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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 computationFri, 04 Dec 2009 04:56:14 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t12599278428529s1fvb4mxlul.htm/, Retrieved Sat, 27 Apr 2024 18:58:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63328, Retrieved Sat, 27 Apr 2024 18:58:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
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] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [workshop 9] [2009-12-04 11:56:14] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataseries X:
0.6348
0.634
0.62915
0.62168
0.61328
0.6089
0.60857
0.62672
0.62291
0.62393
0.61838
0.62012
0.61659
0.6116
0.61573
0.61407
0.62823
0.64405
0.6387
0.63633
0.63059
0.62994
0.63709
0.64217
0.65711
0.66977
0.68255
0.68902
0.71322
0.70224
0.70045
0.69919
0.69693
0.69763
0.69278
0.70196
0.69215
0.6769
0.67124
0.66532
0.67157
0.66428
0.66576
0.66942
0.6813
0.69144
0.69862
0.695
0.69867
0.68968
0.69233
0.68293
0.68399
0.66895
0.68756
0.68527
0.6776
0.68137
0.67933
0.67922




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63328&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63328&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63328&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
10.63480.6348000
20.6340.634043775797653-4.14019524637114e-05-4.37757976527463e-05-0.0565668498210342
30.629150.629298612086866-6.71087687042004e-05-0.000148612086866151-0.586282872673047
40.621680.62187555040857-9.54878514834116e-05-0.000195550408570217-0.916754315403155
50.613280.61350953878865-0.000129179058897638-0.000229538788649658-1.03071856535967
60.60890.609073231484365-0.000146714463847507-0.000173231484365154-0.536774383662716
70.608570.608696633837391-0.000147646354980016-0.000126633837391283-0.0286488862045854
80.626720.62648368734009-7.52387586440856e-050.0002363126599106952.23507033236324
90.622910.623158572386925-8.83067050178318e-05-0.000248572386925168-0.405005076359817
100.623930.623980071198226-8.46629737372267e-05-5.00711982260674e-050.113380616132610
110.618380.618583019749126-0.000105853988011067-0.000203019749126295-0.662028516588124
120.620120.620159255051431-9.91708255689625e-05-3.92550514314409e-050.209619928023016
130.616590.6170031622466812.92164355971801e-05-0.000413162246681057-0.458181136186446
140.61160.61172367305957-9.62159322930973e-05-0.000123673059570553-0.565838730284064
150.615730.615638075615706-8.12126883100111e-059.19243842942786e-050.499172265384509
160.614070.61422240380813-8.35026903415249e-05-0.000152403808130171-0.166136859688344
170.628230.627848756999033-5.70994857941775e-050.0003812430009672661.70679881994831
180.644050.643632160286069-2.59968424773669e-050.0004178397139313481.97205033985364
190.63870.639348957373977-3.43360630986668e-05-0.000648957373976545-0.529996744075801
200.636330.636063640363847-4.06895915146999e-050.000266359636152796-0.404726978093259
210.630590.630973264012296-5.05368691862032e-05-0.000383264012296115-0.628653264124264
220.629940.629839786313649-5.26512621167694e-050.000100213686350915-0.134819617225985
230.637090.637024948473746-3.78276911356679e-056.50515262541327e-050.901158016531734
240.642170.642014441243823-3.09817630615673e-050.0001555587561769720.625615995978485
250.657110.654008914474841-0.0002573478368126890.003101085525158941.63678159256294
