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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationSun, 02 Dec 2012 16:40:01 -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/2012/Dec/02/t1354484428yb5ou4kze7e3ynh.htm/, Retrieved Thu, 28 Mar 2024 16:08:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=195629, Retrieved Thu, 28 Mar 2024 16:08:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
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   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
- RMPD      [Structural Time Series Models] [ws9] [2012-12-02 21:40:01] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]
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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195629&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195629&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1655362655362000
2873127804406.270808494154723.66978177368720.7291915060.792697781426955
311078971106300.63778955252402.5236209041596.362210445960.434957337765308
415559641514241.73456391361658.4156781241722.26543608770.490125219025299
516711591734860.34846805263973.661430785-63701.3484680493-0.413797651271663
614933081606381.34092004-4624.90866164697-113073.340920037-1.14966330898594
729577962589589.23103867673905.400647857368206.7689613262.90713342940484
826386912870691.93283009403718.869641323-232000.932830094-1.15615648951339
913056691764653.85292548-634755.49590711-458984.852925483-4.44410988525324
1012804961125357.48641561-637878.215996821155138.513584387-0.0133636851532883
11921900813555.879851871-413672.625359818108344.1201481290.959470784746266
12867888776975.148345814-154394.44317473890912.85165418651.10956307080438
13652586674263.043038158-118967.081855182-21677.04303815810.15213384048647
14913831817118.29668793961258.581961591196712.70331206080.783697784671904
1511085441100837.19712039209625.3725045977706.802879612090.630196214288936
1615558271402058.78520417270175.012411704153768.2147958250.263216866498606
1716992831685824.30766363279252.43314798713458.69233636870.0389643055827133
1815094582025952.93803814319595.25862276-516494.9380381420.172020783862317
1932689752693984.9264719549922.935103874574990.0735280960.986118300085746
2024250162455590.536141727803.9183325409-30574.5361416965-2.23553728271848
2113127031887240.74218544-367364.789472075-574537.742185438-1.69095075773119
2213654981322291.48776232-498337.76160352543206.512237677-0.560523218749487
23934453883149.57381828-459116.9208404251303.426181720.167847623326227
24775019614397.046614897-333135.170280823160621.9533851030.539354990098468
25651142633505.356674373-99975.501284104417636.64332562681.00094352134782
26843192752375.11799820245074.412128957490816.88200179790.621717163705449
2711467661071980.14153414225370.48677196974785.8584658610.769405262176553
2816526011471062.66649272338941.272522163181538.3335072810.488287356813842
2914659061626318.19939301218206.615206302-160412.199393005-0.518534295016631
3016527342205887.05138043455393.024138037-553153.0513804281.0132020947577
3129223342258299.17733559191716.792063545664034.822664409-1.1274713048644
3227028052485325.18803131214831.173984409217479.8119686880.0989511868682982
3314589562139065.60151173-152861.885175702-680109.601511728-1.57359274743843
3414103631487508.5864575-479723.845012365-77145.5864574963-1.39878280244602
351019279985788.825897975-494133.65495699633490.1741020245-0.0616691420552667
36936574774886.570749488-308681.244419669161687.4292505120.794213859223977
37708917691302.904871889-161180.41115463517614.09512811080.632314912385764
