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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 computationTue, 28 Dec 2010 13:13:55 +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/t1293541937kyuan9v4xe3tzma.htm/, Retrieved Sun, 05 May 2024 04:38:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116339, Retrieved Sun, 05 May 2024 04:38:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2010-12-28 13:13:55] [a8b9961884f5001e2816791dd4ebd90c] [Current]
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Dataseries X:
11100
8962
9173
8738
8459
8078
8411
8291
7810
8616
8312
9692
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116339&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116339&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116339&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'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11110011100000
289629151.72898761441-104.281203113550-189.728987614411-2.68274837761497
391739190.9413920235-101.309899504965-17.94139202349130.316778501194152
487388888.55337711984-102.674843178230-150.553377119834-0.451974962980000
584598586.6089699495-103.886921897370-127.608969949502-0.447458380772994
680788219.34120612973-105.733076635293-141.341206129734-0.591161030112538
784118434.16741232283-103.314927392172-23.16741232283280.719302187539148
882918401.90493558974-102.754022722746-110.9049355897390.159395276188719
978107980.19213107548-105.376775530318-170.192131075485-0.715355766783798
1086168562.73036354003-99.493474754110153.26963645996971.54245675938534
1183128460.45087107921-99.5182102277705-148.450871079213-0.00624530494248919
1296929557.1484360474-88.514551649736134.85156395262.68084656957212
1399119113.96672378358-76.7994245984491797.033276216421-0.919360846580055
1489159109.20202891533-75.2251798631519-194.2020289153330.145083276654696
1594529337.53669264368-69.1343924460746114.4633073563170.659337041965563
1691129234.07569008787-69.5302244463971-122.075690087868-0.0767851238858974
1784728660.21318058797-73.9893549109773-188.213180587970-1.12817793128366
1882308404.86681407355-75.628650278673-174.866814073548-0.40543942166794
1983848361.5458015267-75.317582613238122.45419847330260.0722001140783568
2086258564.59958136614-72.519410809757360.40041863386260.621935553147393
2182218483.61934737145-72.6075162992987-262.619347371449-0.0188996594712316
2286498519.85483161535-71.4235586633613129.1451683846530.243132176382376
2386258846.30200897733-67.0441574738951-221.3020089773260.88863371537594
24104439950.45750632936-58.0669409318259492.5424936706442.61269391652336
25103579755.94502339968-57.6157652759494601.054976600317-0.319682585933325
2685869043.11340159796-68.1491857694916-457.113401597957-1.40566599353232
2788928787.63343182715-71.7921065819403104.366568172847-0.406621488505043
2883298415.52770945267-76.391180753812-86.5277094526656-0.668276694427434
2981018250.08376887579-77.5146815861799-149.083768875786-0.198626920172358
3079228113.14588868574-78.2347955760516-191.145888685738-0.132452643989060
3181208123.30661178185-77.1347604154134-3.306611781849950.19695452312405
3278387832.56967320268-79.88439870284025.43032679732221-0.475823842526044
3377357934.18687690408-77.4732991319601-199.1868769040820.404301991209434
3484068262.37407626163-71.9652576441052143.6259237383730.903678995519527
3582098611.01529122724-66.4992369929473-402.0152912272350.936179492796282
3694518868.11091851488-63.2189464894811582.8890814851180.720963365134581
37100419171.82486683474-60.5965809706119869.1751331652640.833591909929539
