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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 computationFri, 04 Dec 2009 12:54:02 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t12599565296fkpvsmdnmxloc7.htm/, Retrieved Sun, 28 Apr 2024 07:46:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64110, Retrieved Sun, 28 Apr 2024 07:46:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [] [2009-12-04 19:54:02] [90c9838c596c9c0a7d0d4c412ffe5b98] [Current]
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Dataseries X:
6802.96
7132.68
7073.29
7264.5
7105.33
7218.71
7225.72
7354.25
7745.46
8070.26
8366.33
8667.51
8854.34
9218.1
9332.9
9358.31
9248.66
9401.2
9652.04
9957.38
10110.63
10169.26
10343.78
10750.21
11337.5
11786.96
12083.04
12007.74
11745.93
11051.51
11445.9
11924.88
12247.63
12690.91
12910.7
13202.12
13654.67
13862.82
13523.93
14211.17
14510.35
14289.23
14111.82
13086.59
13351.54
13747.69
12855.61
12926.93
12121.95
11731.65
11639.51
12163.78
12029.53
11234.18
9852.13
9709.04
9332.75
7108.6
6691.49
6143.05




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64110&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64110&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64110&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
16802.966802.96000
27132.687115.7756324197521.540370097026316.90436758025090.44932039843554
37073.297056.524167571315.061885343305416.7658324286956-0.187157310312754
47264.57247.4659207062134.503705518122517.03407929378990.400585664535751
57105.337088.551411820428.6018696344703216.7785881795752-0.434618377625661
67218.717201.8140516039824.415700523932416.89594839601580.232785747773486
77225.727208.8403668983621.576145946260916.879633101641-0.0383911627018807
87354.257337.2873174104539.930034716475716.96268258954960.234708154437045
97745.467728.2729314206102.23836375822617.18706857939820.768187935665514
108070.268052.95653538964142.58009516783717.30346461036130.485552554967699
118366.338348.96102145352170.80036065478117.36897854648340.334330505643150
128667.518650.09569932421194.99664839156617.41430067579290.283691475180607
138854.348986.2839086275219.973027741281-131.9439086275010.359635051844466
149218.19206.1084578784219.94581668223511.9915421216060-0.000272798080751261
159332.99320.84782803717200.16300294829212.0521719628253-0.228425201984916
169358.319346.17604535292167.23635464065212.1339546470779-0.379580573024516
179248.669236.42090370243115.01637437658612.2390962975663-0.601357869219266
189401.29388.97246353382122.09764383674112.22753646618380.0814900739143552
199652.049639.84463716831146.40323307011712.19536283168590.279576471329508
209957.389945.21685584504176.41740795871812.16314415495460.345135700576902
2110110.6310098.4630465251172.04161603248212.1669534749371-0.0503076431132427
2210169.2610157.0779212804150.61825898505612.1820787195792-0.246268095762417
2310343.7810331.6005067622155.13362459354412.17949323776520.0519010639206832
2410750.2110738.0525543023202.60934661161212.15744569770010.54567039270012
2511337.511382.6139947891284.885344435431-45.11399478912371.04140885574006
