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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, 01 Dec 2009 12:02:53 -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/01/t1259694235mxcdj199k0vidb7.htm/, Retrieved Fri, 29 Mar 2024 12:04:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62190, Retrieved Fri, 29 Mar 2024 12:04:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
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]
- R PD      [Structural Time Series Models] [structural timese...] [2009-12-01 19:02:53] [4563e36d4b7005634fe3557528d9fcab] [Current]
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Dataseries X:
7291
6820
8031
7862
7357
7213
7079
7012
7319
8148
7599
6908
7878
7407
7911
7323
7179
6758
6934
6696
7688
8296
7697
7907
7592
7710
9011
8225
7733
8062
7859
8221
8330
8868
9053
8811
8120
7953
8878
8601
8361
9116
9310
9891
10147
10317
10682
10276
10614
9413
11068
9772
10350
10541
10049
10714
10759
11684
11462
10485
11056
10184
11082
10554
11315
10847
11104
11026
11073
12073
12328
11172




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=62190&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=62190&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62190&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
172917291000
268207048.68374732168-13.2611654876782-134.840613763128-1.04355479152437
380317493.267503562718.1125206526509390.7003754343121.83592902788528
478627713.328543094528.806650562373568.17159742720990.950525852620733
573577594.138404824523.0798460388473-171.851574428151-0.745883741957134
672137412.8440314866316.7059932940849-106.718099601164-1.05098366565339
770797240.4164814554411.3340005639096-74.4966734139742-0.977067469653487
870127113.35521884077.46874033390386-37.6958479907924-0.714894154450486
973197177.859611797829.0861735775606114.9433701051030.294204356472167
1081487600.4394290829721.1005217792702358.0319491264912.12923293199540
1175997671.1261766735722.5774335036598-94.80644672022980.254901740865258
1269087357.7368768881112.3387736497183-296.377822956583-1.72426220584337
1378787511.470763791668.51365443585555298.7874230216760.832768070543478
1474077659.6698682651311.8478622837356-311.0320259443860.699422629064606
1579117671.2338199182211.8358006325896239.871423136174-0.00130998792483277
1673237480.772062483462.98974315981812-80.5380609496657-0.963915941973353
1771797361.8263009821-1.85757745151146-133.962787909043-0.603078425928941
1867587105.35278873495-10.9982549616414-242.668769655792-1.28167187919449
1969347000.51920549942-14.1551460848484-27.5727771673630-0.475030151478542
2066966900.20396845968-16.9797170770225-168.358073091576-0.436603798980192
2176887226.20463333312-5.76111259305233319.1319131665341.73702942415039
2282967567.812890729125.61695542044239583.8339633194581.75729814634966
2376977675.534183683658.88622142500431-20.92764201113280.515692754377049
2479077951.3612273672316.5890703596405-155.2889794361461.34838690870133
2575927772.5754841477212.6707867788182-97.8375403304805-1.01798928366606
2677107835.4911996850814.1875247848437-146.0813910912290.252633678064310
2790118181.0579411025426.9001528930008701.2006283544531.59972023941562
2882258249.253057370428.5902851500672-40.20751049020410.199606300425917
2977338054.8238194369319.5965575902860-234.012742509691-1.09639231246194
3080628106.6070978511620.8452406792173-57.48650555594690.160177160475840
3178598035.2761175736817.3915861949327-139.097928536477-0.461110355506568
3282218256.7618699381724.8676813862013-118.3056011051741.02230583362258
