Free Statistics

of Irreproducible Research!

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 computationSun, 27 Nov 2011 16:46:46 -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/27/t1322430622jwwdqkx9fl1bqpv.htm/, Retrieved Thu, 25 Apr 2024 01:38:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147626, Retrieved Thu, 25 Apr 2024 01:38:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2011-11-27 21:46:46] [75a32e1bc492240bc1028714aca23077] [Current]
- R P     [Structural Time Series Models] [] [2011-12-21 21:42:52] [493236dcc414c5f9e1823f06b33a5ad6]
Feedback Forum

Post a new message
Dataseries X:
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.249
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684
1.457




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147626&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147626&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147626&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'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.06221.0622000
21.07731.076515122460240.0007910781242642810.0007848775397630470.328240240099415
31.08071.07990491516580.0008076868666076220.0007950848341999520.09933580303149
41.08481.083992131725580.0008326301108970310.0008078682744223630.125279859759595
51.15821.157112836232690.001468798122463510.001087163767308872.75969374998516
61.16631.165187567783570.001534677821844840.001112432216427370.252032000352156
71.13721.136203009141730.001195288299472080.000996990858274954-1.16365902907142
81.11391.112994189537410.0008965452422824360.00090581046259364-0.929928373548654
91.12221.121266997291340.0009949000179946580.0009330027086577620.280906530430957
101.16921.168100448325910.001654803886464240.001099551674093081.74461728835354
111.17021.169102782425380.00164473819695050.00109721757461887-0.024818690572936
121.22861.22730378518230.002573686850855310.001296214817704622.15007732690633
131.26131.261106721904430.001824259017962190.0001932780955712541.42961647320596
141.26461.264577993450030.001884307410102282.20065499674343e-050.053234081152479
151.22621.226240513944810.00117535653373872-4.05139448063186e-05-1.52514771671431
161.19851.198584498121970.000640664631562474-8.44981219697938e-05-1.09269821398411
171.20071.200782168958540.000670917754581231-8.2168958542792e-050.0589816241991564
181.21381.213863979721490.000922614623455504-6.39797214936935e-050.46992450886551
191.22661.226646964014010.0011728148012153-4.6964014008739e-050.448878969916067
201.21761.217661219362870.000950586674349255-6.12193628743883e-05-0.384305369687713
211.22181.221856768768860.0010239943434762-5.67687688630237e-050.12270850142165
221.2491.249021751984570.00163397326138623-2.17519845718105e-050.988131735105905
231.29911.299058471719480.002796268074104554.15282805211078e-051.82893365367462
241.34081.340708927550490.003754378001578319.10724495144878e-051.46760103769023
251.31191.318535728917110.00348746349735618-0.00663572891711172-1.07613397704392
261.30141.301115340467110.002747241471762670.000284659532892683-0.718370938893928
271.32011.319803237535080.003163503843589520.0002967624649207850.601348146751499
281.29381.293524995381870.002378812734267420.000275004618129556-1.1103357429078