3894119683.79890340546-51.567563719338-272.7989034054551.25094095754972
391040510131.9933495994-41.9866910267261273.0066504006441.08875246509051
4084678930.00138421515-62.2764631385297-463.00138421515-2.57097838718554
4184648618.65355396426-66.13527352608-154.653553964258-0.554155910825658
4281028339.82981390013-69.2711024083718-237.829813900126-0.472992881457897
4376277714.38106856821-77.5052380006069-87.381068568208-1.23640009085819
4475137529.72498707655-79.126532537786-16.7249870765458-0.238168978437048
4575107700.8360475885-75.2596348821626-190.8360475884990.556232192170052
4682918099.9160536253-67.9098220517655191.0839463746981.05417898954564
4780648441.9220032502-61.8751923264924-377.9220032501970.910033593969813
4893838800.75567517234-56.4331952858063582.2443248276660.936007505351328
4997068962.06326211932-53.7429832952189743.9367378806760.488438298476534
5085798958.93294881637-52.8983121990589-379.9329488163680.111257269410033
5194748936.91312924544-52.3055326122391537.0868707545640.067514515523591
5283188771.65122495816-54.4145700850778-453.651224958157-0.249736022575718
5382138400.3799976768-59.9217041076765-187.379997676795-0.703551818199042
5480598182.9560451612-62.5471008494056-123.956045161200-0.349681683346025
5591118905.5801382149-49.5374864440732205.4198617851041.74257223952062
5677088062.46282412428-62.8298759849634-354.462824124275-1.76108273650325
5776807956.00680391426-63.568281476183-276.006803914258-0.0968115418605098
5880147911.39102277594-63.2495728139162102.6089772240570.0420393212950883
5980078334.51155727326-55.3951367432464-327.5115572732571.07794649720128
6087188228.53498395817-56.1616933126726489.465016041829-0.112371212684350
6194868617.06919247476-49.3691976811174868.930807525240.991526312624052
6291139302.61886165224-36.394369808097-189.6188616522441.61931213306789
6390258678.00836231039-47.7749399612975346.991637689614-1.28965873073105
6484768788.0940840587-44.7282264609761-312.0940840587010.348560649514962
6579528285.38691990558-53.2137004178575-333.386919905575-1.01536218088255
6677598076.97262358745-56.0011076572134-317.97262358745-0.34416894824201
6778357565.13164355554-64.1103387548892269.868356444461-1.01050523680481
6876007810.25504172191-58.5917565057631-210.2550417219130.685424121090115
6976517898.11225963858-55.9704938546256-247.1122596385850.324561289303625
7083198212.67893060137-49.3998365121464106.3210693986300.820716058692524
7188128922.78923421093-36.304294691432-110.7892342109261.68151954564564
7286308450.52906552328-43.5947036012835179.470934476718-0.967323186872627

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 11100 & 11100 & 0 & 0 & 0 \tabularnewline
2 & 8962 & 9151.72898761441 & -104.281203113550 & -189.728987614411 & -2.68274837761497 \tabularnewline
3 & 9173 & 9190.9413920235 & -101.309899504965 & -17.9413920234913 & 0.316778501194152 \tabularnewline
4 & 8738 & 8888.55337711984 & -102.674843178230 & -150.553377119834 & -0.451974962980000 \tabularnewline
5 & 8459 & 8586.6089699495 & -103.886921897370 & -127.608969949502 & -0.447458380772994 \tabularnewline
6 & 8078 & 8219.34120612973 & -105.733076635293 & -141.341206129734 & -0.591161030112538 \tabularnewline
7 & 8411 & 8434.16741232283 & -103.314927392172 & -23.1674123228328 & 0.719302187539148 \tabularnewline
8 & 8291 & 8401.90493558974 & -102.754022722746 & -110.904935589739 & 0.159395276188719 \tabularnewline
9 & 7810 & 7980.19213107548 & -105.376775530318 & -170.192131075485 & -0.715355766783798 \tabularnewline