2611786.9611781.2103252723305.9242303358525.749674727684380.223580283598754
2712083.0412077.2857953009304.0626223888835.7542046990833-0.0213735503522545
2812007.7412001.8441075777232.3440776080465.89589242226916-0.823691780188378
2911745.9311739.8844309555138.9425468624646.04556904451604-1.07295750145121
3011051.5111045.2597145929-18.55342960862766.25028540714393-1.80950861620265
3111445.911439.731985205159.48151469223116.168014794931440.896647080864921
3211924.8811918.7797689900138.7507510109646.100231010035020.91088637295186
3312247.6312241.5538822784173.5183220739976.0761177216430.399532138145045
3412690.9112684.8625549436224.4898961239336.047445056395160.585756541844059
3512910.712904.6521497850223.6018630583766.047850215031-0.0102053042766483
3613202.1213196.0768914709236.4157882627146.043108529085740.147259758283031
3713654.6713687.6172392769284.183214630968-32.94723927694340.585523185737997
3813862.8213861.0096038404263.5779594104681.81039615962577-0.224104445005227
3913523.9313521.9109588416149.6679408077752.01904115836383-1.30809171207160
4014211.1714209.3019416875251.2838892605311.868058312508901.16722051432893
4114510.3514508.4928513925260.336165989971.857148607470440.103997847996357
4214289.2314287.2839104520169.3511559865961.94608954802054-1.04540852861792
4314111.8214109.8219569976103.8250624837871.99804300243684-0.75294479695948
4413086.5913084.4547606289-109.5185823291842.13523937110385-2.45160251972505
4513351.5413349.4416659797-38.76192409252032.098334020345640.81311467280819
4613747.6913745.626429493143.41405991607442.063570506907710.944362420580778
4712855.6112853.4857819718-133.34397050842.12421802815459-2.03132331885016
4812926.9312924.8165432518-94.67381570064322.113456748210980.444405919288936
4912121.9512206.1650627209-211.779732513143-84.215062720917-1.41287088696063
5011731.6511726.2662482176-261.8088085655665.3837517823917-0.550929871409648
5111639.5111634.1729791624-229.7336472822775.337020837579550.368394716670721
5212163.7812158.6113803851-87.21935824256775.168619614931991.63717218769508
5312029.5312024.3528615403-96.10752138875025.17713845970975-0.102119361652775
5411234.1811228.9001394462-228.2442321182095.27986055382809-1.51830533203426
559852.139846.71266864719-446.2686275165855.41733135281309-2.50534035565272
569709.049703.65196554225-388.9816857094535.388034457755750.65831597897221
579332.759327.36296023724-386.5835912751675.387039762760940.0275585458283186
587108.67103.09615486353-733.7865218227185.50384513647265-3.99007266202074
596691.496686.00248109009-673.9519976469625.48751890990580.687628923348309
606143.056137.56772923764-650.2372587735055.482270762360930.272535980107564

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 6802.96 & 6802.96 & 0 & 0 & 0 \tabularnewline
2 & 7132.68 & 7115.77563241975 & 21.5403700970263 & 16.9043675802509 & 0.44932039843554 \tabularnewline
3 & 7073.29 & 7056.5241675713 & 15.0618853433054 & 16.7658324286956 & -0.187157310312754 \tabularnewline
4 & 7264.5 & 7247.46592070621 & 34.5037055181225 & 17.0340792937899 & 0.400585664535751 \tabularnewline