260.669770.66961027501545-2.56648881213365e-050.0001597249845498671.80766020185603
270.682550.6818087620907371.67866286643057e-050.0007412379092633251.52002415379452
280.689020.6892039080630362.54321665369694e-05-0.0001839080630363990.918347673317461
290.713220.7123399568911855.54116653666675e-050.0008800431088149822.87629889279836
300.702240.7024363201611954.17709080785731e-05-0.000196320161195255-1.23947832711184
310.700450.7009174793983243.96302418008324e-05-0.000467479398324185-0.194229980239109
320.699190.6987572470246073.66210427634495e-050.000432752975393434-0.273789348114225
330.696930.6971660021312533.43985842123414e-05-0.000236002131253049-0.202599722514456
340.697630.6975716342116253.49139008352257e-055.83657883744542e-050.0462037325089274
350.692780.692876649737952.77410337120586e-05-9.66497379507568e-05-0.588777664628847
360.701960.7016439000961793.06730087737229e-050.0003160999038210471.08709314853119
370.692150.6936464650373950.000123725102927740-0.00149646503739542-1.05565262408303
380.67690.677774149404473-4.01267637211755e-05-0.000874149404472806-1.87796195030779
390.671240.670808719195376-6.3736237328819e-050.000431280804623693-0.86034447248294
400.665320.666796169630104-6.77111726352793e-05-0.00147616963010449-0.491449036653395
410.671570.670140900869694-6.43008226161582e-050.001429099130305920.424652272371464
420.664280.664879176953151-6.99999802085563e-05-0.000599176953150763-0.646772973962655
430.665760.666111268803962-6.85552083535888e-05-0.0003512688039622950.162033934744073
440.669420.668880849640045-6.5417368908221e-050.0005391503599553560.353180905761929
450.68130.681053688418615-5.18824598589055e-050.0002463115813847711.52294352820467
460.691440.69083976047504-4.05243196551376e-050.0006002395249602721.22430799130474
470.698620.699022640706795-3.02642941436875e-05-0.0004026407067949361.02354165671426
480.6950.694839967600024-2.92302417333965e-050.000160032399975887-0.51647898515867
490.698670.69773954557356-5.26236211033291e-050.0009304544264402820.378248018047598
500.689680.690621020073305-0.000106723052281708-0.000941020073305363-0.843503353862849
510.692330.691554537969385-0.0001032327138222520.0007754620306146950.129121422277859
520.682930.685073982292132-0.000109541623784688-0.00214398229213170-0.793672419050343
530.683990.682372306989461-0.0001117033526537550.00161769301053887-0.32255152923866
540.668950.67026899634846-0.000122950053434896-0.00131899634845996-1.49214259168660
550.687560.686706940139958-0.0001069688633471860.0008530598600415342.06070863365380
560.685270.685565481695672-0.000107964890983330-0.000295481695672165-0.128723757344901
570.67760.678307580003903-0.000114901390153306-0.000707580003903233-0.889688491672784
580.681370.681022276578974-0.0001119594417273450.0003477234210257530.352123305817686
590.679330.679700968497827-0.000113231299108571-0.00037096849782673-0.150509747255192
600.679220.67925632172993-0.000113030941972195-3.63217299293497e-05-0.0412272507041686

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 0.6348 & 0.6348 & 0 & 0 & 0 \tabularnewline
2 & 0.634 & 0.634043775797653 & -4.14019524637114e-05 & -4.37757976527463e-05 & -0.0565668498210342 \tabularnewline
3 & 0.62915 & 0.629298612086866 & -6.71087687042004e-05 & -0.000148612086866151 & -0.586282872673047 \tabularnewline
