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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 computationWed, 29 Dec 2010 19:20:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293650405e28ic4wwy3qlkoq.htm/, Retrieved Fri, 03 May 2024 12:35:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117055, Retrieved Fri, 03 May 2024 12:35:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Structural Time Series Models] [HPC Retail Sales] [2008-03-06 16:52:55] [74be16979710d4c4e7c6647856088456]
- R  D    [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
-  M D      [Structural Time Series Models] [Paper 'Decomposit...] [2010-12-20 18:19:34] [40c8b935cbad1b0be3c22a481f9723f7]
-               [Structural Time Series Models] [paper (15)] [2010-12-29 19:20:43] [f420459ea4e1f042529d081e77704a0f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9,3
14,2
17,3
23
16,3
18,4
14,2
9,1
5,9
7,2
6,8
8
14,3
14,6
17,5
17,2
17,2
14,1
10,4
6,8
4,1
6,5
6,1
6,3
9,3
16,4
16,1
18
17,6
14
10,5
6,9
2,8
0,7
3,6
6,7
12,5
14,4
16,5
18,7
19,4
15,8
11,3
9,7
2,9
0,1
2,5
6,7
10,3
11,2
17,4
20,5
17
14,2
10,6
6,1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time29 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 29 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117055&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]29 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117055&T=0

As an alternative you can also use a QR Code:  
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 time29 seconds
R Server'George Udny Yule' @ 72.249.76.132






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
19.39.3000
214.213.47364768399314.105285116229110.03418128357742531.68788852455429
317.317.29543016526143.873820436812530.0348490013678868-0.0892405567329994
42322.79534403184425.214232697080060.03228783611895460.509927720206144
516.317.3813416949964-3.567008103006570.0357084361327118-3.31927989853951
618.417.9315456500646-0.1645891673179630.03586533490771241.28582079926591
714.214.5068170063931-2.858751250414220.0357224015960273-1.01815916274151
89.19.30995767617444-4.790891675499040.0356952743436134-0.730177514024716
95.95.73640677955879-3.784930703615650.03569510518038670.380163802782149
107.26.668702396510070.1131895514016170.03568967413455681.47314286086379
116.86.765981886452310.1000421261728290.0356896802857181-0.00496855773546678
1287.859891147224850.921333646640680.03568965563337230.310375169631545
1314.313.44858474948064.721088950881010.3683132531690011.54882440567234
1414.614.92591129292492.36626606932170-0.0829125414169277-0.84958897719745
1517.517.55637979931142.5859998832146-0.08371742453917420.0817086802834417
1617.217.54704154463280.445431463205550-0.077259704946118-0.804871946353704
1717.217.3448573903102-0.089355729033756-0.0768856241706202-0.202078951411528
1814.114.4696852798031-2.39153377730075-0.0769873692803231-0.870014689333337
1910.410.6293146781067-3.58887588211528-0.0770853671519543-0.452486970345829
206.86.89261909028666-3.71103053268482-0.0770883259601337-0.0461635876122528
214.14.0824410548707-2.96660213372933-0.07708785676656440.28132774434708
226.56.057869984119521.11728511764852-0.07709636758474941.54334625758912
236.16.271985877393760.370943139224908-0.0770957827090498-0.282050906804445
246.36.402369772305080.172154736367637-0.0770957679960285-0.0751243410166322
