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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationSat, 17 Dec 2011 12:38:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/17/t1324143567safeepsdgw8skp0.htm/, Retrieved Thu, 25 Apr 2024 11:44:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156517, Retrieved Thu, 25 Apr 2024 11:44:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [Structural time s...] [2011-12-17 17:38:58] [de50302416ae5d0bdedd77e4c0468c33] [Current]
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Dataseries X:
41
39
50
40
43
38
44
35
39
35
29
49
50
59
63
32
39
47
53
60
57
52
70
90
74
62
55
84
94
70
108
139
120
97
126
149
158
124
140
109
114
77
120
133
110
92
97
78
99
107
112
90
98
125
155
190
236
189
174
178
136
161
171
149
184
155
276
224
213
279
268
287




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156517&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156517&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156517&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' @ jenkins.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
14141000
23939.4681786008149-0.0979294436540218-0.124264696701125-0.0638919853075069
35046.54376973502230.1390087590865370.5620482632373630.428901774879656
44042.28031011889660.0522995425459843-0.353964513427608-0.276036645139372
54342.67330323380940.05783211816026480.1758368559063980.0214933581845457
63839.57156258233590.00755165472164264-0.170636625591731-0.199394845746151
74442.43875947261440.05475454201686770.2943919906581290.180327708480934
83537.5424864602514-0.0311422615834274-0.352202703776871-0.311888748349939
93938.4198102839839-0.01457033562275380.1789036244764630.0571669720681654
103536.1532277334162-0.0576370820621674-0.159984380750529-0.141560510440093
112931.3631502606335-0.152178877240164-0.279023441236224-0.297167760879146
124942.97219842834040.0924254510823550.8557244335490170.737780218222501
135046.5115120124834-0.06626558351077921.933968152502580.266246838500981
145955.71881796610750.2994335470399560.1912046437310.492463308392527
156360.31501652456090.4274672118929790.9503911548084850.259785315192187
163242.3586711510299-0.00898801290880507-2.58617255004916-1.1424517720132
173939.5279485357041-0.07165608554070150.672559003404174-0.17589346716942
184744.51698118761520.0411884144557890.3296117218833470.315386752387433
195349.86578651713490.1624195896897460.8777647129507980.330540295157587
206056.90445497327570.3239027993838020.1754106016788090.427896048756906
215757.03675125620490.319278635969590.0445286885340537-0.0119142207694525
225253.72863388904440.229421797654096-0.191516321877984-0.225391921552344
237064.76142874785950.5035134306706250.6651673395694560.670825612085569
249081.25227390092240.9024872899276431.985390597457270.991384292019457
257478.98045532735830.894980344337853-3.62525481524946-0.216919487050872
266267.9448837860780.470231608776671-1.56747771749412-0.678844852185979
275557.88271306008530.1259942590505551.30522505996275-0.636513733079734
288475.86995450509570.6498649704781540.7876173198706991.10145767096802
299488.05637306326490.9759734635639641.178449925666740.713006342990637
307077.22366521814610.642379758312508-2.34568071468906-0.729646151837466
3110896.67453793329631.179298675605363.560399587601561.1616101577344
32139124.7084460458471.955658677240723.21184473104551.65776872568695
33120122.9202469822031.84605010273257-1.37656688893993-0.231014708066358
3497107.4626820232661.33321393213363-3.33210534366404-1.06732218116826
