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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 computationThu, 22 Dec 2011 07:46:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245580078qvneanbzpyehyp.htm/, Retrieved Fri, 03 May 2024 14:14:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159378, Retrieved Fri, 03 May 2024 14:14:21 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [] [2011-12-22 12:46:26] [ef12b3094dcc95645ac503919f1fca4e] [Current]
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Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159378&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159378&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
14646000
26247.91109702948590.4577277443502923.272899216651791.53740429817903
36652.21817402542041.235505914538373.110362186349781.66337970171935
45954.80853709317741.45916987859720.8854167545155370.524668797214267
55856.45329342057531.484955302739881.097503591945890.0712006553256786
66158.30254283358531.528616445126061.787731143614870.143268072165915
74155.50974959612711.0734073799599-3.20477313178717-1.76638936537319
82749.86598779924460.442403029886409-4.36790838932408-2.86761633273592
95851.1348960076430.5124543277263074.469431565002480.368676590817397
107055.14969634150170.7828621835983534.191727386170641.62955537366549
114954.83125546086970.704779764133224-2.32474642955447-0.533007185981333
125955.92696896877990.7304079772382991.775782540583150.196192833634787
134456.33222893566960.736556369921038-10.1265350015043-0.329520700187165
143653.31608386537430.551367235294654-4.85042774181174-1.97251277464884
157256.14620986202530.6784598021036879.181650178529661.08291331779463
164555.0580952420530.582852895980019-4.74356327153115-0.862760661319053
175655.42481675220660.5718122388517771.26021093683455-0.110643642358113
185455.12171324835460.5297871402645381.79122994332995-0.468073674291378
195355.20651822052930.509649758413919-0.661411211431124-0.247201561470065
203553.29285549433330.405993485232927-9.57558067988913-1.38969566178409
216154.04460476969040.4200182908988675.673846579312830.203698345347536
225253.51615852811530.3834512223308182.09133750566887-0.571946593328757
234753.183792446570.357330486447834-3.39304821665302-0.441294777764235
245152.75762546274340.3318218376203161.42351760485177-0.50030873180266
255253.3936814040870.334467383427234-2.912487204330940.238013795260528
266355.75063291824070.386383838953319-1.080510858320751.33111479342128
277457.49726426987970.42979570116153111.33541327998620.836180039173977
284557.00922142443530.399556006675553-8.55539273461992-0.56070958621908
295156.16349051423490.359394654581507-0.409936037262088-0.771341119699042
306457.00853103706030.3744797683708185.100837939447580.306322904980649
313654.46464190096970.287521919247104-6.88315851592293-1.87338887376895
323052.37778967179820.219655898006101-12.7910123563833-1.54839304502992
335551.68264789349330.1945612234399657.06816613686863-0.604975821696486
346452.89496771629560.2212812628020526.87125539319690.681927251137723
353951.85388085545440.190134822516464-7.51335314081002-0.858540758339722
364050.31699401963330.153400464786647-2.78109455127934-1.20701409619885
376352.13364764504650.1728512112501692.991581086849991.25745814675726
384551.87767235273860.165163871679258-4.98983538979901-0.303709522528703
395951.30818176267170.14894075808398810.7700438640505-0.498697830082819
405552.71548556395460.17850651897955-2.912361352751760.844381301213788
