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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, 25 Nov 2011 16:15:08 -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/2011/Nov/25/t1322255822qwllu1kmf23off5.htm/, Retrieved Sun, 16 Jun 2024 22:37:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147361, Retrieved Sun, 16 Jun 2024 22:37:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- R P         [Univariate Data Series] [WS8 Time Series A...] [2010-11-30 09:10:08] [afe9379cca749d06b3d6872e02cc47ed]
- R PD          [Univariate Data Series] [] [2011-11-25 19:20:16] [f1de53e71fac758e9834be8effee591f]
- RMPD              [Structural Time Series Models] [] [2011-11-25 21:15:08] [13d85cac30d4a10947636c080219d4f4] [Current]
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Dataseries X:
9.829
9.125
9.782
9.441
9.162
9.915
10.444
10.209
9.985
9.842
9.429
10.132
9.849
9.172
10.313
9.819
9.955
10.048
10.082
10.541
10.208
10.233
9.439
9.963
10.158
9.225
10.474
9.757
10.490
10.281
10.444
10.640
10.695
10.786
9.832
9.747
10.411
9.511
10.402
9.701
10.540
10.112
10.915
11.183
10.384
10.834
9.886
10.216
10.943
9.867
10.203
10.837
10.573
10.647
11.502
10.656
10.866
10.835
9.945
10.331




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147361&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147361&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'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
19.8299.829000
29.1259.80890668529748-0.00143008314906009-0.375425974024312-2.16410084020367
39.7829.75871855137161-0.01161710135062190.111946729880022-0.711122094591708
49.4419.66599643166279-0.0258247873703129-0.12015415668978-0.906673047112183
59.1629.52364065809337-0.0430868496391284-0.208772812716079-1.32687523224617
69.9159.55377230630157-0.03374742805408780.2553561784522370.904574911665152
710.4449.71808357566759-0.01163052720118080.4079617340251762.6707056131267
810.2099.842313259166130.001839264014856040.1268654182907961.98515728664676
99.9859.898935491437820.00671883031964397-0.01900565858901330.859357029852691
109.8429.907491383096820.00686730299414785-0.06927786159221220.0306667860390292
119.4299.83985602861950.00135730281411214-0.247378592638417-1.31335615873897
1210.1329.864890249774650.00297132827094340.2122447300353750.437829891550462
139.8499.855473156896260.002596012214617070.0362922964242046-0.344054961934685
149.1729.807915027657520.000341680822821636-0.501015564197216-1.11272542487806
1510.3139.830876319854680.001530993401331540.430675937173160.436440100119941
169.8199.842602126990130.00208193486729224-0.04578087986095080.190468933647384
179.9559.902659913237460.00514644346166466-0.07584040875033071.1028472412838
1810.0489.933582955304610.00645178413883730.05522308644330280.508028227556528
1910.0829.931226277256130.006028070809601830.171878480446447-0.180445678390151
2010.5419.997642502664760.008779083346597980.3927046465659481.28309958072438
2110.20810.04559080694690.0104672480323160.06104926020516350.860157625616102
2210.23310.08662276677280.01171063075265740.06459529901943350.691630098790339
239.43910.06354371295270.0103884128039947-0.528310269956603-0.810833129372798
