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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 10:42:50 +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/t1293533028f9ooalxq6jgu2zi.htm/, Retrieved Sun, 05 May 2024 06:36:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116276, Retrieved Sun, 05 May 2024 06:36:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
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] [Unemployment] [2010-11-30 13:26:46] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [] [2010-12-28 10:42:50] [1ee5bf11b725e231963af8d8fe43ab15] [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 time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116276&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116276&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116276&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'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1655362655362000
2873127804406.285730027154723.68343579468720.71426997270.792697786821828
311078971106300.64103651252402.5242667991596.35896348690.434957272742389
415559641514241.73925271361658.42190053541722.26074728750.490125218490501
516711591734860.34344928263973.653008982-63701.3434492812-0.413797699272963
614933081606381.3245862-4624.93070922245-113073.324586201-1.14966332958283
729577962589589.26262942673905.456188023368206.7373705762.90713365936900
826386912870691.93217134403718.853278761-232000.932171341-1.15615675723536
913056691764653.79148623-634755.586934305-458984.791486227-4.44411005156445
1012804961125357.46497959-637878.217048106155138.535020414-0.0133632996398641
11921900813555.896314699-413672.590087513108344.1036853010.959470907078068
12867888776975.173500372-154394.41513097390912.8264996281.10956300157995
13652586674263.051614344-118967.082874995-21677.05161434360.152133710391677
14913831817118.30747920861258.590857129296712.6925207920.783697800299816
1511085441100837.21270453209625.3843289847706.787295470840.630196206051504
1615558271402058.79522844270175.015422369153768.2047715590.263216819291442
1716992831685824.29326389279252.41820775413458.70673610820.0389642270491927
1815094582025952.91210091319595.247558002-516494.9121009080.172020794623345
1932689752693984.95255166549922.975517657574990.0474483360.986118487001621
2024250162455590.5474617927803.9000785844-30574.5474617950-2.23553745599879
2113127031887240.71049179-367364.840682602-574537.710491789-1.69095084036959
2213654981322291.45042066-498337.7880842743206.5495793387-0.56052309358586
23934453883149.563472569-459116.91024800151303.4365274310.167847776186944
24775019614397.061963756-333135.144410646160621.9380362440.539355036853022
25651142633505.38111326-99975.476758447117636.61888674051.00094348095493
26843192752375.1384769245074.423835702490816.86152308070.621717087192154
2711467661071980.15866668225370.49593516774785.84133331690.769405225037162
2816526011471062.66692537338941.269568832181538.3330746280.488287288313136
2914659061626318.17121237218206.590290659-160412.171212373-0.518534370758853
3016527342205887.04772438455393.041312502-553153.0477243841.01320223921361
3129223342258299.19082140191716.798301726664034.809178603-1.12747131346868
3227028052485325.21462377214831.185691687217479.7853762260.0989512068727316
3314589562139065.58578316-152861.924261836-680109.585783157-1.57359291035739
3414103631487508.54590282-479723.888368583-77145.5459028232-1.39878277247667
351019279985788.802204873-494133.65946335433490.1977951272-0.0616689736602361
36936574774886.575725905-308681.219509099161687.4242740950.79421395774286
