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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 12:05:11 -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/t1259953542lbcne2lg2kfz3oe.htm/, Retrieved Sun, 28 Apr 2024 07:48:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64046, Retrieved Sun, 28 Apr 2024 07:48:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91
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]
-   PD      [Structural Time Series Models] [] [2009-12-04 19:05:11] [c5f9f441970441f2f938cd843072158d] [Current]
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Dataseries X:
14.9
18.6
19.1
18.8
18.2
18
19
20.7
21.2
20.7
19.6
18.6
18.7
23.8
24.9
24.8
23.8
22.3
21.7
20.7
19.7
18.4
17.4
17
18
23.8
25.5
25.6
23.7
22
21.3
20.7
20.4
20.3
20.4
19.8
19.5
23.1
23.5
23.5
22.9
21.9
21.5
20.5
20.2
19.4
19.2
18.8
18.8
22.6
23.3
23
21.4
19.9
18.8
18.6
18.4
18.6
19.9
19.2
18.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64046&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]6 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=64046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64046&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
114.914.9000
218.618.49636633637121.959522008949630.1036336636288482.67574541083026
319.119.00179993658950.7891890667212250.0982000634105242-1.09926465742992
418.818.6893326054695-0.08197203010778480.110667394530525-0.88351553292114
518.218.0852634657129-0.4954024087138590.114736534287079-0.421087081668346
61817.8804873802303-0.2651803929172770.1195126197696780.234513407124899
71918.87503957465050.7328057231108060.1249604253494991.01659379848851
820.720.57900404500881.502191784235420.1209959549912070.783731637440376
921.221.09268287475930.7190539880751380.107317125240685-0.797739789429887
1020.720.5906039300775-0.2483750699643520.109396069922484-0.985467253946108
1119.619.4874950900653-0.925528564890050.112504909934693-0.689779358614831
1218.618.4825677346654-0.9884313919602320.117432265334611-0.0640756815651315
1318.719.58024503294320.63599789298586-0.8802450329432041.81717330558587
1423.823.63367046622592.961769532732860.1663295337741432.25596283857556
1524.924.78995533015261.531090069446190.110044669847427-1.4191781479967
1624.824.66723427672330.2306954318525040.132765723276674-1.32112552682345
1723.823.6597219728002-0.7438697023914680.140278027199789-0.992692153068098
1822.322.1596515174833-1.339164822299580.140348482516665-0.606393989794981
1921.721.5433064816194-0.7701366503384260.1566935183805790.579637914084658
2020.720.5478414789925-0.9475240771017230.152158521007479-0.180694901995688
2119.719.5526192416813-0.9850740145220.147380758318688-0.0382500747013373
2218.418.2556005298177-1.230650061156130.144399470182251-0.250154934121929
2317.417.2348323352030-1.065412435206250.1651676647970270.168318936995219
241716.8570602808589-0.5242304970442360.1429397191411270.551288476205146
251819.35464240978771.83813004492188-1.354642409787692.52028624841156
2623.823.59061285656513.562457804464020.2093871434348751.71548337349918
2725.525.40346663232822.186121325550860.0965333676718153-1.38271892324709
2825.625.47615489452880.5326958906039110.123845105471156-1.67984582071548
2923.723.5659805432428-1.380120507871080.134019456757183-1.94841302878142