414051.46079549811650.144740802856046-5.52624056845741-0.964761979746398
426451.91139471494390.15179697920312810.81071486900890.207653088178401
432749.5696035318230.0959865593902811-12.0403514054693-1.7096684150664
442848.13979759904850.0629804906621448-13.629058547596-1.05627505056528
454546.77351618577660.03319272543881544.38732759280101-0.998619200326333
465746.77647078279940.03259046838270210.3552406267113-0.0213268746658416
474547.29311605080110.0415739288817546-4.429472943601820.345395981609572
486950.41294537806550.09094612006187124.704236277148952.23926448303041
496051.59054769793530.1036458185644323.327388349540360.81794039031203
505652.92269897406110.121202104802161-2.489249027341010.89872986428568
515852.66962003640050.1148800335523146.97609368353074-0.266782073171524
525052.58735439330260.111304445477343-1.73148196057455-0.13905037567473
535153.05286753806410.117841824363637-3.588594160120870.249691041457891
545351.55486957997140.08828437567824098.48028199409069-1.14380737714284
553750.8844750724070.0747063062870747-10.5600971636644-0.540288315940446
562248.87960164676940.0385537414840956-17.7021877956234-1.49073086461519
575548.8551829181620.03749732700711446.42479824863928-0.0454485060629214
587050.27518894545520.059622945407921113.52758923260631.00512826720558
596252.78821290930060.096140774407123-1.899241054109691.80000378360204
605853.47354728867390.1038733343517871.818640563927720.438002164070448
613951.83045720059120.0847429648833302-4.65985854113487-1.31979280812915
624951.56944382553080.0805486915413699-0.976649132644919-0.257652819400335
635851.45749947708550.07790196379041827.41299407872505-0.141151231141377
644751.1240338922830.0718439290790956-2.28213391542378-0.299146140347208
654250.23678077602540.0573625571042322-3.9530457314549-0.696224194478585
666250.36813390380070.058478864895805411.30077210750470.0538190337270404
673950.11048058739210.0537830932615876-9.69010283373238-0.230837538211109
684051.05557900263560.0666927955977087-15.08065548788240.653904373425245
697253.13714852807430.09489661635278829.711849438997761.4858866556932
707054.17715138566920.10752061406617611.50254145562760.701034884565534
715454.63672655378890.11191012033054-2.25881072579930.26297748924643
726555.5499315565470.1209273204822745.721237416898810.603908639469392

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 46 & 46 & 0 & 0 & 0 \tabularnewline
2 & 62 & 47.9110970294859 & 0.457727744350292 & 3.27289921665179 & 1.53740429817903 \tabularnewline
3 & 66 & 52.2181740254204 & 1.23550591453837 & 3.11036218634978 & 1.66337970171935 \tabularnewline
4 & 59 & 54.8085370931774 & 1.4591698785972 & 0.885416754515537 & 0.524668797214267 \tabularnewline
5 & 58 & 56.4532934205753 & 1.48495530273988 & 1.09750359194589 & 0.0712006553256786 \tabularnewline
6 & 61 & 58.3025428335853 & 1.52861644512606 & 1.78773114361487 & 0.143268072165915 \tabularnewline
7 & 41 & 55.5097495961271 & 1.0734073799599 & -3.20477313178717 & -1.76638936537319 \tabularnewline
8 & 27 & 49.8659877992446 & 0.442403029886409 & -4.36790838932408 & -2.86761633273592 \tabularnewline
9 & 58 & 51.134896007643 & 0.512454327726307 & 4.46943156500248 & 0.368676590817397 \tabularnewline
10 & 70 & 55.1496963415017 & 0.782862183598353 & 4.19172738617064 & 1.62955537366549 \tabularnewline
11 & 49 & 54.8312554608697 & 0.704779764133224 & -2.32474642955447 & -0.533007185981333 \tabularnewline
12 & 59 & 55.9269689687799 & 0.730407977238299 & 1.77578254058315 & 0.196192833634787 \tabularnewline
