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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 computationMon, 20 Dec 2010 13:28:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t1292851709gn343wqtx2ncdvo.htm/, Retrieved Sat, 04 May 2024 02:17:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112934, Retrieved Sat, 04 May 2024 02:17:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [STSM - bev.aantal] [2010-12-20 13:28:47] [f3d6336ce664ba129edd250394d444d3] [Current]
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Dataseries X:
16306977
16307888
16307482
16308869
16311019
16312596
16315238
16319511
16327575
16330818
16331930
16334210
16334715
16335459
16334090
16333559
16334600
16336676
16337253
16342333
16348917
16352678
16352972
16357992
16359133
16362938
16365065
16367596
16371278
16374541
16377339
16383275
16393843
16399139
16401009
16405399
16409106
16414307
16418055
16423337
16428686
16434935
16440452
16449092
16464859
16473709
16479291
16485787
16489042
16495231
16501683
16506782
16513615
16520661
16528400
16538542
16554596
16562317
16568499
16574989




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112934&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112934&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11630697716306977000
21630788816307769.6479338802.429049481105118.3520661608750.596812355298189
31630748216307743.9254104159.003088416110-261.925410406686-0.480529304271365
41630886916308689.3026867793.076169037753179.6973132604610.442252697891651
51631101916310832.92420331862.70797688004186.0757967338490.742740018322194
61631259616312684.05583171853.52272502671-88.0558316668831-0.00639744523962094
71631523816315142.90430792334.3598198067795.09569206584280.334609318661443
81631951116319237.70200883732.75073843248273.2979911769630.973170392718777
91632757516326957.13346596899.09117146745617.8665341272532.20350686356295
101633081816331475.15748745008.07321314193-657.157487428788-1.31598636352216
111633193016332538.15368151874.89231978793-608.153681536321-2.18042618470508
121633421016334079.24698681609.78577870023130.753013182522-0.184491476964418
131633471516334826.7444229926.091133625024-111.744422934432-0.476802917964902
141633545916335151.4000672447.847366270083307.59993282131-0.33749539563946
151633409016334618.117871-298.049741571975-528.117871009226-0.520077635925116
161633355916333754.7003244-731.900000761233-195.700324355116-0.305053408189535
171633460016334072.783755671.2442703724557527.2162444428280.557250449839938
181633667616336231.48637971662.18099228068444.513620285471.10725994173139
191633725316337524.11666321380.12465440747-271.116663239638-0.196343358778851
201634233316342435.61363164076.61774239966-102.6136316192111.87645403719288
211634891716347890.64880075129.134506237551026.351199326350.732463197758377
221635267816352698.59682884883.8971386203-20.5968287699800-0.170667100787337
231635297216354257.27199082345.22375635811-1285.27199079785-1.76670740081873
241635799216357468.06200393005.63222097429523.9379961118490.459649119766005
251635913316359405.83284302191.04632983813-272.832843039710-0.56827557652956
261636293816362200.87021672652.35595496233737.1297832753670.321476483744732
271636506516365293.32430382984.19505624561-228.3243037981220.230791017358861
281636759616368017.72506922787.99214238032-421.725069207335-0.137297869625761
291637127816371018.27312152948.79360768452259.7268784928110.111818714740832
301637454116373812.84457842832.51974293858728.155421570669-0.080850396004529
