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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, 01 Dec 2009 12:54:37 -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/01/t125969733043fhia1j8lhq69j.htm/, Retrieved Fri, 29 Mar 2024 01:25:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62224, Retrieved Fri, 29 Mar 2024 01:25:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168
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]
-    D      [Structural Time Series Models] [Structurele tijdr...] [2009-12-01 19:54:37] [6c304092df7982e5e12293b2743450a3] [Current]
-    D        [Structural Time Series Models] [Ad hoc forecasting] [2009-12-04 16:16:16] [34d27ebe78dc2d31581e8710befe8733]
-   PD          [Structural Time Series Models] [tijdreeksanalyse ...] [2009-12-16 22:58:04] [34d27ebe78dc2d31581e8710befe8733]
-    D        [Structural Time Series Models] [structurele tijdr...] [2009-12-04 19:23:56] [4f1a20f787b3465111b61213cdeef1a9]
-    D        [Structural Time Series Models] [structurele tijdr...] [2009-12-04 19:23:56] [4f1a20f787b3465111b61213cdeef1a9]
-    D          [Structural Time Series Models] [Structurele tijdr...] [2009-12-11 16:03:21] [4f1a20f787b3465111b61213cdeef1a9]
-   PD            [Structural Time Series Models] [Structurele tijdr...] [2009-12-11 16:46:43] [4f1a20f787b3465111b61213cdeef1a9]
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Dataseries X:
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.4
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.5
8.2
8.1
7.9
8.6
8.7
8.7
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8
8.2
8.1
8.1
8
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.4
6.1
6.5
7.7
7.9
7.5
6.9
6.6
6.9
7.7
8
8
7.7
7.3
7.4
8.1
8.3
8.2




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=62224&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=62224&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62224&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
18.48.4000
28.48.4000
38.48.4000
48.68.594502881849090.1863364984712070.005497118150910830.542139808952547
58.98.898187574877190.2988208786663450.001812425122812850.327604743032395
68.88.81218879419231-0.0701036903600278-0.0121887941923082-1.0744078602474
78.38.30982820712854-0.484497368791315-0.00982820712853705-1.20682693844648
87.57.50619437027392-0.790443779007934-0.00619437027391512-0.890998953192124
97.27.1841981799698-0.3413566543726830.01580182003020041.30786355065082
107.47.387839918495450.1811176244537330.01216008150455001.52158685435875
118.88.769403133279731.331950181557210.03059686672027313.35153664288356
129.39.331417968581670.593835381079452-0.0314179685816659-2.14959055962222
139.39.312060246205030.00614229913265951-0.0120602462050237-1.71219001997661
148.78.71417121390012-0.573184008276645-0.0141712139001166-1.69423863951449
158.28.19291377757677-0.5245900122182040.007086222423232570.142937770280577
168.38.278706518584610.04608477580661030.02129348141539211.66000271945657
178.58.490826570389340.2012334139530790.009173429610654370.451892590507891
188.68.610260113055910.124775165732414-0.0102601130559145-0.222666517111757
198.58.49906162872325-0.09578937517306620.0009383712767499-0.642344401582395
208.28.22059950369853-0.266530263226196-0.0205995036985341-0.497243774358148
218.18.0671242661379-0.1608599020449250.03287573386209840.307740764488693
227.97.9519178929486-0.118188339236399-0.05191789294860330.124271169664202
238.68.519740959738710.5230203674974460.0802590402612891.86737445325227
248.78.74242533811970.242306274407083-0.0424253381196999-0.8175171896451
258.78.69797842724487-0.02564172060564530.00202157275512926-0.780709100960954
268.58.50132251246483-0.185569562503805-0.00132251246482670-0.466614956822868