35126119.7837654084741.661777083273151.690758972928160.677462987476597
36149138.0699006156282.144774140200984.090514727580221.02361043757679
37158151.6957187796762.373433115894071.546269263959080.748559940683804
38124134.9579191120681.70746962061804-3.68439127307395-1.12123337612557
39140138.7115412460781.777683332289330.482720492359090.123626913477546
40109120.8464632684231.13977347234442-3.9127059355202-1.20586918346958
41114114.8403769780190.9132494641220942.05778256723268-0.439500155557618
427793.77561146861560.219053609667765-7.8610744598345-1.35141262669071
43120109.6845485950940.7165283800182413.953533674100540.964467486167056
44133123.25220011581.126293312309894.538686745516590.789775841321633
45110115.6443465829960.846164694427059-2.10508222172593-0.536660586794877
4692103.7816806460460.436317880905144-6.63314268393655-0.780755211690291
479799.58748053478030.286781076802437-0.712329206445536-0.284354627061858
487883.8684351025245-0.217539305212350.603322091440758-0.98193785104253
499988.9844084843725-0.07000671520935697.842365817913090.339627951673189
50107103.2204967048020.425768673665507-1.758913565049230.853175494732954
51112107.7544500413220.5688983656076622.634994314257860.248401840875929
529098.736426675120.244211318394393-4.90362600244276-0.58717557267884
539895.08027747237110.1141999447571234.48494651993072-0.239296444444071
54125120.1170730489790.941456659263719-5.119229457458311.52863004688005
55155141.8911765434431.633583209823734.749914539993531.27747051325415
56190170.2693628068422.524310124150179.001354034594471.63979949374108
57236214.228969870963.908106134559245.150138023812942.54037857174548
58189204.9258813944193.46592657890096-10.6271489379084-0.80990376253078
59174185.4902823209052.70063719988553-2.30934727760813-1.40323816487932
60178181.0619534681862.46649487327162-0.208825554379717-0.436647310873763
61136151.3920088414621.46365697090616-2.42701385222636-2.01939810093448
62161158.577109592881.66144946752640.1884678802043580.344457669000458
63171164.1731219389321.798695152452945.286639060968590.238196628516746
64149157.672655123311.51332509272347-5.37419909730093-0.50770181431007
65184173.2576143364861.992312912359295.129789872245490.862287097220564
66155167.45691966741.72801219500799-9.34851948665104-0.477388271613014
67276235.5767356928863.9791872153281613.94686109723784.0661055587189
68224229.4033436580313.63458751758534-1.35492718662874-0.621745408306792
69213215.9297083397693.053161549093653.89212524524071-1.04770782742818
70279261.0806423267834.484799549035121.13481551972882.57781714868866
71268268.9643229784344.60011955725864-2.318603052042720.207997940599125
72287280.1209208712034.820123150316824.270482247456160.401344183091745

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 41 & 41 & 0 & 0 & 0 \tabularnewline
2 & 39 & 39.4681786008149 & -0.0979294436540218 & -0.124264696701125 & -0.0638919853075069 \tabularnewline
3 & 50 & 46.5437697350223 & 0.139008759086537 & 0.562048263237363 & 0.428901774879656 \tabularnewline
4 & 40 & 42.2803101188966 & 0.0522995425459843 & -0.353964513427608 & -0.276036645139372 \tabularnewline
5 & 43 & 42.6733032338094 & 0.0578321181602648 & 0.175836855906398 & 0.0214933581845457 \tabularnewline
6 & 38 & 39.5715625823359 & 0.00755165472164264 & -0.170636625591731 & -0.199394845746151 \tabularnewline
7 & 44 & 42.4387594726144 & 0.0547545420168677 & 0.294391990658129 & 0.180327708480934 \tabularnewline
8 & 35 & 37.5424864602514 & -0.0311422615834274 & -0.352202703776871 & -0.311888748349939 \tabularnewline
