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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 computationMon, 20 Dec 2010 13:33:30 +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/20/t12928518939uzzi1wlsblmv6s.htm/, Retrieved Fri, 03 May 2024 19:00:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112936, Retrieved Fri, 03 May 2024 19:00:23 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [STSM - failliseme...] [2010-12-20 13:33:30] [f3d6336ce664ba129edd250394d444d3] [Current]
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Dataseries X:
48
49
59
56
47
56
50
54
79
50
54
56
50
46
47
43
52
48
36
41
34
37
37
34
55
37
27
38
43
26
32
29
41
55
50
30
35
29
22
39
24
38
30
31
39
33
57
49
74
74
115
67
51
114
70
73
77
67
60
73




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=112936&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=112936&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112936&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
14848000
24948.48355531416140.04508897252755770.05741944415558910.066091969717463
35953.26434946248290.3603078438673070.5803830012784920.715462380994163
45654.54693220695480.4057588392897830.3622937660806550.147832922569044
54751.36471973788230.2645501800681740.0570801987295856-0.592327924025418
65653.34672565434560.3222725683735610.4901581125502650.287976792902266
75051.99234098500310.2716237514460460.144767701651258-0.283538684676336
85452.86721279929980.2885916093312120.3586761345831250.102501088069710
97963.95411663134480.577922050484041.135152168319131.83975286392058
105058.26455426147760.415674698647807-0.172007923441882-1.06947612345479
115456.47384472536750.3600142820216180.379034307642431-0.376837534559955
125656.30990413255880.3470661124098890.368183901821673-0.0895373068586133
135055.87002455084630.423396028306244-4.67556943058289-0.182115530955458
144651.3181670871120.229352757294846-0.276639463057748-0.711172682161763
154749.28058398047310.1570699768540080.315594236406929-0.356650752245261
164346.54524844305700.0845980918405958-0.00548886692811479-0.47824852972098
175248.90062525860440.1322969089022030.2414956442470690.383363870556341
184848.47835465531180.1219242415839280.228431400695907-0.0944966010247166
193643.3418048720080.0303823272467762-0.59956109984278-0.899954040282278
204142.3972168995780.0141162088807799-0.143414480178913-0.167215267612003
213438.5407702919013-0.04890543128520720.44410939004563-0.664515984663655
223738.0077627533247-0.0566821835261305-0.383849328360152-0.0831515438298939
233737.5300603118559-0.063392392112890.0127225527401368-0.0723288157461003
243435.9655273328857-0.0860159433976338-0.0279746051358467-0.257963606941926
255542.2223784964475-0.3161940263722013.993852844341661.25863970147881
263740.0930989532443-0.363962033787098-1.05887172883997-0.281449568608998
272734.2618258271655-0.494903159563017-0.798999419331397-0.887128446007918
283835.7183980114887-0.456283143823238-0.1237985756501310.326852063380065
294338.639408518804-0.3981281546337610.1204751548543760.573463845087279
302633.2514191353127-0.476859388042633-0.935463211082794-0.85237103158677
313232.7649152503695-0.477004006568099-0.752661380197807-0.0016519921505009
322931.0845224179931-0.494554749528413-0.552813916884883-0.206387555162543
334134.8360934615138-0.4334758297184960.7548949742812660.728638355886549
345543.2995897242132-0.3062264503441720.3631383590031661.52703407745410
