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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 computationSun, 29 Nov 2009 06:44:20 -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/Nov/29/t1259502295286re7ykibky86x.htm/, Retrieved Sat, 27 Apr 2024 04:09:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61598, Retrieved Sat, 27 Apr 2024 04:09:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
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] [ws 9 stru] [2009-11-29 13:44:20] [2e4ef2c1b76db9b31c0a03b96e94ad77] [Current]
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Dataseries X:
103,63
103,64
103,66
103,77
103,88
103,91
103,91
103,92
104,05
104,23
104,30
104,31
104,31
104,34
104,55
104,65
104,73
104,75
104,75
104,76
104,94
105,29
105,38
105,43
105,43
105,42
105,52
105,69
105,72
105,74
105,74
105,74
105,95
106,17
106,34
106,37
106,37
106,36
106,44
106,29
106,23
106,23
106,23
106,23
106,34
106,44
106,44
106,48
106,50
106,57
106,40
106,37
106,25
106,21
106,21
106,24
106,19
106,08
106,13
106,09




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=61598&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=61598&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61598&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
1103.63103.63000
2103.64103.6394870710810.0006489104882088420.0005129289192512490.0696065340614257
3103.66103.6594526068850.002152828187893850.0005473931150855970.228816834469941
4103.77103.7692811523340.01373071627881760.0007188476658782521.25458131126306
5103.88103.8791480557600.02628050088312730.000851944240155381.10537017977190
6103.91103.9091436714710.02682882668412440.0008563285285645790.0422866014374643
7103.91103.909170246960.02254584932954390.00082975304009546-0.302820103381684
8103.92103.9191805842540.02043583721685530.000819415746001782-0.140884834159692
9104.05104.0491060205950.03950587008146750.0008939794052764621.22601287192371
10104.23104.2290274303880.0645192108322720.0009725696121246451.56831943482443
11104.3104.2990249185890.06550970858295710.0009750814105284570.0610814551868003
12104.31104.3090457149980.05537813566930070.00095428500233726-0.617957149978106
13104.31104.3335569550220.0500146811198818-0.0235569550220304-0.402404334177696
14104.34104.3388705708210.04194039266301870.00112942917926398-0.42032620218643
15104.55104.5489631415370.07306445515152390.001036858463097041.86784281917169
16104.65104.6489752262340.07806095710062350.001024773766437530.299310728749698
17104.73104.728975934640.07842103480727050.001024065359924980.0215442088356734
18104.75104.7489585574840.06756457067063050.00104144251627414-0.649048239773017
19104.75104.7489421970100.05500300569107090.00105780299042050-0.750590883487078
20104.76104.7589333263750.04663344963020880.00106667362483166-0.499931189073008
21104.94104.9389547245020.07144180792524650.001045275497763371.48151251059090
22105.29105.2889911031710.1232653147800590.001008896828661743.09433629892012
23105.38105.3789875671450.1170760069314460.0010124328549743-0.369520630496941
24105.43105.4289817638050.1045951653654420.00101823619525671-0.7450943267097
25105.43105.4566621681380.0904927124064977-0.0266621681380912-0.92733967289765