302221.8612771969591-1.634321206657320.138722803040948-0.258940390083650
3121.321.1122099702114-0.9410267745205320.1877900297886230.706221206934525
3220.720.5393365378003-0.652703791446250.1606634621996730.293698904322016
3320.420.2341127210846-0.3805713428527560.1658872789154450.277206496272518
3420.320.1436842126631-0.1533403352038590.1563157873368670.231467846116482
3520.420.20303177991400.01324440590738850.1969682200860330.169692280945363
3619.819.7046967814650-0.3871763086747550.0953032185349647-0.407945161905656
3719.521.12972659548151.02794309669007-1.629726595481541.48271640034508
3823.122.86846193665771.551894712479670.2315380633423160.52597406976628
3923.523.40663476298930.7588025225386380.0933652370107018-0.80197510590265
4023.523.33315674441070.1102181773372890.166843255589294-0.658890555859676
4122.922.7583142710786-0.424027204318890.141685728921434-0.54418400017232
4221.921.7890760967952-0.8493070980783930.110923903204748-0.433209393622026
4321.521.3069770570496-0.562878539573460.1930229429503860.29176916548235
4420.520.3555221305564-0.8659727749560120.144477869443552-0.308745570150847
4520.220.0275731302723-0.446307785741070.1724268697276900.427489865582739
4619.419.2670945002758-0.6913718281482990.132905499724190-0.249633473705347
4719.218.9559171978162-0.3947851201921520.2440828021837890.302121020156532
4818.818.7431486512696-0.2529249548820530.05685134873035110.144553995138398
4918.820.43097652591201.2595856319138-1.630976525911961.56953414634139
5022.622.34426381885451.748049110479230.2557361811455200.492583844589775
5123.323.22001287066811.068461139279520.0799871293318805-0.689780782450303
522322.8526861167906-0.04719809323281690.147313883209369-1.13333102016186
5321.421.2679155967925-1.242583921271000.132084403207512-1.21760144990606
5419.919.8029482758241-1.415526052623460.0970517241758727-0.176166552709623
5518.818.5801599358129-1.265640784209190.219840064187060.152679956194470
5618.618.4499206974265-0.3826936950038750.1500793025734510.899410046192465
5718.418.2165947665365-0.2665373589246060.1834052334634660.118322133278772
5818.618.46512285912480.1340232375651580.1348771408752490.408029936691651
5919.919.60742422640060.9181778335914120.2925757735993690.798786383587909
6019.219.2338836305732-0.085342739094769-0.0338836305732321-1.02281489359146
6118.420.08257647863280.641142144687673-1.682576478632810.749424269891704

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 14.9 & 14.9 & 0 & 0 & 0 \tabularnewline
2 & 18.6 & 18.4963663363712 & 1.95952200894963 & 0.103633663628848 & 2.67574541083026 \tabularnewline
3 & 19.1 & 19.0017999365895 & 0.789189066721225 & 0.0982000634105242 & -1.09926465742992 \tabularnewline
4 & 18.8 & 18.6893326054695 & -0.0819720301077848 & 0.110667394530525 & -0.88351553292114 \tabularnewline
5 & 18.2 & 18.0852634657129 & -0.495402408713859 & 0.114736534287079 & -0.421087081668346 \tabularnewline
6 & 18 & 17.8804873802303 & -0.265180392917277 & 0.119512619769678 & 0.234513407124899 \tabularnewline
7 & 19 & 18.8750395746505 & 0.732805723110806 & 0.124960425349499 & 1.01659379848851 \tabularnewline
8 & 20.7 & 20.5790040450088 & 1.50219178423542 & 0.120995954991207 & 0.783731637440376 \tabularnewline
9 & 21.2 & 21.0926828747593 & 0.719053988075138 & 0.107317125240685 & -0.797739789429887 \tabularnewline
