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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, 23 Nov 2012 10:13:13 -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/2012/Nov/23/t1353683661u68zk1mb02wvsgi.htm/, Retrieved Wed, 01 May 2024 14:25:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=192140, Retrieved Wed, 01 May 2024 14:25:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80
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Dataseries X:
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644
19195
19650
20830
23595
22937
21814
21928
21777
21383
21467
22052
22680
24320
24977
25204
25739
26434
27525
30695
32436
30160
30236
31293
31077
32226
33865
32810
32242
32700
32819
33947
34148
35261
39506
41591
39148
41216
40225
41126
42362
40740
40256
39804
41002
41702
42254
43605
43271




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=192140&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=192140&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=192140&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11557915579000
21634816306.195862687840.453601448972141.80413731221040.405587938190393
31592815898.798903822335.846915349255129.2010961777378-0.415373869861746
41617116127.571647230738.06298910552243.42835276934460.178664845679122
51593715904.769504065434.361911314613432.230495934637-0.2412588543354
61571315679.414394138130.045430276111233.5856058619345-0.239881891802206
71559415559.310600421927.199365457318434.6893995780642-0.138504501974318
81568315645.480631396628.449392717695237.51936860339250.0543296862553791
91643816389.070198494345.141446469359348.92980150567460.658068646435764
101703216986.938942597559.170200230987245.06105740252260.508030501146461
111769617647.370212758875.593381762914648.62978724124340.552029116938942
121774517705.99491728875.098911283813539.0050827119521-0.0155628435370805
131939419196.542219950266.6500957619767197.4577800497551.55207641128412
142014820124.2529174088107.97279318997723.74708259123310.674204866011312
152010820128.2307881213104.4494303227-20.2307881213015-0.0950076133536353
161858418619.957081850349.5995078852376-35.9570818502849-1.47178623781982
171844118433.3288478641.20337049531887.67115213998753-0.21542390719346
181839118389.921597399438.08553570371991.07840260063018-0.0771030386796983
191917819153.010381856965.662302942281424.98961814305720.660217630477964
201807918122.657243094422.7703197555062-43.6572430943596-0.997436582907646
211848318458.042853851235.32080829854524.95714614878750.284328426238659
221964419616.706672244781.469230046684727.2933277552741.02113959483234
231919519203.719248219460.708475535016-8.71924821941574-0.44928268482641
241965019656.603968369577.3532724240444-6.603968369538490.356000416618893
252083020640.0813916719107.149358667192189.9186083280870.896738307937841
262359523499.3083282089245.8221419433195.69167179105762.29338579912395
272293722953.8978594715209.916035505561-16.8978594714905-0.71742523441183
282181421866.2942059305151.551099670352-52.2942059305488-1.17570444433602
292192821881.6469381214145.34633319673146.3530618785535-0.123383773150933
302177721792.0325966357134.52680301648-15.0325966356775-0.212802332556952
312138321325.7844285769106.60788627088157.2155714230673-0.543979182819426
322146721481.0032702228108.885536523065-14.00327022281570.0440050176440973
332205222025.6416994848129.45430312086326.35830051517640.394379985689704
