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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationTue, 28 Dec 2010 18:07:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293559669dy7i6vfn0wzvb8k.htm/, Retrieved Sat, 04 May 2024 23:18:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116450, Retrieved Sat, 04 May 2024 23:18:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [PAPER] [2010-12-28 18:07:59] [d5e0edb7e0239841e94676417b2a1e2e] [Current]
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Dataseries X:
26548
26752
26967
27034
27056
27476
28497
29085
28720
29067
29249
29672
29761
30066
30315
30571
30757
30742
31310
31381
31470
31226
31081
31061
31114
30828
30418
30195
29877
29192
29876
29409
28458
28340
28164
28438
28053
27599
27226
27119
26625
26541
27023
26631
26154
26029
26008
26632
27010
27041
27244
26976
26715
27017
27714
27655
27103
27088
26968
27770
27616
27481
27279
26918
26503
26547
27467
27305
26259
26048
25743





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=116450&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=116450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116450&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
12654826548000
22675226715.071160153720.639042287058936.92883984631910.655157802561565
32696726905.984857443760.652445081584361.01514255630790.797201645473681
42703427023.336842896178.338784825386610.66315710393640.260047571583508
52705627064.502622790365.9119502713695-8.50262279034184-0.163277419399898
62747627371.7479631305148.157340019902104.2520368695051.05359781154324
72849728251.0395063614399.416856521773245.9604936386243.19443636634054
82908529029.5396239157530.19791770960755.46037608434491.65696680734548
92872028972.3331143086327.219979063559-252.333114308634-2.56773172212168
102906729067.6843740869247.046495247978-0.684374086876244-1.01358872503052
112924929237.9329865949220.48475630364111.0670134050717-0.335726035983488
122967229600.61195138269.67163337558471.38804861998610.62164125467645
132976129878.9303215381272.580636832681-117.9303215380660.0404532214802299
143006630100.7268984668255.444053548699-34.7268984668399-0.208407717797834
153031530273.8784421762228.50537359574341.1215578238274-0.335766994045634
163057130507.164335154230.10219649927363.83566484598580.0204911994051311
173075730807.9256950309253.991899944700-50.92569503094470.301274491280909
183074230943.3158692665213.941770040189-201.315869266538-0.502871907000844
193131031170.7751718584218.499799397522139.2248281416460.057433184126269
203138131223.3671993483162.539170526016157.632800651745-0.706683206745636
213147031558.1445332407220.677054601602-88.14453324072250.734636757521303
223122631387.747867198688.630822864254-161.747867198606-1.66873740076928
233108131192.8057916132-7.01679000012729-111.805791613217-1.20870955964957
243106131025.9858320732-60.736463395501235.0141679267804-0.681669535008176
253111431118.5431277041-9.17458033242673-4.543127704077050.667213170070226
263082830922.2405160452-72.0196421270532-94.2405160452136-0.787468975070231
273041830505.9314245896-185.848338749833-87.9314245896289-1.42399743668197
283019530182.3792107237-231.54544942195212.6207892762570-0.582334562156756
292987729903.6335795030-247.348888625837-26.6335795030399-0.200384461419676
302919229474.2476114323-308.373187248733-282.247611432276-0.768536563314524
312987629538.9891794660-183.468990414924337.0108205340461.57354403807739
322940929350.1184862488-185.27675971141858.8815137512226-0.0228176816561006