10 & 8616 & 8562.73036354003 & -99.4934747541101 & 53.2696364599697 & 1.54245675938534 \tabularnewline
11 & 8312 & 8460.45087107921 & -99.5182102277705 & -148.450871079213 & -0.00624530494248919 \tabularnewline
12 & 9692 & 9557.1484360474 & -88.514551649736 & 134.8515639526 & 2.68084656957212 \tabularnewline
13 & 9911 & 9113.96672378358 & -76.7994245984491 & 797.033276216421 & -0.919360846580055 \tabularnewline
14 & 8915 & 9109.20202891533 & -75.2251798631519 & -194.202028915333 & 0.145083276654696 \tabularnewline
15 & 9452 & 9337.53669264368 & -69.1343924460746 & 114.463307356317 & 0.659337041965563 \tabularnewline
16 & 9112 & 9234.07569008787 & -69.5302244463971 & -122.075690087868 & -0.0767851238858974 \tabularnewline
17 & 8472 & 8660.21318058797 & -73.9893549109773 & -188.213180587970 & -1.12817793128366 \tabularnewline
18 & 8230 & 8404.86681407355 & -75.628650278673 & -174.866814073548 & -0.40543942166794 \tabularnewline
19 & 8384 & 8361.5458015267 & -75.3175826132381 & 22.4541984733026 & 0.0722001140783568 \tabularnewline
20 & 8625 & 8564.59958136614 & -72.5194108097573 & 60.4004186338626 & 0.621935553147393 \tabularnewline
21 & 8221 & 8483.61934737145 & -72.6075162992987 & -262.619347371449 & -0.0188996594712316 \tabularnewline
22 & 8649 & 8519.85483161535 & -71.4235586633613 & 129.145168384653 & 0.243132176382376 \tabularnewline
23 & 8625 & 8846.30200897733 & -67.0441574738951 & -221.302008977326 & 0.88863371537594 \tabularnewline
24 & 10443 & 9950.45750632936 & -58.0669409318259 & 492.542493670644 & 2.61269391652336 \tabularnewline
25 & 10357 & 9755.94502339968 & -57.6157652759494 & 601.054976600317 & -0.319682585933325 \tabularnewline
26 & 8586 & 9043.11340159796 & -68.1491857694916 & -457.113401597957 & -1.40566599353232 \tabularnewline
27 & 8892 & 8787.63343182715 & -71.7921065819403 & 104.366568172847 & -0.406621488505043 \tabularnewline
28 & 8329 & 8415.52770945267 & -76.391180753812 & -86.5277094526656 & -0.668276694427434 \tabularnewline
29 & 8101 & 8250.08376887579 & -77.5146815861799 & -149.083768875786 & -0.198626920172358 \tabularnewline
30 & 7922 & 8113.14588868574 & -78.2347955760516 & -191.145888685738 & -0.132452643989060 \tabularnewline
31 & 8120 & 8123.30661178185 & -77.1347604154134 & -3.30661178184995 & 0.19695452312405 \tabularnewline
32 & 7838 & 7832.56967320268 & -79.8843987028402 & 5.43032679732221 & -0.475823842526044 \tabularnewline
33 & 7735 & 7934.18687690408 & -77.4732991319601 & -199.186876904082 & 0.404301991209434 \tabularnewline
34 & 8406 & 8262.37407626163 & -71.9652576441052 & 143.625923738373 & 0.903678995519527 \tabularnewline
35 & 8209 & 8611.01529122724 & -66.4992369929473 & -402.015291227235 & 0.936179492796282 \tabularnewline
36 & 9451 & 8868.11091851488 & -63.2189464894811 & 582.889081485118 & 0.720963365134581 \tabularnewline
37 & 10041 & 9171.82486683474 & -60.5965809706119 & 869.175133165264 & 0.833591909929539 \tabularnewline
38 & 9411 & 9683.79890340546 & -51.567563719338 & -272.798903405455 & 1.25094095754972 \tabularnewline
39 & 10405 & 10131.9933495994 & -41.9866910267261 & 273.006650400644 & 1.08875246509051 \tabularnewline
40 & 8467 & 8930.00138421515 & -62.2764631385297 & -463.00138421515 & -2.57097838718554 \tabularnewline
41 & 8464 & 8618.65355396426 & -66.13527352608 & -154.653553964258 & -0.554155910825658 \tabularnewline
42 & 8102 & 8339.82981390013 & -69.2711024083718 & -237.829813900126 & -0.472992881457897 \tabularnewline
43 & 7627 & 7714.38106856821 & -77.5052380006069 & -87.381068568208 & -1.23640009085819 \tabularnewline