355046.1940842554349-0.260666687655523-0.2732705873715780.549373760889409
363039.7485793548057-0.340508812460426-1.85367823584773-1.06194629241181
373536.0225184471337-0.2878562227064593.47929100434421-0.634153899121415
382932.972665987874-0.345819504485971-0.747464534478297-0.444635919758805
392228.4348817274144-0.433083749095241-1.43193087036311-0.689235293669122
403932.5762165334568-0.350389264977690.7990955875149810.769539450181696
412428.6966790218554-0.407672498944957-0.29646923692442-0.599881827853066
423832.5028141707185-0.3436725407081730.2111848642553370.719460077012531
433031.5980734695633-0.351881782199347-0.892308282386911-0.0959844972251893
443131.4761450543421-0.348581511199892-0.76576246084180.0393732685344838
453934.3520853799691-0.3027015078048190.58459315138740.552306577012687
463333.6694266640623-0.308091597595351-0.190541108537781-0.0650870620941148
475742.8881535856428-0.1739500104327602.102979350003921.63193254999632
484946.1674532643648-0.130653447497648-1.527357068635020.591751687622329
497455.2484107624321-0.1726740101625546.812105551459371.67212696377326
507463.4698221324493-0.01840024634922050.5380951790170071.37828680623751
5111585.93384530887010.4147547188320382.134962318586693.72386248104591
526778.53606965049650.278416861614145-1.96803720545403-1.31659649822396
535167.49294576422760.0965311869526432-2.47124245860172-1.92395211972329
5411485.96117300993360.3768491019659975.173819322817733.13315429375489
557080.52586215634550.290666727290494-3.2766682016665-0.992720931796824
567377.85492355688110.247378627042241-1.15761731004758-0.506167168805854
577777.23598340124120.2347934984723840.845953011599004-0.148098597441190
586773.60765664166460.178817438750667-1.78240821735651-0.660446221293281
596067.32311333803310.08623383752580850.751131174168254-1.10492777802117
607370.54955658828650.126376703068323-1.477465184842960.537001124713946

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 48 & 48 & 0 & 0 & 0 \tabularnewline
2 & 49 & 48.4835553141614 & 0.0450889725275577 & 0.0574194441555891 & 0.066091969717463 \tabularnewline
3 & 59 & 53.2643494624829 & 0.360307843867307 & 0.580383001278492 & 0.715462380994163 \tabularnewline
4 & 56 & 54.5469322069548 & 0.405758839289783 & 0.362293766080655 & 0.147832922569044 \tabularnewline
5 & 47 & 51.3647197378823 & 0.264550180068174 & 0.0570801987295856 & -0.592327924025418 \tabularnewline
6 & 56 & 53.3467256543456 & 0.322272568373561 & 0.490158112550265 & 0.287976792902266 \tabularnewline
7 & 50 & 51.9923409850031 & 0.271623751446046 & 0.144767701651258 & -0.283538684676336 \tabularnewline
8 & 54 & 52.8672127992998 & 0.288591609331212 & 0.358676134583125 & 0.102501088069710 \tabularnewline
9 & 79 & 63.9541166313448 & 0.57792205048404 & 1.13515216831913 & 1.83975286392058 \tabularnewline
10 & 50 & 58.2645542614776 & 0.415674698647807 & -0.172007923441882 & -1.06947612345479 \tabularnewline
11 & 54 & 56.4738447253675 & 0.360014282021618 & 0.379034307642431 & -0.376837534559955 \tabularnewline
12 & 56 & 56.3099041325588 & 0.347066112409889 & 0.368183901821673 & -0.0895373068586133 \tabularnewline
13 & 50 & 55.8700245508463 & 0.423396028306244 & -4.67556943058289 & -0.182115530955458 \tabularnewline
14 & 46 & 51.318167087112 & 0.229352757294846 & -0.276639463057748 & -0.711172682161763 \tabularnewline