249.96310.02718065723690.008791501530555640.0706758268026154-1.13098956559884
2510.15810.02877015040470.008603852566505260.151787324819502-0.188703637437372
269.22510.00944542916710.00784731627872468-0.699177292824764-0.714911534543254
2710.47410.01658546047430.007826228708840030.459476113351375-0.0174023687527663
289.75710.00926771380610.00734975079196719-0.20925081182984-0.364960031624052
2910.4910.07474043911470.009206552059341950.2510545365221141.39637907209921
3010.28110.11768215721590.01027422570839040.0673578342376830.816393825076508
3110.44410.16327253527150.01136526467050230.1789683909096880.865215641956152
3210.6410.19813623589020.01206731143268220.3731072580986050.583954036840811
3310.69510.26105446110030.01352588831461390.2827212021696511.28255849631834
3410.78610.32591697746650.01492758641324590.3048328725623721.31457115863458
359.83210.35088340398340.01518485744997-0.5497969303171180.261274579728104
369.74710.30213329857760.0136846889662288-0.354020488789147-1.69638232496869
3710.41110.29152315082840.01317725257733080.197794542333461-0.65946295490803
389.51110.29047014443780.0128833224075267-0.733699850060103-0.385418242308115
3910.40210.26766010827410.01211553134396770.247460872252597-0.954429757172428
409.70110.24513395721440.0113415332784254-0.435672123924506-0.917060281020503
4110.5410.25720089482130.01135806112998440.2805395404429480.0191311251451253
4210.11210.25414021543780.0110289641249001-0.0971866991354684-0.380822519672974
4310.91510.31292674448930.01210580988886040.4524461270470811.26750891056777
4411.18310.39269781447550.01359700716569390.5767547485087031.80818829274647
4510.38410.40157243464010.0134962154882829-0.00253943489457393-0.127200806928218
4610.83410.42203115350080.01363881360753210.3895917354744310.189175639644208
479.88610.42667426136010.0134638745693697-0.511460375340924-0.24672783672698
4810.21610.44438082221330.0135414025469193-0.2423118276812850.117536154992626
4910.94310.48071813282350.01393328552542370.3867055304695370.63708204975121
509.86710.50471946766350.0141032813973789-0.6711329780130690.281614513017286
5110.20310.46704935811940.01321720437314-0.0930473335481057-1.4421183296814
5210.83710.54116975700660.01428124804748760.09573390686363521.68897789758883
5310.57310.55053957254960.0141942297561810.0385497248482355-0.13590308233299
5410.64710.59122885441540.0146651861597838-0.0310183744555830.733363050986384
5511.50210.66349719751220.01568154903361670.6493799786239591.59813696534619
5610.65610.64062165897430.01501260713899420.142470324995907-1.07373318458846
5710.86610.66679009181930.01520132836073750.1622517427124550.312117479504484
5810.83510.66351689152340.01489892973631890.233041862126717-0.519607089550707
599.94510.65573547178690.0145421397926016-0.634693319004723-0.641491105688102
6010.33110.65863207039570.0143666931449317-0.288348577435209-0.331207289021549

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9.829 & 9.829 & 0 & 0 & 0 \tabularnewline
2 & 9.125 & 9.80890668529748 & -0.00143008314906009 & -0.375425974024312 & -2.16410084020367 \tabularnewline
