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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 03:23:01 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259922225ohkno5kv3pm2ev7.htm/, Retrieved Sat, 27 Apr 2024 17:56:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63244, Retrieved Sat, 27 Apr 2024 17:56:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSHW WS 9 Structural Time Series Models
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [WS 9 Structural T...] [2009-12-04 10:23:01] [a45cc820faa25ce30779915639528ec2] [Current]
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Dataseries X:
14.2
13.5
11.9
14.6
15.6
14.1
14.9
14.2
14.6
17.2
15.4
14.3
17.5
14.5
14.4
16.6
16.7
16.6
16.9
15.7
16.4
18.4
16.9
16.5
18.3
15.1
15.7
18.1
16.8
18.9
19
18.1
17.8
21.5
17.1
18.7
19
16.4
16.9
18.6
19.3
19.4
17.6
18.6
18.1
20.4
18.1
19.6
19.9
19.2
17.8
19.2
22
21.1
19.5
22.2
20.9
22.2
23.5
21.5
24.3
22.8
20.3
23.7
23.3
19.6
18
17.3
16.8
18.2
16.5
16
18.4




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=63244&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=63244&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63244&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
114.214.2000
213.513.6962936202158-0.0218460906336913-0.196293620215784-0.510961302574374
311.912.5702624714998-0.0799509747537027-0.670262471499777-1.28905027878636
414.613.5370438654530-0.04615112368630381.062956134546981.46329923993990
515.614.8464248791018-0.02392546807888680.7535751208981711.95715040105429
614.114.6933425081781-0.0252516531120534-0.593342508178087-0.186791276444708
714.914.7846758745439-0.02417223680943290.1153241254560860.168450679883035
814.214.4884285371524-0.026809952089474-0.288428537152382-0.392773401016692
914.614.5009388600968-0.02641283473224800.09906113990323520.0567382164401477
1017.215.9906933974858-0.01092317548608951.209306602514172.18754213010685
1115.415.9531568795025-0.0111935144893430-0.55315687950249-0.0383985440214962
1214.315.0428351737731-0.0202241712770097-0.742835173773071-1.29732709720659
1317.515.8445774986595-0.04392159038615291.655422501340531.28248841532129
1414.515.1386898929531-0.0440762184398627-0.638689892953074-0.956503842745318
1514.415.3125085376162-0.0395886555083069-0.9125085376162150.290041437516813
1616.615.7595887304915-0.02857558105195300.8404112695084740.669989945447174
1716.715.8833206620207-0.02601999576182790.8166793379792870.217177605013153
1816.616.5663305059722-0.01814429507456290.03366949402776931.02223378168815
1916.916.7138164028435-0.01679637143252050.1861835971564550.239206876790042
2015.716.4580578485087-0.0185438447733307-0.758057848508664-0.345069547894469
2116.416.6882200247152-0.0166973840672004-0.2882200247151940.359040185774239
2218.416.9319367557831-0.01480722907305921.468063244216940.375838750550512
2316.917.0767935804617-0.0139431355412167-0.1767935804616840.230235386400481
2416.517.3491456998188-0.0137442623144000-0.8491456998188030.41346327500924
2518.316.9193860039223-0.01169067008446131.38061399607775-0.609305430076157
2615.116.3534377019843-0.0125862934937511-1.2534377019843-0.79657961809902
2715.716.5418742525155-0.0103600918800077-0.8418742525155070.279518673783194
2818.117.0212199201116-0.002903343624512521.078780079888380.682027894419079
2916.816.7142445080592-0.007230115579343320.0857554919407617-0.43139289913556
3018.917.78384746800020.004876587681267431.116152531999851.54656750785754
311918.42086110981640.01035737652969510.5791388901835530.911993503563147
3218.118.82179042507460.0131587939088284-0.7217904250746430.564106828493199
3317.818.59666721483950.0116609695805094-0.796667214839529-0.344116422627190
3421.519.37172077203380.01560813215372012.128279227966191.10155994887272