15 & 47 & 49.2805839804731 & 0.157069976854008 & 0.315594236406929 & -0.356650752245261 \tabularnewline
16 & 43 & 46.5452484430570 & 0.0845980918405958 & -0.00548886692811479 & -0.47824852972098 \tabularnewline
17 & 52 & 48.9006252586044 & 0.132296908902203 & 0.241495644247069 & 0.383363870556341 \tabularnewline
18 & 48 & 48.4783546553118 & 0.121924241583928 & 0.228431400695907 & -0.0944966010247166 \tabularnewline
19 & 36 & 43.341804872008 & 0.0303823272467762 & -0.59956109984278 & -0.899954040282278 \tabularnewline
20 & 41 & 42.397216899578 & 0.0141162088807799 & -0.143414480178913 & -0.167215267612003 \tabularnewline
21 & 34 & 38.5407702919013 & -0.0489054312852072 & 0.44410939004563 & -0.664515984663655 \tabularnewline
22 & 37 & 38.0077627533247 & -0.0566821835261305 & -0.383849328360152 & -0.0831515438298939 \tabularnewline
23 & 37 & 37.5300603118559 & -0.06339239211289 & 0.0127225527401368 & -0.0723288157461003 \tabularnewline
24 & 34 & 35.9655273328857 & -0.0860159433976338 & -0.0279746051358467 & -0.257963606941926 \tabularnewline
25 & 55 & 42.2223784964475 & -0.316194026372201 & 3.99385284434166 & 1.25863970147881 \tabularnewline
26 & 37 & 40.0930989532443 & -0.363962033787098 & -1.05887172883997 & -0.281449568608998 \tabularnewline
27 & 27 & 34.2618258271655 & -0.494903159563017 & -0.798999419331397 & -0.887128446007918 \tabularnewline
28 & 38 & 35.7183980114887 & -0.456283143823238 & -0.123798575650131 & 0.326852063380065 \tabularnewline
29 & 43 & 38.639408518804 & -0.398128154633761 & 0.120475154854376 & 0.573463845087279 \tabularnewline
30 & 26 & 33.2514191353127 & -0.476859388042633 & -0.935463211082794 & -0.85237103158677 \tabularnewline
31 & 32 & 32.7649152503695 & -0.477004006568099 & -0.752661380197807 & -0.0016519921505009 \tabularnewline
32 & 29 & 31.0845224179931 & -0.494554749528413 & -0.552813916884883 & -0.206387555162543 \tabularnewline
33 & 41 & 34.8360934615138 & -0.433475829718496 & 0.754894974281266 & 0.728638355886549 \tabularnewline
34 & 55 & 43.2995897242132 & -0.306226450344172 & 0.363138359003166 & 1.52703407745410 \tabularnewline
35 & 50 & 46.1940842554349 & -0.260666687655523 & -0.273270587371578 & 0.549373760889409 \tabularnewline
36 & 30 & 39.7485793548057 & -0.340508812460426 & -1.85367823584773 & -1.06194629241181 \tabularnewline
37 & 35 & 36.0225184471337 & -0.287856222706459 & 3.47929100434421 & -0.634153899121415 \tabularnewline
38 & 29 & 32.972665987874 & -0.345819504485971 & -0.747464534478297 & -0.444635919758805 \tabularnewline
39 & 22 & 28.4348817274144 & -0.433083749095241 & -1.43193087036311 & -0.689235293669122 \tabularnewline
40 & 39 & 32.5762165334568 & -0.35038926497769 & 0.799095587514981 & 0.769539450181696 \tabularnewline
41 & 24 & 28.6966790218554 & -0.407672498944957 & -0.29646923692442 & -0.599881827853066 \tabularnewline
42 & 38 & 32.5028141707185 & -0.343672540708173 & 0.211184864255337 & 0.719460077012531 \tabularnewline
43 & 30 & 31.5980734695633 & -0.351881782199347 & -0.892308282386911 & -0.0959844972251893 \tabularnewline
44 & 31 & 31.4761450543421 & -0.348581511199892 & -0.7657624608418 & 0.0393732685344838 \tabularnewline
45 & 39 & 34.3520853799691 & -0.302701507804819 & 0.5845931513874 & 0.552306577012687 \tabularnewline
46 & 33 & 33.6694266640623 & -0.308091597595351 & -0.190541108537781 & -0.0650870620941148 \tabularnewline
47 & 57 & 42.8881535856428 & -0.173950010432760 & 2.10297935000392 & 1.63193254999632 \tabularnewline