9 & 39 & 38.4198102839839 & -0.0145703356227538 & 0.178903624476463 & 0.0571669720681654 \tabularnewline
10 & 35 & 36.1532277334162 & -0.0576370820621674 & -0.159984380750529 & -0.141560510440093 \tabularnewline
11 & 29 & 31.3631502606335 & -0.152178877240164 & -0.279023441236224 & -0.297167760879146 \tabularnewline
12 & 49 & 42.9721984283404 & 0.092425451082355 & 0.855724433549017 & 0.737780218222501 \tabularnewline
13 & 50 & 46.5115120124834 & -0.0662655835107792 & 1.93396815250258 & 0.266246838500981 \tabularnewline
14 & 59 & 55.7188179661075 & 0.299433547039956 & 0.191204643731 & 0.492463308392527 \tabularnewline
15 & 63 & 60.3150165245609 & 0.427467211892979 & 0.950391154808485 & 0.259785315192187 \tabularnewline
16 & 32 & 42.3586711510299 & -0.00898801290880507 & -2.58617255004916 & -1.1424517720132 \tabularnewline
17 & 39 & 39.5279485357041 & -0.0716560855407015 & 0.672559003404174 & -0.17589346716942 \tabularnewline
18 & 47 & 44.5169811876152 & 0.041188414455789 & 0.329611721883347 & 0.315386752387433 \tabularnewline
19 & 53 & 49.8657865171349 & 0.162419589689746 & 0.877764712950798 & 0.330540295157587 \tabularnewline
20 & 60 & 56.9044549732757 & 0.323902799383802 & 0.175410601678809 & 0.427896048756906 \tabularnewline
21 & 57 & 57.0367512562049 & 0.31927863596959 & 0.0445286885340537 & -0.0119142207694525 \tabularnewline
22 & 52 & 53.7286338890444 & 0.229421797654096 & -0.191516321877984 & -0.225391921552344 \tabularnewline
23 & 70 & 64.7614287478595 & 0.503513430670625 & 0.665167339569456 & 0.670825612085569 \tabularnewline
24 & 90 & 81.2522739009224 & 0.902487289927643 & 1.98539059745727 & 0.991384292019457 \tabularnewline
25 & 74 & 78.9804553273583 & 0.894980344337853 & -3.62525481524946 & -0.216919487050872 \tabularnewline
26 & 62 & 67.944883786078 & 0.470231608776671 & -1.56747771749412 & -0.678844852185979 \tabularnewline
27 & 55 & 57.8827130600853 & 0.125994259050555 & 1.30522505996275 & -0.636513733079734 \tabularnewline
28 & 84 & 75.8699545050957 & 0.649864970478154 & 0.787617319870699 & 1.10145767096802 \tabularnewline
29 & 94 & 88.0563730632649 & 0.975973463563964 & 1.17844992566674 & 0.713006342990637 \tabularnewline
30 & 70 & 77.2236652181461 & 0.642379758312508 & -2.34568071468906 & -0.729646151837466 \tabularnewline
31 & 108 & 96.6745379332963 & 1.17929867560536 & 3.56039958760156 & 1.1616101577344 \tabularnewline
32 & 139 & 124.708446045847 & 1.95565867724072 & 3.2118447310455 & 1.65776872568695 \tabularnewline
33 & 120 & 122.920246982203 & 1.84605010273257 & -1.37656688893993 & -0.231014708066358 \tabularnewline
34 & 97 & 107.462682023266 & 1.33321393213363 & -3.33210534366404 & -1.06732218116826 \tabularnewline
35 & 126 & 119.783765408474 & 1.66177708327315 & 1.69075897292816 & 0.677462987476597 \tabularnewline
36 & 149 & 138.069900615628 & 2.14477414020098 & 4.09051472758022 & 1.02361043757679 \tabularnewline
37 & 158 & 151.695718779676 & 2.37343311589407 & 1.54626926395908 & 0.748559940683804 \tabularnewline
38 & 124 & 134.957919112068 & 1.70746962061804 & -3.68439127307395 & -1.12123337612557 \tabularnewline
39 & 140 & 138.711541246078 & 1.77768333228933 & 0.48272049235909 & 0.123626913477546 \tabularnewline
40 & 109 & 120.846463268423 & 1.13977347234442 & -3.9127059355202 & -1.20586918346958 \tabularnewline
41 & 114 & 114.840376978019 & 0.913249464122094 & 2.05778256723268 & -0.439500155557618 \tabularnewline