3 & 9.782 & 9.75871855137161 & -0.0116171013506219 & 0.111946729880022 & -0.711122094591708 \tabularnewline
4 & 9.441 & 9.66599643166279 & -0.0258247873703129 & -0.12015415668978 & -0.906673047112183 \tabularnewline
5 & 9.162 & 9.52364065809337 & -0.0430868496391284 & -0.208772812716079 & -1.32687523224617 \tabularnewline
6 & 9.915 & 9.55377230630157 & -0.0337474280540878 & 0.255356178452237 & 0.904574911665152 \tabularnewline
7 & 10.444 & 9.71808357566759 & -0.0116305272011808 & 0.407961734025176 & 2.6707056131267 \tabularnewline
8 & 10.209 & 9.84231325916613 & 0.00183926401485604 & 0.126865418290796 & 1.98515728664676 \tabularnewline
9 & 9.985 & 9.89893549143782 & 0.00671883031964397 & -0.0190056585890133 & 0.859357029852691 \tabularnewline
10 & 9.842 & 9.90749138309682 & 0.00686730299414785 & -0.0692778615922122 & 0.0306667860390292 \tabularnewline
11 & 9.429 & 9.8398560286195 & 0.00135730281411214 & -0.247378592638417 & -1.31335615873897 \tabularnewline
12 & 10.132 & 9.86489024977465 & 0.0029713282709434 & 0.212244730035375 & 0.437829891550462 \tabularnewline
13 & 9.849 & 9.85547315689626 & 0.00259601221461707 & 0.0362922964242046 & -0.344054961934685 \tabularnewline
14 & 9.172 & 9.80791502765752 & 0.000341680822821636 & -0.501015564197216 & -1.11272542487806 \tabularnewline
15 & 10.313 & 9.83087631985468 & 0.00153099340133154 & 0.43067593717316 & 0.436440100119941 \tabularnewline
16 & 9.819 & 9.84260212699013 & 0.00208193486729224 & -0.0457808798609508 & 0.190468933647384 \tabularnewline
17 & 9.955 & 9.90265991323746 & 0.00514644346166466 & -0.0758404087503307 & 1.1028472412838 \tabularnewline
18 & 10.048 & 9.93358295530461 & 0.0064517841388373 & 0.0552230864433028 & 0.508028227556528 \tabularnewline
19 & 10.082 & 9.93122627725613 & 0.00602807080960183 & 0.171878480446447 & -0.180445678390151 \tabularnewline
20 & 10.541 & 9.99764250266476 & 0.00877908334659798 & 0.392704646565948 & 1.28309958072438 \tabularnewline
21 & 10.208 & 10.0455908069469 & 0.010467248032316 & 0.0610492602051635 & 0.860157625616102 \tabularnewline
22 & 10.233 & 10.0866227667728 & 0.0117106307526574 & 0.0645952990194335 & 0.691630098790339 \tabularnewline
23 & 9.439 & 10.0635437129527 & 0.0103884128039947 & -0.528310269956603 & -0.810833129372798 \tabularnewline
24 & 9.963 & 10.0271806572369 & 0.00879150153055564 & 0.0706758268026154 & -1.13098956559884 \tabularnewline
25 & 10.158 & 10.0287701504047 & 0.00860385256650526 & 0.151787324819502 & -0.188703637437372 \tabularnewline
26 & 9.225 & 10.0094454291671 & 0.00784731627872468 & -0.699177292824764 & -0.714911534543254 \tabularnewline
27 & 10.474 & 10.0165854604743 & 0.00782622870884003 & 0.459476113351375 & -0.0174023687527663 \tabularnewline
28 & 9.757 & 10.0092677138061 & 0.00734975079196719 & -0.20925081182984 & -0.364960031624052 \tabularnewline
29 & 10.49 & 10.0747404391147 & 0.00920655205934195 & 0.251054536522114 & 1.39637907209921 \tabularnewline
30 & 10.281 & 10.1176821572159 & 0.0102742257083904 & 0.067357834237683 & 0.816393825076508 \tabularnewline
31 & 10.444 & 10.1632725352715 & 0.0113652646705023 & 0.178968390909688 & 0.865215641956152 \tabularnewline