311637733916378127.79286333950.47136757629-788.7928633034770.778252166564338
321638327516383598.17589635097.55420346252-323.1758962991540.798293587672257
331639384316392197.43035367740.675299849051645.569646450161.83935192007960
341639913916398728.61180486827.78027766495410.388195184257-0.635329030874804
351640100916403003.4861974901.53774300114-1994.48619698999-1.3404772077725
361640539916405095.45402392782.98676016899303.545976141113-1.47494285795660
371640910616409192.37478163774.29149833876-86.37478164254820.691067023130556
381641430716413596.77631314249.49417686857710.2236868558780.330632798218718
391641805516418160.54309804485.10969395089-105.5430980294880.163895632310000
401642333716423548.73542625162.42715779133-211.7354262373060.472759137459043
411642868616428291.93877464847.38081791848394.061225427853-0.219284536099624
421643493516434135.74104675594.66001902326799.2589532756070.519578141479515
431644045216441304.04049706774.24722315774-852.0404970293850.820976447031814
441644909216450121.27227948306.68993293857-1029.272279387661.06654219208397
451646485916462393.969916611282.40385479282465.030083449672.07080225025958
461647370916472938.926424610729.1228671495770.073575354833-0.385049269862007
471647929116481031.40896338751.70695851332-1740.40896331086-1.37612014232664
481648578716486410.72765736223.40967259856-623.727657251446-1.76040189691263
491648904216489783.69767044085.16478635051-741.697670374698-1.48962248093219
501649523116494443.13405204515.60734023491787.8659480377810.299424906629549
511650168316501480.80146666399.5827438526202.19853335511.31072720351576
521650678216506931.29486155690.51396384101-149.294861518056-0.494327611006217
531651361516513076.17972756030.51567022144538.820272472730.236716011241474
541652066116519847.54361996584.28848106541813.4563800548380.385112131890457
551652840016529189.16504378643.8766878351-789.165043679541.43315819182033
561653854216540342.174405110518.8945209348-1800.174405109351.30495496219462
571655459616552054.331524911410.88481582812541.668475095160.62074453128931
581656231716561519.77475209956.67739629438797.225247964324-1.01201261439228
591656849916569739.08540738658.4253068564-1240.08540732774-0.90352086081914
601657498916575388.48991526410.18136671235-399.489915176402-1.56536530441666

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 16306977 & 16306977 & 0 & 0 & 0 \tabularnewline
2 & 16307888 & 16307769.6479338 & 802.429049481105 & 118.352066160875 & 0.596812355298189 \tabularnewline
3 & 16307482 & 16307743.9254104 & 159.003088416110 & -261.925410406686 & -0.480529304271365 \tabularnewline
4 & 16308869 & 16308689.3026867 & 793.076169037753 & 179.697313260461 & 0.442252697891651 \tabularnewline
5 & 16311019 & 16310832.9242033 & 1862.70797688004 & 186.075796733849 & 0.742740018322194 \tabularnewline
6 & 16312596 & 16312684.0558317 & 1853.52272502671 & -88.0558316668831 & -0.00639744523962094 \tabularnewline
7 & 16315238 & 16315142.9043079 & 2334.35981980677 & 95.0956920658428 & 0.334609318661443 \tabularnewline
8 & 16319511 & 16319237.7020088 & 3732.75073843248 & 273.297991176963 & 0.973170392718777 \tabularnewline
9 & 16327575 & 16326957.1334659 & 6899.09117146745 & 617.866534127253 & 2.20350686356295 \tabularnewline
10 & 16330818 & 16331475.1574874 & 5008.07321314193 & -657.157487428788 & -1.31598636352216 \tabularnewline
11 & 16331930 & 16332538.1536815 & 1874.89231978793 & -608.153681536321 & -2.18042618470508 \tabularnewline
12 & 16334210 & 16334079.2469868 & 1609.78577870023 & 130.753013182522 & -0.184491476964418 \tabularnewline