37708917691302.923382195-161180.387582717614.07661780500.632314884873522
38885295800990.6696077716266.182453737084304.33039222970.759309206825336
3910996631017114.01883775146601.34534249982548.9811622480.557010075370487
4015762201322724.97451576250074.735001702253495.0254842440.443697283395574
4114878701748618.91718001364798.299043137-260748.9171800120.492143367464302
4214886352010215.69909771297435.387932727-521580.699097708-0.288124852859087
4328825302267215.05333694271094.361359321615314.946663062-0.112615994237108
4426770262324713.89115924132015.684770908352312.108840758-0.595133427404052
4514043982019831.92600212-152644.443415346-615433.92600212-1.21828661742676
4613443701491863.05342577-397249.244480888-147493.053425775-1.04681171889161
47936865991231.006925688-464609.907957584-54366.0069256883-0.288319993519011
48872705704400.332736259-348773.915920572168304.6672637410.496064698220531
49628151609708.905283165-183123.00447095318442.09471683480.709492600952514
50953712811251.92351592667500.5843725041142460.0764840741.07209027581935
5111603841091172.38151680205525.40154008169211.61848320140.590213562923718
5214006181248644.16569597174338.279100505151973.834304031-0.133612469497581
5316615111797784.55968851417973.371191647-136273.5596885131.04435187600874
5414953472109124.00036111348610.229218776-613777.000361107-0.296844019695559
5529187862296705.38818896243985.542129311622080.611811039-0.447395876638402
5627756772327785.81065524105763.618165130447891.189344763-0.591320949131602
5714070261999944.58645493-175778.847318824-592918.586454928-1.20487958506547
5813701991527005.27346226-368766.867424514-156806.273462259-0.825997990953125
599645261068224.35481020-427227.557583701-103698.354810196-0.250262185271716
60850851726189.256137018-371883.91773934124661.7438629820.236982880845807
61683118681906.498318281-158988.640218111211.501681719150.9114208826073
62847224711405.312438976-36586.6640156648135818.6875610240.523566333984858
631073256929423.35132979128451.277713616143832.6486702110.705916075044084
6415143261412776.70752070358337.199943239101549.2924793040.984508098733805
6515037341684675.44206594302288.600588527-180941.442065942-0.240149787659462
6615077122069108.48627377355589.330451279-561396.4862737750.228154299283835
6728656982249922.92165048242260.618262726615775.078349524-0.484722186461353
6827881282252724.5518505987120.3993972594535403.44814941-0.663634973325712
6913915961969764.57690924-152592.232197099-578168.576909244-1.02579742665646
7013663781535961.6943205-334761.579187746-169583.694320501-0.779759955153367
719462951084296.47693913-410508.134465556-138001.476939130-0.324294331167833
72859626782388.217916578-340116.39827475777237.78208342240.301390911931582

\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 & 804406.285730027 & 154723.683435794 & 68720.7142699727 & 0.792697786821828 \tabularnewline
3 & 1107897 & 1106300.64103651 & 252402.524266799 & 1596.3589634869 & 0.434957272742389 \tabularnewline
4 & 1555964 & 1514241.73925271 & 361658.421900535 & 41722.2607472875 & 0.490125218490501 \tabularnewline
5 & 1671159 & 1734860.34344928 & 263973.653008982 & -63701.3434492812 & -0.413797699272963 \tabularnewline
6 & 1493308 & 1606381.3245862 & -4624.93070922245 & -113073.324586201 & -1.14966332958283 \tabularnewline
7 & 2957796 & 2589589.26262942 & 673905.456188023 & 368206.737370576 & 2.90713365936900 \tabularnewline
8 & 2638691 & 2870691.93217134 & 403718.853278761 & -232000.932171341 & -1.15615675723536 \tabularnewline
9 & 1305669 & 1764653.79148623 & -634755.586934305 & -458984.791486227 & -4.44411005156445 \tabularnewline