26105.42105.4194283093930.06720616181748160.000571690606598565-1.28507598990847
27105.52105.519443021570.07331392882809360.0005569784299908950.364241115792154
28105.69105.6894783275000.0913163919714020.0005216725003322621.07392673202998
29105.72105.7194601053670.07990171474032850.00053989463314945-0.681077595004771
30105.74105.7394456172140.06875177149104690.000554382785935683-0.665373333127532
31105.74105.7394320835020.05595554966121750.000567916497887755-0.763684705238315
32105.74105.7394231186970.04554155157735370.00057688130312154-0.621549981318638
33105.95105.9494445635020.07614813987155970.0005554364980307251.82679941144869
34106.17106.1694598304410.1029190961499510.00054016955864871.59790640483198
35106.34106.3394656248290.1154027139878900.0005343751708999030.745135995494691
36106.37106.3694596206520.0995096109744130.000540379348171504-0.948656153894987
37106.37106.3867748524440.0843515476891247-0.0167748524439978-0.965145018751399
38106.36106.3596673293780.0639284105418560.000332670621857405-1.15357188907121
39106.44106.4396727597150.06692121060963750.0003272402850062760.178510453829056
40106.29106.2896130758590.02653595721121020.000386924141393101-2.40945134794615
41106.23106.2295936983700.01042755884186320.00040630163009411-0.961214208414722
42106.23106.2295917979940.008486693847639670.000408202005607816-0.115827359427076
43106.23106.2295905391920.006907182777739370.000409460808479994-0.0942693049984303
44106.23106.2295897053440.005621700241609420.000410294656446816-0.0767246436068602
45106.34106.3395999610540.02504678518182980.0004000389461614061.15943279787802
46106.44106.4396059550610.03899551545951050.0003940449385849920.83258115499392
47106.44106.4396034169280.03173857464775650.000396583071970762-0.433163251940526
48106.48106.4796038545790.03327598661656550.0003961454205472060.0917682266489144
49106.5106.5051180722730.0318411940036946-0.00511807227287477-0.0899165989108308
50106.57106.5692608970740.0377789513242720.0007391029259396930.339590706536093
51106.4106.399205020961-0.0009077273812927170.000794979039216034-2.30788000980989
52106.37106.369198654227-0.006323486113877670.000801345772593357-0.323145614471233
53106.25106.249178407179-0.02748275134356130.000821592821104137-1.26269159907276
54106.21106.209176592668-0.02981247486444970.000823407332374508-0.139039868929669
55106.21106.209180110003-0.02426403616388700.0008198899965531540.331154835263960
56106.24106.239185320707-0.01416523573736420.0008146792931000750.602763417353139
57106.19106.189182520052-0.02083410598553290.000817479947739854-0.39805261908213
58106.08106.079176848185-0.03742767755383210.000823151814591872-0.990456617980347
59106.13106.129181374549-0.02115774822276940.0008186254508081030.971149592792165
60106.09106.089180580573-0.02466418995182160.000819419426550609-0.209300528484211

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 103.63 & 103.63 & 0 & 0 & 0 \tabularnewline
2 & 103.64 & 103.639487071081 & 0.000648910488208842 & 0.000512928919251249 & 0.0696065340614257 \tabularnewline
3 & 103.66 & 103.659452606885 & 0.00215282818789385 & 0.000547393115085597 & 0.228816834469941 \tabularnewline
4 & 103.77 & 103.769281152334 & 0.0137307162788176 & 0.000718847665878252 & 1.25458131126306 \tabularnewline