13 & 16334715 & 16334826.7444229 & 926.091133625024 & -111.744422934432 & -0.476802917964902 \tabularnewline
14 & 16335459 & 16335151.4000672 & 447.847366270083 & 307.59993282131 & -0.33749539563946 \tabularnewline
15 & 16334090 & 16334618.117871 & -298.049741571975 & -528.117871009226 & -0.520077635925116 \tabularnewline
16 & 16333559 & 16333754.7003244 & -731.900000761233 & -195.700324355116 & -0.305053408189535 \tabularnewline
17 & 16334600 & 16334072.7837556 & 71.2442703724557 & 527.216244442828 & 0.557250449839938 \tabularnewline
18 & 16336676 & 16336231.4863797 & 1662.18099228068 & 444.51362028547 & 1.10725994173139 \tabularnewline
19 & 16337253 & 16337524.1166632 & 1380.12465440747 & -271.116663239638 & -0.196343358778851 \tabularnewline
20 & 16342333 & 16342435.6136316 & 4076.61774239966 & -102.613631619211 & 1.87645403719288 \tabularnewline
21 & 16348917 & 16347890.6488007 & 5129.13450623755 & 1026.35119932635 & 0.732463197758377 \tabularnewline
22 & 16352678 & 16352698.5968288 & 4883.8971386203 & -20.5968287699800 & -0.170667100787337 \tabularnewline
23 & 16352972 & 16354257.2719908 & 2345.22375635811 & -1285.27199079785 & -1.76670740081873 \tabularnewline
24 & 16357992 & 16357468.0620039 & 3005.63222097429 & 523.937996111849 & 0.459649119766005 \tabularnewline
25 & 16359133 & 16359405.8328430 & 2191.04632983813 & -272.832843039710 & -0.56827557652956 \tabularnewline
26 & 16362938 & 16362200.8702167 & 2652.35595496233 & 737.129783275367 & 0.321476483744732 \tabularnewline
27 & 16365065 & 16365293.3243038 & 2984.19505624561 & -228.324303798122 & 0.230791017358861 \tabularnewline
28 & 16367596 & 16368017.7250692 & 2787.99214238032 & -421.725069207335 & -0.137297869625761 \tabularnewline
29 & 16371278 & 16371018.2731215 & 2948.79360768452 & 259.726878492811 & 0.111818714740832 \tabularnewline
30 & 16374541 & 16373812.8445784 & 2832.51974293858 & 728.155421570669 & -0.080850396004529 \tabularnewline
31 & 16377339 & 16378127.7928633 & 3950.47136757629 & -788.792863303477 & 0.778252166564338 \tabularnewline
32 & 16383275 & 16383598.1758963 & 5097.55420346252 & -323.175896299154 & 0.798293587672257 \tabularnewline
33 & 16393843 & 16392197.4303536 & 7740.67529984905 & 1645.56964645016 & 1.83935192007960 \tabularnewline
34 & 16399139 & 16398728.6118048 & 6827.78027766495 & 410.388195184257 & -0.635329030874804 \tabularnewline
35 & 16401009 & 16403003.486197 & 4901.53774300114 & -1994.48619698999 & -1.3404772077725 \tabularnewline
36 & 16405399 & 16405095.4540239 & 2782.98676016899 & 303.545976141113 & -1.47494285795660 \tabularnewline
37 & 16409106 & 16409192.3747816 & 3774.29149833876 & -86.3747816425482 & 0.691067023130556 \tabularnewline
38 & 16414307 & 16413596.7763131 & 4249.49417686857 & 710.223686855878 & 0.330632798218718 \tabularnewline
39 & 16418055 & 16418160.5430980 & 4485.10969395089 & -105.543098029488 & 0.163895632310000 \tabularnewline
40 & 16423337 & 16423548.7354262 & 5162.42715779133 & -211.735426237306 & 0.472759137459043 \tabularnewline
41 & 16428686 & 16428291.9387746 & 4847.38081791848 & 394.061225427853 & -0.219284536099624 \tabularnewline
42 & 16434935 & 16434135.7410467 & 5594.66001902326 & 799.258953275607 & 0.519578141479515 \tabularnewline
43 & 16440452 & 16441304.0404970 & 6774.24722315774 & -852.040497029385 & 0.820976447031814 \tabularnewline
44 & 16449092 & 16450121.2722794 & 8306.68993293857 & -1029.27227938766 & 1.06654219208397 \tabularnewline
45 & 16464859 & 16462393.9699166 & 11282.4038547928 & 2465.03008344967 & 2.07080225025958 \tabularnewline