10 & 1280496 & 1125357.46497959 & -637878.217048106 & 155138.535020414 & -0.0133632996398641 \tabularnewline
11 & 921900 & 813555.896314699 & -413672.590087513 & 108344.103685301 & 0.959470907078068 \tabularnewline
12 & 867888 & 776975.173500372 & -154394.415130973 & 90912.826499628 & 1.10956300157995 \tabularnewline
13 & 652586 & 674263.051614344 & -118967.082874995 & -21677.0516143436 & 0.152133710391677 \tabularnewline
14 & 913831 & 817118.307479208 & 61258.5908571292 & 96712.692520792 & 0.783697800299816 \tabularnewline
15 & 1108544 & 1100837.21270453 & 209625.384328984 & 7706.78729547084 & 0.630196206051504 \tabularnewline
16 & 1555827 & 1402058.79522844 & 270175.015422369 & 153768.204771559 & 0.263216819291442 \tabularnewline
17 & 1699283 & 1685824.29326389 & 279252.418207754 & 13458.7067361082 & 0.0389642270491927 \tabularnewline
18 & 1509458 & 2025952.91210091 & 319595.247558002 & -516494.912100908 & 0.172020794623345 \tabularnewline
19 & 3268975 & 2693984.95255166 & 549922.975517657 & 574990.047448336 & 0.986118487001621 \tabularnewline
20 & 2425016 & 2455590.54746179 & 27803.9000785844 & -30574.5474617950 & -2.23553745599879 \tabularnewline
21 & 1312703 & 1887240.71049179 & -367364.840682602 & -574537.710491789 & -1.69095084036959 \tabularnewline
22 & 1365498 & 1322291.45042066 & -498337.78808427 & 43206.5495793387 & -0.56052309358586 \tabularnewline
23 & 934453 & 883149.563472569 & -459116.910248001 & 51303.436527431 & 0.167847776186944 \tabularnewline
24 & 775019 & 614397.061963756 & -333135.144410646 & 160621.938036244 & 0.539355036853022 \tabularnewline
25 & 651142 & 633505.38111326 & -99975.4767584471 & 17636.6188867405 & 1.00094348095493 \tabularnewline
26 & 843192 & 752375.13847692 & 45074.4238357024 & 90816.8615230807 & 0.621717087192154 \tabularnewline
27 & 1146766 & 1071980.15866668 & 225370.495935167 & 74785.8413333169 & 0.769405225037162 \tabularnewline
28 & 1652601 & 1471062.66692537 & 338941.269568832 & 181538.333074628 & 0.488287288313136 \tabularnewline
29 & 1465906 & 1626318.17121237 & 218206.590290659 & -160412.171212373 & -0.518534370758853 \tabularnewline
30 & 1652734 & 2205887.04772438 & 455393.041312502 & -553153.047724384 & 1.01320223921361 \tabularnewline
31 & 2922334 & 2258299.19082140 & 191716.798301726 & 664034.809178603 & -1.12747131346868 \tabularnewline
32 & 2702805 & 2485325.21462377 & 214831.185691687 & 217479.785376226 & 0.0989512068727316 \tabularnewline
33 & 1458956 & 2139065.58578316 & -152861.924261836 & -680109.585783157 & -1.57359291035739 \tabularnewline
34 & 1410363 & 1487508.54590282 & -479723.888368583 & -77145.5459028232 & -1.39878277247667 \tabularnewline
35 & 1019279 & 985788.802204873 & -494133.659463354 & 33490.1977951272 & -0.0616689736602361 \tabularnewline
36 & 936574 & 774886.575725905 & -308681.219509099 & 161687.424274095 & 0.79421395774286 \tabularnewline
37 & 708917 & 691302.923382195 & -161180.3875827 & 17614.0766178050 & 0.632314884873522 \tabularnewline
38 & 885295 & 800990.66960777 & 16266.1824537370 & 84304.3303922297 & 0.759309206825336 \tabularnewline
39 & 1099663 & 1017114.01883775 & 146601.345342499 & 82548.981162248 & 0.557010075370487 \tabularnewline
40 & 1576220 & 1322724.97451576 & 250074.735001702 & 253495.025484244 & 0.443697283395574 \tabularnewline
41 & 1487870 & 1748618.91718001 & 364798.299043137 & -260748.917180012 & 0.492143367464302 \tabularnewline
42 & 1488635 & 2010215.69909771 & 297435.387932727 & -521580.699097708 & -0.288124852859087 \tabularnewline