13 & 44 & 56.3322289356696 & 0.736556369921038 & -10.1265350015043 & -0.329520700187165 \tabularnewline
14 & 36 & 53.3160838653743 & 0.551367235294654 & -4.85042774181174 & -1.97251277464884 \tabularnewline
15 & 72 & 56.1462098620253 & 0.678459802103687 & 9.18165017852966 & 1.08291331779463 \tabularnewline
16 & 45 & 55.058095242053 & 0.582852895980019 & -4.74356327153115 & -0.862760661319053 \tabularnewline
17 & 56 & 55.4248167522066 & 0.571812238851777 & 1.26021093683455 & -0.110643642358113 \tabularnewline
18 & 54 & 55.1217132483546 & 0.529787140264538 & 1.79122994332995 & -0.468073674291378 \tabularnewline
19 & 53 & 55.2065182205293 & 0.509649758413919 & -0.661411211431124 & -0.247201561470065 \tabularnewline
20 & 35 & 53.2928554943333 & 0.405993485232927 & -9.57558067988913 & -1.38969566178409 \tabularnewline
21 & 61 & 54.0446047696904 & 0.420018290898867 & 5.67384657931283 & 0.203698345347536 \tabularnewline
22 & 52 & 53.5161585281153 & 0.383451222330818 & 2.09133750566887 & -0.571946593328757 \tabularnewline
23 & 47 & 53.18379244657 & 0.357330486447834 & -3.39304821665302 & -0.441294777764235 \tabularnewline
24 & 51 & 52.7576254627434 & 0.331821837620316 & 1.42351760485177 & -0.50030873180266 \tabularnewline
25 & 52 & 53.393681404087 & 0.334467383427234 & -2.91248720433094 & 0.238013795260528 \tabularnewline
26 & 63 & 55.7506329182407 & 0.386383838953319 & -1.08051085832075 & 1.33111479342128 \tabularnewline
27 & 74 & 57.4972642698797 & 0.429795701161531 & 11.3354132799862 & 0.836180039173977 \tabularnewline
28 & 45 & 57.0092214244353 & 0.399556006675553 & -8.55539273461992 & -0.56070958621908 \tabularnewline
29 & 51 & 56.1634905142349 & 0.359394654581507 & -0.409936037262088 & -0.771341119699042 \tabularnewline
30 & 64 & 57.0085310370603 & 0.374479768370818 & 5.10083793944758 & 0.306322904980649 \tabularnewline
31 & 36 & 54.4646419009697 & 0.287521919247104 & -6.88315851592293 & -1.87338887376895 \tabularnewline
32 & 30 & 52.3777896717982 & 0.219655898006101 & -12.7910123563833 & -1.54839304502992 \tabularnewline
33 & 55 & 51.6826478934933 & 0.194561223439965 & 7.06816613686863 & -0.604975821696486 \tabularnewline
34 & 64 & 52.8949677162956 & 0.221281262802052 & 6.8712553931969 & 0.681927251137723 \tabularnewline
35 & 39 & 51.8538808554544 & 0.190134822516464 & -7.51335314081002 & -0.858540758339722 \tabularnewline
36 & 40 & 50.3169940196333 & 0.153400464786647 & -2.78109455127934 & -1.20701409619885 \tabularnewline
37 & 63 & 52.1336476450465 & 0.172851211250169 & 2.99158108684999 & 1.25745814675726 \tabularnewline
38 & 45 & 51.8776723527386 & 0.165163871679258 & -4.98983538979901 & -0.303709522528703 \tabularnewline
39 & 59 & 51.3081817626717 & 0.148940758083988 & 10.7700438640505 & -0.498697830082819 \tabularnewline
40 & 55 & 52.7154855639546 & 0.17850651897955 & -2.91236135275176 & 0.844381301213788 \tabularnewline
41 & 40 & 51.4607954981165 & 0.144740802856046 & -5.52624056845741 & -0.964761979746398 \tabularnewline
42 & 64 & 51.9113947149439 & 0.151796979203128 & 10.8107148690089 & 0.207653088178401 \tabularnewline
43 & 27 & 49.569603531823 & 0.0959865593902811 & -12.0403514054693 & -1.7096684150664 \tabularnewline
44 & 28 & 48.1397975990485 & 0.0629804906621448 & -13.629058547596 & -1.05627505056528 \tabularnewline
45 & 45 & 46.7735161857766 & 0.0331927254388154 & 4.38732759280101 & -0.998619200326333 \tabularnewline