4 & 0.62168 & 0.62187555040857 & -9.54878514834116e-05 & -0.000195550408570217 & -0.916754315403155 \tabularnewline
5 & 0.61328 & 0.61350953878865 & -0.000129179058897638 & -0.000229538788649658 & -1.03071856535967 \tabularnewline
6 & 0.6089 & 0.609073231484365 & -0.000146714463847507 & -0.000173231484365154 & -0.536774383662716 \tabularnewline
7 & 0.60857 & 0.608696633837391 & -0.000147646354980016 & -0.000126633837391283 & -0.0286488862045854 \tabularnewline
8 & 0.62672 & 0.62648368734009 & -7.52387586440856e-05 & 0.000236312659910695 & 2.23507033236324 \tabularnewline
9 & 0.62291 & 0.623158572386925 & -8.83067050178318e-05 & -0.000248572386925168 & -0.405005076359817 \tabularnewline
10 & 0.62393 & 0.623980071198226 & -8.46629737372267e-05 & -5.00711982260674e-05 & 0.113380616132610 \tabularnewline
11 & 0.61838 & 0.618583019749126 & -0.000105853988011067 & -0.000203019749126295 & -0.662028516588124 \tabularnewline
12 & 0.62012 & 0.620159255051431 & -9.91708255689625e-05 & -3.92550514314409e-05 & 0.209619928023016 \tabularnewline
13 & 0.61659 & 0.617003162246681 & 2.92164355971801e-05 & -0.000413162246681057 & -0.458181136186446 \tabularnewline
14 & 0.6116 & 0.61172367305957 & -9.62159322930973e-05 & -0.000123673059570553 & -0.565838730284064 \tabularnewline
15 & 0.61573 & 0.615638075615706 & -8.12126883100111e-05 & 9.19243842942786e-05 & 0.499172265384509 \tabularnewline
16 & 0.61407 & 0.61422240380813 & -8.35026903415249e-05 & -0.000152403808130171 & -0.166136859688344 \tabularnewline
17 & 0.62823 & 0.627848756999033 & -5.70994857941775e-05 & 0.000381243000967266 & 1.70679881994831 \tabularnewline
18 & 0.64405 & 0.643632160286069 & -2.59968424773669e-05 & 0.000417839713931348 & 1.97205033985364 \tabularnewline
19 & 0.6387 & 0.639348957373977 & -3.43360630986668e-05 & -0.000648957373976545 & -0.529996744075801 \tabularnewline
20 & 0.63633 & 0.636063640363847 & -4.06895915146999e-05 & 0.000266359636152796 & -0.404726978093259 \tabularnewline
21 & 0.63059 & 0.630973264012296 & -5.05368691862032e-05 & -0.000383264012296115 & -0.628653264124264 \tabularnewline
22 & 0.62994 & 0.629839786313649 & -5.26512621167694e-05 & 0.000100213686350915 & -0.134819617225985 \tabularnewline
23 & 0.63709 & 0.637024948473746 & -3.78276911356679e-05 & 6.50515262541327e-05 & 0.901158016531734 \tabularnewline
24 & 0.64217 & 0.642014441243823 & -3.09817630615673e-05 & 0.000155558756176972 & 0.625615995978485 \tabularnewline
25 & 0.65711 & 0.654008914474841 & -0.000257347836812689 & 0.00310108552515894 & 1.63678159256294 \tabularnewline
26 & 0.66977 & 0.66961027501545 & -2.56648881213365e-05 & 0.000159724984549867 & 1.80766020185603 \tabularnewline
27 & 0.68255 & 0.681808762090737 & 1.67866286643057e-05 & 0.000741237909263325 & 1.52002415379452 \tabularnewline
28 & 0.68902 & 0.689203908063036 & 2.54321665369694e-05 & -0.000183908063036399 & 0.918347673317461 \tabularnewline
29 & 0.71322 & 0.712339956891185 & 5.54116653666675e-05 & 0.000880043108814982 & 2.87629889279836 \tabularnewline
30 & 0.70224 & 0.702436320161195 & 4.17709080785731e-05 & -0.000196320161195255 & -1.23947832711184 \tabularnewline
31 & 0.70045 & 0.700917479398324 & 3.96302418008324e-05 & -0.000467479398324185 & -0.194229980239109 \tabularnewline
32 & 0.69919 & 0.698757247024607 & 3.66210427634495e-05 & 0.000432752975393434 & -0.273789348114225 \tabularnewline