5 & 7105.33 & 7088.55141182042 & 8.60186963447032 & 16.7785881795752 & -0.434618377625661 \tabularnewline
6 & 7218.71 & 7201.81405160398 & 24.4157005239324 & 16.8959483960158 & 0.232785747773486 \tabularnewline
7 & 7225.72 & 7208.84036689836 & 21.5761459462609 & 16.879633101641 & -0.0383911627018807 \tabularnewline
8 & 7354.25 & 7337.28731741045 & 39.9300347164757 & 16.9626825895496 & 0.234708154437045 \tabularnewline
9 & 7745.46 & 7728.2729314206 & 102.238363758226 & 17.1870685793982 & 0.768187935665514 \tabularnewline
10 & 8070.26 & 8052.95653538964 & 142.580095167837 & 17.3034646103613 & 0.485552554967699 \tabularnewline
11 & 8366.33 & 8348.96102145352 & 170.800360654781 & 17.3689785464834 & 0.334330505643150 \tabularnewline
12 & 8667.51 & 8650.09569932421 & 194.996648391566 & 17.4143006757929 & 0.283691475180607 \tabularnewline
13 & 8854.34 & 8986.2839086275 & 219.973027741281 & -131.943908627501 & 0.359635051844466 \tabularnewline
14 & 9218.1 & 9206.1084578784 & 219.945816682235 & 11.9915421216060 & -0.000272798080751261 \tabularnewline
15 & 9332.9 & 9320.84782803717 & 200.163002948292 & 12.0521719628253 & -0.228425201984916 \tabularnewline
16 & 9358.31 & 9346.17604535292 & 167.236354640652 & 12.1339546470779 & -0.379580573024516 \tabularnewline
17 & 9248.66 & 9236.42090370243 & 115.016374376586 & 12.2390962975663 & -0.601357869219266 \tabularnewline
18 & 9401.2 & 9388.97246353382 & 122.097643836741 & 12.2275364661838 & 0.0814900739143552 \tabularnewline
19 & 9652.04 & 9639.84463716831 & 146.403233070117 & 12.1953628316859 & 0.279576471329508 \tabularnewline
20 & 9957.38 & 9945.21685584504 & 176.417407958718 & 12.1631441549546 & 0.345135700576902 \tabularnewline
21 & 10110.63 & 10098.4630465251 & 172.041616032482 & 12.1669534749371 & -0.0503076431132427 \tabularnewline
22 & 10169.26 & 10157.0779212804 & 150.618258985056 & 12.1820787195792 & -0.246268095762417 \tabularnewline
23 & 10343.78 & 10331.6005067622 & 155.133624593544 & 12.1794932377652 & 0.0519010639206832 \tabularnewline
24 & 10750.21 & 10738.0525543023 & 202.609346611612 & 12.1574456977001 & 0.54567039270012 \tabularnewline
25 & 11337.5 & 11382.6139947891 & 284.885344435431 & -45.1139947891237 & 1.04140885574006 \tabularnewline
26 & 11786.96 & 11781.2103252723 & 305.924230335852 & 5.74967472768438 & 0.223580283598754 \tabularnewline
27 & 12083.04 & 12077.2857953009 & 304.062622388883 & 5.7542046990833 & -0.0213735503522545 \tabularnewline
28 & 12007.74 & 12001.8441075777 & 232.344077608046 & 5.89589242226916 & -0.823691780188378 \tabularnewline
29 & 11745.93 & 11739.8844309555 & 138.942546862464 & 6.04556904451604 & -1.07295750145121 \tabularnewline
30 & 11051.51 & 11045.2597145929 & -18.5534296086276 & 6.25028540714393 & -1.80950861620265 \tabularnewline
31 & 11445.9 & 11439.7319852051 & 59.4815146922311 & 6.16801479493144 & 0.896647080864921 \tabularnewline
32 & 11924.88 & 11918.7797689900 & 138.750751010964 & 6.10023101003502 & 0.91088637295186 \tabularnewline
33 & 12247.63 & 12241.5538822784 & 173.518322073997 & 6.076117721643 & 0.399532138145045 \tabularnewline