42 & 77 & 93.7756114686156 & 0.219053609667765 & -7.8610744598345 & -1.35141262669071 \tabularnewline
43 & 120 & 109.684548595094 & 0.716528380018241 & 3.95353367410054 & 0.964467486167056 \tabularnewline
44 & 133 & 123.2522001158 & 1.12629331230989 & 4.53868674551659 & 0.789775841321633 \tabularnewline
45 & 110 & 115.644346582996 & 0.846164694427059 & -2.10508222172593 & -0.536660586794877 \tabularnewline
46 & 92 & 103.781680646046 & 0.436317880905144 & -6.63314268393655 & -0.780755211690291 \tabularnewline
47 & 97 & 99.5874805347803 & 0.286781076802437 & -0.712329206445536 & -0.284354627061858 \tabularnewline
48 & 78 & 83.8684351025245 & -0.21753930521235 & 0.603322091440758 & -0.98193785104253 \tabularnewline
49 & 99 & 88.9844084843725 & -0.0700067152093569 & 7.84236581791309 & 0.339627951673189 \tabularnewline
50 & 107 & 103.220496704802 & 0.425768673665507 & -1.75891356504923 & 0.853175494732954 \tabularnewline
51 & 112 & 107.754450041322 & 0.568898365607662 & 2.63499431425786 & 0.248401840875929 \tabularnewline
52 & 90 & 98.73642667512 & 0.244211318394393 & -4.90362600244276 & -0.58717557267884 \tabularnewline
53 & 98 & 95.0802774723711 & 0.114199944757123 & 4.48494651993072 & -0.239296444444071 \tabularnewline
54 & 125 & 120.117073048979 & 0.941456659263719 & -5.11922945745831 & 1.52863004688005 \tabularnewline
55 & 155 & 141.891176543443 & 1.63358320982373 & 4.74991453999353 & 1.27747051325415 \tabularnewline
56 & 190 & 170.269362806842 & 2.52431012415017 & 9.00135403459447 & 1.63979949374108 \tabularnewline
57 & 236 & 214.22896987096 & 3.90810613455924 & 5.15013802381294 & 2.54037857174548 \tabularnewline
58 & 189 & 204.925881394419 & 3.46592657890096 & -10.6271489379084 & -0.80990376253078 \tabularnewline
59 & 174 & 185.490282320905 & 2.70063719988553 & -2.30934727760813 & -1.40323816487932 \tabularnewline
60 & 178 & 181.061953468186 & 2.46649487327162 & -0.208825554379717 & -0.436647310873763 \tabularnewline
61 & 136 & 151.392008841462 & 1.46365697090616 & -2.42701385222636 & -2.01939810093448 \tabularnewline
62 & 161 & 158.57710959288 & 1.6614494675264 & 0.188467880204358 & 0.344457669000458 \tabularnewline
63 & 171 & 164.173121938932 & 1.79869515245294 & 5.28663906096859 & 0.238196628516746 \tabularnewline
64 & 149 & 157.67265512331 & 1.51332509272347 & -5.37419909730093 & -0.50770181431007 \tabularnewline
65 & 184 & 173.257614336486 & 1.99231291235929 & 5.12978987224549 & 0.862287097220564 \tabularnewline
66 & 155 & 167.4569196674 & 1.72801219500799 & -9.34851948665104 & -0.477388271613014 \tabularnewline
67 & 276 & 235.576735692886 & 3.97918721532816 & 13.9468610972378 & 4.0661055587189 \tabularnewline
68 & 224 & 229.403343658031 & 3.63458751758534 & -1.35492718662874 & -0.621745408306792 \tabularnewline
69 & 213 & 215.929708339769 & 3.05316154909365 & 3.89212524524071 & -1.04770782742818 \tabularnewline
70 & 279 & 261.080642326783 & 4.48479954903512 & 1.1348155197288 & 2.57781714868866 \tabularnewline
71 & 268 & 268.964322978434 & 4.60011955725864 & -2.31860305204272 & 0.207997940599125 \tabularnewline
72 & 287 & 280.120920871203 & 4.82012315031682 & 4.27048224745616 & 0.401344183091745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156517&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]41[/C][C]41[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]39.4681786008149[/C][C]-0.0979294436540218[/C][C]-0.124264696701125[/C][C]-0.0638919853075069[/C][/ROW]
[ROW][C]3[/C][C]50[/C][C]46.5437697350223[/C][C]0.139008759086537[/C][C]0.562048263237363[/C][C]0.428901774879656[/C][/ROW]
