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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 11:46:05 -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/t125995254190yrdkcmzbo2uqm.htm/, Retrieved Sun, 28 Apr 2024 11:00:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64025, Retrieved Sun, 28 Apr 2024 11:00:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82
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 18:46:05] [6e025b5370bdd3143fbe248190b38274] [Current]
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Dataseries X:
15836,8
17570,4
18252,1
16196,7
16643
17729
16446,1
15993,8
16373,5
17842,2
22321,5
22786,7
18274,1
22392,9
23899,3
21343,5
22952,3
21374,4
21164,1
20906,5
17877,4
20664,3
22160
19813,6
17735,4
19640,2
20844,4
19823,1
18594,6
21350,6
18574,1
18924,2
17343,4
19961,2
19932,1
19464,6
16165,4
17574,9
19795,4
19439,5
17170
21072,4
17751,8
17515,5
18040,3
19090,1
17746,5
19202,1
15141,6
16258,1
18586,5
17209,4
17838,7
19123,5
16583,6
15991,2
16704,4
17420,4
17872
17823,2




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=64025&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=64025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64025&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
115836.815836.8000
217570.417006.423656665171.5282508611204254.2107502999201.12373986875586
318252.117782.3806555236107.589919641277231.4998540624710.827106576511352
416196.716953.485672311777.115768397848-379.666991451067-1.23079516688193
51664316698.488532807969.16375278492983.6425626710096-0.446609889754032
61772917223.341576672479.0711017916035313.4538281348490.614699393569607
716446.116831.88479786368.7154607102635-187.393961263084-0.634208203573949
815993.816324.075839167355.4511765648218-87.6780057120329-0.775835463654933
916373.516289.861618407553.2857415513582121.282480665723-0.120456727153796
1017842.217120.919421656572.9426490261555395.4907631052381.04310613378845
1122321.520032.3889106053147.7110734382691102.712441073973.80073471910435
1222786.721799.7768054433192.004812185022311.3670719871802.1653642717609
1318274.121130.9443113906219.577046724410-2486.57854476157-1.35672552582924
1422392.921812.1775474369235.448451561349418.7120491958210.565170488932372
1523899.322790.2058505370266.767287434215855.4543466528780.908440018299779
1621343.522334.1083494632239.788298960575-724.703891453059-0.934113507603892
1722952.322655.5597440943242.468708409767265.7481718763440.107623699975615
1821374.421824.7312717257209.28874106058-40.0686183552695-1.41948514133735
1921164.121414.0041054061190.259145592848-12.8182557573681-0.819689602744022
2020906.521121.4785911912175.198196105344-30.5949015115718-0.637576922447044
2117877.419468.2579147779116.834417528627-893.595095603723-2.41203728310275
2220664.320085.1797647043133.137682318068388.6746931818380.659061989705987
232216020861.0641729381154.1671573495641054.445498281730.845974891031146
2419813.620169.6323261097128.590813485337-34.620108128745-1.11140039932139
2517735.420267.2100354703128.035614123778-2519.78783289814-0.0429632306145878