291.26941.269144233905360.001651948812052490.000255766094637071-1.00889810735449
301.21651.216282342585690.000144882287089650.000217657414309721-2.05474730773331
311.20371.20349113161751-0.0002186455675459980.000208868382486066-0.487465957073578
321.22921.228974167640380.000514689732539720.0002258323596157460.968273308593403
331.22561.225376803191960.0003956913644951870.000223196808037161-0.154880254671523
341.20151.20129203345912-0.000322122007549040.000207966540884147-0.921855684392367
351.17861.17840565489167-0.0009918873947746170.00019434510832971-0.849523864253229
361.18561.18540097798448-0.000752095055772610.0001990220155214760.300653153081772
371.21031.2089842469676-0.0001834806860222820.001315753032398550.975939539895688
381.19381.19408288150075-0.00070906161558893-0.000282881500753122-0.518796356248348
391.2021.20227944863906-0.000434355423344117-0.0002794486390568890.335013634804215
401.22711.227369915685780.00035986191558444-0.0002699156857830650.960052646982891
411.2771.277252000403660.00191327771186087-0.0002520004036562071.86239025204319
421.2651.265256872921260.00147370323455676-0.000256872921256574-0.522985629786916
431.26841.268656219783270.00153499078461751-0.0002562197832674560.0723991769144068
441.28111.281352555364240.00189254638073148-0.0002525553642376240.419587723836158
451.27271.272955824594950.00156092171129772-0.000255824594953821-0.386761040413287
461.26111.261359869456780.00113447228904545-0.000259869456781193-0.494498717205803
471.28811.288352178921680.00197701164483501-0.0002521789216784890.971766220927739
481.32131.321543199350050.00299906396540314-0.0002431993500531281.17294858427024
491.29991.299900037709360.00227575340902432-3.77093553283451e-08-0.971411396236973
501.30741.307354917178790.002460964189305664.5082821206326e-050.185162664765
511.32421.324152085088190.002936067246109124.79149118054201e-050.538560797037379
521.35161.351547413904630.003749672883828715.25860953685555e-050.91878363953994
531.35111.351048198263950.003607848336725635.18017360497454e-05-0.159593974867195
541.34191.341850483052780.003179026941665664.95169472189349e-05-0.480964345011559
551.37161.371545910862380.004069657100515145.40891376171052e-050.995871893721284
561.36221.362148154816550.003616047603985575.18451834550106e-05-0.505765701396804
571.38961.389544326353240.004419099240623585.56736467575949e-050.893011552575744
581.42271.422639865896550.005389850690832496.01341034543138e-051.07683054584234
591.46841.468333809524540.006757305030115516.61904754638338e-051.51339308399436
601.4571.456936444817720.006140049005385166.35551822768207e-05-0.681667574587881

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.0622 & 1.0622 & 0 & 0 & 0 \tabularnewline
2 & 1.0773 & 1.07651512246024 & 0.000791078124264281 & 0.000784877539763047 & 0.328240240099415 \tabularnewline
3 & 1.0807 & 1.0799049151658 & 0.000807686866607622 & 0.000795084834199952 & 0.09933580303149 \tabularnewline
4 & 1.0848 & 1.08399213172558 & 0.000832630110897031 & 0.000807868274422363 & 0.125279859759595 \tabularnewline
5 & 1.1582 & 1.15711283623269 & 0.00146879812246351 & 0.00108716376730887 & 2.75969374998516 \tabularnewline