[ROW][C]4[/C][C]40[/C][C]42.2803101188966[/C][C]0.0522995425459843[/C][C]-0.353964513427608[/C][C]-0.276036645139372[/C][/ROW]
[ROW][C]5[/C][C]43[/C][C]42.6733032338094[/C][C]0.0578321181602648[/C][C]0.175836855906398[/C][C]0.0214933581845457[/C][/ROW]
[ROW][C]6[/C][C]38[/C][C]39.5715625823359[/C][C]0.00755165472164264[/C][C]-0.170636625591731[/C][C]-0.199394845746151[/C][/ROW]
[ROW][C]7[/C][C]44[/C][C]42.4387594726144[/C][C]0.0547545420168677[/C][C]0.294391990658129[/C][C]0.180327708480934[/C][/ROW]
[ROW][C]8[/C][C]35[/C][C]37.5424864602514[/C][C]-0.0311422615834274[/C][C]-0.352202703776871[/C][C]-0.311888748349939[/C][/ROW]
[ROW][C]9[/C][C]39[/C][C]38.4198102839839[/C][C]-0.0145703356227538[/C][C]0.178903624476463[/C][C]0.0571669720681654[/C][/ROW]
[ROW][C]10[/C][C]35[/C][C]36.1532277334162[/C][C]-0.0576370820621674[/C][C]-0.159984380750529[/C][C]-0.141560510440093[/C][/ROW]
[ROW][C]11[/C][C]29[/C][C]31.3631502606335[/C][C]-0.152178877240164[/C][C]-0.279023441236224[/C][C]-0.297167760879146[/C][/ROW]
[ROW][C]12[/C][C]49[/C][C]42.9721984283404[/C][C]0.092425451082355[/C][C]0.855724433549017[/C][C]0.737780218222501[/C][/ROW]
[ROW][C]13[/C][C]50[/C][C]46.5115120124834[/C][C]-0.0662655835107792[/C][C]1.93396815250258[/C][C]0.266246838500981[/C][/ROW]
[ROW][C]14[/C][C]59[/C][C]55.7188179661075[/C][C]0.299433547039956[/C][C]0.191204643731[/C][C]0.492463308392527[/C][/ROW]
[ROW][C]15[/C][C]63[/C][C]60.3150165245609[/C][C]0.427467211892979[/C][C]0.950391154808485[/C][C]0.259785315192187[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]42.3586711510299[/C][C]-0.00898801290880507[/C][C]-2.58617255004916[/C][C]-1.1424517720132[/C][/ROW]
[ROW][C]17[/C][C]39[/C][C]39.5279485357041[/C][C]-0.0716560855407015[/C][C]0.672559003404174[/C][C]-0.17589346716942[/C][/ROW]
[ROW][C]18[/C][C]47[/C][C]44.5169811876152[/C][C]0.041188414455789[/C][C]0.329611721883347[/C][C]0.315386752387433[/C][/ROW]
[ROW][C]19[/C][C]53[/C][C]49.8657865171349[/C][C]0.162419589689746[/C][C]0.877764712950798[/C][C]0.330540295157587[/C][/ROW]
[ROW][C]20[/C][C]60[/C][C]56.9044549732757[/C][C]0.323902799383802[/C][C]0.175410601678809[/C][C]0.427896048756906[/C][/ROW]
[ROW][C]21[/C][C]57[/C][C]57.0367512562049[/C][C]0.31927863596959[/C][C]0.0445286885340537[/C][C]-0.0119142207694525[/C][/ROW]
[ROW][C]22[/C][C]52[/C][C]53.7286338890444[/C][C]0.229421797654096[/C][C]-0.191516321877984[/C][C]-0.225391921552344[/C][/ROW]
[ROW][C]23[/C][C]70[/C][C]64.7614287478595[/C][C]0.503513430670625[/C][C]0.665167339569456[/C][C]0.670825612085569[/C][/ROW]
[ROW][C]24[/C][C]90[/C][C]81.2522739009224[/C][C]0.902487289927643[/C][C]1.98539059745727[/C][C]0.991384292019457[/C][/ROW]
[ROW][C]25[/C][C]74[/C][C]78.9804553273583[/C][C]0.894980344337853[/C][C]-3.62525481524946[/C][C]-0.216919487050872[/C][/ROW]
[ROW][C]26[/C][C]62[/C][C]67.944883786078[/C][C]0.470231608776671[/C][C]-1.56747771749412[/C][C]-0.678844852185979[/C][/ROW]
[ROW][C]27[/C][C]55[/C][C]57.8827130600853[/C][C]0.125994259050555[/C][C]1.30522505996275[/C][C]-0.636513733079734[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]75.8699545050957[/C][C]0.649864970478154[/C][C]0.787617319870699[/C][C]1.10145767096802[/C][/ROW]
[ROW][C]29[/C][C]94[/C][C]88.0563730632649[/C][C]0.975973463563964[/C][C]1.17844992566674[/C][C]0.713006342990637[/C][/ROW]
[ROW][C]30[/C][C]70[/C][C]77.2236652181461[/C][C]0.642379758312508[/C][C]-2.34568071468906[/C][C]-0.729646151837466[/C][/ROW]
