Free Statistics

of Irreproducible Research!

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, 24 Nov 2012 07:36:44 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/24/t1353760622w8ms1u8w34h6rzy.htm/, Retrieved Sun, 28 Apr 2024 20:26:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=192378, Retrieved Sun, 28 Apr 2024 20:26:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [Unemployment] [2010-11-30 13:26:46] [b98453cac15ba1066b407e146608df68]
- R  D      [Structural Time Series Models] [periodogram] [2012-11-24 12:36:44] [0bfaf13dc36b9cd1ca4f63e192935829] [Current]
Feedback Forum

Post a new message
Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=192378&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=192378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=192378&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 time5 seconds
R Server'George Udny Yule' @ yule.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
14646000
26247.91107541142070.4577240640763353.272943087229931.53740451313277
36652.21814899943591.235505145455583.110392273791891.66338385981868
45954.80852175620861.459171639290180.8854140052106110.524673408014731
55856.45328514929391.484957890245761.097500045637360.0712027661538959
66158.30253775190661.528619252128191.78773651674030.143268498393585
74155.50975887017441.0734099096598-3.20483061963755-1.76638756205403
82749.8660101407470.442404531957312-4.36798174239947-2.86761758456189
95851.13490220921670.5124545528575794.469478366816960.368669594664296
107055.14968897370370.7828621646972364.191780696555211.62955341180087
114954.83125851251520.704780254689664-2.32478261502626-0.533003784977301
125955.92696915997550.7304083216125451.775792925526550.196191829367788
134456.33223229314260.736556156443668-10.126556923625-0.329518875291752
143653.31611071605470.551367794191178-4.85049356139445-1.97251266875634
157256.14620782883590.6784588794101449.181727680976351.0829045791418
164555.05810420104480.582852455113798-4.74360346518054-0.862758514104173
175655.42482274875920.571811646231541.26021349167067-0.110645403517929
185455.12171601882510.5297863236433411.79123894463907-0.468077174016439
195355.20652334114140.509649035251215-0.661425283752519-0.247200975527775
203553.29288491300080.405993565354842-9.57568592668394-1.38968830854626
216154.04462101103470.4200178702052095.673877013042830.203691617158301
225253.51616907971040.3834505017052372.09135497981137-0.571952965863289
234753.18380749209350.357329888105564-3.39307651914128-0.441294159689625
245152.75763918801780.3318211355219851.42351617032134-0.500312333658597
255253.39369107581780.334466823605864-2.91248292823240.238012400905332
266355.75063121958210.386383165372465-1.080501170533141.33111768071526
277457.49724302005930.4297944351647111.33548370907470.836174663071383
284557.00921497262170.399555254030157-8.55543306600882-0.560703747525154
295156.16349194965160.359394137608602-0.409952723865391-0.771341091736933
306457.00852386293560.3744790226692795.100869671335950.306319904421563
313654.4646584785290.287521746911311-6.88322926351031-1.87338611168286
323052.37783043363860.219656286362561-12.7911149501929-1.54838802068709
335551.68268155906070.1945613536517687.06817788990424-0.604985084620498
346452.89498168085650.2212809294315196.871306685693520.681918919419113
353951.85390509725010.190134681432901-7.51339958697585-0.858539926568995
364050.31702904873960.153400414367847-2.7811443918811-1.20701560273348
376352.13366513141320.1728514500968722.99161854338131.2574534386736