46 & 16473709 & 16472938.9264246 & 10729.1228671495 & 770.073575354833 & -0.385049269862007 \tabularnewline
47 & 16479291 & 16481031.4089633 & 8751.70695851332 & -1740.40896331086 & -1.37612014232664 \tabularnewline
48 & 16485787 & 16486410.7276573 & 6223.40967259856 & -623.727657251446 & -1.76040189691263 \tabularnewline
49 & 16489042 & 16489783.6976704 & 4085.16478635051 & -741.697670374698 & -1.48962248093219 \tabularnewline
50 & 16495231 & 16494443.1340520 & 4515.60734023491 & 787.865948037781 & 0.299424906629549 \tabularnewline
51 & 16501683 & 16501480.8014666 & 6399.5827438526 & 202.1985333551 & 1.31072720351576 \tabularnewline
52 & 16506782 & 16506931.2948615 & 5690.51396384101 & -149.294861518056 & -0.494327611006217 \tabularnewline
53 & 16513615 & 16513076.1797275 & 6030.51567022144 & 538.82027247273 & 0.236716011241474 \tabularnewline
54 & 16520661 & 16519847.5436199 & 6584.28848106541 & 813.456380054838 & 0.385112131890457 \tabularnewline
55 & 16528400 & 16529189.1650437 & 8643.8766878351 & -789.16504367954 & 1.43315819182033 \tabularnewline
56 & 16538542 & 16540342.1744051 & 10518.8945209348 & -1800.17440510935 & 1.30495496219462 \tabularnewline
57 & 16554596 & 16552054.3315249 & 11410.8848158281 & 2541.66847509516 & 0.62074453128931 \tabularnewline
58 & 16562317 & 16561519.7747520 & 9956.67739629438 & 797.225247964324 & -1.01201261439228 \tabularnewline
59 & 16568499 & 16569739.0854073 & 8658.4253068564 & -1240.08540732774 & -0.90352086081914 \tabularnewline
60 & 16574989 & 16575388.4899152 & 6410.18136671235 & -399.489915176402 & -1.56536530441666 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112934&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]16306977[/C][C]16306977[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]16307888[/C][C]16307769.6479338[/C][C]802.429049481105[/C][C]118.352066160875[/C][C]0.596812355298189[/C][/ROW]
[ROW][C]3[/C][C]16307482[/C][C]16307743.9254104[/C][C]159.003088416110[/C][C]-261.925410406686[/C][C]-0.480529304271365[/C][/ROW]
[ROW][C]4[/C][C]16308869[/C][C]16308689.3026867[/C][C]793.076169037753[/C][C]179.697313260461[/C][C]0.442252697891651[/C][/ROW]
[ROW][C]5[/C][C]16311019[/C][C]16310832.9242033[/C][C]1862.70797688004[/C][C]186.075796733849[/C][C]0.742740018322194[/C][/ROW]
[ROW][C]6[/C][C]16312596[/C][C]16312684.0558317[/C][C]1853.52272502671[/C][C]-88.0558316668831[/C][C]-0.00639744523962094[/C][/ROW]
[ROW][C]7[/C][C]16315238[/C][C]16315142.9043079[/C][C]2334.35981980677[/C][C]95.0956920658428[/C][C]0.334609318661443[/C][/ROW]
[ROW][C]8[/C][C]16319511[/C][C]16319237.7020088[/C][C]3732.75073843248[/C][C]273.297991176963[/C][C]0.973170392718777[/C][/ROW]
[ROW][C]9[/C][C]16327575[/C][C]16326957.1334659[/C][C]6899.09117146745[/C][C]617.866534127253[/C][C]2.20350686356295[/C][/ROW]
[ROW][C]10[/C][C]16330818[/C][C]16331475.1574874[/C][C]5008.07321314193[/C][C]-657.157487428788[/C][C]-1.31598636352216[/C][/ROW]
[ROW][C]11[/C][C]16331930[/C][C]16332538.1536815[/C][C]1874.89231978793[/C][C]-608.153681536321[/C][C]-2.18042618470508[/C][/ROW]
[ROW][C]12[/C][C]16334210[/C][C]16334079.2469868[/C][C]1609.78577870023[/C][C]130.753013182522[/C][C]-0.184491476964418[/C][/ROW]
[ROW][C]13[/C][C]16334715[/C][C]16334826.7444229[/C][C]926.091133625024[/C][C]-111.744422934432[/C][C]-0.476802917964902[/C][/ROW]
[ROW][C]14[/C][C]16335459[/C][C]16335151.4000672[/C][C]447.847366270083[/C][C]307.59993282131[/C][C]-0.33749539563946[/C][/ROW]