2619640.219879.6228078065109.927827922258-52.1198325460708-0.661367949249112
2720844.419836.1159858550103.6158039707971061.73533694789-0.192114901066006
2819823.120243.1523852152115.893053278604-528.7288382659970.38923055279357
2918594.619184.345842741470.74089627384-159.743698351054-1.52970107006852
3021350.620131.0662550679103.233011908406896.6523990297121.14498227069708
3118574.119345.492900383570.6934405645057-443.458477805689-1.16177958984990
3218924.218821.005310471548.8746783380265322.695535058381-0.777525859554788
3317343.418594.454559177138.7104699713628-1149.54657729049-0.359604182559487
3419961.219169.887064507358.5884287380442593.65480859490.700237532497955
3519932.118960.741626924848.78468740651431069.83783127356-0.348800529914243
3619464.619213.77280905355.9339490043494175.7519533320480.266294714446732
3716165.418890.646826911443.6753017678191-2584.25392812131-0.504715175846393
3817574.918213.857894567016.5056463046039-377.256155106772-0.932196104908674
3919795.418438.521226039725.00039473137081283.348816775750.264226301443945
4019439.519073.769615509250.0760316368887148.2466328314910.782323454465578
411717018380.345334995420.0999405178627-941.68529448744-0.963150779895513
4221072.419060.634118537746.22396098148071771.806047844600.858225916776422
4317751.818585.754832247525.816791646834-644.339283097093-0.677530939829128
4417515.517822.1699185985-4.99424605538028-19.4957875547972-1.02584771277166
4518040.318524.907372962922.6148986732703-741.9420747356560.919187227464885
4619090.118536.497171474122.1859325713249557.608261235116-0.0143062581572489
4717746.517622.1037554812-13.8978599687285464.211195289801-1.21396646459755
4819202.118154.36909294926.74630349630789849.5089468782930.709405830562182
4915141.617873.8012913649-3.97293182122392-2627.16786146480-0.376872586045862
5016258.117283.3262373944-26.9626249849905-812.930187773772-0.759780808887915
5118586.517311.5999100973-24.71810398711271255.241154139790.0706504319302863
5217209.417067.7191993396-33.6969428968974219.821704348346-0.281338870284398
5317838.717985.1302916595.02408292795899-488.4417905293081.2295650077008
5419123.517626.2553183700-9.657136862084331628.72523469236-0.471911781970573
5516583.617291.5288029145-22.6847832980374-590.351540747061-0.421658514101497
5615991.216724.6279143943-44.4134680598639-536.651228692926-0.705466953957897
5716704.416984.3910546103-32.2987801507951-389.8940022257690.393973919286167
5817420.416785.7497764931-38.9011055608934694.68806597599-0.215228780753669
591787217145.6470795065-23.1676424270715582.5637289523360.515722636454629
6017823.217004.0195611814-27.8065496158891861.940251084041-0.153574250829237

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 15836.8 & 15836.8 & 0 & 0 & 0 \tabularnewline
2 & 17570.4 & 17006.4236566651 & 71.5282508611204 & 254.210750299920 & 1.12373986875586 \tabularnewline
3 & 18252.1 & 17782.3806555236 & 107.589919641277 & 231.499854062471 & 0.827106576511352 \tabularnewline
4 & 16196.7 & 16953.4856723117 & 77.115768397848 & -379.666991451067 & -1.23079516688193 \tabularnewline