259.38.1337421361771.448918804338751.003929671060680.505960885246378
2616.415.89561718916466.21046188506448-0.02217718355538061.74806724176669
2716.116.67471622502001.70141134881811-0.0102880954333830-1.68436251440989
281818.0442913219421.42757394164245-0.00969374634490636-0.103111505668207
2917.617.78598816364460.0351377567760029-0.00899320686874115-0.526173893353219
301414.3715206331808-2.81555846617776-0.00908382354259766-1.07730621607979
3110.510.6086611234191-3.59840825732458-0.00912990866609655-0.295846728177796
326.96.91874545358752-3.67402735983675-0.00913122608017145-0.0285772931052124
332.82.85054464704267-3.99975645083775-0.0091313737432062-0.123096634323273
340.70.532452111570659-2.61009923175077-0.009133456741976960.52516686633244
353.63.068467124493311.64243914227045-0.009135853685973441.6070813907992
366.76.519154934816293.13670252794617-0.009135933232716120.56469869741193
3712.511.98797148988355.051237151566730.2686140103151070.748990607230812
3814.414.65499244936793.21514514973727-0.0447757660747581-0.679502314694195
3916.516.66795514907982.21811894259122-0.0427222348421502-0.373392408485900
4018.718.75611309362692.11084027258293-0.0425404052439581-0.0404271951585167
4119.419.57751750692951.04572338673201-0.0421220105536339-0.402493691940765
4215.816.2967735967418-2.52957437782729-0.0422107480296036-1.35114124319275
4311.311.5728483521771-4.34296682632066-0.0422940992592825-0.685299505689963
449.79.50340183540177-2.46420071737202-0.04226854276257480.710006461658452
452.93.33178409840791-5.52786085726114-0.0422696271729196-1.15779112849407
460.1-0.0800458133226463-3.77926028947159-0.04227167366358040.660815536727616
472.51.933645950556351.00779888758642-0.04227378042942021.80908272748409
486.76.380911379470483.85003461507723-0.04227389856838621.07411238770690
4910.39.889864605188243.569623118686190.445786991852384-0.108893898966875
5011.211.43896926195701.99312965582028-0.0546680209929926-0.58610852315939
5117.417.07926042239905.01592935522485-0.05977603209672361.13390587630879
5220.520.70366183298423.86704460238085-0.0581787089543906-0.433169661578381
531717.7704896773926-1.75055927423806-0.0563687872568442-2.12284323097155
5414.214.4240655329188-3.06934169632711-0.0563956340049576-0.498381463079877
5510.610.7228084720207-3.59154743805578-0.0564153212829056-0.197346960054362
566.16.24910655899945-4.32053085181914-0.0564234546434887-0.275490888330518

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9.3 & 9.3 & 0 & 0 & 0 \tabularnewline
2 & 14.2 & 13.4736476839931 & 4.10528511622911 & 0.0341812835774253 & 1.68788852455429 \tabularnewline
3 & 17.3 & 17.2954301652614 & 3.87382043681253 & 0.0348490013678868 & -0.0892405567329994 \tabularnewline
4 & 23 & 22.7953440318442 & 5.21423269708006 & 0.0322878361189546 & 0.509927720206144 \tabularnewline
5 & 16.3 & 17.3813416949964 & -3.56700810300657 & 0.0357084361327118 & -3.31927989853951 \tabularnewline
6 & 18.4 & 17.9315456500646 & -0.164589167317963 & 0.0358653349077124 & 1.28582079926591 \tabularnewline
7 & 14.2 & 14.5068170063931 & -2.85875125041422 & 0.0357224015960273 & -1.01815916274151 \tabularnewline
8 & 9.1 & 9.30995767617444 & -4.79089167549904 & 0.0356952743436134 & -0.730177514024716 \tabularnewline
9 & 5.9 & 5.73640677955879 & -3.78493070361565 & 0.0356951051803867 & 0.380163802782149 \tabularnewline
10 & 7.2 & 6.66870239651007 & 0.113189551401617 & 0.0356896741345568 & 1.47314286086379 \tabularnewline