38885295800990.6480343916266.164973563684304.35196560990.759309259134497
3910996631017114.00518108146601.339085982548.99481891570.557010142471582
4015762201322724.97615589250074.738474914253495.0238441090.443697340219723
4114878701748618.92955223364798.30245567-260748.9295522270.492143384427458
4214886352010215.70544274297435.387940507-521580.705442741-0.288124877517037
4328825302267215.03926471271094.349139319615314.960735292-0.112616050368239
4426770262324713.86846994132015.680676348352312.131530064-0.595133413021073
4514043982019831.92800634-152644.416879769-615433.928006339-1.21828652841736
4613443701491863.08350446-397249.206772976-147493.083504465-1.04681170727029
47936865991231.037098372-464609.891967184-54366.0370983722-0.288320096454099
48872705704400.339663046-348773.930277393168304.6603369540.496064585402981
49628151609708.893542012-183123.02847961418442.10645798790.709492584035874
50953712811251.90204337167500.5593269039142460.0979566281.07209030836604
5111603841091172.37079537205525.39408767269211.62920463190.5902136584617
5214006181248644.17488525174338.290696041151973.825114752-0.133612392469709
5316615111797784.55866331417973.358481966-136273.5586633141.04435180810324
5414953472109124.00099439348610.228705699-613777.000994386-0.296843977869487
5529187862296705.37528847243985.537434921622080.624711526-0.447395909931677
5627756772327785.78881514105763.616427456447891.211184865-0.591320956742762
5714070261999944.5843592-175778.823378698-592918.584359196-1.20487951670167
5813701991527005.29417037-368766.836186885-156806.294170367-0.82599798831194
599645261068224.38312333-427227.53927071-103698.383123328-0.250262249273532
60850851726189.274209709-371883.920263003124661.7257902910.236982799813938
61683118681906.495437457-158988.6635241671211.504562543060.91142082502849
62847224711405.302362507-36586.6819214064135818.6976374930.523566375130336
631073256929423.34310166128451.26580181143832.656898340.705916124983534
6415143261412776.70478339358337.189777416101549.2952166120.984508140051157
6515037341684675.44723148302288.604470334-180941.447231481-0.24014973579156
6615077122069108.47663503355589.320005798-561396.4766350250.228154245901793
6728656982249922.902911242260.613348987615775.097089004-0.484722179546206
6827881282252724.5292097287120.4015606262535403.470790283-0.663634965826439
6913915961969764.57136228-152592.210357643-578168.571362279-1.02579737776963
7013663781535961.71045447-334761.549837292-169583.71045447-0.779759949991332
719462951084296.50476855-410508.113400359-138001.504768546-0.324294377883474
72859626782388.239899389-340116.39757595677237.76010061070.30139083515193

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 655362 & 655362 & 0 & 0 & 0 \tabularnewline
2 & 873127 & 804406.270808494 & 154723.669781773 & 68720.729191506 & 0.792697781426955 \tabularnewline
3 & 1107897 & 1106300.63778955 & 252402.523620904 & 1596.36221044596 & 0.434957337765308 \tabularnewline
4 & 1555964 & 1514241.73456391 & 361658.41567812 & 41722.2654360877 & 0.490125219025299 \tabularnewline
5 & 1671159 & 1734860.34846805 & 263973.661430785 & -63701.3484680493 & -0.413797651271663 \tabularnewline
6 & 1493308 & 1606381.34092004 & -4624.90866164697 & -113073.340920037 & -1.14966330898594 \tabularnewline
7 & 2957796 & 2589589.23103867 & 673905.400647857 & 368206.768961326 & 2.90713342940484 \tabularnewline
8 & 2638691 & 2870691.93283009 & 403718.869641323 & -232000.932830094 & -1.15615648951339 \tabularnewline
9 & 1305669 & 1764653.85292548 & -634755.49590711 & -458984.852925483 & -4.44410988525324 \tabularnewline