44 & 7513 & 7529.72498707655 & -79.126532537786 & -16.7249870765458 & -0.238168978437048 \tabularnewline
45 & 7510 & 7700.8360475885 & -75.2596348821626 & -190.836047588499 & 0.556232192170052 \tabularnewline
46 & 8291 & 8099.9160536253 & -67.9098220517655 & 191.083946374698 & 1.05417898954564 \tabularnewline
47 & 8064 & 8441.9220032502 & -61.8751923264924 & -377.922003250197 & 0.910033593969813 \tabularnewline
48 & 9383 & 8800.75567517234 & -56.4331952858063 & 582.244324827666 & 0.936007505351328 \tabularnewline
49 & 9706 & 8962.06326211932 & -53.7429832952189 & 743.936737880676 & 0.488438298476534 \tabularnewline
50 & 8579 & 8958.93294881637 & -52.8983121990589 & -379.932948816368 & 0.111257269410033 \tabularnewline
51 & 9474 & 8936.91312924544 & -52.3055326122391 & 537.086870754564 & 0.067514515523591 \tabularnewline
52 & 8318 & 8771.65122495816 & -54.4145700850778 & -453.651224958157 & -0.249736022575718 \tabularnewline
53 & 8213 & 8400.3799976768 & -59.9217041076765 & -187.379997676795 & -0.703551818199042 \tabularnewline
54 & 8059 & 8182.9560451612 & -62.5471008494056 & -123.956045161200 & -0.349681683346025 \tabularnewline
55 & 9111 & 8905.5801382149 & -49.5374864440732 & 205.419861785104 & 1.74257223952062 \tabularnewline
56 & 7708 & 8062.46282412428 & -62.8298759849634 & -354.462824124275 & -1.76108273650325 \tabularnewline
57 & 7680 & 7956.00680391426 & -63.568281476183 & -276.006803914258 & -0.0968115418605098 \tabularnewline
58 & 8014 & 7911.39102277594 & -63.2495728139162 & 102.608977224057 & 0.0420393212950883 \tabularnewline
59 & 8007 & 8334.51155727326 & -55.3951367432464 & -327.511557273257 & 1.07794649720128 \tabularnewline
60 & 8718 & 8228.53498395817 & -56.1616933126726 & 489.465016041829 & -0.112371212684350 \tabularnewline
61 & 9486 & 8617.06919247476 & -49.3691976811174 & 868.93080752524 & 0.991526312624052 \tabularnewline
62 & 9113 & 9302.61886165224 & -36.394369808097 & -189.618861652244 & 1.61931213306789 \tabularnewline
63 & 9025 & 8678.00836231039 & -47.7749399612975 & 346.991637689614 & -1.28965873073105 \tabularnewline
64 & 8476 & 8788.0940840587 & -44.7282264609761 & -312.094084058701 & 0.348560649514962 \tabularnewline
65 & 7952 & 8285.38691990558 & -53.2137004178575 & -333.386919905575 & -1.01536218088255 \tabularnewline
66 & 7759 & 8076.97262358745 & -56.0011076572134 & -317.97262358745 & -0.34416894824201 \tabularnewline
67 & 7835 & 7565.13164355554 & -64.1103387548892 & 269.868356444461 & -1.01050523680481 \tabularnewline
68 & 7600 & 7810.25504172191 & -58.5917565057631 & -210.255041721913 & 0.685424121090115 \tabularnewline
69 & 7651 & 7898.11225963858 & -55.9704938546256 & -247.112259638585 & 0.324561289303625 \tabularnewline
70 & 8319 & 8212.67893060137 & -49.3998365121464 & 106.321069398630 & 0.820716058692524 \tabularnewline
71 & 8812 & 8922.78923421093 & -36.304294691432 & -110.789234210926 & 1.68151954564564 \tabularnewline
72 & 8630 & 8450.52906552328 & -43.5947036012835 & 179.470934476718 & -0.967323186872627 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116339&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]11100[/C][C]11100[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8962[/C][C]9151.72898761441[/C][C]-104.281203113550[/C][C]-189.728987614411[/C][C]-2.68274837761497[/C][/ROW]
[ROW][C]3[/C][C]9173[/C][C]9190.9413920235[/C][C]-101.309899504965[/C][C]-17.9413920234913[/C][C]0.316778501194152[/C][/ROW]
[ROW][C]4[/C][C]8738[/C][C]8888.55337711984[/C][C]-102.674843178230[/C][C]-150.553377119834[/C][C]-0.451974962980000[/C][/ROW]