332845828638.7705933574-361.412617010191-180.770593357424-2.22515972296892
342834028337.9811361967-341.1168307147492.018863803266320.256453293310451
352816428175.7477639450-281.308069745666-11.74776394503510.756318845029209
362843828297.0663171147-146.845539897833140.9336828852791.70787769093953
372805328049.3665674207-180.6028596230563.63343257928044-0.430305741362482
382759927645.856288222-255.093863319666-46.8562882220065-0.93750543967464
392722627286.7875299428-289.55776432753-60.7875299428079-0.432883522008801
402711927050.9348063584-271.76029384992868.06519364160540.225823654725565
412662526672.4922305923-307.302956533675-47.4922305923384-0.451044796960724
422654126777.099526657-169.785940146996-236.0995266569861.73628166490649
432702326661.0145033727-151.872400451269361.9854966273290.225817688612905
442663126422.958069467-180.60104905316208.041930533002-0.36248289620195
452615426323.5026762431-153.555466889015-169.5026762430910.341553227503716
462602926142.7316996139-162.622893656690-113.731699613897-0.114577876487621
472600826107.9018622941-120.079539737631-99.90186229409920.538356921112227
482663226315.6778733876-10.9104029941995316.3221266123731.38542830379758
492701026750.8104979997137.862005144066259.1895020003041.88731956683512
502704127023.3869063740182.73239144848617.61309362604930.565419162152878
512724427287.3208057622209.649665502674-43.32080576216170.338836017499217
522697627081.585946214572.0876033019442-105.58594621454-1.74201812991879
532671526962.47194435138.56889468535878-247.471944351268-0.805671148517118
542701727168.543214714274.3147573654074-151.5432147142030.831287997757693
552771427306.092518918695.3583562041105407.9074810813780.265488155779644
562765527439.9359010357108.154237456146215.0640989643310.161431691995990
572710327338.144392694538.3912689838747-235.144392694536-0.880788600479137
582708827276.07393198765.02562760095843-188.073931987641-0.421668242536758
592696827224.1795054151-13.8739147793201-256.179505415110-0.239236439101584
602777027497.806007415581.6591018419422272.1939925845191.21123276927662
612761627480.496171810948.7349271617143135.503828189074-0.416863820866641
622748127475.658517435530.93342591748245.34148256450683-0.224460930549671
632727927259.1910362491-51.018177595388919.8089637508565-1.03283995756840
642691827026.6762417456-111.080188538592-108.676241745629-0.759908093071657
652650326845.9197240207-134.182259343628-342.919724020668-0.292829396133781
662654726720.4351493483-131.293367897765-173.4351493483310.0365501859303891
672746726921.4242579236-20.92553380533545.5757420764271.3933213515838
682730526995.430348607410.5778877407435309.5696513926150.397467970756622
692625926624.1528325546-116.033069044342-365.152832554619-1.59832629851900
702604826300.1312311149-184.958543233422-252.131231114932-0.871155018129837
712574326098.731979908-190.407261880979-355.731979908003-0.0689744663970854

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 26548 & 26548 & 0 & 0 & 0 \tabularnewline
2 & 26752 & 26715.0711601537 & 20.6390422870589 & 36.9288398463191 & 0.655157802561565 \tabularnewline
3 & 26967 & 26905.9848574437 & 60.6524450815843 & 61.0151425563079 & 0.797201645473681 \tabularnewline
4 & 27034 & 27023.3368428961 & 78.3387848253866 & 10.6631571039364 & 0.260047571583508 \tabularnewline
5 & 27056 & 27064.5026227903 & 65.9119502713695 & -8.50262279034184 & -0.163277419399898 \tabularnewline
6 & 27476 & 27371.7479631305 & 148.157340019902 & 104.252036869505 & 1.05359781154324 \tabularnewline