[ROW][C]15[/C][C]16334090[/C][C]16334618.117871[/C][C]-298.049741571975[/C][C]-528.117871009226[/C][C]-0.520077635925116[/C][/ROW]
[ROW][C]16[/C][C]16333559[/C][C]16333754.7003244[/C][C]-731.900000761233[/C][C]-195.700324355116[/C][C]-0.305053408189535[/C][/ROW]
[ROW][C]17[/C][C]16334600[/C][C]16334072.7837556[/C][C]71.2442703724557[/C][C]527.216244442828[/C][C]0.557250449839938[/C][/ROW]
[ROW][C]18[/C][C]16336676[/C][C]16336231.4863797[/C][C]1662.18099228068[/C][C]444.51362028547[/C][C]1.10725994173139[/C][/ROW]
[ROW][C]19[/C][C]16337253[/C][C]16337524.1166632[/C][C]1380.12465440747[/C][C]-271.116663239638[/C][C]-0.196343358778851[/C][/ROW]
[ROW][C]20[/C][C]16342333[/C][C]16342435.6136316[/C][C]4076.61774239966[/C][C]-102.613631619211[/C][C]1.87645403719288[/C][/ROW]
[ROW][C]21[/C][C]16348917[/C][C]16347890.6488007[/C][C]5129.13450623755[/C][C]1026.35119932635[/C][C]0.732463197758377[/C][/ROW]
[ROW][C]22[/C][C]16352678[/C][C]16352698.5968288[/C][C]4883.8971386203[/C][C]-20.5968287699800[/C][C]-0.170667100787337[/C][/ROW]
[ROW][C]23[/C][C]16352972[/C][C]16354257.2719908[/C][C]2345.22375635811[/C][C]-1285.27199079785[/C][C]-1.76670740081873[/C][/ROW]
[ROW][C]24[/C][C]16357992[/C][C]16357468.0620039[/C][C]3005.63222097429[/C][C]523.937996111849[/C][C]0.459649119766005[/C][/ROW]
[ROW][C]25[/C][C]16359133[/C][C]16359405.8328430[/C][C]2191.04632983813[/C][C]-272.832843039710[/C][C]-0.56827557652956[/C][/ROW]
[ROW][C]26[/C][C]16362938[/C][C]16362200.8702167[/C][C]2652.35595496233[/C][C]737.129783275367[/C][C]0.321476483744732[/C][/ROW]
[ROW][C]27[/C][C]16365065[/C][C]16365293.3243038[/C][C]2984.19505624561[/C][C]-228.324303798122[/C][C]0.230791017358861[/C][/ROW]
[ROW][C]28[/C][C]16367596[/C][C]16368017.7250692[/C][C]2787.99214238032[/C][C]-421.725069207335[/C][C]-0.137297869625761[/C][/ROW]
[ROW][C]29[/C][C]16371278[/C][C]16371018.2731215[/C][C]2948.79360768452[/C][C]259.726878492811[/C][C]0.111818714740832[/C][/ROW]
[ROW][C]30[/C][C]16374541[/C][C]16373812.8445784[/C][C]2832.51974293858[/C][C]728.155421570669[/C][C]-0.080850396004529[/C][/ROW]
[ROW][C]31[/C][C]16377339[/C][C]16378127.7928633[/C][C]3950.47136757629[/C][C]-788.792863303477[/C][C]0.778252166564338[/C][/ROW]
[ROW][C]32[/C][C]16383275[/C][C]16383598.1758963[/C][C]5097.55420346252[/C][C]-323.175896299154[/C][C]0.798293587672257[/C][/ROW]
[ROW][C]33[/C][C]16393843[/C][C]16392197.4303536[/C][C]7740.67529984905[/C][C]1645.56964645016[/C][C]1.83935192007960[/C][/ROW]
[ROW][C]34[/C][C]16399139[/C][C]16398728.6118048[/C][C]6827.78027766495[/C][C]410.388195184257[/C][C]-0.635329030874804[/C][/ROW]
[ROW][C]35[/C][C]16401009[/C][C]16403003.486197[/C][C]4901.53774300114[/C][C]-1994.48619698999[/C][C]-1.3404772077725[/C][/ROW]
[ROW][C]36[/C][C]16405399[/C][C]16405095.4540239[/C][C]2782.98676016899[/C][C]303.545976141113[/C][C]-1.47494285795660[/C][/ROW]
[ROW][C]37[/C][C]16409106[/C][C]16409192.3747816[/C][C]3774.29149833876[/C][C]-86.3747816425482[/C][C]0.691067023130556[/C][/ROW]
[ROW][C]38[/C][C]16414307[/C][C]16413596.7763131[/C][C]4249.49417686857[/C][C]710.223686855878[/C][C]0.330632798218718[/C][/ROW]
[ROW][C]39[/C][C]16418055[/C][C]16418160.5430980[/C][C]4485.10969395089[/C][C]-105.543098029488[/C][C]0.163895632310000[/C][/ROW]
[ROW][C]40[/C][C]16423337[/C][C]16423548.7354262[/C][C]5162.42715779133[/C][C]-211.735426237306[/C][C]0.472759137459043[/C][/ROW]
[ROW][C]41[/C][C]16428686[/C][C]16428291.9387746[/C][C]4847.38081791848[/C][C]394.061225427853[/C][C]-0.219284536099624[/C][/ROW]
[ROW][C]42[/C][C]16434935[/C][C]16434135.7410467[/C][C]5594.66001902326[/C][C]799.258953275607[/C][C]0.519578141479515[/C][/ROW]