43 & 2882530 & 2267215.05333694 & 271094.361359321 & 615314.946663062 & -0.112615994237108 \tabularnewline
44 & 2677026 & 2324713.89115924 & 132015.684770908 & 352312.108840758 & -0.595133427404052 \tabularnewline
45 & 1404398 & 2019831.92600212 & -152644.443415346 & -615433.92600212 & -1.21828661742676 \tabularnewline
46 & 1344370 & 1491863.05342577 & -397249.244480888 & -147493.053425775 & -1.04681171889161 \tabularnewline
47 & 936865 & 991231.006925688 & -464609.907957584 & -54366.0069256883 & -0.288319993519011 \tabularnewline
48 & 872705 & 704400.332736259 & -348773.915920572 & 168304.667263741 & 0.496064698220531 \tabularnewline
49 & 628151 & 609708.905283165 & -183123.004470953 & 18442.0947168348 & 0.709492600952514 \tabularnewline
50 & 953712 & 811251.923515926 & 67500.5843725041 & 142460.076484074 & 1.07209027581935 \tabularnewline
51 & 1160384 & 1091172.38151680 & 205525.401540081 & 69211.6184832014 & 0.590213562923718 \tabularnewline
52 & 1400618 & 1248644.16569597 & 174338.279100505 & 151973.834304031 & -0.133612469497581 \tabularnewline
53 & 1661511 & 1797784.55968851 & 417973.371191647 & -136273.559688513 & 1.04435187600874 \tabularnewline
54 & 1495347 & 2109124.00036111 & 348610.229218776 & -613777.000361107 & -0.296844019695559 \tabularnewline
55 & 2918786 & 2296705.38818896 & 243985.542129311 & 622080.611811039 & -0.447395876638402 \tabularnewline
56 & 2775677 & 2327785.81065524 & 105763.618165130 & 447891.189344763 & -0.591320949131602 \tabularnewline
57 & 1407026 & 1999944.58645493 & -175778.847318824 & -592918.586454928 & -1.20487958506547 \tabularnewline
58 & 1370199 & 1527005.27346226 & -368766.867424514 & -156806.273462259 & -0.825997990953125 \tabularnewline
59 & 964526 & 1068224.35481020 & -427227.557583701 & -103698.354810196 & -0.250262185271716 \tabularnewline
60 & 850851 & 726189.256137018 & -371883.91773934 & 124661.743862982 & 0.236982880845807 \tabularnewline
61 & 683118 & 681906.498318281 & -158988.64021811 & 1211.50168171915 & 0.9114208826073 \tabularnewline
62 & 847224 & 711405.312438976 & -36586.6640156648 & 135818.687561024 & 0.523566333984858 \tabularnewline
63 & 1073256 & 929423.35132979 & 128451.277713616 & 143832.648670211 & 0.705916075044084 \tabularnewline
64 & 1514326 & 1412776.70752070 & 358337.199943239 & 101549.292479304 & 0.984508098733805 \tabularnewline
65 & 1503734 & 1684675.44206594 & 302288.600588527 & -180941.442065942 & -0.240149787659462 \tabularnewline
66 & 1507712 & 2069108.48627377 & 355589.330451279 & -561396.486273775 & 0.228154299283835 \tabularnewline
67 & 2865698 & 2249922.92165048 & 242260.618262726 & 615775.078349524 & -0.484722186461353 \tabularnewline
68 & 2788128 & 2252724.55185059 & 87120.3993972594 & 535403.44814941 & -0.663634973325712 \tabularnewline
69 & 1391596 & 1969764.57690924 & -152592.232197099 & -578168.576909244 & -1.02579742665646 \tabularnewline
70 & 1366378 & 1535961.6943205 & -334761.579187746 & -169583.694320501 & -0.779759955153367 \tabularnewline
71 & 946295 & 1084296.47693913 & -410508.134465556 & -138001.476939130 & -0.324294331167833 \tabularnewline
72 & 859626 & 782388.217916578 & -340116.398274757 & 77237.7820834224 & 0.301390911931582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116276&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]804406.285730027[/C][C]154723.683435794[/C][C]68720.7142699727[/C][C]0.792697786821828[/C][/ROW]
[ROW][C]3[/C][C]1107897[/C][C]1106300.64103651[/C][C]252402.524266799[/C][C]1596.3589634869[/C][C]0.434957272742389[/C][/ROW]
[ROW][C]4[/C][C]1555964[/C][C]1514241.73925271[/C][C]361658.421900535[/C][C]41722.2607472875[/C][C]0.490125218490501[/C][/ROW]