3383308277.292730655624.711005942631954.4618614152546-0.0217149244208512
3488688326.5354665345825.5848595513574531.5467017031020.122682031477188
3590538685.6374622203237.171791607188232.6744748672621.66557172067662
3688118842.1500822278641.1452234701889-79.37670227883190.596870614182408
3781208678.0494611536534.5785186295075-474.561646742321-1.03559255897059
3879538507.4309124312227.4212054571413-471.798870485392-1.02413989936943
3988788350.2586709003320.3902199643988600.399169125844-0.904397560154914
4086018403.5397175784721.6954792645921184.6176849751620.160550046591718
4183618495.061950536924.4795444720377-161.5102873685060.343326489198481
4291168780.5539695329934.7536852443553231.8905599134011.29321068413719
4393109143.9212101419747.469967646299934.95614722125681.63466389998092
4498919572.8559108547362.0073688716162165.6155066386691.89931762784982
45101479905.7268614521872.1962862258242132.9773774505951.34762897451159
461031710016.546327816373.6304710877073285.0284096398670.191897580410769
471068210249.386663283179.4557132360774369.0911169095030.79040497743052
481027610306.330706658278.64376581864-21.3410179580736-0.111945866857702
491061410610.389371701186.7552629680137-86.6697376372481.12474976872109
50941310373.687158491974.7790798188108-831.620064995144-1.60719749047823
511106810428.398548449574.0102312779966647.526101810366-0.098833694891011
52977210169.496444553160.9846697124375-266.778910394404-1.63410867673940
531035010377.953641369266.789656224051-85.98596968083950.72612670344276
541054110505.521100566369.17320396640511.43735401064500.300654683936889
551004910455.982789178764.5548874606687-359.844378799897-0.588897114626286
561071410576.959152581466.7301718431311114.6068202348710.280032877243103
571075910665.792171460267.57486984407484.42375025832550.109597200580330
581168411037.519886117679.1010212184423525.7512211869441.50623759587080
591146211154.641852674780.5308555217278292.2766583634250.188225193229656
601048510971.811347249770.6750543478657-382.248618588742-1.30565169501504
611105610973.861862325568.102014984308109.431398351198-0.340815452641316
621018410996.624736520466.3824462910915-794.619894168715-0.224752029060345
631108210748.352202884254.2780137671281457.965437368703-1.55274557482400
641055410796.133494028654.0255517929649-239.575872566221-0.0319850409632741
651131511072.329958213862.6939124156583155.1524188655761.09528742526485
661084711027.843152658858.5156672325497-138.498157362335-0.529881669715866
671110411242.885045946064.5950269826568-200.8795181522560.775309707590191
681102611197.467035501260.3431224231802-127.859921089333-0.545048847437742
691107311193.467325927757.8691224108759-94.97905511919-0.318490297321043
701207311347.072894904161.532449989892688.0440193109760.473401533514601
711232811610.550698181969.2300751248162637.5741314236620.998473944486447
721117211632.871801660867.445216111687-442.303003545541-0.232186314431388

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 7291 & 7291 & 0 & 0 & 0 \tabularnewline
2 & 6820 & 7048.68374732168 & -13.2611654876782 & -134.840613763128 & -1.04355479152437 \tabularnewline
3 & 8031 & 7493.2675035627 & 18.1125206526509 & 390.700375434312 & 1.83592902788528 \tabularnewline
4 & 7862 & 7713.3285430945 & 28.8066505623735 & 68.1715974272099 & 0.950525852620733 \tabularnewline
5 & 7357 & 7594.1384048245 & 23.0798460388473 & -171.851574428151 & -0.745883741957134 \tabularnewline
6 & 7213 & 7412.84403148663 & 16.7059932940849 & -106.718099601164 & -1.05098366565339 \tabularnewline