342268022603.5132118529150.76566824873676.48678814714020.405770391002382
352432024252.6697110019222.46640377259267.33028899805571.35582711734647
362497724984.9608615425246.824725721672-7.960861542536470.460873535244501
372520425451.0679139085256.581572524978-247.0679139084970.209367453806386
382573925632.5405406573252.751839540031106.459459342723-0.0643314811034533
392643426381.0271554574277.13648479745452.97284454256430.448111469724433
402752527541.9093520889320.265832027167-16.90935208889750.798609307086338
413069530532.9401354873451.060324543482162.0598645127232.41329120677962
423243632398.5728505906520.56989527765437.42714940939741.27818023925559
433016030251.0439793205389.109125180475-91.0439793205483-2.41066317079283
443023630244.8213352482369.584098758731-8.82133524816191-0.357159780325342
453129331230.1210880892400.05947914958962.87891191081380.556226220026645
463107731088.0570782432373.170040367189-11.0570782431667-0.489734486557007
473222632139.889049224406.90285860653686.11095077596890.613115457925133
483386533829.8585417522470.4675600836235.141458247831.15810976158779
493281033198.5072936285417.228667007958-388.50729362854-1.03331504846916
503224232196.5436338831345.03830781626145.4563661168712-1.23457280624209
513270032629.6314339134349.46029155638670.36856608659020.0794785349157572
523281932952.1741661019348.114998555033-133.174166101921-0.0243014850300842
533394733853.3017266093375.77363317556493.69827339067120.499199652414732
543414834011.8077899086364.895028194755136.192210091416-0.196140112664762
553526135279.7206583229410.146644876369-18.72065832285760.815203234865504
563950639363.9502329537594.388312712379142.0497670463213.3167110803234
574159141467.0084776979670.091088254203123.9915223020841.3619033224019
583914839360.2544413861530.656058905741-212.254441386113-2.50682159942303
594121641106.3351563174591.721734439272109.6648436825741.09726714078851
604022540238.7885289125518.597897547144-13.7885289125073-1.3165147120102
614112641301.7301741092545.616824462463-175.7301741092360.505082290473058
624236242300.6983004094568.59082757266661.30169959058940.398258524959874
634074040795.3864970677463.969573778092-55.3864970676852-1.87042665860416
644025640440.4141298902422.759925378408-184.414129890174-0.73911543232672
653980439726.4874903871365.56150885119277.5125096128667-1.02561931550166
664100240810.1139999705401.708286428696191.8860000294980.647969119858316
674170241876.3145016617435.167283325914-174.3145016617350.599643136863894
684225442236.8146766749431.40681852921317.1853233250541-0.0673798705066036
694360543297.4599803507463.103551239671307.5400196492970.567831853541199
704327143583.2429006157454.169097057867-312.242900615681-0.160027895096661

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 15579 & 15579 & 0 & 0 & 0 \tabularnewline
2 & 16348 & 16306.1958626878 & 40.4536014489721 & 41.8041373122104 & 0.405587938190393 \tabularnewline
3 & 15928 & 15898.7989038223 & 35.8469153492551 & 29.2010961777378 & -0.415373869861746 \tabularnewline
4 & 16171 & 16127.5716472307 & 38.062989105522 & 43.4283527693446 & 0.178664845679122 \tabularnewline
5 & 15937 & 15904.7695040654 & 34.3619113146134 & 32.230495934637 & -0.2412588543354 \tabularnewline
6 & 15713 & 15679.4143941381 & 30.0454302761112 & 33.5856058619345 & -0.239881891802206 \tabularnewline
7 & 15594 & 15559.3106004219 & 27.1993654573184 & 34.6893995780642 & -0.138504501974318 \tabularnewline