33 & 0.69693 & 0.697166002131253 & 3.43985842123414e-05 & -0.000236002131253049 & -0.202599722514456 \tabularnewline
34 & 0.69763 & 0.697571634211625 & 3.49139008352257e-05 & 5.83657883744542e-05 & 0.0462037325089274 \tabularnewline
35 & 0.69278 & 0.69287664973795 & 2.77410337120586e-05 & -9.66497379507568e-05 & -0.588777664628847 \tabularnewline
36 & 0.70196 & 0.701643900096179 & 3.06730087737229e-05 & 0.000316099903821047 & 1.08709314853119 \tabularnewline
37 & 0.69215 & 0.693646465037395 & 0.000123725102927740 & -0.00149646503739542 & -1.05565262408303 \tabularnewline
38 & 0.6769 & 0.677774149404473 & -4.01267637211755e-05 & -0.000874149404472806 & -1.87796195030779 \tabularnewline
39 & 0.67124 & 0.670808719195376 & -6.3736237328819e-05 & 0.000431280804623693 & -0.86034447248294 \tabularnewline
40 & 0.66532 & 0.666796169630104 & -6.77111726352793e-05 & -0.00147616963010449 & -0.491449036653395 \tabularnewline
41 & 0.67157 & 0.670140900869694 & -6.43008226161582e-05 & 0.00142909913030592 & 0.424652272371464 \tabularnewline
42 & 0.66428 & 0.664879176953151 & -6.99999802085563e-05 & -0.000599176953150763 & -0.646772973962655 \tabularnewline
43 & 0.66576 & 0.666111268803962 & -6.85552083535888e-05 & -0.000351268803962295 & 0.162033934744073 \tabularnewline
44 & 0.66942 & 0.668880849640045 & -6.5417368908221e-05 & 0.000539150359955356 & 0.353180905761929 \tabularnewline
45 & 0.6813 & 0.681053688418615 & -5.18824598589055e-05 & 0.000246311581384771 & 1.52294352820467 \tabularnewline
46 & 0.69144 & 0.69083976047504 & -4.05243196551376e-05 & 0.000600239524960272 & 1.22430799130474 \tabularnewline
47 & 0.69862 & 0.699022640706795 & -3.02642941436875e-05 & -0.000402640706794936 & 1.02354165671426 \tabularnewline
48 & 0.695 & 0.694839967600024 & -2.92302417333965e-05 & 0.000160032399975887 & -0.51647898515867 \tabularnewline
49 & 0.69867 & 0.69773954557356 & -5.26236211033291e-05 & 0.000930454426440282 & 0.378248018047598 \tabularnewline
50 & 0.68968 & 0.690621020073305 & -0.000106723052281708 & -0.000941020073305363 & -0.843503353862849 \tabularnewline
51 & 0.69233 & 0.691554537969385 & -0.000103232713822252 & 0.000775462030614695 & 0.129121422277859 \tabularnewline
52 & 0.68293 & 0.685073982292132 & -0.000109541623784688 & -0.00214398229213170 & -0.793672419050343 \tabularnewline
53 & 0.68399 & 0.682372306989461 & -0.000111703352653755 & 0.00161769301053887 & -0.32255152923866 \tabularnewline
54 & 0.66895 & 0.67026899634846 & -0.000122950053434896 & -0.00131899634845996 & -1.49214259168660 \tabularnewline
55 & 0.68756 & 0.686706940139958 & -0.000106968863347186 & 0.000853059860041534 & 2.06070863365380 \tabularnewline
56 & 0.68527 & 0.685565481695672 & -0.000107964890983330 & -0.000295481695672165 & -0.128723757344901 \tabularnewline
57 & 0.6776 & 0.678307580003903 & -0.000114901390153306 & -0.000707580003903233 & -0.889688491672784 \tabularnewline
58 & 0.68137 & 0.681022276578974 & -0.000111959441727345 & 0.000347723421025753 & 0.352123305817686 \tabularnewline
59 & 0.67933 & 0.679700968497827 & -0.000113231299108571 & -0.00037096849782673 & -0.150509747255192 \tabularnewline
60 & 0.67922 & 0.67925632172993 & -0.000113030941972195 & -3.63217299293497e-05 & -0.0412272507041686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63328&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]0.6348[/C][C]0.6348[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.634[/C][C]0.634043775797653[/C][C]-4.14019524637114e-05[/C][C]-4.37757976527463e-05[/C][C]-0.0565668498210342[/C][/ROW]