7 & 7079 & 7240.41648145544 & 11.3340005639096 & -74.4966734139742 & -0.977067469653487 \tabularnewline
8 & 7012 & 7113.3552188407 & 7.46874033390386 & -37.6958479907924 & -0.714894154450486 \tabularnewline
9 & 7319 & 7177.85961179782 & 9.0861735775606 & 114.943370105103 & 0.294204356472167 \tabularnewline
10 & 8148 & 7600.43942908297 & 21.1005217792702 & 358.031949126491 & 2.12923293199540 \tabularnewline
11 & 7599 & 7671.12617667357 & 22.5774335036598 & -94.8064467202298 & 0.254901740865258 \tabularnewline
12 & 6908 & 7357.73687688811 & 12.3387736497183 & -296.377822956583 & -1.72426220584337 \tabularnewline
13 & 7878 & 7511.47076379166 & 8.51365443585555 & 298.787423021676 & 0.832768070543478 \tabularnewline
14 & 7407 & 7659.66986826513 & 11.8478622837356 & -311.032025944386 & 0.699422629064606 \tabularnewline
15 & 7911 & 7671.23381991822 & 11.8358006325896 & 239.871423136174 & -0.00130998792483277 \tabularnewline
16 & 7323 & 7480.77206248346 & 2.98974315981812 & -80.5380609496657 & -0.963915941973353 \tabularnewline
17 & 7179 & 7361.8263009821 & -1.85757745151146 & -133.962787909043 & -0.603078425928941 \tabularnewline
18 & 6758 & 7105.35278873495 & -10.9982549616414 & -242.668769655792 & -1.28167187919449 \tabularnewline
19 & 6934 & 7000.51920549942 & -14.1551460848484 & -27.5727771673630 & -0.475030151478542 \tabularnewline
20 & 6696 & 6900.20396845968 & -16.9797170770225 & -168.358073091576 & -0.436603798980192 \tabularnewline
21 & 7688 & 7226.20463333312 & -5.76111259305233 & 319.131913166534 & 1.73702942415039 \tabularnewline
22 & 8296 & 7567.81289072912 & 5.61695542044239 & 583.833963319458 & 1.75729814634966 \tabularnewline
23 & 7697 & 7675.53418368365 & 8.88622142500431 & -20.9276420111328 & 0.515692754377049 \tabularnewline
24 & 7907 & 7951.36122736723 & 16.5890703596405 & -155.288979436146 & 1.34838690870133 \tabularnewline
25 & 7592 & 7772.57548414772 & 12.6707867788182 & -97.8375403304805 & -1.01798928366606 \tabularnewline
26 & 7710 & 7835.49119968508 & 14.1875247848437 & -146.081391091229 & 0.252633678064310 \tabularnewline
27 & 9011 & 8181.05794110254 & 26.9001528930008 & 701.200628354453 & 1.59972023941562 \tabularnewline
28 & 8225 & 8249.2530573704 & 28.5902851500672 & -40.2075104902041 & 0.199606300425917 \tabularnewline
29 & 7733 & 8054.82381943693 & 19.5965575902860 & -234.012742509691 & -1.09639231246194 \tabularnewline
30 & 8062 & 8106.60709785116 & 20.8452406792173 & -57.4865055559469 & 0.160177160475840 \tabularnewline
31 & 7859 & 8035.27611757368 & 17.3915861949327 & -139.097928536477 & -0.461110355506568 \tabularnewline
32 & 8221 & 8256.76186993817 & 24.8676813862013 & -118.305601105174 & 1.02230583362258 \tabularnewline
33 & 8330 & 8277.2927306556 & 24.7110059426319 & 54.4618614152546 & -0.0217149244208512 \tabularnewline
34 & 8868 & 8326.53546653458 & 25.5848595513574 & 531.546701703102 & 0.122682031477188 \tabularnewline
35 & 9053 & 8685.63746222032 & 37.171791607188 & 232.674474867262 & 1.66557172067662 \tabularnewline
36 & 8811 & 8842.15008222786 & 41.1452234701889 & -79.3767022788319 & 0.596870614182408 \tabularnewline
37 & 8120 & 8678.04946115365 & 34.5785186295075 & -474.561646742321 & -1.03559255897059 \tabularnewline
38 & 7953 & 8507.43091243122 & 27.4212054571413 & -471.798870485392 & -1.02413989936943 \tabularnewline
39 & 8878 & 8350.25867090033 & 20.3902199643988 & 600.399169125844 & -0.904397560154914 \tabularnewline
40 & 8601 & 8403.53971757847 & 21.6954792645921 & 184.617684975162 & 0.160550046591718 \tabularnewline
41 & 8361 & 8495.0619505369 & 24.4795444720377 & -161.510287368506 & 0.343326489198481 \tabularnewline
