0102146364963925[/C][C]-0.267829279109299[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.01043086295789[/C][C]-0.0321499223549408[/C][C]-0.0104308629578906[/C][C]-0.419696617100316[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]1.70969460975183[/C][C]0.0659163658073972[/C][C]-0.0096946097518321[/C][C]1.61746170371269[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.40915875823275[/C][C]0.167134886666108[/C][C]-0.00915875823275107[/C][C]1.37988419523486[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.00955301572114[/C][C]0.0665326347807482[/C][C]-0.00955301572114442[/C][C]-1.22108225604380[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.1095341526333[/C][C]0.0728656639364329[/C][C]-0.00953415263330204[/C][C]0.0715370596606255[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.00961239598364[/C][C]0.0388213214879076[/C][C]-0.00961239598363637[/C][C]-0.367741955205151[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.80969866963719[/C][C]-0.00939271723135667[/C][C]-0.009698669637191[/C][C]-0.506485501705009[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.70943750567578[/C][C]0.177050179052900[/C][C]-0.0094375056757775[/C][C]1.92482183922168[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]2.30956892242742[/C][C]0.0576030943148097[/C][C]-0.00956892242742046[/C][C]-1.21986561752445[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]1.90965143519821[/C][C]-0.0376869115642739[/C][C]-0.00965143519821298[/C][C]-0.966600937801458[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.88741471682334[/C][C]-0.034599735415535[/C][C]0.112585283176656[/C][C]0.0381016776443702[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]2.30015347313978[/C][C]0.0561466540338654[/C][C]-0.000153473139779047[/C][C]0.795823001790371[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]2.80049230379502[/C][C]0.149403093529309[/C][C]-0.000492303795022896[/C][C]0.936449824744887[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]2.40016104852481[/C][C]0.0339282693520079[/C][C]-0.000161048524806911[/C][C]-1.15908489643239[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.30009727436292[/C][C]0.005772571716646[/C][C]-9.72743629246727e-05[/C][C]-0.282542240483822[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.70024552750499[/C][C]0.088662521672981[/C][C]-0.000245527504990482[/C][C]0.83166771870982[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.70021919624834[/C][C]0.0700187763253945[/C][C]-0.000219196248337363[/C][C]-0.187041519589758[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.90024968083065[/C][C]0.0973524136888926[/C][C]-0.000249680830649947[/C][C]0.274205022819628[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]3.00025017118941[/C][C]0.0979091912553759[/C][C]-0.000250171189406988[/C][C]0.00558525417386132[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.20011884300216[/C][C]-0.0909217876818235[/C][C]-0.000118843002161278[/C][C]-1.89419202375357[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.30014089465854[/C][C]-0.0507702600785963[/C][C]-0.000140894658542150[/C][C]0.402759981721342[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.80019113070371[/C][C]0.065059631657089[/C][C]-0.000191130703706305[/C][C]1.16187879105915[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.80896759302726[/C][C]0.0533968710683159[/C][C]-0.00896759302726437[/C][C]-0.128662803824972[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.80008543281844[/C][C]0.0406146265049172[/C][C]-8.54328184351252e-05[/C][C]-0.118724163697982[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]2.19973221676550[/C][C]-0.094267060696716[/C][C]0.000267783234502387[/C][C]-1.35120041722536[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]2.59994738591858[/C][C]0.00975599639880526[/C][C]5.26140814156697e-05[/C][C]1.04258069269520[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.80001277950673[/C][C]0.0497837810949955[/C][C]-1.27795067309705e-05[/C][C]0.401305050926975[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.49991783951061[/C][C]-0.0237991449174350[/C][C]8.2160489385259e-05[/C][C]-0.737858471396365[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.39990150722762[/C][C]-0.0398275970824514[/C][C]9.84927723785144e-05[/C][C]-0.160745685189536[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.29989132296714[/C][C]-0.0524837184422896[/C][C]0.000108677032861557[/C][C]-0.126934777