48 & 49 & 46.1674532643648 & -0.130653447497648 & -1.52735706863502 & 0.591751687622329 \tabularnewline
49 & 74 & 55.2484107624321 & -0.172674010162554 & 6.81210555145937 & 1.67212696377326 \tabularnewline
50 & 74 & 63.4698221324493 & -0.0184002463492205 & 0.538095179017007 & 1.37828680623751 \tabularnewline
51 & 115 & 85.9338453088701 & 0.414754718832038 & 2.13496231858669 & 3.72386248104591 \tabularnewline
52 & 67 & 78.5360696504965 & 0.278416861614145 & -1.96803720545403 & -1.31659649822396 \tabularnewline
53 & 51 & 67.4929457642276 & 0.0965311869526432 & -2.47124245860172 & -1.92395211972329 \tabularnewline
54 & 114 & 85.9611730099336 & 0.376849101965997 & 5.17381932281773 & 3.13315429375489 \tabularnewline
55 & 70 & 80.5258621563455 & 0.290666727290494 & -3.2766682016665 & -0.992720931796824 \tabularnewline
56 & 73 & 77.8549235568811 & 0.247378627042241 & -1.15761731004758 & -0.506167168805854 \tabularnewline
57 & 77 & 77.2359834012412 & 0.234793498472384 & 0.845953011599004 & -0.148098597441190 \tabularnewline
58 & 67 & 73.6076566416646 & 0.178817438750667 & -1.78240821735651 & -0.660446221293281 \tabularnewline
59 & 60 & 67.3231133380331 & 0.0862338375258085 & 0.751131174168254 & -1.10492777802117 \tabularnewline
60 & 73 & 70.5495565882865 & 0.126376703068323 & -1.47746518484296 & 0.537001124713946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112936&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]48[/C][C]48[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]49[/C][C]48.4835553141614[/C][C]0.0450889725275577[/C][C]0.0574194441555891[/C][C]0.066091969717463[/C][/ROW]
[ROW][C]3[/C][C]59[/C][C]53.2643494624829[/C][C]0.360307843867307[/C][C]0.580383001278492[/C][C]0.715462380994163[/C][/ROW]
[ROW][C]4[/C][C]56[/C][C]54.5469322069548[/C][C]0.405758839289783[/C][C]0.362293766080655[/C][C]0.147832922569044[/C][/ROW]
[ROW][C]5[/C][C]47[/C][C]51.3647197378823[/C][C]0.264550180068174[/C][C]0.0570801987295856[/C][C]-0.592327924025418[/C][/ROW]
[ROW][C]6[/C][C]56[/C][C]53.3467256543456[/C][C]0.322272568373561[/C][C]0.490158112550265[/C][C]0.287976792902266[/C][/ROW]
[ROW][C]7[/C][C]50[/C][C]51.9923409850031[/C][C]0.271623751446046[/C][C]0.144767701651258[/C][C]-0.283538684676336[/C][/ROW]
[ROW][C]8[/C][C]54[/C][C]52.8672127992998[/C][C]0.288591609331212[/C][C]0.358676134583125[/C][C]0.102501088069710[/C][/ROW]
[ROW][C]9[/C][C]79[/C][C]63.9541166313448[/C][C]0.57792205048404[/C][C]1.13515216831913[/C][C]1.83975286392058[/C][/ROW]
[ROW][C]10[/C][C]50[/C][C]58.2645542614776[/C][C]0.415674698647807[/C][C]-0.172007923441882[/C][C]-1.06947612345479[/C][/ROW]
[ROW][C]11[/C][C]54[/C][C]56.4738447253675[/C][C]0.360014282021618[/C][C]0.379034307642431[/C][C]-0.376837534559955[/C][/ROW]
[ROW][C]12[/C][C]56[/C][C]56.3099041325588[/C][C]0.347066112409889[/C][C]0.368183901821673[/C][C]-0.0895373068586133[/C][/ROW]
[ROW][C]13[/C][C]50[/C][C]55.8700245508463[/C][C]0.423396028306244[/C][C]-4.67556943058289[/C][C]-0.182115530955458[/C][/ROW]
[ROW][C]14[/C][C]46[/C][C]51.318167087112[/C][C]0.229352757294846[/C][C]-0.276639463057748[/C][C]-0.711172682161763[/C][/ROW]
[ROW][C]15[/C][C]47[/C][C]49.2805839804731[/C][C]0.157069976854008[/C][C]0.315594236406929[/C][C]-0.356650752245261[/C][/ROW]
[ROW][C]16[/C][C]43[/C][C]46.5452484430570[/C][C]0.0845980918405958[/C][C]-0.00548886692811479[/C][C]-0.47824852972098[/C][/ROW]