10 & 20.7 & 20.5906039300775 & -0.248375069964352 & 0.109396069922484 & -0.985467253946108 \tabularnewline
11 & 19.6 & 19.4874950900653 & -0.92552856489005 & 0.112504909934693 & -0.689779358614831 \tabularnewline
12 & 18.6 & 18.4825677346654 & -0.988431391960232 & 0.117432265334611 & -0.0640756815651315 \tabularnewline
13 & 18.7 & 19.5802450329432 & 0.63599789298586 & -0.880245032943204 & 1.81717330558587 \tabularnewline
14 & 23.8 & 23.6336704662259 & 2.96176953273286 & 0.166329533774143 & 2.25596283857556 \tabularnewline
15 & 24.9 & 24.7899553301526 & 1.53109006944619 & 0.110044669847427 & -1.4191781479967 \tabularnewline
16 & 24.8 & 24.6672342767233 & 0.230695431852504 & 0.132765723276674 & -1.32112552682345 \tabularnewline
17 & 23.8 & 23.6597219728002 & -0.743869702391468 & 0.140278027199789 & -0.992692153068098 \tabularnewline
18 & 22.3 & 22.1596515174833 & -1.33916482229958 & 0.140348482516665 & -0.606393989794981 \tabularnewline
19 & 21.7 & 21.5433064816194 & -0.770136650338426 & 0.156693518380579 & 0.579637914084658 \tabularnewline
20 & 20.7 & 20.5478414789925 & -0.947524077101723 & 0.152158521007479 & -0.180694901995688 \tabularnewline
21 & 19.7 & 19.5526192416813 & -0.985074014522 & 0.147380758318688 & -0.0382500747013373 \tabularnewline
22 & 18.4 & 18.2556005298177 & -1.23065006115613 & 0.144399470182251 & -0.250154934121929 \tabularnewline
23 & 17.4 & 17.2348323352030 & -1.06541243520625 & 0.165167664797027 & 0.168318936995219 \tabularnewline
24 & 17 & 16.8570602808589 & -0.524230497044236 & 0.142939719141127 & 0.551288476205146 \tabularnewline
25 & 18 & 19.3546424097877 & 1.83813004492188 & -1.35464240978769 & 2.52028624841156 \tabularnewline
26 & 23.8 & 23.5906128565651 & 3.56245780446402 & 0.209387143434875 & 1.71548337349918 \tabularnewline
27 & 25.5 & 25.4034666323282 & 2.18612132555086 & 0.0965333676718153 & -1.38271892324709 \tabularnewline
28 & 25.6 & 25.4761548945288 & 0.532695890603911 & 0.123845105471156 & -1.67984582071548 \tabularnewline
29 & 23.7 & 23.5659805432428 & -1.38012050787108 & 0.134019456757183 & -1.94841302878142 \tabularnewline
30 & 22 & 21.8612771969591 & -1.63432120665732 & 0.138722803040948 & -0.258940390083650 \tabularnewline
31 & 21.3 & 21.1122099702114 & -0.941026774520532 & 0.187790029788623 & 0.706221206934525 \tabularnewline
32 & 20.7 & 20.5393365378003 & -0.65270379144625 & 0.160663462199673 & 0.293698904322016 \tabularnewline
33 & 20.4 & 20.2341127210846 & -0.380571342852756 & 0.165887278915445 & 0.277206496272518 \tabularnewline
34 & 20.3 & 20.1436842126631 & -0.153340335203859 & 0.156315787336867 & 0.231467846116482 \tabularnewline
35 & 20.4 & 20.2030317799140 & 0.0132444059073885 & 0.196968220086033 & 0.169692280945363 \tabularnewline
36 & 19.8 & 19.7046967814650 & -0.387176308674755 & 0.0953032185349647 & -0.407945161905656 \tabularnewline
37 & 19.5 & 21.1297265954815 & 1.02794309669007 & -1.62972659548154 & 1.48271640034508 \tabularnewline
38 & 23.1 & 22.8684619366577 & 1.55189471247967 & 0.231538063342316 & 0.52597406976628 \tabularnewline
39 & 23.5 & 23.4066347629893 & 0.758802522538638 & 0.0933652370107018 & -0.80197510590265 \tabularnewline
40 & 23.5 & 23.3331567444107 & 0.110218177337289 & 0.166843255589294 & -0.658890555859676 \tabularnewline
41 & 22.9 & 22.7583142710786 & -0.42402720431889 & 0.141685728921434 & -0.54418400017232 \tabularnewline