7 & 28497 & 28251.0395063614 & 399.416856521773 & 245.960493638624 & 3.19443636634054 \tabularnewline
8 & 29085 & 29029.5396239157 & 530.197917709607 & 55.4603760843449 & 1.65696680734548 \tabularnewline
9 & 28720 & 28972.3331143086 & 327.219979063559 & -252.333114308634 & -2.56773172212168 \tabularnewline
10 & 29067 & 29067.6843740869 & 247.046495247978 & -0.684374086876244 & -1.01358872503052 \tabularnewline
11 & 29249 & 29237.9329865949 & 220.484756303641 & 11.0670134050717 & -0.335726035983488 \tabularnewline
12 & 29672 & 29600.61195138 & 269.671633375584 & 71.3880486199861 & 0.62164125467645 \tabularnewline
13 & 29761 & 29878.9303215381 & 272.580636832681 & -117.930321538066 & 0.0404532214802299 \tabularnewline
14 & 30066 & 30100.7268984668 & 255.444053548699 & -34.7268984668399 & -0.208407717797834 \tabularnewline
15 & 30315 & 30273.8784421762 & 228.505373595743 & 41.1215578238274 & -0.335766994045634 \tabularnewline
16 & 30571 & 30507.164335154 & 230.102196499273 & 63.8356648459858 & 0.0204911994051311 \tabularnewline
17 & 30757 & 30807.9256950309 & 253.991899944700 & -50.9256950309447 & 0.301274491280909 \tabularnewline
18 & 30742 & 30943.3158692665 & 213.941770040189 & -201.315869266538 & -0.502871907000844 \tabularnewline
19 & 31310 & 31170.7751718584 & 218.499799397522 & 139.224828141646 & 0.057433184126269 \tabularnewline
20 & 31381 & 31223.3671993483 & 162.539170526016 & 157.632800651745 & -0.706683206745636 \tabularnewline
21 & 31470 & 31558.1445332407 & 220.677054601602 & -88.1445332407225 & 0.734636757521303 \tabularnewline
22 & 31226 & 31387.7478671986 & 88.630822864254 & -161.747867198606 & -1.66873740076928 \tabularnewline
23 & 31081 & 31192.8057916132 & -7.01679000012729 & -111.805791613217 & -1.20870955964957 \tabularnewline
24 & 31061 & 31025.9858320732 & -60.7364633955012 & 35.0141679267804 & -0.681669535008176 \tabularnewline
25 & 31114 & 31118.5431277041 & -9.17458033242673 & -4.54312770407705 & 0.667213170070226 \tabularnewline
26 & 30828 & 30922.2405160452 & -72.0196421270532 & -94.2405160452136 & -0.787468975070231 \tabularnewline
27 & 30418 & 30505.9314245896 & -185.848338749833 & -87.9314245896289 & -1.42399743668197 \tabularnewline
28 & 30195 & 30182.3792107237 & -231.545449421952 & 12.6207892762570 & -0.582334562156756 \tabularnewline
29 & 29877 & 29903.6335795030 & -247.348888625837 & -26.6335795030399 & -0.200384461419676 \tabularnewline
30 & 29192 & 29474.2476114323 & -308.373187248733 & -282.247611432276 & -0.768536563314524 \tabularnewline
31 & 29876 & 29538.9891794660 & -183.468990414924 & 337.010820534046 & 1.57354403807739 \tabularnewline
32 & 29409 & 29350.1184862488 & -185.276759711418 & 58.8815137512226 & -0.0228176816561006 \tabularnewline
33 & 28458 & 28638.7705933574 & -361.412617010191 & -180.770593357424 & -2.22515972296892 \tabularnewline
34 & 28340 & 28337.9811361967 & -341.116830714749 & 2.01886380326632 & 0.256453293310451 \tabularnewline
35 & 28164 & 28175.7477639450 & -281.308069745666 & -11.7477639450351 & 0.756318845029209 \tabularnewline
36 & 28438 & 28297.0663171147 & -146.845539897833 & 140.933682885279 & 1.70787769093953 \tabularnewline
37 & 28053 & 28049.3665674207 & -180.602859623056 & 3.63343257928044 & -0.430305741362482 \tabularnewline
38 & 27599 & 27645.856288222 & -255.093863319666 & -46.8562882220065 & -0.93750543967464 \tabularnewline
39 & 27226 & 27286.7875299428 & -289.55776432753 & -60.7875299428079 & -0.432883522008801 \tabularnewline
40 & 27119 & 27050.9348063584 & -271.760293849928 & 68.0651936416054 & 0.225823654725565 \tabularnewline