3517.118.44814321371710.0125830048153362-1.34814321371715-1.35410045289478
3618.718.74565774622860.0128163868799370-0.04565774622862470.411320851329926
371918.10602381193630.01282280942285990.89397618806374-0.94318038422607
3816.417.86389160263680.012031102129274-1.46389160263678-0.364909874229781
3916.917.87196295580720.0119996193423882-0.971962955807191-0.00558288949162498
4018.617.69896947581170.009935327894282760.901030524188335-0.260187794118112
4119.318.77535440153830.02237817980862320.5246455984616831.51362961246257
4219.418.86005314265830.02302389001560750.5399468573416710.0893083145149912
4317.618.02975078906550.0157359945700344-0.429750789065486-1.22942482034335
4418.618.58096011489360.01944609424342230.01903988510638780.77294592443537
4518.118.95411578078900.0214121214346974-0.8541157807889670.51059396860266
4620.418.44427030953370.01920265703125511.95572969046633-0.766288873106567
4718.118.90655249142770.0203767240500655-0.806552491427690.638717058582607
4819.619.17616005399870.02075270111841420.4238399460013020.359315647383104
4919.919.07052804774790.02054025297818290.829471952252136-0.181938014290561
5019.219.89807075787930.0235132400234149-0.6980707578793281.15412229369438
5117.819.49015068041050.0206687710323164-1.69015068041052-0.611959182569259
5219.219.08183665491790.01686031296745560.118163345082099-0.60712352175694
532220.22481694081390.02778710436901811.775183059186091.60147036004313
5421.120.39668855829170.02911230258974940.7033114417082820.206319304538089
5519.520.33148614175590.0283612770781370-0.831486141755915-0.135715902666664
5622.221.32433728683150.03465254446283670.8756627131684591.39088119093849
5720.921.59881553165940.0358768768914523-0.6988155316594210.345947200063953
5822.221.08109327142450.03379274491603741.11890672857555-0.7981356278653
5923.522.74525612725020.03808309583546730.7547438727497912.3493292309355
6021.522.16711613110250.0368199501355184-0.667116131102471-0.887476799362390
6124.322.93006120001300.03858771689290821.369938799987031.04361331108593
6222.823.23088317963780.0396008318279125-0.4308831796378320.375146174328563
6320.322.63756747405280.0359232147716827-2.33756747405279-0.900854601113942
6423.723.35171885473940.04098330048934640.3482811452605550.963769543552018
6523.322.56562922251580.03416082272937570.73437077748417-1.17859557606987
6619.620.63885844696450.0182954704975575-1.03885844696454-2.80788066228598
671819.71385625618040.0114594678733042-1.71385625618036-1.35616078818596
6817.317.96302263698320.000778064530874426-0.663022636983215-2.53892632526007
6916.817.4806259685557-0.00153895237867139-0.680625968555701-0.696448362145635
7018.217.5804328557332-0.001170674450694410.6195671442668060.146037657290146
7116.516.5566294477317-0.00400215643104431-0.0566294477316673-1.47289973064485
721616.6527506842698-0.00375817701028089-0.6527506842697530.144091047663971
7318.416.8769270349950-0.003114788990107971.523072965004970.327384222430384

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 14.2 & 14.2 & 0 & 0 & 0 \tabularnewline
2 & 13.5 & 13.6962936202158 & -0.0218460906336913 & -0.196293620215784 & -0.510961302574374 \tabularnewline
3 & 11.9 & 12.5702624714998 & -0.0799509747537027 & -0.670262471499777 & -1.28905027878636 \tabularnewline
4 & 14.6 & 13.5370438654530 & -0.0461511236863038 & 1.06295613454698 & 1.46329923993990 \tabularnewline
5 & 15.6 & 14.8464248791018 & -0.0239254680788868 & 0.753575120898171 & 1.95715040105429 \tabularnewline
6 & 14.1 & 14.6933425081781 & -0.0252516531120534 & -0.593342508178087 & -0.186791276444708 \tabularnewline
7 & 14.9 & 14.7846758745439 & -0.0241722368094329 & 0.115324125456086 & 0.168450679883035 \tabularnewline