[ROW][C]3[/C][C]0.62915[/C][C]0.629298612086866[/C][C]-6.71087687042004e-05[/C][C]-0.000148612086866151[/C][C]-0.586282872673047[/C][/ROW]
[ROW][C]4[/C][C]0.62168[/C][C]0.62187555040857[/C][C]-9.54878514834116e-05[/C][C]-0.000195550408570217[/C][C]-0.916754315403155[/C][/ROW]
[ROW][C]5[/C][C]0.61328[/C][C]0.61350953878865[/C][C]-0.000129179058897638[/C][C]-0.000229538788649658[/C][C]-1.03071856535967[/C][/ROW]
[ROW][C]6[/C][C]0.6089[/C][C]0.609073231484365[/C][C]-0.000146714463847507[/C][C]-0.000173231484365154[/C][C]-0.536774383662716[/C][/ROW]
[ROW][C]7[/C][C]0.60857[/C][C]0.608696633837391[/C][C]-0.000147646354980016[/C][C]-0.000126633837391283[/C][C]-0.0286488862045854[/C][/ROW]
[ROW][C]8[/C][C]0.62672[/C][C]0.62648368734009[/C][C]-7.52387586440856e-05[/C][C]0.000236312659910695[/C][C]2.23507033236324[/C][/ROW]
[ROW][C]9[/C][C]0.62291[/C][C]0.623158572386925[/C][C]-8.83067050178318e-05[/C][C]-0.000248572386925168[/C][C]-0.405005076359817[/C][/ROW]
[ROW][C]10[/C][C]0.62393[/C][C]0.623980071198226[/C][C]-8.46629737372267e-05[/C][C]-5.00711982260674e-05[/C][C]0.113380616132610[/C][/ROW]
[ROW][C]11[/C][C]0.61838[/C][C]0.618583019749126[/C][C]-0.000105853988011067[/C][C]-0.000203019749126295[/C][C]-0.662028516588124[/C][/ROW]
[ROW][C]12[/C][C]0.62012[/C][C]0.620159255051431[/C][C]-9.91708255689625e-05[/C][C]-3.92550514314409e-05[/C][C]0.209619928023016[/C][/ROW]
[ROW][C]13[/C][C]0.61659[/C][C]0.617003162246681[/C][C]2.92164355971801e-05[/C][C]-0.000413162246681057[/C][C]-0.458181136186446[/C][/ROW]
[ROW][C]14[/C][C]0.6116[/C][C]0.61172367305957[/C][C]-9.62159322930973e-05[/C][C]-0.000123673059570553[/C][C]-0.565838730284064[/C][/ROW]
[ROW][C]15[/C][C]0.61573[/C][C]0.615638075615706[/C][C]-8.12126883100111e-05[/C][C]9.19243842942786e-05[/C][C]0.499172265384509[/C][/ROW]
[ROW][C]16[/C][C]0.61407[/C][C]0.61422240380813[/C][C]-8.35026903415249e-05[/C][C]-0.000152403808130171[/C][C]-0.166136859688344[/C][/ROW]
[ROW][C]17[/C][C]0.62823[/C][C]0.627848756999033[/C][C]-5.70994857941775e-05[/C][C]0.000381243000967266[/C][C]1.70679881994831[/C][/ROW]
[ROW][C]18[/C][C]0.64405[/C][C]0.643632160286069[/C][C]-2.59968424773669e-05[/C][C]0.000417839713931348[/C][C]1.97205033985364[/C][/ROW]
[ROW][C]19[/C][C]0.6387[/C][C]0.639348957373977[/C][C]-3.43360630986668e-05[/C][C]-0.000648957373976545[/C][C]-0.529996744075801[/C][/ROW]
[ROW][C]20[/C][C]0.63633[/C][C]0.636063640363847[/C][C]-4.06895915146999e-05[/C][C]0.000266359636152796[/C][C]-0.404726978093259[/C][/ROW]
[ROW][C]21[/C][C]0.63059[/C][C]0.630973264012296[/C][C]-5.05368691862032e-05[/C][C]-0.000383264012296115[/C][C]-0.628653264124264[/C][/ROW]
[ROW][C]22[/C][C]0.62994[/C][C]0.629839786313649[/C][C]-5.26512621167694e-05[/C][C]0.000100213686350915[/C][C]-0.134819617225985[/C][/ROW]
[ROW][C]23[/C][C]0.63709[/C][C]0.637024948473746[/C][C]-3.78276911356679e-05[/C][C]6.50515262541327e-05[/C][C]0.901158016531734[/C][/ROW]
[ROW][C]24[/C][C]0.64217[/C][C]0.642014441243823[/C][C]-3.09817630615673e-05[/C][C]0.000155558756176972[/C][C]0.625615995978485[/C][/ROW]
[ROW][C]25[/C][C]0.65711[/C][C]0.654008914474841[/C][C]-0.000257347836812689[/C][C]0.00310108552515894[/C][C]1.63678159256294[/C][/ROW]