41 & 26625 & 26672.4922305923 & -307.302956533675 & -47.4922305923384 & -0.451044796960724 \tabularnewline
42 & 26541 & 26777.099526657 & -169.785940146996 & -236.099526656986 & 1.73628166490649 \tabularnewline
43 & 27023 & 26661.0145033727 & -151.872400451269 & 361.985496627329 & 0.225817688612905 \tabularnewline
44 & 26631 & 26422.958069467 & -180.60104905316 & 208.041930533002 & -0.36248289620195 \tabularnewline
45 & 26154 & 26323.5026762431 & -153.555466889015 & -169.502676243091 & 0.341553227503716 \tabularnewline
46 & 26029 & 26142.7316996139 & -162.622893656690 & -113.731699613897 & -0.114577876487621 \tabularnewline
47 & 26008 & 26107.9018622941 & -120.079539737631 & -99.9018622940992 & 0.538356921112227 \tabularnewline
48 & 26632 & 26315.6778733876 & -10.9104029941995 & 316.322126612373 & 1.38542830379758 \tabularnewline
49 & 27010 & 26750.8104979997 & 137.862005144066 & 259.189502000304 & 1.88731956683512 \tabularnewline
50 & 27041 & 27023.3869063740 & 182.732391448486 & 17.6130936260493 & 0.565419162152878 \tabularnewline
51 & 27244 & 27287.3208057622 & 209.649665502674 & -43.3208057621617 & 0.338836017499217 \tabularnewline
52 & 26976 & 27081.5859462145 & 72.0876033019442 & -105.58594621454 & -1.74201812991879 \tabularnewline
53 & 26715 & 26962.4719443513 & 8.56889468535878 & -247.471944351268 & -0.805671148517118 \tabularnewline
54 & 27017 & 27168.5432147142 & 74.3147573654074 & -151.543214714203 & 0.831287997757693 \tabularnewline
55 & 27714 & 27306.0925189186 & 95.3583562041105 & 407.907481081378 & 0.265488155779644 \tabularnewline
56 & 27655 & 27439.9359010357 & 108.154237456146 & 215.064098964331 & 0.161431691995990 \tabularnewline
57 & 27103 & 27338.1443926945 & 38.3912689838747 & -235.144392694536 & -0.880788600479137 \tabularnewline
58 & 27088 & 27276.0739319876 & 5.02562760095843 & -188.073931987641 & -0.421668242536758 \tabularnewline
59 & 26968 & 27224.1795054151 & -13.8739147793201 & -256.179505415110 & -0.239236439101584 \tabularnewline
60 & 27770 & 27497.8060074155 & 81.6591018419422 & 272.193992584519 & 1.21123276927662 \tabularnewline
61 & 27616 & 27480.4961718109 & 48.7349271617143 & 135.503828189074 & -0.416863820866641 \tabularnewline
62 & 27481 & 27475.6585174355 & 30.9334259174824 & 5.34148256450683 & -0.224460930549671 \tabularnewline
63 & 27279 & 27259.1910362491 & -51.0181775953889 & 19.8089637508565 & -1.03283995756840 \tabularnewline
64 & 26918 & 27026.6762417456 & -111.080188538592 & -108.676241745629 & -0.759908093071657 \tabularnewline
65 & 26503 & 26845.9197240207 & -134.182259343628 & -342.919724020668 & -0.292829396133781 \tabularnewline
66 & 26547 & 26720.4351493483 & -131.293367897765 & -173.435149348331 & 0.0365501859303891 \tabularnewline
67 & 27467 & 26921.4242579236 & -20.92553380533 & 545.575742076427 & 1.3933213515838 \tabularnewline
68 & 27305 & 26995.4303486074 & 10.5778877407435 & 309.569651392615 & 0.397467970756622 \tabularnewline
69 & 26259 & 26624.1528325546 & -116.033069044342 & -365.152832554619 & -1.59832629851900 \tabularnewline
70 & 26048 & 26300.1312311149 & -184.958543233422 & -252.131231114932 & -0.871155018129837 \tabularnewline
71 & 25743 & 26098.731979908 & -190.407261880979 & -355.731979908003 & -0.0689744663970854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116450&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]26548[/C][C]26548[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]26752[/C][C]26715.0711601537[/C][C]20.6390422870589[/C][C]36.9288398463191[/C][C]0.655157802561565[/C][/ROW]