42 & 9116 & 8780.55396953299 & 34.7536852443553 & 231.890559913401 & 1.29321068413719 \tabularnewline
43 & 9310 & 9143.92121014197 & 47.4699676462999 & 34.9561472212568 & 1.63466389998092 \tabularnewline
44 & 9891 & 9572.85591085473 & 62.0073688716162 & 165.615506638669 & 1.89931762784982 \tabularnewline
45 & 10147 & 9905.72686145218 & 72.1962862258242 & 132.977377450595 & 1.34762897451159 \tabularnewline
46 & 10317 & 10016.5463278163 & 73.6304710877073 & 285.028409639867 & 0.191897580410769 \tabularnewline
47 & 10682 & 10249.3866632831 & 79.4557132360774 & 369.091116909503 & 0.79040497743052 \tabularnewline
48 & 10276 & 10306.3307066582 & 78.64376581864 & -21.3410179580736 & -0.111945866857702 \tabularnewline
49 & 10614 & 10610.3893717011 & 86.7552629680137 & -86.669737637248 & 1.12474976872109 \tabularnewline
50 & 9413 & 10373.6871584919 & 74.7790798188108 & -831.620064995144 & -1.60719749047823 \tabularnewline
51 & 11068 & 10428.3985484495 & 74.0102312779966 & 647.526101810366 & -0.098833694891011 \tabularnewline
52 & 9772 & 10169.4964445531 & 60.9846697124375 & -266.778910394404 & -1.63410867673940 \tabularnewline
53 & 10350 & 10377.9536413692 & 66.789656224051 & -85.9859696808395 & 0.72612670344276 \tabularnewline
54 & 10541 & 10505.5211005663 & 69.173203966405 & 11.4373540106450 & 0.300654683936889 \tabularnewline
55 & 10049 & 10455.9827891787 & 64.5548874606687 & -359.844378799897 & -0.588897114626286 \tabularnewline
56 & 10714 & 10576.9591525814 & 66.7301718431311 & 114.606820234871 & 0.280032877243103 \tabularnewline
57 & 10759 & 10665.7921714602 & 67.574869844074 & 84.4237502583255 & 0.109597200580330 \tabularnewline
58 & 11684 & 11037.5198861176 & 79.1010212184423 & 525.751221186944 & 1.50623759587080 \tabularnewline
59 & 11462 & 11154.6418526747 & 80.5308555217278 & 292.276658363425 & 0.188225193229656 \tabularnewline
60 & 10485 & 10971.8113472497 & 70.6750543478657 & -382.248618588742 & -1.30565169501504 \tabularnewline
61 & 11056 & 10973.8618623255 & 68.102014984308 & 109.431398351198 & -0.340815452641316 \tabularnewline
62 & 10184 & 10996.6247365204 & 66.3824462910915 & -794.619894168715 & -0.224752029060345 \tabularnewline
63 & 11082 & 10748.3522028842 & 54.2780137671281 & 457.965437368703 & -1.55274557482400 \tabularnewline
64 & 10554 & 10796.1334940286 & 54.0255517929649 & -239.575872566221 & -0.0319850409632741 \tabularnewline
65 & 11315 & 11072.3299582138 & 62.6939124156583 & 155.152418865576 & 1.09528742526485 \tabularnewline
66 & 10847 & 11027.8431526588 & 58.5156672325497 & -138.498157362335 & -0.529881669715866 \tabularnewline
67 & 11104 & 11242.8850459460 & 64.5950269826568 & -200.879518152256 & 0.775309707590191 \tabularnewline
68 & 11026 & 11197.4670355012 & 60.3431224231802 & -127.859921089333 & -0.545048847437742 \tabularnewline
69 & 11073 & 11193.4673259277 & 57.8691224108759 & -94.97905511919 & -0.318490297321043 \tabularnewline
70 & 12073 & 11347.0728949041 & 61.532449989892 & 688.044019310976 & 0.473401533514601 \tabularnewline
71 & 12328 & 11610.5506981819 & 69.2300751248162 & 637.574131423662 & 0.998473944486447 \tabularnewline
72 & 11172 & 11632.8718016608 & 67.445216111687 & -442.303003545541 & -0.232186314431388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62190&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]7291[/C][C]7291[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]6820[/C][C]7048.68374732168[/C][C]-13.2611654876782[/C][C]-134.840613763128[/C][C]-1.04355479152437[/C][/ROW]