278.48.41811469353935-0.0911547063213842-0.01811469353934720.276352870301122
288.58.48365115932580.05359920368471950.01634884067419420.421298118657457
298.78.687386178159330.1921288006513340.01261382184067340.403448763895573
308.78.711843948787780.0373675017945224-0.0118439487877778-0.450712780267858
318.68.59304152670997-0.1067835638814930.00695847329002688-0.419806817830184
328.58.52145780871233-0.0742934064926835-0.02145780871233120.0946201826656657
338.38.25530817586004-0.2513788777267350.0446918241399638-0.515720954263528
3488.09985758883582-0.162835388833424-0.09985758883582170.257862671210054
358.28.10177341255155-0.01076520224174150.09822658744845160.442870737944124
368.18.129558093753150.0248150432402895-0.02955809375314510.103619748756330
378.18.08810141986608-0.03633945337132690.0118985801339163-0.178202264373939
3888.00409711361563-0.0803437981701123-0.00409711361562836-0.128225093271845
397.97.9241028133476-0.080023604231224-0.02410281334760760.000934916941762015
407.97.89861201034747-0.02996892477011550.001387989652525510.145753330538329
4187.973794758511150.06642683458414620.02620524148884800.280701723197123
4288.002770873011010.0320854763466518-0.00277087301101287-0.100014939197683
437.97.91018055910298-0.0822476986425896-0.0101805591029809-0.332968048500968
4487.993013015216620.06913587958990480.006986984783376030.440870367703134
457.77.67726132357204-0.2838129574916610.0227386764279626-1.02788278615441
467.27.3141959239161-0.356489541200404-0.114195923916094-0.211653986485931
477.57.382784120374870.03332074690735760.1172158796251261.13523790722274
487.37.33239981069436-0.0434311786450603-0.032399810694358-0.223525681529458
4977.00145157623927-0.307046239928848-0.00145157623927222-0.768172856093289
5076.98466972743133-0.04090070300360840.01533027256867400.77512340161062
5177.020539724264480.0291743944877572-0.02053972426447950.204371834297510
527.27.201629426630880.168063168540456-0.001629426630883530.404538839841147
537.37.278294178038540.08458778039075320.0217058219614643-0.243056860738686
547.17.10447766873797-0.151449040374118-0.00447766873797478-0.687434972770834
556.86.84491095858611-0.250218854362103-0.044910958586107-0.287643356539499
566.46.37048585356149-0.4550395149456620.0295141464385114-0.596493447011743
576.16.04662841713655-0.3352013672063790.05337158286344610.349001215668825
586.56.606301588231620.482295506431774-0.1063015882316192.38077547677439
597.77.561005900963380.9138541150889240.1389940990366241.25681899661878
607.97.931980330918780.417984603653717-0.0319803309187814-1.44415926819459
617.57.56674274440778-0.297445557429344-0.06674274440778-2.08462594732407
626.96.90371487086696-0.631307123974338-0.00371487086695706-0.972161148286413
636.66.61464816362608-0.319647774563612-0.01464816362608010.908425293627625
646.96.868996013287740.2037193049208180.03100398671226091.52458534731289
657.77.635114487737150.7161262300786770.06488551226285171.49193598767762
6688.02666095087850.420394582704619-0.0266609508785065-0.861286443508214
6788.037308867851950.0470167974500614-0.0373088678519463-1.08737666402777
687.77.66381937553885-0.3361689853065710.0361806244611525-1.11593967357225
697.37.30718183205484-0.354820772858166-0.00718183205484464-0.0543190866513019
707.47.539106789830840.17984804454146-0.1391067898308351.55710462546365
718.17.935712053004360.3773627611220930.1642879469956360.575216160067762
728.38.280997119486550.3481367688556240.0190028805134527-0.0851193254992055
738.28.24143453405873-0.00513654574708017-0.0414345340587352-1.02927863813055

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8.4 & 8.4 & 0 & 0 & 0 \tabularnewline
2 & 8.4 & 8.4 & 0 & 0 & 0 \tabularnewline
3 & 8.4 & 8.4 & 0 & 0 & 0 \tabularnewline
4 & 8.6 & 8.59450288184909 & 0.186336498471207 & 0.00549711815091083 & 0.542139808952547 \tabularnewline