[ROW][C]5[/C][C]8459[/C][C]8586.6089699495[/C][C]-103.886921897370[/C][C]-127.608969949502[/C][C]-0.447458380772994[/C][/ROW]
[ROW][C]6[/C][C]8078[/C][C]8219.34120612973[/C][C]-105.733076635293[/C][C]-141.341206129734[/C][C]-0.591161030112538[/C][/ROW]
[ROW][C]7[/C][C]8411[/C][C]8434.16741232283[/C][C]-103.314927392172[/C][C]-23.1674123228328[/C][C]0.719302187539148[/C][/ROW]
[ROW][C]8[/C][C]8291[/C][C]8401.90493558974[/C][C]-102.754022722746[/C][C]-110.904935589739[/C][C]0.159395276188719[/C][/ROW]
[ROW][C]9[/C][C]7810[/C][C]7980.19213107548[/C][C]-105.376775530318[/C][C]-170.192131075485[/C][C]-0.715355766783798[/C][/ROW]
[ROW][C]10[/C][C]8616[/C][C]8562.73036354003[/C][C]-99.4934747541101[/C][C]53.2696364599697[/C][C]1.54245675938534[/C][/ROW]
[ROW][C]11[/C][C]8312[/C][C]8460.45087107921[/C][C]-99.5182102277705[/C][C]-148.450871079213[/C][C]-0.00624530494248919[/C][/ROW]
[ROW][C]12[/C][C]9692[/C][C]9557.1484360474[/C][C]-88.514551649736[/C][C]134.8515639526[/C][C]2.68084656957212[/C][/ROW]
[ROW][C]13[/C][C]9911[/C][C]9113.96672378358[/C][C]-76.7994245984491[/C][C]797.033276216421[/C][C]-0.919360846580055[/C][/ROW]
[ROW][C]14[/C][C]8915[/C][C]9109.20202891533[/C][C]-75.2251798631519[/C][C]-194.202028915333[/C][C]0.145083276654696[/C][/ROW]
[ROW][C]15[/C][C]9452[/C][C]9337.53669264368[/C][C]-69.1343924460746[/C][C]114.463307356317[/C][C]0.659337041965563[/C][/ROW]
[ROW][C]16[/C][C]9112[/C][C]9234.07569008787[/C][C]-69.5302244463971[/C][C]-122.075690087868[/C][C]-0.0767851238858974[/C][/ROW]
[ROW][C]17[/C][C]8472[/C][C]8660.21318058797[/C][C]-73.9893549109773[/C][C]-188.213180587970[/C][C]-1.12817793128366[/C][/ROW]
[ROW][C]18[/C][C]8230[/C][C]8404.86681407355[/C][C]-75.628650278673[/C][C]-174.866814073548[/C][C]-0.40543942166794[/C][/ROW]
[ROW][C]19[/C][C]8384[/C][C]8361.5458015267[/C][C]-75.3175826132381[/C][C]22.4541984733026[/C][C]0.0722001140783568[/C][/ROW]
[ROW][C]20[/C][C]8625[/C][C]8564.59958136614[/C][C]-72.5194108097573[/C][C]60.4004186338626[/C][C]0.621935553147393[/C][/ROW]
[ROW][C]21[/C][C]8221[/C][C]8483.61934737145[/C][C]-72.6075162992987[/C][C]-262.619347371449[/C][C]-0.0188996594712316[/C][/ROW]
[ROW][C]22[/C][C]8649[/C][C]8519.85483161535[/C][C]-71.4235586633613[/C][C]129.145168384653[/C][C]0.243132176382376[/C][/ROW]
[ROW][C]23[/C][C]8625[/C][C]8846.30200897733[/C][C]-67.0441574738951[/C][C]-221.302008977326[/C][C]0.88863371537594[/C][/ROW]
[ROW][C]24[/C][C]10443[/C][C]9950.45750632936[/C][C]-58.0669409318259[/C][C]492.542493670644[/C][C]2.61269391652336[/C][/ROW]
[ROW][C]25[/C][C]10357[/C][C]9755.94502339968[/C][C]-57.6157652759494[/C][C]601.054976600317[/C][C]-0.319682585933325[/C][/ROW]
[ROW][C]26[/C][C]8586[/C][C]9043.11340159796[/C][C]-68.1491857694916[/C][C]-457.113401597957[/C][C]-1.40566599353232[/C][/ROW]
[ROW][C]27[/C][C]8892[/C][C]8787.63343182715[/C][C]-71.7921065819403[/C][C]104.366568172847[/C][C]-0.406621488505043[/C][/ROW]
[ROW][C]28[/C][C]8329[/C][C]8415.52770945267[/C][C]-76.391180753812[/C][C]-86.5277094526656[/C][C]-0.668276694427434[/C][/ROW]
[ROW][C]29[/C][C]8101[/C][C]8250.08376887579[/C][C]-77.5146815861799[/C][C]-149.083768875786[/C][C]-0.198626920172358[/C][/ROW]
[ROW][C]30[/C][C]7922[/C][C]8113.14588868574[/C][C]-78.2347955760516[/C][C]-191.145888685738[/C][C]-0.132452643989060[/C][/ROW]
[ROW][C]31[/C][C]8120[/C][C]8123.30661178185[/C][C]-77.1347604154134[/C][C]-3.30661178184995[/C][C]0.19695452312405[/C][/ROW]
