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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 computationTue, 28 Dec 2010 19:26:09 +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/28/t12935642411vwsqeqgt1136sm.htm/, Retrieved Sat, 04 May 2024 22:56:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116514, Retrieved Sat, 04 May 2024 22:56:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Structural Time Series Models] [Paper: analyse (m...] [2010-12-28 19:22:02] [a48e3f697f1471e9c9650f8bf805cc06]
-         [Structural Time Series Models] [Paper: analyse (1...] [2010-12-28 19:26:09] [35c3410767ea63f72c8afa35bf7b6164] [Current]
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Dataseries X:
49915
47469
45652
43492
41087
42931
67256
72316
65624
59450
52851
51214
44092
43752
40320
40551
38329
39530
59648
61031
55560
43877
38510
36085
35994
32617
30001
27894
26083
28817
48742
49915
40264
34276
30426
30793
29855
28081
26820
25782
22654
27373
43675
45096
38145
34017
31537
33814
36531
36935
36497
35110
33137
37407
53963
56602
49694
43957
41723
45599
42503
42153
39098
37449
34748
36548
53639
55289
47774
42156
38019




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116514&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116514&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116514&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 time6 seconds
R Server'George Udny Yule' @ 72.249.76.132







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
14991549915000
24746948054.9100273512-1908.51228848179-585.910027351172-0.625488585550989
34565245445.8356321007-2397.35531297182206.164367899272-0.149024276652523
44349243332.3435485845-2187.19966673854159.6564514154500.0621313871758061
54108741106.6751305862-2215.0849477409-19.6751305861678-0.0079565128619767
64293142052.439767165464.936513275486878.5602328345820.65680302011448
76725662050.985128138414488.87330196795205.014871861614.15121270134145
87231674531.934874442113035.1129719894-2215.93487444214-0.418188710152325
96562470219.9496262154477.104421390131-4595.94962621536-3.61273986015508
105945060738.6456728403-6730.69754420941-1288.64567284032-2.07352923660390
115285152451.876074245-7856.89420889287399.123925755013-0.323981538875216
125121449691.5554151534-4168.213765509521522.444584846651.06115169088050
134409244728.4750105224-4742.0600082226-636.47501052238-0.165575793548647
144375243191.9851680310-2419.20896499232560.0148319690470.678694016392548
154032040188.5200983374-2827.23952674978131.479901662643-0.116793934872384
164055138604.2225628837-1960.674428249401946.777437116250.253193319462149
173832938299.0404129765-799.62244040314429.95958702353520.333835640316512
183953043672.16252514073495.66130739115-4142.162525140681.23296671383039
195964853673.29229026398021.825474787445974.707709736141.30306337569509
206103159842.37045139626730.773020328151188.62954860378-0.371467632964803
215556059281.74466530091648.19521816235-3721.7446653009-1.46205106993328
224387747941.0955180597-7405.47819353161-4064.09551805966-2.60471367796243
233851039063.8362282919-8430.99906663948-553.83622829186-0.295022139652995
243608533352.9073539625-6537.605631961632732.092646037550.544858643081406
253599435089.7456208396-777.407116033018904.2543791603631.66203971211854
263261732448.582261804-2076.48225280578168.417738196016-0.374255852490388
273000129617.92542403-2596.69678035483383.074575970006-0.149313969071453
282789426136.2776898046-3205.523536969011757.72231019536-0.176114056035855
292608326954.1104165456-423.977156476301-871.1104165455960.801730506581558