[ROW][C]43[/C][C]16440452[/C][C]16441304.0404970[/C][C]6774.24722315774[/C][C]-852.040497029385[/C][C]0.820976447031814[/C][/ROW]
[ROW][C]44[/C][C]16449092[/C][C]16450121.2722794[/C][C]8306.68993293857[/C][C]-1029.27227938766[/C][C]1.06654219208397[/C][/ROW]
[ROW][C]45[/C][C]16464859[/C][C]16462393.9699166[/C][C]11282.4038547928[/C][C]2465.03008344967[/C][C]2.07080225025958[/C][/ROW]
[ROW][C]46[/C][C]16473709[/C][C]16472938.9264246[/C][C]10729.1228671495[/C][C]770.073575354833[/C][C]-0.385049269862007[/C][/ROW]
[ROW][C]47[/C][C]16479291[/C][C]16481031.4089633[/C][C]8751.70695851332[/C][C]-1740.40896331086[/C][C]-1.37612014232664[/C][/ROW]
[ROW][C]48[/C][C]16485787[/C][C]16486410.7276573[/C][C]6223.40967259856[/C][C]-623.727657251446[/C][C]-1.76040189691263[/C][/ROW]
[ROW][C]49[/C][C]16489042[/C][C]16489783.6976704[/C][C]4085.16478635051[/C][C]-741.697670374698[/C][C]-1.48962248093219[/C][/ROW]
[ROW][C]50[/C][C]16495231[/C][C]16494443.1340520[/C][C]4515.60734023491[/C][C]787.865948037781[/C][C]0.299424906629549[/C][/ROW]
[ROW][C]51[/C][C]16501683[/C][C]16501480.8014666[/C][C]6399.5827438526[/C][C]202.1985333551[/C][C]1.31072720351576[/C][/ROW]
[ROW][C]52[/C][C]16506782[/C][C]16506931.2948615[/C][C]5690.51396384101[/C][C]-149.294861518056[/C][C]-0.494327611006217[/C][/ROW]
[ROW][C]53[/C][C]16513615[/C][C]16513076.1797275[/C][C]6030.51567022144[/C][C]538.82027247273[/C][C]0.236716011241474[/C][/ROW]
[ROW][C]54[/C][C]16520661[/C][C]16519847.5436199[/C][C]6584.28848106541[/C][C]813.456380054838[/C][C]0.385112131890457[/C][/ROW]
[ROW][C]55[/C][C]16528400[/C][C]16529189.1650437[/C][C]8643.8766878351[/C][C]-789.16504367954[/C][C]1.43315819182033[/C][/ROW]
[ROW][C]56[/C][C]16538542[/C][C]16540342.1744051[/C][C]10518.8945209348[/C][C]-1800.17440510935[/C][C]1.30495496219462[/C][/ROW]
[ROW][C]57[/C][C]16554596[/C][C]16552054.3315249[/C][C]11410.8848158281[/C][C]2541.66847509516[/C][C]0.62074453128931[/C][/ROW]
[ROW][C]58[/C][C]16562317[/C][C]16561519.7747520[/C][C]9956.67739629438[/C][C]797.225247964324[/C][C]-1.01201261439228[/C][/ROW]
[ROW][C]59[/C][C]16568499[/C][C]16569739.0854073[/C][C]8658.4253068564[/C][C]-1240.08540732774[/C][C]-0.90352086081914[/C][/ROW]
[ROW][C]60[/C][C]16574989[/C][C]16575388.4899152[/C][C]6410.18136671235[/C][C]-399.489915176402[/C][C]-1.56536530441666[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112934&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
11630697716306977000
21630788816307769.6479338802.429049481105118.3520661608750.596812355298189
31630748216307743.9254104159.003088416110-261.925410406686-0.480529304271365
41630886916308689.3026867793.076169037753179.6973132604610.442252697891651
51631101916310832.92420331862.70797688004186.0757967338490.742740018322194
61631259616312684.05583171853.52272502671-88.0558316668831-0.00639744523962094
71631523816315142.90430792334.3598198067795.09569206584280.334609318661443
81631951116319237.70200883732.75073843248273.2979911769630.973170392718777
91632757516326957.13346596899.09117146745617.8665341272532.20350686356295
101633081816331475.15748745008.07321314193-657.157487428788-1.31598636352216
111633193016332538.15368151874.89231978793-608.153681536321-2.18042618470508
121633421016334079.24698681609.78577870023130.753013182522-0.184491476964418
131633471516334826.7444229926.091133625024-111.744422934432-0.476802917964902
141633545916335151.4000672447.847366270083307.59993282131-0.33749539563946
151633409016334618.117871-298.049741571975-528.117871009226-0.520077635925116