11 & 6.8 & 6.76598188645231 & 0.100042126172829 & 0.0356896802857181 & -0.00496855773546678 \tabularnewline
12 & 8 & 7.85989114722485 & 0.92133364664068 & 0.0356896556333723 & 0.310375169631545 \tabularnewline
13 & 14.3 & 13.4485847494806 & 4.72108895088101 & 0.368313253169001 & 1.54882440567234 \tabularnewline
14 & 14.6 & 14.9259112929249 & 2.36626606932170 & -0.0829125414169277 & -0.84958897719745 \tabularnewline
15 & 17.5 & 17.5563797993114 & 2.5859998832146 & -0.0837174245391742 & 0.0817086802834417 \tabularnewline
16 & 17.2 & 17.5470415446328 & 0.445431463205550 & -0.077259704946118 & -0.804871946353704 \tabularnewline
17 & 17.2 & 17.3448573903102 & -0.089355729033756 & -0.0768856241706202 & -0.202078951411528 \tabularnewline
18 & 14.1 & 14.4696852798031 & -2.39153377730075 & -0.0769873692803231 & -0.870014689333337 \tabularnewline
19 & 10.4 & 10.6293146781067 & -3.58887588211528 & -0.0770853671519543 & -0.452486970345829 \tabularnewline
20 & 6.8 & 6.89261909028666 & -3.71103053268482 & -0.0770883259601337 & -0.0461635876122528 \tabularnewline
21 & 4.1 & 4.0824410548707 & -2.96660213372933 & -0.0770878567665644 & 0.28132774434708 \tabularnewline
22 & 6.5 & 6.05786998411952 & 1.11728511764852 & -0.0770963675847494 & 1.54334625758912 \tabularnewline
23 & 6.1 & 6.27198587739376 & 0.370943139224908 & -0.0770957827090498 & -0.282050906804445 \tabularnewline
24 & 6.3 & 6.40236977230508 & 0.172154736367637 & -0.0770957679960285 & -0.0751243410166322 \tabularnewline
25 & 9.3 & 8.133742136177 & 1.44891880433875 & 1.00392967106068 & 0.505960885246378 \tabularnewline
26 & 16.4 & 15.8956171891646 & 6.21046188506448 & -0.0221771835553806 & 1.74806724176669 \tabularnewline
27 & 16.1 & 16.6747162250200 & 1.70141134881811 & -0.0102880954333830 & -1.68436251440989 \tabularnewline
28 & 18 & 18.044291321942 & 1.42757394164245 & -0.00969374634490636 & -0.103111505668207 \tabularnewline
29 & 17.6 & 17.7859881636446 & 0.0351377567760029 & -0.00899320686874115 & -0.526173893353219 \tabularnewline
30 & 14 & 14.3715206331808 & -2.81555846617776 & -0.00908382354259766 & -1.07730621607979 \tabularnewline
31 & 10.5 & 10.6086611234191 & -3.59840825732458 & -0.00912990866609655 & -0.295846728177796 \tabularnewline
32 & 6.9 & 6.91874545358752 & -3.67402735983675 & -0.00913122608017145 & -0.0285772931052124 \tabularnewline
33 & 2.8 & 2.85054464704267 & -3.99975645083775 & -0.0091313737432062 & -0.123096634323273 \tabularnewline
34 & 0.7 & 0.532452111570659 & -2.61009923175077 & -0.00913345674197696 & 0.52516686633244 \tabularnewline
35 & 3.6 & 3.06846712449331 & 1.64243914227045 & -0.00913585368597344 & 1.6070813907992 \tabularnewline
36 & 6.7 & 6.51915493481629 & 3.13670252794617 & -0.00913593323271612 & 0.56469869741193 \tabularnewline
37 & 12.5 & 11.9879714898835 & 5.05123715156673 & 0.268614010315107 & 0.748990607230812 \tabularnewline
38 & 14.4 & 14.6549924493679 & 3.21514514973727 & -0.0447757660747581 & -0.679502314694195 \tabularnewline
39 & 16.5 & 16.6679551490798 & 2.21811894259122 & -0.0427222348421502 & -0.373392408485900 \tabularnewline
40 & 18.7 & 18.7561130936269 & 2.11084027258293 & -0.0425404052439581 & -0.0404271951585167 \tabularnewline
41 & 19.4 & 19.5775175069295 & 1.04572338673201 & -0.0421220105536339 & -0.402493691940765 \tabularnewline