10 & 1280496 & 1125357.48641561 & -637878.215996821 & 155138.513584387 & -0.0133636851532883 \tabularnewline
11 & 921900 & 813555.879851871 & -413672.625359818 & 108344.120148129 & 0.959470784746266 \tabularnewline
12 & 867888 & 776975.148345814 & -154394.443174738 & 90912.8516541865 & 1.10956307080438 \tabularnewline
13 & 652586 & 674263.043038158 & -118967.081855182 & -21677.0430381581 & 0.15213384048647 \tabularnewline
14 & 913831 & 817118.296687939 & 61258.5819615911 & 96712.7033120608 & 0.783697784671904 \tabularnewline
15 & 1108544 & 1100837.19712039 & 209625.372504597 & 7706.80287961209 & 0.630196214288936 \tabularnewline
16 & 1555827 & 1402058.78520417 & 270175.012411704 & 153768.214795825 & 0.263216866498606 \tabularnewline
17 & 1699283 & 1685824.30766363 & 279252.433147987 & 13458.6923363687 & 0.0389643055827133 \tabularnewline
18 & 1509458 & 2025952.93803814 & 319595.25862276 & -516494.938038142 & 0.172020783862317 \tabularnewline
19 & 3268975 & 2693984.9264719 & 549922.935103874 & 574990.073528096 & 0.986118300085746 \tabularnewline
20 & 2425016 & 2455590.5361417 & 27803.9183325409 & -30574.5361416965 & -2.23553728271848 \tabularnewline
21 & 1312703 & 1887240.74218544 & -367364.789472075 & -574537.742185438 & -1.69095075773119 \tabularnewline
22 & 1365498 & 1322291.48776232 & -498337.761603525 & 43206.512237677 & -0.560523218749487 \tabularnewline
23 & 934453 & 883149.57381828 & -459116.92084042 & 51303.42618172 & 0.167847623326227 \tabularnewline
24 & 775019 & 614397.046614897 & -333135.170280823 & 160621.953385103 & 0.539354990098468 \tabularnewline
25 & 651142 & 633505.356674373 & -99975.5012841044 & 17636.6433256268 & 1.00094352134782 \tabularnewline
26 & 843192 & 752375.117998202 & 45074.4121289574 & 90816.8820017979 & 0.621717163705449 \tabularnewline
27 & 1146766 & 1071980.14153414 & 225370.486771969 & 74785.858465861 & 0.769405262176553 \tabularnewline
28 & 1652601 & 1471062.66649272 & 338941.272522163 & 181538.333507281 & 0.488287356813842 \tabularnewline
29 & 1465906 & 1626318.19939301 & 218206.615206302 & -160412.199393005 & -0.518534295016631 \tabularnewline
30 & 1652734 & 2205887.05138043 & 455393.024138037 & -553153.051380428 & 1.0132020947577 \tabularnewline
31 & 2922334 & 2258299.17733559 & 191716.792063545 & 664034.822664409 & -1.1274713048644 \tabularnewline
32 & 2702805 & 2485325.18803131 & 214831.173984409 & 217479.811968688 & 0.0989511868682982 \tabularnewline
33 & 1458956 & 2139065.60151173 & -152861.885175702 & -680109.601511728 & -1.57359274743843 \tabularnewline
34 & 1410363 & 1487508.5864575 & -479723.845012365 & -77145.5864574963 & -1.39878280244602 \tabularnewline
35 & 1019279 & 985788.825897975 & -494133.654956996 & 33490.1741020245 & -0.0616691420552667 \tabularnewline
36 & 936574 & 774886.570749488 & -308681.244419669 & 161687.429250512 & 0.794213859223977 \tabularnewline
37 & 708917 & 691302.904871889 & -161180.411154635 & 17614.0951281108 & 0.632314912385764 \tabularnewline
38 & 885295 & 800990.64803439 & 16266.1649735636 & 84304.3519656099 & 0.759309259134497 \tabularnewline
39 & 1099663 & 1017114.00518108 & 146601.3390859 & 82548.9948189157 & 0.557010142471582 \tabularnewline
40 & 1576220 & 1322724.97615589 & 250074.738474914 & 253495.023844109 & 0.443697340219723 \tabularnewline
41 & 1487870 & 1748618.92955223 & 364798.30245567 & -260748.929552227 & 0.492143384427458 \tabularnewline
42 & 1488635 & 2010215.70544274 & 297435.387940507 & -521580.705442741 & -0.288124877517037 \tabularnewline
43 & 2882530 & 2267215.03926471 & 271094.349139319 & 615314.960735292 & -0.112616050368239 \tabularnewline