302881733937.79931022244682.14216449458-5120.799310222421.46613502676548
314874242505.88167324217355.450302398926236.118326757880.768965229592107
324991547606.19073825535802.457342032712308.80926174471-0.44689822013063
334026442698.4952616616-1578.18058637246-2434.49526166159-2.12317699958105
343427637913.0160557226-3788.44063477600-3637.01605572264-0.635865599434165
353042632318.9473430505-5032.14094508802-1892.94734305054-0.357789863224785
363079329861.0322013219-3260.16339595976931.967798678060.510085538939467
372985527944.6870626057-2334.475210770881910.312937394250.26677926209369
382808126763.0836287648-1540.587247205241317.916371235180.228354021685662
392682025427.3442948253-1400.233217294751392.655705174740.0403314770097582
402578224835.0564856024-847.4490756093946.9435143976060.159424302657798
412265425819.3657289281409.440685043375-3165.365728928060.362209382122214
422737332765.19620846384891.25096588271-5392.19620846381.28804991139661
434367537433.72563512764738.809963007356241.27436487237-0.0438297637262487
444509640069.83868093003299.286617446805026.16131906995-0.414172084362699
453814540244.80765032211158.92368420508-2099.80765032213-0.61575276386306
463401737902.8099065578-1239.66626997443-3885.80990655779-0.690023240910395
473153734644.3626614841-2622.29163776583-3107.36266148412-0.397788229981384
483381432934.1573268921-1997.71738675355879.8426731079180.179793888019627
493653133968.268194151379.6629482371552562.731805848730.59818564696341
503693535099.4269041232799.6344482505831835.573095876780.207037952097699
513649735141.9145688030282.7376250654231355.08543119695-0.148605247383081
523511034899.7900591218-75.2895058861104210.209940878196-0.103140945482274
533313737795.49328820381954.96630953774-4658.493288203820.584835370851633
543740742466.88701281233811.37294234474-5059.887012812310.533830023832569
555396347190.5016686244434.012970057446772.498331375960.17901246348803
565660250896.43724594543937.193090183525705.56275405458-0.142913943442061
574969451468.27708176641639.83201388054-1774.27708176640-0.660928068411421
584395748267.747066966-1664.88036798074-4310.74706696604-0.950733351828158
594172345313.1318324563-2545.29819128717-3590.13183245635-0.253325187313365
604559945066.0271299942-976.293296516713532.9728700057650.451620286142561
614250341079.2149010603-3032.421019633581423.78509893975-0.591783983916273
624215339241.9741741646-2216.740498334992911.025825835360.234553621817085
633909837323.8791520904-2013.316724557911774.120847909640.0584970524155923
643744937534.0864154188-499.512856155734-85.08641541878980.43587967697749
653474839775.05375117421368.48646741695-5027.053751174240.53793176246557
663654842079.26989587292006.50762033808-5531.269895872920.183526344978030
675363946329.90161023823535.347208097427309.098389761760.439587940458182
685528948919.24370570342891.171950947466369.75629429663-0.185278399253649
694777448789.9463504437834.151091208721-1015.94635044374-0.591773603848368
704215646880.6414977795-1034.44293477845-4724.64149777951-0.537612854741253
713801942992.6012693490-2978.1031007798-4973.60126934904-0.559299922580377

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 49915 & 49915 & 0 & 0 & 0 \tabularnewline
2 & 47469 & 48054.9100273512 & -1908.51228848179 & -585.910027351172 & -0.625488585550989 \tabularnewline
3 & 45652 & 45445.8356321007 & -2397.35531297182 & 206.164367899272 & -0.149024276652523 \tabularnewline
4 & 43492 & 43332.3435485845 & -2187.19966673854 & 159.656451415450 & 0.0621313871758061 \tabularnewline