32 & 10.64 & 10.1981362358902 & 0.0120673114326822 & 0.373107258098605 & 0.583954036840811 \tabularnewline
33 & 10.695 & 10.2610544611003 & 0.0135258883146139 & 0.282721202169651 & 1.28255849631834 \tabularnewline
34 & 10.786 & 10.3259169774665 & 0.0149275864132459 & 0.304832872562372 & 1.31457115863458 \tabularnewline
35 & 9.832 & 10.3508834039834 & 0.01518485744997 & -0.549796930317118 & 0.261274579728104 \tabularnewline
36 & 9.747 & 10.3021332985776 & 0.0136846889662288 & -0.354020488789147 & -1.69638232496869 \tabularnewline
37 & 10.411 & 10.2915231508284 & 0.0131772525773308 & 0.197794542333461 & -0.65946295490803 \tabularnewline
38 & 9.511 & 10.2904701444378 & 0.0128833224075267 & -0.733699850060103 & -0.385418242308115 \tabularnewline
39 & 10.402 & 10.2676601082741 & 0.0121155313439677 & 0.247460872252597 & -0.954429757172428 \tabularnewline
40 & 9.701 & 10.2451339572144 & 0.0113415332784254 & -0.435672123924506 & -0.917060281020503 \tabularnewline
41 & 10.54 & 10.2572008948213 & 0.0113580611299844 & 0.280539540442948 & 0.0191311251451253 \tabularnewline
42 & 10.112 & 10.2541402154378 & 0.0110289641249001 & -0.0971866991354684 & -0.380822519672974 \tabularnewline
43 & 10.915 & 10.3129267444893 & 0.0121058098888604 & 0.452446127047081 & 1.26750891056777 \tabularnewline
44 & 11.183 & 10.3926978144755 & 0.0135970071656939 & 0.576754748508703 & 1.80818829274647 \tabularnewline
45 & 10.384 & 10.4015724346401 & 0.0134962154882829 & -0.00253943489457393 & -0.127200806928218 \tabularnewline
46 & 10.834 & 10.4220311535008 & 0.0136388136075321 & 0.389591735474431 & 0.189175639644208 \tabularnewline
47 & 9.886 & 10.4266742613601 & 0.0134638745693697 & -0.511460375340924 & -0.24672783672698 \tabularnewline
48 & 10.216 & 10.4443808222133 & 0.0135414025469193 & -0.242311827681285 & 0.117536154992626 \tabularnewline
49 & 10.943 & 10.4807181328235 & 0.0139332855254237 & 0.386705530469537 & 0.63708204975121 \tabularnewline
50 & 9.867 & 10.5047194676635 & 0.0141032813973789 & -0.671132978013069 & 0.281614513017286 \tabularnewline
51 & 10.203 & 10.4670493581194 & 0.01321720437314 & -0.0930473335481057 & -1.4421183296814 \tabularnewline
52 & 10.837 & 10.5411697570066 & 0.0142812480474876 & 0.0957339068636352 & 1.68897789758883 \tabularnewline
53 & 10.573 & 10.5505395725496 & 0.014194229756181 & 0.0385497248482355 & -0.13590308233299 \tabularnewline
54 & 10.647 & 10.5912288544154 & 0.0146651861597838 & -0.031018374455583 & 0.733363050986384 \tabularnewline
55 & 11.502 & 10.6634971975122 & 0.0156815490336167 & 0.649379978623959 & 1.59813696534619 \tabularnewline
56 & 10.656 & 10.6406216589743 & 0.0150126071389942 & 0.142470324995907 & -1.07373318458846 \tabularnewline
57 & 10.866 & 10.6667900918193 & 0.0152013283607375 & 0.162251742712455 & 0.312117479504484 \tabularnewline
58 & 10.835 & 10.6635168915234 & 0.0148989297363189 & 0.233041862126717 & -0.519607089550707 \tabularnewline
59 & 9.945 & 10.6557354717869 & 0.0145421397926016 & -0.634693319004723 & -0.641491105688102 \tabularnewline