[ROW][C]3[/C][C]8031[/C][C]7493.2675035627[/C][C]18.1125206526509[/C][C]390.700375434312[/C][C]1.83592902788528[/C][/ROW]
[ROW][C]4[/C][C]7862[/C][C]7713.3285430945[/C][C]28.8066505623735[/C][C]68.1715974272099[/C][C]0.950525852620733[/C][/ROW]
[ROW][C]5[/C][C]7357[/C][C]7594.1384048245[/C][C]23.0798460388473[/C][C]-171.851574428151[/C][C]-0.745883741957134[/C][/ROW]
[ROW][C]6[/C][C]7213[/C][C]7412.84403148663[/C][C]16.7059932940849[/C][C]-106.718099601164[/C][C]-1.05098366565339[/C][/ROW]
[ROW][C]7[/C][C]7079[/C][C]7240.41648145544[/C][C]11.3340005639096[/C][C]-74.4966734139742[/C][C]-0.977067469653487[/C][/ROW]
[ROW][C]8[/C][C]7012[/C][C]7113.3552188407[/C][C]7.46874033390386[/C][C]-37.6958479907924[/C][C]-0.714894154450486[/C][/ROW]
[ROW][C]9[/C][C]7319[/C][C]7177.85961179782[/C][C]9.0861735775606[/C][C]114.943370105103[/C][C]0.294204356472167[/C][/ROW]
[ROW][C]10[/C][C]8148[/C][C]7600.43942908297[/C][C]21.1005217792702[/C][C]358.031949126491[/C][C]2.12923293199540[/C][/ROW]
[ROW][C]11[/C][C]7599[/C][C]7671.12617667357[/C][C]22.5774335036598[/C][C]-94.8064467202298[/C][C]0.254901740865258[/C][/ROW]
[ROW][C]12[/C][C]6908[/C][C]7357.73687688811[/C][C]12.3387736497183[/C][C]-296.377822956583[/C][C]-1.72426220584337[/C][/ROW]
[ROW][C]13[/C][C]7878[/C][C]7511.47076379166[/C][C]8.51365443585555[/C][C]298.787423021676[/C][C]0.832768070543478[/C][/ROW]
[ROW][C]14[/C][C]7407[/C][C]7659.66986826513[/C][C]11.8478622837356[/C][C]-311.032025944386[/C][C]0.699422629064606[/C][/ROW]
[ROW][C]15[/C][C]7911[/C][C]7671.23381991822[/C][C]11.8358006325896[/C][C]239.871423136174[/C][C]-0.00130998792483277[/C][/ROW]
[ROW][C]16[/C][C]7323[/C][C]7480.77206248346[/C][C]2.98974315981812[/C][C]-80.5380609496657[/C][C]-0.963915941973353[/C][/ROW]
[ROW][C]17[/C][C]7179[/C][C]7361.8263009821[/C][C]-1.85757745151146[/C][C]-133.962787909043[/C][C]-0.603078425928941[/C][/ROW]
[ROW][C]18[/C][C]6758[/C][C]7105.35278873495[/C][C]-10.9982549616414[/C][C]-242.668769655792[/C][C]-1.28167187919449[/C][/ROW]
[ROW][C]19[/C][C]6934[/C][C]7000.51920549942[/C][C]-14.1551460848484[/C][C]-27.5727771673630[/C][C]-0.475030151478542[/C][/ROW]
[ROW][C]20[/C][C]6696[/C][C]6900.20396845968[/C][C]-16.9797170770225[/C][C]-168.358073091576[/C][C]-0.436603798980192[/C][/ROW]
[ROW][C]21[/C][C]7688[/C][C]7226.20463333312[/C][C]-5.76111259305233[/C][C]319.131913166534[/C][C]1.73702942415039[/C][/ROW]
[ROW][C]22[/C][C]8296[/C][C]7567.81289072912[/C][C]5.61695542044239[/C][C]583.833963319458[/C][C]1.75729814634966[/C][/ROW]
[ROW][C]23[/C][C]7697[/C][C]7675.53418368365[/C][C]8.88622142500431[/C][C]-20.9276420111328[/C][C]0.515692754377049[/C][/ROW]
[ROW][C]24[/C][C]7907[/C][C]7951.36122736723[/C][C]16.5890703596405[/C][C]-155.288979436146[/C][C]1.34838690870133[/C][/ROW]
[ROW][C]25[/C][C]7592[/C][C]7772.57548414772[/C][C]12.6707867788182[/C][C]-97.8375403304805[/C][C]-1.01798928366606[/C][/ROW]
[ROW][C]26[/C][C]7710[/C][C]7835.49119968508[/C][C]14.1875247848437[/C][C]-146.081391091229[/C][C]0.252633678064310[/C][/ROW]
[ROW][C]27[/C][C]9011[/C][C]8181.05794110254[/C][C]26.9001528930008[/C][C]701.200628354453[/C][C]1.59972023941562[/C][/ROW]
[ROW][C]28[/C][C]8225[/C][C]8249.2530573704[/C][C]28.5902851500672[/C][C]-40.2075104902041[/C][C]0.199606300425917[/C][/ROW]
[ROW][C]29[/C][C]7733[/C][C]8054.82381943693[/C][C]19.5965575902860[/C][C]-234.012742509691[/C][C]-1.09639231246194[/C][/ROW]
[ROW][C]30[/C][C]8062[/C][C]8106.60709785116[/C][C]20.8452406792173[/C][C]-57.4865055559469[/C][C]0.160177160475840[/C][/ROW]
[ROW][C]31[/C][C]7859[/C][C]8035.27611757368[/C][C]17.3915861949327[/C][C]-139.097928536477[/C][C]-0.461110355506568[/C][/ROW]