34 & 12690.91 & 12684.8625549436 & 224.489896123933 & 6.04744505639516 & 0.585756541844059 \tabularnewline
35 & 12910.7 & 12904.6521497850 & 223.601863058376 & 6.047850215031 & -0.0102053042766483 \tabularnewline
36 & 13202.12 & 13196.0768914709 & 236.415788262714 & 6.04310852908574 & 0.147259758283031 \tabularnewline
37 & 13654.67 & 13687.6172392769 & 284.183214630968 & -32.9472392769434 & 0.585523185737997 \tabularnewline
38 & 13862.82 & 13861.0096038404 & 263.577959410468 & 1.81039615962577 & -0.224104445005227 \tabularnewline
39 & 13523.93 & 13521.9109588416 & 149.667940807775 & 2.01904115836383 & -1.30809171207160 \tabularnewline
40 & 14211.17 & 14209.3019416875 & 251.283889260531 & 1.86805831250890 & 1.16722051432893 \tabularnewline
41 & 14510.35 & 14508.4928513925 & 260.33616598997 & 1.85714860747044 & 0.103997847996357 \tabularnewline
42 & 14289.23 & 14287.2839104520 & 169.351155986596 & 1.94608954802054 & -1.04540852861792 \tabularnewline
43 & 14111.82 & 14109.8219569976 & 103.825062483787 & 1.99804300243684 & -0.75294479695948 \tabularnewline
44 & 13086.59 & 13084.4547606289 & -109.518582329184 & 2.13523937110385 & -2.45160251972505 \tabularnewline
45 & 13351.54 & 13349.4416659797 & -38.7619240925203 & 2.09833402034564 & 0.81311467280819 \tabularnewline
46 & 13747.69 & 13745.6264294931 & 43.4140599160744 & 2.06357050690771 & 0.944362420580778 \tabularnewline
47 & 12855.61 & 12853.4857819718 & -133.3439705084 & 2.12421802815459 & -2.03132331885016 \tabularnewline
48 & 12926.93 & 12924.8165432518 & -94.6738157006432 & 2.11345674821098 & 0.444405919288936 \tabularnewline
49 & 12121.95 & 12206.1650627209 & -211.779732513143 & -84.215062720917 & -1.41287088696063 \tabularnewline
50 & 11731.65 & 11726.2662482176 & -261.808808565566 & 5.3837517823917 & -0.550929871409648 \tabularnewline
51 & 11639.51 & 11634.1729791624 & -229.733647282277 & 5.33702083757955 & 0.368394716670721 \tabularnewline
52 & 12163.78 & 12158.6113803851 & -87.2193582425677 & 5.16861961493199 & 1.63717218769508 \tabularnewline
53 & 12029.53 & 12024.3528615403 & -96.1075213887502 & 5.17713845970975 & -0.102119361652775 \tabularnewline
54 & 11234.18 & 11228.9001394462 & -228.244232118209 & 5.27986055382809 & -1.51830533203426 \tabularnewline
55 & 9852.13 & 9846.71266864719 & -446.268627516585 & 5.41733135281309 & -2.50534035565272 \tabularnewline
56 & 9709.04 & 9703.65196554225 & -388.981685709453 & 5.38803445775575 & 0.65831597897221 \tabularnewline
57 & 9332.75 & 9327.36296023724 & -386.583591275167 & 5.38703976276094 & 0.0275585458283186 \tabularnewline
58 & 7108.6 & 7103.09615486353 & -733.786521822718 & 5.50384513647265 & -3.99007266202074 \tabularnewline
59 & 6691.49 & 6686.00248109009 & -673.951997646962 & 5.4875189099058 & 0.687628923348309 \tabularnewline
60 & 6143.05 & 6137.56772923764 & -650.237258773505 & 5.48227076236093 & 0.272535980107564 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64110&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]6802.96[/C][C]6802.96[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]7132.68[/C][C]7115.77563241975[/C][C]21.5403700970263[/C][C]16.9043675802509[/C][C]0.44932039843554[/C][/ROW]