60 & 10.331 & 10.6586320703957 & 0.0143666931449317 & -0.288348577435209 & -0.331207289021549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147361&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]9.829[/C][C]9.829[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]9.125[/C][C]9.80890668529748[/C][C]-0.00143008314906009[/C][C]-0.375425974024312[/C][C]-2.16410084020367[/C][/ROW]
[ROW][C]3[/C][C]9.782[/C][C]9.75871855137161[/C][C]-0.0116171013506219[/C][C]0.111946729880022[/C][C]-0.711122094591708[/C][/ROW]
[ROW][C]4[/C][C]9.441[/C][C]9.66599643166279[/C][C]-0.0258247873703129[/C][C]-0.12015415668978[/C][C]-0.906673047112183[/C][/ROW]
[ROW][C]5[/C][C]9.162[/C][C]9.52364065809337[/C][C]-0.0430868496391284[/C][C]-0.208772812716079[/C][C]-1.32687523224617[/C][/ROW]
[ROW][C]6[/C][C]9.915[/C][C]9.55377230630157[/C][C]-0.0337474280540878[/C][C]0.255356178452237[/C][C]0.904574911665152[/C][/ROW]
[ROW][C]7[/C][C]10.444[/C][C]9.71808357566759[/C][C]-0.0116305272011808[/C][C]0.407961734025176[/C][C]2.6707056131267[/C][/ROW]
[ROW][C]8[/C][C]10.209[/C][C]9.84231325916613[/C][C]0.00183926401485604[/C][C]0.126865418290796[/C][C]1.98515728664676[/C][/ROW]
[ROW][C]9[/C][C]9.985[/C][C]9.89893549143782[/C][C]0.00671883031964397[/C][C]-0.0190056585890133[/C][C]0.859357029852691[/C][/ROW]
[ROW][C]10[/C][C]9.842[/C][C]9.90749138309682[/C][C]0.00686730299414785[/C][C]-0.0692778615922122[/C][C]0.0306667860390292[/C][/ROW]
[ROW][C]11[/C][C]9.429[/C][C]9.8398560286195[/C][C]0.00135730281411214[/C][C]-0.247378592638417[/C][C]-1.31335615873897[/C][/ROW]
[ROW][C]12[/C][C]10.132[/C][C]9.86489024977465[/C][C]0.0029713282709434[/C][C]0.212244730035375[/C][C]0.437829891550462[/C][/ROW]
[ROW][C]13[/C][C]9.849[/C][C]9.85547315689626[/C][C]0.00259601221461707[/C][C]0.0362922964242046[/C][C]-0.344054961934685[/C][/ROW]
[ROW][C]14[/C][C]9.172[/C][C]9.80791502765752[/C][C]0.000341680822821636[/C][C]-0.501015564197216[/C][C]-1.11272542487806[/C][/ROW]
[ROW][C]15[/C][C]10.313[/C][C]9.83087631985468[/C][C]0.00153099340133154[/C][C]0.43067593717316[/C][C]0.436440100119941[/C][/ROW]
[ROW][C]16[/C][C]9.819[/C][C]9.84260212699013[/C][C]0.00208193486729224[/C][C]-0.0457808798609508[/C][C]0.190468933647384[/C][/ROW]
[ROW][C]17[/C][C]9.955[/C][C]9.90265991323746[/C][C]0.00514644346166466[/C][C]-0.0758404087503307[/C][C]1.1028472412838[/C][/ROW]
[ROW][C]18[/C][C]10.048[/C][C]9.93358295530461[/C][C]0.0064517841388373[/C][C]0.0552230864433028[/C][C]0.508028227556528[/C][/ROW]
[ROW][C]19[/C][C]10.082[/C][C]9.93122627725613[/C][C]0.00602807080960183[/C][C]0.171878480446447[/C][C]-0.180445678390151[/C][/ROW]
[ROW][C]20[/C][C]10.541[/C][C]9.99764250266476[/C][C]0.00877908334659798[/C][C]0.392704646565948[/C][C]1.28309958072438[/C][/ROW]
[ROW][C]21[/C][C]10.208[/C][C]10.0455908069469[/C][C]0.010467248032316[/C][C]0.0610492602051635[/C][C]0.860157625616102[/C][/ROW]
[ROW][C]22[/C][C]10.233[/C][C]10.0866227667728[/C][C]0.0117106307526574[/C][C]0.0645952990194335[/C][C]0.691630098790339[/C][/ROW]
[ROW][C]23[/C][C]9.439[/C][C]10.0635437129527[/C][C]0.0103884128039947[/C][C]-0.528310269956603[/C][C]-0.810833129372798[/C][/ROW]
[ROW][C]24[/C][C]9.963[/C][C]10.0271806572369[/C][C]0.00879150153055564[/C][C]0.0706758268026154[/C][C]-1.13098956559884[/C][/ROW]