384551.87769114980030.165164096194881-4.98984936008479-0.303711231996393
395951.30819449356820.14894084063807310.770074064912-0.498706282591717
405552.71548559269470.178506299104309-2.912335747535580.844379674342226
414051.46080899115760.1447408929574-5.52628082172351-0.964760550187544
426451.91139569774280.15179678455750610.81076023788420.207646188691917
432749.56963183292430.0959869126440519-12.0404444680315-1.70966312770991
442848.13984305300160.0629811587275342-13.6291438327967-1.05627182496688
454546.77356407957240.03319338199726814.38730248849458-0.998625955797365
465746.77650230329230.032590784703975310.3552839453647-0.0213390581254067
474547.29314090576480.0415741350006108-4.429477852433130.345393817367777
486950.41294525926030.09094614287001014.704279729673282.23926443378491
496051.59053756563960.1036459191650843.327422219848620.817939127603087
505652.922680248490.121202167048085-2.489221140086210.898731108415806
515852.66960541317890.1148801604815966.97610008227742-0.26678153121337
525052.58733890570350.111304555302155-1.73145722124216-0.13905228945575
535153.05285295790850.1178419485459-3.588591740245530.24969375812056
545351.55486634977290.08828469503499378.48027550292194-1.14380919818932
553750.88448688192230.0747068793134794-10.5601551721981-0.540282419626302
562248.87963964345840.0385547231961313-17.7022884925543-1.49072513761177
575548.85521779844660.03749823866342136.42478312745999-0.0454518495909033
587050.27520318692560.059623545539515913.52763737239131.00512116311249
596252.78820634537760.0961411373412126-1.899199089023691.80000346365486
605853.47353544965760.1038736594019461.818663515109430.438001694681637
613951.83046035899710.0847432447440065-4.65988790368577-1.31979260862075
624951.56944423930820.0805489142617881-0.976626541235048-0.257657313757062
635851.45749905914120.0779021693989667.41300449816337-0.141153269186039
644751.12403378308410.0718441373811094-2.28212309857134-0.299148756258948
654250.23678729940120.0573628663726305-3.95305521348947-0.69622575467493
666250.36813854523140.058479141099225411.30077530662670.0538179173818217
673950.11049440849880.0537835001667511-9.69014830920612-0.230833073695431
684051.05559486714490.066693234494052-15.08070227537460.65391132704504
697253.13714842362270.09489685223272039.711870659577051.48588761992048
707054.17714127905060.10752073050816111.50257315340930.701033457994749
715454.63671046195550.111910170782766-2.258775961649440.262975244069043
726555.5499061902680.1209273069778815.721284760600190.603906887014906

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 46 & 46 & 0 & 0 & 0 \tabularnewline
2 & 62 & 47.9110754114207 & 0.457724064076335 & 3.27294308722993 & 1.53740451313277 \tabularnewline
3 & 66 & 52.2181489994359 & 1.23550514545558 & 3.11039227379189 & 1.66338385981868 \tabularnewline
4 & 59 & 54.8085217562086 & 1.45917163929018 & 0.885414005210611 & 0.524673408014731 \tabularnewline
5 & 58 & 56.4532851492939 & 1.48495789024576 & 1.09750004563736 & 0.0712027661538959 \tabularnewline
6 & 61 & 58.3025377519066 & 1.52861925212819 & 1.7877365167403 & 0.143268498393585 \tabularnewline
7 & 41 & 55.5097588701744 & 1.0734099096598 & -3.20483061963755 & -1.76638756205403 \tabularnewline
8 & 27 & 49.866010140747 & 0.442404531957312 & -4.36798174239947 & -2.86761758456189 \tabularnewline
9 & 58 & 51.1349022092167 & 0.512454552857579 & 4.46947836681696 & 0.368669594664296 \tabularnewline
10 & 70 & 55.1496889737037 & 0.782862164697236 & 4.19178069655521 & 1.62955341180087 \tabularnewline