289846[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]1.89984487602571[/C][C]-0.125574176081934[/C][C]0.000155123974291983[/C][C]-0.7330959007705[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]1.69983702080515[/C][C]-0.141227194840171[/C][C]0.000162979194849584[/C][C]-0.157004015417349[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.99987379571457[/C][C]-0.0484310736674697[/C][C]0.000126204285434117[/C][C]0.930786980775447[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.09988356516023[/C][C]-0.0172142957278159[/C][C]0.000116434839765361[/C][C]0.313121890255179[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]1.74478650525524[/C][C]-0.08760710254893[/C][C]-0.0447865052552376[/C][C]-0.75259889458892[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]1.79446394239242[/C][C]-0.0592526349538608[/C][C]0.00553605760757542[/C][C]0.269399271041521[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]1.79448828045119[/C][C]-0.0467804881305101[/C][C]0.0055117195488096[/C][C]0.124981841330634[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]1.79450345341155[/C][C]-0.0369368362281804[/C][C]0.00549654658844678[/C][C]0.0986785923979794[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]1.29438485896281[/C][C]-0.134355717726365[/C][C]0.00561514103719068[/C][C]-0.976810310368645[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]1.29441203045880[/C][C]-0.106093658114930[/C][C]0.00558796954119686[/C][C]0.283421970463178[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]1.29442897341819[/C][C]-0.0837783635350552[/C][C]0.00557102658180739[/C][C]0.223805818398663[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]1.19442692771472[/C][C]-0.087190185634343[/C][C]0.00557307228527782[/C][C]-0.0342199581975331[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]1.39445552793331[/C][C]-0.0267886599711349[/C][C]0.0055444720666912[/C][C]0.605837552048657[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]2.19452054812348[/C][C]0.147097280816108[/C][C]0.00547945187652305[/C][C]1.74414351975505[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.89455488476415[/C][C]0.263379543229989[/C][C]0.00544511523584917[/C][C]1.16637211201761[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]3.09455177652517[/C][C]0.250050143181212[/C][C]0.00544822347482587[/C][C]-0.133702010837959[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]3.54112457085871[/C][C]0.29109269001546[/C][C]-0.0411245708587135[/C][C]0.431964445297103[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.59823906159672[/C][C]0.24258446917230[/C][C]0.00176093840328068[/C][C]-0.466574855304682[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]4.39842095037545[/C][C]0.359894499156932[/C][C]0.00157904962455254[/C][C]1.17578700212252[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]4.09825092558534[/C][C]0.22105351340172[/C][C]0.00174907441466244[/C][C]-1.39199970347889[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]5.0984094046655[/C][C]0.384916322122279[/C][C]0.00159059533449898[/C][C]1.64316674544985[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]5.79846002540692[/C][C]0.451192090244237[/C][C]0.00153997459308262[/C][C]0.664669527999577[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]5.89841547077178[/C][C]0.377325813731851[/C][C]0.00158452922821826[/C][C]-0.740846503152483[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]5.39832757677942[/C][C]0.192805020254142[/C][C]0.00167242322058052[/C][C]-1.85074545419029[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]5.49832023463863[/C][C]0.173286574740376[/C][C]0.00167976536136757[/C][C]-0.195775648573712[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]4.79826567609804[/C][C]-0.0103773665627493[/C][C]0.00173432390195885[/C][C]-1.84223432328944[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]3.19818725086379[/C][C]-0.344693251536486[/C][C]0.00181274913620764[/C][C]-3.35337926816011[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.6981812001219[/C][C]-0.377355849067603[/C][C]0.00181879987810306[/C][C]-0.327626715890255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63988&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63988&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
11.41.4000
21.21.21021463649639-0.0137793058873016-0.0102146364963925-0.267829279109299
311.01043086295789-0.0321499223549408-0.0104308629578906-0.419696617100316