5 & 8.9 & 8.89818757487719 & 0.298820878666345 & 0.00181242512281285 & 0.327604743032395 \tabularnewline
6 & 8.8 & 8.81218879419231 & -0.0701036903600278 & -0.0121887941923082 & -1.0744078602474 \tabularnewline
7 & 8.3 & 8.30982820712854 & -0.484497368791315 & -0.00982820712853705 & -1.20682693844648 \tabularnewline
8 & 7.5 & 7.50619437027392 & -0.790443779007934 & -0.00619437027391512 & -0.890998953192124 \tabularnewline
9 & 7.2 & 7.1841981799698 & -0.341356654372683 & 0.0158018200302004 & 1.30786355065082 \tabularnewline
10 & 7.4 & 7.38783991849545 & 0.181117624453733 & 0.0121600815045500 & 1.52158685435875 \tabularnewline
11 & 8.8 & 8.76940313327973 & 1.33195018155721 & 0.0305968667202731 & 3.35153664288356 \tabularnewline
12 & 9.3 & 9.33141796858167 & 0.593835381079452 & -0.0314179685816659 & -2.14959055962222 \tabularnewline
13 & 9.3 & 9.31206024620503 & 0.00614229913265951 & -0.0120602462050237 & -1.71219001997661 \tabularnewline
14 & 8.7 & 8.71417121390012 & -0.573184008276645 & -0.0141712139001166 & -1.69423863951449 \tabularnewline
15 & 8.2 & 8.19291377757677 & -0.524590012218204 & 0.00708622242323257 & 0.142937770280577 \tabularnewline
16 & 8.3 & 8.27870651858461 & 0.0460847758066103 & 0.0212934814153921 & 1.66000271945657 \tabularnewline
17 & 8.5 & 8.49082657038934 & 0.201233413953079 & 0.00917342961065437 & 0.451892590507891 \tabularnewline
18 & 8.6 & 8.61026011305591 & 0.124775165732414 & -0.0102601130559145 & -0.222666517111757 \tabularnewline
19 & 8.5 & 8.49906162872325 & -0.0957893751730662 & 0.0009383712767499 & -0.642344401582395 \tabularnewline
20 & 8.2 & 8.22059950369853 & -0.266530263226196 & -0.0205995036985341 & -0.497243774358148 \tabularnewline
21 & 8.1 & 8.0671242661379 & -0.160859902044925 & 0.0328757338620984 & 0.307740764488693 \tabularnewline
22 & 7.9 & 7.9519178929486 & -0.118188339236399 & -0.0519178929486033 & 0.124271169664202 \tabularnewline
23 & 8.6 & 8.51974095973871 & 0.523020367497446 & 0.080259040261289 & 1.86737445325227 \tabularnewline
24 & 8.7 & 8.7424253381197 & 0.242306274407083 & -0.0424253381196999 & -0.8175171896451 \tabularnewline
25 & 8.7 & 8.69797842724487 & -0.0256417206056453 & 0.00202157275512926 & -0.780709100960954 \tabularnewline
26 & 8.5 & 8.50132251246483 & -0.185569562503805 & -0.00132251246482670 & -0.466614956822868 \tabularnewline
27 & 8.4 & 8.41811469353935 & -0.0911547063213842 & -0.0181146935393472 & 0.276352870301122 \tabularnewline
28 & 8.5 & 8.4836511593258 & 0.0535992036847195 & 0.0163488406741942 & 0.421298118657457 \tabularnewline
29 & 8.7 & 8.68738617815933 & 0.192128800651334 & 0.0126138218406734 & 0.403448763895573 \tabularnewline
30 & 8.7 & 8.71184394878778 & 0.0373675017945224 & -0.0118439487877778 & -0.450712780267858 \tabularnewline
31 & 8.6 & 8.59304152670997 & -0.106783563881493 & 0.00695847329002688 & -0.419806817830184 \tabularnewline
32 & 8.5 & 8.52145780871233 & -0.0742934064926835 & -0.0214578087123312 & 0.0946201826656657 \tabularnewline
33 & 8.3 & 8.25530817586004 & -0.251378877726735 & 0.0446918241399638 & -0.515720954263528 \tabularnewline
34 & 8 & 8.09985758883582 & -0.162835388833424 & -0.0998575888358217 & 0.257862671210054 \tabularnewline
35 & 8.2 & 8.10177341255155 & -0.0107652022417415 & 0.0982265874484516 & 0.442870737944124 \tabularnewline
36 & 8.1 & 8.12955809375315 & 0.0248150432402895 & -0.0295580937531451 & 0.103619748756330 \tabularnewline
37 & 8.1 & 8.08810141986608 & -0.0363394533713269 & 0.0118985801339163 & -0.178202264373939 \tabularnewline
38 & 8 & 8.00409711361563 & -0.0803437981701123 & -0.00409711361562836 & -0.128225093271845 \tabularnewline
39 & 7.9 & 7.9241028133476 & -0.080023604231224 & -0.0241028133476076 & 0.000934916941762015 \tabularnewline