[ROW][C]5[/C][C]1671159[/C][C]1734860.34344928[/C][C]263973.653008982[/C][C]-63701.3434492812[/C][C]-0.413797699272963[/C][/ROW]
[ROW][C]6[/C][C]1493308[/C][C]1606381.3245862[/C][C]-4624.93070922245[/C][C]-113073.324586201[/C][C]-1.14966332958283[/C][/ROW]
[ROW][C]7[/C][C]2957796[/C][C]2589589.26262942[/C][C]673905.456188023[/C][C]368206.737370576[/C][C]2.90713365936900[/C][/ROW]
[ROW][C]8[/C][C]2638691[/C][C]2870691.93217134[/C][C]403718.853278761[/C][C]-232000.932171341[/C][C]-1.15615675723536[/C][/ROW]
[ROW][C]9[/C][C]1305669[/C][C]1764653.79148623[/C][C]-634755.586934305[/C][C]-458984.791486227[/C][C]-4.44411005156445[/C][/ROW]
[ROW][C]10[/C][C]1280496[/C][C]1125357.46497959[/C][C]-637878.217048106[/C][C]155138.535020414[/C][C]-0.0133632996398641[/C][/ROW]
[ROW][C]11[/C][C]921900[/C][C]813555.896314699[/C][C]-413672.590087513[/C][C]108344.103685301[/C][C]0.959470907078068[/C][/ROW]
[ROW][C]12[/C][C]867888[/C][C]776975.173500372[/C][C]-154394.415130973[/C][C]90912.826499628[/C][C]1.10956300157995[/C][/ROW]
[ROW][C]13[/C][C]652586[/C][C]674263.051614344[/C][C]-118967.082874995[/C][C]-21677.0516143436[/C][C]0.152133710391677[/C][/ROW]
[ROW][C]14[/C][C]913831[/C][C]817118.307479208[/C][C]61258.5908571292[/C][C]96712.692520792[/C][C]0.783697800299816[/C][/ROW]
[ROW][C]15[/C][C]1108544[/C][C]1100837.21270453[/C][C]209625.384328984[/C][C]7706.78729547084[/C][C]0.630196206051504[/C][/ROW]
[ROW][C]16[/C][C]1555827[/C][C]1402058.79522844[/C][C]270175.015422369[/C][C]153768.204771559[/C][C]0.263216819291442[/C][/ROW]
[ROW][C]17[/C][C]1699283[/C][C]1685824.29326389[/C][C]279252.418207754[/C][C]13458.7067361082[/C][C]0.0389642270491927[/C][/ROW]
[ROW][C]18[/C][C]1509458[/C][C]2025952.91210091[/C][C]319595.247558002[/C][C]-516494.912100908[/C][C]0.172020794623345[/C][/ROW]
[ROW][C]19[/C][C]3268975[/C][C]2693984.95255166[/C][C]549922.975517657[/C][C]574990.047448336[/C][C]0.986118487001621[/C][/ROW]
[ROW][C]20[/C][C]2425016[/C][C]2455590.54746179[/C][C]27803.9000785844[/C][C]-30574.5474617950[/C][C]-2.23553745599879[/C][/ROW]
[ROW][C]21[/C][C]1312703[/C][C]1887240.71049179[/C][C]-367364.840682602[/C][C]-574537.710491789[/C][C]-1.69095084036959[/C][/ROW]
[ROW][C]22[/C][C]1365498[/C][C]1322291.45042066[/C][C]-498337.78808427[/C][C]43206.5495793387[/C][C]-0.56052309358586[/C][/ROW]
[ROW][C]23[/C][C]934453[/C][C]883149.563472569[/C][C]-459116.910248001[/C][C]51303.436527431[/C][C]0.167847776186944[/C][/ROW]
[ROW][C]24[/C][C]775019[/C][C]614397.061963756[/C][C]-333135.144410646[/C][C]160621.938036244[/C][C]0.539355036853022[/C][/ROW]
[ROW][C]25[/C][C]651142[/C][C]633505.38111326[/C][C]-99975.4767584471[/C][C]17636.6188867405[/C][C]1.00094348095493[/C][/ROW]
[ROW][C]26[/C][C]843192[/C][C]752375.13847692[/C][C]45074.4238357024[/C][C]90816.8615230807[/C][C]0.621717087192154[/C][/ROW]
[ROW][C]27[/C][C]1146766[/C][C]1071980.15866668[/C][C]225370.495935167[/C][C]74785.8413333169[/C][C]0.769405225037162[/C][/ROW]
[ROW][C]28[/C][C]1652601[/C][C]1471062.66692537[/C][C]338941.269568832[/C][C]181538.333074628[/C][C]0.488287288313136[/C][/ROW]
[ROW][C]29[/C][C]1465906[/C][C]1626318.17121237[/C][C]218206.590290659[/C][C]-160412.171212373[/C][C]-0.518534370758853[/C][/ROW]
[ROW][C]30[/C][C]1652734[/C][C]2205887.04772438[/C][C]455393.041312502[/C][C]-553153.047724384[/C][C]1.01320223921361[/C][/ROW]
[ROW][C]31[/C][C]2922334[/C][C]2258299.19082140[/C][C]191716.798301726[/C][C]664034.809178603[/C][C]-1.12747131346868[/C][/ROW]
[ROW][C]32[/C][C]2702805[/C][C]2485325.21462377[/C][C]214831.185691687[/C][C]217479.785376226[/C][C]0.0989512068727316[/C][/ROW]