11 & 49 & 54.8312585125152 & 0.704780254689664 & -2.32478261502626 & -0.533003784977301 \tabularnewline
12 & 59 & 55.9269691599755 & 0.730408321612545 & 1.77579292552655 & 0.196191829367788 \tabularnewline
13 & 44 & 56.3322322931426 & 0.736556156443668 & -10.126556923625 & -0.329518875291752 \tabularnewline
14 & 36 & 53.3161107160547 & 0.551367794191178 & -4.85049356139445 & -1.97251266875634 \tabularnewline
15 & 72 & 56.1462078288359 & 0.678458879410144 & 9.18172768097635 & 1.0829045791418 \tabularnewline
16 & 45 & 55.0581042010448 & 0.582852455113798 & -4.74360346518054 & -0.862758514104173 \tabularnewline
17 & 56 & 55.4248227487592 & 0.57181164623154 & 1.26021349167067 & -0.110645403517929 \tabularnewline
18 & 54 & 55.1217160188251 & 0.529786323643341 & 1.79123894463907 & -0.468077174016439 \tabularnewline
19 & 53 & 55.2065233411414 & 0.509649035251215 & -0.661425283752519 & -0.247200975527775 \tabularnewline
20 & 35 & 53.2928849130008 & 0.405993565354842 & -9.57568592668394 & -1.38968830854626 \tabularnewline
21 & 61 & 54.0446210110347 & 0.420017870205209 & 5.67387701304283 & 0.203691617158301 \tabularnewline
22 & 52 & 53.5161690797104 & 0.383450501705237 & 2.09135497981137 & -0.571952965863289 \tabularnewline
23 & 47 & 53.1838074920935 & 0.357329888105564 & -3.39307651914128 & -0.441294159689625 \tabularnewline
24 & 51 & 52.7576391880178 & 0.331821135521985 & 1.42351617032134 & -0.500312333658597 \tabularnewline
25 & 52 & 53.3936910758178 & 0.334466823605864 & -2.9124829282324 & 0.238012400905332 \tabularnewline
26 & 63 & 55.7506312195821 & 0.386383165372465 & -1.08050117053314 & 1.33111768071526 \tabularnewline
27 & 74 & 57.4972430200593 & 0.42979443516471 & 11.3354837090747 & 0.836174663071383 \tabularnewline
28 & 45 & 57.0092149726217 & 0.399555254030157 & -8.55543306600882 & -0.560703747525154 \tabularnewline
29 & 51 & 56.1634919496516 & 0.359394137608602 & -0.409952723865391 & -0.771341091736933 \tabularnewline
30 & 64 & 57.0085238629356 & 0.374479022669279 & 5.10086967133595 & 0.306319904421563 \tabularnewline
31 & 36 & 54.464658478529 & 0.287521746911311 & -6.88322926351031 & -1.87338611168286 \tabularnewline
32 & 30 & 52.3778304336386 & 0.219656286362561 & -12.7911149501929 & -1.54838802068709 \tabularnewline
33 & 55 & 51.6826815590607 & 0.194561353651768 & 7.06817788990424 & -0.604985084620498 \tabularnewline
34 & 64 & 52.8949816808565 & 0.221280929431519 & 6.87130668569352 & 0.681918919419113 \tabularnewline
35 & 39 & 51.8539050972501 & 0.190134681432901 & -7.51339958697585 & -0.858539926568995 \tabularnewline
36 & 40 & 50.3170290487396 & 0.153400414367847 & -2.7811443918811 & -1.20701560273348 \tabularnewline
37 & 63 & 52.1336651314132 & 0.172851450096872 & 2.9916185433813 & 1.2574534386736 \tabularnewline
38 & 45 & 51.8776911498003 & 0.165164096194881 & -4.98984936008479 & -0.303711231996393 \tabularnewline
39 & 59 & 51.3081944935682 & 0.148940840638073 & 10.770074064912 & -0.498706282591717 \tabularnewline
40 & 55 & 52.7154855926947 & 0.178506299104309 & -2.91233574753558 & 0.844379674342226 \tabularnewline
41 & 40 & 51.4608089911576 & 0.1447408929574 & -5.52628082172351 & -0.964760550187544 \tabularnewline
42 & 64 & 51.9113956977428 & 0.151796784557506 & 10.8107602378842 & 0.207646188691917 \tabularnewline
43 & 27 & 49.5696318329243 & 0.0959869126440519 & -12.0404444680315 & -1.70966312770991 \tabularnewline
44 & 28 & 48.1398430530016 & 0.0629811587275342 & -13.6291438327967 & -1.05627182496688 \tabularnewline