8 & 15683 & 15645.4806313966 & 28.4493927176952 & 37.5193686033925 & 0.0543296862553791 \tabularnewline
9 & 16438 & 16389.0701984943 & 45.1414464693593 & 48.9298015056746 & 0.658068646435764 \tabularnewline
10 & 17032 & 16986.9389425975 & 59.1702002309872 & 45.0610574025226 & 0.508030501146461 \tabularnewline
11 & 17696 & 17647.3702127588 & 75.5933817629146 & 48.6297872412434 & 0.552029116938942 \tabularnewline
12 & 17745 & 17705.994917288 & 75.0989112838135 & 39.0050827119521 & -0.0155628435370805 \tabularnewline
13 & 19394 & 19196.5422199502 & 66.6500957619767 & 197.457780049755 & 1.55207641128412 \tabularnewline
14 & 20148 & 20124.2529174088 & 107.972793189977 & 23.7470825912331 & 0.674204866011312 \tabularnewline
15 & 20108 & 20128.2307881213 & 104.4494303227 & -20.2307881213015 & -0.0950076133536353 \tabularnewline
16 & 18584 & 18619.9570818503 & 49.5995078852376 & -35.9570818502849 & -1.47178623781982 \tabularnewline
17 & 18441 & 18433.32884786 & 41.2033704953188 & 7.67115213998753 & -0.21542390719346 \tabularnewline
18 & 18391 & 18389.9215973994 & 38.0855357037199 & 1.07840260063018 & -0.0771030386796983 \tabularnewline
19 & 19178 & 19153.0103818569 & 65.6623029422814 & 24.9896181430572 & 0.660217630477964 \tabularnewline
20 & 18079 & 18122.6572430944 & 22.7703197555062 & -43.6572430943596 & -0.997436582907646 \tabularnewline
21 & 18483 & 18458.0428538512 & 35.320808298545 & 24.9571461487875 & 0.284328426238659 \tabularnewline
22 & 19644 & 19616.7066722447 & 81.4692300466847 & 27.293327755274 & 1.02113959483234 \tabularnewline
23 & 19195 & 19203.7192482194 & 60.708475535016 & -8.71924821941574 & -0.44928268482641 \tabularnewline
24 & 19650 & 19656.6039683695 & 77.3532724240444 & -6.60396836953849 & 0.356000416618893 \tabularnewline
25 & 20830 & 20640.0813916719 & 107.149358667192 & 189.918608328087 & 0.896738307937841 \tabularnewline
26 & 23595 & 23499.3083282089 & 245.82214194331 & 95.6916717910576 & 2.29338579912395 \tabularnewline
27 & 22937 & 22953.8978594715 & 209.916035505561 & -16.8978594714905 & -0.71742523441183 \tabularnewline
28 & 21814 & 21866.2942059305 & 151.551099670352 & -52.2942059305488 & -1.17570444433602 \tabularnewline
29 & 21928 & 21881.6469381214 & 145.346333196731 & 46.3530618785535 & -0.123383773150933 \tabularnewline
30 & 21777 & 21792.0325966357 & 134.52680301648 & -15.0325966356775 & -0.212802332556952 \tabularnewline
31 & 21383 & 21325.7844285769 & 106.607886270881 & 57.2155714230673 & -0.543979182819426 \tabularnewline
32 & 21467 & 21481.0032702228 & 108.885536523065 & -14.0032702228157 & 0.0440050176440973 \tabularnewline
33 & 22052 & 22025.6416994848 & 129.454303120863 & 26.3583005151764 & 0.394379985689704 \tabularnewline
34 & 22680 & 22603.5132118529 & 150.765668248736 & 76.4867881471402 & 0.405770391002382 \tabularnewline
35 & 24320 & 24252.6697110019 & 222.466403772592 & 67.3302889980557 & 1.35582711734647 \tabularnewline
36 & 24977 & 24984.9608615425 & 246.824725721672 & -7.96086154253647 & 0.460873535244501 \tabularnewline
37 & 25204 & 25451.0679139085 & 256.581572524978 & -247.067913908497 & 0.209367453806386 \tabularnewline
38 & 25739 & 25632.5405406573 & 252.751839540031 & 106.459459342723 & -0.0643314811034533 \tabularnewline
39 & 26434 & 26381.0271554574 & 277.136484797454 & 52.9728445425643 & 0.448111469724433 \tabularnewline
40 & 27525 & 27541.9093520889 & 320.265832027167 & -16.9093520888975 & 0.798609307086338 \tabularnewline