46 & 57 & 46.7764707827994 & 0.032590468382702 & 10.3552406267113 & -0.0213268746658416 \tabularnewline
47 & 45 & 47.2931160508011 & 0.0415739288817546 & -4.42947294360182 & 0.345395981609572 \tabularnewline
48 & 69 & 50.4129453780655 & 0.0909461200618712 & 4.70423627714895 & 2.23926448303041 \tabularnewline
49 & 60 & 51.5905476979353 & 0.103645818564432 & 3.32738834954036 & 0.81794039031203 \tabularnewline
50 & 56 & 52.9226989740611 & 0.121202104802161 & -2.48924902734101 & 0.89872986428568 \tabularnewline
51 & 58 & 52.6696200364005 & 0.114880033552314 & 6.97609368353074 & -0.266782073171524 \tabularnewline
52 & 50 & 52.5873543933026 & 0.111304445477343 & -1.73148196057455 & -0.13905037567473 \tabularnewline
53 & 51 & 53.0528675380641 & 0.117841824363637 & -3.58859416012087 & 0.249691041457891 \tabularnewline
54 & 53 & 51.5548695799714 & 0.0882843756782409 & 8.48028199409069 & -1.14380737714284 \tabularnewline
55 & 37 & 50.884475072407 & 0.0747063062870747 & -10.5600971636644 & -0.540288315940446 \tabularnewline
56 & 22 & 48.8796016467694 & 0.0385537414840956 & -17.7021877956234 & -1.49073086461519 \tabularnewline
57 & 55 & 48.855182918162 & 0.0374973270071144 & 6.42479824863928 & -0.0454485060629214 \tabularnewline
58 & 70 & 50.2751889454552 & 0.0596229454079211 & 13.5275892326063 & 1.00512826720558 \tabularnewline
59 & 62 & 52.7882129093006 & 0.096140774407123 & -1.89924105410969 & 1.80000378360204 \tabularnewline
60 & 58 & 53.4735472886739 & 0.103873334351787 & 1.81864056392772 & 0.438002164070448 \tabularnewline
61 & 39 & 51.8304572005912 & 0.0847429648833302 & -4.65985854113487 & -1.31979280812915 \tabularnewline
62 & 49 & 51.5694438255308 & 0.0805486915413699 & -0.976649132644919 & -0.257652819400335 \tabularnewline
63 & 58 & 51.4574994770855 & 0.0779019637904182 & 7.41299407872505 & -0.141151231141377 \tabularnewline
64 & 47 & 51.124033892283 & 0.0718439290790956 & -2.28213391542378 & -0.299146140347208 \tabularnewline
65 & 42 & 50.2367807760254 & 0.0573625571042322 & -3.9530457314549 & -0.696224194478585 \tabularnewline
66 & 62 & 50.3681339038007 & 0.0584788648958054 & 11.3007721075047 & 0.0538190337270404 \tabularnewline
67 & 39 & 50.1104805873921 & 0.0537830932615876 & -9.69010283373238 & -0.230837538211109 \tabularnewline
68 & 40 & 51.0555790026356 & 0.0666927955977087 & -15.0806554878824 & 0.653904373425245 \tabularnewline
69 & 72 & 53.1371485280743 & 0.0948966163527882 & 9.71184943899776 & 1.4858866556932 \tabularnewline
70 & 70 & 54.1771513856692 & 0.107520614066176 & 11.5025414556276 & 0.701034884565534 \tabularnewline
71 & 54 & 54.6367265537889 & 0.11191012033054 & -2.2588107257993 & 0.26297748924643 \tabularnewline
72 & 65 & 55.549931556547 & 0.120927320482274 & 5.72123741689881 & 0.603908639469392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159378&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]46[/C][C]46[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]62[/C][C]47.9110970294859[/C][C]0.457727744350292[/C][C]3.27289921665179[/C][C]1.53740429817903[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]52.2181740254204[/C][C]1.23550591453837[/C][C]3.11036218634978[/C][C]1.66337970171935[/C][/ROW]
[ROW][C]4[/C][C]59[/C][C]54.8085370931774[/C][C]1.4591698785972[/C][C]0.885416754515537[/C][C]0.524668797214267[/C][/ROW]
[ROW][C]5[/C][C]58[/C][C]56.4532934205753[/C][C]1.48495530273988[/C][C]1.09750359194589[/C][C]0.0712006553256786[/C][/ROW]
[ROW][C]6[/C][C]61[/C][C]58.3025428335853[/C][C]1.52861644512606[/C][C]1.78773114361487[/C][C]0.143268072165915[/C][/ROW]