44 & 2677026 & 2324713.86846994 & 132015.680676348 & 352312.131530064 & -0.595133413021073 \tabularnewline
45 & 1404398 & 2019831.92800634 & -152644.416879769 & -615433.928006339 & -1.21828652841736 \tabularnewline
46 & 1344370 & 1491863.08350446 & -397249.206772976 & -147493.083504465 & -1.04681170727029 \tabularnewline
47 & 936865 & 991231.037098372 & -464609.891967184 & -54366.0370983722 & -0.288320096454099 \tabularnewline
48 & 872705 & 704400.339663046 & -348773.930277393 & 168304.660336954 & 0.496064585402981 \tabularnewline
49 & 628151 & 609708.893542012 & -183123.028479614 & 18442.1064579879 & 0.709492584035874 \tabularnewline
50 & 953712 & 811251.902043371 & 67500.5593269039 & 142460.097956628 & 1.07209030836604 \tabularnewline
51 & 1160384 & 1091172.37079537 & 205525.394087672 & 69211.6292046319 & 0.5902136584617 \tabularnewline
52 & 1400618 & 1248644.17488525 & 174338.290696041 & 151973.825114752 & -0.133612392469709 \tabularnewline
53 & 1661511 & 1797784.55866331 & 417973.358481966 & -136273.558663314 & 1.04435180810324 \tabularnewline
54 & 1495347 & 2109124.00099439 & 348610.228705699 & -613777.000994386 & -0.296843977869487 \tabularnewline
55 & 2918786 & 2296705.37528847 & 243985.537434921 & 622080.624711526 & -0.447395909931677 \tabularnewline
56 & 2775677 & 2327785.78881514 & 105763.616427456 & 447891.211184865 & -0.591320956742762 \tabularnewline
57 & 1407026 & 1999944.5843592 & -175778.823378698 & -592918.584359196 & -1.20487951670167 \tabularnewline
58 & 1370199 & 1527005.29417037 & -368766.836186885 & -156806.294170367 & -0.82599798831194 \tabularnewline
59 & 964526 & 1068224.38312333 & -427227.53927071 & -103698.383123328 & -0.250262249273532 \tabularnewline
60 & 850851 & 726189.274209709 & -371883.920263003 & 124661.725790291 & 0.236982799813938 \tabularnewline
61 & 683118 & 681906.495437457 & -158988.663524167 & 1211.50456254306 & 0.91142082502849 \tabularnewline
62 & 847224 & 711405.302362507 & -36586.6819214064 & 135818.697637493 & 0.523566375130336 \tabularnewline
63 & 1073256 & 929423.34310166 & 128451.26580181 & 143832.65689834 & 0.705916124983534 \tabularnewline
64 & 1514326 & 1412776.70478339 & 358337.189777416 & 101549.295216612 & 0.984508140051157 \tabularnewline
65 & 1503734 & 1684675.44723148 & 302288.604470334 & -180941.447231481 & -0.24014973579156 \tabularnewline
66 & 1507712 & 2069108.47663503 & 355589.320005798 & -561396.476635025 & 0.228154245901793 \tabularnewline
67 & 2865698 & 2249922.902911 & 242260.613348987 & 615775.097089004 & -0.484722179546206 \tabularnewline
68 & 2788128 & 2252724.52920972 & 87120.4015606262 & 535403.470790283 & -0.663634965826439 \tabularnewline
69 & 1391596 & 1969764.57136228 & -152592.210357643 & -578168.571362279 & -1.02579737776963 \tabularnewline
70 & 1366378 & 1535961.71045447 & -334761.549837292 & -169583.71045447 & -0.779759949991332 \tabularnewline
71 & 946295 & 1084296.50476855 & -410508.113400359 & -138001.504768546 & -0.324294377883474 \tabularnewline
72 & 859626 & 782388.239899389 & -340116.397575956 & 77237.7601006107 & 0.30139083515193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=195629&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]655362[/C][C]655362[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]873127[/C][C]804406.270808494[/C][C]154723.669781773[/C][C]68720.729191506[/C][C]0.792697781426955[/C][/ROW]
[ROW][C]3[/C][C]1107897[/C][C]1106300.63778955[/C][C]252402.523620904[/C][C]1596.36221044596[/C][C]0.434957337765308[/C][/ROW]
[ROW][C]4[/C][C]1555964[/C][C]1514241.73456391[/C][C]361658.41567812[/C][C]41722.2654360877[/C][C]0.490125219025299[/C][/ROW]