41 & 30695 & 30532.9401354873 & 451.060324543482 & 162.059864512723 & 2.41329120677962 \tabularnewline
42 & 32436 & 32398.5728505906 & 520.569895277654 & 37.4271494093974 & 1.27818023925559 \tabularnewline
43 & 30160 & 30251.0439793205 & 389.109125180475 & -91.0439793205483 & -2.41066317079283 \tabularnewline
44 & 30236 & 30244.8213352482 & 369.584098758731 & -8.82133524816191 & -0.357159780325342 \tabularnewline
45 & 31293 & 31230.1210880892 & 400.059479149589 & 62.8789119108138 & 0.556226220026645 \tabularnewline
46 & 31077 & 31088.0570782432 & 373.170040367189 & -11.0570782431667 & -0.489734486557007 \tabularnewline
47 & 32226 & 32139.889049224 & 406.902858606536 & 86.1109507759689 & 0.613115457925133 \tabularnewline
48 & 33865 & 33829.8585417522 & 470.46756008362 & 35.14145824783 & 1.15810976158779 \tabularnewline
49 & 32810 & 33198.5072936285 & 417.228667007958 & -388.50729362854 & -1.03331504846916 \tabularnewline
50 & 32242 & 32196.5436338831 & 345.038307816261 & 45.4563661168712 & -1.23457280624209 \tabularnewline
51 & 32700 & 32629.6314339134 & 349.460291556386 & 70.3685660865902 & 0.0794785349157572 \tabularnewline
52 & 32819 & 32952.1741661019 & 348.114998555033 & -133.174166101921 & -0.0243014850300842 \tabularnewline
53 & 33947 & 33853.3017266093 & 375.773633175564 & 93.6982733906712 & 0.499199652414732 \tabularnewline
54 & 34148 & 34011.8077899086 & 364.895028194755 & 136.192210091416 & -0.196140112664762 \tabularnewline
55 & 35261 & 35279.7206583229 & 410.146644876369 & -18.7206583228576 & 0.815203234865504 \tabularnewline
56 & 39506 & 39363.9502329537 & 594.388312712379 & 142.049767046321 & 3.3167110803234 \tabularnewline
57 & 41591 & 41467.0084776979 & 670.091088254203 & 123.991522302084 & 1.3619033224019 \tabularnewline
58 & 39148 & 39360.2544413861 & 530.656058905741 & -212.254441386113 & -2.50682159942303 \tabularnewline
59 & 41216 & 41106.3351563174 & 591.721734439272 & 109.664843682574 & 1.09726714078851 \tabularnewline
60 & 40225 & 40238.7885289125 & 518.597897547144 & -13.7885289125073 & -1.3165147120102 \tabularnewline
61 & 41126 & 41301.7301741092 & 545.616824462463 & -175.730174109236 & 0.505082290473058 \tabularnewline
62 & 42362 & 42300.6983004094 & 568.590827572666 & 61.3016995905894 & 0.398258524959874 \tabularnewline
63 & 40740 & 40795.3864970677 & 463.969573778092 & -55.3864970676852 & -1.87042665860416 \tabularnewline
64 & 40256 & 40440.4141298902 & 422.759925378408 & -184.414129890174 & -0.73911543232672 \tabularnewline
65 & 39804 & 39726.4874903871 & 365.561508851192 & 77.5125096128667 & -1.02561931550166 \tabularnewline
66 & 41002 & 40810.1139999705 & 401.708286428696 & 191.886000029498 & 0.647969119858316 \tabularnewline
67 & 41702 & 41876.3145016617 & 435.167283325914 & -174.314501661735 & 0.599643136863894 \tabularnewline
68 & 42254 & 42236.8146766749 & 431.406818529213 & 17.1853233250541 & -0.0673798705066036 \tabularnewline
69 & 43605 & 43297.4599803507 & 463.103551239671 & 307.540019649297 & 0.567831853541199 \tabularnewline
70 & 43271 & 43583.2429006157 & 454.169097057867 & -312.242900615681 & -0.160027895096661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=192140&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]15579[/C][C]15579[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]16348[/C][C]16306.1958626878[/C][C]40.4536014489721[/C][C]41.8041373122104[/C][C]0.405587938190393[/C][/ROW]
[ROW][C]3[/C][C]15928[/C][C]15898.7989038223[/C][C]35.8469153492551[/C][C]29.2010961777378[/C][C]-0.415373869861746[/C][/ROW]