161633355916333754.7003244-731.900000761233-195.700324355116-0.305053408189535
171633460016334072.783755671.2442703724557527.2162444428280.557250449839938
181633667616336231.48637971662.18099228068444.513620285471.10725994173139
191633725316337524.11666321380.12465440747-271.116663239638-0.196343358778851
201634233316342435.61363164076.61774239966-102.6136316192111.87645403719288
211634891716347890.64880075129.134506237551026.351199326350.732463197758377
221635267816352698.59682884883.8971386203-20.5968287699800-0.170667100787337
231635297216354257.27199082345.22375635811-1285.27199079785-1.76670740081873
241635799216357468.06200393005.63222097429523.9379961118490.459649119766005
251635913316359405.83284302191.04632983813-272.832843039710-0.56827557652956
261636293816362200.87021672652.35595496233737.1297832753670.321476483744732
271636506516365293.32430382984.19505624561-228.3243037981220.230791017358861
281636759616368017.72506922787.99214238032-421.725069207335-0.137297869625761
291637127816371018.27312152948.79360768452259.7268784928110.111818714740832
301637454116373812.84457842832.51974293858728.155421570669-0.080850396004529
311637733916378127.79286333950.47136757629-788.7928633034770.778252166564338
321638327516383598.17589635097.55420346252-323.1758962991540.798293587672257
331639384316392197.43035367740.675299849051645.569646450161.83935192007960
341639913916398728.61180486827.78027766495410.388195184257-0.635329030874804
351640100916403003.4861974901.53774300114-1994.48619698999-1.3404772077725
361640539916405095.45402392782.98676016899303.545976141113-1.47494285795660
371640910616409192.37478163774.29149833876-86.37478164254820.691067023130556
381641430716413596.77631314249.49417686857710.2236868558780.330632798218718
391641805516418160.54309804485.10969395089-105.5430980294880.163895632310000
401642333716423548.73542625162.42715779133-211.7354262373060.472759137459043
411642868616428291.93877464847.38081791848394.061225427853-0.219284536099624
421643493516434135.74104675594.66001902326799.2589532756070.519578141479515
431644045216441304.04049706774.24722315774-852.0404970293850.820976447031814
441644909216450121.27227948306.68993293857-1029.272279387661.06654219208397
451646485916462393.969916611282.40385479282465.030083449672.07080225025958
461647370916472938.926424610729.1228671495770.073575354833-0.385049269862007
471647929116481031.40896338751.70695851332-1740.40896331086-1.37612014232664
481648578716486410.72765736223.40967259856-623.727657251446-1.76040189691263
491648904216489783.69767044085.16478635051-741.697670374698-1.48962248093219
501649523116494443.13405204515.60734023491787.8659480377810.299424906629549
511650168316501480.80146666399.5827438526202.19853335511.31072720351576
521650678216506931.29486155690.51396384101-149.294861518056-0.494327611006217
531651361516513076.17972756030.51567022144538.820272472730.236716011241474
541652066116519847.54361996584.28848106541813.4563800548380.385112131890457
551652840016529189.16504378643.8766878351-789.165043679541.43315819182033
561653854216540342.174405110518.8945209348-1800.174405109351.30495496219462
571655459616552054.331524911410.88481582812541.668475095160.62074453128931
581656231716561519.77475209956.67739629438797.225247964324-1.01201261439228
591656849916569739.08540738658.4253068564-1240.08540732774-0.90352086081914
601657498916575388.48991526410.18136671235-399.489915176402-1.56536530441666



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')