6 & 1.1663 & 1.16518756778357 & 0.00153467782184484 & 0.00111243221642737 & 0.252032000352156 \tabularnewline
7 & 1.1372 & 1.13620300914173 & 0.00119528829947208 & 0.000996990858274954 & -1.16365902907142 \tabularnewline
8 & 1.1139 & 1.11299418953741 & 0.000896545242282436 & 0.00090581046259364 & -0.929928373548654 \tabularnewline
9 & 1.1222 & 1.12126699729134 & 0.000994900017994658 & 0.000933002708657762 & 0.280906530430957 \tabularnewline
10 & 1.1692 & 1.16810044832591 & 0.00165480388646424 & 0.00109955167409308 & 1.74461728835354 \tabularnewline
11 & 1.1702 & 1.16910278242538 & 0.0016447381969505 & 0.00109721757461887 & -0.024818690572936 \tabularnewline
12 & 1.2286 & 1.2273037851823 & 0.00257368685085531 & 0.00129621481770462 & 2.15007732690633 \tabularnewline
13 & 1.2613 & 1.26110672190443 & 0.00182425901796219 & 0.000193278095571254 & 1.42961647320596 \tabularnewline
14 & 1.2646 & 1.26457799345003 & 0.00188430741010228 & 2.20065499674343e-05 & 0.053234081152479 \tabularnewline
15 & 1.2262 & 1.22624051394481 & 0.00117535653373872 & -4.05139448063186e-05 & -1.52514771671431 \tabularnewline
16 & 1.1985 & 1.19858449812197 & 0.000640664631562474 & -8.44981219697938e-05 & -1.09269821398411 \tabularnewline
17 & 1.2007 & 1.20078216895854 & 0.000670917754581231 & -8.2168958542792e-05 & 0.0589816241991564 \tabularnewline
18 & 1.2138 & 1.21386397972149 & 0.000922614623455504 & -6.39797214936935e-05 & 0.46992450886551 \tabularnewline
19 & 1.2266 & 1.22664696401401 & 0.0011728148012153 & -4.6964014008739e-05 & 0.448878969916067 \tabularnewline
20 & 1.2176 & 1.21766121936287 & 0.000950586674349255 & -6.12193628743883e-05 & -0.384305369687713 \tabularnewline
21 & 1.2218 & 1.22185676876886 & 0.0010239943434762 & -5.67687688630237e-05 & 0.12270850142165 \tabularnewline
22 & 1.249 & 1.24902175198457 & 0.00163397326138623 & -2.17519845718105e-05 & 0.988131735105905 \tabularnewline
23 & 1.2991 & 1.29905847171948 & 0.00279626807410455 & 4.15282805211078e-05 & 1.82893365367462 \tabularnewline
24 & 1.3408 & 1.34070892755049 & 0.00375437800157831 & 9.10724495144878e-05 & 1.46760103769023 \tabularnewline
25 & 1.3119 & 1.31853572891711 & 0.00348746349735618 & -0.00663572891711172 & -1.07613397704392 \tabularnewline
26 & 1.3014 & 1.30111534046711 & 0.00274724147176267 & 0.000284659532892683 & -0.718370938893928 \tabularnewline
27 & 1.3201 & 1.31980323753508 & 0.00316350384358952 & 0.000296762464920785 & 0.601348146751499 \tabularnewline
28 & 1.2938 & 1.29352499538187 & 0.00237881273426742 & 0.000275004618129556 & -1.1103357429078 \tabularnewline
29 & 1.2694 & 1.26914423390536 & 0.00165194881205249 & 0.000255766094637071 & -1.00889810735449 \tabularnewline
30 & 1.2165 & 1.21628234258569 & 0.00014488228708965 & 0.000217657414309721 & -2.05474730773331 \tabularnewline
31 & 1.2037 & 1.20349113161751 & -0.000218645567545998 & 0.000208868382486066 & -0.487465957073578 \tabularnewline
32 & 1.2292 & 1.22897416764038 & 0.00051468973253972 & 0.000225832359615746 & 0.968273308593403 \tabularnewline
33 & 1.2256 & 1.22537680319196 & 0.000395691364495187 & 0.000223196808037161 & -0.154880254671523 \tabularnewline
34 & 1.2015 & 1.20129203345912 & -0.00032212200754904 & 0.000207966540884147 & -0.921855684392367 \tabularnewline