[ROW][C]4[/C][C]16171[/C][C]16127.5716472307[/C][C]38.062989105522[/C][C]43.4283527693446[/C][C]0.178664845679122[/C][/ROW]
[ROW][C]5[/C][C]15937[/C][C]15904.7695040654[/C][C]34.3619113146134[/C][C]32.230495934637[/C][C]-0.2412588543354[/C][/ROW]
[ROW][C]6[/C][C]15713[/C][C]15679.4143941381[/C][C]30.0454302761112[/C][C]33.5856058619345[/C][C]-0.239881891802206[/C][/ROW]
[ROW][C]7[/C][C]15594[/C][C]15559.3106004219[/C][C]27.1993654573184[/C][C]34.6893995780642[/C][C]-0.138504501974318[/C][/ROW]
[ROW][C]8[/C][C]15683[/C][C]15645.4806313966[/C][C]28.4493927176952[/C][C]37.5193686033925[/C][C]0.0543296862553791[/C][/ROW]
[ROW][C]9[/C][C]16438[/C][C]16389.0701984943[/C][C]45.1414464693593[/C][C]48.9298015056746[/C][C]0.658068646435764[/C][/ROW]
[ROW][C]10[/C][C]17032[/C][C]16986.9389425975[/C][C]59.1702002309872[/C][C]45.0610574025226[/C][C]0.508030501146461[/C][/ROW]
[ROW][C]11[/C][C]17696[/C][C]17647.3702127588[/C][C]75.5933817629146[/C][C]48.6297872412434[/C][C]0.552029116938942[/C][/ROW]
[ROW][C]12[/C][C]17745[/C][C]17705.994917288[/C][C]75.0989112838135[/C][C]39.0050827119521[/C][C]-0.0155628435370805[/C][/ROW]
[ROW][C]13[/C][C]19394[/C][C]19196.5422199502[/C][C]66.6500957619767[/C][C]197.457780049755[/C][C]1.55207641128412[/C][/ROW]
[ROW][C]14[/C][C]20148[/C][C]20124.2529174088[/C][C]107.972793189977[/C][C]23.7470825912331[/C][C]0.674204866011312[/C][/ROW]
[ROW][C]15[/C][C]20108[/C][C]20128.2307881213[/C][C]104.4494303227[/C][C]-20.2307881213015[/C][C]-0.0950076133536353[/C][/ROW]
[ROW][C]16[/C][C]18584[/C][C]18619.9570818503[/C][C]49.5995078852376[/C][C]-35.9570818502849[/C][C]-1.47178623781982[/C][/ROW]
[ROW][C]17[/C][C]18441[/C][C]18433.32884786[/C][C]41.2033704953188[/C][C]7.67115213998753[/C][C]-0.21542390719346[/C][/ROW]
[ROW][C]18[/C][C]18391[/C][C]18389.9215973994[/C][C]38.0855357037199[/C][C]1.07840260063018[/C][C]-0.0771030386796983[/C][/ROW]
[ROW][C]19[/C][C]19178[/C][C]19153.0103818569[/C][C]65.6623029422814[/C][C]24.9896181430572[/C][C]0.660217630477964[/C][/ROW]
[ROW][C]20[/C][C]18079[/C][C]18122.6572430944[/C][C]22.7703197555062[/C][C]-43.6572430943596[/C][C]-0.997436582907646[/C][/ROW]
[ROW][C]21[/C][C]18483[/C][C]18458.0428538512[/C][C]35.320808298545[/C][C]24.9571461487875[/C][C]0.284328426238659[/C][/ROW]
[ROW][C]22[/C][C]19644[/C][C]19616.7066722447[/C][C]81.4692300466847[/C][C]27.293327755274[/C][C]1.02113959483234[/C][/ROW]
[ROW][C]23[/C][C]19195[/C][C]19203.7192482194[/C][C]60.708475535016[/C][C]-8.71924821941574[/C][C]-0.44928268482641[/C][/ROW]
[ROW][C]24[/C][C]19650[/C][C]19656.6039683695[/C][C]77.3532724240444[/C][C]-6.60396836953849[/C][C]0.356000416618893[/C][/ROW]
[ROW][C]25[/C][C]20830[/C][C]20640.0813916719[/C][C]107.149358667192[/C][C]189.918608328087[/C][C]0.896738307937841[/C][/ROW]
[ROW][C]26[/C][C]23595[/C][C]23499.3083282089[/C][C]245.82214194331[/C][C]95.6916717910576[/C][C]2.29338579912395[/C][/ROW]
[ROW][C]27[/C][C]22937[/C][C]22953.8978594715[/C][C]209.916035505561[/C][C]-16.8978594714905[/C][C]-0.71742523441183[/C][/ROW]
[ROW][C]28[/C][C]21814[/C][C]21866.2942059305[/C][C]151.551099670352[/C][C]-52.2942059305488[/C][C]-1.17570444433602[/C][/ROW]
[ROW][C]29[/C][C]21928[/C][C]21881.6469381214[/C][C]145.346333196731[/C][C]46.3530618785535[/C][C]-0.123383773150933[/C][/ROW]
[ROW][C]30[/C][C]21777[/C][C]21792.0325966357[/C][C]134.52680301648[/C][C]-15.0325966356775[/C][C]-0.212802332556952[/C][/ROW]