8 & 14.2 & 14.4884285371524 & -0.026809952089474 & -0.288428537152382 & -0.392773401016692 \tabularnewline
9 & 14.6 & 14.5009388600968 & -0.0264128347322480 & 0.0990611399032352 & 0.0567382164401477 \tabularnewline
10 & 17.2 & 15.9906933974858 & -0.0109231754860895 & 1.20930660251417 & 2.18754213010685 \tabularnewline
11 & 15.4 & 15.9531568795025 & -0.0111935144893430 & -0.55315687950249 & -0.0383985440214962 \tabularnewline
12 & 14.3 & 15.0428351737731 & -0.0202241712770097 & -0.742835173773071 & -1.29732709720659 \tabularnewline
13 & 17.5 & 15.8445774986595 & -0.0439215903861529 & 1.65542250134053 & 1.28248841532129 \tabularnewline
14 & 14.5 & 15.1386898929531 & -0.0440762184398627 & -0.638689892953074 & -0.956503842745318 \tabularnewline
15 & 14.4 & 15.3125085376162 & -0.0395886555083069 & -0.912508537616215 & 0.290041437516813 \tabularnewline
16 & 16.6 & 15.7595887304915 & -0.0285755810519530 & 0.840411269508474 & 0.669989945447174 \tabularnewline
17 & 16.7 & 15.8833206620207 & -0.0260199957618279 & 0.816679337979287 & 0.217177605013153 \tabularnewline
18 & 16.6 & 16.5663305059722 & -0.0181442950745629 & 0.0336694940277693 & 1.02223378168815 \tabularnewline
19 & 16.9 & 16.7138164028435 & -0.0167963714325205 & 0.186183597156455 & 0.239206876790042 \tabularnewline
20 & 15.7 & 16.4580578485087 & -0.0185438447733307 & -0.758057848508664 & -0.345069547894469 \tabularnewline
21 & 16.4 & 16.6882200247152 & -0.0166973840672004 & -0.288220024715194 & 0.359040185774239 \tabularnewline
22 & 18.4 & 16.9319367557831 & -0.0148072290730592 & 1.46806324421694 & 0.375838750550512 \tabularnewline
23 & 16.9 & 17.0767935804617 & -0.0139431355412167 & -0.176793580461684 & 0.230235386400481 \tabularnewline
24 & 16.5 & 17.3491456998188 & -0.0137442623144000 & -0.849145699818803 & 0.41346327500924 \tabularnewline
25 & 18.3 & 16.9193860039223 & -0.0116906700844613 & 1.38061399607775 & -0.609305430076157 \tabularnewline
26 & 15.1 & 16.3534377019843 & -0.0125862934937511 & -1.2534377019843 & -0.79657961809902 \tabularnewline
27 & 15.7 & 16.5418742525155 & -0.0103600918800077 & -0.841874252515507 & 0.279518673783194 \tabularnewline
28 & 18.1 & 17.0212199201116 & -0.00290334362451252 & 1.07878007988838 & 0.682027894419079 \tabularnewline
29 & 16.8 & 16.7142445080592 & -0.00723011557934332 & 0.0857554919407617 & -0.43139289913556 \tabularnewline
30 & 18.9 & 17.7838474680002 & 0.00487658768126743 & 1.11615253199985 & 1.54656750785754 \tabularnewline
31 & 19 & 18.4208611098164 & 0.0103573765296951 & 0.579138890183553 & 0.911993503563147 \tabularnewline
32 & 18.1 & 18.8217904250746 & 0.0131587939088284 & -0.721790425074643 & 0.564106828493199 \tabularnewline
33 & 17.8 & 18.5966672148395 & 0.0116609695805094 & -0.796667214839529 & -0.344116422627190 \tabularnewline
34 & 21.5 & 19.3717207720338 & 0.0156081321537201 & 2.12827922796619 & 1.10155994887272 \tabularnewline
35 & 17.1 & 18.4481432137171 & 0.0125830048153362 & -1.34814321371715 & -1.35410045289478 \tabularnewline
36 & 18.7 & 18.7456577462286 & 0.0128163868799370 & -0.0456577462286247 & 0.411320851329926 \tabularnewline
37 & 19 & 18.1060238119363 & 0.0128228094228599 & 0.89397618806374 & -0.94318038422607 \tabularnewline
38 & 16.4 & 17.8638916026368 & 0.012031102129274 & -1.46389160263678 & -0.364909874229781 \tabularnewline
39 & 16.9 & 17.8719629558072 & 0.0119996193423882 & -0.971962955807191 & -0.00558288949162498 \tabularnewline
40 & 18.6 & 17.6989694758117 & 0.00993532789428276 & 0.901030524188335 & -0.260187794118112 \tabularnewline
41 & 19.3 & 18.7753544015383 & 0.0223781798086232 & 0.524645598461683 & 1.51362961246257 \tabularnewline
42 & 19.4 & 18.8600531426583 & 0.0230238900156075 & 0.539946857341671 & 0.0893083145149912 \tabularnewline