5 & 41087 & 41106.6751305862 & -2215.0849477409 & -19.6751305861678 & -0.0079565128619767 \tabularnewline
6 & 42931 & 42052.4397671654 & 64.936513275486 & 878.560232834582 & 0.65680302011448 \tabularnewline
7 & 67256 & 62050.9851281384 & 14488.8733019679 & 5205.01487186161 & 4.15121270134145 \tabularnewline
8 & 72316 & 74531.9348744421 & 13035.1129719894 & -2215.93487444214 & -0.418188710152325 \tabularnewline
9 & 65624 & 70219.9496262154 & 477.104421390131 & -4595.94962621536 & -3.61273986015508 \tabularnewline
10 & 59450 & 60738.6456728403 & -6730.69754420941 & -1288.64567284032 & -2.07352923660390 \tabularnewline
11 & 52851 & 52451.876074245 & -7856.89420889287 & 399.123925755013 & -0.323981538875216 \tabularnewline
12 & 51214 & 49691.5554151534 & -4168.21376550952 & 1522.44458484665 & 1.06115169088050 \tabularnewline
13 & 44092 & 44728.4750105224 & -4742.0600082226 & -636.47501052238 & -0.165575793548647 \tabularnewline
14 & 43752 & 43191.9851680310 & -2419.20896499232 & 560.014831969047 & 0.678694016392548 \tabularnewline
15 & 40320 & 40188.5200983374 & -2827.23952674978 & 131.479901662643 & -0.116793934872384 \tabularnewline
16 & 40551 & 38604.2225628837 & -1960.67442824940 & 1946.77743711625 & 0.253193319462149 \tabularnewline
17 & 38329 & 38299.0404129765 & -799.622440403144 & 29.9595870235352 & 0.333835640316512 \tabularnewline
18 & 39530 & 43672.1625251407 & 3495.66130739115 & -4142.16252514068 & 1.23296671383039 \tabularnewline
19 & 59648 & 53673.2922902639 & 8021.82547478744 & 5974.70770973614 & 1.30306337569509 \tabularnewline
20 & 61031 & 59842.3704513962 & 6730.77302032815 & 1188.62954860378 & -0.371467632964803 \tabularnewline
21 & 55560 & 59281.7446653009 & 1648.19521816235 & -3721.7446653009 & -1.46205106993328 \tabularnewline
22 & 43877 & 47941.0955180597 & -7405.47819353161 & -4064.09551805966 & -2.60471367796243 \tabularnewline
23 & 38510 & 39063.8362282919 & -8430.99906663948 & -553.83622829186 & -0.295022139652995 \tabularnewline
24 & 36085 & 33352.9073539625 & -6537.60563196163 & 2732.09264603755 & 0.544858643081406 \tabularnewline
25 & 35994 & 35089.7456208396 & -777.407116033018 & 904.254379160363 & 1.66203971211854 \tabularnewline
26 & 32617 & 32448.582261804 & -2076.48225280578 & 168.417738196016 & -0.374255852490388 \tabularnewline
27 & 30001 & 29617.92542403 & -2596.69678035483 & 383.074575970006 & -0.149313969071453 \tabularnewline
28 & 27894 & 26136.2776898046 & -3205.52353696901 & 1757.72231019536 & -0.176114056035855 \tabularnewline
29 & 26083 & 26954.1104165456 & -423.977156476301 & -871.110416545596 & 0.801730506581558 \tabularnewline
30 & 28817 & 33937.7993102224 & 4682.14216449458 & -5120.79931022242 & 1.46613502676548 \tabularnewline
31 & 48742 & 42505.8816732421 & 7355.45030239892 & 6236.11832675788 & 0.768965229592107 \tabularnewline
32 & 49915 & 47606.1907382553 & 5802.45734203271 & 2308.80926174471 & -0.44689822013063 \tabularnewline
33 & 40264 & 42698.4952616616 & -1578.18058637246 & -2434.49526166159 & -2.12317699958105 \tabularnewline
34 & 34276 & 37913.0160557226 & -3788.44063477600 & -3637.01605572264 & -0.635865599434165 \tabularnewline
35 & 30426 & 32318.9473430505 & -5032.14094508802 & -1892.94734305054 & -0.357789863224785 \tabularnewline
36 & 30793 & 29861.0322013219 & -3260.16339595976 & 931.96779867806 & 0.510085538939467 \tabularnewline
37 & 29855 & 27944.6870626057 & -2334.47521077088 & 1910.31293739425 & 0.26677926209369 \tabularnewline
38 & 28081 & 26763.0836287648 & -1540.58724720524 & 1317.91637123518 & 0.228354021685662 \tabularnewline