5 & 103.88 & 103.879148055760 & 0.0262805008831273 & 0.00085194424015538 & 1.10537017977190 \tabularnewline
6 & 103.91 & 103.909143671471 & 0.0268288266841244 & 0.000856328528564579 & 0.0422866014374643 \tabularnewline
7 & 103.91 & 103.90917024696 & 0.0225458493295439 & 0.00082975304009546 & -0.302820103381684 \tabularnewline
8 & 103.92 & 103.919180584254 & 0.0204358372168553 & 0.000819415746001782 & -0.140884834159692 \tabularnewline
9 & 104.05 & 104.049106020595 & 0.0395058700814675 & 0.000893979405276462 & 1.22601287192371 \tabularnewline
10 & 104.23 & 104.229027430388 & 0.064519210832272 & 0.000972569612124645 & 1.56831943482443 \tabularnewline
11 & 104.3 & 104.299024918589 & 0.0655097085829571 & 0.000975081410528457 & 0.0610814551868003 \tabularnewline
12 & 104.31 & 104.309045714998 & 0.0553781356693007 & 0.00095428500233726 & -0.617957149978106 \tabularnewline
13 & 104.31 & 104.333556955022 & 0.0500146811198818 & -0.0235569550220304 & -0.402404334177696 \tabularnewline
14 & 104.34 & 104.338870570821 & 0.0419403926630187 & 0.00112942917926398 & -0.42032620218643 \tabularnewline
15 & 104.55 & 104.548963141537 & 0.0730644551515239 & 0.00103685846309704 & 1.86784281917169 \tabularnewline
16 & 104.65 & 104.648975226234 & 0.0780609571006235 & 0.00102477376643753 & 0.299310728749698 \tabularnewline
17 & 104.73 & 104.72897593464 & 0.0784210348072705 & 0.00102406535992498 & 0.0215442088356734 \tabularnewline
18 & 104.75 & 104.748958557484 & 0.0675645706706305 & 0.00104144251627414 & -0.649048239773017 \tabularnewline
19 & 104.75 & 104.748942197010 & 0.0550030056910709 & 0.00105780299042050 & -0.750590883487078 \tabularnewline
20 & 104.76 & 104.758933326375 & 0.0466334496302088 & 0.00106667362483166 & -0.499931189073008 \tabularnewline
21 & 104.94 & 104.938954724502 & 0.0714418079252465 & 0.00104527549776337 & 1.48151251059090 \tabularnewline
22 & 105.29 & 105.288991103171 & 0.123265314780059 & 0.00100889682866174 & 3.09433629892012 \tabularnewline
23 & 105.38 & 105.378987567145 & 0.117076006931446 & 0.0010124328549743 & -0.369520630496941 \tabularnewline
24 & 105.43 & 105.428981763805 & 0.104595165365442 & 0.00101823619525671 & -0.7450943267097 \tabularnewline
25 & 105.43 & 105.456662168138 & 0.0904927124064977 & -0.0266621681380912 & -0.92733967289765 \tabularnewline
26 & 105.42 & 105.419428309393 & 0.0672061618174816 & 0.000571690606598565 & -1.28507598990847 \tabularnewline
27 & 105.52 & 105.51944302157 & 0.0733139288280936 & 0.000556978429990895 & 0.364241115792154 \tabularnewline
28 & 105.69 & 105.689478327500 & 0.091316391971402 & 0.000521672500332262 & 1.07392673202998 \tabularnewline
29 & 105.72 & 105.719460105367 & 0.0799017147403285 & 0.00053989463314945 & -0.681077595004771 \tabularnewline
30 & 105.74 & 105.739445617214 & 0.0687517714910469 & 0.000554382785935683 & -0.665373333127532 \tabularnewline
31 & 105.74 & 105.739432083502 & 0.0559555496612175 & 0.000567916497887755 & -0.763684705238315 \tabularnewline
32 & 105.74 & 105.739423118697 & 0.0455415515773537 & 0.00057688130312154 & -0.621549981318638 \tabularnewline
33 & 105.95 & 105.949444563502 & 0.0761481398715597 & 0.000555436498030725 & 1.82679941144869 \tabularnewline