[ROW][C]32[/C][C]7838[/C][C]7832.56967320268[/C][C]-79.8843987028402[/C][C]5.43032679732221[/C][C]-0.475823842526044[/C][/ROW]
[ROW][C]33[/C][C]7735[/C][C]7934.18687690408[/C][C]-77.4732991319601[/C][C]-199.186876904082[/C][C]0.404301991209434[/C][/ROW]
[ROW][C]34[/C][C]8406[/C][C]8262.37407626163[/C][C]-71.9652576441052[/C][C]143.625923738373[/C][C]0.903678995519527[/C][/ROW]
[ROW][C]35[/C][C]8209[/C][C]8611.01529122724[/C][C]-66.4992369929473[/C][C]-402.015291227235[/C][C]0.936179492796282[/C][/ROW]
[ROW][C]36[/C][C]9451[/C][C]8868.11091851488[/C][C]-63.2189464894811[/C][C]582.889081485118[/C][C]0.720963365134581[/C][/ROW]
[ROW][C]37[/C][C]10041[/C][C]9171.82486683474[/C][C]-60.5965809706119[/C][C]869.175133165264[/C][C]0.833591909929539[/C][/ROW]
[ROW][C]38[/C][C]9411[/C][C]9683.79890340546[/C][C]-51.567563719338[/C][C]-272.798903405455[/C][C]1.25094095754972[/C][/ROW]
[ROW][C]39[/C][C]10405[/C][C]10131.9933495994[/C][C]-41.9866910267261[/C][C]273.006650400644[/C][C]1.08875246509051[/C][/ROW]
[ROW][C]40[/C][C]8467[/C][C]8930.00138421515[/C][C]-62.2764631385297[/C][C]-463.00138421515[/C][C]-2.57097838718554[/C][/ROW]
[ROW][C]41[/C][C]8464[/C][C]8618.65355396426[/C][C]-66.13527352608[/C][C]-154.653553964258[/C][C]-0.554155910825658[/C][/ROW]
[ROW][C]42[/C][C]8102[/C][C]8339.82981390013[/C][C]-69.2711024083718[/C][C]-237.829813900126[/C][C]-0.472992881457897[/C][/ROW]
[ROW][C]43[/C][C]7627[/C][C]7714.38106856821[/C][C]-77.5052380006069[/C][C]-87.381068568208[/C][C]-1.23640009085819[/C][/ROW]
[ROW][C]44[/C][C]7513[/C][C]7529.72498707655[/C][C]-79.126532537786[/C][C]-16.7249870765458[/C][C]-0.238168978437048[/C][/ROW]
[ROW][C]45[/C][C]7510[/C][C]7700.8360475885[/C][C]-75.2596348821626[/C][C]-190.836047588499[/C][C]0.556232192170052[/C][/ROW]
[ROW][C]46[/C][C]8291[/C][C]8099.9160536253[/C][C]-67.9098220517655[/C][C]191.083946374698[/C][C]1.05417898954564[/C][/ROW]
[ROW][C]47[/C][C]8064[/C][C]8441.9220032502[/C][C]-61.8751923264924[/C][C]-377.922003250197[/C][C]0.910033593969813[/C][/ROW]
[ROW][C]48[/C][C]9383[/C][C]8800.75567517234[/C][C]-56.4331952858063[/C][C]582.244324827666[/C][C]0.936007505351328[/C][/ROW]
[ROW][C]49[/C][C]9706[/C][C]8962.06326211932[/C][C]-53.7429832952189[/C][C]743.936737880676[/C][C]0.488438298476534[/C][/ROW]
[ROW][C]50[/C][C]8579[/C][C]8958.93294881637[/C][C]-52.8983121990589[/C][C]-379.932948816368[/C][C]0.111257269410033[/C][/ROW]
[ROW][C]51[/C][C]9474[/C][C]8936.91312924544[/C][C]-52.3055326122391[/C][C]537.086870754564[/C][C]0.067514515523591[/C][/ROW]
[ROW][C]52[/C][C]8318[/C][C]8771.65122495816[/C][C]-54.4145700850778[/C][C]-453.651224958157[/C][C]-0.249736022575718[/C][/ROW]
[ROW][C]53[/C][C]8213[/C][C]8400.3799976768[/C][C]-59.9217041076765[/C][C]-187.379997676795[/C][C]-0.703551818199042[/C][/ROW]
[ROW][C]54[/C][C]8059[/C][C]8182.9560451612[/C][C]-62.5471008494056[/C][C]-123.956045161200[/C][C]-0.349681683346025[/C][/ROW]
[ROW][C]55[/C][C]9111[/C][C]8905.5801382149[/C][C]-49.5374864440732[/C][C]205.419861785104[/C][C]1.74257223952062[/C][/ROW]
[ROW][C]56[/C][C]7708[/C][C]8062.46282412428[/C][C]-62.8298759849634[/C][C]-354.462824124275[/C][C]-1.76108273650325[/C][/ROW]
[ROW][C]57[/C][C]7680[/C][C]7956.00680391426[/C][C]-63.568281476183[/C][C]-276.006803914258[/C][C]-0.0968115418605098[/C][/ROW]
[ROW][C]58[/C][C]8014[/C][C]7911.39102277594[/C][C]-63.2495728139162[/C][C]102.608977224057[/C][C]0.0420393212950883[/C][/ROW]
[ROW][C]59[/C][C]8007[/C][C]8334.51155727326[/C][C]-55.3951367432464[/C][C]-327.511557273257[/C][C]1.07794649720128[/C][/ROW]