[ROW][C]33[/C][C]1458956[/C][C]2139065.58578316[/C][C]-152861.924261836[/C][C]-680109.585783157[/C][C]-1.57359291035739[/C][/ROW]
[ROW][C]34[/C][C]1410363[/C][C]1487508.54590282[/C][C]-479723.888368583[/C][C]-77145.5459028232[/C][C]-1.39878277247667[/C][/ROW]
[ROW][C]35[/C][C]1019279[/C][C]985788.802204873[/C][C]-494133.659463354[/C][C]33490.1977951272[/C][C]-0.0616689736602361[/C][/ROW]
[ROW][C]36[/C][C]936574[/C][C]774886.575725905[/C][C]-308681.219509099[/C][C]161687.424274095[/C][C]0.79421395774286[/C][/ROW]
[ROW][C]37[/C][C]708917[/C][C]691302.923382195[/C][C]-161180.3875827[/C][C]17614.0766178050[/C][C]0.632314884873522[/C][/ROW]
[ROW][C]38[/C][C]885295[/C][C]800990.66960777[/C][C]16266.1824537370[/C][C]84304.3303922297[/C][C]0.759309206825336[/C][/ROW]
[ROW][C]39[/C][C]1099663[/C][C]1017114.01883775[/C][C]146601.345342499[/C][C]82548.981162248[/C][C]0.557010075370487[/C][/ROW]
[ROW][C]40[/C][C]1576220[/C][C]1322724.97451576[/C][C]250074.735001702[/C][C]253495.025484244[/C][C]0.443697283395574[/C][/ROW]
[ROW][C]41[/C][C]1487870[/C][C]1748618.91718001[/C][C]364798.299043137[/C][C]-260748.917180012[/C][C]0.492143367464302[/C][/ROW]
[ROW][C]42[/C][C]1488635[/C][C]2010215.69909771[/C][C]297435.387932727[/C][C]-521580.699097708[/C][C]-0.288124852859087[/C][/ROW]
[ROW][C]43[/C][C]2882530[/C][C]2267215.05333694[/C][C]271094.361359321[/C][C]615314.946663062[/C][C]-0.112615994237108[/C][/ROW]
[ROW][C]44[/C][C]2677026[/C][C]2324713.89115924[/C][C]132015.684770908[/C][C]352312.108840758[/C][C]-0.595133427404052[/C][/ROW]
[ROW][C]45[/C][C]1404398[/C][C]2019831.92600212[/C][C]-152644.443415346[/C][C]-615433.92600212[/C][C]-1.21828661742676[/C][/ROW]
[ROW][C]46[/C][C]1344370[/C][C]1491863.05342577[/C][C]-397249.244480888[/C][C]-147493.053425775[/C][C]-1.04681171889161[/C][/ROW]
[ROW][C]47[/C][C]936865[/C][C]991231.006925688[/C][C]-464609.907957584[/C][C]-54366.0069256883[/C][C]-0.288319993519011[/C][/ROW]
[ROW][C]48[/C][C]872705[/C][C]704400.332736259[/C][C]-348773.915920572[/C][C]168304.667263741[/C][C]0.496064698220531[/C][/ROW]
[ROW][C]49[/C][C]628151[/C][C]609708.905283165[/C][C]-183123.004470953[/C][C]18442.0947168348[/C][C]0.709492600952514[/C][/ROW]
[ROW][C]50[/C][C]953712[/C][C]811251.923515926[/C][C]67500.5843725041[/C][C]142460.076484074[/C][C]1.07209027581935[/C][/ROW]
[ROW][C]51[/C][C]1160384[/C][C]1091172.38151680[/C][C]205525.401540081[/C][C]69211.6184832014[/C][C]0.590213562923718[/C][/ROW]
[ROW][C]52[/C][C]1400618[/C][C]1248644.16569597[/C][C]174338.279100505[/C][C]151973.834304031[/C][C]-0.133612469497581[/C][/ROW]
[ROW][C]53[/C][C]1661511[/C][C]1797784.55968851[/C][C]417973.371191647[/C][C]-136273.559688513[/C][C]1.04435187600874[/C][/ROW]
[ROW][C]54[/C][C]1495347[/C][C]2109124.00036111[/C][C]348610.229218776[/C][C]-613777.000361107[/C][C]-0.296844019695559[/C][/ROW]
[ROW][C]55[/C][C]2918786[/C][C]2296705.38818896[/C][C]243985.542129311[/C][C]622080.611811039[/C][C]-0.447395876638402[/C][/ROW]
[ROW][C]56[/C][C]2775677[/C][C]2327785.81065524[/C][C]105763.618165130[/C][C]447891.189344763[/C][C]-0.591320949131602[/C][/ROW]
[ROW][C]57[/C][C]1407026[/C][C]1999944.58645493[/C][C]-175778.847318824[/C][C]-592918.586454928[/C][C]-1.20487958506547[/C][/ROW]
[ROW][C]58[/C][C]1370199[/C][C]1527005.27346226[/C][C]-368766.867424514[/C][C]-156806.273462259[/C][C]-0.825997990953125[/C][/ROW]
[ROW][C]59[/C][C]964526[/C][C]1068224.35481020[/C][C]-427227.557583701[/C][C]-103698.354810196[/C][C]-0.250262185271716[/C][/ROW]
[ROW][C]60[/C][C]850851[/C][C]726189.256137018[/C][C]-371883.91773934[/C][C]124661.743862982[/C][C]0.236982880845807[/C][/ROW]
