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 computationFri, 04 Dec 2009 10:56:50 -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/t1259949547b00hdvseiqlxmxi.htm/, Retrieved Sun, 28 Apr 2024 07:38:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63985, Retrieved Sun, 28 Apr 2024 07:38:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
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] [] [2009-12-04 17:56:50] [aa8eb70c35ea8a87edcd21d6427e653e] [Current]
Feedback Forum

Post a new message
Dataseries X:
2849,27
2921,44
2981,85
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68
3844,49
3720,98
3674,4
3857,62
3801,06
3504,37
3032,6
3047,03
2962,34
2197,82
2014,45
1862,83
1905,41
1810,99
1670,07
1864,44
2052,02
2029,6
2070,83
2293,41
2443,27
2513,17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63985&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63985&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
12849.272849.27000
22921.442917.763133991155.137905834803853.676866008845350.264174667852236
32981.852978.1278980311911.23236166301193.722101968812980.338719245348942
43080.583076.7969032280924.20282191391443.783096771913550.524636601294583
53106.223102.4360766187924.4542209290673.78392338120640.0084812578178234
63119.313115.5313533318122.26431775995253.77864666818843-0.0663376075692007
73061.263057.511033255915.870325678917593.74896674408907-0.465499328144555
83097.313093.5522369470712.24705693817273.757763052930640.174130835969007
93161.693157.9203199875423.49353683618063.769680012457830.299950075187489
103257.163253.3774611068239.21644848150033.782538893183450.413402697369485
113277.013273.2301594701834.95385355107383.77984052981891-0.111114812339812
123295.323291.5419657344431.27375577508833.77803426556331-0.0954323118956617
133363.993381.4269533339243.7382114887806-17.43695333391940.391778289159546
143494.173491.1020052475657.85186384621923.067994752439780.317896052319807
153667.033664.0617818767683.4643930673972.968218123237140.658757311629081
163813.063810.1339744125197.39666399441962.926025587494360.358375195739271
173917.963915.0379077324899.06743869484432.922092267521850.0429793591223882
183895.513892.5383909581972.00989596540472.97160904181063-0.696059101657574
193801.063798.0356637723334.94600100599363.02433622767383-0.953494777019992
203570.123567.03019516236-24.25538701104253.08980483764101-1.52301749022897
213701.613698.5500054648510.42217647632423.0599945351510.892123635120283
223862.273859.2323589138743.87330958599443.037641086134880.860576334564032
233970.13967.0697560843158.11352058717393.030243915690380.366350051019184
244138.524135.4996734252682.67365824332263.020326574738870.631846223109648
254199.754230.5640568471385.3916521685081-30.81405684712540.0768490048576036
264290.894288.4854038561279.43420603590862.4045961438822-0.142027442385313
274443.914441.549983775295.83984644786552.360016224795370.421427346514012
284502.644500.2625106666587.57066280996172.37748933335368-0.212544699497747
294356.984354.5171570654935.61621780988462.46284293451407-1.33587653254883
304591.274588.8636710034279.86437503021132.40632899657891.13797584921890
314696.964694.559381303585.61553775242362.400618696502930.147928025980703
324621.44618.9716794439949.72550601318892.42832055600669-0.923215444774458
334562.844560.3972121589425.61386019208372.44278784106401-0.620264419110675
344202.524200.03713105689-60.31883188683362.48286894311003-2.21065512929778