[ROW][C]3[/C][C]7073.29[/C][C]7056.5241675713[/C][C]15.0618853433054[/C][C]16.7658324286956[/C][C]-0.187157310312754[/C][/ROW]
[ROW][C]4[/C][C]7264.5[/C][C]7247.46592070621[/C][C]34.5037055181225[/C][C]17.0340792937899[/C][C]0.400585664535751[/C][/ROW]
[ROW][C]5[/C][C]7105.33[/C][C]7088.55141182042[/C][C]8.60186963447032[/C][C]16.7785881795752[/C][C]-0.434618377625661[/C][/ROW]
[ROW][C]6[/C][C]7218.71[/C][C]7201.81405160398[/C][C]24.4157005239324[/C][C]16.8959483960158[/C][C]0.232785747773486[/C][/ROW]
[ROW][C]7[/C][C]7225.72[/C][C]7208.84036689836[/C][C]21.5761459462609[/C][C]16.879633101641[/C][C]-0.0383911627018807[/C][/ROW]
[ROW][C]8[/C][C]7354.25[/C][C]7337.28731741045[/C][C]39.9300347164757[/C][C]16.9626825895496[/C][C]0.234708154437045[/C][/ROW]
[ROW][C]9[/C][C]7745.46[/C][C]7728.2729314206[/C][C]102.238363758226[/C][C]17.1870685793982[/C][C]0.768187935665514[/C][/ROW]
[ROW][C]10[/C][C]8070.26[/C][C]8052.95653538964[/C][C]142.580095167837[/C][C]17.3034646103613[/C][C]0.485552554967699[/C][/ROW]
[ROW][C]11[/C][C]8366.33[/C][C]8348.96102145352[/C][C]170.800360654781[/C][C]17.3689785464834[/C][C]0.334330505643150[/C][/ROW]
[ROW][C]12[/C][C]8667.51[/C][C]8650.09569932421[/C][C]194.996648391566[/C][C]17.4143006757929[/C][C]0.283691475180607[/C][/ROW]
[ROW][C]13[/C][C]8854.34[/C][C]8986.2839086275[/C][C]219.973027741281[/C][C]-131.943908627501[/C][C]0.359635051844466[/C][/ROW]
[ROW][C]14[/C][C]9218.1[/C][C]9206.1084578784[/C][C]219.945816682235[/C][C]11.9915421216060[/C][C]-0.000272798080751261[/C][/ROW]
[ROW][C]15[/C][C]9332.9[/C][C]9320.84782803717[/C][C]200.163002948292[/C][C]12.0521719628253[/C][C]-0.228425201984916[/C][/ROW]
[ROW][C]16[/C][C]9358.31[/C][C]9346.17604535292[/C][C]167.236354640652[/C][C]12.1339546470779[/C][C]-0.379580573024516[/C][/ROW]
[ROW][C]17[/C][C]9248.66[/C][C]9236.42090370243[/C][C]115.016374376586[/C][C]12.2390962975663[/C][C]-0.601357869219266[/C][/ROW]
[ROW][C]18[/C][C]9401.2[/C][C]9388.97246353382[/C][C]122.097643836741[/C][C]12.2275364661838[/C][C]0.0814900739143552[/C][/ROW]
[ROW][C]19[/C][C]9652.04[/C][C]9639.84463716831[/C][C]146.403233070117[/C][C]12.1953628316859[/C][C]0.279576471329508[/C][/ROW]
[ROW][C]20[/C][C]9957.38[/C][C]9945.21685584504[/C][C]176.417407958718[/C][C]12.1631441549546[/C][C]0.345135700576902[/C][/ROW]
[ROW][C]21[/C][C]10110.63[/C][C]10098.4630465251[/C][C]172.041616032482[/C][C]12.1669534749371[/C][C]-0.0503076431132427[/C][/ROW]
[ROW][C]22[/C][C]10169.26[/C][C]10157.0779212804[/C][C]150.618258985056[/C][C]12.1820787195792[/C][C]-0.246268095762417[/C][/ROW]
[ROW][C]23[/C][C]10343.78[/C][C]10331.6005067622[/C][C]155.133624593544[/C][C]12.1794932377652[/C][C]0.0519010639206832[/C][/ROW]
[ROW][C]24[/C][C]10750.21[/C][C]10738.0525543023[/C][C]202.609346611612[/C][C]12.1574456977001[/C][C]0.54567039270012[/C][/ROW]
[ROW][C]25[/C][C]11337.5[/C][C]11382.6139947891[/C][C]284.885344435431[/C][C]-45.1139947891237[/C][C]1.04140885574006[/C][/ROW]