43 & 17.6 & 18.0297507890655 & 0.0157359945700344 & -0.429750789065486 & -1.22942482034335 \tabularnewline
44 & 18.6 & 18.5809601148936 & 0.0194460942434223 & 0.0190398851063878 & 0.77294592443537 \tabularnewline
45 & 18.1 & 18.9541157807890 & 0.0214121214346974 & -0.854115780788967 & 0.51059396860266 \tabularnewline
46 & 20.4 & 18.4442703095337 & 0.0192026570312551 & 1.95572969046633 & -0.766288873106567 \tabularnewline
47 & 18.1 & 18.9065524914277 & 0.0203767240500655 & -0.80655249142769 & 0.638717058582607 \tabularnewline
48 & 19.6 & 19.1761600539987 & 0.0207527011184142 & 0.423839946001302 & 0.359315647383104 \tabularnewline
49 & 19.9 & 19.0705280477479 & 0.0205402529781829 & 0.829471952252136 & -0.181938014290561 \tabularnewline
50 & 19.2 & 19.8980707578793 & 0.0235132400234149 & -0.698070757879328 & 1.15412229369438 \tabularnewline
51 & 17.8 & 19.4901506804105 & 0.0206687710323164 & -1.69015068041052 & -0.611959182569259 \tabularnewline
52 & 19.2 & 19.0818366549179 & 0.0168603129674556 & 0.118163345082099 & -0.60712352175694 \tabularnewline
53 & 22 & 20.2248169408139 & 0.0277871043690181 & 1.77518305918609 & 1.60147036004313 \tabularnewline
54 & 21.1 & 20.3966885582917 & 0.0291123025897494 & 0.703311441708282 & 0.206319304538089 \tabularnewline
55 & 19.5 & 20.3314861417559 & 0.0283612770781370 & -0.831486141755915 & -0.135715902666664 \tabularnewline
56 & 22.2 & 21.3243372868315 & 0.0346525444628367 & 0.875662713168459 & 1.39088119093849 \tabularnewline
57 & 20.9 & 21.5988155316594 & 0.0358768768914523 & -0.698815531659421 & 0.345947200063953 \tabularnewline
58 & 22.2 & 21.0810932714245 & 0.0337927449160374 & 1.11890672857555 & -0.7981356278653 \tabularnewline
59 & 23.5 & 22.7452561272502 & 0.0380830958354673 & 0.754743872749791 & 2.3493292309355 \tabularnewline
60 & 21.5 & 22.1671161311025 & 0.0368199501355184 & -0.667116131102471 & -0.887476799362390 \tabularnewline
61 & 24.3 & 22.9300612000130 & 0.0385877168929082 & 1.36993879998703 & 1.04361331108593 \tabularnewline
62 & 22.8 & 23.2308831796378 & 0.0396008318279125 & -0.430883179637832 & 0.375146174328563 \tabularnewline
63 & 20.3 & 22.6375674740528 & 0.0359232147716827 & -2.33756747405279 & -0.900854601113942 \tabularnewline
64 & 23.7 & 23.3517188547394 & 0.0409833004893464 & 0.348281145260555 & 0.963769543552018 \tabularnewline
65 & 23.3 & 22.5656292225158 & 0.0341608227293757 & 0.73437077748417 & -1.17859557606987 \tabularnewline
66 & 19.6 & 20.6388584469645 & 0.0182954704975575 & -1.03885844696454 & -2.80788066228598 \tabularnewline
67 & 18 & 19.7138562561804 & 0.0114594678733042 & -1.71385625618036 & -1.35616078818596 \tabularnewline
68 & 17.3 & 17.9630226369832 & 0.000778064530874426 & -0.663022636983215 & -2.53892632526007 \tabularnewline
69 & 16.8 & 17.4806259685557 & -0.00153895237867139 & -0.680625968555701 & -0.696448362145635 \tabularnewline
70 & 18.2 & 17.5804328557332 & -0.00117067445069441 & 0.619567144266806 & 0.146037657290146 \tabularnewline
71 & 16.5 & 16.5566294477317 & -0.00400215643104431 & -0.0566294477316673 & -1.47289973064485 \tabularnewline
72 & 16 & 16.6527506842698 & -0.00375817701028089 & -0.652750684269753 & 0.144091047663971 \tabularnewline
73 & 18.4 & 16.8769270349950 & -0.00311478899010797 & 1.52307296500497 & 0.327384222430384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63244&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]14.2[/C][C]14.2[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]13.5[/C][C]13.6962936202158[/C][C]-0.0218460906336913[/C][C]-0.196293620215784[/C][C]-0.510961302574374[/C][/ROW]
[ROW][C]3[/C][C]11.9[/C][C]12.5702624714998[/C][C]-0.0799509747537027[/C][C]-0.670262471499777[/C][C]-1.28905027878636[/C][/ROW]