[ROW][C]61[/C][C]683118[/C][C]681906.498318281[/C][C]-158988.64021811[/C][C]1211.50168171915[/C][C]0.9114208826073[/C][/ROW]
[ROW][C]62[/C][C]847224[/C][C]711405.312438976[/C][C]-36586.6640156648[/C][C]135818.687561024[/C][C]0.523566333984858[/C][/ROW]
[ROW][C]63[/C][C]1073256[/C][C]929423.35132979[/C][C]128451.277713616[/C][C]143832.648670211[/C][C]0.705916075044084[/C][/ROW]
[ROW][C]64[/C][C]1514326[/C][C]1412776.70752070[/C][C]358337.199943239[/C][C]101549.292479304[/C][C]0.984508098733805[/C][/ROW]
[ROW][C]65[/C][C]1503734[/C][C]1684675.44206594[/C][C]302288.600588527[/C][C]-180941.442065942[/C][C]-0.240149787659462[/C][/ROW]
[ROW][C]66[/C][C]1507712[/C][C]2069108.48627377[/C][C]355589.330451279[/C][C]-561396.486273775[/C][C]0.228154299283835[/C][/ROW]
[ROW][C]67[/C][C]2865698[/C][C]2249922.92165048[/C][C]242260.618262726[/C][C]615775.078349524[/C][C]-0.484722186461353[/C][/ROW]
[ROW][C]68[/C][C]2788128[/C][C]2252724.55185059[/C][C]87120.3993972594[/C][C]535403.44814941[/C][C]-0.663634973325712[/C][/ROW]
[ROW][C]69[/C][C]1391596[/C][C]1969764.57690924[/C][C]-152592.232197099[/C][C]-578168.576909244[/C][C]-1.02579742665646[/C][/ROW]
[ROW][C]70[/C][C]1366378[/C][C]1535961.6943205[/C][C]-334761.579187746[/C][C]-169583.694320501[/C][C]-0.779759955153367[/C][/ROW]
[ROW][C]71[/C][C]946295[/C][C]1084296.47693913[/C][C]-410508.134465556[/C][C]-138001.476939130[/C][C]-0.324294331167833[/C][/ROW]
[ROW][C]72[/C][C]859626[/C][C]782388.217916578[/C][C]-340116.398274757[/C][C]77237.7820834224[/C][C]0.301390911931582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116276&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116276&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
2873127804406.285730027154723.68343579468720.71426997270.792697786821828
311078971106300.64103651252402.5242667991596.35896348690.434957272742389
415559641514241.73925271361658.42190053541722.26074728750.490125218490501
516711591734860.34344928263973.653008982-63701.3434492812-0.413797699272963
614933081606381.3245862-4624.93070922245-113073.324586201-1.14966332958283
729577962589589.26262942673905.456188023368206.7373705762.90713365936900
826386912870691.93217134403718.853278761-232000.932171341-1.15615675723536
913056691764653.79148623-634755.586934305-458984.791486227-4.44411005156445
1012804961125357.46497959-637878.217048106155138.535020414-0.0133632996398641
11921900813555.896314699-413672.590087513108344.1036853010.959470907078068
12867888776975.173500372-154394.41513097390912.8264996281.10956300157995
13652586674263.051614344-118967.082874995-21677.05161434360.152133710391677
14913831817118.30747920861258.590857129296712.6925207920.783697800299816
1511085441100837.21270453209625.3843289847706.787295470840.630196206051504
1615558271402058.79522844270175.015422369153768.2047715590.263216819291442
1716992831685824.29326389279252.41820775413458.70673610820.0389642270491927
1815094582025952.91210091319595.247558002-516494.9121009080.172020794623345
1932689752693984.95255166549922.975517657574990.0474483360.986118487001621
2024250162455590.5474617927803.9000785844-30574.5474617950-2.23553745599879
2113127031887240.71049179-367364.840682602-574537.710491789-1.69095084036959
2213654981322291.45042066-498337.7880842743206.5495793387-0.56052309358586
23934453883149.563472569-459116.91024800151303.4365274310.167847776186944
24775019614397.061963756-333135.144410646160621.9380362440.539355036853022
25651142633505.38111326-99975.476758447117636.61888674051.00094348095493