[ROW][C]31[/C][C]21383[/C][C]21325.7844285769[/C][C]106.607886270881[/C][C]57.2155714230673[/C][C]-0.543979182819426[/C][/ROW]
[ROW][C]32[/C][C]21467[/C][C]21481.0032702228[/C][C]108.885536523065[/C][C]-14.0032702228157[/C][C]0.0440050176440973[/C][/ROW]
[ROW][C]33[/C][C]22052[/C][C]22025.6416994848[/C][C]129.454303120863[/C][C]26.3583005151764[/C][C]0.394379985689704[/C][/ROW]
[ROW][C]34[/C][C]22680[/C][C]22603.5132118529[/C][C]150.765668248736[/C][C]76.4867881471402[/C][C]0.405770391002382[/C][/ROW]
[ROW][C]35[/C][C]24320[/C][C]24252.6697110019[/C][C]222.466403772592[/C][C]67.3302889980557[/C][C]1.35582711734647[/C][/ROW]
[ROW][C]36[/C][C]24977[/C][C]24984.9608615425[/C][C]246.824725721672[/C][C]-7.96086154253647[/C][C]0.460873535244501[/C][/ROW]
[ROW][C]37[/C][C]25204[/C][C]25451.0679139085[/C][C]256.581572524978[/C][C]-247.067913908497[/C][C]0.209367453806386[/C][/ROW]
[ROW][C]38[/C][C]25739[/C][C]25632.5405406573[/C][C]252.751839540031[/C][C]106.459459342723[/C][C]-0.0643314811034533[/C][/ROW]
[ROW][C]39[/C][C]26434[/C][C]26381.0271554574[/C][C]277.136484797454[/C][C]52.9728445425643[/C][C]0.448111469724433[/C][/ROW]
[ROW][C]40[/C][C]27525[/C][C]27541.9093520889[/C][C]320.265832027167[/C][C]-16.9093520888975[/C][C]0.798609307086338[/C][/ROW]
[ROW][C]41[/C][C]30695[/C][C]30532.9401354873[/C][C]451.060324543482[/C][C]162.059864512723[/C][C]2.41329120677962[/C][/ROW]
[ROW][C]42[/C][C]32436[/C][C]32398.5728505906[/C][C]520.569895277654[/C][C]37.4271494093974[/C][C]1.27818023925559[/C][/ROW]
[ROW][C]43[/C][C]30160[/C][C]30251.0439793205[/C][C]389.109125180475[/C][C]-91.0439793205483[/C][C]-2.41066317079283[/C][/ROW]
[ROW][C]44[/C][C]30236[/C][C]30244.8213352482[/C][C]369.584098758731[/C][C]-8.82133524816191[/C][C]-0.357159780325342[/C][/ROW]
[ROW][C]45[/C][C]31293[/C][C]31230.1210880892[/C][C]400.059479149589[/C][C]62.8789119108138[/C][C]0.556226220026645[/C][/ROW]
[ROW][C]46[/C][C]31077[/C][C]31088.0570782432[/C][C]373.170040367189[/C][C]-11.0570782431667[/C][C]-0.489734486557007[/C][/ROW]
[ROW][C]47[/C][C]32226[/C][C]32139.889049224[/C][C]406.902858606536[/C][C]86.1109507759689[/C][C]0.613115457925133[/C][/ROW]
[ROW][C]48[/C][C]33865[/C][C]33829.8585417522[/C][C]470.46756008362[/C][C]35.14145824783[/C][C]1.15810976158779[/C][/ROW]
[ROW][C]49[/C][C]32810[/C][C]33198.5072936285[/C][C]417.228667007958[/C][C]-388.50729362854[/C][C]-1.03331504846916[/C][/ROW]
[ROW][C]50[/C][C]32242[/C][C]32196.5436338831[/C][C]345.038307816261[/C][C]45.4563661168712[/C][C]-1.23457280624209[/C][/ROW]
[ROW][C]51[/C][C]32700[/C][C]32629.6314339134[/C][C]349.460291556386[/C][C]70.3685660865902[/C][C]0.0794785349157572[/C][/ROW]
[ROW][C]52[/C][C]32819[/C][C]32952.1741661019[/C][C]348.114998555033[/C][C]-133.174166101921[/C][C]-0.0243014850300842[/C][/ROW]
[ROW][C]53[/C][C]33947[/C][C]33853.3017266093[/C][C]375.773633175564[/C][C]93.6982733906712[/C][C]0.499199652414732[/C][/ROW]
[ROW][C]54[/C][C]34148[/C][C]34011.8077899086[/C][C]364.895028194755[/C][C]136.192210091416[/C][C]-0.196140112664762[/C][/ROW]
[ROW][C]55[/C][C]35261[/C][C]35279.7206583229[/C][C]410.146644876369[/C][C]-18.7206583228576[/C][C]0.815203234865504[/C][/ROW]
[ROW][C]56[/C][C]39506[/C][C]39363.9502329537[/C][C]594.388312712379[/C][C]142.049767046321[/C][C]3.3167110803234[/C][/ROW]
[ROW][C]57[/C][C]41591[/C][C]41467.0084776979[/C][C]670.091088254203[/C][C]123.991522302084[/C][C]1.3619033224019[/C][/ROW]