39 & 26820 & 25427.3442948253 & -1400.23321729475 & 1392.65570517474 & 0.0403314770097582 \tabularnewline
40 & 25782 & 24835.0564856024 & -847.4490756093 & 946.943514397606 & 0.159424302657798 \tabularnewline
41 & 22654 & 25819.3657289281 & 409.440685043375 & -3165.36572892806 & 0.362209382122214 \tabularnewline
42 & 27373 & 32765.1962084638 & 4891.25096588271 & -5392.1962084638 & 1.28804991139661 \tabularnewline
43 & 43675 & 37433.7256351276 & 4738.80996300735 & 6241.27436487237 & -0.0438297637262487 \tabularnewline
44 & 45096 & 40069.8386809300 & 3299.28661744680 & 5026.16131906995 & -0.414172084362699 \tabularnewline
45 & 38145 & 40244.8076503221 & 1158.92368420508 & -2099.80765032213 & -0.61575276386306 \tabularnewline
46 & 34017 & 37902.8099065578 & -1239.66626997443 & -3885.80990655779 & -0.690023240910395 \tabularnewline
47 & 31537 & 34644.3626614841 & -2622.29163776583 & -3107.36266148412 & -0.397788229981384 \tabularnewline
48 & 33814 & 32934.1573268921 & -1997.71738675355 & 879.842673107918 & 0.179793888019627 \tabularnewline
49 & 36531 & 33968.2681941513 & 79.662948237155 & 2562.73180584873 & 0.59818564696341 \tabularnewline
50 & 36935 & 35099.4269041232 & 799.634448250583 & 1835.57309587678 & 0.207037952097699 \tabularnewline
51 & 36497 & 35141.9145688030 & 282.737625065423 & 1355.08543119695 & -0.148605247383081 \tabularnewline
52 & 35110 & 34899.7900591218 & -75.2895058861104 & 210.209940878196 & -0.103140945482274 \tabularnewline
53 & 33137 & 37795.4932882038 & 1954.96630953774 & -4658.49328820382 & 0.584835370851633 \tabularnewline
54 & 37407 & 42466.8870128123 & 3811.37294234474 & -5059.88701281231 & 0.533830023832569 \tabularnewline
55 & 53963 & 47190.501668624 & 4434.01297005744 & 6772.49833137596 & 0.17901246348803 \tabularnewline
56 & 56602 & 50896.4372459454 & 3937.19309018352 & 5705.56275405458 & -0.142913943442061 \tabularnewline
57 & 49694 & 51468.2770817664 & 1639.83201388054 & -1774.27708176640 & -0.660928068411421 \tabularnewline
58 & 43957 & 48267.747066966 & -1664.88036798074 & -4310.74706696604 & -0.950733351828158 \tabularnewline
59 & 41723 & 45313.1318324563 & -2545.29819128717 & -3590.13183245635 & -0.253325187313365 \tabularnewline
60 & 45599 & 45066.0271299942 & -976.293296516713 & 532.972870005765 & 0.451620286142561 \tabularnewline
61 & 42503 & 41079.2149010603 & -3032.42101963358 & 1423.78509893975 & -0.591783983916273 \tabularnewline
62 & 42153 & 39241.9741741646 & -2216.74049833499 & 2911.02582583536 & 0.234553621817085 \tabularnewline
63 & 39098 & 37323.8791520904 & -2013.31672455791 & 1774.12084790964 & 0.0584970524155923 \tabularnewline
64 & 37449 & 37534.0864154188 & -499.512856155734 & -85.0864154187898 & 0.43587967697749 \tabularnewline
65 & 34748 & 39775.0537511742 & 1368.48646741695 & -5027.05375117424 & 0.53793176246557 \tabularnewline
66 & 36548 & 42079.2698958729 & 2006.50762033808 & -5531.26989587292 & 0.183526344978030 \tabularnewline
67 & 53639 & 46329.9016102382 & 3535.34720809742 & 7309.09838976176 & 0.439587940458182 \tabularnewline
68 & 55289 & 48919.2437057034 & 2891.17195094746 & 6369.75629429663 & -0.185278399253649 \tabularnewline
69 & 47774 & 48789.9463504437 & 834.151091208721 & -1015.94635044374 & -0.591773603848368 \tabularnewline
70 & 42156 & 46880.6414977795 & -1034.44293477845 & -4724.64149777951 & -0.537612854741253 \tabularnewline
71 & 38019 & 42992.6012693490 & -2978.1031007798 & -4973.60126934904 & -0.559299922580377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116514&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]49915[/C][C]49915[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]47469[/C][C]48054.9100273512[/C][C]-1908.51228848179[/C][C]-585.910027351172[/C][C]-0.625488585550989[/C][/ROW]