5 & 16643 & 16698.4885328079 & 69.163752784929 & 83.6425626710096 & -0.446609889754032 \tabularnewline
6 & 17729 & 17223.3415766724 & 79.0711017916035 & 313.453828134849 & 0.614699393569607 \tabularnewline
7 & 16446.1 & 16831.884797863 & 68.7154607102635 & -187.393961263084 & -0.634208203573949 \tabularnewline
8 & 15993.8 & 16324.0758391673 & 55.4511765648218 & -87.6780057120329 & -0.775835463654933 \tabularnewline
9 & 16373.5 & 16289.8616184075 & 53.2857415513582 & 121.282480665723 & -0.120456727153796 \tabularnewline
10 & 17842.2 & 17120.9194216565 & 72.9426490261555 & 395.490763105238 & 1.04310613378845 \tabularnewline
11 & 22321.5 & 20032.3889106053 & 147.711073438269 & 1102.71244107397 & 3.80073471910435 \tabularnewline
12 & 22786.7 & 21799.7768054433 & 192.004812185022 & 311.367071987180 & 2.1653642717609 \tabularnewline
13 & 18274.1 & 21130.9443113906 & 219.577046724410 & -2486.57854476157 & -1.35672552582924 \tabularnewline
14 & 22392.9 & 21812.1775474369 & 235.448451561349 & 418.712049195821 & 0.565170488932372 \tabularnewline
15 & 23899.3 & 22790.2058505370 & 266.767287434215 & 855.454346652878 & 0.908440018299779 \tabularnewline
16 & 21343.5 & 22334.1083494632 & 239.788298960575 & -724.703891453059 & -0.934113507603892 \tabularnewline
17 & 22952.3 & 22655.5597440943 & 242.468708409767 & 265.748171876344 & 0.107623699975615 \tabularnewline
18 & 21374.4 & 21824.7312717257 & 209.28874106058 & -40.0686183552695 & -1.41948514133735 \tabularnewline
19 & 21164.1 & 21414.0041054061 & 190.259145592848 & -12.8182557573681 & -0.819689602744022 \tabularnewline
20 & 20906.5 & 21121.4785911912 & 175.198196105344 & -30.5949015115718 & -0.637576922447044 \tabularnewline
21 & 17877.4 & 19468.2579147779 & 116.834417528627 & -893.595095603723 & -2.41203728310275 \tabularnewline
22 & 20664.3 & 20085.1797647043 & 133.137682318068 & 388.674693181838 & 0.659061989705987 \tabularnewline
23 & 22160 & 20861.0641729381 & 154.167157349564 & 1054.44549828173 & 0.845974891031146 \tabularnewline
24 & 19813.6 & 20169.6323261097 & 128.590813485337 & -34.620108128745 & -1.11140039932139 \tabularnewline
25 & 17735.4 & 20267.2100354703 & 128.035614123778 & -2519.78783289814 & -0.0429632306145878 \tabularnewline
26 & 19640.2 & 19879.6228078065 & 109.927827922258 & -52.1198325460708 & -0.661367949249112 \tabularnewline
27 & 20844.4 & 19836.1159858550 & 103.615803970797 & 1061.73533694789 & -0.192114901066006 \tabularnewline
28 & 19823.1 & 20243.1523852152 & 115.893053278604 & -528.728838265997 & 0.38923055279357 \tabularnewline
29 & 18594.6 & 19184.3458427414 & 70.74089627384 & -159.743698351054 & -1.52970107006852 \tabularnewline
30 & 21350.6 & 20131.0662550679 & 103.233011908406 & 896.652399029712 & 1.14498227069708 \tabularnewline
31 & 18574.1 & 19345.4929003835 & 70.6934405645057 & -443.458477805689 & -1.16177958984990 \tabularnewline
32 & 18924.2 & 18821.0053104715 & 48.8746783380265 & 322.695535058381 & -0.777525859554788 \tabularnewline
33 & 17343.4 & 18594.4545591771 & 38.7104699713628 & -1149.54657729049 & -0.359604182559487 \tabularnewline