[ROW][C]3[/C][C]26967[/C][C]26905.9848574437[/C][C]60.6524450815843[/C][C]61.0151425563079[/C][C]0.797201645473681[/C][/ROW]
[ROW][C]4[/C][C]27034[/C][C]27023.3368428961[/C][C]78.3387848253866[/C][C]10.6631571039364[/C][C]0.260047571583508[/C][/ROW]
[ROW][C]5[/C][C]27056[/C][C]27064.5026227903[/C][C]65.9119502713695[/C][C]-8.50262279034184[/C][C]-0.163277419399898[/C][/ROW]
[ROW][C]6[/C][C]27476[/C][C]27371.7479631305[/C][C]148.157340019902[/C][C]104.252036869505[/C][C]1.05359781154324[/C][/ROW]
[ROW][C]7[/C][C]28497[/C][C]28251.0395063614[/C][C]399.416856521773[/C][C]245.960493638624[/C][C]3.19443636634054[/C][/ROW]
[ROW][C]8[/C][C]29085[/C][C]29029.5396239157[/C][C]530.197917709607[/C][C]55.4603760843449[/C][C]1.65696680734548[/C][/ROW]
[ROW][C]9[/C][C]28720[/C][C]28972.3331143086[/C][C]327.219979063559[/C][C]-252.333114308634[/C][C]-2.56773172212168[/C][/ROW]
[ROW][C]10[/C][C]29067[/C][C]29067.6843740869[/C][C]247.046495247978[/C][C]-0.684374086876244[/C][C]-1.01358872503052[/C][/ROW]
[ROW][C]11[/C][C]29249[/C][C]29237.9329865949[/C][C]220.484756303641[/C][C]11.0670134050717[/C][C]-0.335726035983488[/C][/ROW]
[ROW][C]12[/C][C]29672[/C][C]29600.61195138[/C][C]269.671633375584[/C][C]71.3880486199861[/C][C]0.62164125467645[/C][/ROW]
[ROW][C]13[/C][C]29761[/C][C]29878.9303215381[/C][C]272.580636832681[/C][C]-117.930321538066[/C][C]0.0404532214802299[/C][/ROW]
[ROW][C]14[/C][C]30066[/C][C]30100.7268984668[/C][C]255.444053548699[/C][C]-34.7268984668399[/C][C]-0.208407717797834[/C][/ROW]
[ROW][C]15[/C][C]30315[/C][C]30273.8784421762[/C][C]228.505373595743[/C][C]41.1215578238274[/C][C]-0.335766994045634[/C][/ROW]
[ROW][C]16[/C][C]30571[/C][C]30507.164335154[/C][C]230.102196499273[/C][C]63.8356648459858[/C][C]0.0204911994051311[/C][/ROW]
[ROW][C]17[/C][C]30757[/C][C]30807.9256950309[/C][C]253.991899944700[/C][C]-50.9256950309447[/C][C]0.301274491280909[/C][/ROW]
[ROW][C]18[/C][C]30742[/C][C]30943.3158692665[/C][C]213.941770040189[/C][C]-201.315869266538[/C][C]-0.502871907000844[/C][/ROW]
[ROW][C]19[/C][C]31310[/C][C]31170.7751718584[/C][C]218.499799397522[/C][C]139.224828141646[/C][C]0.057433184126269[/C][/ROW]
[ROW][C]20[/C][C]31381[/C][C]31223.3671993483[/C][C]162.539170526016[/C][C]157.632800651745[/C][C]-0.706683206745636[/C][/ROW]
[ROW][C]21[/C][C]31470[/C][C]31558.1445332407[/C][C]220.677054601602[/C][C]-88.1445332407225[/C][C]0.734636757521303[/C][/ROW]
[ROW][C]22[/C][C]31226[/C][C]31387.7478671986[/C][C]88.630822864254[/C][C]-161.747867198606[/C][C]-1.66873740076928[/C][/ROW]
[ROW][C]23[/C][C]31081[/C][C]31192.8057916132[/C][C]-7.01679000012729[/C][C]-111.805791613217[/C][C]-1.20870955964957[/C][/ROW]
[ROW][C]24[/C][C]31061[/C][C]31025.9858320732[/C][C]-60.7364633955012[/C][C]35.0141679267804[/C][C]-0.681669535008176[/C][/ROW]
[ROW][C]25[/C][C]31114[/C][C]31118.5431277041[/C][C]-9.17458033242673[/C][C]-4.54312770407705[/C][C]0.667213170070226[/C][/ROW]
[ROW][C]26[/C][C]30828[/C][C]30922.2405160452[/C][C]-72.0196421270532[/C][C]-94.2405160452136[/C][C]-0.787468975070231[/C][/ROW]
[ROW][C]27[/C][C]30418[/C][C]30505.9314245896[/C][C]-185.848338749833[/C][C]-87.9314245896289[/C][C]-1.42399743668197[/C][/ROW]
[ROW][C]28[/C][C]30195[/C][C]30182.3792107237[/C][C]-231.545449421952[/C][C]12.6207892762570[/C][C]-0.582334562156756[/C][/ROW]
[ROW][C]29[/C][C]29877[/C][C]29903.6335795030[/C][C]-247.348888625837[/C][C]-26.6335795030399[/C][C]-0.200384461419676[/C][/ROW]
[ROW][C]30[/C][C]29192[/C][C]29474.2476114323[/C][C]-308.373187248733[/C][C]-282.247611432276[/C][C]-0.768536563314524[/C][/ROW]