[ROW][C]31[/C][C]108[/C][C]96.6745379332963[/C][C]1.17929867560536[/C][C]3.56039958760156[/C][C]1.1616101577344[/C][/ROW]
[ROW][C]32[/C][C]139[/C][C]124.708446045847[/C][C]1.95565867724072[/C][C]3.2118447310455[/C][C]1.65776872568695[/C][/ROW]
[ROW][C]33[/C][C]120[/C][C]122.920246982203[/C][C]1.84605010273257[/C][C]-1.37656688893993[/C][C]-0.231014708066358[/C][/ROW]
[ROW][C]34[/C][C]97[/C][C]107.462682023266[/C][C]1.33321393213363[/C][C]-3.33210534366404[/C][C]-1.06732218116826[/C][/ROW]
[ROW][C]35[/C][C]126[/C][C]119.783765408474[/C][C]1.66177708327315[/C][C]1.69075897292816[/C][C]0.677462987476597[/C][/ROW]
[ROW][C]36[/C][C]149[/C][C]138.069900615628[/C][C]2.14477414020098[/C][C]4.09051472758022[/C][C]1.02361043757679[/C][/ROW]
[ROW][C]37[/C][C]158[/C][C]151.695718779676[/C][C]2.37343311589407[/C][C]1.54626926395908[/C][C]0.748559940683804[/C][/ROW]
[ROW][C]38[/C][C]124[/C][C]134.957919112068[/C][C]1.70746962061804[/C][C]-3.68439127307395[/C][C]-1.12123337612557[/C][/ROW]
[ROW][C]39[/C][C]140[/C][C]138.711541246078[/C][C]1.77768333228933[/C][C]0.48272049235909[/C][C]0.123626913477546[/C][/ROW]
[ROW][C]40[/C][C]109[/C][C]120.846463268423[/C][C]1.13977347234442[/C][C]-3.9127059355202[/C][C]-1.20586918346958[/C][/ROW]
[ROW][C]41[/C][C]114[/C][C]114.840376978019[/C][C]0.913249464122094[/C][C]2.05778256723268[/C][C]-0.439500155557618[/C][/ROW]
[ROW][C]42[/C][C]77[/C][C]93.7756114686156[/C][C]0.219053609667765[/C][C]-7.8610744598345[/C][C]-1.35141262669071[/C][/ROW]
[ROW][C]43[/C][C]120[/C][C]109.684548595094[/C][C]0.716528380018241[/C][C]3.95353367410054[/C][C]0.964467486167056[/C][/ROW]
[ROW][C]44[/C][C]133[/C][C]123.2522001158[/C][C]1.12629331230989[/C][C]4.53868674551659[/C][C]0.789775841321633[/C][/ROW]
[ROW][C]45[/C][C]110[/C][C]115.644346582996[/C][C]0.846164694427059[/C][C]-2.10508222172593[/C][C]-0.536660586794877[/C][/ROW]
[ROW][C]46[/C][C]92[/C][C]103.781680646046[/C][C]0.436317880905144[/C][C]-6.63314268393655[/C][C]-0.780755211690291[/C][/ROW]
[ROW][C]47[/C][C]97[/C][C]99.5874805347803[/C][C]0.286781076802437[/C][C]-0.712329206445536[/C][C]-0.284354627061858[/C][/ROW]
[ROW][C]48[/C][C]78[/C][C]83.8684351025245[/C][C]-0.21753930521235[/C][C]0.603322091440758[/C][C]-0.98193785104253[/C][/ROW]
[ROW][C]49[/C][C]99[/C][C]88.9844084843725[/C][C]-0.0700067152093569[/C][C]7.84236581791309[/C][C]0.339627951673189[/C][/ROW]
[ROW][C]50[/C][C]107[/C][C]103.220496704802[/C][C]0.425768673665507[/C][C]-1.75891356504923[/C][C]0.853175494732954[/C][/ROW]
[ROW][C]51[/C][C]112[/C][C]107.754450041322[/C][C]0.568898365607662[/C][C]2.63499431425786[/C][C]0.248401840875929[/C][/ROW]
[ROW][C]52[/C][C]90[/C][C]98.73642667512[/C][C]0.244211318394393[/C][C]-4.90362600244276[/C][C]-0.58717557267884[/C][/ROW]
[ROW][C]53[/C][C]98[/C][C]95.0802774723711[/C][C]0.114199944757123[/C][C]4.48494651993072[/C][C]-0.239296444444071[/C][/ROW]
[ROW][C]54[/C][C]125[/C][C]120.117073048979[/C][C]0.941456659263719[/C][C]-5.11922945745831[/C][C]1.52863004688005[/C][/ROW]
[ROW][C]55[/C][C]155[/C][C]141.891176543443[/C][C]1.63358320982373[/C][C]4.74991453999353[/C][C]1.27747051325415[/C][/ROW]
[ROW][C]56[/C][C]190[/C][C]170.269362806842[/C][C]2.52431012415017[/C][C]9.00135403459447[/C][C]1.63979949374108[/C][/ROW]
[ROW][C]57[/C][C]236[/C][C]214.22896987096[/C][C]3.90810613455924[/C][C]5.15013802381294[/C][C]2.54037857174548[/C][/ROW]
[ROW][C]58[/C][C]189[/C][C]204.925881394419[/C][C]3.46592657890096[/C][C]-10.6271489379084[/C][C]-0.80990376253078[/C][/ROW]
[ROW][C]59[/C][C]174[/C][C]185.490282320905[/C][C]2.70063719988553[/C][C]-2.30934727760813[/C][C]-1.40323816487932[/C][/ROW]