41.71.709694609751830.0659163658073972-0.00969460975183211.61746170371269
52.42.409158758232750.167134886666108-0.009158758232751071.37988419523486
622.009553015721140.0665326347807482-0.00955301572114442-1.22108225604380
72.12.10953415263330.0728656639364329-0.009534152633302040.0715370596606255
822.009612395983640.0388213214879076-0.00961239598363637-0.367741955205151
91.81.80969866963719-0.00939271723135667-0.009698669637191-0.506485501705009
102.72.709437505675780.177050179052900-0.00943750567577751.92482183922168
112.32.309568922427420.0576030943148097-0.00956892242742046-1.21986561752445
121.91.90965143519821-0.0376869115642739-0.00965143519821298-0.966600937801458
1321.88741471682334-0.0345997354155350.1125852831766560.0381016776443702
142.32.300153473139780.0561466540338654-0.0001534731397790470.795823001790371
152.82.800492303795020.149403093529309-0.0004923037950228960.936449824744887
162.42.400161048524810.0339282693520079-0.000161048524806911-1.15908489643239
172.32.300097274362920.005772571716646-9.72743629246727e-05-0.282542240483822
182.72.700245527504990.088662521672981-0.0002455275049904820.83166771870982
192.72.700219196248340.0700187763253945-0.000219196248337363-0.187041519589758
202.92.900249680830650.0973524136888926-0.0002496808306499470.274205022819628
2133.000250171189410.0979091912553759-0.0002501711894069880.00558525417386132
222.22.20011884300216-0.0909217876818235-0.000118843002161278-1.89419202375357
232.32.30014089465854-0.0507702600785963-0.0001408946585421500.402759981721342
242.82.800191130703710.065059631657089-0.0001911307037063051.16187879105915
252.82.808967593027260.0533968710683159-0.00896759302726437-0.128662803824972
262.82.800085432818440.0406146265049172-8.54328184351252e-05-0.118724163697982
272.22.19973221676550-0.0942670606967160.000267783234502387-1.35120041722536
282.62.599947385918580.009755996398805265.26140814156697e-051.04258069269520
292.82.800012779506730.0497837810949955-1.27795067309705e-050.401305050926975
302.52.49991783951061-0.02379914491743508.2160489385259e-05-0.737858471396365
312.42.39990150722762-0.03982759708245149.84927723785144e-05-0.160745685189536
322.32.29989132296714-0.05248371844228960.000108677032861557-0.126934777289846
331.91.89984487602571-0.1255741760819340.000155123974291983-0.7330959007705
341.71.69983702080515-0.1412271948401710.000162979194849584-0.157004015417349
3521.99987379571457-0.04843107366746970.0001262042854341170.930786980775447
362.12.09988356516023-0.01721429572781590.0001164348397653610.313121890255179
371.71.74478650525524-0.08760710254893-0.0447865052552376-0.75259889458892
381.81.79446394239242-0.05925263495386080.005536057607575420.269399271041521
391.81.79448828045119-0.04678048813051010.00551171954880960.124981841330634
401.81.79450345341155-0.03693683622818040.005496546588446780.0986785923979794
411.31.29438485896281-0.1343557177263650.00561514103719068-0.976810310368645
421.31.29441203045880-0.1060936581149300.005587969541196860.283421970463178
431.31.29442897341819-0.08377836353505520.005571026581807390.223805818398663
441.21.19442692771472-0.0871901856343430.00557307228527782-0.0342199581975331
451.41.39445552793331-0.02678865997113490.00554447206669120.605837552048657
462.22.194520548123480.1470972808161080.005479451876523051.74414351975505
472.92.894554884764150.2633795432299890.005445115235849171.16637211201761
483.13.094551776525170.2500501431812120.00544822347482587-0.133702010837959
493.53.541124570858710.29109269001546-0.04112457085871350.431964445297103
503.63.598239061596720.242584469172300.00176093840328068-0.466574855304682
514.44.398420950375450.3598944991569320.001579049624552541.17578700212252
524.14.098250925585340.221053513401720.00174907441466244-1.39199970347889
535.15.09840940466550.3849163221222790.001590595334498981.64316674544985
545.85.798460025406920.4511920902442370.001539974593082620.664669527999577
555.95.898415470771780.3773258137318510.00158452922821826-0.740846503152483
565.45.398327576779420.1928050202541420.00167242322058052-1.85074545419029
575.55.498320234638630.1732865747403760.00167976536136757-0.195775648573712
584.84.79826567609804-0.01037736656274930.00173432390195885-1.84223432328944
593.23.19818725086379-0.3446932515364860.00181274913620764-3.35337926816011
602.72.6981812001219-0.3773558490676030.00181879987810306-0.327626715890255



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')