35 & 1.1786 & 1.17840565489167 & -0.000991887394774617 & 0.00019434510832971 & -0.849523864253229 \tabularnewline
36 & 1.1856 & 1.18540097798448 & -0.00075209505577261 & 0.000199022015521476 & 0.300653153081772 \tabularnewline
37 & 1.2103 & 1.2089842469676 & -0.000183480686022282 & 0.00131575303239855 & 0.975939539895688 \tabularnewline
38 & 1.1938 & 1.19408288150075 & -0.00070906161558893 & -0.000282881500753122 & -0.518796356248348 \tabularnewline
39 & 1.202 & 1.20227944863906 & -0.000434355423344117 & -0.000279448639056889 & 0.335013634804215 \tabularnewline
40 & 1.2271 & 1.22736991568578 & 0.00035986191558444 & -0.000269915685783065 & 0.960052646982891 \tabularnewline
41 & 1.277 & 1.27725200040366 & 0.00191327771186087 & -0.000252000403656207 & 1.86239025204319 \tabularnewline
42 & 1.265 & 1.26525687292126 & 0.00147370323455676 & -0.000256872921256574 & -0.522985629786916 \tabularnewline
43 & 1.2684 & 1.26865621978327 & 0.00153499078461751 & -0.000256219783267456 & 0.0723991769144068 \tabularnewline
44 & 1.2811 & 1.28135255536424 & 0.00189254638073148 & -0.000252555364237624 & 0.419587723836158 \tabularnewline
45 & 1.2727 & 1.27295582459495 & 0.00156092171129772 & -0.000255824594953821 & -0.386761040413287 \tabularnewline
46 & 1.2611 & 1.26135986945678 & 0.00113447228904545 & -0.000259869456781193 & -0.494498717205803 \tabularnewline
47 & 1.2881 & 1.28835217892168 & 0.00197701164483501 & -0.000252178921678489 & 0.971766220927739 \tabularnewline
48 & 1.3213 & 1.32154319935005 & 0.00299906396540314 & -0.000243199350053128 & 1.17294858427024 \tabularnewline
49 & 1.2999 & 1.29990003770936 & 0.00227575340902432 & -3.77093553283451e-08 & -0.971411396236973 \tabularnewline
50 & 1.3074 & 1.30735491717879 & 0.00246096418930566 & 4.5082821206326e-05 & 0.185162664765 \tabularnewline
51 & 1.3242 & 1.32415208508819 & 0.00293606724610912 & 4.79149118054201e-05 & 0.538560797037379 \tabularnewline
52 & 1.3516 & 1.35154741390463 & 0.00374967288382871 & 5.25860953685555e-05 & 0.91878363953994 \tabularnewline
53 & 1.3511 & 1.35104819826395 & 0.00360784833672563 & 5.18017360497454e-05 & -0.159593974867195 \tabularnewline
54 & 1.3419 & 1.34185048305278 & 0.00317902694166566 & 4.95169472189349e-05 & -0.480964345011559 \tabularnewline
55 & 1.3716 & 1.37154591086238 & 0.00406965710051514 & 5.40891376171052e-05 & 0.995871893721284 \tabularnewline
56 & 1.3622 & 1.36214815481655 & 0.00361604760398557 & 5.18451834550106e-05 & -0.505765701396804 \tabularnewline
57 & 1.3896 & 1.38954432635324 & 0.00441909924062358 & 5.56736467575949e-05 & 0.893011552575744 \tabularnewline
58 & 1.4227 & 1.42263986589655 & 0.00538985069083249 & 6.01341034543138e-05 & 1.07683054584234 \tabularnewline
59 & 1.4684 & 1.46833380952454 & 0.00675730503011551 & 6.61904754638338e-05 & 1.51339308399436 \tabularnewline
60 & 1.457 & 1.45693644481772 & 0.00614004900538516 & 6.35551822768207e-05 & -0.681667574587881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147626&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]1.0622[/C][C]1.0622[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.0773[/C][C]1.07651512246024[/C][C]0.000791078124264281[/C][C]0.000784877539763047[/C][C]0.328240240099415[/C][/ROW]