[ROW][C]60[/C][C]178[/C][C]181.061953468186[/C][C]2.46649487327162[/C][C]-0.208825554379717[/C][C]-0.436647310873763[/C][/ROW]
[ROW][C]61[/C][C]136[/C][C]151.392008841462[/C][C]1.46365697090616[/C][C]-2.42701385222636[/C][C]-2.01939810093448[/C][/ROW]
[ROW][C]62[/C][C]161[/C][C]158.57710959288[/C][C]1.6614494675264[/C][C]0.188467880204358[/C][C]0.344457669000458[/C][/ROW]
[ROW][C]63[/C][C]171[/C][C]164.173121938932[/C][C]1.79869515245294[/C][C]5.28663906096859[/C][C]0.238196628516746[/C][/ROW]
[ROW][C]64[/C][C]149[/C][C]157.67265512331[/C][C]1.51332509272347[/C][C]-5.37419909730093[/C][C]-0.50770181431007[/C][/ROW]
[ROW][C]65[/C][C]184[/C][C]173.257614336486[/C][C]1.99231291235929[/C][C]5.12978987224549[/C][C]0.862287097220564[/C][/ROW]
[ROW][C]66[/C][C]155[/C][C]167.4569196674[/C][C]1.72801219500799[/C][C]-9.34851948665104[/C][C]-0.477388271613014[/C][/ROW]
[ROW][C]67[/C][C]276[/C][C]235.576735692886[/C][C]3.97918721532816[/C][C]13.9468610972378[/C][C]4.0661055587189[/C][/ROW]
[ROW][C]68[/C][C]224[/C][C]229.403343658031[/C][C]3.63458751758534[/C][C]-1.35492718662874[/C][C]-0.621745408306792[/C][/ROW]
[ROW][C]69[/C][C]213[/C][C]215.929708339769[/C][C]3.05316154909365[/C][C]3.89212524524071[/C][C]-1.04770782742818[/C][/ROW]
[ROW][C]70[/C][C]279[/C][C]261.080642326783[/C][C]4.48479954903512[/C][C]1.1348155197288[/C][C]2.57781714868866[/C][/ROW]
[ROW][C]71[/C][C]268[/C][C]268.964322978434[/C][C]4.60011955725864[/C][C]-2.31860305204272[/C][C]0.207997940599125[/C][/ROW]
[ROW][C]72[/C][C]287[/C][C]280.120920871203[/C][C]4.82012315031682[/C][C]4.27048224745616[/C][C]0.401344183091745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156517&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156517&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
14141000
23939.4681786008149-0.0979294436540218-0.124264696701125-0.0638919853075069
35046.54376973502230.1390087590865370.5620482632373630.428901774879656
44042.28031011889660.0522995425459843-0.353964513427608-0.276036645139372
54342.67330323380940.05783211816026480.1758368559063980.0214933581845457
63839.57156258233590.00755165472164264-0.170636625591731-0.199394845746151
74442.43875947261440.05475454201686770.2943919906581290.180327708480934
83537.5424864602514-0.0311422615834274-0.352202703776871-0.311888748349939
93938.4198102839839-0.01457033562275380.1789036244764630.0571669720681654
103536.1532277334162-0.0576370820621674-0.159984380750529-0.141560510440093
112931.3631502606335-0.152178877240164-0.279023441236224-0.297167760879146
124942.97219842834040.0924254510823550.8557244335490170.737780218222501
135046.5115120124834-0.06626558351077921.933968152502580.266246838500981
145955.71881796610750.2994335470399560.1912046437310.492463308392527
156360.31501652456090.4274672118929790.9503911548084850.259785315192187
163242.3586711510299-0.00898801290880507-2.58617255004916-1.1424517720132
173939.5279485357041-0.07165608554070150.672559003404174-0.17589346716942
184744.51698118761520.0411884144557890.3296117218833470.315386752387433
195349.86578651713490.1624195896897460.8777647129507980.330540295157587
206056.90445497327570.3239027993838020.1754106016788090.427896048756906
215757.03675125620490.319278635969590.0445286885340537-0.0119142207694525
225253.72863388904440.229421797654096-0.191516321877984-0.225391921552344
237064.76142874785950.5035134306706250.6651673395694560.670825612085569
249081.25227390092240.9024872899276431.985390597457270.991384292019457