40 & 7.9 & 7.89861201034747 & -0.0299689247701155 & 0.00138798965252551 & 0.145753330538329 \tabularnewline
41 & 8 & 7.97379475851115 & 0.0664268345841462 & 0.0262052414888480 & 0.280701723197123 \tabularnewline
42 & 8 & 8.00277087301101 & 0.0320854763466518 & -0.00277087301101287 & -0.100014939197683 \tabularnewline
43 & 7.9 & 7.91018055910298 & -0.0822476986425896 & -0.0101805591029809 & -0.332968048500968 \tabularnewline
44 & 8 & 7.99301301521662 & 0.0691358795899048 & 0.00698698478337603 & 0.440870367703134 \tabularnewline
45 & 7.7 & 7.67726132357204 & -0.283812957491661 & 0.0227386764279626 & -1.02788278615441 \tabularnewline
46 & 7.2 & 7.3141959239161 & -0.356489541200404 & -0.114195923916094 & -0.211653986485931 \tabularnewline
47 & 7.5 & 7.38278412037487 & 0.0333207469073576 & 0.117215879625126 & 1.13523790722274 \tabularnewline
48 & 7.3 & 7.33239981069436 & -0.0434311786450603 & -0.032399810694358 & -0.223525681529458 \tabularnewline
49 & 7 & 7.00145157623927 & -0.307046239928848 & -0.00145157623927222 & -0.768172856093289 \tabularnewline
50 & 7 & 6.98466972743133 & -0.0409007030036084 & 0.0153302725686740 & 0.77512340161062 \tabularnewline
51 & 7 & 7.02053972426448 & 0.0291743944877572 & -0.0205397242644795 & 0.204371834297510 \tabularnewline
52 & 7.2 & 7.20162942663088 & 0.168063168540456 & -0.00162942663088353 & 0.404538839841147 \tabularnewline
53 & 7.3 & 7.27829417803854 & 0.0845877803907532 & 0.0217058219614643 & -0.243056860738686 \tabularnewline
54 & 7.1 & 7.10447766873797 & -0.151449040374118 & -0.00447766873797478 & -0.687434972770834 \tabularnewline
55 & 6.8 & 6.84491095858611 & -0.250218854362103 & -0.044910958586107 & -0.287643356539499 \tabularnewline
56 & 6.4 & 6.37048585356149 & -0.455039514945662 & 0.0295141464385114 & -0.596493447011743 \tabularnewline
57 & 6.1 & 6.04662841713655 & -0.335201367206379 & 0.0533715828634461 & 0.349001215668825 \tabularnewline
58 & 6.5 & 6.60630158823162 & 0.482295506431774 & -0.106301588231619 & 2.38077547677439 \tabularnewline
59 & 7.7 & 7.56100590096338 & 0.913854115088924 & 0.138994099036624 & 1.25681899661878 \tabularnewline
60 & 7.9 & 7.93198033091878 & 0.417984603653717 & -0.0319803309187814 & -1.44415926819459 \tabularnewline
61 & 7.5 & 7.56674274440778 & -0.297445557429344 & -0.06674274440778 & -2.08462594732407 \tabularnewline
62 & 6.9 & 6.90371487086696 & -0.631307123974338 & -0.00371487086695706 & -0.972161148286413 \tabularnewline
63 & 6.6 & 6.61464816362608 & -0.319647774563612 & -0.0146481636260801 & 0.908425293627625 \tabularnewline
64 & 6.9 & 6.86899601328774 & 0.203719304920818 & 0.0310039867122609 & 1.52458534731289 \tabularnewline
65 & 7.7 & 7.63511448773715 & 0.716126230078677 & 0.0648855122628517 & 1.49193598767762 \tabularnewline
66 & 8 & 8.0266609508785 & 0.420394582704619 & -0.0266609508785065 & -0.861286443508214 \tabularnewline
67 & 8 & 8.03730886785195 & 0.0470167974500614 & -0.0373088678519463 & -1.08737666402777 \tabularnewline
68 & 7.7 & 7.66381937553885 & -0.336168985306571 & 0.0361806244611525 & -1.11593967357225 \tabularnewline
69 & 7.3 & 7.30718183205484 & -0.354820772858166 & -0.00718183205484464 & -0.0543190866513019 \tabularnewline
70 & 7.4 & 7.53910678983084 & 0.17984804454146 & -0.139106789830835 & 1.55710462546365 \tabularnewline
71 & 8.1 & 7.93571205300436 & 0.377362761122093 & 0.164287946995636 & 0.575216160067762 \tabularnewline
72 & 8.3 & 8.28099711948655 & 0.348136768855624 & 0.0190028805134527 & -0.0851193254992055 \tabularnewline
73 & 8.2 & 8.24143453405873 & -0.00513654574708017 & -0.0414345340587352 & -1.02927863813055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62224&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]8.4[/C][C]8.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.4[/C][C]8.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]3[/C][C]8.4[/C][C]8.4[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]8.59450288184909[/C][C]0.186336498471207[/C][C]0.00549711815091083[/C][C]0.542139808952547[/C][/ROW]