34 & 19961.2 & 19169.8870645073 & 58.5884287380442 & 593.6548085949 & 0.700237532497955 \tabularnewline
35 & 19932.1 & 18960.7416269248 & 48.7846874065143 & 1069.83783127356 & -0.348800529914243 \tabularnewline
36 & 19464.6 & 19213.772809053 & 55.9339490043494 & 175.751953332048 & 0.266294714446732 \tabularnewline
37 & 16165.4 & 18890.6468269114 & 43.6753017678191 & -2584.25392812131 & -0.504715175846393 \tabularnewline
38 & 17574.9 & 18213.8578945670 & 16.5056463046039 & -377.256155106772 & -0.932196104908674 \tabularnewline
39 & 19795.4 & 18438.5212260397 & 25.0003947313708 & 1283.34881677575 & 0.264226301443945 \tabularnewline
40 & 19439.5 & 19073.7696155092 & 50.0760316368887 & 148.246632831491 & 0.782323454465578 \tabularnewline
41 & 17170 & 18380.3453349954 & 20.0999405178627 & -941.68529448744 & -0.963150779895513 \tabularnewline
42 & 21072.4 & 19060.6341185377 & 46.2239609814807 & 1771.80604784460 & 0.858225916776422 \tabularnewline
43 & 17751.8 & 18585.7548322475 & 25.816791646834 & -644.339283097093 & -0.677530939829128 \tabularnewline
44 & 17515.5 & 17822.1699185985 & -4.99424605538028 & -19.4957875547972 & -1.02584771277166 \tabularnewline
45 & 18040.3 & 18524.9073729629 & 22.6148986732703 & -741.942074735656 & 0.919187227464885 \tabularnewline
46 & 19090.1 & 18536.4971714741 & 22.1859325713249 & 557.608261235116 & -0.0143062581572489 \tabularnewline
47 & 17746.5 & 17622.1037554812 & -13.8978599687285 & 464.211195289801 & -1.21396646459755 \tabularnewline
48 & 19202.1 & 18154.3690929492 & 6.74630349630789 & 849.508946878293 & 0.709405830562182 \tabularnewline
49 & 15141.6 & 17873.8012913649 & -3.97293182122392 & -2627.16786146480 & -0.376872586045862 \tabularnewline
50 & 16258.1 & 17283.3262373944 & -26.9626249849905 & -812.930187773772 & -0.759780808887915 \tabularnewline
51 & 18586.5 & 17311.5999100973 & -24.7181039871127 & 1255.24115413979 & 0.0706504319302863 \tabularnewline
52 & 17209.4 & 17067.7191993396 & -33.6969428968974 & 219.821704348346 & -0.281338870284398 \tabularnewline
53 & 17838.7 & 17985.130291659 & 5.02408292795899 & -488.441790529308 & 1.2295650077008 \tabularnewline
54 & 19123.5 & 17626.2553183700 & -9.65713686208433 & 1628.72523469236 & -0.471911781970573 \tabularnewline
55 & 16583.6 & 17291.5288029145 & -22.6847832980374 & -590.351540747061 & -0.421658514101497 \tabularnewline
56 & 15991.2 & 16724.6279143943 & -44.4134680598639 & -536.651228692926 & -0.705466953957897 \tabularnewline
57 & 16704.4 & 16984.3910546103 & -32.2987801507951 & -389.894002225769 & 0.393973919286167 \tabularnewline
58 & 17420.4 & 16785.7497764931 & -38.9011055608934 & 694.68806597599 & -0.215228780753669 \tabularnewline
59 & 17872 & 17145.6470795065 & -23.1676424270715 & 582.563728952336 & 0.515722636454629 \tabularnewline
60 & 17823.2 & 17004.0195611814 & -27.8065496158891 & 861.940251084041 & -0.153574250829237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64025&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]15836.8[/C][C]15836.8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]17570.4[/C][C]17006.4236566651[/C][C]71.5282508611204[/C][C]254.210750299920[/C][C]1.12373986875586[/C][/ROW]