[ROW][C]7[/C][C]41[/C][C]55.5097495961271[/C][C]1.0734073799599[/C][C]-3.20477313178717[/C][C]-1.76638936537319[/C][/ROW]
[ROW][C]8[/C][C]27[/C][C]49.8659877992446[/C][C]0.442403029886409[/C][C]-4.36790838932408[/C][C]-2.86761633273592[/C][/ROW]
[ROW][C]9[/C][C]58[/C][C]51.134896007643[/C][C]0.512454327726307[/C][C]4.46943156500248[/C][C]0.368676590817397[/C][/ROW]
[ROW][C]10[/C][C]70[/C][C]55.1496963415017[/C][C]0.782862183598353[/C][C]4.19172738617064[/C][C]1.62955537366549[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]54.8312554608697[/C][C]0.704779764133224[/C][C]-2.32474642955447[/C][C]-0.533007185981333[/C][/ROW]
[ROW][C]12[/C][C]59[/C][C]55.9269689687799[/C][C]0.730407977238299[/C][C]1.77578254058315[/C][C]0.196192833634787[/C][/ROW]
[ROW][C]13[/C][C]44[/C][C]56.3322289356696[/C][C]0.736556369921038[/C][C]-10.1265350015043[/C][C]-0.329520700187165[/C][/ROW]
[ROW][C]14[/C][C]36[/C][C]53.3160838653743[/C][C]0.551367235294654[/C][C]-4.85042774181174[/C][C]-1.97251277464884[/C][/ROW]
[ROW][C]15[/C][C]72[/C][C]56.1462098620253[/C][C]0.678459802103687[/C][C]9.18165017852966[/C][C]1.08291331779463[/C][/ROW]
[ROW][C]16[/C][C]45[/C][C]55.058095242053[/C][C]0.582852895980019[/C][C]-4.74356327153115[/C][C]-0.862760661319053[/C][/ROW]
[ROW][C]17[/C][C]56[/C][C]55.4248167522066[/C][C]0.571812238851777[/C][C]1.26021093683455[/C][C]-0.110643642358113[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]55.1217132483546[/C][C]0.529787140264538[/C][C]1.79122994332995[/C][C]-0.468073674291378[/C][/ROW]
[ROW][C]19[/C][C]53[/C][C]55.2065182205293[/C][C]0.509649758413919[/C][C]-0.661411211431124[/C][C]-0.247201561470065[/C][/ROW]
[ROW][C]20[/C][C]35[/C][C]53.2928554943333[/C][C]0.405993485232927[/C][C]-9.57558067988913[/C][C]-1.38969566178409[/C][/ROW]
[ROW][C]21[/C][C]61[/C][C]54.0446047696904[/C][C]0.420018290898867[/C][C]5.67384657931283[/C][C]0.203698345347536[/C][/ROW]
[ROW][C]22[/C][C]52[/C][C]53.5161585281153[/C][C]0.383451222330818[/C][C]2.09133750566887[/C][C]-0.571946593328757[/C][/ROW]
[ROW][C]23[/C][C]47[/C][C]53.18379244657[/C][C]0.357330486447834[/C][C]-3.39304821665302[/C][C]-0.441294777764235[/C][/ROW]
[ROW][C]24[/C][C]51[/C][C]52.7576254627434[/C][C]0.331821837620316[/C][C]1.42351760485177[/C][C]-0.50030873180266[/C][/ROW]
[ROW][C]25[/C][C]52[/C][C]53.393681404087[/C][C]0.334467383427234[/C][C]-2.91248720433094[/C][C]0.238013795260528[/C][/ROW]
[ROW][C]26[/C][C]63[/C][C]55.7506329182407[/C][C]0.386383838953319[/C][C]-1.08051085832075[/C][C]1.33111479342128[/C][/ROW]
[ROW][C]27[/C][C]74[/C][C]57.4972642698797[/C][C]0.429795701161531[/C][C]11.3354132799862[/C][C]0.836180039173977[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]57.0092214244353[/C][C]0.399556006675553[/C][C]-8.55539273461992[/C][C]-0.56070958621908[/C][/ROW]
[ROW][C]29[/C][C]51[/C][C]56.1634905142349[/C][C]0.359394654581507[/C][C]-0.409936037262088[/C][C]-0.771341119699042[/C][/ROW]
[ROW][C]30[/C][C]64[/C][C]57.0085310370603[/C][C]0.374479768370818[/C][C]5.10083793944758[/C][C]0.306322904980649[/C][/ROW]
[ROW][C]31[/C][C]36[/C][C]54.4646419009697[/C][C]0.287521919247104[/C][C]-6.88315851592293[/C][C]-1.87338887376895[/C][/ROW]
[ROW][C]32[/C][C]30[/C][C]52.3777896717982[/C][C]0.219655898006101[/C][C]-12.7910123563833[/C][C]-1.54839304502992[/C][/ROW]
[ROW][C]33[/C][C]55[/C][C]51.6826478934933[/C][C]0.194561223439965[/C][C]7.06816613686863[/C][C]-0.604975821696486[/C][/ROW]