[ROW][C]5[/C][C]8.9[/C][C]8.89818757487719[/C][C]0.298820878666345[/C][C]0.00181242512281285[/C][C]0.327604743032395[/C][/ROW]
[ROW][C]6[/C][C]8.8[/C][C]8.81218879419231[/C][C]-0.0701036903600278[/C][C]-0.0121887941923082[/C][C]-1.0744078602474[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.30982820712854[/C][C]-0.484497368791315[/C][C]-0.00982820712853705[/C][C]-1.20682693844648[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.50619437027392[/C][C]-0.790443779007934[/C][C]-0.00619437027391512[/C][C]-0.890998953192124[/C][/ROW]
[ROW][C]9[/C][C]7.2[/C][C]7.1841981799698[/C][C]-0.341356654372683[/C][C]0.0158018200302004[/C][C]1.30786355065082[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]7.38783991849545[/C][C]0.181117624453733[/C][C]0.0121600815045500[/C][C]1.52158685435875[/C][/ROW]
[ROW][C]11[/C][C]8.8[/C][C]8.76940313327973[/C][C]1.33195018155721[/C][C]0.0305968667202731[/C][C]3.35153664288356[/C][/ROW]
[ROW][C]12[/C][C]9.3[/C][C]9.33141796858167[/C][C]0.593835381079452[/C][C]-0.0314179685816659[/C][C]-2.14959055962222[/C][/ROW]
[ROW][C]13[/C][C]9.3[/C][C]9.31206024620503[/C][C]0.00614229913265951[/C][C]-0.0120602462050237[/C][C]-1.71219001997661[/C][/ROW]
[ROW][C]14[/C][C]8.7[/C][C]8.71417121390012[/C][C]-0.573184008276645[/C][C]-0.0141712139001166[/C][C]-1.69423863951449[/C][/ROW]
[ROW][C]15[/C][C]8.2[/C][C]8.19291377757677[/C][C]-0.524590012218204[/C][C]0.00708622242323257[/C][C]0.142937770280577[/C][/ROW]
[ROW][C]16[/C][C]8.3[/C][C]8.27870651858461[/C][C]0.0460847758066103[/C][C]0.0212934814153921[/C][C]1.66000271945657[/C][/ROW]
[ROW][C]17[/C][C]8.5[/C][C]8.49082657038934[/C][C]0.201233413953079[/C][C]0.00917342961065437[/C][C]0.451892590507891[/C][/ROW]
[ROW][C]18[/C][C]8.6[/C][C]8.61026011305591[/C][C]0.124775165732414[/C][C]-0.0102601130559145[/C][C]-0.222666517111757[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.49906162872325[/C][C]-0.0957893751730662[/C][C]0.0009383712767499[/C][C]-0.642344401582395[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.22059950369853[/C][C]-0.266530263226196[/C][C]-0.0205995036985341[/C][C]-0.497243774358148[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]8.0671242661379[/C][C]-0.160859902044925[/C][C]0.0328757338620984[/C][C]0.307740764488693[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.9519178929486[/C][C]-0.118188339236399[/C][C]-0.0519178929486033[/C][C]0.124271169664202[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]8.51974095973871[/C][C]0.523020367497446[/C][C]0.080259040261289[/C][C]1.86737445325227[/C][/ROW]
[ROW][C]24[/C][C]8.7[/C][C]8.7424253381197[/C][C]0.242306274407083[/C][C]-0.0424253381196999[/C][C]-0.8175171896451[/C][/ROW]
[ROW][C]25[/C][C]8.7[/C][C]8.69797842724487[/C][C]-0.0256417206056453[/C][C]0.00202157275512926[/C][C]-0.780709100960954[/C][/ROW]
[ROW][C]26[/C][C]8.5[/C][C]8.50132251246483[/C][C]-0.185569562503805[/C][C]-0.00132251246482670[/C][C]-0.466614956822868[/C][/ROW]
[ROW][C]27[/C][C]8.4[/C][C]8.41811469353935[/C][C]-0.0911547063213842[/C][C]-0.0181146935393472[/C][C]0.276352870301122[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.4836511593258[/C][C]0.0535992036847195[/C][C]0.0163488406741942[/C][C]0.421298118657457[/C][/ROW]
[ROW][C]29[/C][C]8.7[/C][C]8.68738617815933[/C][C]0.192128800651334[/C][C]0.0126138218406734[/C][C]0.403448763895573[/C][/ROW]
[ROW][C]30[/C][C]8.7[/C][C]8.71184394878778[/C][C]0.0373675017945224[/C][C]-0.0118439487877778[/C][C]-0.450712780267858[/C][/ROW]
[ROW][C]31[/C][C]8.6[/C][C]8.59304152670997[/C][C]-0.106783563881493[/C][C]0.00695847329002688[/C][C]-0.419806817830184[/C][/ROW]