[ROW][C]3[/C][C]18252.1[/C][C]17782.3806555236[/C][C]107.589919641277[/C][C]231.499854062471[/C][C]0.827106576511352[/C][/ROW]
[ROW][C]4[/C][C]16196.7[/C][C]16953.4856723117[/C][C]77.115768397848[/C][C]-379.666991451067[/C][C]-1.23079516688193[/C][/ROW]
[ROW][C]5[/C][C]16643[/C][C]16698.4885328079[/C][C]69.163752784929[/C][C]83.6425626710096[/C][C]-0.446609889754032[/C][/ROW]
[ROW][C]6[/C][C]17729[/C][C]17223.3415766724[/C][C]79.0711017916035[/C][C]313.453828134849[/C][C]0.614699393569607[/C][/ROW]
[ROW][C]7[/C][C]16446.1[/C][C]16831.884797863[/C][C]68.7154607102635[/C][C]-187.393961263084[/C][C]-0.634208203573949[/C][/ROW]
[ROW][C]8[/C][C]15993.8[/C][C]16324.0758391673[/C][C]55.4511765648218[/C][C]-87.6780057120329[/C][C]-0.775835463654933[/C][/ROW]
[ROW][C]9[/C][C]16373.5[/C][C]16289.8616184075[/C][C]53.2857415513582[/C][C]121.282480665723[/C][C]-0.120456727153796[/C][/ROW]
[ROW][C]10[/C][C]17842.2[/C][C]17120.9194216565[/C][C]72.9426490261555[/C][C]395.490763105238[/C][C]1.04310613378845[/C][/ROW]
[ROW][C]11[/C][C]22321.5[/C][C]20032.3889106053[/C][C]147.711073438269[/C][C]1102.71244107397[/C][C]3.80073471910435[/C][/ROW]
[ROW][C]12[/C][C]22786.7[/C][C]21799.7768054433[/C][C]192.004812185022[/C][C]311.367071987180[/C][C]2.1653642717609[/C][/ROW]
[ROW][C]13[/C][C]18274.1[/C][C]21130.9443113906[/C][C]219.577046724410[/C][C]-2486.57854476157[/C][C]-1.35672552582924[/C][/ROW]
[ROW][C]14[/C][C]22392.9[/C][C]21812.1775474369[/C][C]235.448451561349[/C][C]418.712049195821[/C][C]0.565170488932372[/C][/ROW]
[ROW][C]15[/C][C]23899.3[/C][C]22790.2058505370[/C][C]266.767287434215[/C][C]855.454346652878[/C][C]0.908440018299779[/C][/ROW]
[ROW][C]16[/C][C]21343.5[/C][C]22334.1083494632[/C][C]239.788298960575[/C][C]-724.703891453059[/C][C]-0.934113507603892[/C][/ROW]
[ROW][C]17[/C][C]22952.3[/C][C]22655.5597440943[/C][C]242.468708409767[/C][C]265.748171876344[/C][C]0.107623699975615[/C][/ROW]
[ROW][C]18[/C][C]21374.4[/C][C]21824.7312717257[/C][C]209.28874106058[/C][C]-40.0686183552695[/C][C]-1.41948514133735[/C][/ROW]
[ROW][C]19[/C][C]21164.1[/C][C]21414.0041054061[/C][C]190.259145592848[/C][C]-12.8182557573681[/C][C]-0.819689602744022[/C][/ROW]
[ROW][C]20[/C][C]20906.5[/C][C]21121.4785911912[/C][C]175.198196105344[/C][C]-30.5949015115718[/C][C]-0.637576922447044[/C][/ROW]
[ROW][C]21[/C][C]17877.4[/C][C]19468.2579147779[/C][C]116.834417528627[/C][C]-893.595095603723[/C][C]-2.41203728310275[/C][/ROW]
[ROW][C]22[/C][C]20664.3[/C][C]20085.1797647043[/C][C]133.137682318068[/C][C]388.674693181838[/C][C]0.659061989705987[/C][/ROW]
[ROW][C]23[/C][C]22160[/C][C]20861.0641729381[/C][C]154.167157349564[/C][C]1054.44549828173[/C][C]0.845974891031146[/C][/ROW]
[ROW][C]24[/C][C]19813.6[/C][C]20169.6323261097[/C][C]128.590813485337[/C][C]-34.620108128745[/C][C]-1.11140039932139[/C][/ROW]
[ROW][C]25[/C][C]17735.4[/C][C]20267.2100354703[/C][C]128.035614123778[/C][C]-2519.78783289814[/C][C]-0.0429632306145878[/C][/ROW]