45 & 45 & 46.7735640795724 & 0.0331933819972681 & 4.38730248849458 & -0.998625955797365 \tabularnewline
46 & 57 & 46.7765023032923 & 0.0325907847039753 & 10.3552839453647 & -0.0213390581254067 \tabularnewline
47 & 45 & 47.2931409057648 & 0.0415741350006108 & -4.42947785243313 & 0.345393817367777 \tabularnewline
48 & 69 & 50.4129452592603 & 0.0909461428700101 & 4.70427972967328 & 2.23926443378491 \tabularnewline
49 & 60 & 51.5905375656396 & 0.103645919165084 & 3.32742221984862 & 0.817939127603087 \tabularnewline
50 & 56 & 52.92268024849 & 0.121202167048085 & -2.48922114008621 & 0.898731108415806 \tabularnewline
51 & 58 & 52.6696054131789 & 0.114880160481596 & 6.97610008227742 & -0.26678153121337 \tabularnewline
52 & 50 & 52.5873389057035 & 0.111304555302155 & -1.73145722124216 & -0.13905228945575 \tabularnewline
53 & 51 & 53.0528529579085 & 0.1178419485459 & -3.58859174024553 & 0.24969375812056 \tabularnewline
54 & 53 & 51.5548663497729 & 0.0882846950349937 & 8.48027550292194 & -1.14380919818932 \tabularnewline
55 & 37 & 50.8844868819223 & 0.0747068793134794 & -10.5601551721981 & -0.540282419626302 \tabularnewline
56 & 22 & 48.8796396434584 & 0.0385547231961313 & -17.7022884925543 & -1.49072513761177 \tabularnewline
57 & 55 & 48.8552177984466 & 0.0374982386634213 & 6.42478312745999 & -0.0454518495909033 \tabularnewline
58 & 70 & 50.2752031869256 & 0.0596235455395159 & 13.5276373723913 & 1.00512116311249 \tabularnewline
59 & 62 & 52.7882063453776 & 0.0961411373412126 & -1.89919908902369 & 1.80000346365486 \tabularnewline
60 & 58 & 53.4735354496576 & 0.103873659401946 & 1.81866351510943 & 0.438001694681637 \tabularnewline
61 & 39 & 51.8304603589971 & 0.0847432447440065 & -4.65988790368577 & -1.31979260862075 \tabularnewline
62 & 49 & 51.5694442393082 & 0.0805489142617881 & -0.976626541235048 & -0.257657313757062 \tabularnewline
63 & 58 & 51.4574990591412 & 0.077902169398966 & 7.41300449816337 & -0.141153269186039 \tabularnewline
64 & 47 & 51.1240337830841 & 0.0718441373811094 & -2.28212309857134 & -0.299148756258948 \tabularnewline
65 & 42 & 50.2367872994012 & 0.0573628663726305 & -3.95305521348947 & -0.69622575467493 \tabularnewline
66 & 62 & 50.3681385452314 & 0.0584791410992254 & 11.3007753066267 & 0.0538179173818217 \tabularnewline
67 & 39 & 50.1104944084988 & 0.0537835001667511 & -9.69014830920612 & -0.230833073695431 \tabularnewline
68 & 40 & 51.0555948671449 & 0.066693234494052 & -15.0807022753746 & 0.65391132704504 \tabularnewline
69 & 72 & 53.1371484236227 & 0.0948968522327203 & 9.71187065957705 & 1.48588761992048 \tabularnewline
70 & 70 & 54.1771412790506 & 0.107520730508161 & 11.5025731534093 & 0.701033457994749 \tabularnewline
71 & 54 & 54.6367104619555 & 0.111910170782766 & -2.25877596164944 & 0.262975244069043 \tabularnewline
72 & 65 & 55.549906190268 & 0.120927306977881 & 5.72128476060019 & 0.603906887014906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=192378&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]46[/C][C]46[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]62[/C][C]47.9110754114207[/C][C]0.457724064076335[/C][C]3.27294308722993[/C][C]1.53740451313277[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]52.2181489994359[/C][C]1.23550514545558[/C][C]3.11039227379189[/C][C]1.66338385981868[/C][/ROW]
[ROW][C]4[/C][C]59[/C][C]54.8085217562086[/C][C]1.45917163929018[/C][C]0.885414005210611[/C][C]0.524673408014731[/C][/ROW]
[ROW][C]5[/C][C]58[/C][C]56.4532851492939[/C][C]1.48495789024576[/C][C]1.09750004563736[/C][C]0.0712027661538959[/C][/ROW]