[ROW][C]26[/C][C]11786.96[/C][C]11781.2103252723[/C][C]305.924230335852[/C][C]5.74967472768438[/C][C]0.223580283598754[/C][/ROW]
[ROW][C]27[/C][C]12083.04[/C][C]12077.2857953009[/C][C]304.062622388883[/C][C]5.7542046990833[/C][C]-0.0213735503522545[/C][/ROW]
[ROW][C]28[/C][C]12007.74[/C][C]12001.8441075777[/C][C]232.344077608046[/C][C]5.89589242226916[/C][C]-0.823691780188378[/C][/ROW]
[ROW][C]29[/C][C]11745.93[/C][C]11739.8844309555[/C][C]138.942546862464[/C][C]6.04556904451604[/C][C]-1.07295750145121[/C][/ROW]
[ROW][C]30[/C][C]11051.51[/C][C]11045.2597145929[/C][C]-18.5534296086276[/C][C]6.25028540714393[/C][C]-1.80950861620265[/C][/ROW]
[ROW][C]31[/C][C]11445.9[/C][C]11439.7319852051[/C][C]59.4815146922311[/C][C]6.16801479493144[/C][C]0.896647080864921[/C][/ROW]
[ROW][C]32[/C][C]11924.88[/C][C]11918.7797689900[/C][C]138.750751010964[/C][C]6.10023101003502[/C][C]0.91088637295186[/C][/ROW]
[ROW][C]33[/C][C]12247.63[/C][C]12241.5538822784[/C][C]173.518322073997[/C][C]6.076117721643[/C][C]0.399532138145045[/C][/ROW]
[ROW][C]34[/C][C]12690.91[/C][C]12684.8625549436[/C][C]224.489896123933[/C][C]6.04744505639516[/C][C]0.585756541844059[/C][/ROW]
[ROW][C]35[/C][C]12910.7[/C][C]12904.6521497850[/C][C]223.601863058376[/C][C]6.047850215031[/C][C]-0.0102053042766483[/C][/ROW]
[ROW][C]36[/C][C]13202.12[/C][C]13196.0768914709[/C][C]236.415788262714[/C][C]6.04310852908574[/C][C]0.147259758283031[/C][/ROW]
[ROW][C]37[/C][C]13654.67[/C][C]13687.6172392769[/C][C]284.183214630968[/C][C]-32.9472392769434[/C][C]0.585523185737997[/C][/ROW]
[ROW][C]38[/C][C]13862.82[/C][C]13861.0096038404[/C][C]263.577959410468[/C][C]1.81039615962577[/C][C]-0.224104445005227[/C][/ROW]
[ROW][C]39[/C][C]13523.93[/C][C]13521.9109588416[/C][C]149.667940807775[/C][C]2.01904115836383[/C][C]-1.30809171207160[/C][/ROW]
[ROW][C]40[/C][C]14211.17[/C][C]14209.3019416875[/C][C]251.283889260531[/C][C]1.86805831250890[/C][C]1.16722051432893[/C][/ROW]
[ROW][C]41[/C][C]14510.35[/C][C]14508.4928513925[/C][C]260.33616598997[/C][C]1.85714860747044[/C][C]0.103997847996357[/C][/ROW]
[ROW][C]42[/C][C]14289.23[/C][C]14287.2839104520[/C][C]169.351155986596[/C][C]1.94608954802054[/C][C]-1.04540852861792[/C][/ROW]
[ROW][C]43[/C][C]14111.82[/C][C]14109.8219569976[/C][C]103.825062483787[/C][C]1.99804300243684[/C][C]-0.75294479695948[/C][/ROW]
[ROW][C]44[/C][C]13086.59[/C][C]13084.4547606289[/C][C]-109.518582329184[/C][C]2.13523937110385[/C][C]-2.45160251972505[/C][/ROW]
[ROW][C]45[/C][C]13351.54[/C][C]13349.4416659797[/C][C]-38.7619240925203[/C][C]2.09833402034564[/C][C]0.81311467280819[/C][/ROW]
[ROW][C]46[/C][C]13747.69[/C][C]13745.6264294931[/C][C]43.4140599160744[/C][C]2.06357050690771[/C][C]0.944362420580778[/C][/ROW]
[ROW][C]47[/C][C]12855.61[/C][C]12853.4857819718[/C][C]-133.3439705084[/C][C]2.12421802815459[/C][C]-2.03132331885016[/C][/ROW]
[ROW][C]48[/C][C]12926.93[/C][C]12924.8165432518[/C][C]-94.6738157006432[/C][C]2.11345674821098[/C][C]0.444405919288936[/C][/ROW]
[ROW][C]49[/C][C]12121.95[/C][C]12206.1650627209[/C][C]-211.779732513143[/C][C]-84.215062720917[/C][C]-1.41287088696063[/C][/ROW]