[ROW][C]5[/C][C]1671159[/C][C]1734860.34846805[/C][C]263973.661430785[/C][C]-63701.3484680493[/C][C]-0.413797651271663[/C][/ROW]
[ROW][C]6[/C][C]1493308[/C][C]1606381.34092004[/C][C]-4624.90866164697[/C][C]-113073.340920037[/C][C]-1.14966330898594[/C][/ROW]
[ROW][C]7[/C][C]2957796[/C][C]2589589.23103867[/C][C]673905.400647857[/C][C]368206.768961326[/C][C]2.90713342940484[/C][/ROW]
[ROW][C]8[/C][C]2638691[/C][C]2870691.93283009[/C][C]403718.869641323[/C][C]-232000.932830094[/C][C]-1.15615648951339[/C][/ROW]
[ROW][C]9[/C][C]1305669[/C][C]1764653.85292548[/C][C]-634755.49590711[/C][C]-458984.852925483[/C][C]-4.44410988525324[/C][/ROW]
[ROW][C]10[/C][C]1280496[/C][C]1125357.48641561[/C][C]-637878.215996821[/C][C]155138.513584387[/C][C]-0.0133636851532883[/C][/ROW]
[ROW][C]11[/C][C]921900[/C][C]813555.879851871[/C][C]-413672.625359818[/C][C]108344.120148129[/C][C]0.959470784746266[/C][/ROW]
[ROW][C]12[/C][C]867888[/C][C]776975.148345814[/C][C]-154394.443174738[/C][C]90912.8516541865[/C][C]1.10956307080438[/C][/ROW]
[ROW][C]13[/C][C]652586[/C][C]674263.043038158[/C][C]-118967.081855182[/C][C]-21677.0430381581[/C][C]0.15213384048647[/C][/ROW]
[ROW][C]14[/C][C]913831[/C][C]817118.296687939[/C][C]61258.5819615911[/C][C]96712.7033120608[/C][C]0.783697784671904[/C][/ROW]
[ROW][C]15[/C][C]1108544[/C][C]1100837.19712039[/C][C]209625.372504597[/C][C]7706.80287961209[/C][C]0.630196214288936[/C][/ROW]
[ROW][C]16[/C][C]1555827[/C][C]1402058.78520417[/C][C]270175.012411704[/C][C]153768.214795825[/C][C]0.263216866498606[/C][/ROW]
[ROW][C]17[/C][C]1699283[/C][C]1685824.30766363[/C][C]279252.433147987[/C][C]13458.6923363687[/C][C]0.0389643055827133[/C][/ROW]
[ROW][C]18[/C][C]1509458[/C][C]2025952.93803814[/C][C]319595.25862276[/C][C]-516494.938038142[/C][C]0.172020783862317[/C][/ROW]
[ROW][C]19[/C][C]3268975[/C][C]2693984.9264719[/C][C]549922.935103874[/C][C]574990.073528096[/C][C]0.986118300085746[/C][/ROW]
[ROW][C]20[/C][C]2425016[/C][C]2455590.5361417[/C][C]27803.9183325409[/C][C]-30574.5361416965[/C][C]-2.23553728271848[/C][/ROW]
[ROW][C]21[/C][C]1312703[/C][C]1887240.74218544[/C][C]-367364.789472075[/C][C]-574537.742185438[/C][C]-1.69095075773119[/C][/ROW]
[ROW][C]22[/C][C]1365498[/C][C]1322291.48776232[/C][C]-498337.761603525[/C][C]43206.512237677[/C][C]-0.560523218749487[/C][/ROW]
[ROW][C]23[/C][C]934453[/C][C]883149.57381828[/C][C]-459116.92084042[/C][C]51303.42618172[/C][C]0.167847623326227[/C][/ROW]
[ROW][C]24[/C][C]775019[/C][C]614397.046614897[/C][C]-333135.170280823[/C][C]160621.953385103[/C][C]0.539354990098468[/C][/ROW]
[ROW][C]25[/C][C]651142[/C][C]633505.356674373[/C][C]-99975.5012841044[/C][C]17636.6433256268[/C][C]1.00094352134782[/C][/ROW]
[ROW][C]26[/C][C]843192[/C][C]752375.117998202[/C][C]45074.4121289574[/C][C]90816.8820017979[/C][C]0.621717163705449[/C][/ROW]
[ROW][C]27[/C][C]1146766[/C][C]1071980.14153414[/C][C]225370.486771969[/C][C]74785.858465861[/C][C]0.769405262176553[/C][/ROW]
[ROW][C]28[/C][C]1652601[/C][C]1471062.66649272[/C][C]338941.272522163[/C][C]181538.333507281[/C][C]0.488287356813842[/C][/ROW]
[ROW][C]29[/C][C]1465906[/C][C]1626318.19939301[/C][C]218206.615206302[/C][C]-160412.199393005[/C][C]-0.518534295016631[/C][/ROW]
[ROW][C]30[/C][C]1652734[/C][C]2205887.05138043[/C][C]455393.024138037[/C][C]-553153.051380428[/C][C]1.0132020947577[/C][/ROW]
[ROW][C]31[/C][C]2922334[/C][C]2258299.17733559[/C][C]191716.792063545[/C][C]664034.822664409[/C][C]-1.1274713048644[/C][/ROW]
[ROW][C]32[/C][C]2702805[/C][C]2485325.18803131[/C][C]214831.173984409[/C][C]217479.811968688[/C][C]0.0989511868682982[/C][/ROW]