42 & 15.8 & 16.2967735967418 & -2.52957437782729 & -0.0422107480296036 & -1.35114124319275 \tabularnewline
43 & 11.3 & 11.5728483521771 & -4.34296682632066 & -0.0422940992592825 & -0.685299505689963 \tabularnewline
44 & 9.7 & 9.50340183540177 & -2.46420071737202 & -0.0422685427625748 & 0.710006461658452 \tabularnewline
45 & 2.9 & 3.33178409840791 & -5.52786085726114 & -0.0422696271729196 & -1.15779112849407 \tabularnewline
46 & 0.1 & -0.0800458133226463 & -3.77926028947159 & -0.0422716736635804 & 0.660815536727616 \tabularnewline
47 & 2.5 & 1.93364595055635 & 1.00779888758642 & -0.0422737804294202 & 1.80908272748409 \tabularnewline
48 & 6.7 & 6.38091137947048 & 3.85003461507723 & -0.0422738985683862 & 1.07411238770690 \tabularnewline
49 & 10.3 & 9.88986460518824 & 3.56962311868619 & 0.445786991852384 & -0.108893898966875 \tabularnewline
50 & 11.2 & 11.4389692619570 & 1.99312965582028 & -0.0546680209929926 & -0.58610852315939 \tabularnewline
51 & 17.4 & 17.0792604223990 & 5.01592935522485 & -0.0597760320967236 & 1.13390587630879 \tabularnewline
52 & 20.5 & 20.7036618329842 & 3.86704460238085 & -0.0581787089543906 & -0.433169661578381 \tabularnewline
53 & 17 & 17.7704896773926 & -1.75055927423806 & -0.0563687872568442 & -2.12284323097155 \tabularnewline
54 & 14.2 & 14.4240655329188 & -3.06934169632711 & -0.0563956340049576 & -0.498381463079877 \tabularnewline
55 & 10.6 & 10.7228084720207 & -3.59154743805578 & -0.0564153212829056 & -0.197346960054362 \tabularnewline
56 & 6.1 & 6.24910655899945 & -4.32053085181914 & -0.0564234546434887 & -0.275490888330518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117055&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]9.3[/C][C]9.3[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]14.2[/C][C]13.4736476839931[/C][C]4.10528511622911[/C][C]0.0341812835774253[/C][C]1.68788852455429[/C][/ROW]
[ROW][C]3[/C][C]17.3[/C][C]17.2954301652614[/C][C]3.87382043681253[/C][C]0.0348490013678868[/C][C]-0.0892405567329994[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]22.7953440318442[/C][C]5.21423269708006[/C][C]0.0322878361189546[/C][C]0.509927720206144[/C][/ROW]
[ROW][C]5[/C][C]16.3[/C][C]17.3813416949964[/C][C]-3.56700810300657[/C][C]0.0357084361327118[/C][C]-3.31927989853951[/C][/ROW]
[ROW][C]6[/C][C]18.4[/C][C]17.9315456500646[/C][C]-0.164589167317963[/C][C]0.0358653349077124[/C][C]1.28582079926591[/C][/ROW]
[ROW][C]7[/C][C]14.2[/C][C]14.5068170063931[/C][C]-2.85875125041422[/C][C]0.0357224015960273[/C][C]-1.01815916274151[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]9.30995767617444[/C][C]-4.79089167549904[/C][C]0.0356952743436134[/C][C]-0.730177514024716[/C][/ROW]
[ROW][C]9[/C][C]5.9[/C][C]5.73640677955879[/C][C]-3.78493070361565[/C][C]0.0356951051803867[/C][C]0.380163802782149[/C][/ROW]
[ROW][C]10[/C][C]7.2[/C][C]6.66870239651007[/C][C]0.113189551401617[/C][C]0.0356896741345568[/C][C]1.47314286086379[/C][/ROW]
[ROW][C]11[/C][C]6.8[/C][C]6.76598188645231[/C][C]0.100042126172829[/C][C]0.0356896802857181[/C][C]-0.00496855773546678[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]7.85989114722485[/C][C]0.92133364664068[/C][C]0.0356896556333723[/C][C]0.310375169631545[/C][/ROW]
[ROW][C]13[/C][C]14.3[/C][C]13.4485847494806[/C][C]4.72108895088101[/C][C]0.368313253169001[/C][C]1.54882440567234[/C][/ROW]
[ROW][C]14[/C][C]14.6[/C][C]14.9259112929249[/C][C]2.36626606932170[/C][C]-0.0829125414169277[/C][C]-0.84958897719745[/C][/ROW]