354296.494294.01958690279-25.96514259035452.470413097212810.883778750285135
364435.234432.7699230576910.70748586244052.460076942311460.94344545845217
374105.184167.37148713995-50.1819026991242-62.1914871399531-1.66893610767262
384116.684111.09118841473-51.51358008122065.58881158527523-0.0324681561925759
393844.493838.8018394913-100.6961533051205.6881605086983-1.26387849756210
403720.983715.28385723677-105.7787427679235.69614276323134-0.130668861223294
413674.43668.71995664016-92.59323089774745.680043359837040.339078839393447
423857.623851.99826090203-31.16924883098295.62173909797331.57983645428615
433801.063795.43408875761-36.82331875537885.625911242391-0.145438008795883
443504.373498.71089630002-94.6880204719795.6591036999766-1.48852521020575
453032.63026.90345650104-178.6505079515005.69654349895634-2.15994812306076
463047.033041.34835859982-135.6592916168365.681641400176211.10597969054765
472962.342956.66141654926-124.3106225595885.678583450736420.291956419034426
482197.822192.11155878457-266.8567305988195.708441215433-3.66717716307664
492014.452064.11497959758-236.158079925354-49.66497959757940.828389947333573
501862.831858.06098260740-229.5576007178044.769017392598090.162901085914849
511905.411900.73827178121-168.9180272814174.671728218785721.55865684837942
521810.991806.33897240724-152.3229882892434.651027592762650.426703256060083
531670.071665.4214352507-149.7833541487784.64856474929910.065314780566455
541864.441859.84921339512-73.14334947247814.590786604880221.97129588133215
552052.022047.46323802728-15.08644767222914.556761972717621.49342691045499
562029.62025.04249409234-16.71938849362044.55750590765862-0.0420069356825406
572070.832066.27706370684-3.816287254334224.5529362931590.331938029389666
582293.412288.8709412319346.59270549075844.539058768069521.29681608710483
592443.272438.7358618387569.58580789364194.534138161249970.591524112987543
602513.172508.6358734763969.65576432717154.534126523608810.00179972133477979

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 2849.27 & 2849.27 & 0 & 0 & 0 \tabularnewline
2 & 2921.44 & 2917.76313399115 & 5.13790583480385 & 3.67686600884535 & 0.264174667852236 \tabularnewline
3 & 2981.85 & 2978.12789803119 & 11.2323616630119 & 3.72210196881298 & 0.338719245348942 \tabularnewline
4 & 3080.58 & 3076.79690322809 & 24.2028219139144 & 3.78309677191355 & 0.524636601294583 \tabularnewline
5 & 3106.22 & 3102.43607661879 & 24.454220929067 & 3.7839233812064 & 0.0084812578178234 \tabularnewline
6 & 3119.31 & 3115.53135333181 & 22.2643177599525 & 3.77864666818843 & -0.0663376075692007 \tabularnewline
7 & 3061.26 & 3057.51103325591 & 5.87032567891759 & 3.74896674408907 & -0.465499328144555 \tabularnewline
8 & 3097.31 & 3093.55223694707 & 12.2470569381727 & 3.75776305293064 & 0.174130835969007 \tabularnewline
9 & 3161.69 & 3157.92031998754 & 23.4935368361806 & 3.76968001245783 & 0.299950075187489 \tabularnewline
10 & 3257.16 & 3253.37746110682 & 39.2164484815003 & 3.78253889318345 & 0.413402697369485 \tabularnewline
11 & 3277.01 & 3273.23015947018 & 34.9538535510738 & 3.77984052981891 & -0.111114812339812 \tabularnewline
12 & 3295.32 & 3291.54196573444 & 31.2737557750883 & 3.77803426556331 & -0.0954323118956617 \tabularnewline
13 & 3363.99 & 3381.42695333392 & 43.7382114887806 & -17.4369533339194 & 0.391778289159546 \tabularnewline
14 & 3494.17 & 3491.10200524756 & 57.8518638462192 & 3.06799475243978 & 0.317896052319807 \tabularnewline