[ROW][C]58[/C][C]39148[/C][C]39360.2544413861[/C][C]530.656058905741[/C][C]-212.254441386113[/C][C]-2.50682159942303[/C][/ROW]
[ROW][C]59[/C][C]41216[/C][C]41106.3351563174[/C][C]591.721734439272[/C][C]109.664843682574[/C][C]1.09726714078851[/C][/ROW]
[ROW][C]60[/C][C]40225[/C][C]40238.7885289125[/C][C]518.597897547144[/C][C]-13.7885289125073[/C][C]-1.3165147120102[/C][/ROW]
[ROW][C]61[/C][C]41126[/C][C]41301.7301741092[/C][C]545.616824462463[/C][C]-175.730174109236[/C][C]0.505082290473058[/C][/ROW]
[ROW][C]62[/C][C]42362[/C][C]42300.6983004094[/C][C]568.590827572666[/C][C]61.3016995905894[/C][C]0.398258524959874[/C][/ROW]
[ROW][C]63[/C][C]40740[/C][C]40795.3864970677[/C][C]463.969573778092[/C][C]-55.3864970676852[/C][C]-1.87042665860416[/C][/ROW]
[ROW][C]64[/C][C]40256[/C][C]40440.4141298902[/C][C]422.759925378408[/C][C]-184.414129890174[/C][C]-0.73911543232672[/C][/ROW]
[ROW][C]65[/C][C]39804[/C][C]39726.4874903871[/C][C]365.561508851192[/C][C]77.5125096128667[/C][C]-1.02561931550166[/C][/ROW]
[ROW][C]66[/C][C]41002[/C][C]40810.1139999705[/C][C]401.708286428696[/C][C]191.886000029498[/C][C]0.647969119858316[/C][/ROW]
[ROW][C]67[/C][C]41702[/C][C]41876.3145016617[/C][C]435.167283325914[/C][C]-174.314501661735[/C][C]0.599643136863894[/C][/ROW]
[ROW][C]68[/C][C]42254[/C][C]42236.8146766749[/C][C]431.406818529213[/C][C]17.1853233250541[/C][C]-0.0673798705066036[/C][/ROW]
[ROW][C]69[/C][C]43605[/C][C]43297.4599803507[/C][C]463.103551239671[/C][C]307.540019649297[/C][C]0.567831853541199[/C][/ROW]
[ROW][C]70[/C][C]43271[/C][C]43583.2429006157[/C][C]454.169097057867[/C][C]-312.242900615681[/C][C]-0.160027895096661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=192140&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=192140&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
11557915579000
21634816306.195862687840.453601448972141.80413731221040.405587938190393
31592815898.798903822335.846915349255129.2010961777378-0.415373869861746
41617116127.571647230738.06298910552243.42835276934460.178664845679122
51593715904.769504065434.361911314613432.230495934637-0.2412588543354
61571315679.414394138130.045430276111233.5856058619345-0.239881891802206
71559415559.310600421927.199365457318434.6893995780642-0.138504501974318
81568315645.480631396628.449392717695237.51936860339250.0543296862553791
91643816389.070198494345.141446469359348.92980150567460.658068646435764
101703216986.938942597559.170200230987245.06105740252260.508030501146461
111769617647.370212758875.593381762914648.62978724124340.552029116938942
121774517705.99491728875.098911283813539.0050827119521-0.0155628435370805
131939419196.542219950266.6500957619767197.4577800497551.55207641128412
142014820124.2529174088107.97279318997723.74708259123310.674204866011312
152010820128.2307881213104.4494303227-20.2307881213015-0.0950076133536353
161858418619.957081850349.5995078852376-35.9570818502849-1.47178623781982
171844118433.3288478641.20337049531887.67115213998753-0.21542390719346
181839118389.921597399438.08553570371991.07840260063018-0.0771030386796983
191917819153.010381856965.662302942281424.98961814305720.660217630477964
201807918122.657243094422.7703197555062-43.6572430943596-0.997436582907646
211848318458.042853851235.32080829854524.95714614878750.284328426238659
221964419616.706672244781.469230046684727.2933277552741.02113959483234
231919519203.719248219460.708475535016-8.71924821941574-0.44928268482641
241965019656.603968369577.3532724240444-6.603968369538490.356000416618893