[ROW][C]26[/C][C]19640.2[/C][C]19879.6228078065[/C][C]109.927827922258[/C][C]-52.1198325460708[/C][C]-0.661367949249112[/C][/ROW]
[ROW][C]27[/C][C]20844.4[/C][C]19836.1159858550[/C][C]103.615803970797[/C][C]1061.73533694789[/C][C]-0.192114901066006[/C][/ROW]
[ROW][C]28[/C][C]19823.1[/C][C]20243.1523852152[/C][C]115.893053278604[/C][C]-528.728838265997[/C][C]0.38923055279357[/C][/ROW]
[ROW][C]29[/C][C]18594.6[/C][C]19184.3458427414[/C][C]70.74089627384[/C][C]-159.743698351054[/C][C]-1.52970107006852[/C][/ROW]
[ROW][C]30[/C][C]21350.6[/C][C]20131.0662550679[/C][C]103.233011908406[/C][C]896.652399029712[/C][C]1.14498227069708[/C][/ROW]
[ROW][C]31[/C][C]18574.1[/C][C]19345.4929003835[/C][C]70.6934405645057[/C][C]-443.458477805689[/C][C]-1.16177958984990[/C][/ROW]
[ROW][C]32[/C][C]18924.2[/C][C]18821.0053104715[/C][C]48.8746783380265[/C][C]322.695535058381[/C][C]-0.777525859554788[/C][/ROW]
[ROW][C]33[/C][C]17343.4[/C][C]18594.4545591771[/C][C]38.7104699713628[/C][C]-1149.54657729049[/C][C]-0.359604182559487[/C][/ROW]
[ROW][C]34[/C][C]19961.2[/C][C]19169.8870645073[/C][C]58.5884287380442[/C][C]593.6548085949[/C][C]0.700237532497955[/C][/ROW]
[ROW][C]35[/C][C]19932.1[/C][C]18960.7416269248[/C][C]48.7846874065143[/C][C]1069.83783127356[/C][C]-0.348800529914243[/C][/ROW]
[ROW][C]36[/C][C]19464.6[/C][C]19213.772809053[/C][C]55.9339490043494[/C][C]175.751953332048[/C][C]0.266294714446732[/C][/ROW]
[ROW][C]37[/C][C]16165.4[/C][C]18890.6468269114[/C][C]43.6753017678191[/C][C]-2584.25392812131[/C][C]-0.504715175846393[/C][/ROW]
[ROW][C]38[/C][C]17574.9[/C][C]18213.8578945670[/C][C]16.5056463046039[/C][C]-377.256155106772[/C][C]-0.932196104908674[/C][/ROW]
[ROW][C]39[/C][C]19795.4[/C][C]18438.5212260397[/C][C]25.0003947313708[/C][C]1283.34881677575[/C][C]0.264226301443945[/C][/ROW]
[ROW][C]40[/C][C]19439.5[/C][C]19073.7696155092[/C][C]50.0760316368887[/C][C]148.246632831491[/C][C]0.782323454465578[/C][/ROW]
[ROW][C]41[/C][C]17170[/C][C]18380.3453349954[/C][C]20.0999405178627[/C][C]-941.68529448744[/C][C]-0.963150779895513[/C][/ROW]
[ROW][C]42[/C][C]21072.4[/C][C]19060.6341185377[/C][C]46.2239609814807[/C][C]1771.80604784460[/C][C]0.858225916776422[/C][/ROW]
[ROW][C]43[/C][C]17751.8[/C][C]18585.7548322475[/C][C]25.816791646834[/C][C]-644.339283097093[/C][C]-0.677530939829128[/C][/ROW]
[ROW][C]44[/C][C]17515.5[/C][C]17822.1699185985[/C][C]-4.99424605538028[/C][C]-19.4957875547972[/C][C]-1.02584771277166[/C][/ROW]
[ROW][C]45[/C][C]18040.3[/C][C]18524.9073729629[/C][C]22.6148986732703[/C][C]-741.942074735656[/C][C]0.919187227464885[/C][/ROW]
[ROW][C]46[/C][C]19090.1[/C][C]18536.4971714741[/C][C]22.1859325713249[/C][C]557.608261235116[/C][C]-0.0143062581572489[/C][/ROW]
[ROW][C]47[/C][C]17746.5[/C][C]17622.1037554812[/C][C]-13.8978599687285[/C][C]464.211195289801[/C][C]-1.21396646459755[/C][/ROW]
[ROW][C]48[/C][C]19202.1[/C][C]18154.3690929492[/C][C]6.74630349630789[/C][C]849.508946878293[/C][C]0.709405830562182[/C][/ROW]