[ROW][C]3[/C][C]45652[/C][C]45445.8356321007[/C][C]-2397.35531297182[/C][C]206.164367899272[/C][C]-0.149024276652523[/C][/ROW]
[ROW][C]4[/C][C]43492[/C][C]43332.3435485845[/C][C]-2187.19966673854[/C][C]159.656451415450[/C][C]0.0621313871758061[/C][/ROW]
[ROW][C]5[/C][C]41087[/C][C]41106.6751305862[/C][C]-2215.0849477409[/C][C]-19.6751305861678[/C][C]-0.0079565128619767[/C][/ROW]
[ROW][C]6[/C][C]42931[/C][C]42052.4397671654[/C][C]64.936513275486[/C][C]878.560232834582[/C][C]0.65680302011448[/C][/ROW]
[ROW][C]7[/C][C]67256[/C][C]62050.9851281384[/C][C]14488.8733019679[/C][C]5205.01487186161[/C][C]4.15121270134145[/C][/ROW]
[ROW][C]8[/C][C]72316[/C][C]74531.9348744421[/C][C]13035.1129719894[/C][C]-2215.93487444214[/C][C]-0.418188710152325[/C][/ROW]
[ROW][C]9[/C][C]65624[/C][C]70219.9496262154[/C][C]477.104421390131[/C][C]-4595.94962621536[/C][C]-3.61273986015508[/C][/ROW]
[ROW][C]10[/C][C]59450[/C][C]60738.6456728403[/C][C]-6730.69754420941[/C][C]-1288.64567284032[/C][C]-2.07352923660390[/C][/ROW]
[ROW][C]11[/C][C]52851[/C][C]52451.876074245[/C][C]-7856.89420889287[/C][C]399.123925755013[/C][C]-0.323981538875216[/C][/ROW]
[ROW][C]12[/C][C]51214[/C][C]49691.5554151534[/C][C]-4168.21376550952[/C][C]1522.44458484665[/C][C]1.06115169088050[/C][/ROW]
[ROW][C]13[/C][C]44092[/C][C]44728.4750105224[/C][C]-4742.0600082226[/C][C]-636.47501052238[/C][C]-0.165575793548647[/C][/ROW]
[ROW][C]14[/C][C]43752[/C][C]43191.9851680310[/C][C]-2419.20896499232[/C][C]560.014831969047[/C][C]0.678694016392548[/C][/ROW]
[ROW][C]15[/C][C]40320[/C][C]40188.5200983374[/C][C]-2827.23952674978[/C][C]131.479901662643[/C][C]-0.116793934872384[/C][/ROW]
[ROW][C]16[/C][C]40551[/C][C]38604.2225628837[/C][C]-1960.67442824940[/C][C]1946.77743711625[/C][C]0.253193319462149[/C][/ROW]
[ROW][C]17[/C][C]38329[/C][C]38299.0404129765[/C][C]-799.622440403144[/C][C]29.9595870235352[/C][C]0.333835640316512[/C][/ROW]
[ROW][C]18[/C][C]39530[/C][C]43672.1625251407[/C][C]3495.66130739115[/C][C]-4142.16252514068[/C][C]1.23296671383039[/C][/ROW]
[ROW][C]19[/C][C]59648[/C][C]53673.2922902639[/C][C]8021.82547478744[/C][C]5974.70770973614[/C][C]1.30306337569509[/C][/ROW]
[ROW][C]20[/C][C]61031[/C][C]59842.3704513962[/C][C]6730.77302032815[/C][C]1188.62954860378[/C][C]-0.371467632964803[/C][/ROW]
[ROW][C]21[/C][C]55560[/C][C]59281.7446653009[/C][C]1648.19521816235[/C][C]-3721.7446653009[/C][C]-1.46205106993328[/C][/ROW]
[ROW][C]22[/C][C]43877[/C][C]47941.0955180597[/C][C]-7405.47819353161[/C][C]-4064.09551805966[/C][C]-2.60471367796243[/C][/ROW]
[ROW][C]23[/C][C]38510[/C][C]39063.8362282919[/C][C]-8430.99906663948[/C][C]-553.83622829186[/C][C]-0.295022139652995[/C][/ROW]
[ROW][C]24[/C][C]36085[/C][C]33352.9073539625[/C][C]-6537.60563196163[/C][C]2732.09264603755[/C][C]0.544858643081406[/C][/ROW]
[ROW][C]25[/C][C]35994[/C][C]35089.7456208396[/C][C]-777.407116033018[/C][C]904.254379160363[/C][C]1.66203971211854[/C][/ROW]
[ROW][C]26[/C][C]32617[/C][C]32448.582261804[/C][C]-2076.48225280578[/C][C]168.417738196016[/C][C]-0.374255852490388[/C][/ROW]
[ROW][C]27[/C][C]30001[/C][C]29617.92542403[/C][C]-2596.69678035483[/C][C]383.074575970006[/C][C]-0.149313969071453[/C][/ROW]
[ROW][C]28[/C][C]27894[/C][C]26136.2776898046[/C][C]-3205.52353696901[/C][C]1757.72231019536[/C][C]-0.176114056035855[/C][/ROW]
[ROW][C]29[/C][C]26083[/C][C]26954.1104165456[/C][C]-423.977156476301[/C][C]-871.110416545596[/C][C]0.801730506581558[/C][/ROW]