[ROW][C]31[/C][C]29876[/C][C]29538.9891794660[/C][C]-183.468990414924[/C][C]337.010820534046[/C][C]1.57354403807739[/C][/ROW]
[ROW][C]32[/C][C]29409[/C][C]29350.1184862488[/C][C]-185.276759711418[/C][C]58.8815137512226[/C][C]-0.0228176816561006[/C][/ROW]
[ROW][C]33[/C][C]28458[/C][C]28638.7705933574[/C][C]-361.412617010191[/C][C]-180.770593357424[/C][C]-2.22515972296892[/C][/ROW]
[ROW][C]34[/C][C]28340[/C][C]28337.9811361967[/C][C]-341.116830714749[/C][C]2.01886380326632[/C][C]0.256453293310451[/C][/ROW]
[ROW][C]35[/C][C]28164[/C][C]28175.7477639450[/C][C]-281.308069745666[/C][C]-11.7477639450351[/C][C]0.756318845029209[/C][/ROW]
[ROW][C]36[/C][C]28438[/C][C]28297.0663171147[/C][C]-146.845539897833[/C][C]140.933682885279[/C][C]1.70787769093953[/C][/ROW]
[ROW][C]37[/C][C]28053[/C][C]28049.3665674207[/C][C]-180.602859623056[/C][C]3.63343257928044[/C][C]-0.430305741362482[/C][/ROW]
[ROW][C]38[/C][C]27599[/C][C]27645.856288222[/C][C]-255.093863319666[/C][C]-46.8562882220065[/C][C]-0.93750543967464[/C][/ROW]
[ROW][C]39[/C][C]27226[/C][C]27286.7875299428[/C][C]-289.55776432753[/C][C]-60.7875299428079[/C][C]-0.432883522008801[/C][/ROW]
[ROW][C]40[/C][C]27119[/C][C]27050.9348063584[/C][C]-271.760293849928[/C][C]68.0651936416054[/C][C]0.225823654725565[/C][/ROW]
[ROW][C]41[/C][C]26625[/C][C]26672.4922305923[/C][C]-307.302956533675[/C][C]-47.4922305923384[/C][C]-0.451044796960724[/C][/ROW]
[ROW][C]42[/C][C]26541[/C][C]26777.099526657[/C][C]-169.785940146996[/C][C]-236.099526656986[/C][C]1.73628166490649[/C][/ROW]
[ROW][C]43[/C][C]27023[/C][C]26661.0145033727[/C][C]-151.872400451269[/C][C]361.985496627329[/C][C]0.225817688612905[/C][/ROW]
[ROW][C]44[/C][C]26631[/C][C]26422.958069467[/C][C]-180.60104905316[/C][C]208.041930533002[/C][C]-0.36248289620195[/C][/ROW]
[ROW][C]45[/C][C]26154[/C][C]26323.5026762431[/C][C]-153.555466889015[/C][C]-169.502676243091[/C][C]0.341553227503716[/C][/ROW]
[ROW][C]46[/C][C]26029[/C][C]26142.7316996139[/C][C]-162.622893656690[/C][C]-113.731699613897[/C][C]-0.114577876487621[/C][/ROW]
[ROW][C]47[/C][C]26008[/C][C]26107.9018622941[/C][C]-120.079539737631[/C][C]-99.9018622940992[/C][C]0.538356921112227[/C][/ROW]
[ROW][C]48[/C][C]26632[/C][C]26315.6778733876[/C][C]-10.9104029941995[/C][C]316.322126612373[/C][C]1.38542830379758[/C][/ROW]
[ROW][C]49[/C][C]27010[/C][C]26750.8104979997[/C][C]137.862005144066[/C][C]259.189502000304[/C][C]1.88731956683512[/C][/ROW]
[ROW][C]50[/C][C]27041[/C][C]27023.3869063740[/C][C]182.732391448486[/C][C]17.6130936260493[/C][C]0.565419162152878[/C][/ROW]
[ROW][C]51[/C][C]27244[/C][C]27287.3208057622[/C][C]209.649665502674[/C][C]-43.3208057621617[/C][C]0.338836017499217[/C][/ROW]
[ROW][C]52[/C][C]26976[/C][C]27081.5859462145[/C][C]72.0876033019442[/C][C]-105.58594621454[/C][C]-1.74201812991879[/C][/ROW]
[ROW][C]53[/C][C]26715[/C][C]26962.4719443513[/C][C]8.56889468535878[/C][C]-247.471944351268[/C][C]-0.805671148517118[/C][/ROW]
[ROW][C]54[/C][C]27017[/C][C]27168.5432147142[/C][C]74.3147573654074[/C][C]-151.543214714203[/C][C]0.831287997757693[/C][/ROW]
[ROW][C]55[/C][C]27714[/C][C]27306.0925189186[/C][C]95.3583562041105[/C][C]407.907481081378[/C][C]0.265488155779644[/C][/ROW]
[ROW][C]56[/C][C]27655[/C][C]27439.9359010357[/C][C]108.154237456146[/C][C]215.064098964331[/C][C]0.161431691995990[/C][/ROW]
[ROW][C]57[/C][C]27103[/C][C]27338.1443926945[/C][C]38.3912689838747[/C][C]-235.144392694536[/C][C]-0.880788600479137[/C][/ROW]
[ROW][C]58[/C][C]27088[/C][C]27276.0739319876[/C][C]5.02562760095843[/C][C]-188.073931987641[/C][C]-0.421668242536758[/C][/ROW]