[ROW][C]3[/C][C]1.0807[/C][C]1.0799049151658[/C][C]0.000807686866607622[/C][C]0.000795084834199952[/C][C]0.09933580303149[/C][/ROW]
[ROW][C]4[/C][C]1.0848[/C][C]1.08399213172558[/C][C]0.000832630110897031[/C][C]0.000807868274422363[/C][C]0.125279859759595[/C][/ROW]
[ROW][C]5[/C][C]1.1582[/C][C]1.15711283623269[/C][C]0.00146879812246351[/C][C]0.00108716376730887[/C][C]2.75969374998516[/C][/ROW]
[ROW][C]6[/C][C]1.1663[/C][C]1.16518756778357[/C][C]0.00153467782184484[/C][C]0.00111243221642737[/C][C]0.252032000352156[/C][/ROW]
[ROW][C]7[/C][C]1.1372[/C][C]1.13620300914173[/C][C]0.00119528829947208[/C][C]0.000996990858274954[/C][C]-1.16365902907142[/C][/ROW]
[ROW][C]8[/C][C]1.1139[/C][C]1.11299418953741[/C][C]0.000896545242282436[/C][C]0.00090581046259364[/C][C]-0.929928373548654[/C][/ROW]
[ROW][C]9[/C][C]1.1222[/C][C]1.12126699729134[/C][C]0.000994900017994658[/C][C]0.000933002708657762[/C][C]0.280906530430957[/C][/ROW]
[ROW][C]10[/C][C]1.1692[/C][C]1.16810044832591[/C][C]0.00165480388646424[/C][C]0.00109955167409308[/C][C]1.74461728835354[/C][/ROW]
[ROW][C]11[/C][C]1.1702[/C][C]1.16910278242538[/C][C]0.0016447381969505[/C][C]0.00109721757461887[/C][C]-0.024818690572936[/C][/ROW]
[ROW][C]12[/C][C]1.2286[/C][C]1.2273037851823[/C][C]0.00257368685085531[/C][C]0.00129621481770462[/C][C]2.15007732690633[/C][/ROW]
[ROW][C]13[/C][C]1.2613[/C][C]1.26110672190443[/C][C]0.00182425901796219[/C][C]0.000193278095571254[/C][C]1.42961647320596[/C][/ROW]
[ROW][C]14[/C][C]1.2646[/C][C]1.26457799345003[/C][C]0.00188430741010228[/C][C]2.20065499674343e-05[/C][C]0.053234081152479[/C][/ROW]
[ROW][C]15[/C][C]1.2262[/C][C]1.22624051394481[/C][C]0.00117535653373872[/C][C]-4.05139448063186e-05[/C][C]-1.52514771671431[/C][/ROW]
[ROW][C]16[/C][C]1.1985[/C][C]1.19858449812197[/C][C]0.000640664631562474[/C][C]-8.44981219697938e-05[/C][C]-1.09269821398411[/C][/ROW]
[ROW][C]17[/C][C]1.2007[/C][C]1.20078216895854[/C][C]0.000670917754581231[/C][C]-8.2168958542792e-05[/C][C]0.0589816241991564[/C][/ROW]
[ROW][C]18[/C][C]1.2138[/C][C]1.21386397972149[/C][C]0.000922614623455504[/C][C]-6.39797214936935e-05[/C][C]0.46992450886551[/C][/ROW]
[ROW][C]19[/C][C]1.2266[/C][C]1.22664696401401[/C][C]0.0011728148012153[/C][C]-4.6964014008739e-05[/C][C]0.448878969916067[/C][/ROW]
[ROW][C]20[/C][C]1.2176[/C][C]1.21766121936287[/C][C]0.000950586674349255[/C][C]-6.12193628743883e-05[/C][C]-0.384305369687713[/C][/ROW]
[ROW][C]21[/C][C]1.2218[/C][C]1.22185676876886[/C][C]0.0010239943434762[/C][C]-5.67687688630237e-05[/C][C]0.12270850142165[/C][/ROW]
[ROW][C]22[/C][C]1.249[/C][C]1.24902175198457[/C][C]0.00163397326138623[/C][C]-2.17519845718105e-05[/C][C]0.988131735105905[/C][/ROW]
[ROW][C]23[/C][C]1.2991[/C][C]1.29905847171948[/C][C]0.00279626807410455[/C][C]4.15282805211078e-05[/C][C]1.82893365367462[/C][/ROW]
[ROW][C]24[/C][C]1.3408[/C][C]1.34070892755049[/C][C]0.00375437800157831[/C][C]9.10724495144878e-05[/C][C]1.46760103769023[/C][/ROW]
[ROW][C]25[/C][C]1.3119[/C][C]1.31853572891711[/C][C]0.00348746349735618[/C][C]-0.00663572891711172[/C][C]-1.07613397704392[/C][/ROW]
[ROW][C]26[/C][C]1.3014[/C][C]1.30111534046711[/C][C]0.00274724147176267[/C][C]0.000284659532892683[/C][C]-0.718370938893928[/C][/ROW]