[ROW][C]25[/C][C]10.158[/C][C]10.0287701504047[/C][C]0.00860385256650526[/C][C]0.151787324819502[/C][C]-0.188703637437372[/C][/ROW]
[ROW][C]26[/C][C]9.225[/C][C]10.0094454291671[/C][C]0.00784731627872468[/C][C]-0.699177292824764[/C][C]-0.714911534543254[/C][/ROW]
[ROW][C]27[/C][C]10.474[/C][C]10.0165854604743[/C][C]0.00782622870884003[/C][C]0.459476113351375[/C][C]-0.0174023687527663[/C][/ROW]
[ROW][C]28[/C][C]9.757[/C][C]10.0092677138061[/C][C]0.00734975079196719[/C][C]-0.20925081182984[/C][C]-0.364960031624052[/C][/ROW]
[ROW][C]29[/C][C]10.49[/C][C]10.0747404391147[/C][C]0.00920655205934195[/C][C]0.251054536522114[/C][C]1.39637907209921[/C][/ROW]
[ROW][C]30[/C][C]10.281[/C][C]10.1176821572159[/C][C]0.0102742257083904[/C][C]0.067357834237683[/C][C]0.816393825076508[/C][/ROW]
[ROW][C]31[/C][C]10.444[/C][C]10.1632725352715[/C][C]0.0113652646705023[/C][C]0.178968390909688[/C][C]0.865215641956152[/C][/ROW]
[ROW][C]32[/C][C]10.64[/C][C]10.1981362358902[/C][C]0.0120673114326822[/C][C]0.373107258098605[/C][C]0.583954036840811[/C][/ROW]
[ROW][C]33[/C][C]10.695[/C][C]10.2610544611003[/C][C]0.0135258883146139[/C][C]0.282721202169651[/C][C]1.28255849631834[/C][/ROW]
[ROW][C]34[/C][C]10.786[/C][C]10.3259169774665[/C][C]0.0149275864132459[/C][C]0.304832872562372[/C][C]1.31457115863458[/C][/ROW]
[ROW][C]35[/C][C]9.832[/C][C]10.3508834039834[/C][C]0.01518485744997[/C][C]-0.549796930317118[/C][C]0.261274579728104[/C][/ROW]
[ROW][C]36[/C][C]9.747[/C][C]10.3021332985776[/C][C]0.0136846889662288[/C][C]-0.354020488789147[/C][C]-1.69638232496869[/C][/ROW]
[ROW][C]37[/C][C]10.411[/C][C]10.2915231508284[/C][C]0.0131772525773308[/C][C]0.197794542333461[/C][C]-0.65946295490803[/C][/ROW]
[ROW][C]38[/C][C]9.511[/C][C]10.2904701444378[/C][C]0.0128833224075267[/C][C]-0.733699850060103[/C][C]-0.385418242308115[/C][/ROW]
[ROW][C]39[/C][C]10.402[/C][C]10.2676601082741[/C][C]0.0121155313439677[/C][C]0.247460872252597[/C][C]-0.954429757172428[/C][/ROW]
[ROW][C]40[/C][C]9.701[/C][C]10.2451339572144[/C][C]0.0113415332784254[/C][C]-0.435672123924506[/C][C]-0.917060281020503[/C][/ROW]
[ROW][C]41[/C][C]10.54[/C][C]10.2572008948213[/C][C]0.0113580611299844[/C][C]0.280539540442948[/C][C]0.0191311251451253[/C][/ROW]
[ROW][C]42[/C][C]10.112[/C][C]10.2541402154378[/C][C]0.0110289641249001[/C][C]-0.0971866991354684[/C][C]-0.380822519672974[/C][/ROW]
[ROW][C]43[/C][C]10.915[/C][C]10.3129267444893[/C][C]0.0121058098888604[/C][C]0.452446127047081[/C][C]1.26750891056777[/C][/ROW]
[ROW][C]44[/C][C]11.183[/C][C]10.3926978144755[/C][C]0.0135970071656939[/C][C]0.576754748508703[/C][C]1.80818829274647[/C][/ROW]
[ROW][C]45[/C][C]10.384[/C][C]10.4015724346401[/C][C]0.0134962154882829[/C][C]-0.00253943489457393[/C][C]-0.127200806928218[/C][/ROW]
[ROW][C]46[/C][C]10.834[/C][C]10.4220311535008[/C][C]0.0136388136075321[/C][C]0.389591735474431[/C][C]0.189175639644208[/C][/ROW]
[ROW][C]47[/C][C]9.886[/C][C]10.4266742613601[/C][C]0.0134638745693697[/C][C]-0.511460375340924[/C][C]-0.24672783672698[/C][/ROW]
[ROW][C]48[/C][C]10.216[/C][C]10.4443808222133[/C][C]0.0135414025469193[/C][C]-0.242311827681285[/C][C]0.117536154992626[/C][/ROW]