[ROW][C]59[/C][C]26968[/C][C]27224.1795054151[/C][C]-13.8739147793201[/C][C]-256.179505415110[/C][C]-0.239236439101584[/C][/ROW]
[ROW][C]60[/C][C]27770[/C][C]27497.8060074155[/C][C]81.6591018419422[/C][C]272.193992584519[/C][C]1.21123276927662[/C][/ROW]
[ROW][C]61[/C][C]27616[/C][C]27480.4961718109[/C][C]48.7349271617143[/C][C]135.503828189074[/C][C]-0.416863820866641[/C][/ROW]
[ROW][C]62[/C][C]27481[/C][C]27475.6585174355[/C][C]30.9334259174824[/C][C]5.34148256450683[/C][C]-0.224460930549671[/C][/ROW]
[ROW][C]63[/C][C]27279[/C][C]27259.1910362491[/C][C]-51.0181775953889[/C][C]19.8089637508565[/C][C]-1.03283995756840[/C][/ROW]
[ROW][C]64[/C][C]26918[/C][C]27026.6762417456[/C][C]-111.080188538592[/C][C]-108.676241745629[/C][C]-0.759908093071657[/C][/ROW]
[ROW][C]65[/C][C]26503[/C][C]26845.9197240207[/C][C]-134.182259343628[/C][C]-342.919724020668[/C][C]-0.292829396133781[/C][/ROW]
[ROW][C]66[/C][C]26547[/C][C]26720.4351493483[/C][C]-131.293367897765[/C][C]-173.435149348331[/C][C]0.0365501859303891[/C][/ROW]
[ROW][C]67[/C][C]27467[/C][C]26921.4242579236[/C][C]-20.92553380533[/C][C]545.575742076427[/C][C]1.3933213515838[/C][/ROW]
[ROW][C]68[/C][C]27305[/C][C]26995.4303486074[/C][C]10.5778877407435[/C][C]309.569651392615[/C][C]0.397467970756622[/C][/ROW]
[ROW][C]69[/C][C]26259[/C][C]26624.1528325546[/C][C]-116.033069044342[/C][C]-365.152832554619[/C][C]-1.59832629851900[/C][/ROW]
[ROW][C]70[/C][C]26048[/C][C]26300.1312311149[/C][C]-184.958543233422[/C][C]-252.131231114932[/C][C]-0.871155018129837[/C][/ROW]
[ROW][C]71[/C][C]25743[/C][C]26098.731979908[/C][C]-190.407261880979[/C][C]-355.731979908003[/C][C]-0.0689744663970854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116450&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
12654826548000
22675226715.071160153720.639042287058936.92883984631910.655157802561565
32696726905.984857443760.652445081584361.01514255630790.797201645473681
42703427023.336842896178.338784825386610.66315710393640.260047571583508
52705627064.502622790365.9119502713695-8.50262279034184-0.163277419399898
62747627371.7479631305148.157340019902104.2520368695051.05359781154324
72849728251.0395063614399.416856521773245.9604936386243.19443636634054
82908529029.5396239157530.19791770960755.46037608434491.65696680734548
92872028972.3331143086327.219979063559-252.333114308634-2.56773172212168
102906729067.6843740869247.046495247978-0.684374086876244-1.01358872503052
112924929237.9329865949220.48475630364111.0670134050717-0.335726035983488
122967229600.61195138269.67163337558471.38804861998610.62164125467645
132976129878.9303215381272.580636832681-117.9303215380660.0404532214802299
143006630100.7268984668255.444053548699-34.7268984668399-0.208407717797834
153031530273.8784421762228.50537359574341.1215578238274-0.335766994045634
163057130507.164335154230.10219649927363.83566484598580.0204911994051311
173075730807.9256950309253.991899944700-50.92569503094470.301274491280909
183074230943.3158692665213.941770040189-201.315869266538-0.502871907000844
193131031170.7751718584218.499799397522139.2248281416460.057433184126269
203138131223.3671993483162.539170526016157.632800651745-0.706683206745636
213147031558.1445332407220.677054601602-88.14453324072250.734636757521303
223122631387.747867198688.630822864254-161.747867198606-1.66873740076928
233108131192.8057916132-7.01679000012729-111.805791613217-1.20870955964957
243106131025.9858320732-60.736463395501235.0141679267804-0.681669535008176