[ROW][C]60[/C][C]8718[/C][C]8228.53498395817[/C][C]-56.1616933126726[/C][C]489.465016041829[/C][C]-0.112371212684350[/C][/ROW]
[ROW][C]61[/C][C]9486[/C][C]8617.06919247476[/C][C]-49.3691976811174[/C][C]868.93080752524[/C][C]0.991526312624052[/C][/ROW]
[ROW][C]62[/C][C]9113[/C][C]9302.61886165224[/C][C]-36.394369808097[/C][C]-189.618861652244[/C][C]1.61931213306789[/C][/ROW]
[ROW][C]63[/C][C]9025[/C][C]8678.00836231039[/C][C]-47.7749399612975[/C][C]346.991637689614[/C][C]-1.28965873073105[/C][/ROW]
[ROW][C]64[/C][C]8476[/C][C]8788.0940840587[/C][C]-44.7282264609761[/C][C]-312.094084058701[/C][C]0.348560649514962[/C][/ROW]
[ROW][C]65[/C][C]7952[/C][C]8285.38691990558[/C][C]-53.2137004178575[/C][C]-333.386919905575[/C][C]-1.01536218088255[/C][/ROW]
[ROW][C]66[/C][C]7759[/C][C]8076.97262358745[/C][C]-56.0011076572134[/C][C]-317.97262358745[/C][C]-0.34416894824201[/C][/ROW]
[ROW][C]67[/C][C]7835[/C][C]7565.13164355554[/C][C]-64.1103387548892[/C][C]269.868356444461[/C][C]-1.01050523680481[/C][/ROW]
[ROW][C]68[/C][C]7600[/C][C]7810.25504172191[/C][C]-58.5917565057631[/C][C]-210.255041721913[/C][C]0.685424121090115[/C][/ROW]
[ROW][C]69[/C][C]7651[/C][C]7898.11225963858[/C][C]-55.9704938546256[/C][C]-247.112259638585[/C][C]0.324561289303625[/C][/ROW]
[ROW][C]70[/C][C]8319[/C][C]8212.67893060137[/C][C]-49.3998365121464[/C][C]106.321069398630[/C][C]0.820716058692524[/C][/ROW]
[ROW][C]71[/C][C]8812[/C][C]8922.78923421093[/C][C]-36.304294691432[/C][C]-110.789234210926[/C][C]1.68151954564564[/C][/ROW]
[ROW][C]72[/C][C]8630[/C][C]8450.52906552328[/C][C]-43.5947036012835[/C][C]179.470934476718[/C][C]-0.967323186872627[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116339&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116339&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
11110011100000
289629151.72898761441-104.281203113550-189.728987614411-2.68274837761497
391739190.9413920235-101.309899504965-17.94139202349130.316778501194152
487388888.55337711984-102.674843178230-150.553377119834-0.451974962980000
584598586.6089699495-103.886921897370-127.608969949502-0.447458380772994
680788219.34120612973-105.733076635293-141.341206129734-0.591161030112538
784118434.16741232283-103.314927392172-23.16741232283280.719302187539148
882918401.90493558974-102.754022722746-110.9049355897390.159395276188719
978107980.19213107548-105.376775530318-170.192131075485-0.715355766783798
1086168562.73036354003-99.493474754110153.26963645996971.54245675938534
1183128460.45087107921-99.5182102277705-148.450871079213-0.00624530494248919
1296929557.1484360474-88.514551649736134.85156395262.68084656957212
1399119113.96672378358-76.7994245984491797.033276216421-0.919360846580055
1489159109.20202891533-75.2251798631519-194.2020289153330.145083276654696
1594529337.53669264368-69.1343924460746114.4633073563170.659337041965563
1691129234.07569008787-69.5302244463971-122.075690087868-0.0767851238858974
1784728660.21318058797-73.9893549109773-188.213180587970-1.12817793128366
1882308404.86681407355-75.628650278673-174.866814073548-0.40543942166794
1983848361.5458015267-75.317582613238122.45419847330260.0722001140783568
2086258564.59958136614-72.519410809757360.40041863386260.621935553147393
2182218483.61934737145-72.6075162992987-262.619347371449-0.0188996594712316
2286498519.85483161535-71.4235586633613129.1451683846530.243132176382376
2386258846.30200897733-67.0441574738951-221.3020089773260.88863371537594
24104439950.45750632936-58.0669409318259492.5424936706442.61269391652336
25103579755.94502339968-57.6157652759494601.054976600317-0.319682585933325