[ROW][C]15[/C][C]17.5[/C][C]17.5563797993114[/C][C]2.5859998832146[/C][C]-0.0837174245391742[/C][C]0.0817086802834417[/C][/ROW]
[ROW][C]16[/C][C]17.2[/C][C]17.5470415446328[/C][C]0.445431463205550[/C][C]-0.077259704946118[/C][C]-0.804871946353704[/C][/ROW]
[ROW][C]17[/C][C]17.2[/C][C]17.3448573903102[/C][C]-0.089355729033756[/C][C]-0.0768856241706202[/C][C]-0.202078951411528[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]14.4696852798031[/C][C]-2.39153377730075[/C][C]-0.0769873692803231[/C][C]-0.870014689333337[/C][/ROW]
[ROW][C]19[/C][C]10.4[/C][C]10.6293146781067[/C][C]-3.58887588211528[/C][C]-0.0770853671519543[/C][C]-0.452486970345829[/C][/ROW]
[ROW][C]20[/C][C]6.8[/C][C]6.89261909028666[/C][C]-3.71103053268482[/C][C]-0.0770883259601337[/C][C]-0.0461635876122528[/C][/ROW]
[ROW][C]21[/C][C]4.1[/C][C]4.0824410548707[/C][C]-2.96660213372933[/C][C]-0.0770878567665644[/C][C]0.28132774434708[/C][/ROW]
[ROW][C]22[/C][C]6.5[/C][C]6.05786998411952[/C][C]1.11728511764852[/C][C]-0.0770963675847494[/C][C]1.54334625758912[/C][/ROW]
[ROW][C]23[/C][C]6.1[/C][C]6.27198587739376[/C][C]0.370943139224908[/C][C]-0.0770957827090498[/C][C]-0.282050906804445[/C][/ROW]
[ROW][C]24[/C][C]6.3[/C][C]6.40236977230508[/C][C]0.172154736367637[/C][C]-0.0770957679960285[/C][C]-0.0751243410166322[/C][/ROW]
[ROW][C]25[/C][C]9.3[/C][C]8.133742136177[/C][C]1.44891880433875[/C][C]1.00392967106068[/C][C]0.505960885246378[/C][/ROW]
[ROW][C]26[/C][C]16.4[/C][C]15.8956171891646[/C][C]6.21046188506448[/C][C]-0.0221771835553806[/C][C]1.74806724176669[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]16.6747162250200[/C][C]1.70141134881811[/C][C]-0.0102880954333830[/C][C]-1.68436251440989[/C][/ROW]
[ROW][C]28[/C][C]18[/C][C]18.044291321942[/C][C]1.42757394164245[/C][C]-0.00969374634490636[/C][C]-0.103111505668207[/C][/ROW]
[ROW][C]29[/C][C]17.6[/C][C]17.7859881636446[/C][C]0.0351377567760029[/C][C]-0.00899320686874115[/C][C]-0.526173893353219[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3715206331808[/C][C]-2.81555846617776[/C][C]-0.00908382354259766[/C][C]-1.07730621607979[/C][/ROW]
[ROW][C]31[/C][C]10.5[/C][C]10.6086611234191[/C][C]-3.59840825732458[/C][C]-0.00912990866609655[/C][C]-0.295846728177796[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]6.91874545358752[/C][C]-3.67402735983675[/C][C]-0.00913122608017145[/C][C]-0.0285772931052124[/C][/ROW]
[ROW][C]33[/C][C]2.8[/C][C]2.85054464704267[/C][C]-3.99975645083775[/C][C]-0.0091313737432062[/C][C]-0.123096634323273[/C][/ROW]
[ROW][C]34[/C][C]0.7[/C][C]0.532452111570659[/C][C]-2.61009923175077[/C][C]-0.00913345674197696[/C][C]0.52516686633244[/C][/ROW]
[ROW][C]35[/C][C]3.6[/C][C]3.06846712449331[/C][C]1.64243914227045[/C][C]-0.00913585368597344[/C][C]1.6070813907992[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]6.51915493481629[/C][C]3.13670252794617[/C][C]-0.00913593323271612[/C][C]0.56469869741193[/C][/ROW]
[ROW][C]37[/C][C]12.5[/C][C]11.9879714898835[/C][C]5.05123715156673[/C][C]0.268614010315107[/C][C]0.748990607230812[/C][/ROW]
[ROW][C]38[/C][C]14.4[/C][C]14.6549924493679[/C][C]3.21514514973727[/C][C]-0.0447757660747581[/C][C]-0.679502314694195[/C][/ROW]
[ROW][C]39[/C][C]16.5[/C][C]16.6679551490798[/C][C]2.21811894259122[/C][C]-0.0427222348421502[/C][C]-0.373392408485900[/C][/ROW]