[ROW][C]17[/C][C]52[/C][C]48.9006252586044[/C][C]0.132296908902203[/C][C]0.241495644247069[/C][C]0.383363870556341[/C][/ROW]
[ROW][C]18[/C][C]48[/C][C]48.4783546553118[/C][C]0.121924241583928[/C][C]0.228431400695907[/C][C]-0.0944966010247166[/C][/ROW]
[ROW][C]19[/C][C]36[/C][C]43.341804872008[/C][C]0.0303823272467762[/C][C]-0.59956109984278[/C][C]-0.899954040282278[/C][/ROW]
[ROW][C]20[/C][C]41[/C][C]42.397216899578[/C][C]0.0141162088807799[/C][C]-0.143414480178913[/C][C]-0.167215267612003[/C][/ROW]
[ROW][C]21[/C][C]34[/C][C]38.5407702919013[/C][C]-0.0489054312852072[/C][C]0.44410939004563[/C][C]-0.664515984663655[/C][/ROW]
[ROW][C]22[/C][C]37[/C][C]38.0077627533247[/C][C]-0.0566821835261305[/C][C]-0.383849328360152[/C][C]-0.0831515438298939[/C][/ROW]
[ROW][C]23[/C][C]37[/C][C]37.5300603118559[/C][C]-0.06339239211289[/C][C]0.0127225527401368[/C][C]-0.0723288157461003[/C][/ROW]
[ROW][C]24[/C][C]34[/C][C]35.9655273328857[/C][C]-0.0860159433976338[/C][C]-0.0279746051358467[/C][C]-0.257963606941926[/C][/ROW]
[ROW][C]25[/C][C]55[/C][C]42.2223784964475[/C][C]-0.316194026372201[/C][C]3.99385284434166[/C][C]1.25863970147881[/C][/ROW]
[ROW][C]26[/C][C]37[/C][C]40.0930989532443[/C][C]-0.363962033787098[/C][C]-1.05887172883997[/C][C]-0.281449568608998[/C][/ROW]
[ROW][C]27[/C][C]27[/C][C]34.2618258271655[/C][C]-0.494903159563017[/C][C]-0.798999419331397[/C][C]-0.887128446007918[/C][/ROW]
[ROW][C]28[/C][C]38[/C][C]35.7183980114887[/C][C]-0.456283143823238[/C][C]-0.123798575650131[/C][C]0.326852063380065[/C][/ROW]
[ROW][C]29[/C][C]43[/C][C]38.639408518804[/C][C]-0.398128154633761[/C][C]0.120475154854376[/C][C]0.573463845087279[/C][/ROW]
[ROW][C]30[/C][C]26[/C][C]33.2514191353127[/C][C]-0.476859388042633[/C][C]-0.935463211082794[/C][C]-0.85237103158677[/C][/ROW]
[ROW][C]31[/C][C]32[/C][C]32.7649152503695[/C][C]-0.477004006568099[/C][C]-0.752661380197807[/C][C]-0.0016519921505009[/C][/ROW]
[ROW][C]32[/C][C]29[/C][C]31.0845224179931[/C][C]-0.494554749528413[/C][C]-0.552813916884883[/C][C]-0.206387555162543[/C][/ROW]
[ROW][C]33[/C][C]41[/C][C]34.8360934615138[/C][C]-0.433475829718496[/C][C]0.754894974281266[/C][C]0.728638355886549[/C][/ROW]
[ROW][C]34[/C][C]55[/C][C]43.2995897242132[/C][C]-0.306226450344172[/C][C]0.363138359003166[/C][C]1.52703407745410[/C][/ROW]
[ROW][C]35[/C][C]50[/C][C]46.1940842554349[/C][C]-0.260666687655523[/C][C]-0.273270587371578[/C][C]0.549373760889409[/C][/ROW]
[ROW][C]36[/C][C]30[/C][C]39.7485793548057[/C][C]-0.340508812460426[/C][C]-1.85367823584773[/C][C]-1.06194629241181[/C][/ROW]
[ROW][C]37[/C][C]35[/C][C]36.0225184471337[/C][C]-0.287856222706459[/C][C]3.47929100434421[/C][C]-0.634153899121415[/C][/ROW]
[ROW][C]38[/C][C]29[/C][C]32.972665987874[/C][C]-0.345819504485971[/C][C]-0.747464534478297[/C][C]-0.444635919758805[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]28.4348817274144[/C][C]-0.433083749095241[/C][C]-1.43193087036311[/C][C]-0.689235293669122[/C][/ROW]
[ROW][C]40[/C][C]39[/C][C]32.5762165334568[/C][C]-0.35038926497769[/C][C]0.799095587514981[/C][C]0.769539450181696[/C][/ROW]
[ROW][C]41[/C][C]24[/C][C]28.6966790218554[/C][C]-0.407672498944957[/C][C]-0.29646923692442[/C][C]-0.599881827853066[/C][/ROW]
[ROW][C]42[/C][C]38[/C][C]32.5028141707185[/C][C]-0.343672540708173[/C][C]0.211184864255337[/C][C]0.719460077012531[/C][/ROW]