[ROW][C]50[/C][C]11731.65[/C][C]11726.2662482176[/C][C]-261.808808565566[/C][C]5.3837517823917[/C][C]-0.550929871409648[/C][/ROW]
[ROW][C]51[/C][C]11639.51[/C][C]11634.1729791624[/C][C]-229.733647282277[/C][C]5.33702083757955[/C][C]0.368394716670721[/C][/ROW]
[ROW][C]52[/C][C]12163.78[/C][C]12158.6113803851[/C][C]-87.2193582425677[/C][C]5.16861961493199[/C][C]1.63717218769508[/C][/ROW]
[ROW][C]53[/C][C]12029.53[/C][C]12024.3528615403[/C][C]-96.1075213887502[/C][C]5.17713845970975[/C][C]-0.102119361652775[/C][/ROW]
[ROW][C]54[/C][C]11234.18[/C][C]11228.9001394462[/C][C]-228.244232118209[/C][C]5.27986055382809[/C][C]-1.51830533203426[/C][/ROW]
[ROW][C]55[/C][C]9852.13[/C][C]9846.71266864719[/C][C]-446.268627516585[/C][C]5.41733135281309[/C][C]-2.50534035565272[/C][/ROW]
[ROW][C]56[/C][C]9709.04[/C][C]9703.65196554225[/C][C]-388.981685709453[/C][C]5.38803445775575[/C][C]0.65831597897221[/C][/ROW]
[ROW][C]57[/C][C]9332.75[/C][C]9327.36296023724[/C][C]-386.583591275167[/C][C]5.38703976276094[/C][C]0.0275585458283186[/C][/ROW]
[ROW][C]58[/C][C]7108.6[/C][C]7103.09615486353[/C][C]-733.786521822718[/C][C]5.50384513647265[/C][C]-3.99007266202074[/C][/ROW]
[ROW][C]59[/C][C]6691.49[/C][C]6686.00248109009[/C][C]-673.951997646962[/C][C]5.4875189099058[/C][C]0.687628923348309[/C][/ROW]
[ROW][C]60[/C][C]6143.05[/C][C]6137.56772923764[/C][C]-650.237258773505[/C][C]5.48227076236093[/C][C]0.272535980107564[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64110&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64110&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
16802.966802.96000
27132.687115.7756324197521.540370097026316.90436758025090.44932039843554
37073.297056.524167571315.061885343305416.7658324286956-0.187157310312754
47264.57247.4659207062134.503705518122517.03407929378990.400585664535751
57105.337088.551411820428.6018696344703216.7785881795752-0.434618377625661
67218.717201.8140516039824.415700523932416.89594839601580.232785747773486
77225.727208.8403668983621.576145946260916.879633101641-0.0383911627018807
87354.257337.2873174104539.930034716475716.96268258954960.234708154437045
97745.467728.2729314206102.23836375822617.18706857939820.768187935665514
108070.268052.95653538964142.58009516783717.30346461036130.485552554967699
118366.338348.96102145352170.80036065478117.36897854648340.334330505643150
128667.518650.09569932421194.99664839156617.41430067579290.283691475180607
138854.348986.2839086275219.973027741281-131.9439086275010.359635051844466
149218.19206.1084578784219.94581668223511.9915421216060-0.000272798080751261
159332.99320.84782803717200.16300294829212.0521719628253-0.228425201984916
169358.319346.17604535292167.23635464065212.1339546470779-0.379580573024516
179248.669236.42090370243115.01637437658612.2390962975663-0.601357869219266
189401.29388.97246353382122.09764383674112.22753646618380.0814900739143552
199652.049639.84463716831146.40323307011712.19536283168590.279576471329508
209957.389945.21685584504176.41740795871812.16314415495460.345135700576902
2110110.6310098.4630465251172.04161603248212.1669534749371-0.0503076431132427