252083020640.0813916719107.149358667192189.9186083280870.896738307937841
262359523499.3083282089245.8221419433195.69167179105762.29338579912395
272293722953.8978594715209.916035505561-16.8978594714905-0.71742523441183
282181421866.2942059305151.551099670352-52.2942059305488-1.17570444433602
292192821881.6469381214145.34633319673146.3530618785535-0.123383773150933
302177721792.0325966357134.52680301648-15.0325966356775-0.212802332556952
312138321325.7844285769106.60788627088157.2155714230673-0.543979182819426
322146721481.0032702228108.885536523065-14.00327022281570.0440050176440973
332205222025.6416994848129.45430312086326.35830051517640.394379985689704
342268022603.5132118529150.76566824873676.48678814714020.405770391002382
352432024252.6697110019222.46640377259267.33028899805571.35582711734647
362497724984.9608615425246.824725721672-7.960861542536470.460873535244501
372520425451.0679139085256.581572524978-247.0679139084970.209367453806386
382573925632.5405406573252.751839540031106.459459342723-0.0643314811034533
392643426381.0271554574277.13648479745452.97284454256430.448111469724433
402752527541.9093520889320.265832027167-16.90935208889750.798609307086338
413069530532.9401354873451.060324543482162.0598645127232.41329120677962
423243632398.5728505906520.56989527765437.42714940939741.27818023925559
433016030251.0439793205389.109125180475-91.0439793205483-2.41066317079283
443023630244.8213352482369.584098758731-8.82133524816191-0.357159780325342
453129331230.1210880892400.05947914958962.87891191081380.556226220026645
463107731088.0570782432373.170040367189-11.0570782431667-0.489734486557007
473222632139.889049224406.90285860653686.11095077596890.613115457925133
483386533829.8585417522470.4675600836235.141458247831.15810976158779
493281033198.5072936285417.228667007958-388.50729362854-1.03331504846916
503224232196.5436338831345.03830781626145.4563661168712-1.23457280624209
513270032629.6314339134349.46029155638670.36856608659020.0794785349157572
523281932952.1741661019348.114998555033-133.174166101921-0.0243014850300842
533394733853.3017266093375.77363317556493.69827339067120.499199652414732
543414834011.8077899086364.895028194755136.192210091416-0.196140112664762
553526135279.7206583229410.146644876369-18.72065832285760.815203234865504
563950639363.9502329537594.388312712379142.0497670463213.3167110803234
574159141467.0084776979670.091088254203123.9915223020841.3619033224019
583914839360.2544413861530.656058905741-212.254441386113-2.50682159942303
594121641106.3351563174591.721734439272109.6648436825741.09726714078851
604022540238.7885289125518.597897547144-13.7885289125073-1.3165147120102
614112641301.7301741092545.616824462463-175.7301741092360.505082290473058
624236242300.6983004094568.59082757266661.30169959058940.398258524959874
634074040795.3864970677463.969573778092-55.3864970676852-1.87042665860416
644025640440.4141298902422.759925378408-184.414129890174-0.73911543232672
653980439726.4874903871365.56150885119277.5125096128667-1.02561931550166
664100240810.1139999705401.708286428696191.8860000294980.647969119858316
674170241876.3145016617435.167283325914-174.3145016617350.599643136863894
684225442236.8146766749431.40681852921317.1853233250541-0.0673798705066036
694360543297.4599803507463.103551239671307.5400196492970.567831853541199
704327143583.2429006157454.169097057867-312.242900615681-0.160027895096661



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')