[ROW][C]34[/C][C]64[/C][C]52.8949677162956[/C][C]0.221281262802052[/C][C]6.8712553931969[/C][C]0.681927251137723[/C][/ROW]
[ROW][C]35[/C][C]39[/C][C]51.8538808554544[/C][C]0.190134822516464[/C][C]-7.51335314081002[/C][C]-0.858540758339722[/C][/ROW]
[ROW][C]36[/C][C]40[/C][C]50.3169940196333[/C][C]0.153400464786647[/C][C]-2.78109455127934[/C][C]-1.20701409619885[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]52.1336476450465[/C][C]0.172851211250169[/C][C]2.99158108684999[/C][C]1.25745814675726[/C][/ROW]
[ROW][C]38[/C][C]45[/C][C]51.8776723527386[/C][C]0.165163871679258[/C][C]-4.98983538979901[/C][C]-0.303709522528703[/C][/ROW]
[ROW][C]39[/C][C]59[/C][C]51.3081817626717[/C][C]0.148940758083988[/C][C]10.7700438640505[/C][C]-0.498697830082819[/C][/ROW]
[ROW][C]40[/C][C]55[/C][C]52.7154855639546[/C][C]0.17850651897955[/C][C]-2.91236135275176[/C][C]0.844381301213788[/C][/ROW]
[ROW][C]41[/C][C]40[/C][C]51.4607954981165[/C][C]0.144740802856046[/C][C]-5.52624056845741[/C][C]-0.964761979746398[/C][/ROW]
[ROW][C]42[/C][C]64[/C][C]51.9113947149439[/C][C]0.151796979203128[/C][C]10.8107148690089[/C][C]0.207653088178401[/C][/ROW]
[ROW][C]43[/C][C]27[/C][C]49.569603531823[/C][C]0.0959865593902811[/C][C]-12.0403514054693[/C][C]-1.7096684150664[/C][/ROW]
[ROW][C]44[/C][C]28[/C][C]48.1397975990485[/C][C]0.0629804906621448[/C][C]-13.629058547596[/C][C]-1.05627505056528[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]46.7735161857766[/C][C]0.0331927254388154[/C][C]4.38732759280101[/C][C]-0.998619200326333[/C][/ROW]
[ROW][C]46[/C][C]57[/C][C]46.7764707827994[/C][C]0.032590468382702[/C][C]10.3552406267113[/C][C]-0.0213268746658416[/C][/ROW]
[ROW][C]47[/C][C]45[/C][C]47.2931160508011[/C][C]0.0415739288817546[/C][C]-4.42947294360182[/C][C]0.345395981609572[/C][/ROW]
[ROW][C]48[/C][C]69[/C][C]50.4129453780655[/C][C]0.0909461200618712[/C][C]4.70423627714895[/C][C]2.23926448303041[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]51.5905476979353[/C][C]0.103645818564432[/C][C]3.32738834954036[/C][C]0.81794039031203[/C][/ROW]
[ROW][C]50[/C][C]56[/C][C]52.9226989740611[/C][C]0.121202104802161[/C][C]-2.48924902734101[/C][C]0.89872986428568[/C][/ROW]
[ROW][C]51[/C][C]58[/C][C]52.6696200364005[/C][C]0.114880033552314[/C][C]6.97609368353074[/C][C]-0.266782073171524[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]52.5873543933026[/C][C]0.111304445477343[/C][C]-1.73148196057455[/C][C]-0.13905037567473[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]53.0528675380641[/C][C]0.117841824363637[/C][C]-3.58859416012087[/C][C]0.249691041457891[/C][/ROW]
[ROW][C]54[/C][C]53[/C][C]51.5548695799714[/C][C]0.0882843756782409[/C][C]8.48028199409069[/C][C]-1.14380737714284[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]50.884475072407[/C][C]0.0747063062870747[/C][C]-10.5600971636644[/C][C]-0.540288315940446[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]48.8796016467694[/C][C]0.0385537414840956[/C][C]-17.7021877956234[/C][C]-1.49073086461519[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]48.855182918162[/C][C]0.0374973270071144[/C][C]6.42479824863928[/C][C]-0.0454485060629214[/C][/ROW]
[ROW][C]58[/C][C]70[/C][C]50.2751889454552[/C][C]0.0596229454079211[/C][C]13.5275892326063[/C][C]1.00512826720558[/C][/ROW]
[ROW][C]59[/C][C]62[/C][C]52.7882129093006[/C][C]0.096140774407123[/C][C]-1.89924105410969[/C][C]1.80000378360204[/C][/ROW]
[ROW][C]60[/C][C]58[/C][C]53.4735472886739[/C][C]0.103873334351787[/C][C]1.81864056392772[/C][C]0.438002164070448[/C][/ROW]