[ROW][C]4[/C][C]14.6[/C][C]13.5370438654530[/C][C]-0.0461511236863038[/C][C]1.06295613454698[/C][C]1.46329923993990[/C][/ROW]
[ROW][C]5[/C][C]15.6[/C][C]14.8464248791018[/C][C]-0.0239254680788868[/C][C]0.753575120898171[/C][C]1.95715040105429[/C][/ROW]
[ROW][C]6[/C][C]14.1[/C][C]14.6933425081781[/C][C]-0.0252516531120534[/C][C]-0.593342508178087[/C][C]-0.186791276444708[/C][/ROW]
[ROW][C]7[/C][C]14.9[/C][C]14.7846758745439[/C][C]-0.0241722368094329[/C][C]0.115324125456086[/C][C]0.168450679883035[/C][/ROW]
[ROW][C]8[/C][C]14.2[/C][C]14.4884285371524[/C][C]-0.026809952089474[/C][C]-0.288428537152382[/C][C]-0.392773401016692[/C][/ROW]
[ROW][C]9[/C][C]14.6[/C][C]14.5009388600968[/C][C]-0.0264128347322480[/C][C]0.0990611399032352[/C][C]0.0567382164401477[/C][/ROW]
[ROW][C]10[/C][C]17.2[/C][C]15.9906933974858[/C][C]-0.0109231754860895[/C][C]1.20930660251417[/C][C]2.18754213010685[/C][/ROW]
[ROW][C]11[/C][C]15.4[/C][C]15.9531568795025[/C][C]-0.0111935144893430[/C][C]-0.55315687950249[/C][C]-0.0383985440214962[/C][/ROW]
[ROW][C]12[/C][C]14.3[/C][C]15.0428351737731[/C][C]-0.0202241712770097[/C][C]-0.742835173773071[/C][C]-1.29732709720659[/C][/ROW]
[ROW][C]13[/C][C]17.5[/C][C]15.8445774986595[/C][C]-0.0439215903861529[/C][C]1.65542250134053[/C][C]1.28248841532129[/C][/ROW]
[ROW][C]14[/C][C]14.5[/C][C]15.1386898929531[/C][C]-0.0440762184398627[/C][C]-0.638689892953074[/C][C]-0.956503842745318[/C][/ROW]
[ROW][C]15[/C][C]14.4[/C][C]15.3125085376162[/C][C]-0.0395886555083069[/C][C]-0.912508537616215[/C][C]0.290041437516813[/C][/ROW]
[ROW][C]16[/C][C]16.6[/C][C]15.7595887304915[/C][C]-0.0285755810519530[/C][C]0.840411269508474[/C][C]0.669989945447174[/C][/ROW]
[ROW][C]17[/C][C]16.7[/C][C]15.8833206620207[/C][C]-0.0260199957618279[/C][C]0.816679337979287[/C][C]0.217177605013153[/C][/ROW]
[ROW][C]18[/C][C]16.6[/C][C]16.5663305059722[/C][C]-0.0181442950745629[/C][C]0.0336694940277693[/C][C]1.02223378168815[/C][/ROW]
[ROW][C]19[/C][C]16.9[/C][C]16.7138164028435[/C][C]-0.0167963714325205[/C][C]0.186183597156455[/C][C]0.239206876790042[/C][/ROW]
[ROW][C]20[/C][C]15.7[/C][C]16.4580578485087[/C][C]-0.0185438447733307[/C][C]-0.758057848508664[/C][C]-0.345069547894469[/C][/ROW]
[ROW][C]21[/C][C]16.4[/C][C]16.6882200247152[/C][C]-0.0166973840672004[/C][C]-0.288220024715194[/C][C]0.359040185774239[/C][/ROW]
[ROW][C]22[/C][C]18.4[/C][C]16.9319367557831[/C][C]-0.0148072290730592[/C][C]1.46806324421694[/C][C]0.375838750550512[/C][/ROW]
[ROW][C]23[/C][C]16.9[/C][C]17.0767935804617[/C][C]-0.0139431355412167[/C][C]-0.176793580461684[/C][C]0.230235386400481[/C][/ROW]
[ROW][C]24[/C][C]16.5[/C][C]17.3491456998188[/C][C]-0.0137442623144000[/C][C]-0.849145699818803[/C][C]0.41346327500924[/C][/ROW]
[ROW][C]25[/C][C]18.3[/C][C]16.9193860039223[/C][C]-0.0116906700844613[/C][C]1.38061399607775[/C][C]-0.609305430076157[/C][/ROW]
[ROW][C]26[/C][C]15.1[/C][C]16.3534377019843[/C][C]-0.0125862934937511[/C][C]-1.2534377019843[/C][C]-0.79657961809902[/C][/ROW]
[ROW][C]27[/C][C]15.7[/C][C]16.5418742525155[/C][C]-0.0103600918800077[/C][C]-0.841874252515507[/C][C]0.279518673783194[/C][/ROW]
[ROW][C]28[/C][C]18.1[/C][C]17.0212199201116[/C][C]-0.00290334362451252[/C][C]1.07878007988838[/C][C]0.682027894419079[/C][/ROW]
[ROW][C]29[/C][C]16.8[/C][C]16.7142445080592[/C][C]-0.00723011557934332[/C][C]0.0857554919407617[/C][C]-0.43139289913556[/C][/ROW]
[ROW][C]30[/C][C]18.9[/C][C]17.7838474680002[/C][C]0.00487658768126743[/C][C]1.11615253199985[/C][C]1.54656750785754[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]18.4208611098164[/C][C]0.0103573765296951[/C][C]0.579138890183553[/C][C]0.911993503563147[/C][/ROW]