[ROW][C]6[/C][C]61[/C][C]58.3025377519066[/C][C]1.52861925212819[/C][C]1.7877365167403[/C][C]0.143268498393585[/C][/ROW]
[ROW][C]7[/C][C]41[/C][C]55.5097588701744[/C][C]1.0734099096598[/C][C]-3.20483061963755[/C][C]-1.76638756205403[/C][/ROW]
[ROW][C]8[/C][C]27[/C][C]49.866010140747[/C][C]0.442404531957312[/C][C]-4.36798174239947[/C][C]-2.86761758456189[/C][/ROW]
[ROW][C]9[/C][C]58[/C][C]51.1349022092167[/C][C]0.512454552857579[/C][C]4.46947836681696[/C][C]0.368669594664296[/C][/ROW]
[ROW][C]10[/C][C]70[/C][C]55.1496889737037[/C][C]0.782862164697236[/C][C]4.19178069655521[/C][C]1.62955341180087[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]54.8312585125152[/C][C]0.704780254689664[/C][C]-2.32478261502626[/C][C]-0.533003784977301[/C][/ROW]
[ROW][C]12[/C][C]59[/C][C]55.9269691599755[/C][C]0.730408321612545[/C][C]1.77579292552655[/C][C]0.196191829367788[/C][/ROW]
[ROW][C]13[/C][C]44[/C][C]56.3322322931426[/C][C]0.736556156443668[/C][C]-10.126556923625[/C][C]-0.329518875291752[/C][/ROW]
[ROW][C]14[/C][C]36[/C][C]53.3161107160547[/C][C]0.551367794191178[/C][C]-4.85049356139445[/C][C]-1.97251266875634[/C][/ROW]
[ROW][C]15[/C][C]72[/C][C]56.1462078288359[/C][C]0.678458879410144[/C][C]9.18172768097635[/C][C]1.0829045791418[/C][/ROW]
[ROW][C]16[/C][C]45[/C][C]55.0581042010448[/C][C]0.582852455113798[/C][C]-4.74360346518054[/C][C]-0.862758514104173[/C][/ROW]
[ROW][C]17[/C][C]56[/C][C]55.4248227487592[/C][C]0.57181164623154[/C][C]1.26021349167067[/C][C]-0.110645403517929[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]55.1217160188251[/C][C]0.529786323643341[/C][C]1.79123894463907[/C][C]-0.468077174016439[/C][/ROW]
[ROW][C]19[/C][C]53[/C][C]55.2065233411414[/C][C]0.509649035251215[/C][C]-0.661425283752519[/C][C]-0.247200975527775[/C][/ROW]
[ROW][C]20[/C][C]35[/C][C]53.2928849130008[/C][C]0.405993565354842[/C][C]-9.57568592668394[/C][C]-1.38968830854626[/C][/ROW]
[ROW][C]21[/C][C]61[/C][C]54.0446210110347[/C][C]0.420017870205209[/C][C]5.67387701304283[/C][C]0.203691617158301[/C][/ROW]
[ROW][C]22[/C][C]52[/C][C]53.5161690797104[/C][C]0.383450501705237[/C][C]2.09135497981137[/C][C]-0.571952965863289[/C][/ROW]
[ROW][C]23[/C][C]47[/C][C]53.1838074920935[/C][C]0.357329888105564[/C][C]-3.39307651914128[/C][C]-0.441294159689625[/C][/ROW]
[ROW][C]24[/C][C]51[/C][C]52.7576391880178[/C][C]0.331821135521985[/C][C]1.42351617032134[/C][C]-0.500312333658597[/C][/ROW]
[ROW][C]25[/C][C]52[/C][C]53.3936910758178[/C][C]0.334466823605864[/C][C]-2.9124829282324[/C][C]0.238012400905332[/C][/ROW]
[ROW][C]26[/C][C]63[/C][C]55.7506312195821[/C][C]0.386383165372465[/C][C]-1.08050117053314[/C][C]1.33111768071526[/C][/ROW]
[ROW][C]27[/C][C]74[/C][C]57.4972430200593[/C][C]0.42979443516471[/C][C]11.3354837090747[/C][C]0.836174663071383[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]57.0092149726217[/C][C]0.399555254030157[/C][C]-8.55543306600882[/C][C]-0.560703747525154[/C][/ROW]
[ROW][C]29[/C][C]51[/C][C]56.1634919496516[/C][C]0.359394137608602[/C][C]-0.409952723865391[/C][C]-0.771341091736933[/C][/ROW]
[ROW][C]30[/C][C]64[/C][C]57.0085238629356[/C][C]0.374479022669279[/C][C]5.10086967133595[/C][C]0.306319904421563[/C][/ROW]
[ROW][C]31[/C][C]36[/C][C]54.464658478529[/C][C]0.287521746911311[/C][C]-6.88322926351031[/C][C]-1.87338611168286[/C][/ROW]
[ROW][C]32[/C][C]30[/C][C]52.3778304336386[/C][C]0.219656286362561[/C][C]-12.7911149501929[/C][C]-1.54838802068709[/C][/ROW]
[ROW][C]33[/C][C]55[/C][C]51.6826815590607[/C][C]0.194561353651768[/C][C]7.06817788990424[/C][C]-0.604985084620498[/C][/ROW]