[ROW][C]43[/C][C]30[/C][C]31.5980734695633[/C][C]-0.351881782199347[/C][C]-0.892308282386911[/C][C]-0.0959844972251893[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]31.4761450543421[/C][C]-0.348581511199892[/C][C]-0.7657624608418[/C][C]0.0393732685344838[/C][/ROW]
[ROW][C]45[/C][C]39[/C][C]34.3520853799691[/C][C]-0.302701507804819[/C][C]0.5845931513874[/C][C]0.552306577012687[/C][/ROW]
[ROW][C]46[/C][C]33[/C][C]33.6694266640623[/C][C]-0.308091597595351[/C][C]-0.190541108537781[/C][C]-0.0650870620941148[/C][/ROW]
[ROW][C]47[/C][C]57[/C][C]42.8881535856428[/C][C]-0.173950010432760[/C][C]2.10297935000392[/C][C]1.63193254999632[/C][/ROW]
[ROW][C]48[/C][C]49[/C][C]46.1674532643648[/C][C]-0.130653447497648[/C][C]-1.52735706863502[/C][C]0.591751687622329[/C][/ROW]
[ROW][C]49[/C][C]74[/C][C]55.2484107624321[/C][C]-0.172674010162554[/C][C]6.81210555145937[/C][C]1.67212696377326[/C][/ROW]
[ROW][C]50[/C][C]74[/C][C]63.4698221324493[/C][C]-0.0184002463492205[/C][C]0.538095179017007[/C][C]1.37828680623751[/C][/ROW]
[ROW][C]51[/C][C]115[/C][C]85.9338453088701[/C][C]0.414754718832038[/C][C]2.13496231858669[/C][C]3.72386248104591[/C][/ROW]
[ROW][C]52[/C][C]67[/C][C]78.5360696504965[/C][C]0.278416861614145[/C][C]-1.96803720545403[/C][C]-1.31659649822396[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]67.4929457642276[/C][C]0.0965311869526432[/C][C]-2.47124245860172[/C][C]-1.92395211972329[/C][/ROW]
[ROW][C]54[/C][C]114[/C][C]85.9611730099336[/C][C]0.376849101965997[/C][C]5.17381932281773[/C][C]3.13315429375489[/C][/ROW]
[ROW][C]55[/C][C]70[/C][C]80.5258621563455[/C][C]0.290666727290494[/C][C]-3.2766682016665[/C][C]-0.992720931796824[/C][/ROW]
[ROW][C]56[/C][C]73[/C][C]77.8549235568811[/C][C]0.247378627042241[/C][C]-1.15761731004758[/C][C]-0.506167168805854[/C][/ROW]
[ROW][C]57[/C][C]77[/C][C]77.2359834012412[/C][C]0.234793498472384[/C][C]0.845953011599004[/C][C]-0.148098597441190[/C][/ROW]
[ROW][C]58[/C][C]67[/C][C]73.6076566416646[/C][C]0.178817438750667[/C][C]-1.78240821735651[/C][C]-0.660446221293281[/C][/ROW]
[ROW][C]59[/C][C]60[/C][C]67.3231133380331[/C][C]0.0862338375258085[/C][C]0.751131174168254[/C][C]-1.10492777802117[/C][/ROW]
[ROW][C]60[/C][C]73[/C][C]70.5495565882865[/C][C]0.126376703068323[/C][C]-1.47746518484296[/C][C]0.537001124713946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112936&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112936&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
14848000
24948.48355531416140.04508897252755770.05741944415558910.066091969717463
35953.26434946248290.3603078438673070.5803830012784920.715462380994163
45654.54693220695480.4057588392897830.3622937660806550.147832922569044
54751.36471973788230.2645501800681740.0570801987295856-0.592327924025418
65653.34672565434560.3222725683735610.4901581125502650.287976792902266
75051.99234098500310.2716237514460460.144767701651258-0.283538684676336
85452.86721279929980.2885916093312120.3586761345831250.102501088069710
97963.95411663134480.577922050484041.135152168319131.83975286392058
105058.26455426147760.415674698647807-0.172007923441882-1.06947612345479
115456.47384472536750.3600142820216180.379034307642431-0.376837534559955
125656.30990413255880.3470661124098890.368183901821673-0.0895373068586133
135055.87002455084630.423396028306244-4.67556943058289-0.182115530955458
144651.3181670871120.229352757294846-0.276639463057748-0.711172682161763
154749.28058398047310.1570699768540080.315594236406929-0.356650752245261