34 & 106.17 & 106.169459830441 & 0.102919096149951 & 0.0005401695586487 & 1.59790640483198 \tabularnewline
35 & 106.34 & 106.339465624829 & 0.115402713987890 & 0.000534375170899903 & 0.745135995494691 \tabularnewline
36 & 106.37 & 106.369459620652 & 0.099509610974413 & 0.000540379348171504 & -0.948656153894987 \tabularnewline
37 & 106.37 & 106.386774852444 & 0.0843515476891247 & -0.0167748524439978 & -0.965145018751399 \tabularnewline
38 & 106.36 & 106.359667329378 & 0.063928410541856 & 0.000332670621857405 & -1.15357188907121 \tabularnewline
39 & 106.44 & 106.439672759715 & 0.0669212106096375 & 0.000327240285006276 & 0.178510453829056 \tabularnewline
40 & 106.29 & 106.289613075859 & 0.0265359572112102 & 0.000386924141393101 & -2.40945134794615 \tabularnewline
41 & 106.23 & 106.229593698370 & 0.0104275588418632 & 0.00040630163009411 & -0.961214208414722 \tabularnewline
42 & 106.23 & 106.229591797994 & 0.00848669384763967 & 0.000408202005607816 & -0.115827359427076 \tabularnewline
43 & 106.23 & 106.229590539192 & 0.00690718277773937 & 0.000409460808479994 & -0.0942693049984303 \tabularnewline
44 & 106.23 & 106.229589705344 & 0.00562170024160942 & 0.000410294656446816 & -0.0767246436068602 \tabularnewline
45 & 106.34 & 106.339599961054 & 0.0250467851818298 & 0.000400038946161406 & 1.15943279787802 \tabularnewline
46 & 106.44 & 106.439605955061 & 0.0389955154595105 & 0.000394044938584992 & 0.83258115499392 \tabularnewline
47 & 106.44 & 106.439603416928 & 0.0317385746477565 & 0.000396583071970762 & -0.433163251940526 \tabularnewline
48 & 106.48 & 106.479603854579 & 0.0332759866165655 & 0.000396145420547206 & 0.0917682266489144 \tabularnewline
49 & 106.5 & 106.505118072273 & 0.0318411940036946 & -0.00511807227287477 & -0.0899165989108308 \tabularnewline
50 & 106.57 & 106.569260897074 & 0.037778951324272 & 0.000739102925939693 & 0.339590706536093 \tabularnewline
51 & 106.4 & 106.399205020961 & -0.000907727381292717 & 0.000794979039216034 & -2.30788000980989 \tabularnewline
52 & 106.37 & 106.369198654227 & -0.00632348611387767 & 0.000801345772593357 & -0.323145614471233 \tabularnewline
53 & 106.25 & 106.249178407179 & -0.0274827513435613 & 0.000821592821104137 & -1.26269159907276 \tabularnewline
54 & 106.21 & 106.209176592668 & -0.0298124748644497 & 0.000823407332374508 & -0.139039868929669 \tabularnewline
55 & 106.21 & 106.209180110003 & -0.0242640361638870 & 0.000819889996553154 & 0.331154835263960 \tabularnewline
56 & 106.24 & 106.239185320707 & -0.0141652357373642 & 0.000814679293100075 & 0.602763417353139 \tabularnewline
57 & 106.19 & 106.189182520052 & -0.0208341059855329 & 0.000817479947739854 & -0.39805261908213 \tabularnewline
58 & 106.08 & 106.079176848185 & -0.0374276775538321 & 0.000823151814591872 & -0.990456617980347 \tabularnewline
59 & 106.13 & 106.129181374549 & -0.0211577482227694 & 0.000818625450808103 & 0.971149592792165 \tabularnewline
60 & 106.09 & 106.089180580573 & -0.0246641899518216 & 0.000819419426550609 & -0.209300528484211 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61598&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]103.63[/C][C]103.63[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]103.64[/C][C]103.639487071081[/C][C]0.000648910488208842[/C][C]0.000512928919251249[/C][C]0.0696065340614257[/C][/ROW]