[ROW][C]34[/C][C]64[/C][C]52.8949816808565[/C][C]0.221280929431519[/C][C]6.87130668569352[/C][C]0.681918919419113[/C][/ROW]
[ROW][C]35[/C][C]39[/C][C]51.8539050972501[/C][C]0.190134681432901[/C][C]-7.51339958697585[/C][C]-0.858539926568995[/C][/ROW]
[ROW][C]36[/C][C]40[/C][C]50.3170290487396[/C][C]0.153400414367847[/C][C]-2.7811443918811[/C][C]-1.20701560273348[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]52.1336651314132[/C][C]0.172851450096872[/C][C]2.9916185433813[/C][C]1.2574534386736[/C][/ROW]
[ROW][C]38[/C][C]45[/C][C]51.8776911498003[/C][C]0.165164096194881[/C][C]-4.98984936008479[/C][C]-0.303711231996393[/C][/ROW]
[ROW][C]39[/C][C]59[/C][C]51.3081944935682[/C][C]0.148940840638073[/C][C]10.770074064912[/C][C]-0.498706282591717[/C][/ROW]
[ROW][C]40[/C][C]55[/C][C]52.7154855926947[/C][C]0.178506299104309[/C][C]-2.91233574753558[/C][C]0.844379674342226[/C][/ROW]
[ROW][C]41[/C][C]40[/C][C]51.4608089911576[/C][C]0.1447408929574[/C][C]-5.52628082172351[/C][C]-0.964760550187544[/C][/ROW]
[ROW][C]42[/C][C]64[/C][C]51.9113956977428[/C][C]0.151796784557506[/C][C]10.8107602378842[/C][C]0.207646188691917[/C][/ROW]
[ROW][C]43[/C][C]27[/C][C]49.5696318329243[/C][C]0.0959869126440519[/C][C]-12.0404444680315[/C][C]-1.70966312770991[/C][/ROW]
[ROW][C]44[/C][C]28[/C][C]48.1398430530016[/C][C]0.0629811587275342[/C][C]-13.6291438327967[/C][C]-1.05627182496688[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]46.7735640795724[/C][C]0.0331933819972681[/C][C]4.38730248849458[/C][C]-0.998625955797365[/C][/ROW]
[ROW][C]46[/C][C]57[/C][C]46.7765023032923[/C][C]0.0325907847039753[/C][C]10.3552839453647[/C][C]-0.0213390581254067[/C][/ROW]
[ROW][C]47[/C][C]45[/C][C]47.2931409057648[/C][C]0.0415741350006108[/C][C]-4.42947785243313[/C][C]0.345393817367777[/C][/ROW]
[ROW][C]48[/C][C]69[/C][C]50.4129452592603[/C][C]0.0909461428700101[/C][C]4.70427972967328[/C][C]2.23926443378491[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]51.5905375656396[/C][C]0.103645919165084[/C][C]3.32742221984862[/C][C]0.817939127603087[/C][/ROW]
[ROW][C]50[/C][C]56[/C][C]52.92268024849[/C][C]0.121202167048085[/C][C]-2.48922114008621[/C][C]0.898731108415806[/C][/ROW]
[ROW][C]51[/C][C]58[/C][C]52.6696054131789[/C][C]0.114880160481596[/C][C]6.97610008227742[/C][C]-0.26678153121337[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]52.5873389057035[/C][C]0.111304555302155[/C][C]-1.73145722124216[/C][C]-0.13905228945575[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]53.0528529579085[/C][C]0.1178419485459[/C][C]-3.58859174024553[/C][C]0.24969375812056[/C][/ROW]
[ROW][C]54[/C][C]53[/C][C]51.5548663497729[/C][C]0.0882846950349937[/C][C]8.48027550292194[/C][C]-1.14380919818932[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]50.8844868819223[/C][C]0.0747068793134794[/C][C]-10.5601551721981[/C][C]-0.540282419626302[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]48.8796396434584[/C][C]0.0385547231961313[/C][C]-17.7022884925543[/C][C]-1.49072513761177[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]48.8552177984466[/C][C]0.0374982386634213[/C][C]6.42478312745999[/C][C]-0.0454518495909033[/C][/ROW]
[ROW][C]58[/C][C]70[/C][C]50.2752031869256[/C][C]0.0596235455395159[/C][C]13.5276373723913[/C][C]1.00512116311249[/C][/ROW]
[ROW][C]59[/C][C]62[/C][C]52.7882063453776[/C][C]0.0961411373412126[/C][C]-1.89919908902369[/C][C]1.80000346365486[/C][/ROW]
[ROW][C]60[/C][C]58[/C][C]53.4735354496576[/C][C]0.103873659401946[/C][C]1.81866351510943[/C][C]0.438001694681637[/C][/ROW]