257478.98045532735830.894980344337853-3.62525481524946-0.216919487050872
266267.9448837860780.470231608776671-1.56747771749412-0.678844852185979
275557.88271306008530.1259942590505551.30522505996275-0.636513733079734
288475.86995450509570.6498649704781540.7876173198706991.10145767096802
299488.05637306326490.9759734635639641.178449925666740.713006342990637
307077.22366521814610.642379758312508-2.34568071468906-0.729646151837466
3110896.67453793329631.179298675605363.560399587601561.1616101577344
32139124.7084460458471.955658677240723.21184473104551.65776872568695
33120122.9202469822031.84605010273257-1.37656688893993-0.231014708066358
3497107.4626820232661.33321393213363-3.33210534366404-1.06732218116826
35126119.7837654084741.661777083273151.690758972928160.677462987476597
36149138.0699006156282.144774140200984.090514727580221.02361043757679
37158151.6957187796762.373433115894071.546269263959080.748559940683804
38124134.9579191120681.70746962061804-3.68439127307395-1.12123337612557
39140138.7115412460781.777683332289330.482720492359090.123626913477546
40109120.8464632684231.13977347234442-3.9127059355202-1.20586918346958
41114114.8403769780190.9132494641220942.05778256723268-0.439500155557618
427793.77561146861560.219053609667765-7.8610744598345-1.35141262669071
43120109.6845485950940.7165283800182413.953533674100540.964467486167056
44133123.25220011581.126293312309894.538686745516590.789775841321633
45110115.6443465829960.846164694427059-2.10508222172593-0.536660586794877
4692103.7816806460460.436317880905144-6.63314268393655-0.780755211690291
479799.58748053478030.286781076802437-0.712329206445536-0.284354627061858
487883.8684351025245-0.217539305212350.603322091440758-0.98193785104253
499988.9844084843725-0.07000671520935697.842365817913090.339627951673189
50107103.2204967048020.425768673665507-1.758913565049230.853175494732954
51112107.7544500413220.5688983656076622.634994314257860.248401840875929
529098.736426675120.244211318394393-4.90362600244276-0.58717557267884
539895.08027747237110.1141999447571234.48494651993072-0.239296444444071
54125120.1170730489790.941456659263719-5.119229457458311.52863004688005
55155141.8911765434431.633583209823734.749914539993531.27747051325415
56190170.2693628068422.524310124150179.001354034594471.63979949374108
57236214.228969870963.908106134559245.150138023812942.54037857174548
58189204.9258813944193.46592657890096-10.6271489379084-0.80990376253078
59174185.4902823209052.70063719988553-2.30934727760813-1.40323816487932
60178181.0619534681862.46649487327162-0.208825554379717-0.436647310873763
61136151.3920088414621.46365697090616-2.42701385222636-2.01939810093448
62161158.577109592881.66144946752640.1884678802043580.344457669000458
63171164.1731219389321.798695152452945.286639060968590.238196628516746
64149157.672655123311.51332509272347-5.37419909730093-0.50770181431007
65184173.2576143364861.992312912359295.129789872245490.862287097220564
66155167.45691966741.72801219500799-9.34851948665104-0.477388271613014
67276235.5767356928863.9791872153281613.94686109723784.0661055587189
68224229.4033436580313.63458751758534-1.35492718662874-0.621745408306792
69213215.9297083397693.053161549093653.89212524524071-1.04770782742818
70279261.0806423267834.484799549035121.13481551972882.57781714868866
71268268.9643229784344.60011955725864-2.318603052042720.207997940599125
72287280.1209208712034.820123150316824.270482247456160.401344183091745



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')