[ROW][C]40[/C][C]18.7[/C][C]18.7561130936269[/C][C]2.11084027258293[/C][C]-0.0425404052439581[/C][C]-0.0404271951585167[/C][/ROW]
[ROW][C]41[/C][C]19.4[/C][C]19.5775175069295[/C][C]1.04572338673201[/C][C]-0.0421220105536339[/C][C]-0.402493691940765[/C][/ROW]
[ROW][C]42[/C][C]15.8[/C][C]16.2967735967418[/C][C]-2.52957437782729[/C][C]-0.0422107480296036[/C][C]-1.35114124319275[/C][/ROW]
[ROW][C]43[/C][C]11.3[/C][C]11.5728483521771[/C][C]-4.34296682632066[/C][C]-0.0422940992592825[/C][C]-0.685299505689963[/C][/ROW]
[ROW][C]44[/C][C]9.7[/C][C]9.50340183540177[/C][C]-2.46420071737202[/C][C]-0.0422685427625748[/C][C]0.710006461658452[/C][/ROW]
[ROW][C]45[/C][C]2.9[/C][C]3.33178409840791[/C][C]-5.52786085726114[/C][C]-0.0422696271729196[/C][C]-1.15779112849407[/C][/ROW]
[ROW][C]46[/C][C]0.1[/C][C]-0.0800458133226463[/C][C]-3.77926028947159[/C][C]-0.0422716736635804[/C][C]0.660815536727616[/C][/ROW]
[ROW][C]47[/C][C]2.5[/C][C]1.93364595055635[/C][C]1.00779888758642[/C][C]-0.0422737804294202[/C][C]1.80908272748409[/C][/ROW]
[ROW][C]48[/C][C]6.7[/C][C]6.38091137947048[/C][C]3.85003461507723[/C][C]-0.0422738985683862[/C][C]1.07411238770690[/C][/ROW]
[ROW][C]49[/C][C]10.3[/C][C]9.88986460518824[/C][C]3.56962311868619[/C][C]0.445786991852384[/C][C]-0.108893898966875[/C][/ROW]
[ROW][C]50[/C][C]11.2[/C][C]11.4389692619570[/C][C]1.99312965582028[/C][C]-0.0546680209929926[/C][C]-0.58610852315939[/C][/ROW]
[ROW][C]51[/C][C]17.4[/C][C]17.0792604223990[/C][C]5.01592935522485[/C][C]-0.0597760320967236[/C][C]1.13390587630879[/C][/ROW]
[ROW][C]52[/C][C]20.5[/C][C]20.7036618329842[/C][C]3.86704460238085[/C][C]-0.0581787089543906[/C][C]-0.433169661578381[/C][/ROW]
[ROW][C]53[/C][C]17[/C][C]17.7704896773926[/C][C]-1.75055927423806[/C][C]-0.0563687872568442[/C][C]-2.12284323097155[/C][/ROW]
[ROW][C]54[/C][C]14.2[/C][C]14.4240655329188[/C][C]-3.06934169632711[/C][C]-0.0563956340049576[/C][C]-0.498381463079877[/C][/ROW]
[ROW][C]55[/C][C]10.6[/C][C]10.7228084720207[/C][C]-3.59154743805578[/C][C]-0.0564153212829056[/C][C]-0.197346960054362[/C][/ROW]
[ROW][C]56[/C][C]6.1[/C][C]6.24910655899945[/C][C]-4.32053085181914[/C][C]-0.0564234546434887[/C][C]-0.275490888330518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117055&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
19.39.3000
214.213.47364768399314.105285116229110.03418128357742531.68788852455429
317.317.29543016526143.873820436812530.0348490013678868-0.0892405567329994
42322.79534403184425.214232697080060.03228783611895460.509927720206144
516.317.3813416949964-3.567008103006570.0357084361327118-3.31927989853951
618.417.9315456500646-0.1645891673179630.03586533490771241.28582079926591
714.214.5068170063931-2.858751250414220.0357224015960273-1.01815916274151
89.19.30995767617444-4.790891675499040.0356952743436134-0.730177514024716
95.95.73640677955879-3.784930703615650.03569510518038670.380163802782149
107.26.668702396510070.1131895514016170.03568967413455681.47314286086379
116.86.765981886452310.1000421261728290.0356896802857181-0.00496855773546678
1287.859891147224850.921333646640680.03568965563337230.310375169631545
1314.313.44858474948064.721088950881010.3683132531690011.54882440567234
1414.614.92591129292492.36626606932170-0.0829125414169277-0.84958897719745
1517.517.55637979931142.5859998832146-0.08371742453917420.0817086802834417
1617.217.54704154463280.445431463205550-0.077259704946118-0.804871946353704