[ROW][C]30[/C][C]28817[/C][C]33937.7993102224[/C][C]4682.14216449458[/C][C]-5120.79931022242[/C][C]1.46613502676548[/C][/ROW]
[ROW][C]31[/C][C]48742[/C][C]42505.8816732421[/C][C]7355.45030239892[/C][C]6236.11832675788[/C][C]0.768965229592107[/C][/ROW]
[ROW][C]32[/C][C]49915[/C][C]47606.1907382553[/C][C]5802.45734203271[/C][C]2308.80926174471[/C][C]-0.44689822013063[/C][/ROW]
[ROW][C]33[/C][C]40264[/C][C]42698.4952616616[/C][C]-1578.18058637246[/C][C]-2434.49526166159[/C][C]-2.12317699958105[/C][/ROW]
[ROW][C]34[/C][C]34276[/C][C]37913.0160557226[/C][C]-3788.44063477600[/C][C]-3637.01605572264[/C][C]-0.635865599434165[/C][/ROW]
[ROW][C]35[/C][C]30426[/C][C]32318.9473430505[/C][C]-5032.14094508802[/C][C]-1892.94734305054[/C][C]-0.357789863224785[/C][/ROW]
[ROW][C]36[/C][C]30793[/C][C]29861.0322013219[/C][C]-3260.16339595976[/C][C]931.96779867806[/C][C]0.510085538939467[/C][/ROW]
[ROW][C]37[/C][C]29855[/C][C]27944.6870626057[/C][C]-2334.47521077088[/C][C]1910.31293739425[/C][C]0.26677926209369[/C][/ROW]
[ROW][C]38[/C][C]28081[/C][C]26763.0836287648[/C][C]-1540.58724720524[/C][C]1317.91637123518[/C][C]0.228354021685662[/C][/ROW]
[ROW][C]39[/C][C]26820[/C][C]25427.3442948253[/C][C]-1400.23321729475[/C][C]1392.65570517474[/C][C]0.0403314770097582[/C][/ROW]
[ROW][C]40[/C][C]25782[/C][C]24835.0564856024[/C][C]-847.4490756093[/C][C]946.943514397606[/C][C]0.159424302657798[/C][/ROW]
[ROW][C]41[/C][C]22654[/C][C]25819.3657289281[/C][C]409.440685043375[/C][C]-3165.36572892806[/C][C]0.362209382122214[/C][/ROW]
[ROW][C]42[/C][C]27373[/C][C]32765.1962084638[/C][C]4891.25096588271[/C][C]-5392.1962084638[/C][C]1.28804991139661[/C][/ROW]
[ROW][C]43[/C][C]43675[/C][C]37433.7256351276[/C][C]4738.80996300735[/C][C]6241.27436487237[/C][C]-0.0438297637262487[/C][/ROW]
[ROW][C]44[/C][C]45096[/C][C]40069.8386809300[/C][C]3299.28661744680[/C][C]5026.16131906995[/C][C]-0.414172084362699[/C][/ROW]
[ROW][C]45[/C][C]38145[/C][C]40244.8076503221[/C][C]1158.92368420508[/C][C]-2099.80765032213[/C][C]-0.61575276386306[/C][/ROW]
[ROW][C]46[/C][C]34017[/C][C]37902.8099065578[/C][C]-1239.66626997443[/C][C]-3885.80990655779[/C][C]-0.690023240910395[/C][/ROW]
[ROW][C]47[/C][C]31537[/C][C]34644.3626614841[/C][C]-2622.29163776583[/C][C]-3107.36266148412[/C][C]-0.397788229981384[/C][/ROW]
[ROW][C]48[/C][C]33814[/C][C]32934.1573268921[/C][C]-1997.71738675355[/C][C]879.842673107918[/C][C]0.179793888019627[/C][/ROW]
[ROW][C]49[/C][C]36531[/C][C]33968.2681941513[/C][C]79.662948237155[/C][C]2562.73180584873[/C][C]0.59818564696341[/C][/ROW]
[ROW][C]50[/C][C]36935[/C][C]35099.4269041232[/C][C]799.634448250583[/C][C]1835.57309587678[/C][C]0.207037952097699[/C][/ROW]
[ROW][C]51[/C][C]36497[/C][C]35141.9145688030[/C][C]282.737625065423[/C][C]1355.08543119695[/C][C]-0.148605247383081[/C][/ROW]
[ROW][C]52[/C][C]35110[/C][C]34899.7900591218[/C][C]-75.2895058861104[/C][C]210.209940878196[/C][C]-0.103140945482274[/C][/ROW]
[ROW][C]53[/C][C]33137[/C][C]37795.4932882038[/C][C]1954.96630953774[/C][C]-4658.49328820382[/C][C]0.584835370851633[/C][/ROW]
[ROW][C]54[/C][C]37407[/C][C]42466.8870128123[/C][C]3811.37294234474[/C][C]-5059.88701281231[/C][C]0.533830023832569[/C][/ROW]
[ROW][C]55[/C][C]53963[/C][C]47190.501668624[/C][C]4434.01297005744[/C][C]6772.49833137596[/C][C]0.17901246348803[/C][/ROW]
[ROW][C]56[/C][C]56602[/C][C]50896.4372459454[/C][C]3937.19309018352[/C][C]5705.56275405458[/C][C]-0.142913943442061[/C][/ROW]
[ROW][C]57[/C][C]49694[/C][C]51468.2770817664[/C][C]1639.83201388054[/C][C]-1774.27708176640[/C][C]-0.660928068411421[/C][/ROW]