15 & 3667.03 & 3664.06178187676 & 83.464393067397 & 2.96821812323714 & 0.658757311629081 \tabularnewline
16 & 3813.06 & 3810.13397441251 & 97.3966639944196 & 2.92602558749436 & 0.358375195739271 \tabularnewline
17 & 3917.96 & 3915.03790773248 & 99.0674386948443 & 2.92209226752185 & 0.0429793591223882 \tabularnewline
18 & 3895.51 & 3892.53839095819 & 72.0098959654047 & 2.97160904181063 & -0.696059101657574 \tabularnewline
19 & 3801.06 & 3798.03566377233 & 34.9460010059936 & 3.02433622767383 & -0.953494777019992 \tabularnewline
20 & 3570.12 & 3567.03019516236 & -24.2553870110425 & 3.08980483764101 & -1.52301749022897 \tabularnewline
21 & 3701.61 & 3698.55000546485 & 10.4221764763242 & 3.059994535151 & 0.892123635120283 \tabularnewline
22 & 3862.27 & 3859.23235891387 & 43.8733095859944 & 3.03764108613488 & 0.860576334564032 \tabularnewline
23 & 3970.1 & 3967.06975608431 & 58.1135205871739 & 3.03024391569038 & 0.366350051019184 \tabularnewline
24 & 4138.52 & 4135.49967342526 & 82.6736582433226 & 3.02032657473887 & 0.631846223109648 \tabularnewline
25 & 4199.75 & 4230.56405684713 & 85.3916521685081 & -30.8140568471254 & 0.0768490048576036 \tabularnewline
26 & 4290.89 & 4288.48540385612 & 79.4342060359086 & 2.4045961438822 & -0.142027442385313 \tabularnewline
27 & 4443.91 & 4441.5499837752 & 95.8398464478655 & 2.36001622479537 & 0.421427346514012 \tabularnewline
28 & 4502.64 & 4500.26251066665 & 87.5706628099617 & 2.37748933335368 & -0.212544699497747 \tabularnewline
29 & 4356.98 & 4354.51715706549 & 35.6162178098846 & 2.46284293451407 & -1.33587653254883 \tabularnewline
30 & 4591.27 & 4588.86367100342 & 79.8643750302113 & 2.4063289965789 & 1.13797584921890 \tabularnewline
31 & 4696.96 & 4694.5593813035 & 85.6155377524236 & 2.40061869650293 & 0.147928025980703 \tabularnewline
32 & 4621.4 & 4618.97167944399 & 49.7255060131889 & 2.42832055600669 & -0.923215444774458 \tabularnewline
33 & 4562.84 & 4560.39721215894 & 25.6138601920837 & 2.44278784106401 & -0.620264419110675 \tabularnewline
34 & 4202.52 & 4200.03713105689 & -60.3188318868336 & 2.48286894311003 & -2.21065512929778 \tabularnewline
35 & 4296.49 & 4294.01958690279 & -25.9651425903545 & 2.47041309721281 & 0.883778750285135 \tabularnewline
36 & 4435.23 & 4432.76992305769 & 10.7074858624405 & 2.46007694231146 & 0.94344545845217 \tabularnewline
37 & 4105.18 & 4167.37148713995 & -50.1819026991242 & -62.1914871399531 & -1.66893610767262 \tabularnewline
38 & 4116.68 & 4111.09118841473 & -51.5135800812206 & 5.58881158527523 & -0.0324681561925759 \tabularnewline
39 & 3844.49 & 3838.8018394913 & -100.696153305120 & 5.6881605086983 & -1.26387849756210 \tabularnewline
40 & 3720.98 & 3715.28385723677 & -105.778742767923 & 5.69614276323134 & -0.130668861223294 \tabularnewline
41 & 3674.4 & 3668.71995664016 & -92.5932308977474 & 5.68004335983704 & 0.339078839393447 \tabularnewline
42 & 3857.62 & 3851.99826090203 & -31.1692488309829 & 5.6217390979733 & 1.57983645428615 \tabularnewline
43 & 3801.06 & 3795.43408875761 & -36.8233187553788 & 5.625911242391 & -0.145438008795883 \tabularnewline
44 & 3504.37 & 3498.71089630002 & -94.688020471979 & 5.6591036999766 & -1.48852521020575 \tabularnewline
45 & 3032.6 & 3026.90345650104 & -178.650507951500 & 5.69654349895634 & -2.15994812306076 \tabularnewline
46 & 3047.03 & 3041.34835859982 & -135.659291616836 & 5.68164140017621 & 1.10597969054765 \tabularnewline