[ROW][C]33[/C][C]1458956[/C][C]2139065.60151173[/C][C]-152861.885175702[/C][C]-680109.601511728[/C][C]-1.57359274743843[/C][/ROW]
[ROW][C]34[/C][C]1410363[/C][C]1487508.5864575[/C][C]-479723.845012365[/C][C]-77145.5864574963[/C][C]-1.39878280244602[/C][/ROW]
[ROW][C]35[/C][C]1019279[/C][C]985788.825897975[/C][C]-494133.654956996[/C][C]33490.1741020245[/C][C]-0.0616691420552667[/C][/ROW]
[ROW][C]36[/C][C]936574[/C][C]774886.570749488[/C][C]-308681.244419669[/C][C]161687.429250512[/C][C]0.794213859223977[/C][/ROW]
[ROW][C]37[/C][C]708917[/C][C]691302.904871889[/C][C]-161180.411154635[/C][C]17614.0951281108[/C][C]0.632314912385764[/C][/ROW]
[ROW][C]38[/C][C]885295[/C][C]800990.64803439[/C][C]16266.1649735636[/C][C]84304.3519656099[/C][C]0.759309259134497[/C][/ROW]
[ROW][C]39[/C][C]1099663[/C][C]1017114.00518108[/C][C]146601.3390859[/C][C]82548.9948189157[/C][C]0.557010142471582[/C][/ROW]
[ROW][C]40[/C][C]1576220[/C][C]1322724.97615589[/C][C]250074.738474914[/C][C]253495.023844109[/C][C]0.443697340219723[/C][/ROW]
[ROW][C]41[/C][C]1487870[/C][C]1748618.92955223[/C][C]364798.30245567[/C][C]-260748.929552227[/C][C]0.492143384427458[/C][/ROW]
[ROW][C]42[/C][C]1488635[/C][C]2010215.70544274[/C][C]297435.387940507[/C][C]-521580.705442741[/C][C]-0.288124877517037[/C][/ROW]
[ROW][C]43[/C][C]2882530[/C][C]2267215.03926471[/C][C]271094.349139319[/C][C]615314.960735292[/C][C]-0.112616050368239[/C][/ROW]
[ROW][C]44[/C][C]2677026[/C][C]2324713.86846994[/C][C]132015.680676348[/C][C]352312.131530064[/C][C]-0.595133413021073[/C][/ROW]
[ROW][C]45[/C][C]1404398[/C][C]2019831.92800634[/C][C]-152644.416879769[/C][C]-615433.928006339[/C][C]-1.21828652841736[/C][/ROW]
[ROW][C]46[/C][C]1344370[/C][C]1491863.08350446[/C][C]-397249.206772976[/C][C]-147493.083504465[/C][C]-1.04681170727029[/C][/ROW]
[ROW][C]47[/C][C]936865[/C][C]991231.037098372[/C][C]-464609.891967184[/C][C]-54366.0370983722[/C][C]-0.288320096454099[/C][/ROW]
[ROW][C]48[/C][C]872705[/C][C]704400.339663046[/C][C]-348773.930277393[/C][C]168304.660336954[/C][C]0.496064585402981[/C][/ROW]
[ROW][C]49[/C][C]628151[/C][C]609708.893542012[/C][C]-183123.028479614[/C][C]18442.1064579879[/C][C]0.709492584035874[/C][/ROW]
[ROW][C]50[/C][C]953712[/C][C]811251.902043371[/C][C]67500.5593269039[/C][C]142460.097956628[/C][C]1.07209030836604[/C][/ROW]
[ROW][C]51[/C][C]1160384[/C][C]1091172.37079537[/C][C]205525.394087672[/C][C]69211.6292046319[/C][C]0.5902136584617[/C][/ROW]
[ROW][C]52[/C][C]1400618[/C][C]1248644.17488525[/C][C]174338.290696041[/C][C]151973.825114752[/C][C]-0.133612392469709[/C][/ROW]
[ROW][C]53[/C][C]1661511[/C][C]1797784.55866331[/C][C]417973.358481966[/C][C]-136273.558663314[/C][C]1.04435180810324[/C][/ROW]
[ROW][C]54[/C][C]1495347[/C][C]2109124.00099439[/C][C]348610.228705699[/C][C]-613777.000994386[/C][C]-0.296843977869487[/C][/ROW]
[ROW][C]55[/C][C]2918786[/C][C]2296705.37528847[/C][C]243985.537434921[/C][C]622080.624711526[/C][C]-0.447395909931677[/C][/ROW]
[ROW][C]56[/C][C]2775677[/C][C]2327785.78881514[/C][C]105763.616427456[/C][C]447891.211184865[/C][C]-0.591320956742762[/C][/ROW]
[ROW][C]57[/C][C]1407026[/C][C]1999944.5843592[/C][C]-175778.823378698[/C][C]-592918.584359196[/C][C]-1.20487951670167[/C][/ROW]
[ROW][C]58[/C][C]1370199[/C][C]1527005.29417037[/C][C]-368766.836186885[/C][C]-156806.294170367[/C][C]-0.82599798831194[/C][/ROW]
[ROW][C]59[/C][C]964526[/C][C]1068224.38312333[/C][C]-427227.53927071[/C][C]-103698.383123328[/C][C]-0.250262249273532[/C][/ROW]
[ROW][C]60[/C][C]850851[/C][C]726189.274209709[/C][C]-371883.920263003[/C][C]124661.725790291[/C][C]0.236982799813938[/C][/ROW]