[ROW][C]32[/C][C]8.5[/C][C]8.52145780871233[/C][C]-0.0742934064926835[/C][C]-0.0214578087123312[/C][C]0.0946201826656657[/C][/ROW]
[ROW][C]33[/C][C]8.3[/C][C]8.25530817586004[/C][C]-0.251378877726735[/C][C]0.0446918241399638[/C][C]-0.515720954263528[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.09985758883582[/C][C]-0.162835388833424[/C][C]-0.0998575888358217[/C][C]0.257862671210054[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]8.10177341255155[/C][C]-0.0107652022417415[/C][C]0.0982265874484516[/C][C]0.442870737944124[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.12955809375315[/C][C]0.0248150432402895[/C][C]-0.0295580937531451[/C][C]0.103619748756330[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]8.08810141986608[/C][C]-0.0363394533713269[/C][C]0.0118985801339163[/C][C]-0.178202264373939[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]8.00409711361563[/C][C]-0.0803437981701123[/C][C]-0.00409711361562836[/C][C]-0.128225093271845[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.9241028133476[/C][C]-0.080023604231224[/C][C]-0.0241028133476076[/C][C]0.000934916941762015[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]7.89861201034747[/C][C]-0.0299689247701155[/C][C]0.00138798965252551[/C][C]0.145753330538329[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]7.97379475851115[/C][C]0.0664268345841462[/C][C]0.0262052414888480[/C][C]0.280701723197123[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]8.00277087301101[/C][C]0.0320854763466518[/C][C]-0.00277087301101287[/C][C]-0.100014939197683[/C][/ROW]
[ROW][C]43[/C][C]7.9[/C][C]7.91018055910298[/C][C]-0.0822476986425896[/C][C]-0.0101805591029809[/C][C]-0.332968048500968[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]7.99301301521662[/C][C]0.0691358795899048[/C][C]0.00698698478337603[/C][C]0.440870367703134[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]7.67726132357204[/C][C]-0.283812957491661[/C][C]0.0227386764279626[/C][C]-1.02788278615441[/C][/ROW]
[ROW][C]46[/C][C]7.2[/C][C]7.3141959239161[/C][C]-0.356489541200404[/C][C]-0.114195923916094[/C][C]-0.211653986485931[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]7.38278412037487[/C][C]0.0333207469073576[/C][C]0.117215879625126[/C][C]1.13523790722274[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]7.33239981069436[/C][C]-0.0434311786450603[/C][C]-0.032399810694358[/C][C]-0.223525681529458[/C][/ROW]
[ROW][C]49[/C][C]7[/C][C]7.00145157623927[/C][C]-0.307046239928848[/C][C]-0.00145157623927222[/C][C]-0.768172856093289[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]6.98466972743133[/C][C]-0.0409007030036084[/C][C]0.0153302725686740[/C][C]0.77512340161062[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]7.02053972426448[/C][C]0.0291743944877572[/C][C]-0.0205397242644795[/C][C]0.204371834297510[/C][/ROW]
[ROW][C]52[/C][C]7.2[/C][C]7.20162942663088[/C][C]0.168063168540456[/C][C]-0.00162942663088353[/C][C]0.404538839841147[/C][/ROW]
[ROW][C]53[/C][C]7.3[/C][C]7.27829417803854[/C][C]0.0845877803907532[/C][C]0.0217058219614643[/C][C]-0.243056860738686[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.10447766873797[/C][C]-0.151449040374118[/C][C]-0.00447766873797478[/C][C]-0.687434972770834[/C][/ROW]
[ROW][C]55[/C][C]6.8[/C][C]6.84491095858611[/C][C]-0.250218854362103[/C][C]-0.044910958586107[/C][C]-0.287643356539499[/C][/ROW]
[ROW][C]56[/C][C]6.4[/C][C]6.37048585356149[/C][C]-0.455039514945662[/C][C]0.0295141464385114[/C][C]-0.596493447011743[/C][/ROW]
[ROW][C]57[/C][C]6.1[/C][C]6.04662841713655[/C][C]-0.335201367206379[/C][C]0.0533715828634461[/C][C]0.349001215668825[/C][/ROW]