[ROW][C]32[/C][C]8221[/C][C]8256.76186993817[/C][C]24.8676813862013[/C][C]-118.305601105174[/C][C]1.02230583362258[/C][/ROW]
[ROW][C]33[/C][C]8330[/C][C]8277.2927306556[/C][C]24.7110059426319[/C][C]54.4618614152546[/C][C]-0.0217149244208512[/C][/ROW]
[ROW][C]34[/C][C]8868[/C][C]8326.53546653458[/C][C]25.5848595513574[/C][C]531.546701703102[/C][C]0.122682031477188[/C][/ROW]
[ROW][C]35[/C][C]9053[/C][C]8685.63746222032[/C][C]37.171791607188[/C][C]232.674474867262[/C][C]1.66557172067662[/C][/ROW]
[ROW][C]36[/C][C]8811[/C][C]8842.15008222786[/C][C]41.1452234701889[/C][C]-79.3767022788319[/C][C]0.596870614182408[/C][/ROW]
[ROW][C]37[/C][C]8120[/C][C]8678.04946115365[/C][C]34.5785186295075[/C][C]-474.561646742321[/C][C]-1.03559255897059[/C][/ROW]
[ROW][C]38[/C][C]7953[/C][C]8507.43091243122[/C][C]27.4212054571413[/C][C]-471.798870485392[/C][C]-1.02413989936943[/C][/ROW]
[ROW][C]39[/C][C]8878[/C][C]8350.25867090033[/C][C]20.3902199643988[/C][C]600.399169125844[/C][C]-0.904397560154914[/C][/ROW]
[ROW][C]40[/C][C]8601[/C][C]8403.53971757847[/C][C]21.6954792645921[/C][C]184.617684975162[/C][C]0.160550046591718[/C][/ROW]
[ROW][C]41[/C][C]8361[/C][C]8495.0619505369[/C][C]24.4795444720377[/C][C]-161.510287368506[/C][C]0.343326489198481[/C][/ROW]
[ROW][C]42[/C][C]9116[/C][C]8780.55396953299[/C][C]34.7536852443553[/C][C]231.890559913401[/C][C]1.29321068413719[/C][/ROW]
[ROW][C]43[/C][C]9310[/C][C]9143.92121014197[/C][C]47.4699676462999[/C][C]34.9561472212568[/C][C]1.63466389998092[/C][/ROW]
[ROW][C]44[/C][C]9891[/C][C]9572.85591085473[/C][C]62.0073688716162[/C][C]165.615506638669[/C][C]1.89931762784982[/C][/ROW]
[ROW][C]45[/C][C]10147[/C][C]9905.72686145218[/C][C]72.1962862258242[/C][C]132.977377450595[/C][C]1.34762897451159[/C][/ROW]
[ROW][C]46[/C][C]10317[/C][C]10016.5463278163[/C][C]73.6304710877073[/C][C]285.028409639867[/C][C]0.191897580410769[/C][/ROW]
[ROW][C]47[/C][C]10682[/C][C]10249.3866632831[/C][C]79.4557132360774[/C][C]369.091116909503[/C][C]0.79040497743052[/C][/ROW]
[ROW][C]48[/C][C]10276[/C][C]10306.3307066582[/C][C]78.64376581864[/C][C]-21.3410179580736[/C][C]-0.111945866857702[/C][/ROW]
[ROW][C]49[/C][C]10614[/C][C]10610.3893717011[/C][C]86.7552629680137[/C][C]-86.669737637248[/C][C]1.12474976872109[/C][/ROW]
[ROW][C]50[/C][C]9413[/C][C]10373.6871584919[/C][C]74.7790798188108[/C][C]-831.620064995144[/C][C]-1.60719749047823[/C][/ROW]
[ROW][C]51[/C][C]11068[/C][C]10428.3985484495[/C][C]74.0102312779966[/C][C]647.526101810366[/C][C]-0.098833694891011[/C][/ROW]
[ROW][C]52[/C][C]9772[/C][C]10169.4964445531[/C][C]60.9846697124375[/C][C]-266.778910394404[/C][C]-1.63410867673940[/C][/ROW]
[ROW][C]53[/C][C]10350[/C][C]10377.9536413692[/C][C]66.789656224051[/C][C]-85.9859696808395[/C][C]0.72612670344276[/C][/ROW]
[ROW][C]54[/C][C]10541[/C][C]10505.5211005663[/C][C]69.173203966405[/C][C]11.4373540106450[/C][C]0.300654683936889[/C][/ROW]
[ROW][C]55[/C][C]10049[/C][C]10455.9827891787[/C][C]64.5548874606687[/C][C]-359.844378799897[/C][C]-0.588897114626286[/C][/ROW]
[ROW][C]56[/C][C]10714[/C][C]10576.9591525814[/C][C]66.7301718431311[/C][C]114.606820234871[/C][C]0.280032877243103[/C][/ROW]
[ROW][C]57[/C][C]10759[/C][C]10665.7921714602[/C][C]67.574869844074[/C][C]84.4237502583255[/C][C]0.109597200580330[/C][/ROW]
[ROW][C]58[/C][C]11684[/C][C]11037.5198861176[/C][C]79.1010212184423[/C][C]525.751221186944[/C][C]1.50623759587080[/C][/ROW]
[ROW][C]59[/C][C]11462[/C][C]11154.6418526747[/C][C]80.5308555217278[/C][C]292.276658363425[/C][C]0.188225193229656[/C][/ROW]