[ROW][C]58[/C][C]43957[/C][C]48267.747066966[/C][C]-1664.88036798074[/C][C]-4310.74706696604[/C][C]-0.950733351828158[/C][/ROW]
[ROW][C]59[/C][C]41723[/C][C]45313.1318324563[/C][C]-2545.29819128717[/C][C]-3590.13183245635[/C][C]-0.253325187313365[/C][/ROW]
[ROW][C]60[/C][C]45599[/C][C]45066.0271299942[/C][C]-976.293296516713[/C][C]532.972870005765[/C][C]0.451620286142561[/C][/ROW]
[ROW][C]61[/C][C]42503[/C][C]41079.2149010603[/C][C]-3032.42101963358[/C][C]1423.78509893975[/C][C]-0.591783983916273[/C][/ROW]
[ROW][C]62[/C][C]42153[/C][C]39241.9741741646[/C][C]-2216.74049833499[/C][C]2911.02582583536[/C][C]0.234553621817085[/C][/ROW]
[ROW][C]63[/C][C]39098[/C][C]37323.8791520904[/C][C]-2013.31672455791[/C][C]1774.12084790964[/C][C]0.0584970524155923[/C][/ROW]
[ROW][C]64[/C][C]37449[/C][C]37534.0864154188[/C][C]-499.512856155734[/C][C]-85.0864154187898[/C][C]0.43587967697749[/C][/ROW]
[ROW][C]65[/C][C]34748[/C][C]39775.0537511742[/C][C]1368.48646741695[/C][C]-5027.05375117424[/C][C]0.53793176246557[/C][/ROW]
[ROW][C]66[/C][C]36548[/C][C]42079.2698958729[/C][C]2006.50762033808[/C][C]-5531.26989587292[/C][C]0.183526344978030[/C][/ROW]
[ROW][C]67[/C][C]53639[/C][C]46329.9016102382[/C][C]3535.34720809742[/C][C]7309.09838976176[/C][C]0.439587940458182[/C][/ROW]
[ROW][C]68[/C][C]55289[/C][C]48919.2437057034[/C][C]2891.17195094746[/C][C]6369.75629429663[/C][C]-0.185278399253649[/C][/ROW]
[ROW][C]69[/C][C]47774[/C][C]48789.9463504437[/C][C]834.151091208721[/C][C]-1015.94635044374[/C][C]-0.591773603848368[/C][/ROW]
[ROW][C]70[/C][C]42156[/C][C]46880.6414977795[/C][C]-1034.44293477845[/C][C]-4724.64149777951[/C][C]-0.537612854741253[/C][/ROW]
[ROW][C]71[/C][C]38019[/C][C]42992.6012693490[/C][C]-2978.1031007798[/C][C]-4973.60126934904[/C][C]-0.559299922580377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116514&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116514&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
14991549915000
24746948054.9100273512-1908.51228848179-585.910027351172-0.625488585550989
34565245445.8356321007-2397.35531297182206.164367899272-0.149024276652523
44349243332.3435485845-2187.19966673854159.6564514154500.0621313871758061
54108741106.6751305862-2215.0849477409-19.6751305861678-0.0079565128619767
64293142052.439767165464.936513275486878.5602328345820.65680302011448
76725662050.985128138414488.87330196795205.014871861614.15121270134145
87231674531.934874442113035.1129719894-2215.93487444214-0.418188710152325
96562470219.9496262154477.104421390131-4595.94962621536-3.61273986015508
105945060738.6456728403-6730.69754420941-1288.64567284032-2.07352923660390
115285152451.876074245-7856.89420889287399.123925755013-0.323981538875216
125121449691.5554151534-4168.213765509521522.444584846651.06115169088050
134409244728.4750105224-4742.0600082226-636.47501052238-0.165575793548647
144375243191.9851680310-2419.20896499232560.0148319690470.678694016392548
154032040188.5200983374-2827.23952674978131.479901662643-0.116793934872384
164055138604.2225628837-1960.674428249401946.777437116250.253193319462149
173832938299.0404129765-799.62244040314429.95958702353520.333835640316512
183953043672.16252514073495.66130739115-4142.162525140681.23296671383039
195964853673.29229026398021.825474787445974.707709736141.30306337569509
206103159842.37045139626730.773020328151188.62954860378-0.371467632964803
215556059281.74466530091648.19521816235-3721.7446653009-1.46205106993328
224387747941.0955180597-7405.47819353161-4064.09551805966-2.60471367796243
233851039063.8362282919-8430.99906663948-553.83622829186-0.295022139652995
243608533352.9073539625-6537.605631961632732.092646037550.544858643081406