47 & 2962.34 & 2956.66141654926 & -124.310622559588 & 5.67858345073642 & 0.291956419034426 \tabularnewline
48 & 2197.82 & 2192.11155878457 & -266.856730598819 & 5.708441215433 & -3.66717716307664 \tabularnewline
49 & 2014.45 & 2064.11497959758 & -236.158079925354 & -49.6649795975794 & 0.828389947333573 \tabularnewline
50 & 1862.83 & 1858.06098260740 & -229.557600717804 & 4.76901739259809 & 0.162901085914849 \tabularnewline
51 & 1905.41 & 1900.73827178121 & -168.918027281417 & 4.67172821878572 & 1.55865684837942 \tabularnewline
52 & 1810.99 & 1806.33897240724 & -152.322988289243 & 4.65102759276265 & 0.426703256060083 \tabularnewline
53 & 1670.07 & 1665.4214352507 & -149.783354148778 & 4.6485647492991 & 0.065314780566455 \tabularnewline
54 & 1864.44 & 1859.84921339512 & -73.1433494724781 & 4.59078660488022 & 1.97129588133215 \tabularnewline
55 & 2052.02 & 2047.46323802728 & -15.0864476722291 & 4.55676197271762 & 1.49342691045499 \tabularnewline
56 & 2029.6 & 2025.04249409234 & -16.7193884936204 & 4.55750590765862 & -0.0420069356825406 \tabularnewline
57 & 2070.83 & 2066.27706370684 & -3.81628725433422 & 4.552936293159 & 0.331938029389666 \tabularnewline
58 & 2293.41 & 2288.87094123193 & 46.5927054907584 & 4.53905876806952 & 1.29681608710483 \tabularnewline
59 & 2443.27 & 2438.73586183875 & 69.5858078936419 & 4.53413816124997 & 0.591524112987543 \tabularnewline
60 & 2513.17 & 2508.63587347639 & 69.6557643271715 & 4.53412652360881 & 0.00179972133477979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63985&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]2849.27[/C][C]2849.27[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2921.44[/C][C]2917.76313399115[/C][C]5.13790583480385[/C][C]3.67686600884535[/C][C]0.264174667852236[/C][/ROW]
[ROW][C]3[/C][C]2981.85[/C][C]2978.12789803119[/C][C]11.2323616630119[/C][C]3.72210196881298[/C][C]0.338719245348942[/C][/ROW]
[ROW][C]4[/C][C]3080.58[/C][C]3076.79690322809[/C][C]24.2028219139144[/C][C]3.78309677191355[/C][C]0.524636601294583[/C][/ROW]
[ROW][C]5[/C][C]3106.22[/C][C]3102.43607661879[/C][C]24.454220929067[/C][C]3.7839233812064[/C][C]0.0084812578178234[/C][/ROW]
[ROW][C]6[/C][C]3119.31[/C][C]3115.53135333181[/C][C]22.2643177599525[/C][C]3.77864666818843[/C][C]-0.0663376075692007[/C][/ROW]
[ROW][C]7[/C][C]3061.26[/C][C]3057.51103325591[/C][C]5.87032567891759[/C][C]3.74896674408907[/C][C]-0.465499328144555[/C][/ROW]
[ROW][C]8[/C][C]3097.31[/C][C]3093.55223694707[/C][C]12.2470569381727[/C][C]3.75776305293064[/C][C]0.174130835969007[/C][/ROW]
[ROW][C]9[/C][C]3161.69[/C][C]3157.92031998754[/C][C]23.4935368361806[/C][C]3.76968001245783[/C][C]0.299950075187489[/C][/ROW]
[ROW][C]10[/C][C]3257.16[/C][C]3253.37746110682[/C][C]39.2164484815003[/C][C]3.78253889318345[/C][C]0.413402697369485[/C][/ROW]
[ROW][C]11[/C][C]3277.01[/C][C]3273.23015947018[/C][C]34.9538535510738[/C][C]3.77984052981891[/C][C]-0.111114812339812[/C][/ROW]
[ROW][C]12[/C][C]3295.32[/C][C]3291.54196573444[/C][C]31.2737557750883[/C][C]3.77803426556331[/C][C]-0.0954323118956617[/C][/ROW]
[ROW][C]13[/C][C]3363.99[/C][C]3381.42695333392[/C][C]43.7382114887806[/C][C]-17.4369533339194[/C][C]0.391778289159546[/C][/ROW]
[ROW][C]14[/C][C]3494.17[/C][C]3491.10200524756[/C][C]57.8518638462192[/C][C]3.06799475243978[/C][C]0.317896052319807[/C][/ROW]
[ROW][C]15[/C][C]3667.03[/C][C]3664.06178187676[/C][C]83.464393067397[/C][C]2.96821812323714[/C][C]0.658757311629081[/C][/ROW]