[ROW][C]32[/C][C]18.1[/C][C]18.8217904250746[/C][C]0.0131587939088284[/C][C]-0.721790425074643[/C][C]0.564106828493199[/C][/ROW]
[ROW][C]33[/C][C]17.8[/C][C]18.5966672148395[/C][C]0.0116609695805094[/C][C]-0.796667214839529[/C][C]-0.344116422627190[/C][/ROW]
[ROW][C]34[/C][C]21.5[/C][C]19.3717207720338[/C][C]0.0156081321537201[/C][C]2.12827922796619[/C][C]1.10155994887272[/C][/ROW]
[ROW][C]35[/C][C]17.1[/C][C]18.4481432137171[/C][C]0.0125830048153362[/C][C]-1.34814321371715[/C][C]-1.35410045289478[/C][/ROW]
[ROW][C]36[/C][C]18.7[/C][C]18.7456577462286[/C][C]0.0128163868799370[/C][C]-0.0456577462286247[/C][C]0.411320851329926[/C][/ROW]
[ROW][C]37[/C][C]19[/C][C]18.1060238119363[/C][C]0.0128228094228599[/C][C]0.89397618806374[/C][C]-0.94318038422607[/C][/ROW]
[ROW][C]38[/C][C]16.4[/C][C]17.8638916026368[/C][C]0.012031102129274[/C][C]-1.46389160263678[/C][C]-0.364909874229781[/C][/ROW]
[ROW][C]39[/C][C]16.9[/C][C]17.8719629558072[/C][C]0.0119996193423882[/C][C]-0.971962955807191[/C][C]-0.00558288949162498[/C][/ROW]
[ROW][C]40[/C][C]18.6[/C][C]17.6989694758117[/C][C]0.00993532789428276[/C][C]0.901030524188335[/C][C]-0.260187794118112[/C][/ROW]
[ROW][C]41[/C][C]19.3[/C][C]18.7753544015383[/C][C]0.0223781798086232[/C][C]0.524645598461683[/C][C]1.51362961246257[/C][/ROW]
[ROW][C]42[/C][C]19.4[/C][C]18.8600531426583[/C][C]0.0230238900156075[/C][C]0.539946857341671[/C][C]0.0893083145149912[/C][/ROW]
[ROW][C]43[/C][C]17.6[/C][C]18.0297507890655[/C][C]0.0157359945700344[/C][C]-0.429750789065486[/C][C]-1.22942482034335[/C][/ROW]
[ROW][C]44[/C][C]18.6[/C][C]18.5809601148936[/C][C]0.0194460942434223[/C][C]0.0190398851063878[/C][C]0.77294592443537[/C][/ROW]
[ROW][C]45[/C][C]18.1[/C][C]18.9541157807890[/C][C]0.0214121214346974[/C][C]-0.854115780788967[/C][C]0.51059396860266[/C][/ROW]
[ROW][C]46[/C][C]20.4[/C][C]18.4442703095337[/C][C]0.0192026570312551[/C][C]1.95572969046633[/C][C]-0.766288873106567[/C][/ROW]
[ROW][C]47[/C][C]18.1[/C][C]18.9065524914277[/C][C]0.0203767240500655[/C][C]-0.80655249142769[/C][C]0.638717058582607[/C][/ROW]
[ROW][C]48[/C][C]19.6[/C][C]19.1761600539987[/C][C]0.0207527011184142[/C][C]0.423839946001302[/C][C]0.359315647383104[/C][/ROW]
[ROW][C]49[/C][C]19.9[/C][C]19.0705280477479[/C][C]0.0205402529781829[/C][C]0.829471952252136[/C][C]-0.181938014290561[/C][/ROW]
[ROW][C]50[/C][C]19.2[/C][C]19.8980707578793[/C][C]0.0235132400234149[/C][C]-0.698070757879328[/C][C]1.15412229369438[/C][/ROW]
[ROW][C]51[/C][C]17.8[/C][C]19.4901506804105[/C][C]0.0206687710323164[/C][C]-1.69015068041052[/C][C]-0.611959182569259[/C][/ROW]
[ROW][C]52[/C][C]19.2[/C][C]19.0818366549179[/C][C]0.0168603129674556[/C][C]0.118163345082099[/C][C]-0.60712352175694[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]20.2248169408139[/C][C]0.0277871043690181[/C][C]1.77518305918609[/C][C]1.60147036004313[/C][/ROW]
[ROW][C]54[/C][C]21.1[/C][C]20.3966885582917[/C][C]0.0291123025897494[/C][C]0.703311441708282[/C][C]0.206319304538089[/C][/ROW]
[ROW][C]55[/C][C]19.5[/C][C]20.3314861417559[/C][C]0.0283612770781370[/C][C]-0.831486141755915[/C][C]-0.135715902666664[/C][/ROW]
[ROW][C]56[/C][C]22.2[/C][C]21.3243372868315[/C][C]0.0346525444628367[/C][C]0.875662713168459[/C][C]1.39088119093849[/C][/ROW]
[ROW][C]57[/C][C]20.9[/C][C]21.5988155316594[/C][C]0.0358768768914523[/C][C]-0.698815531659421[/C][C]0.345947200063953[/C][/ROW]
[ROW][C]58[/C][C]22.2[/C][C]21.0810932714245[/C][C]0.0337927449160374[/C][C]1.11890672857555[/C][C]-0.7981356278653[/C][/ROW]
[ROW][C]59[/C][C]23.5[/C][C]22.7452561272502[/C][C]0.0380830958354673[/C][C]0.754743872749791[/C][C]2.3493292309355[/C][/ROW]
[ROW][C]60[/C][C]21.5[/C][C]22.1671161311025[/C][C]0.0368199501355184[/C][C]-0.667116131102471[/C][C]-0.887476799362390[/C][/ROW]