[ROW][C]58[/C][C]6.5[/C][C]6.60630158823162[/C][C]0.482295506431774[/C][C]-0.106301588231619[/C][C]2.38077547677439[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]7.56100590096338[/C][C]0.913854115088924[/C][C]0.138994099036624[/C][C]1.25681899661878[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]7.93198033091878[/C][C]0.417984603653717[/C][C]-0.0319803309187814[/C][C]-1.44415926819459[/C][/ROW]
[ROW][C]61[/C][C]7.5[/C][C]7.56674274440778[/C][C]-0.297445557429344[/C][C]-0.06674274440778[/C][C]-2.08462594732407[/C][/ROW]
[ROW][C]62[/C][C]6.9[/C][C]6.90371487086696[/C][C]-0.631307123974338[/C][C]-0.00371487086695706[/C][C]-0.972161148286413[/C][/ROW]
[ROW][C]63[/C][C]6.6[/C][C]6.61464816362608[/C][C]-0.319647774563612[/C][C]-0.0146481636260801[/C][C]0.908425293627625[/C][/ROW]
[ROW][C]64[/C][C]6.9[/C][C]6.86899601328774[/C][C]0.203719304920818[/C][C]0.0310039867122609[/C][C]1.52458534731289[/C][/ROW]
[ROW][C]65[/C][C]7.7[/C][C]7.63511448773715[/C][C]0.716126230078677[/C][C]0.0648855122628517[/C][C]1.49193598767762[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]8.0266609508785[/C][C]0.420394582704619[/C][C]-0.0266609508785065[/C][C]-0.861286443508214[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]8.03730886785195[/C][C]0.0470167974500614[/C][C]-0.0373088678519463[/C][C]-1.08737666402777[/C][/ROW]
[ROW][C]68[/C][C]7.7[/C][C]7.66381937553885[/C][C]-0.336168985306571[/C][C]0.0361806244611525[/C][C]-1.11593967357225[/C][/ROW]
[ROW][C]69[/C][C]7.3[/C][C]7.30718183205484[/C][C]-0.354820772858166[/C][C]-0.00718183205484464[/C][C]-0.0543190866513019[/C][/ROW]
[ROW][C]70[/C][C]7.4[/C][C]7.53910678983084[/C][C]0.17984804454146[/C][C]-0.139106789830835[/C][C]1.55710462546365[/C][/ROW]
[ROW][C]71[/C][C]8.1[/C][C]7.93571205300436[/C][C]0.377362761122093[/C][C]0.164287946995636[/C][C]0.575216160067762[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]8.28099711948655[/C][C]0.348136768855624[/C][C]0.0190028805134527[/C][C]-0.0851193254992055[/C][/ROW]
[ROW][C]73[/C][C]8.2[/C][C]8.24143453405873[/C][C]-0.00513654574708017[/C][C]-0.0414345340587352[/C][C]-1.02927863813055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62224&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62224&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
18.48.4000
28.48.4000
38.48.4000
48.68.594502881849090.1863364984712070.005497118150910830.542139808952547
58.98.898187574877190.2988208786663450.001812425122812850.327604743032395
68.88.81218879419231-0.0701036903600278-0.0121887941923082-1.0744078602474
78.38.30982820712854-0.484497368791315-0.00982820712853705-1.20682693844648
87.57.50619437027392-0.790443779007934-0.00619437027391512-0.890998953192124
97.27.1841981799698-0.3413566543726830.01580182003020041.30786355065082
107.47.387839918495450.1811176244537330.01216008150455001.52158685435875
118.88.769403133279731.331950181557210.03059686672027313.35153664288356
129.39.331417968581670.593835381079452-0.0314179685816659-2.14959055962222
139.39.312060246205030.00614229913265951-0.0120602462050237-1.71219001997661
148.78.71417121390012-0.573184008276645-0.0141712139001166-1.69423863951449
158.28.19291377757677-0.5245900122182040.007086222423232570.142937770280577
168.38.278706518584610.04608477580661030.02129348141539211.66000271945657
178.58.490826570389340.2012334139530790.009173429610654370.451892590507891
188.68.610260113055910.124775165732414-0.0102601130559145-0.222666517111757
198.58.49906162872325-0.09578937517306620.0009383712767499-0.642344401582395
208.28.22059950369853-0.266530263226196-0.0205995036985341-0.497243774358148
218.18.0671242661379-0.1608599020449250.03287573386209840.307740764488693
227.97.9519178929486-0.118188339236399-0.05191789294860330.124271169664202
238.68.519740959738710.5230203674974460.0802590402612891.86737445325227
248.78.74242533811970.242306274407083-0.0424253381196999-0.8175171896451