[ROW][C]61[/C][C]683118[/C][C]681906.495437457[/C][C]-158988.663524167[/C][C]1211.50456254306[/C][C]0.91142082502849[/C][/ROW]
[ROW][C]62[/C][C]847224[/C][C]711405.302362507[/C][C]-36586.6819214064[/C][C]135818.697637493[/C][C]0.523566375130336[/C][/ROW]
[ROW][C]63[/C][C]1073256[/C][C]929423.34310166[/C][C]128451.26580181[/C][C]143832.65689834[/C][C]0.705916124983534[/C][/ROW]
[ROW][C]64[/C][C]1514326[/C][C]1412776.70478339[/C][C]358337.189777416[/C][C]101549.295216612[/C][C]0.984508140051157[/C][/ROW]
[ROW][C]65[/C][C]1503734[/C][C]1684675.44723148[/C][C]302288.604470334[/C][C]-180941.447231481[/C][C]-0.24014973579156[/C][/ROW]
[ROW][C]66[/C][C]1507712[/C][C]2069108.47663503[/C][C]355589.320005798[/C][C]-561396.476635025[/C][C]0.228154245901793[/C][/ROW]
[ROW][C]67[/C][C]2865698[/C][C]2249922.902911[/C][C]242260.613348987[/C][C]615775.097089004[/C][C]-0.484722179546206[/C][/ROW]
[ROW][C]68[/C][C]2788128[/C][C]2252724.52920972[/C][C]87120.4015606262[/C][C]535403.470790283[/C][C]-0.663634965826439[/C][/ROW]
[ROW][C]69[/C][C]1391596[/C][C]1969764.57136228[/C][C]-152592.210357643[/C][C]-578168.571362279[/C][C]-1.02579737776963[/C][/ROW]
[ROW][C]70[/C][C]1366378[/C][C]1535961.71045447[/C][C]-334761.549837292[/C][C]-169583.71045447[/C][C]-0.779759949991332[/C][/ROW]
[ROW][C]71[/C][C]946295[/C][C]1084296.50476855[/C][C]-410508.113400359[/C][C]-138001.504768546[/C][C]-0.324294377883474[/C][/ROW]
[ROW][C]72[/C][C]859626[/C][C]782388.239899389[/C][C]-340116.397575956[/C][C]77237.7601006107[/C][C]0.30139083515193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=195629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=195629&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
1655362655362000
2873127804406.270808494154723.66978177368720.7291915060.792697781426955
311078971106300.63778955252402.5236209041596.362210445960.434957337765308
415559641514241.73456391361658.4156781241722.26543608770.490125219025299
516711591734860.34846805263973.661430785-63701.3484680493-0.413797651271663
614933081606381.34092004-4624.90866164697-113073.340920037-1.14966330898594
729577962589589.23103867673905.400647857368206.7689613262.90713342940484
826386912870691.93283009403718.869641323-232000.932830094-1.15615648951339
913056691764653.85292548-634755.49590711-458984.852925483-4.44410988525324
1012804961125357.48641561-637878.215996821155138.513584387-0.0133636851532883
11921900813555.879851871-413672.625359818108344.1201481290.959470784746266
12867888776975.148345814-154394.44317473890912.85165418651.10956307080438
13652586674263.043038158-118967.081855182-21677.04303815810.15213384048647
14913831817118.29668793961258.581961591196712.70331206080.783697784671904
1511085441100837.19712039209625.3725045977706.802879612090.630196214288936
1615558271402058.78520417270175.012411704153768.2147958250.263216866498606
1716992831685824.30766363279252.43314798713458.69233636870.0389643055827133
1815094582025952.93803814319595.25862276-516494.9380381420.172020783862317
1932689752693984.9264719549922.935103874574990.0735280960.986118300085746
2024250162455590.536141727803.9183325409-30574.5361416965-2.23553728271848
2113127031887240.74218544-367364.789472075-574537.742185438-1.69095075773119
2213654981322291.48776232-498337.76160352543206.512237677-0.560523218749487
23934453883149.57381828-459116.9208404251303.426181720.167847623326227
24775019614397.046614897-333135.170280823160621.9533851030.539354990098468
25651142633505.356674373-99975.501284104417636.64332562681.00094352134782