253111431118.5431277041-9.17458033242673-4.543127704077050.667213170070226
263082830922.2405160452-72.0196421270532-94.2405160452136-0.787468975070231
273041830505.9314245896-185.848338749833-87.9314245896289-1.42399743668197
283019530182.3792107237-231.54544942195212.6207892762570-0.582334562156756
292987729903.6335795030-247.348888625837-26.6335795030399-0.200384461419676
302919229474.2476114323-308.373187248733-282.247611432276-0.768536563314524
312987629538.9891794660-183.468990414924337.0108205340461.57354403807739
322940929350.1184862488-185.27675971141858.8815137512226-0.0228176816561006
332845828638.7705933574-361.412617010191-180.770593357424-2.22515972296892
342834028337.9811361967-341.1168307147492.018863803266320.256453293310451
352816428175.7477639450-281.308069745666-11.74776394503510.756318845029209
362843828297.0663171147-146.845539897833140.9336828852791.70787769093953
372805328049.3665674207-180.6028596230563.63343257928044-0.430305741362482
382759927645.856288222-255.093863319666-46.8562882220065-0.93750543967464
392722627286.7875299428-289.55776432753-60.7875299428079-0.432883522008801
402711927050.9348063584-271.76029384992868.06519364160540.225823654725565
412662526672.4922305923-307.302956533675-47.4922305923384-0.451044796960724
422654126777.099526657-169.785940146996-236.0995266569861.73628166490649
432702326661.0145033727-151.872400451269361.9854966273290.225817688612905
442663126422.958069467-180.60104905316208.041930533002-0.36248289620195
452615426323.5026762431-153.555466889015-169.5026762430910.341553227503716
462602926142.7316996139-162.622893656690-113.731699613897-0.114577876487621
472600826107.9018622941-120.079539737631-99.90186229409920.538356921112227
482663226315.6778733876-10.9104029941995316.3221266123731.38542830379758
492701026750.8104979997137.862005144066259.1895020003041.88731956683512
502704127023.3869063740182.73239144848617.61309362604930.565419162152878
512724427287.3208057622209.649665502674-43.32080576216170.338836017499217
522697627081.585946214572.0876033019442-105.58594621454-1.74201812991879
532671526962.47194435138.56889468535878-247.471944351268-0.805671148517118
542701727168.543214714274.3147573654074-151.5432147142030.831287997757693
552771427306.092518918695.3583562041105407.9074810813780.265488155779644
562765527439.9359010357108.154237456146215.0640989643310.161431691995990
572710327338.144392694538.3912689838747-235.144392694536-0.880788600479137
582708827276.07393198765.02562760095843-188.073931987641-0.421668242536758
592696827224.1795054151-13.8739147793201-256.179505415110-0.239236439101584
602777027497.806007415581.6591018419422272.1939925845191.21123276927662
612761627480.496171810948.7349271617143135.503828189074-0.416863820866641
622748127475.658517435530.93342591748245.34148256450683-0.224460930549671
632727927259.1910362491-51.018177595388919.8089637508565-1.03283995756840
642691827026.6762417456-111.080188538592-108.676241745629-0.759908093071657
652650326845.9197240207-134.182259343628-342.919724020668-0.292829396133781
662654726720.4351493483-131.293367897765-173.4351493483310.0365501859303891
672746726921.4242579236-20.92553380533545.5757420764271.3933213515838
682730526995.430348607410.5778877407435309.5696513926150.397467970756622
692625926624.1528325546-116.033069044342-365.152832554619-1.59832629851900
702604826300.1312311149-184.958543233422-252.131231114932-0.871155018129837
712574326098.731979908-190.407261880979-355.731979908003-0.0689744663970854



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')