[ROW][C]49[/C][C]10.943[/C][C]10.4807181328235[/C][C]0.0139332855254237[/C][C]0.386705530469537[/C][C]0.63708204975121[/C][/ROW]
[ROW][C]50[/C][C]9.867[/C][C]10.5047194676635[/C][C]0.0141032813973789[/C][C]-0.671132978013069[/C][C]0.281614513017286[/C][/ROW]
[ROW][C]51[/C][C]10.203[/C][C]10.4670493581194[/C][C]0.01321720437314[/C][C]-0.0930473335481057[/C][C]-1.4421183296814[/C][/ROW]
[ROW][C]52[/C][C]10.837[/C][C]10.5411697570066[/C][C]0.0142812480474876[/C][C]0.0957339068636352[/C][C]1.68897789758883[/C][/ROW]
[ROW][C]53[/C][C]10.573[/C][C]10.5505395725496[/C][C]0.014194229756181[/C][C]0.0385497248482355[/C][C]-0.13590308233299[/C][/ROW]
[ROW][C]54[/C][C]10.647[/C][C]10.5912288544154[/C][C]0.0146651861597838[/C][C]-0.031018374455583[/C][C]0.733363050986384[/C][/ROW]
[ROW][C]55[/C][C]11.502[/C][C]10.6634971975122[/C][C]0.0156815490336167[/C][C]0.649379978623959[/C][C]1.59813696534619[/C][/ROW]
[ROW][C]56[/C][C]10.656[/C][C]10.6406216589743[/C][C]0.0150126071389942[/C][C]0.142470324995907[/C][C]-1.07373318458846[/C][/ROW]
[ROW][C]57[/C][C]10.866[/C][C]10.6667900918193[/C][C]0.0152013283607375[/C][C]0.162251742712455[/C][C]0.312117479504484[/C][/ROW]
[ROW][C]58[/C][C]10.835[/C][C]10.6635168915234[/C][C]0.0148989297363189[/C][C]0.233041862126717[/C][C]-0.519607089550707[/C][/ROW]
[ROW][C]59[/C][C]9.945[/C][C]10.6557354717869[/C][C]0.0145421397926016[/C][C]-0.634693319004723[/C][C]-0.641491105688102[/C][/ROW]
[ROW][C]60[/C][C]10.331[/C][C]10.6586320703957[/C][C]0.0143666931449317[/C][C]-0.288348577435209[/C][C]-0.331207289021549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147361&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
19.8299.829000
29.1259.80890668529748-0.00143008314906009-0.375425974024312-2.16410084020367
39.7829.75871855137161-0.01161710135062190.111946729880022-0.711122094591708
49.4419.66599643166279-0.0258247873703129-0.12015415668978-0.906673047112183
59.1629.52364065809337-0.0430868496391284-0.208772812716079-1.32687523224617
69.9159.55377230630157-0.03374742805408780.2553561784522370.904574911665152
710.4449.71808357566759-0.01163052720118080.4079617340251762.6707056131267
810.2099.842313259166130.001839264014856040.1268654182907961.98515728664676
99.9859.898935491437820.00671883031964397-0.01900565858901330.859357029852691
109.8429.907491383096820.00686730299414785-0.06927786159221220.0306667860390292
119.4299.83985602861950.00135730281411214-0.247378592638417-1.31335615873897
1210.1329.864890249774650.00297132827094340.2122447300353750.437829891550462
139.8499.855473156896260.002596012214617070.0362922964242046-0.344054961934685
149.1729.807915027657520.000341680822821636-0.501015564197216-1.11272542487806
1510.3139.830876319854680.001530993401331540.430675937173160.436440100119941
169.8199.842602126990130.00208193486729224-0.04578087986095080.190468933647384
179.9559.902659913237460.00514644346166466-0.07584040875033071.1028472412838
1810.0489.933582955304610.00645178413883730.05522308644330280.508028227556528
1910.0829.931226277256130.006028070809601830.171878480446447-0.180445678390151
2010.5419.997642502664760.008779083346597980.3927046465659481.28309958072438