[ROW][C]61[/C][C]39[/C][C]51.8304572005912[/C][C]0.0847429648833302[/C][C]-4.65985854113487[/C][C]-1.31979280812915[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]51.5694438255308[/C][C]0.0805486915413699[/C][C]-0.976649132644919[/C][C]-0.257652819400335[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]51.4574994770855[/C][C]0.0779019637904182[/C][C]7.41299407872505[/C][C]-0.141151231141377[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]51.124033892283[/C][C]0.0718439290790956[/C][C]-2.28213391542378[/C][C]-0.299146140347208[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]50.2367807760254[/C][C]0.0573625571042322[/C][C]-3.9530457314549[/C][C]-0.696224194478585[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]50.3681339038007[/C][C]0.0584788648958054[/C][C]11.3007721075047[/C][C]0.0538190337270404[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]50.1104805873921[/C][C]0.0537830932615876[/C][C]-9.69010283373238[/C][C]-0.230837538211109[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]51.0555790026356[/C][C]0.0666927955977087[/C][C]-15.0806554878824[/C][C]0.653904373425245[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]53.1371485280743[/C][C]0.0948966163527882[/C][C]9.71184943899776[/C][C]1.4858866556932[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]54.1771513856692[/C][C]0.107520614066176[/C][C]11.5025414556276[/C][C]0.701034884565534[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]54.6367265537889[/C][C]0.11191012033054[/C][C]-2.2588107257993[/C][C]0.26297748924643[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]55.549931556547[/C][C]0.120927320482274[/C][C]5.72123741689881[/C][C]0.603908639469392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159378&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
14646000
26247.91109702948590.4577277443502923.272899216651791.53740429817903
36652.21817402542041.235505914538373.110362186349781.66337970171935
45954.80853709317741.45916987859720.8854167545155370.524668797214267
55856.45329342057531.484955302739881.097503591945890.0712006553256786
66158.30254283358531.528616445126061.787731143614870.143268072165915
74155.50974959612711.0734073799599-3.20477313178717-1.76638936537319
82749.86598779924460.442403029886409-4.36790838932408-2.86761633273592
95851.1348960076430.5124543277263074.469431565002480.368676590817397
107055.14969634150170.7828621835983534.191727386170641.62955537366549
114954.83125546086970.704779764133224-2.32474642955447-0.533007185981333
125955.92696896877990.7304079772382991.775782540583150.196192833634787
134456.33222893566960.736556369921038-10.1265350015043-0.329520700187165
143653.31608386537430.551367235294654-4.85042774181174-1.97251277464884
157256.14620986202530.6784598021036879.181650178529661.08291331779463
164555.0580952420530.582852895980019-4.74356327153115-0.862760661319053
175655.42481675220660.5718122388517771.26021093683455-0.110643642358113
185455.12171324835460.5297871402645381.79122994332995-0.468073674291378
195355.20651822052930.509649758413919-0.661411211431124-0.247201561470065
203553.29285549433330.405993485232927-9.57558067988913-1.38969566178409
216154.04460476969040.4200182908988675.673846579312830.203698345347536
225253.51615852811530.3834512223308182.09133750566887-0.571946593328757
234753.183792446570.357330486447834-3.39304821665302-0.441294777764235
245152.75762546274340.3318218376203161.42351760485177-0.50030873180266