26843192752375.11799820245074.412128957490816.88200179790.621717163705449
2711467661071980.14153414225370.48677196974785.8584658610.769405262176553
2816526011471062.66649272338941.272522163181538.3335072810.488287356813842
2914659061626318.19939301218206.615206302-160412.199393005-0.518534295016631
3016527342205887.05138043455393.024138037-553153.0513804281.0132020947577
3129223342258299.17733559191716.792063545664034.822664409-1.1274713048644
3227028052485325.18803131214831.173984409217479.8119686880.0989511868682982
3314589562139065.60151173-152861.885175702-680109.601511728-1.57359274743843
3414103631487508.5864575-479723.845012365-77145.5864574963-1.39878280244602
351019279985788.825897975-494133.65495699633490.1741020245-0.0616691420552667
36936574774886.570749488-308681.244419669161687.4292505120.794213859223977
37708917691302.904871889-161180.41115463517614.09512811080.632314912385764
38885295800990.6480343916266.164973563684304.35196560990.759309259134497
3910996631017114.00518108146601.339085982548.99481891570.557010142471582
4015762201322724.97615589250074.738474914253495.0238441090.443697340219723
4114878701748618.92955223364798.30245567-260748.9295522270.492143384427458
4214886352010215.70544274297435.387940507-521580.705442741-0.288124877517037
4328825302267215.03926471271094.349139319615314.960735292-0.112616050368239
4426770262324713.86846994132015.680676348352312.131530064-0.595133413021073
4514043982019831.92800634-152644.416879769-615433.928006339-1.21828652841736
4613443701491863.08350446-397249.206772976-147493.083504465-1.04681170727029
47936865991231.037098372-464609.891967184-54366.0370983722-0.288320096454099
48872705704400.339663046-348773.930277393168304.6603369540.496064585402981
49628151609708.893542012-183123.02847961418442.10645798790.709492584035874
50953712811251.90204337167500.5593269039142460.0979566281.07209030836604
5111603841091172.37079537205525.39408767269211.62920463190.5902136584617
5214006181248644.17488525174338.290696041151973.825114752-0.133612392469709
5316615111797784.55866331417973.358481966-136273.5586633141.04435180810324
5414953472109124.00099439348610.228705699-613777.000994386-0.296843977869487
5529187862296705.37528847243985.537434921622080.624711526-0.447395909931677
5627756772327785.78881514105763.616427456447891.211184865-0.591320956742762
5714070261999944.5843592-175778.823378698-592918.584359196-1.20487951670167
5813701991527005.29417037-368766.836186885-156806.294170367-0.82599798831194
599645261068224.38312333-427227.53927071-103698.383123328-0.250262249273532
60850851726189.274209709-371883.920263003124661.7257902910.236982799813938
61683118681906.495437457-158988.6635241671211.504562543060.91142082502849
62847224711405.302362507-36586.6819214064135818.6976374930.523566375130336
631073256929423.34310166128451.26580181143832.656898340.705916124983534
6415143261412776.70478339358337.189777416101549.2952166120.984508140051157
6515037341684675.44723148302288.604470334-180941.447231481-0.24014973579156
6615077122069108.47663503355589.320005798-561396.4766350250.228154245901793
6728656982249922.902911242260.613348987615775.097089004-0.484722179546206
6827881282252724.5292097287120.4015606262535403.470790283-0.663634965826439
6913915961969764.57136228-152592.210357643-578168.571362279-1.02579737776963
7013663781535961.71045447-334761.549837292-169583.71045447-0.779759949991332
719462951084296.50476855-410508.113400359-138001.504768546-0.324294377883474
72859626782388.239899389-340116.39757595677237.76010061070.30139083515193



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')