[ROW][C]26[/C][C]0.66977[/C][C]0.66961027501545[/C][C]-2.56648881213365e-05[/C][C]0.000159724984549867[/C][C]1.80766020185603[/C][/ROW]
[ROW][C]27[/C][C]0.68255[/C][C]0.681808762090737[/C][C]1.67866286643057e-05[/C][C]0.000741237909263325[/C][C]1.52002415379452[/C][/ROW]
[ROW][C]28[/C][C]0.68902[/C][C]0.689203908063036[/C][C]2.54321665369694e-05[/C][C]-0.000183908063036399[/C][C]0.918347673317461[/C][/ROW]
[ROW][C]29[/C][C]0.71322[/C][C]0.712339956891185[/C][C]5.54116653666675e-05[/C][C]0.000880043108814982[/C][C]2.87629889279836[/C][/ROW]
[ROW][C]30[/C][C]0.70224[/C][C]0.702436320161195[/C][C]4.17709080785731e-05[/C][C]-0.000196320161195255[/C][C]-1.23947832711184[/C][/ROW]
[ROW][C]31[/C][C]0.70045[/C][C]0.700917479398324[/C][C]3.96302418008324e-05[/C][C]-0.000467479398324185[/C][C]-0.194229980239109[/C][/ROW]
[ROW][C]32[/C][C]0.69919[/C][C]0.698757247024607[/C][C]3.66210427634495e-05[/C][C]0.000432752975393434[/C][C]-0.273789348114225[/C][/ROW]
[ROW][C]33[/C][C]0.69693[/C][C]0.697166002131253[/C][C]3.43985842123414e-05[/C][C]-0.000236002131253049[/C][C]-0.202599722514456[/C][/ROW]
[ROW][C]34[/C][C]0.69763[/C][C]0.697571634211625[/C][C]3.49139008352257e-05[/C][C]5.83657883744542e-05[/C][C]0.0462037325089274[/C][/ROW]
[ROW][C]35[/C][C]0.69278[/C][C]0.69287664973795[/C][C]2.77410337120586e-05[/C][C]-9.66497379507568e-05[/C][C]-0.588777664628847[/C][/ROW]
[ROW][C]36[/C][C]0.70196[/C][C]0.701643900096179[/C][C]3.06730087737229e-05[/C][C]0.000316099903821047[/C][C]1.08709314853119[/C][/ROW]
[ROW][C]37[/C][C]0.69215[/C][C]0.693646465037395[/C][C]0.000123725102927740[/C][C]-0.00149646503739542[/C][C]-1.05565262408303[/C][/ROW]
[ROW][C]38[/C][C]0.6769[/C][C]0.677774149404473[/C][C]-4.01267637211755e-05[/C][C]-0.000874149404472806[/C][C]-1.87796195030779[/C][/ROW]
[ROW][C]39[/C][C]0.67124[/C][C]0.670808719195376[/C][C]-6.3736237328819e-05[/C][C]0.000431280804623693[/C][C]-0.86034447248294[/C][/ROW]
[ROW][C]40[/C][C]0.66532[/C][C]0.666796169630104[/C][C]-6.77111726352793e-05[/C][C]-0.00147616963010449[/C][C]-0.491449036653395[/C][/ROW]
[ROW][C]41[/C][C]0.67157[/C][C]0.670140900869694[/C][C]-6.43008226161582e-05[/C][C]0.00142909913030592[/C][C]0.424652272371464[/C][/ROW]
[ROW][C]42[/C][C]0.66428[/C][C]0.664879176953151[/C][C]-6.99999802085563e-05[/C][C]-0.000599176953150763[/C][C]-0.646772973962655[/C][/ROW]
[ROW][C]43[/C][C]0.66576[/C][C]0.666111268803962[/C][C]-6.85552083535888e-05[/C][C]-0.000351268803962295[/C][C]0.162033934744073[/C][/ROW]
[ROW][C]44[/C][C]0.66942[/C][C]0.668880849640045[/C][C]-6.5417368908221e-05[/C][C]0.000539150359955356[/C][C]0.353180905761929[/C][/ROW]
[ROW][C]45[/C][C]0.6813[/C][C]0.681053688418615[/C][C]-5.18824598589055e-05[/C][C]0.000246311581384771[/C][C]1.52294352820467[/C][/ROW]
[ROW][C]46[/C][C]0.69144[/C][C]0.69083976047504[/C][C]-4.05243196551376e-05[/C][C]0.000600239524960272[/C][C]1.22430799130474[/C][/ROW]
[ROW][C]47[/C][C]0.69862[/C][C]0.699022640706795[/C][C]-3.02642941436875e-05[/C][C]-0.000402640706794936[/C][C]1.02354165671426[/C][/ROW]
[ROW][C]48[/C][C]0.695[/C][C]0.694839967600024[/C][C]-2.92302417333965e-05[/C][C]0.000160032399975887[/C][C]-0.51647898515867[/C][/ROW]
[ROW][C]49[/C][C]0.69867[/C][C]0.69773954557356[/C][C]-5.26236211033291e-05[/C][C]0.000930454426440282[/C][C]0.378248018047598[/C][/ROW]