[ROW][C]61[/C][C]24.3[/C][C]22.9300612000130[/C][C]0.0385877168929082[/C][C]1.36993879998703[/C][C]1.04361331108593[/C][/ROW]
[ROW][C]62[/C][C]22.8[/C][C]23.2308831796378[/C][C]0.0396008318279125[/C][C]-0.430883179637832[/C][C]0.375146174328563[/C][/ROW]
[ROW][C]63[/C][C]20.3[/C][C]22.6375674740528[/C][C]0.0359232147716827[/C][C]-2.33756747405279[/C][C]-0.900854601113942[/C][/ROW]
[ROW][C]64[/C][C]23.7[/C][C]23.3517188547394[/C][C]0.0409833004893464[/C][C]0.348281145260555[/C][C]0.963769543552018[/C][/ROW]
[ROW][C]65[/C][C]23.3[/C][C]22.5656292225158[/C][C]0.0341608227293757[/C][C]0.73437077748417[/C][C]-1.17859557606987[/C][/ROW]
[ROW][C]66[/C][C]19.6[/C][C]20.6388584469645[/C][C]0.0182954704975575[/C][C]-1.03885844696454[/C][C]-2.80788066228598[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]19.7138562561804[/C][C]0.0114594678733042[/C][C]-1.71385625618036[/C][C]-1.35616078818596[/C][/ROW]
[ROW][C]68[/C][C]17.3[/C][C]17.9630226369832[/C][C]0.000778064530874426[/C][C]-0.663022636983215[/C][C]-2.53892632526007[/C][/ROW]
[ROW][C]69[/C][C]16.8[/C][C]17.4806259685557[/C][C]-0.00153895237867139[/C][C]-0.680625968555701[/C][C]-0.696448362145635[/C][/ROW]
[ROW][C]70[/C][C]18.2[/C][C]17.5804328557332[/C][C]-0.00117067445069441[/C][C]0.619567144266806[/C][C]0.146037657290146[/C][/ROW]
[ROW][C]71[/C][C]16.5[/C][C]16.5566294477317[/C][C]-0.00400215643104431[/C][C]-0.0566294477316673[/C][C]-1.47289973064485[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]16.6527506842698[/C][C]-0.00375817701028089[/C][C]-0.652750684269753[/C][C]0.144091047663971[/C][/ROW]
[ROW][C]73[/C][C]18.4[/C][C]16.8769270349950[/C][C]-0.00311478899010797[/C][C]1.52307296500497[/C][C]0.327384222430384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63244&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63244&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
114.214.2000
213.513.6962936202158-0.0218460906336913-0.196293620215784-0.510961302574374
311.912.5702624714998-0.0799509747537027-0.670262471499777-1.28905027878636
414.613.5370438654530-0.04615112368630381.062956134546981.46329923993990
515.614.8464248791018-0.02392546807888680.7535751208981711.95715040105429
614.114.6933425081781-0.0252516531120534-0.593342508178087-0.186791276444708
714.914.7846758745439-0.02417223680943290.1153241254560860.168450679883035
814.214.4884285371524-0.026809952089474-0.288428537152382-0.392773401016692
914.614.5009388600968-0.02641283473224800.09906113990323520.0567382164401477
1017.215.9906933974858-0.01092317548608951.209306602514172.18754213010685
1115.415.9531568795025-0.0111935144893430-0.55315687950249-0.0383985440214962
1214.315.0428351737731-0.0202241712770097-0.742835173773071-1.29732709720659
1317.515.8445774986595-0.04392159038615291.655422501340531.28248841532129
1414.515.1386898929531-0.0440762184398627-0.638689892953074-0.956503842745318
1514.415.3125085376162-0.0395886555083069-0.9125085376162150.290041437516813
1616.615.7595887304915-0.02857558105195300.8404112695084740.669989945447174
1716.715.8833206620207-0.02601999576182790.8166793379792870.217177605013153
1816.616.5663305059722-0.01814429507456290.03366949402776931.02223378168815
1916.916.7138164028435-0.01679637143252050.1861835971564550.239206876790042
2015.716.4580578485087-0.0185438447733307-0.758057848508664-0.345069547894469
2116.416.6882200247152-0.0166973840672004-0.2882200247151940.359040185774239
2218.416.9319367557831-0.01480722907305921.468063244216940.375838750550512
2316.917.0767935804617-0.0139431355412167-0.1767935804616840.230235386400481
2416.517.3491456998188-0.0137442623144000-0.8491456998188030.41346327500924
2518.316.9193860039223-0.01169067008446131.38061399607775-0.609305430076157