[ROW][C]60[/C][C]10485[/C][C]10971.8113472497[/C][C]70.6750543478657[/C][C]-382.248618588742[/C][C]-1.30565169501504[/C][/ROW]
[ROW][C]61[/C][C]11056[/C][C]10973.8618623255[/C][C]68.102014984308[/C][C]109.431398351198[/C][C]-0.340815452641316[/C][/ROW]
[ROW][C]62[/C][C]10184[/C][C]10996.6247365204[/C][C]66.3824462910915[/C][C]-794.619894168715[/C][C]-0.224752029060345[/C][/ROW]
[ROW][C]63[/C][C]11082[/C][C]10748.3522028842[/C][C]54.2780137671281[/C][C]457.965437368703[/C][C]-1.55274557482400[/C][/ROW]
[ROW][C]64[/C][C]10554[/C][C]10796.1334940286[/C][C]54.0255517929649[/C][C]-239.575872566221[/C][C]-0.0319850409632741[/C][/ROW]
[ROW][C]65[/C][C]11315[/C][C]11072.3299582138[/C][C]62.6939124156583[/C][C]155.152418865576[/C][C]1.09528742526485[/C][/ROW]
[ROW][C]66[/C][C]10847[/C][C]11027.8431526588[/C][C]58.5156672325497[/C][C]-138.498157362335[/C][C]-0.529881669715866[/C][/ROW]
[ROW][C]67[/C][C]11104[/C][C]11242.8850459460[/C][C]64.5950269826568[/C][C]-200.879518152256[/C][C]0.775309707590191[/C][/ROW]
[ROW][C]68[/C][C]11026[/C][C]11197.4670355012[/C][C]60.3431224231802[/C][C]-127.859921089333[/C][C]-0.545048847437742[/C][/ROW]
[ROW][C]69[/C][C]11073[/C][C]11193.4673259277[/C][C]57.8691224108759[/C][C]-94.97905511919[/C][C]-0.318490297321043[/C][/ROW]
[ROW][C]70[/C][C]12073[/C][C]11347.0728949041[/C][C]61.532449989892[/C][C]688.044019310976[/C][C]0.473401533514601[/C][/ROW]
[ROW][C]71[/C][C]12328[/C][C]11610.5506981819[/C][C]69.2300751248162[/C][C]637.574131423662[/C][C]0.998473944486447[/C][/ROW]
[ROW][C]72[/C][C]11172[/C][C]11632.8718016608[/C][C]67.445216111687[/C][C]-442.303003545541[/C][C]-0.232186314431388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62190&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62190&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
172917291000
268207048.68374732168-13.2611654876782-134.840613763128-1.04355479152437
380317493.267503562718.1125206526509390.7003754343121.83592902788528
478627713.328543094528.806650562373568.17159742720990.950525852620733
573577594.138404824523.0798460388473-171.851574428151-0.745883741957134
672137412.8440314866316.7059932940849-106.718099601164-1.05098366565339
770797240.4164814554411.3340005639096-74.4966734139742-0.977067469653487
870127113.35521884077.46874033390386-37.6958479907924-0.714894154450486
973197177.859611797829.0861735775606114.9433701051030.294204356472167
1081487600.4394290829721.1005217792702358.0319491264912.12923293199540
1175997671.1261766735722.5774335036598-94.80644672022980.254901740865258
1269087357.7368768881112.3387736497183-296.377822956583-1.72426220584337
1378787511.470763791668.51365443585555298.7874230216760.832768070543478
1474077659.6698682651311.8478622837356-311.0320259443860.699422629064606
1579117671.2338199182211.8358006325896239.871423136174-0.00130998792483277
1673237480.772062483462.98974315981812-80.5380609496657-0.963915941973353
1771797361.8263009821-1.85757745151146-133.962787909043-0.603078425928941
1867587105.35278873495-10.9982549616414-242.668769655792-1.28167187919449
1969347000.51920549942-14.1551460848484-27.5727771673630-0.475030151478542
2066966900.20396845968-16.9797170770225-168.358073091576-0.436603798980192
2176887226.20463333312-5.76111259305233319.1319131665341.73702942415039
2282967567.812890729125.61695542044239583.8339633194581.75729814634966
2376977675.534183683658.88622142500431-20.92764201113280.515692754377049
2479077951.3612273672316.5890703596405-155.2889794361461.34838690870133
2575927772.5754841477212.6707867788182-97.8375403304805-1.01798928366606