26843192752375.1384769245074.423835702490816.86152308070.621717087192154
2711467661071980.15866668225370.49593516774785.84133331690.769405225037162
2816526011471062.66692537338941.269568832181538.3330746280.488287288313136
2914659061626318.17121237218206.590290659-160412.171212373-0.518534370758853
3016527342205887.04772438455393.041312502-553153.0477243841.01320223921361
3129223342258299.19082140191716.798301726664034.809178603-1.12747131346868
3227028052485325.21462377214831.185691687217479.7853762260.0989512068727316
3314589562139065.58578316-152861.924261836-680109.585783157-1.57359291035739
3414103631487508.54590282-479723.888368583-77145.5459028232-1.39878277247667
351019279985788.802204873-494133.65946335433490.1977951272-0.0616689736602361
36936574774886.575725905-308681.219509099161687.4242740950.79421395774286
37708917691302.923382195-161180.387582717614.07661780500.632314884873522
38885295800990.6696077716266.182453737084304.33039222970.759309206825336
3910996631017114.01883775146601.34534249982548.9811622480.557010075370487
4015762201322724.97451576250074.735001702253495.0254842440.443697283395574
4114878701748618.91718001364798.299043137-260748.9171800120.492143367464302
4214886352010215.69909771297435.387932727-521580.699097708-0.288124852859087
4328825302267215.05333694271094.361359321615314.946663062-0.112615994237108
4426770262324713.89115924132015.684770908352312.108840758-0.595133427404052
4514043982019831.92600212-152644.443415346-615433.92600212-1.21828661742676
4613443701491863.05342577-397249.244480888-147493.053425775-1.04681171889161
47936865991231.006925688-464609.907957584-54366.0069256883-0.288319993519011
48872705704400.332736259-348773.915920572168304.6672637410.496064698220531
49628151609708.905283165-183123.00447095318442.09471683480.709492600952514
50953712811251.92351592667500.5843725041142460.0764840741.07209027581935
5111603841091172.38151680205525.40154008169211.61848320140.590213562923718
5214006181248644.16569597174338.279100505151973.834304031-0.133612469497581
5316615111797784.55968851417973.371191647-136273.5596885131.04435187600874
5414953472109124.00036111348610.229218776-613777.000361107-0.296844019695559
5529187862296705.38818896243985.542129311622080.611811039-0.447395876638402
5627756772327785.81065524105763.618165130447891.189344763-0.591320949131602
5714070261999944.58645493-175778.847318824-592918.586454928-1.20487958506547
5813701991527005.27346226-368766.867424514-156806.273462259-0.825997990953125
599645261068224.35481020-427227.557583701-103698.354810196-0.250262185271716
60850851726189.256137018-371883.91773934124661.7438629820.236982880845807
61683118681906.498318281-158988.640218111211.501681719150.9114208826073
62847224711405.312438976-36586.6640156648135818.6875610240.523566333984858
631073256929423.35132979128451.277713616143832.6486702110.705916075044084
6415143261412776.70752070358337.199943239101549.2924793040.984508098733805
6515037341684675.44206594302288.600588527-180941.442065942-0.240149787659462
6615077122069108.48627377355589.330451279-561396.4862737750.228154299283835
6728656982249922.92165048242260.618262726615775.078349524-0.484722186461353
6827881282252724.5518505987120.3993972594535403.44814941-0.663634973325712
6913915961969764.57690924-152592.232197099-578168.576909244-1.02579742665646
7013663781535961.6943205-334761.579187746-169583.694320501-0.779759955153367
719462951084296.47693913-410508.134465556-138001.476939130-0.324294331167833
72859626782388.217916578-340116.39827475777237.78208342240.301390911931582



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')