[ROW][C]61[/C][C]39[/C][C]51.8304603589971[/C][C]0.0847432447440065[/C][C]-4.65988790368577[/C][C]-1.31979260862075[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]51.5694442393082[/C][C]0.0805489142617881[/C][C]-0.976626541235048[/C][C]-0.257657313757062[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]51.4574990591412[/C][C]0.077902169398966[/C][C]7.41300449816337[/C][C]-0.141153269186039[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]51.1240337830841[/C][C]0.0718441373811094[/C][C]-2.28212309857134[/C][C]-0.299148756258948[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]50.2367872994012[/C][C]0.0573628663726305[/C][C]-3.95305521348947[/C][C]-0.69622575467493[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]50.3681385452314[/C][C]0.0584791410992254[/C][C]11.3007753066267[/C][C]0.0538179173818217[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]50.1104944084988[/C][C]0.0537835001667511[/C][C]-9.69014830920612[/C][C]-0.230833073695431[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]51.0555948671449[/C][C]0.066693234494052[/C][C]-15.0807022753746[/C][C]0.65391132704504[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]53.1371484236227[/C][C]0.0948968522327203[/C][C]9.71187065957705[/C][C]1.48588761992048[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]54.1771412790506[/C][C]0.107520730508161[/C][C]11.5025731534093[/C][C]0.701033457994749[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]54.6367104619555[/C][C]0.111910170782766[/C][C]-2.25877596164944[/C][C]0.262975244069043[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]55.549906190268[/C][C]0.120927306977881[/C][C]5.72128476060019[/C][C]0.603906887014906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=192378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=192378&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
14646000
26247.91107541142070.4577240640763353.272943087229931.53740451313277
36652.21814899943591.235505145455583.110392273791891.66338385981868
45954.80852175620861.459171639290180.8854140052106110.524673408014731
55856.45328514929391.484957890245761.097500045637360.0712027661538959
66158.30253775190661.528619252128191.78773651674030.143268498393585
74155.50975887017441.0734099096598-3.20483061963755-1.76638756205403
82749.8660101407470.442404531957312-4.36798174239947-2.86761758456189
95851.13490220921670.5124545528575794.469478366816960.368669594664296
107055.14968897370370.7828621646972364.191780696555211.62955341180087
114954.83125851251520.704780254689664-2.32478261502626-0.533003784977301
125955.92696915997550.7304083216125451.775792925526550.196191829367788
134456.33223229314260.736556156443668-10.126556923625-0.329518875291752
143653.31611071605470.551367794191178-4.85049356139445-1.97251266875634
157256.14620782883590.6784588794101449.181727680976351.0829045791418
164555.05810420104480.582852455113798-4.74360346518054-0.862758514104173
175655.42482274875920.571811646231541.26021349167067-0.110645403517929
185455.12171601882510.5297863236433411.79123894463907-0.468077174016439
195355.20652334114140.509649035251215-0.661425283752519-0.247200975527775
203553.29288491300080.405993565354842-9.57568592668394-1.38968830854626
216154.04462101103470.4200178702052095.673877013042830.203691617158301
225253.51616907971040.3834505017052372.09135497981137-0.571952965863289
234753.18380749209350.357329888105564-3.39307651914128-0.441294159689625
245152.75763918801780.3318211355219851.42351617032134-0.500312333658597