42 & 21.9 & 21.7890760967952 & -0.849307098078393 & 0.110923903204748 & -0.433209393622026 \tabularnewline
43 & 21.5 & 21.3069770570496 & -0.56287853957346 & 0.193022942950386 & 0.29176916548235 \tabularnewline
44 & 20.5 & 20.3555221305564 & -0.865972774956012 & 0.144477869443552 & -0.308745570150847 \tabularnewline
45 & 20.2 & 20.0275731302723 & -0.44630778574107 & 0.172426869727690 & 0.427489865582739 \tabularnewline
46 & 19.4 & 19.2670945002758 & -0.691371828148299 & 0.132905499724190 & -0.249633473705347 \tabularnewline
47 & 19.2 & 18.9559171978162 & -0.394785120192152 & 0.244082802183789 & 0.302121020156532 \tabularnewline
48 & 18.8 & 18.7431486512696 & -0.252924954882053 & 0.0568513487303511 & 0.144553995138398 \tabularnewline
49 & 18.8 & 20.4309765259120 & 1.2595856319138 & -1.63097652591196 & 1.56953414634139 \tabularnewline
50 & 22.6 & 22.3442638188545 & 1.74804911047923 & 0.255736181145520 & 0.492583844589775 \tabularnewline
51 & 23.3 & 23.2200128706681 & 1.06846113927952 & 0.0799871293318805 & -0.689780782450303 \tabularnewline
52 & 23 & 22.8526861167906 & -0.0471980932328169 & 0.147313883209369 & -1.13333102016186 \tabularnewline
53 & 21.4 & 21.2679155967925 & -1.24258392127100 & 0.132084403207512 & -1.21760144990606 \tabularnewline
54 & 19.9 & 19.8029482758241 & -1.41552605262346 & 0.0970517241758727 & -0.176166552709623 \tabularnewline
55 & 18.8 & 18.5801599358129 & -1.26564078420919 & 0.21984006418706 & 0.152679956194470 \tabularnewline
56 & 18.6 & 18.4499206974265 & -0.382693695003875 & 0.150079302573451 & 0.899410046192465 \tabularnewline
57 & 18.4 & 18.2165947665365 & -0.266537358924606 & 0.183405233463466 & 0.118322133278772 \tabularnewline
58 & 18.6 & 18.4651228591248 & 0.134023237565158 & 0.134877140875249 & 0.408029936691651 \tabularnewline
59 & 19.9 & 19.6074242264006 & 0.918177833591412 & 0.292575773599369 & 0.798786383587909 \tabularnewline
60 & 19.2 & 19.2338836305732 & -0.085342739094769 & -0.0338836305732321 & -1.02281489359146 \tabularnewline
61 & 18.4 & 20.0825764786328 & 0.641142144687673 & -1.68257647863281 & 0.749424269891704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64046&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]14.9[/C][C]14.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]18.6[/C][C]18.4963663363712[/C][C]1.95952200894963[/C][C]0.103633663628848[/C][C]2.67574541083026[/C][/ROW]
[ROW][C]3[/C][C]19.1[/C][C]19.0017999365895[/C][C]0.789189066721225[/C][C]0.0982000634105242[/C][C]-1.09926465742992[/C][/ROW]
[ROW][C]4[/C][C]18.8[/C][C]18.6893326054695[/C][C]-0.0819720301077848[/C][C]0.110667394530525[/C][C]-0.88351553292114[/C][/ROW]
[ROW][C]5[/C][C]18.2[/C][C]18.0852634657129[/C][C]-0.495402408713859[/C][C]0.114736534287079[/C][C]-0.421087081668346[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]17.8804873802303[/C][C]-0.265180392917277[/C][C]0.119512619769678[/C][C]0.234513407124899[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]18.8750395746505[/C][C]0.732805723110806[/C][C]0.124960425349499[/C][C]1.01659379848851[/C][/ROW]
[ROW][C]8[/C][C]20.7[/C][C]20.5790040450088[/C][C]1.50219178423542[/C][C]0.120995954991207[/C][C]0.783731637440376[/C][/ROW]
[ROW][C]9[/C][C]21.2[/C][C]21.0926828747593[/C][C]0.719053988075138[/C][C]0.107317125240685[/C][C]-0.797739789429887[/C][/ROW]