[ROW][C]27[/C][C]1.3201[/C][C]1.31980323753508[/C][C]0.00316350384358952[/C][C]0.000296762464920785[/C][C]0.601348146751499[/C][/ROW]
[ROW][C]28[/C][C]1.2938[/C][C]1.29352499538187[/C][C]0.00237881273426742[/C][C]0.000275004618129556[/C][C]-1.1103357429078[/C][/ROW]
[ROW][C]29[/C][C]1.2694[/C][C]1.26914423390536[/C][C]0.00165194881205249[/C][C]0.000255766094637071[/C][C]-1.00889810735449[/C][/ROW]
[ROW][C]30[/C][C]1.2165[/C][C]1.21628234258569[/C][C]0.00014488228708965[/C][C]0.000217657414309721[/C][C]-2.05474730773331[/C][/ROW]
[ROW][C]31[/C][C]1.2037[/C][C]1.20349113161751[/C][C]-0.000218645567545998[/C][C]0.000208868382486066[/C][C]-0.487465957073578[/C][/ROW]
[ROW][C]32[/C][C]1.2292[/C][C]1.22897416764038[/C][C]0.00051468973253972[/C][C]0.000225832359615746[/C][C]0.968273308593403[/C][/ROW]
[ROW][C]33[/C][C]1.2256[/C][C]1.22537680319196[/C][C]0.000395691364495187[/C][C]0.000223196808037161[/C][C]-0.154880254671523[/C][/ROW]
[ROW][C]34[/C][C]1.2015[/C][C]1.20129203345912[/C][C]-0.00032212200754904[/C][C]0.000207966540884147[/C][C]-0.921855684392367[/C][/ROW]
[ROW][C]35[/C][C]1.1786[/C][C]1.17840565489167[/C][C]-0.000991887394774617[/C][C]0.00019434510832971[/C][C]-0.849523864253229[/C][/ROW]
[ROW][C]36[/C][C]1.1856[/C][C]1.18540097798448[/C][C]-0.00075209505577261[/C][C]0.000199022015521476[/C][C]0.300653153081772[/C][/ROW]
[ROW][C]37[/C][C]1.2103[/C][C]1.2089842469676[/C][C]-0.000183480686022282[/C][C]0.00131575303239855[/C][C]0.975939539895688[/C][/ROW]
[ROW][C]38[/C][C]1.1938[/C][C]1.19408288150075[/C][C]-0.00070906161558893[/C][C]-0.000282881500753122[/C][C]-0.518796356248348[/C][/ROW]
[ROW][C]39[/C][C]1.202[/C][C]1.20227944863906[/C][C]-0.000434355423344117[/C][C]-0.000279448639056889[/C][C]0.335013634804215[/C][/ROW]
[ROW][C]40[/C][C]1.2271[/C][C]1.22736991568578[/C][C]0.00035986191558444[/C][C]-0.000269915685783065[/C][C]0.960052646982891[/C][/ROW]
[ROW][C]41[/C][C]1.277[/C][C]1.27725200040366[/C][C]0.00191327771186087[/C][C]-0.000252000403656207[/C][C]1.86239025204319[/C][/ROW]
[ROW][C]42[/C][C]1.265[/C][C]1.26525687292126[/C][C]0.00147370323455676[/C][C]-0.000256872921256574[/C][C]-0.522985629786916[/C][/ROW]
[ROW][C]43[/C][C]1.2684[/C][C]1.26865621978327[/C][C]0.00153499078461751[/C][C]-0.000256219783267456[/C][C]0.0723991769144068[/C][/ROW]
[ROW][C]44[/C][C]1.2811[/C][C]1.28135255536424[/C][C]0.00189254638073148[/C][C]-0.000252555364237624[/C][C]0.419587723836158[/C][/ROW]
[ROW][C]45[/C][C]1.2727[/C][C]1.27295582459495[/C][C]0.00156092171129772[/C][C]-0.000255824594953821[/C][C]-0.386761040413287[/C][/ROW]
[ROW][C]46[/C][C]1.2611[/C][C]1.26135986945678[/C][C]0.00113447228904545[/C][C]-0.000259869456781193[/C][C]-0.494498717205803[/C][/ROW]
[ROW][C]47[/C][C]1.2881[/C][C]1.28835217892168[/C][C]0.00197701164483501[/C][C]-0.000252178921678489[/C][C]0.971766220927739[/C][/ROW]
[ROW][C]48[/C][C]1.3213[/C][C]1.32154319935005[/C][C]0.00299906396540314[/C][C]-0.000243199350053128[/C][C]1.17294858427024[/C][/ROW]
[ROW][C]49[/C][C]1.2999[/C][C]1.29990003770936[/C][C]0.00227575340902432[/C][C]-3.77093553283451e-08[/C][C]-0.971411396236973[/C][/ROW]
[ROW][C]50[/C][C]1.3074[/C][C]1.30735491717879[/C][C]0.00246096418930566[/C][C]4.5082821206326e-05[/C][C]0.185162664765[/C][/ROW]