2110.20810.04559080694690.0104672480323160.06104926020516350.860157625616102
2210.23310.08662276677280.01171063075265740.06459529901943350.691630098790339
239.43910.06354371295270.0103884128039947-0.528310269956603-0.810833129372798
249.96310.02718065723690.008791501530555640.0706758268026154-1.13098956559884
2510.15810.02877015040470.008603852566505260.151787324819502-0.188703637437372
269.22510.00944542916710.00784731627872468-0.699177292824764-0.714911534543254
2710.47410.01658546047430.007826228708840030.459476113351375-0.0174023687527663
289.75710.00926771380610.00734975079196719-0.20925081182984-0.364960031624052
2910.4910.07474043911470.009206552059341950.2510545365221141.39637907209921
3010.28110.11768215721590.01027422570839040.0673578342376830.816393825076508
3110.44410.16327253527150.01136526467050230.1789683909096880.865215641956152
3210.6410.19813623589020.01206731143268220.3731072580986050.583954036840811
3310.69510.26105446110030.01352588831461390.2827212021696511.28255849631834
3410.78610.32591697746650.01492758641324590.3048328725623721.31457115863458
359.83210.35088340398340.01518485744997-0.5497969303171180.261274579728104
369.74710.30213329857760.0136846889662288-0.354020488789147-1.69638232496869
3710.41110.29152315082840.01317725257733080.197794542333461-0.65946295490803
389.51110.29047014443780.0128833224075267-0.733699850060103-0.385418242308115
3910.40210.26766010827410.01211553134396770.247460872252597-0.954429757172428
409.70110.24513395721440.0113415332784254-0.435672123924506-0.917060281020503
4110.5410.25720089482130.01135806112998440.2805395404429480.0191311251451253
4210.11210.25414021543780.0110289641249001-0.0971866991354684-0.380822519672974
4310.91510.31292674448930.01210580988886040.4524461270470811.26750891056777
4411.18310.39269781447550.01359700716569390.5767547485087031.80818829274647
4510.38410.40157243464010.0134962154882829-0.00253943489457393-0.127200806928218
4610.83410.42203115350080.01363881360753210.3895917354744310.189175639644208
479.88610.42667426136010.0134638745693697-0.511460375340924-0.24672783672698
4810.21610.44438082221330.0135414025469193-0.2423118276812850.117536154992626
4910.94310.48071813282350.01393328552542370.3867055304695370.63708204975121
509.86710.50471946766350.0141032813973789-0.6711329780130690.281614513017286
5110.20310.46704935811940.01321720437314-0.0930473335481057-1.4421183296814
5210.83710.54116975700660.01428124804748760.09573390686363521.68897789758883
5310.57310.55053957254960.0141942297561810.0385497248482355-0.13590308233299
5410.64710.59122885441540.0146651861597838-0.0310183744555830.733363050986384
5511.50210.66349719751220.01568154903361670.6493799786239591.59813696534619
5610.65610.64062165897430.01501260713899420.142470324995907-1.07373318458846
5710.86610.66679009181930.01520132836073750.1622517427124550.312117479504484
5810.83510.66351689152340.01489892973631890.233041862126717-0.519607089550707
599.94510.65573547178690.0145421397926016-0.634693319004723-0.641491105688102
6010.33110.65863207039570.0143666931449317-0.288348577435209-0.331207289021549



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')