2210169.2610157.0779212804150.61825898505612.1820787195792-0.246268095762417
2310343.7810331.6005067622155.13362459354412.17949323776520.0519010639206832
2410750.2110738.0525543023202.60934661161212.15744569770010.54567039270012
2511337.511382.6139947891284.885344435431-45.11399478912371.04140885574006
2611786.9611781.2103252723305.9242303358525.749674727684380.223580283598754
2712083.0412077.2857953009304.0626223888835.7542046990833-0.0213735503522545
2812007.7412001.8441075777232.3440776080465.89589242226916-0.823691780188378
2911745.9311739.8844309555138.9425468624646.04556904451604-1.07295750145121
3011051.5111045.2597145929-18.55342960862766.25028540714393-1.80950861620265
3111445.911439.731985205159.48151469223116.168014794931440.896647080864921
3211924.8811918.7797689900138.7507510109646.100231010035020.91088637295186
3312247.6312241.5538822784173.5183220739976.0761177216430.399532138145045
3412690.9112684.8625549436224.4898961239336.047445056395160.585756541844059
3512910.712904.6521497850223.6018630583766.047850215031-0.0102053042766483
3613202.1213196.0768914709236.4157882627146.043108529085740.147259758283031
3713654.6713687.6172392769284.183214630968-32.94723927694340.585523185737997
3813862.8213861.0096038404263.5779594104681.81039615962577-0.224104445005227
3913523.9313521.9109588416149.6679408077752.01904115836383-1.30809171207160
4014211.1714209.3019416875251.2838892605311.868058312508901.16722051432893
4114510.3514508.4928513925260.336165989971.857148607470440.103997847996357
4214289.2314287.2839104520169.3511559865961.94608954802054-1.04540852861792
4314111.8214109.8219569976103.8250624837871.99804300243684-0.75294479695948
4413086.5913084.4547606289-109.5185823291842.13523937110385-2.45160251972505
4513351.5413349.4416659797-38.76192409252032.098334020345640.81311467280819
4613747.6913745.626429493143.41405991607442.063570506907710.944362420580778
4712855.6112853.4857819718-133.34397050842.12421802815459-2.03132331885016
4812926.9312924.8165432518-94.67381570064322.113456748210980.444405919288936
4912121.9512206.1650627209-211.779732513143-84.215062720917-1.41287088696063
5011731.6511726.2662482176-261.8088085655665.3837517823917-0.550929871409648
5111639.5111634.1729791624-229.7336472822775.337020837579550.368394716670721
5212163.7812158.6113803851-87.21935824256775.168619614931991.63717218769508
5312029.5312024.3528615403-96.10752138875025.17713845970975-0.102119361652775
5411234.1811228.9001394462-228.2442321182095.27986055382809-1.51830533203426
559852.139846.71266864719-446.2686275165855.41733135281309-2.50534035565272
569709.049703.65196554225-388.9816857094535.388034457755750.65831597897221
579332.759327.36296023724-386.5835912751675.387039762760940.0275585458283186
587108.67103.09615486353-733.7865218227185.50384513647265-3.99007266202074
596691.496686.00248109009-673.9519976469625.48751890990580.687628923348309
606143.056137.56772923764-650.2372587735055.482270762360930.272535980107564



Parameters (Session):
par1 = Aandelenkoers ; par2 = belgostat ; par3 = euronext brussel ;
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')