[ROW][C]49[/C][C]15141.6[/C][C]17873.8012913649[/C][C]-3.97293182122392[/C][C]-2627.16786146480[/C][C]-0.376872586045862[/C][/ROW]
[ROW][C]50[/C][C]16258.1[/C][C]17283.3262373944[/C][C]-26.9626249849905[/C][C]-812.930187773772[/C][C]-0.759780808887915[/C][/ROW]
[ROW][C]51[/C][C]18586.5[/C][C]17311.5999100973[/C][C]-24.7181039871127[/C][C]1255.24115413979[/C][C]0.0706504319302863[/C][/ROW]
[ROW][C]52[/C][C]17209.4[/C][C]17067.7191993396[/C][C]-33.6969428968974[/C][C]219.821704348346[/C][C]-0.281338870284398[/C][/ROW]
[ROW][C]53[/C][C]17838.7[/C][C]17985.130291659[/C][C]5.02408292795899[/C][C]-488.441790529308[/C][C]1.2295650077008[/C][/ROW]
[ROW][C]54[/C][C]19123.5[/C][C]17626.2553183700[/C][C]-9.65713686208433[/C][C]1628.72523469236[/C][C]-0.471911781970573[/C][/ROW]
[ROW][C]55[/C][C]16583.6[/C][C]17291.5288029145[/C][C]-22.6847832980374[/C][C]-590.351540747061[/C][C]-0.421658514101497[/C][/ROW]
[ROW][C]56[/C][C]15991.2[/C][C]16724.6279143943[/C][C]-44.4134680598639[/C][C]-536.651228692926[/C][C]-0.705466953957897[/C][/ROW]
[ROW][C]57[/C][C]16704.4[/C][C]16984.3910546103[/C][C]-32.2987801507951[/C][C]-389.894002225769[/C][C]0.393973919286167[/C][/ROW]
[ROW][C]58[/C][C]17420.4[/C][C]16785.7497764931[/C][C]-38.9011055608934[/C][C]694.68806597599[/C][C]-0.215228780753669[/C][/ROW]
[ROW][C]59[/C][C]17872[/C][C]17145.6470795065[/C][C]-23.1676424270715[/C][C]582.563728952336[/C][C]0.515722636454629[/C][/ROW]
[ROW][C]60[/C][C]17823.2[/C][C]17004.0195611814[/C][C]-27.8065496158891[/C][C]861.940251084041[/C][C]-0.153574250829237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64025&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
115836.815836.8000
217570.417006.423656665171.5282508611204254.2107502999201.12373986875586
318252.117782.3806555236107.589919641277231.4998540624710.827106576511352
416196.716953.485672311777.115768397848-379.666991451067-1.23079516688193
51664316698.488532807969.16375278492983.6425626710096-0.446609889754032
61772917223.341576672479.0711017916035313.4538281348490.614699393569607
716446.116831.88479786368.7154607102635-187.393961263084-0.634208203573949
815993.816324.075839167355.4511765648218-87.6780057120329-0.775835463654933
916373.516289.861618407553.2857415513582121.282480665723-0.120456727153796
1017842.217120.919421656572.9426490261555395.4907631052381.04310613378845
1122321.520032.3889106053147.7110734382691102.712441073973.80073471910435
1222786.721799.7768054433192.004812185022311.3670719871802.1653642717609
1318274.121130.9443113906219.577046724410-2486.57854476157-1.35672552582924
1422392.921812.1775474369235.448451561349418.7120491958210.565170488932372
1523899.322790.2058505370266.767287434215855.4543466528780.908440018299779
1621343.522334.1083494632239.788298960575-724.703891453059-0.934113507603892
1722952.322655.5597440943242.468708409767265.7481718763440.107623699975615
1821374.421824.7312717257209.28874106058-40.0686183552695-1.41948514133735
1921164.121414.0041054061190.259145592848-12.8182557573681-0.819689602744022
2020906.521121.4785911912175.198196105344-30.5949015115718-0.637576922447044