[ROW][C]51[/C][C]1.3242[/C][C]1.32415208508819[/C][C]0.00293606724610912[/C][C]4.79149118054201e-05[/C][C]0.538560797037379[/C][/ROW]
[ROW][C]52[/C][C]1.3516[/C][C]1.35154741390463[/C][C]0.00374967288382871[/C][C]5.25860953685555e-05[/C][C]0.91878363953994[/C][/ROW]
[ROW][C]53[/C][C]1.3511[/C][C]1.35104819826395[/C][C]0.00360784833672563[/C][C]5.18017360497454e-05[/C][C]-0.159593974867195[/C][/ROW]
[ROW][C]54[/C][C]1.3419[/C][C]1.34185048305278[/C][C]0.00317902694166566[/C][C]4.95169472189349e-05[/C][C]-0.480964345011559[/C][/ROW]
[ROW][C]55[/C][C]1.3716[/C][C]1.37154591086238[/C][C]0.00406965710051514[/C][C]5.40891376171052e-05[/C][C]0.995871893721284[/C][/ROW]
[ROW][C]56[/C][C]1.3622[/C][C]1.36214815481655[/C][C]0.00361604760398557[/C][C]5.18451834550106e-05[/C][C]-0.505765701396804[/C][/ROW]
[ROW][C]57[/C][C]1.3896[/C][C]1.38954432635324[/C][C]0.00441909924062358[/C][C]5.56736467575949e-05[/C][C]0.893011552575744[/C][/ROW]
[ROW][C]58[/C][C]1.4227[/C][C]1.42263986589655[/C][C]0.00538985069083249[/C][C]6.01341034543138e-05[/C][C]1.07683054584234[/C][/ROW]
[ROW][C]59[/C][C]1.4684[/C][C]1.46833380952454[/C][C]0.00675730503011551[/C][C]6.61904754638338e-05[/C][C]1.51339308399436[/C][/ROW]
[ROW][C]60[/C][C]1.457[/C][C]1.45693644481772[/C][C]0.00614004900538516[/C][C]6.35551822768207e-05[/C][C]-0.681667574587881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147626&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147626&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
11.06221.0622000
21.07731.076515122460240.0007910781242642810.0007848775397630470.328240240099415
31.08071.07990491516580.0008076868666076220.0007950848341999520.09933580303149
41.08481.083992131725580.0008326301108970310.0008078682744223630.125279859759595
51.15821.157112836232690.001468798122463510.001087163767308872.75969374998516
61.16631.165187567783570.001534677821844840.001112432216427370.252032000352156
71.13721.136203009141730.001195288299472080.000996990858274954-1.16365902907142
81.11391.112994189537410.0008965452422824360.00090581046259364-0.929928373548654
91.12221.121266997291340.0009949000179946580.0009330027086577620.280906530430957
101.16921.168100448325910.001654803886464240.001099551674093081.74461728835354
111.17021.169102782425380.00164473819695050.00109721757461887-0.024818690572936
121.22861.22730378518230.002573686850855310.001296214817704622.15007732690633
131.26131.261106721904430.001824259017962190.0001932780955712541.42961647320596
141.26461.264577993450030.001884307410102282.20065499674343e-050.053234081152479
151.22621.226240513944810.00117535653373872-4.05139448063186e-05-1.52514771671431
161.19851.198584498121970.000640664631562474-8.44981219697938e-05-1.09269821398411
171.20071.200782168958540.000670917754581231-8.2168958542792e-050.0589816241991564
181.21381.213863979721490.000922614623455504-6.39797214936935e-050.46992450886551
191.22661.226646964014010.0011728148012153-4.6964014008739e-050.448878969916067
201.21761.217661219362870.000950586674349255-6.12193628743883e-05-0.384305369687713
211.22181.221856768768860.0010239943434762-5.67687688630237e-050.12270850142165