164346.54524844305700.0845980918405958-0.00548886692811479-0.47824852972098
175248.90062525860440.1322969089022030.2414956442470690.383363870556341
184848.47835465531180.1219242415839280.228431400695907-0.0944966010247166
193643.3418048720080.0303823272467762-0.59956109984278-0.899954040282278
204142.3972168995780.0141162088807799-0.143414480178913-0.167215267612003
213438.5407702919013-0.04890543128520720.44410939004563-0.664515984663655
223738.0077627533247-0.0566821835261305-0.383849328360152-0.0831515438298939
233737.5300603118559-0.063392392112890.0127225527401368-0.0723288157461003
243435.9655273328857-0.0860159433976338-0.0279746051358467-0.257963606941926
255542.2223784964475-0.3161940263722013.993852844341661.25863970147881
263740.0930989532443-0.363962033787098-1.05887172883997-0.281449568608998
272734.2618258271655-0.494903159563017-0.798999419331397-0.887128446007918
283835.7183980114887-0.456283143823238-0.1237985756501310.326852063380065
294338.639408518804-0.3981281546337610.1204751548543760.573463845087279
302633.2514191353127-0.476859388042633-0.935463211082794-0.85237103158677
313232.7649152503695-0.477004006568099-0.752661380197807-0.0016519921505009
322931.0845224179931-0.494554749528413-0.552813916884883-0.206387555162543
334134.8360934615138-0.4334758297184960.7548949742812660.728638355886549
345543.2995897242132-0.3062264503441720.3631383590031661.52703407745410
355046.1940842554349-0.260666687655523-0.2732705873715780.549373760889409
363039.7485793548057-0.340508812460426-1.85367823584773-1.06194629241181
373536.0225184471337-0.2878562227064593.47929100434421-0.634153899121415
382932.972665987874-0.345819504485971-0.747464534478297-0.444635919758805
392228.4348817274144-0.433083749095241-1.43193087036311-0.689235293669122
403932.5762165334568-0.350389264977690.7990955875149810.769539450181696
412428.6966790218554-0.407672498944957-0.29646923692442-0.599881827853066
423832.5028141707185-0.3436725407081730.2111848642553370.719460077012531
433031.5980734695633-0.351881782199347-0.892308282386911-0.0959844972251893
443131.4761450543421-0.348581511199892-0.76576246084180.0393732685344838
453934.3520853799691-0.3027015078048190.58459315138740.552306577012687
463333.6694266640623-0.308091597595351-0.190541108537781-0.0650870620941148
475742.8881535856428-0.1739500104327602.102979350003921.63193254999632
484946.1674532643648-0.130653447497648-1.527357068635020.591751687622329
497455.2484107624321-0.1726740101625546.812105551459371.67212696377326
507463.4698221324493-0.01840024634922050.5380951790170071.37828680623751
5111585.93384530887010.4147547188320382.134962318586693.72386248104591
526778.53606965049650.278416861614145-1.96803720545403-1.31659649822396
535167.49294576422760.0965311869526432-2.47124245860172-1.92395211972329
5411485.96117300993360.3768491019659975.173819322817733.13315429375489
557080.52586215634550.290666727290494-3.2766682016665-0.992720931796824
567377.85492355688110.247378627042241-1.15761731004758-0.506167168805854
577777.23598340124120.2347934984723840.845953011599004-0.148098597441190
586773.60765664166460.178817438750667-1.78240821735651-0.660446221293281
596067.32311333803310.08623383752580850.751131174168254-1.10492777802117
607370.54955658828650.126376703068323-1.477465184842960.537001124713946



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')