[ROW][C]16[/C][C]3813.06[/C][C]3810.13397441251[/C][C]97.3966639944196[/C][C]2.92602558749436[/C][C]0.358375195739271[/C][/ROW]
[ROW][C]17[/C][C]3917.96[/C][C]3915.03790773248[/C][C]99.0674386948443[/C][C]2.92209226752185[/C][C]0.0429793591223882[/C][/ROW]
[ROW][C]18[/C][C]3895.51[/C][C]3892.53839095819[/C][C]72.0098959654047[/C][C]2.97160904181063[/C][C]-0.696059101657574[/C][/ROW]
[ROW][C]19[/C][C]3801.06[/C][C]3798.03566377233[/C][C]34.9460010059936[/C][C]3.02433622767383[/C][C]-0.953494777019992[/C][/ROW]
[ROW][C]20[/C][C]3570.12[/C][C]3567.03019516236[/C][C]-24.2553870110425[/C][C]3.08980483764101[/C][C]-1.52301749022897[/C][/ROW]
[ROW][C]21[/C][C]3701.61[/C][C]3698.55000546485[/C][C]10.4221764763242[/C][C]3.059994535151[/C][C]0.892123635120283[/C][/ROW]
[ROW][C]22[/C][C]3862.27[/C][C]3859.23235891387[/C][C]43.8733095859944[/C][C]3.03764108613488[/C][C]0.860576334564032[/C][/ROW]
[ROW][C]23[/C][C]3970.1[/C][C]3967.06975608431[/C][C]58.1135205871739[/C][C]3.03024391569038[/C][C]0.366350051019184[/C][/ROW]
[ROW][C]24[/C][C]4138.52[/C][C]4135.49967342526[/C][C]82.6736582433226[/C][C]3.02032657473887[/C][C]0.631846223109648[/C][/ROW]
[ROW][C]25[/C][C]4199.75[/C][C]4230.56405684713[/C][C]85.3916521685081[/C][C]-30.8140568471254[/C][C]0.0768490048576036[/C][/ROW]
[ROW][C]26[/C][C]4290.89[/C][C]4288.48540385612[/C][C]79.4342060359086[/C][C]2.4045961438822[/C][C]-0.142027442385313[/C][/ROW]
[ROW][C]27[/C][C]4443.91[/C][C]4441.5499837752[/C][C]95.8398464478655[/C][C]2.36001622479537[/C][C]0.421427346514012[/C][/ROW]
[ROW][C]28[/C][C]4502.64[/C][C]4500.26251066665[/C][C]87.5706628099617[/C][C]2.37748933335368[/C][C]-0.212544699497747[/C][/ROW]
[ROW][C]29[/C][C]4356.98[/C][C]4354.51715706549[/C][C]35.6162178098846[/C][C]2.46284293451407[/C][C]-1.33587653254883[/C][/ROW]
[ROW][C]30[/C][C]4591.27[/C][C]4588.86367100342[/C][C]79.8643750302113[/C][C]2.4063289965789[/C][C]1.13797584921890[/C][/ROW]
[ROW][C]31[/C][C]4696.96[/C][C]4694.5593813035[/C][C]85.6155377524236[/C][C]2.40061869650293[/C][C]0.147928025980703[/C][/ROW]
[ROW][C]32[/C][C]4621.4[/C][C]4618.97167944399[/C][C]49.7255060131889[/C][C]2.42832055600669[/C][C]-0.923215444774458[/C][/ROW]
[ROW][C]33[/C][C]4562.84[/C][C]4560.39721215894[/C][C]25.6138601920837[/C][C]2.44278784106401[/C][C]-0.620264419110675[/C][/ROW]
[ROW][C]34[/C][C]4202.52[/C][C]4200.03713105689[/C][C]-60.3188318868336[/C][C]2.48286894311003[/C][C]-2.21065512929778[/C][/ROW]
[ROW][C]35[/C][C]4296.49[/C][C]4294.01958690279[/C][C]-25.9651425903545[/C][C]2.47041309721281[/C][C]0.883778750285135[/C][/ROW]
[ROW][C]36[/C][C]4435.23[/C][C]4432.76992305769[/C][C]10.7074858624405[/C][C]2.46007694231146[/C][C]0.94344545845217[/C][/ROW]
[ROW][C]37[/C][C]4105.18[/C][C]4167.37148713995[/C][C]-50.1819026991242[/C][C]-62.1914871399531[/C][C]-1.66893610767262[/C][/ROW]
[ROW][C]38[/C][C]4116.68[/C][C]4111.09118841473[/C][C]-51.5135800812206[/C][C]5.58881158527523[/C][C]-0.0324681561925759[/C][/ROW]
[ROW][C]39[/C][C]3844.49[/C][C]3838.8018394913[/C][C]-100.696153305120[/C][C]5.6881605086983[/C][C]-1.26387849756210[/C][/ROW]
[ROW][C]40[/C][C]3720.98[/C][C]3715.28385723677[/C][C]-105.778742767923[/C][C]5.69614276323134[/C][C]-0.130668861223294[/C][/ROW]
[ROW][C]41[/C][C]3674.4[/C][C]3668.71995664016[/C][C]-92.5932308977474[/C][C]5.68004335983704[/C][C]0.339078839393447[/C][/ROW]
[ROW][C]42[/C][C]3857.62[/C][C]3851.99826090203[/C][C]-31.1692488309829[/C][C]5.6217390979733[/C][C]1.57983645428615[/C][/ROW]