[ROW][C]3[/C][C]103.66[/C][C]103.659452606885[/C][C]0.00215282818789385[/C][C]0.000547393115085597[/C][C]0.228816834469941[/C][/ROW]
[ROW][C]4[/C][C]103.77[/C][C]103.769281152334[/C][C]0.0137307162788176[/C][C]0.000718847665878252[/C][C]1.25458131126306[/C][/ROW]
[ROW][C]5[/C][C]103.88[/C][C]103.879148055760[/C][C]0.0262805008831273[/C][C]0.00085194424015538[/C][C]1.10537017977190[/C][/ROW]
[ROW][C]6[/C][C]103.91[/C][C]103.909143671471[/C][C]0.0268288266841244[/C][C]0.000856328528564579[/C][C]0.0422866014374643[/C][/ROW]
[ROW][C]7[/C][C]103.91[/C][C]103.90917024696[/C][C]0.0225458493295439[/C][C]0.00082975304009546[/C][C]-0.302820103381684[/C][/ROW]
[ROW][C]8[/C][C]103.92[/C][C]103.919180584254[/C][C]0.0204358372168553[/C][C]0.000819415746001782[/C][C]-0.140884834159692[/C][/ROW]
[ROW][C]9[/C][C]104.05[/C][C]104.049106020595[/C][C]0.0395058700814675[/C][C]0.000893979405276462[/C][C]1.22601287192371[/C][/ROW]
[ROW][C]10[/C][C]104.23[/C][C]104.229027430388[/C][C]0.064519210832272[/C][C]0.000972569612124645[/C][C]1.56831943482443[/C][/ROW]
[ROW][C]11[/C][C]104.3[/C][C]104.299024918589[/C][C]0.0655097085829571[/C][C]0.000975081410528457[/C][C]0.0610814551868003[/C][/ROW]
[ROW][C]12[/C][C]104.31[/C][C]104.309045714998[/C][C]0.0553781356693007[/C][C]0.00095428500233726[/C][C]-0.617957149978106[/C][/ROW]
[ROW][C]13[/C][C]104.31[/C][C]104.333556955022[/C][C]0.0500146811198818[/C][C]-0.0235569550220304[/C][C]-0.402404334177696[/C][/ROW]
[ROW][C]14[/C][C]104.34[/C][C]104.338870570821[/C][C]0.0419403926630187[/C][C]0.00112942917926398[/C][C]-0.42032620218643[/C][/ROW]
[ROW][C]15[/C][C]104.55[/C][C]104.548963141537[/C][C]0.0730644551515239[/C][C]0.00103685846309704[/C][C]1.86784281917169[/C][/ROW]
[ROW][C]16[/C][C]104.65[/C][C]104.648975226234[/C][C]0.0780609571006235[/C][C]0.00102477376643753[/C][C]0.299310728749698[/C][/ROW]
[ROW][C]17[/C][C]104.73[/C][C]104.72897593464[/C][C]0.0784210348072705[/C][C]0.00102406535992498[/C][C]0.0215442088356734[/C][/ROW]
[ROW][C]18[/C][C]104.75[/C][C]104.748958557484[/C][C]0.0675645706706305[/C][C]0.00104144251627414[/C][C]-0.649048239773017[/C][/ROW]
[ROW][C]19[/C][C]104.75[/C][C]104.748942197010[/C][C]0.0550030056910709[/C][C]0.00105780299042050[/C][C]-0.750590883487078[/C][/ROW]
[ROW][C]20[/C][C]104.76[/C][C]104.758933326375[/C][C]0.0466334496302088[/C][C]0.00106667362483166[/C][C]-0.499931189073008[/C][/ROW]
[ROW][C]21[/C][C]104.94[/C][C]104.938954724502[/C][C]0.0714418079252465[/C][C]0.00104527549776337[/C][C]1.48151251059090[/C][/ROW]
[ROW][C]22[/C][C]105.29[/C][C]105.288991103171[/C][C]0.123265314780059[/C][C]0.00100889682866174[/C][C]3.09433629892012[/C][/ROW]
[ROW][C]23[/C][C]105.38[/C][C]105.378987567145[/C][C]0.117076006931446[/C][C]0.0010124328549743[/C][C]-0.369520630496941[/C][/ROW]
[ROW][C]24[/C][C]105.43[/C][C]105.428981763805[/C][C]0.104595165365442[/C][C]0.00101823619525671[/C][C]-0.7450943267097[/C][/ROW]
[ROW][C]25[/C][C]105.43[/C][C]105.456662168138[/C][C]0.0904927124064977[/C][C]-0.0266621681380912[/C][C]-0.92733967289765[/C][/ROW]