2117877.419468.2579147779116.834417528627-893.595095603723-2.41203728310275
2220664.320085.1797647043133.137682318068388.6746931818380.659061989705987
232216020861.0641729381154.1671573495641054.445498281730.845974891031146
2419813.620169.6323261097128.590813485337-34.620108128745-1.11140039932139
2517735.420267.2100354703128.035614123778-2519.78783289814-0.0429632306145878
2619640.219879.6228078065109.927827922258-52.1198325460708-0.661367949249112
2720844.419836.1159858550103.6158039707971061.73533694789-0.192114901066006
2819823.120243.1523852152115.893053278604-528.7288382659970.38923055279357
2918594.619184.345842741470.74089627384-159.743698351054-1.52970107006852
3021350.620131.0662550679103.233011908406896.6523990297121.14498227069708
3118574.119345.492900383570.6934405645057-443.458477805689-1.16177958984990
3218924.218821.005310471548.8746783380265322.695535058381-0.777525859554788
3317343.418594.454559177138.7104699713628-1149.54657729049-0.359604182559487
3419961.219169.887064507358.5884287380442593.65480859490.700237532497955
3519932.118960.741626924848.78468740651431069.83783127356-0.348800529914243
3619464.619213.77280905355.9339490043494175.7519533320480.266294714446732
3716165.418890.646826911443.6753017678191-2584.25392812131-0.504715175846393
3817574.918213.857894567016.5056463046039-377.256155106772-0.932196104908674
3919795.418438.521226039725.00039473137081283.348816775750.264226301443945
4019439.519073.769615509250.0760316368887148.2466328314910.782323454465578
411717018380.345334995420.0999405178627-941.68529448744-0.963150779895513
4221072.419060.634118537746.22396098148071771.806047844600.858225916776422
4317751.818585.754832247525.816791646834-644.339283097093-0.677530939829128
4417515.517822.1699185985-4.99424605538028-19.4957875547972-1.02584771277166
4518040.318524.907372962922.6148986732703-741.9420747356560.919187227464885
4619090.118536.497171474122.1859325713249557.608261235116-0.0143062581572489
4717746.517622.1037554812-13.8978599687285464.211195289801-1.21396646459755
4819202.118154.36909294926.74630349630789849.5089468782930.709405830562182
4915141.617873.8012913649-3.97293182122392-2627.16786146480-0.376872586045862
5016258.117283.3262373944-26.9626249849905-812.930187773772-0.759780808887915
5118586.517311.5999100973-24.71810398711271255.241154139790.0706504319302863
5217209.417067.7191993396-33.6969428968974219.821704348346-0.281338870284398
5317838.717985.1302916595.02408292795899-488.4417905293081.2295650077008
5419123.517626.2553183700-9.657136862084331628.72523469236-0.471911781970573
5516583.617291.5288029145-22.6847832980374-590.351540747061-0.421658514101497
5615991.216724.6279143943-44.4134680598639-536.651228692926-0.705466953957897
5716704.416984.3910546103-32.2987801507951-389.8940022257690.393973919286167
5817420.416785.7497764931-38.9011055608934694.68806597599-0.215228780753669
591787217145.6470795065-23.1676424270715582.5637289523360.515722636454629
6017823.217004.0195611814-27.8065496158891861.940251084041-0.153574250829237



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')