[ROW][C]43[/C][C]3801.06[/C][C]3795.43408875761[/C][C]-36.8233187553788[/C][C]5.625911242391[/C][C]-0.145438008795883[/C][/ROW]
[ROW][C]44[/C][C]3504.37[/C][C]3498.71089630002[/C][C]-94.688020471979[/C][C]5.6591036999766[/C][C]-1.48852521020575[/C][/ROW]
[ROW][C]45[/C][C]3032.6[/C][C]3026.90345650104[/C][C]-178.650507951500[/C][C]5.69654349895634[/C][C]-2.15994812306076[/C][/ROW]
[ROW][C]46[/C][C]3047.03[/C][C]3041.34835859982[/C][C]-135.659291616836[/C][C]5.68164140017621[/C][C]1.10597969054765[/C][/ROW]
[ROW][C]47[/C][C]2962.34[/C][C]2956.66141654926[/C][C]-124.310622559588[/C][C]5.67858345073642[/C][C]0.291956419034426[/C][/ROW]
[ROW][C]48[/C][C]2197.82[/C][C]2192.11155878457[/C][C]-266.856730598819[/C][C]5.708441215433[/C][C]-3.66717716307664[/C][/ROW]
[ROW][C]49[/C][C]2014.45[/C][C]2064.11497959758[/C][C]-236.158079925354[/C][C]-49.6649795975794[/C][C]0.828389947333573[/C][/ROW]
[ROW][C]50[/C][C]1862.83[/C][C]1858.06098260740[/C][C]-229.557600717804[/C][C]4.76901739259809[/C][C]0.162901085914849[/C][/ROW]
[ROW][C]51[/C][C]1905.41[/C][C]1900.73827178121[/C][C]-168.918027281417[/C][C]4.67172821878572[/C][C]1.55865684837942[/C][/ROW]
[ROW][C]52[/C][C]1810.99[/C][C]1806.33897240724[/C][C]-152.322988289243[/C][C]4.65102759276265[/C][C]0.426703256060083[/C][/ROW]
[ROW][C]53[/C][C]1670.07[/C][C]1665.4214352507[/C][C]-149.783354148778[/C][C]4.6485647492991[/C][C]0.065314780566455[/C][/ROW]
[ROW][C]54[/C][C]1864.44[/C][C]1859.84921339512[/C][C]-73.1433494724781[/C][C]4.59078660488022[/C][C]1.97129588133215[/C][/ROW]
[ROW][C]55[/C][C]2052.02[/C][C]2047.46323802728[/C][C]-15.0864476722291[/C][C]4.55676197271762[/C][C]1.49342691045499[/C][/ROW]
[ROW][C]56[/C][C]2029.6[/C][C]2025.04249409234[/C][C]-16.7193884936204[/C][C]4.55750590765862[/C][C]-0.0420069356825406[/C][/ROW]
[ROW][C]57[/C][C]2070.83[/C][C]2066.27706370684[/C][C]-3.81628725433422[/C][C]4.552936293159[/C][C]0.331938029389666[/C][/ROW]
[ROW][C]58[/C][C]2293.41[/C][C]2288.87094123193[/C][C]46.5927054907584[/C][C]4.53905876806952[/C][C]1.29681608710483[/C][/ROW]
[ROW][C]59[/C][C]2443.27[/C][C]2438.73586183875[/C][C]69.5858078936419[/C][C]4.53413816124997[/C][C]0.591524112987543[/C][/ROW]
[ROW][C]60[/C][C]2513.17[/C][C]2508.63587347639[/C][C]69.6557643271715[/C][C]4.53412652360881[/C][C]0.00179972133477979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63985&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
12849.272849.27000
22921.442917.763133991155.137905834803853.676866008845350.264174667852236
32981.852978.1278980311911.23236166301193.722101968812980.338719245348942
43080.583076.7969032280924.20282191391443.783096771913550.524636601294583
53106.223102.4360766187924.4542209290673.78392338120640.0084812578178234
63119.313115.5313533318122.26431775995253.77864666818843-0.0663376075692007
73061.263057.511033255915.870325678917593.74896674408907-0.465499328144555
83097.313093.5522369470712.24705693817273.757763052930640.174130835969007
93161.693157.9203199875423.49353683618063.769680012457830.299950075187489
103257.163253.3774611068239.21644848150033.782538893183450.413402697369485
113277.013273.2301594701834.95385355107383.77984052981891-0.111114812339812
123295.323291.5419657344431.27375577508833.77803426556331-0.0954323118956617
133363.993381.4269533339243.7382114887806-17.43695333391940.391778289159546
143494.173491.1020052475657.85186384621923.067994752439780.317896052319807
153667.033664.0617818767683.4643930673972.968218123237140.658757311629081