2615.116.3534377019843-0.0125862934937511-1.2534377019843-0.79657961809902
2715.716.5418742525155-0.0103600918800077-0.8418742525155070.279518673783194
2818.117.0212199201116-0.002903343624512521.078780079888380.682027894419079
2916.816.7142445080592-0.007230115579343320.0857554919407617-0.43139289913556
3018.917.78384746800020.004876587681267431.116152531999851.54656750785754
311918.42086110981640.01035737652969510.5791388901835530.911993503563147
3218.118.82179042507460.0131587939088284-0.7217904250746430.564106828493199
3317.818.59666721483950.0116609695805094-0.796667214839529-0.344116422627190
3421.519.37172077203380.01560813215372012.128279227966191.10155994887272
3517.118.44814321371710.0125830048153362-1.34814321371715-1.35410045289478
3618.718.74565774622860.0128163868799370-0.04565774622862470.411320851329926
371918.10602381193630.01282280942285990.89397618806374-0.94318038422607
3816.417.86389160263680.012031102129274-1.46389160263678-0.364909874229781
3916.917.87196295580720.0119996193423882-0.971962955807191-0.00558288949162498
4018.617.69896947581170.009935327894282760.901030524188335-0.260187794118112
4119.318.77535440153830.02237817980862320.5246455984616831.51362961246257
4219.418.86005314265830.02302389001560750.5399468573416710.0893083145149912
4317.618.02975078906550.0157359945700344-0.429750789065486-1.22942482034335
4418.618.58096011489360.01944609424342230.01903988510638780.77294592443537
4518.118.95411578078900.0214121214346974-0.8541157807889670.51059396860266
4620.418.44427030953370.01920265703125511.95572969046633-0.766288873106567
4718.118.90655249142770.0203767240500655-0.806552491427690.638717058582607
4819.619.17616005399870.02075270111841420.4238399460013020.359315647383104
4919.919.07052804774790.02054025297818290.829471952252136-0.181938014290561
5019.219.89807075787930.0235132400234149-0.6980707578793281.15412229369438
5117.819.49015068041050.0206687710323164-1.69015068041052-0.611959182569259
5219.219.08183665491790.01686031296745560.118163345082099-0.60712352175694
532220.22481694081390.02778710436901811.775183059186091.60147036004313
5421.120.39668855829170.02911230258974940.7033114417082820.206319304538089
5519.520.33148614175590.0283612770781370-0.831486141755915-0.135715902666664
5622.221.32433728683150.03465254446283670.8756627131684591.39088119093849
5720.921.59881553165940.0358768768914523-0.6988155316594210.345947200063953
5822.221.08109327142450.03379274491603741.11890672857555-0.7981356278653
5923.522.74525612725020.03808309583546730.7547438727497912.3493292309355
6021.522.16711613110250.0368199501355184-0.667116131102471-0.887476799362390
6124.322.93006120001300.03858771689290821.369938799987031.04361331108593
6222.823.23088317963780.0396008318279125-0.4308831796378320.375146174328563
6320.322.63756747405280.0359232147716827-2.33756747405279-0.900854601113942
6423.723.35171885473940.04098330048934640.3482811452605550.963769543552018
6523.322.56562922251580.03416082272937570.73437077748417-1.17859557606987
6619.620.63885844696450.0182954704975575-1.03885844696454-2.80788066228598
671819.71385625618040.0114594678733042-1.71385625618036-1.35616078818596
6817.317.96302263698320.000778064530874426-0.663022636983215-2.53892632526007
6916.817.4806259685557-0.00153895237867139-0.680625968555701-0.696448362145635
7018.217.5804328557332-0.001170674450694410.6195671442668060.146037657290146
7116.516.5566294477317-0.00400215643104431-0.0566294477316673-1.47289973064485
721616.6527506842698-0.00375817701028089-0.6527506842697530.144091047663971
7318.416.8769270349950-0.003114788990107971.523072965004970.327384222430384



Parameters (Session):
par1 = multiplicative ; par2 = 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')