258.78.69797842724487-0.02564172060564530.00202157275512926-0.780709100960954
268.58.50132251246483-0.185569562503805-0.00132251246482670-0.466614956822868
278.48.41811469353935-0.0911547063213842-0.01811469353934720.276352870301122
288.58.48365115932580.05359920368471950.01634884067419420.421298118657457
298.78.687386178159330.1921288006513340.01261382184067340.403448763895573
308.78.711843948787780.0373675017945224-0.0118439487877778-0.450712780267858
318.68.59304152670997-0.1067835638814930.00695847329002688-0.419806817830184
328.58.52145780871233-0.0742934064926835-0.02145780871233120.0946201826656657
338.38.25530817586004-0.2513788777267350.0446918241399638-0.515720954263528
3488.09985758883582-0.162835388833424-0.09985758883582170.257862671210054
358.28.10177341255155-0.01076520224174150.09822658744845160.442870737944124
368.18.129558093753150.0248150432402895-0.02955809375314510.103619748756330
378.18.08810141986608-0.03633945337132690.0118985801339163-0.178202264373939
3888.00409711361563-0.0803437981701123-0.00409711361562836-0.128225093271845
397.97.9241028133476-0.080023604231224-0.02410281334760760.000934916941762015
407.97.89861201034747-0.02996892477011550.001387989652525510.145753330538329
4187.973794758511150.06642683458414620.02620524148884800.280701723197123
4288.002770873011010.0320854763466518-0.00277087301101287-0.100014939197683
437.97.91018055910298-0.0822476986425896-0.0101805591029809-0.332968048500968
4487.993013015216620.06913587958990480.006986984783376030.440870367703134
457.77.67726132357204-0.2838129574916610.0227386764279626-1.02788278615441
467.27.3141959239161-0.356489541200404-0.114195923916094-0.211653986485931
477.57.382784120374870.03332074690735760.1172158796251261.13523790722274
487.37.33239981069436-0.0434311786450603-0.032399810694358-0.223525681529458
4977.00145157623927-0.307046239928848-0.00145157623927222-0.768172856093289
5076.98466972743133-0.04090070300360840.01533027256867400.77512340161062
5177.020539724264480.0291743944877572-0.02053972426447950.204371834297510
527.27.201629426630880.168063168540456-0.001629426630883530.404538839841147
537.37.278294178038540.08458778039075320.0217058219614643-0.243056860738686
547.17.10447766873797-0.151449040374118-0.00447766873797478-0.687434972770834
556.86.84491095858611-0.250218854362103-0.044910958586107-0.287643356539499
566.46.37048585356149-0.4550395149456620.0295141464385114-0.596493447011743
576.16.04662841713655-0.3352013672063790.05337158286344610.349001215668825
586.56.606301588231620.482295506431774-0.1063015882316192.38077547677439
597.77.561005900963380.9138541150889240.1389940990366241.25681899661878
607.97.931980330918780.417984603653717-0.0319803309187814-1.44415926819459
617.57.56674274440778-0.297445557429344-0.06674274440778-2.08462594732407
626.96.90371487086696-0.631307123974338-0.00371487086695706-0.972161148286413
636.66.61464816362608-0.319647774563612-0.01464816362608010.908425293627625
646.96.868996013287740.2037193049208180.03100398671226091.52458534731289
657.77.635114487737150.7161262300786770.06488551226285171.49193598767762
6688.02666095087850.420394582704619-0.0266609508785065-0.861286443508214
6788.037308867851950.0470167974500614-0.0373088678519463-1.08737666402777
687.77.66381937553885-0.3361689853065710.0361806244611525-1.11593967357225
697.37.30718183205484-0.354820772858166-0.00718183205484464-0.0543190866513019
707.47.539106789830840.17984804454146-0.1391067898308351.55710462546365
718.17.935712053004360.3773627611220930.1642879469956360.575216160067762
728.38.280997119486550.3481367688556240.0190028805134527-0.0851193254992055
738.28.24143453405873-0.00513654574708017-0.0414345340587352-1.02927863813055



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')