163813.063810.1339744125197.39666399441962.926025587494360.358375195739271
173917.963915.0379077324899.06743869484432.922092267521850.0429793591223882
183895.513892.5383909581972.00989596540472.97160904181063-0.696059101657574
193801.063798.0356637723334.94600100599363.02433622767383-0.953494777019992
203570.123567.03019516236-24.25538701104253.08980483764101-1.52301749022897
213701.613698.5500054648510.42217647632423.0599945351510.892123635120283
223862.273859.2323589138743.87330958599443.037641086134880.860576334564032
233970.13967.0697560843158.11352058717393.030243915690380.366350051019184
244138.524135.4996734252682.67365824332263.020326574738870.631846223109648
254199.754230.5640568471385.3916521685081-30.81405684712540.0768490048576036
264290.894288.4854038561279.43420603590862.4045961438822-0.142027442385313
274443.914441.549983775295.83984644786552.360016224795370.421427346514012
284502.644500.2625106666587.57066280996172.37748933335368-0.212544699497747
294356.984354.5171570654935.61621780988462.46284293451407-1.33587653254883
304591.274588.8636710034279.86437503021132.40632899657891.13797584921890
314696.964694.559381303585.61553775242362.400618696502930.147928025980703
324621.44618.9716794439949.72550601318892.42832055600669-0.923215444774458
334562.844560.3972121589425.61386019208372.44278784106401-0.620264419110675
344202.524200.03713105689-60.31883188683362.48286894311003-2.21065512929778
354296.494294.01958690279-25.96514259035452.470413097212810.883778750285135
364435.234432.7699230576910.70748586244052.460076942311460.94344545845217
374105.184167.37148713995-50.1819026991242-62.1914871399531-1.66893610767262
384116.684111.09118841473-51.51358008122065.58881158527523-0.0324681561925759
393844.493838.8018394913-100.6961533051205.6881605086983-1.26387849756210
403720.983715.28385723677-105.7787427679235.69614276323134-0.130668861223294
413674.43668.71995664016-92.59323089774745.680043359837040.339078839393447
423857.623851.99826090203-31.16924883098295.62173909797331.57983645428615
433801.063795.43408875761-36.82331875537885.625911242391-0.145438008795883
443504.373498.71089630002-94.6880204719795.6591036999766-1.48852521020575
453032.63026.90345650104-178.6505079515005.69654349895634-2.15994812306076
463047.033041.34835859982-135.6592916168365.681641400176211.10597969054765
472962.342956.66141654926-124.3106225595885.678583450736420.291956419034426
482197.822192.11155878457-266.8567305988195.708441215433-3.66717716307664
492014.452064.11497959758-236.158079925354-49.66497959757940.828389947333573
501862.831858.06098260740-229.5576007178044.769017392598090.162901085914849
511905.411900.73827178121-168.9180272814174.671728218785721.55865684837942
521810.991806.33897240724-152.3229882892434.651027592762650.426703256060083
531670.071665.4214352507-149.7833541487784.64856474929910.065314780566455
541864.441859.84921339512-73.14334947247814.590786604880221.97129588133215
552052.022047.46323802728-15.08644767222914.556761972717621.49342691045499
562029.62025.04249409234-16.71938849362044.55750590765862-0.0420069356825406
572070.832066.27706370684-3.816287254334224.5529362931590.331938029389666
582293.412288.8709412319346.59270549075844.539058768069521.29681608710483
592443.272438.7358618387569.58580789364194.534138161249970.591524112987543
602513.172508.6358734763969.65576432717154.534126523608810.00179972133477979



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')