[ROW][C]26[/C][C]105.42[/C][C]105.419428309393[/C][C]0.0672061618174816[/C][C]0.000571690606598565[/C][C]-1.28507598990847[/C][/ROW]
[ROW][C]27[/C][C]105.52[/C][C]105.51944302157[/C][C]0.0733139288280936[/C][C]0.000556978429990895[/C][C]0.364241115792154[/C][/ROW]
[ROW][C]28[/C][C]105.69[/C][C]105.689478327500[/C][C]0.091316391971402[/C][C]0.000521672500332262[/C][C]1.07392673202998[/C][/ROW]
[ROW][C]29[/C][C]105.72[/C][C]105.719460105367[/C][C]0.0799017147403285[/C][C]0.00053989463314945[/C][C]-0.681077595004771[/C][/ROW]
[ROW][C]30[/C][C]105.74[/C][C]105.739445617214[/C][C]0.0687517714910469[/C][C]0.000554382785935683[/C][C]-0.665373333127532[/C][/ROW]
[ROW][C]31[/C][C]105.74[/C][C]105.739432083502[/C][C]0.0559555496612175[/C][C]0.000567916497887755[/C][C]-0.763684705238315[/C][/ROW]
[ROW][C]32[/C][C]105.74[/C][C]105.739423118697[/C][C]0.0455415515773537[/C][C]0.00057688130312154[/C][C]-0.621549981318638[/C][/ROW]
[ROW][C]33[/C][C]105.95[/C][C]105.949444563502[/C][C]0.0761481398715597[/C][C]0.000555436498030725[/C][C]1.82679941144869[/C][/ROW]
[ROW][C]34[/C][C]106.17[/C][C]106.169459830441[/C][C]0.102919096149951[/C][C]0.0005401695586487[/C][C]1.59790640483198[/C][/ROW]
[ROW][C]35[/C][C]106.34[/C][C]106.339465624829[/C][C]0.115402713987890[/C][C]0.000534375170899903[/C][C]0.745135995494691[/C][/ROW]
[ROW][C]36[/C][C]106.37[/C][C]106.369459620652[/C][C]0.099509610974413[/C][C]0.000540379348171504[/C][C]-0.948656153894987[/C][/ROW]
[ROW][C]37[/C][C]106.37[/C][C]106.386774852444[/C][C]0.0843515476891247[/C][C]-0.0167748524439978[/C][C]-0.965145018751399[/C][/ROW]
[ROW][C]38[/C][C]106.36[/C][C]106.359667329378[/C][C]0.063928410541856[/C][C]0.000332670621857405[/C][C]-1.15357188907121[/C][/ROW]
[ROW][C]39[/C][C]106.44[/C][C]106.439672759715[/C][C]0.0669212106096375[/C][C]0.000327240285006276[/C][C]0.178510453829056[/C][/ROW]
[ROW][C]40[/C][C]106.29[/C][C]106.289613075859[/C][C]0.0265359572112102[/C][C]0.000386924141393101[/C][C]-2.40945134794615[/C][/ROW]
[ROW][C]41[/C][C]106.23[/C][C]106.229593698370[/C][C]0.0104275588418632[/C][C]0.00040630163009411[/C][C]-0.961214208414722[/C][/ROW]
[ROW][C]42[/C][C]106.23[/C][C]106.229591797994[/C][C]0.00848669384763967[/C][C]0.000408202005607816[/C][C]-0.115827359427076[/C][/ROW]
[ROW][C]43[/C][C]106.23[/C][C]106.229590539192[/C][C]0.00690718277773937[/C][C]0.000409460808479994[/C][C]-0.0942693049984303[/C][/ROW]
[ROW][C]44[/C][C]106.23[/C][C]106.229589705344[/C][C]0.00562170024160942[/C][C]0.000410294656446816[/C][C]-0.0767246436068602[/C][/ROW]
[ROW][C]45[/C][C]106.34[/C][C]106.339599961054[/C][C]0.0250467851818298[/C][C]0.000400038946161406[/C][C]1.15943279787802[/C][/ROW]
[ROW][C]46[/C][C]106.44[/C][C]106.439605955061[/C][C]0.0389955154595105[/C][C]0.000394044938584992[/C][C]0.83258115499392[/C][/ROW]
[ROW][C]47[/C][C]106.44[/C][C]106.439603416928[/C][C]0.0317385746477565[/C][C]0.000396583071970762[/C][C]-0.433163251940526[/C][/ROW]
[ROW][C]48[/C][C]106.48[/C][C]106.479603854579[/C][C]0.0332759866165655[/C][C]0.000396145420547206[/C][C]0.0917682266489144[/C][/ROW]
[ROW][C]49[/C][C]106.5[/C][C]106.505118072273[/C][C]0.0318411940036946[/C][C]-0.00511807227287477[/C][C]-0.0899165989108308[/C][/ROW]