[ROW][C]50[/C][C]0.68968[/C][C]0.690621020073305[/C][C]-0.000106723052281708[/C][C]-0.000941020073305363[/C][C]-0.843503353862849[/C][/ROW]
[ROW][C]51[/C][C]0.69233[/C][C]0.691554537969385[/C][C]-0.000103232713822252[/C][C]0.000775462030614695[/C][C]0.129121422277859[/C][/ROW]
[ROW][C]52[/C][C]0.68293[/C][C]0.685073982292132[/C][C]-0.000109541623784688[/C][C]-0.00214398229213170[/C][C]-0.793672419050343[/C][/ROW]
[ROW][C]53[/C][C]0.68399[/C][C]0.682372306989461[/C][C]-0.000111703352653755[/C][C]0.00161769301053887[/C][C]-0.32255152923866[/C][/ROW]
[ROW][C]54[/C][C]0.66895[/C][C]0.67026899634846[/C][C]-0.000122950053434896[/C][C]-0.00131899634845996[/C][C]-1.49214259168660[/C][/ROW]
[ROW][C]55[/C][C]0.68756[/C][C]0.686706940139958[/C][C]-0.000106968863347186[/C][C]0.000853059860041534[/C][C]2.06070863365380[/C][/ROW]
[ROW][C]56[/C][C]0.68527[/C][C]0.685565481695672[/C][C]-0.000107964890983330[/C][C]-0.000295481695672165[/C][C]-0.128723757344901[/C][/ROW]
[ROW][C]57[/C][C]0.6776[/C][C]0.678307580003903[/C][C]-0.000114901390153306[/C][C]-0.000707580003903233[/C][C]-0.889688491672784[/C][/ROW]
[ROW][C]58[/C][C]0.68137[/C][C]0.681022276578974[/C][C]-0.000111959441727345[/C][C]0.000347723421025753[/C][C]0.352123305817686[/C][/ROW]
[ROW][C]59[/C][C]0.67933[/C][C]0.679700968497827[/C][C]-0.000113231299108571[/C][C]-0.00037096849782673[/C][C]-0.150509747255192[/C][/ROW]
[ROW][C]60[/C][C]0.67922[/C][C]0.67925632172993[/C][C]-0.000113030941972195[/C][C]-3.63217299293497e-05[/C][C]-0.0412272507041686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63328&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63328&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
10.63480.6348000
20.6340.634043775797653-4.14019524637114e-05-4.37757976527463e-05-0.0565668498210342
30.629150.629298612086866-6.71087687042004e-05-0.000148612086866151-0.586282872673047
40.621680.62187555040857-9.54878514834116e-05-0.000195550408570217-0.916754315403155
50.613280.61350953878865-0.000129179058897638-0.000229538788649658-1.03071856535967
60.60890.609073231484365-0.000146714463847507-0.000173231484365154-0.536774383662716
70.608570.608696633837391-0.000147646354980016-0.000126633837391283-0.0286488862045854
80.626720.62648368734009-7.52387586440856e-050.0002363126599106952.23507033236324
90.622910.623158572386925-8.83067050178318e-05-0.000248572386925168-0.405005076359817
100.623930.623980071198226-8.46629737372267e-05-5.00711982260674e-050.113380616132610
110.618380.618583019749126-0.000105853988011067-0.000203019749126295-0.662028516588124
120.620120.620159255051431-9.91708255689625e-05-3.92550514314409e-050.209619928023016
130.616590.6170031622466812.92164355971801e-05-0.000413162246681057-0.458181136186446
140.61160.61172367305957-9.62159322930973e-05-0.000123673059570553-0.565838730284064
150.615730.615638075615706-8.12126883100111e-059.19243842942786e-050.499172265384509
160.614070.61422240380813-8.35026903415249e-05-0.000152403808130171-0.166136859688344
170.628230.627848756999033-5.70994857941775e-050.0003812430009672661.70679881994831
180.644050.643632160286069-2.59968424773669e-050.0004178397139313481.97205033985364
190.63870.639348957373977-3.43360630986668e-05-0.000648957373976545-0.529996744075801
200.636330.636063640363847-4.06895915146999e-050.000266359636152796-0.404726978093259
210.630590.630973264012296-5.05368691862032e-05-0.000383264012296115-0.628653264124264