255253.3936814040870.334467383427234-2.912487204330940.238013795260528
266355.75063291824070.386383838953319-1.080510858320751.33111479342128
277457.49726426987970.42979570116153111.33541327998620.836180039173977
284557.00922142443530.399556006675553-8.55539273461992-0.56070958621908
295156.16349051423490.359394654581507-0.409936037262088-0.771341119699042
306457.00853103706030.3744797683708185.100837939447580.306322904980649
313654.46464190096970.287521919247104-6.88315851592293-1.87338887376895
323052.37778967179820.219655898006101-12.7910123563833-1.54839304502992
335551.68264789349330.1945612234399657.06816613686863-0.604975821696486
346452.89496771629560.2212812628020526.87125539319690.681927251137723
353951.85388085545440.190134822516464-7.51335314081002-0.858540758339722
364050.31699401963330.153400464786647-2.78109455127934-1.20701409619885
376352.13364764504650.1728512112501692.991581086849991.25745814675726
384551.87767235273860.165163871679258-4.98983538979901-0.303709522528703
395951.30818176267170.14894075808398810.7700438640505-0.498697830082819
405552.71548556395460.17850651897955-2.912361352751760.844381301213788
414051.46079549811650.144740802856046-5.52624056845741-0.964761979746398
426451.91139471494390.15179697920312810.81071486900890.207653088178401
432749.5696035318230.0959865593902811-12.0403514054693-1.7096684150664
442848.13979759904850.0629804906621448-13.629058547596-1.05627505056528
454546.77351618577660.03319272543881544.38732759280101-0.998619200326333
465746.77647078279940.03259046838270210.3552406267113-0.0213268746658416
474547.29311605080110.0415739288817546-4.429472943601820.345395981609572
486950.41294537806550.09094612006187124.704236277148952.23926448303041
496051.59054769793530.1036458185644323.327388349540360.81794039031203
505652.92269897406110.121202104802161-2.489249027341010.89872986428568
515852.66962003640050.1148800335523146.97609368353074-0.266782073171524
525052.58735439330260.111304445477343-1.73148196057455-0.13905037567473
535153.05286753806410.117841824363637-3.588594160120870.249691041457891
545351.55486957997140.08828437567824098.48028199409069-1.14380737714284
553750.8844750724070.0747063062870747-10.5600971636644-0.540288315940446
562248.87960164676940.0385537414840956-17.7021877956234-1.49073086461519
575548.8551829181620.03749732700711446.42479824863928-0.0454485060629214
587050.27518894545520.059622945407921113.52758923260631.00512826720558
596252.78821290930060.096140774407123-1.899241054109691.80000378360204
605853.47354728867390.1038733343517871.818640563927720.438002164070448
613951.83045720059120.0847429648833302-4.65985854113487-1.31979280812915
624951.56944382553080.0805486915413699-0.976649132644919-0.257652819400335
635851.45749947708550.07790196379041827.41299407872505-0.141151231141377
644751.1240338922830.0718439290790956-2.28213391542378-0.299146140347208
654250.23678077602540.0573625571042322-3.9530457314549-0.696224194478585
666250.36813390380070.058478864895805411.30077210750470.0538190337270404
673950.11048058739210.0537830932615876-9.69010283373238-0.230837538211109
684051.05557900263560.0666927955977087-15.08065548788240.653904373425245
697253.13714852807430.09489661635278829.711849438997761.4858866556932
707054.17715138566920.10752061406617611.50254145562760.701034884565534
715454.63672655378890.11191012033054-2.25881072579930.26297748924643
726555.5499315565470.1209273204822745.721237416898810.603908639469392



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')