2677107835.4911996850814.1875247848437-146.0813910912290.252633678064310
2790118181.0579411025426.9001528930008701.2006283544531.59972023941562
2882258249.253057370428.5902851500672-40.20751049020410.199606300425917
2977338054.8238194369319.5965575902860-234.012742509691-1.09639231246194
3080628106.6070978511620.8452406792173-57.48650555594690.160177160475840
3178598035.2761175736817.3915861949327-139.097928536477-0.461110355506568
3282218256.7618699381724.8676813862013-118.3056011051741.02230583362258
3383308277.292730655624.711005942631954.4618614152546-0.0217149244208512
3488688326.5354665345825.5848595513574531.5467017031020.122682031477188
3590538685.6374622203237.171791607188232.6744748672621.66557172067662
3688118842.1500822278641.1452234701889-79.37670227883190.596870614182408
3781208678.0494611536534.5785186295075-474.561646742321-1.03559255897059
3879538507.4309124312227.4212054571413-471.798870485392-1.02413989936943
3988788350.2586709003320.3902199643988600.399169125844-0.904397560154914
4086018403.5397175784721.6954792645921184.6176849751620.160550046591718
4183618495.061950536924.4795444720377-161.5102873685060.343326489198481
4291168780.5539695329934.7536852443553231.8905599134011.29321068413719
4393109143.9212101419747.469967646299934.95614722125681.63466389998092
4498919572.8559108547362.0073688716162165.6155066386691.89931762784982
45101479905.7268614521872.1962862258242132.9773774505951.34762897451159
461031710016.546327816373.6304710877073285.0284096398670.191897580410769
471068210249.386663283179.4557132360774369.0911169095030.79040497743052
481027610306.330706658278.64376581864-21.3410179580736-0.111945866857702
491061410610.389371701186.7552629680137-86.6697376372481.12474976872109
50941310373.687158491974.7790798188108-831.620064995144-1.60719749047823
511106810428.398548449574.0102312779966647.526101810366-0.098833694891011
52977210169.496444553160.9846697124375-266.778910394404-1.63410867673940
531035010377.953641369266.789656224051-85.98596968083950.72612670344276
541054110505.521100566369.17320396640511.43735401064500.300654683936889
551004910455.982789178764.5548874606687-359.844378799897-0.588897114626286
561071410576.959152581466.7301718431311114.6068202348710.280032877243103
571075910665.792171460267.57486984407484.42375025832550.109597200580330
581168411037.519886117679.1010212184423525.7512211869441.50623759587080
591146211154.641852674780.5308555217278292.2766583634250.188225193229656
601048510971.811347249770.6750543478657-382.248618588742-1.30565169501504
611105610973.861862325568.102014984308109.431398351198-0.340815452641316
621018410996.624736520466.3824462910915-794.619894168715-0.224752029060345
631108210748.352202884254.2780137671281457.965437368703-1.55274557482400
641055410796.133494028654.0255517929649-239.575872566221-0.0319850409632741
651131511072.329958213862.6939124156583155.1524188655761.09528742526485
661084711027.843152658858.5156672325497-138.498157362335-0.529881669715866
671110411242.885045946064.5950269826568-200.8795181522560.775309707590191
681102611197.467035501260.3431224231802-127.859921089333-0.545048847437742
691107311193.467325927757.8691224108759-94.97905511919-0.318490297321043
701207311347.072894904161.532449989892688.0440193109760.473401533514601
711232811610.550698181969.2300751248162637.5741314236620.998473944486447
721117211632.871801660867.445216111687-442.303003545541-0.232186314431388



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
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')