255253.39369107581780.334466823605864-2.91248292823240.238012400905332
266355.75063121958210.386383165372465-1.080501170533141.33111768071526
277457.49724302005930.4297944351647111.33548370907470.836174663071383
284557.00921497262170.399555254030157-8.55543306600882-0.560703747525154
295156.16349194965160.359394137608602-0.409952723865391-0.771341091736933
306457.00852386293560.3744790226692795.100869671335950.306319904421563
313654.4646584785290.287521746911311-6.88322926351031-1.87338611168286
323052.37783043363860.219656286362561-12.7911149501929-1.54838802068709
335551.68268155906070.1945613536517687.06817788990424-0.604985084620498
346452.89498168085650.2212809294315196.871306685693520.681918919419113
353951.85390509725010.190134681432901-7.51339958697585-0.858539926568995
364050.31702904873960.153400414367847-2.7811443918811-1.20701560273348
376352.13366513141320.1728514500968722.99161854338131.2574534386736
384551.87769114980030.165164096194881-4.98984936008479-0.303711231996393
395951.30819449356820.14894084063807310.770074064912-0.498706282591717
405552.71548559269470.178506299104309-2.912335747535580.844379674342226
414051.46080899115760.1447408929574-5.52628082172351-0.964760550187544
426451.91139569774280.15179678455750610.81076023788420.207646188691917
432749.56963183292430.0959869126440519-12.0404444680315-1.70966312770991
442848.13984305300160.0629811587275342-13.6291438327967-1.05627182496688
454546.77356407957240.03319338199726814.38730248849458-0.998625955797365
465746.77650230329230.032590784703975310.3552839453647-0.0213390581254067
474547.29314090576480.0415741350006108-4.429477852433130.345393817367777
486950.41294525926030.09094614287001014.704279729673282.23926443378491
496051.59053756563960.1036459191650843.327422219848620.817939127603087
505652.922680248490.121202167048085-2.489221140086210.898731108415806
515852.66960541317890.1148801604815966.97610008227742-0.26678153121337
525052.58733890570350.111304555302155-1.73145722124216-0.13905228945575
535153.05285295790850.1178419485459-3.588591740245530.24969375812056
545351.55486634977290.08828469503499378.48027550292194-1.14380919818932
553750.88448688192230.0747068793134794-10.5601551721981-0.540282419626302
562248.87963964345840.0385547231961313-17.7022884925543-1.49072513761177
575548.85521779844660.03749823866342136.42478312745999-0.0454518495909033
587050.27520318692560.059623545539515913.52763737239131.00512116311249
596252.78820634537760.0961411373412126-1.899199089023691.80000346365486
605853.47353544965760.1038736594019461.818663515109430.438001694681637
613951.83046035899710.0847432447440065-4.65988790368577-1.31979260862075
624951.56944423930820.0805489142617881-0.976626541235048-0.257657313757062
635851.45749905914120.0779021693989667.41300449816337-0.141153269186039
644751.12403378308410.0718441373811094-2.28212309857134-0.299148756258948
654250.23678729940120.0573628663726305-3.95305521348947-0.69622575467493
666250.36813854523140.058479141099225411.30077530662670.0538179173818217
673950.11049440849880.0537835001667511-9.69014830920612-0.230833073695431
684051.05559486714490.066693234494052-15.08070227537460.65391132704504
697253.13714842362270.09489685223272039.711870659577051.48588761992048
707054.17714127905060.10752073050816111.50257315340930.701033457994749
715454.63671046195550.111910170782766-2.258775961649440.262975244069043
726555.5499061902680.1209273069778815.721284760600190.603906887014906



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')