1717.217.3448573903102-0.089355729033756-0.0768856241706202-0.202078951411528
1814.114.4696852798031-2.39153377730075-0.0769873692803231-0.870014689333337
1910.410.6293146781067-3.58887588211528-0.0770853671519543-0.452486970345829
206.86.89261909028666-3.71103053268482-0.0770883259601337-0.0461635876122528
214.14.0824410548707-2.96660213372933-0.07708785676656440.28132774434708
226.56.057869984119521.11728511764852-0.07709636758474941.54334625758912
236.16.271985877393760.370943139224908-0.0770957827090498-0.282050906804445
246.36.402369772305080.172154736367637-0.0770957679960285-0.0751243410166322
259.38.1337421361771.448918804338751.003929671060680.505960885246378
2616.415.89561718916466.21046188506448-0.02217718355538061.74806724176669
2716.116.67471622502001.70141134881811-0.0102880954333830-1.68436251440989
281818.0442913219421.42757394164245-0.00969374634490636-0.103111505668207
2917.617.78598816364460.0351377567760029-0.00899320686874115-0.526173893353219
301414.3715206331808-2.81555846617776-0.00908382354259766-1.07730621607979
3110.510.6086611234191-3.59840825732458-0.00912990866609655-0.295846728177796
326.96.91874545358752-3.67402735983675-0.00913122608017145-0.0285772931052124
332.82.85054464704267-3.99975645083775-0.0091313737432062-0.123096634323273
340.70.532452111570659-2.61009923175077-0.009133456741976960.52516686633244
353.63.068467124493311.64243914227045-0.009135853685973441.6070813907992
366.76.519154934816293.13670252794617-0.009135933232716120.56469869741193
3712.511.98797148988355.051237151566730.2686140103151070.748990607230812
3814.414.65499244936793.21514514973727-0.0447757660747581-0.679502314694195
3916.516.66795514907982.21811894259122-0.0427222348421502-0.373392408485900
4018.718.75611309362692.11084027258293-0.0425404052439581-0.0404271951585167
4119.419.57751750692951.04572338673201-0.0421220105536339-0.402493691940765
4215.816.2967735967418-2.52957437782729-0.0422107480296036-1.35114124319275
4311.311.5728483521771-4.34296682632066-0.0422940992592825-0.685299505689963
449.79.50340183540177-2.46420071737202-0.04226854276257480.710006461658452
452.93.33178409840791-5.52786085726114-0.0422696271729196-1.15779112849407
460.1-0.0800458133226463-3.77926028947159-0.04227167366358040.660815536727616
472.51.933645950556351.00779888758642-0.04227378042942021.80908272748409
486.76.380911379470483.85003461507723-0.04227389856838621.07411238770690
4910.39.889864605188243.569623118686190.445786991852384-0.108893898966875
5011.211.43896926195701.99312965582028-0.0546680209929926-0.58610852315939
5117.417.07926042239905.01592935522485-0.05977603209672361.13390587630879
5220.520.70366183298423.86704460238085-0.0581787089543906-0.433169661578381
531717.7704896773926-1.75055927423806-0.0563687872568442-2.12284323097155
5414.214.4240655329188-3.06934169632711-0.0563956340049576-0.498381463079877
5510.610.7228084720207-3.59154743805578-0.0564153212829056-0.197346960054362
566.16.24910655899945-4.32053085181914-0.0564234546434887-0.275490888330518


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ; par2 = ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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$resid)
myfit <- mylevel+myseas
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time')
grid()
dev.off()
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='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')