[ROW][C]50[/C][C]106.57[/C][C]106.569260897074[/C][C]0.037778951324272[/C][C]0.000739102925939693[/C][C]0.339590706536093[/C][/ROW]
[ROW][C]51[/C][C]106.4[/C][C]106.399205020961[/C][C]-0.000907727381292717[/C][C]0.000794979039216034[/C][C]-2.30788000980989[/C][/ROW]
[ROW][C]52[/C][C]106.37[/C][C]106.369198654227[/C][C]-0.00632348611387767[/C][C]0.000801345772593357[/C][C]-0.323145614471233[/C][/ROW]
[ROW][C]53[/C][C]106.25[/C][C]106.249178407179[/C][C]-0.0274827513435613[/C][C]0.000821592821104137[/C][C]-1.26269159907276[/C][/ROW]
[ROW][C]54[/C][C]106.21[/C][C]106.209176592668[/C][C]-0.0298124748644497[/C][C]0.000823407332374508[/C][C]-0.139039868929669[/C][/ROW]
[ROW][C]55[/C][C]106.21[/C][C]106.209180110003[/C][C]-0.0242640361638870[/C][C]0.000819889996553154[/C][C]0.331154835263960[/C][/ROW]
[ROW][C]56[/C][C]106.24[/C][C]106.239185320707[/C][C]-0.0141652357373642[/C][C]0.000814679293100075[/C][C]0.602763417353139[/C][/ROW]
[ROW][C]57[/C][C]106.19[/C][C]106.189182520052[/C][C]-0.0208341059855329[/C][C]0.000817479947739854[/C][C]-0.39805261908213[/C][/ROW]
[ROW][C]58[/C][C]106.08[/C][C]106.079176848185[/C][C]-0.0374276775538321[/C][C]0.000823151814591872[/C][C]-0.990456617980347[/C][/ROW]
[ROW][C]59[/C][C]106.13[/C][C]106.129181374549[/C][C]-0.0211577482227694[/C][C]0.000818625450808103[/C][C]0.971149592792165[/C][/ROW]
[ROW][C]60[/C][C]106.09[/C][C]106.089180580573[/C][C]-0.0246641899518216[/C][C]0.000819419426550609[/C][C]-0.209300528484211[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61598&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61598&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
1103.63103.63000
2103.64103.6394870710810.0006489104882088420.0005129289192512490.0696065340614257
3103.66103.6594526068850.002152828187893850.0005473931150855970.228816834469941
4103.77103.7692811523340.01373071627881760.0007188476658782521.25458131126306
5103.88103.8791480557600.02628050088312730.000851944240155381.10537017977190
6103.91103.9091436714710.02682882668412440.0008563285285645790.0422866014374643
7103.91103.909170246960.02254584932954390.00082975304009546-0.302820103381684
8103.92103.9191805842540.02043583721685530.000819415746001782-0.140884834159692
9104.05104.0491060205950.03950587008146750.0008939794052764621.22601287192371
10104.23104.2290274303880.0645192108322720.0009725696121246451.56831943482443
11104.3104.2990249185890.06550970858295710.0009750814105284570.0610814551868003
12104.31104.3090457149980.05537813566930070.00095428500233726-0.617957149978106
13104.31104.3335569550220.0500146811198818-0.0235569550220304-0.402404334177696
14104.34104.3388705708210.04194039266301870.00112942917926398-0.42032620218643
15104.55104.5489631415370.07306445515152390.001036858463097041.86784281917169
16104.65104.6489752262340.07806095710062350.001024773766437530.299310728749698
17104.73104.728975934640.07842103480727050.001024065359924980.0215442088356734
18104.75104.7489585574840.06756457067063050.00104144251627414-0.649048239773017
19104.75104.7489421970100.05500300569107090.00105780299042050-0.750590883487078
20104.76104.7589333263750.04663344963020880.00106667362483166-0.499931189073008
21104.94104.9389547245020.07144180792524650.001045275497763371.48151251059090