220.629940.629839786313649-5.26512621167694e-050.000100213686350915-0.134819617225985
230.637090.637024948473746-3.78276911356679e-056.50515262541327e-050.901158016531734
240.642170.642014441243823-3.09817630615673e-050.0001555587561769720.625615995978485
250.657110.654008914474841-0.0002573478368126890.003101085525158941.63678159256294
260.669770.66961027501545-2.56648881213365e-050.0001597249845498671.80766020185603
270.682550.6818087620907371.67866286643057e-050.0007412379092633251.52002415379452
280.689020.6892039080630362.54321665369694e-05-0.0001839080630363990.918347673317461
290.713220.7123399568911855.54116653666675e-050.0008800431088149822.87629889279836
300.702240.7024363201611954.17709080785731e-05-0.000196320161195255-1.23947832711184
310.700450.7009174793983243.96302418008324e-05-0.000467479398324185-0.194229980239109
320.699190.6987572470246073.66210427634495e-050.000432752975393434-0.273789348114225
330.696930.6971660021312533.43985842123414e-05-0.000236002131253049-0.202599722514456
340.697630.6975716342116253.49139008352257e-055.83657883744542e-050.0462037325089274
350.692780.692876649737952.77410337120586e-05-9.66497379507568e-05-0.588777664628847
360.701960.7016439000961793.06730087737229e-050.0003160999038210471.08709314853119
370.692150.6936464650373950.000123725102927740-0.00149646503739542-1.05565262408303
380.67690.677774149404473-4.01267637211755e-05-0.000874149404472806-1.87796195030779
390.671240.670808719195376-6.3736237328819e-050.000431280804623693-0.86034447248294
400.665320.666796169630104-6.77111726352793e-05-0.00147616963010449-0.491449036653395
410.671570.670140900869694-6.43008226161582e-050.001429099130305920.424652272371464
420.664280.664879176953151-6.99999802085563e-05-0.000599176953150763-0.646772973962655
430.665760.666111268803962-6.85552083535888e-05-0.0003512688039622950.162033934744073
440.669420.668880849640045-6.5417368908221e-050.0005391503599553560.353180905761929
450.68130.681053688418615-5.18824598589055e-050.0002463115813847711.52294352820467
460.691440.69083976047504-4.05243196551376e-050.0006002395249602721.22430799130474
470.698620.699022640706795-3.02642941436875e-05-0.0004026407067949361.02354165671426
480.6950.694839967600024-2.92302417333965e-050.000160032399975887-0.51647898515867
490.698670.69773954557356-5.26236211033291e-050.0009304544264402820.378248018047598
500.689680.690621020073305-0.000106723052281708-0.000941020073305363-0.843503353862849
510.692330.691554537969385-0.0001032327138222520.0007754620306146950.129121422277859
520.682930.685073982292132-0.000109541623784688-0.00214398229213170-0.793672419050343
530.683990.682372306989461-0.0001117033526537550.00161769301053887-0.32255152923866
540.668950.67026899634846-0.000122950053434896-0.00131899634845996-1.49214259168660
550.687560.686706940139958-0.0001069688633471860.0008530598600415342.06070863365380
560.685270.685565481695672-0.000107964890983330-0.000295481695672165-0.128723757344901
570.67760.678307580003903-0.000114901390153306-0.000707580003903233-0.889688491672784
580.681370.681022276578974-0.0001119594417273450.0003477234210257530.352123305817686
590.679330.679700968497827-0.000113231299108571-0.00037096849782673-0.150509747255192
600.679220.67925632172993-0.000113030941972195-3.63217299293497e-05-0.0412272507041686



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')