2685869043.11340159796-68.1491857694916-457.113401597957-1.40566599353232
2788928787.63343182715-71.7921065819403104.366568172847-0.406621488505043
2883298415.52770945267-76.391180753812-86.5277094526656-0.668276694427434
2981018250.08376887579-77.5146815861799-149.083768875786-0.198626920172358
3079228113.14588868574-78.2347955760516-191.145888685738-0.132452643989060
3181208123.30661178185-77.1347604154134-3.306611781849950.19695452312405
3278387832.56967320268-79.88439870284025.43032679732221-0.475823842526044
3377357934.18687690408-77.4732991319601-199.1868769040820.404301991209434
3484068262.37407626163-71.9652576441052143.6259237383730.903678995519527
3582098611.01529122724-66.4992369929473-402.0152912272350.936179492796282
3694518868.11091851488-63.2189464894811582.8890814851180.720963365134581
37100419171.82486683474-60.5965809706119869.1751331652640.833591909929539
3894119683.79890340546-51.567563719338-272.7989034054551.25094095754972
391040510131.9933495994-41.9866910267261273.0066504006441.08875246509051
4084678930.00138421515-62.2764631385297-463.00138421515-2.57097838718554
4184648618.65355396426-66.13527352608-154.653553964258-0.554155910825658
4281028339.82981390013-69.2711024083718-237.829813900126-0.472992881457897
4376277714.38106856821-77.5052380006069-87.381068568208-1.23640009085819
4475137529.72498707655-79.126532537786-16.7249870765458-0.238168978437048
4575107700.8360475885-75.2596348821626-190.8360475884990.556232192170052
4682918099.9160536253-67.9098220517655191.0839463746981.05417898954564
4780648441.9220032502-61.8751923264924-377.9220032501970.910033593969813
4893838800.75567517234-56.4331952858063582.2443248276660.936007505351328
4997068962.06326211932-53.7429832952189743.9367378806760.488438298476534
5085798958.93294881637-52.8983121990589-379.9329488163680.111257269410033
5194748936.91312924544-52.3055326122391537.0868707545640.067514515523591
5283188771.65122495816-54.4145700850778-453.651224958157-0.249736022575718
5382138400.3799976768-59.9217041076765-187.379997676795-0.703551818199042
5480598182.9560451612-62.5471008494056-123.956045161200-0.349681683346025
5591118905.5801382149-49.5374864440732205.4198617851041.74257223952062
5677088062.46282412428-62.8298759849634-354.462824124275-1.76108273650325
5776807956.00680391426-63.568281476183-276.006803914258-0.0968115418605098
5880147911.39102277594-63.2495728139162102.6089772240570.0420393212950883
5980078334.51155727326-55.3951367432464-327.5115572732571.07794649720128
6087188228.53498395817-56.1616933126726489.465016041829-0.112371212684350
6194868617.06919247476-49.3691976811174868.930807525240.991526312624052
6291139302.61886165224-36.394369808097-189.6188616522441.61931213306789
6390258678.00836231039-47.7749399612975346.991637689614-1.28965873073105
6484768788.0940840587-44.7282264609761-312.0940840587010.348560649514962
6579528285.38691990558-53.2137004178575-333.386919905575-1.01536218088255
6677598076.97262358745-56.0011076572134-317.97262358745-0.34416894824201
6778357565.13164355554-64.1103387548892269.868356444461-1.01050523680481
6876007810.25504172191-58.5917565057631-210.2550417219130.685424121090115
6976517898.11225963858-55.9704938546256-247.1122596385850.324561289303625
7083198212.67893060137-49.3998365121464106.3210693986300.820716058692524
7188128922.78923421093-36.304294691432-110.7892342109261.68151954564564
7286308450.52906552328-43.5947036012835179.470934476718-0.967323186872627



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')