22105.29105.2889911031710.1232653147800590.001008896828661743.09433629892012
23105.38105.3789875671450.1170760069314460.0010124328549743-0.369520630496941
24105.43105.4289817638050.1045951653654420.00101823619525671-0.7450943267097
25105.43105.4566621681380.0904927124064977-0.0266621681380912-0.92733967289765
26105.42105.4194283093930.06720616181748160.000571690606598565-1.28507598990847
27105.52105.519443021570.07331392882809360.0005569784299908950.364241115792154
28105.69105.6894783275000.0913163919714020.0005216725003322621.07392673202998
29105.72105.7194601053670.07990171474032850.00053989463314945-0.681077595004771
30105.74105.7394456172140.06875177149104690.000554382785935683-0.665373333127532
31105.74105.7394320835020.05595554966121750.000567916497887755-0.763684705238315
32105.74105.7394231186970.04554155157735370.00057688130312154-0.621549981318638
33105.95105.9494445635020.07614813987155970.0005554364980307251.82679941144869
34106.17106.1694598304410.1029190961499510.00054016955864871.59790640483198
35106.34106.3394656248290.1154027139878900.0005343751708999030.745135995494691
36106.37106.3694596206520.0995096109744130.000540379348171504-0.948656153894987
37106.37106.3867748524440.0843515476891247-0.0167748524439978-0.965145018751399
38106.36106.3596673293780.0639284105418560.000332670621857405-1.15357188907121
39106.44106.4396727597150.06692121060963750.0003272402850062760.178510453829056
40106.29106.2896130758590.02653595721121020.000386924141393101-2.40945134794615
41106.23106.2295936983700.01042755884186320.00040630163009411-0.961214208414722
42106.23106.2295917979940.008486693847639670.000408202005607816-0.115827359427076
43106.23106.2295905391920.006907182777739370.000409460808479994-0.0942693049984303
44106.23106.2295897053440.005621700241609420.000410294656446816-0.0767246436068602
45106.34106.3395999610540.02504678518182980.0004000389461614061.15943279787802
46106.44106.4396059550610.03899551545951050.0003940449385849920.83258115499392
47106.44106.4396034169280.03173857464775650.000396583071970762-0.433163251940526
48106.48106.4796038545790.03327598661656550.0003961454205472060.0917682266489144
49106.5106.5051180722730.0318411940036946-0.00511807227287477-0.0899165989108308
50106.57106.5692608970740.0377789513242720.0007391029259396930.339590706536093
51106.4106.399205020961-0.0009077273812927170.000794979039216034-2.30788000980989
52106.37106.369198654227-0.006323486113877670.000801345772593357-0.323145614471233
53106.25106.249178407179-0.02748275134356130.000821592821104137-1.26269159907276
54106.21106.209176592668-0.02981247486444970.000823407332374508-0.139039868929669
55106.21106.209180110003-0.02426403616388700.0008198899965531540.331154835263960
56106.24106.239185320707-0.01416523573736420.0008146792931000750.602763417353139
57106.19106.189182520052-0.02083410598553290.000817479947739854-0.39805261908213
58106.08106.079176848185-0.03742767755383210.000823151814591872-0.990456617980347
59106.13106.129181374549-0.02115774822276940.0008186254508081030.971149592792165
60106.09106.089180580573-0.02466418995182160.000819419426550609-0.209300528484211



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')