221.2491.249021751984570.00163397326138623-2.17519845718105e-050.988131735105905
231.29911.299058471719480.002796268074104554.15282805211078e-051.82893365367462
241.34081.340708927550490.003754378001578319.10724495144878e-051.46760103769023
251.31191.318535728917110.00348746349735618-0.00663572891711172-1.07613397704392
261.30141.301115340467110.002747241471762670.000284659532892683-0.718370938893928
271.32011.319803237535080.003163503843589520.0002967624649207850.601348146751499
281.29381.293524995381870.002378812734267420.000275004618129556-1.1103357429078
291.26941.269144233905360.001651948812052490.000255766094637071-1.00889810735449
301.21651.216282342585690.000144882287089650.000217657414309721-2.05474730773331
311.20371.20349113161751-0.0002186455675459980.000208868382486066-0.487465957073578
321.22921.228974167640380.000514689732539720.0002258323596157460.968273308593403
331.22561.225376803191960.0003956913644951870.000223196808037161-0.154880254671523
341.20151.20129203345912-0.000322122007549040.000207966540884147-0.921855684392367
351.17861.17840565489167-0.0009918873947746170.00019434510832971-0.849523864253229
361.18561.18540097798448-0.000752095055772610.0001990220155214760.300653153081772
371.21031.2089842469676-0.0001834806860222820.001315753032398550.975939539895688
381.19381.19408288150075-0.00070906161558893-0.000282881500753122-0.518796356248348
391.2021.20227944863906-0.000434355423344117-0.0002794486390568890.335013634804215
401.22711.227369915685780.00035986191558444-0.0002699156857830650.960052646982891
411.2771.277252000403660.00191327771186087-0.0002520004036562071.86239025204319
421.2651.265256872921260.00147370323455676-0.000256872921256574-0.522985629786916
431.26841.268656219783270.00153499078461751-0.0002562197832674560.0723991769144068
441.28111.281352555364240.00189254638073148-0.0002525553642376240.419587723836158
451.27271.272955824594950.00156092171129772-0.000255824594953821-0.386761040413287
461.26111.261359869456780.00113447228904545-0.000259869456781193-0.494498717205803
471.28811.288352178921680.00197701164483501-0.0002521789216784890.971766220927739
481.32131.321543199350050.00299906396540314-0.0002431993500531281.17294858427024
491.29991.299900037709360.00227575340902432-3.77093553283451e-08-0.971411396236973
501.30741.307354917178790.002460964189305664.5082821206326e-050.185162664765
511.32421.324152085088190.002936067246109124.79149118054201e-050.538560797037379
521.35161.351547413904630.003749672883828715.25860953685555e-050.91878363953994
531.35111.351048198263950.003607848336725635.18017360497454e-05-0.159593974867195
541.34191.341850483052780.003179026941665664.95169472189349e-05-0.480964345011559
551.37161.371545910862380.004069657100515145.40891376171052e-050.995871893721284
561.36221.362148154816550.003616047603985575.18451834550106e-05-0.505765701396804
571.38961.389544326353240.004419099240623585.56736467575949e-050.893011552575744
581.42271.422639865896550.005389850690832496.01341034543138e-051.07683054584234
591.46841.468333809524540.006757305030115516.61904754638338e-051.51339308399436
601.4571.456936444817720.006140049005385166.35551822768207e-05-0.681667574587881



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')