253599435089.7456208396-777.407116033018904.2543791603631.66203971211854
263261732448.582261804-2076.48225280578168.417738196016-0.374255852490388
273000129617.92542403-2596.69678035483383.074575970006-0.149313969071453
282789426136.2776898046-3205.523536969011757.72231019536-0.176114056035855
292608326954.1104165456-423.977156476301-871.1104165455960.801730506581558
302881733937.79931022244682.14216449458-5120.799310222421.46613502676548
314874242505.88167324217355.450302398926236.118326757880.768965229592107
324991547606.19073825535802.457342032712308.80926174471-0.44689822013063
334026442698.4952616616-1578.18058637246-2434.49526166159-2.12317699958105
343427637913.0160557226-3788.44063477600-3637.01605572264-0.635865599434165
353042632318.9473430505-5032.14094508802-1892.94734305054-0.357789863224785
363079329861.0322013219-3260.16339595976931.967798678060.510085538939467
372985527944.6870626057-2334.475210770881910.312937394250.26677926209369
382808126763.0836287648-1540.587247205241317.916371235180.228354021685662
392682025427.3442948253-1400.233217294751392.655705174740.0403314770097582
402578224835.0564856024-847.4490756093946.9435143976060.159424302657798
412265425819.3657289281409.440685043375-3165.365728928060.362209382122214
422737332765.19620846384891.25096588271-5392.19620846381.28804991139661
434367537433.72563512764738.809963007356241.27436487237-0.0438297637262487
444509640069.83868093003299.286617446805026.16131906995-0.414172084362699
453814540244.80765032211158.92368420508-2099.80765032213-0.61575276386306
463401737902.8099065578-1239.66626997443-3885.80990655779-0.690023240910395
473153734644.3626614841-2622.29163776583-3107.36266148412-0.397788229981384
483381432934.1573268921-1997.71738675355879.8426731079180.179793888019627
493653133968.268194151379.6629482371552562.731805848730.59818564696341
503693535099.4269041232799.6344482505831835.573095876780.207037952097699
513649735141.9145688030282.7376250654231355.08543119695-0.148605247383081
523511034899.7900591218-75.2895058861104210.209940878196-0.103140945482274
533313737795.49328820381954.96630953774-4658.493288203820.584835370851633
543740742466.88701281233811.37294234474-5059.887012812310.533830023832569
555396347190.5016686244434.012970057446772.498331375960.17901246348803
565660250896.43724594543937.193090183525705.56275405458-0.142913943442061
574969451468.27708176641639.83201388054-1774.27708176640-0.660928068411421
584395748267.747066966-1664.88036798074-4310.74706696604-0.950733351828158
594172345313.1318324563-2545.29819128717-3590.13183245635-0.253325187313365
604559945066.0271299942-976.293296516713532.9728700057650.451620286142561
614250341079.2149010603-3032.421019633581423.78509893975-0.591783983916273
624215339241.9741741646-2216.740498334992911.025825835360.234553621817085
633909837323.8791520904-2013.316724557911774.120847909640.0584970524155923
643744937534.0864154188-499.512856155734-85.08641541878980.43587967697749
653474839775.05375117421368.48646741695-5027.053751174240.53793176246557
663654842079.26989587292006.50762033808-5531.269895872920.183526344978030
675363946329.90161023823535.347208097427309.098389761760.439587940458182
685528948919.24370570342891.171950947466369.75629429663-0.185278399253649
694777448789.9463504437834.151091208721-1015.94635044374-0.591773603848368
704215646880.6414977795-1034.44293477845-4724.64149777951-0.537612854741253
713801942992.6012693490-2978.1031007798-4973.60126934904-0.559299922580377



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')