[ROW][C]10[/C][C]20.7[/C][C]20.5906039300775[/C][C]-0.248375069964352[/C][C]0.109396069922484[/C][C]-0.985467253946108[/C][/ROW]
[ROW][C]11[/C][C]19.6[/C][C]19.4874950900653[/C][C]-0.92552856489005[/C][C]0.112504909934693[/C][C]-0.689779358614831[/C][/ROW]
[ROW][C]12[/C][C]18.6[/C][C]18.4825677346654[/C][C]-0.988431391960232[/C][C]0.117432265334611[/C][C]-0.0640756815651315[/C][/ROW]
[ROW][C]13[/C][C]18.7[/C][C]19.5802450329432[/C][C]0.63599789298586[/C][C]-0.880245032943204[/C][C]1.81717330558587[/C][/ROW]
[ROW][C]14[/C][C]23.8[/C][C]23.6336704662259[/C][C]2.96176953273286[/C][C]0.166329533774143[/C][C]2.25596283857556[/C][/ROW]
[ROW][C]15[/C][C]24.9[/C][C]24.7899553301526[/C][C]1.53109006944619[/C][C]0.110044669847427[/C][C]-1.4191781479967[/C][/ROW]
[ROW][C]16[/C][C]24.8[/C][C]24.6672342767233[/C][C]0.230695431852504[/C][C]0.132765723276674[/C][C]-1.32112552682345[/C][/ROW]
[ROW][C]17[/C][C]23.8[/C][C]23.6597219728002[/C][C]-0.743869702391468[/C][C]0.140278027199789[/C][C]-0.992692153068098[/C][/ROW]
[ROW][C]18[/C][C]22.3[/C][C]22.1596515174833[/C][C]-1.33916482229958[/C][C]0.140348482516665[/C][C]-0.606393989794981[/C][/ROW]
[ROW][C]19[/C][C]21.7[/C][C]21.5433064816194[/C][C]-0.770136650338426[/C][C]0.156693518380579[/C][C]0.579637914084658[/C][/ROW]
[ROW][C]20[/C][C]20.7[/C][C]20.5478414789925[/C][C]-0.947524077101723[/C][C]0.152158521007479[/C][C]-0.180694901995688[/C][/ROW]
[ROW][C]21[/C][C]19.7[/C][C]19.5526192416813[/C][C]-0.985074014522[/C][C]0.147380758318688[/C][C]-0.0382500747013373[/C][/ROW]
[ROW][C]22[/C][C]18.4[/C][C]18.2556005298177[/C][C]-1.23065006115613[/C][C]0.144399470182251[/C][C]-0.250154934121929[/C][/ROW]
[ROW][C]23[/C][C]17.4[/C][C]17.2348323352030[/C][C]-1.06541243520625[/C][C]0.165167664797027[/C][C]0.168318936995219[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]16.8570602808589[/C][C]-0.524230497044236[/C][C]0.142939719141127[/C][C]0.551288476205146[/C][/ROW]
[ROW][C]25[/C][C]18[/C][C]19.3546424097877[/C][C]1.83813004492188[/C][C]-1.35464240978769[/C][C]2.52028624841156[/C][/ROW]
[ROW][C]26[/C][C]23.8[/C][C]23.5906128565651[/C][C]3.56245780446402[/C][C]0.209387143434875[/C][C]1.71548337349918[/C][/ROW]
[ROW][C]27[/C][C]25.5[/C][C]25.4034666323282[/C][C]2.18612132555086[/C][C]0.0965333676718153[/C][C]-1.38271892324709[/C][/ROW]
[ROW][C]28[/C][C]25.6[/C][C]25.4761548945288[/C][C]0.532695890603911[/C][C]0.123845105471156[/C][C]-1.67984582071548[/C][/ROW]
[ROW][C]29[/C][C]23.7[/C][C]23.5659805432428[/C][C]-1.38012050787108[/C][C]0.134019456757183[/C][C]-1.94841302878142[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.8612771969591[/C][C]-1.63432120665732[/C][C]0.138722803040948[/C][C]-0.258940390083650[/C][/ROW]
[ROW][C]31[/C][C]21.3[/C][C]21.1122099702114[/C][C]-0.941026774520532[/C][C]0.187790029788623[/C][C]0.706221206934525[/C][/ROW]
[ROW][C]32[/C][C]20.7[/C][C]20.5393365378003[/C][C]-0.65270379144625[/C][C]0.160663462199673[/C][C]0.293698904322016[/C][/ROW]
[ROW][C]33[/C][C]20.4[/C][C]20.2341127210846[/C][C]-0.380571342852756[/C][C]0.165887278915445[/C][C]0.277206496272518[/C][/ROW]
[ROW][C]34[/C][C]20.3[/C][C]20.1436842126631[/C][C]-0.153340335203859[/C][C]0.156315787336867[/C][C]0.231467846116482[/C][/ROW]
[ROW][C]35[/C][C]20.4[/C][C]20.2030317799140[/C][C]0.0132444059073885[/C][C]0.196968220086033[/C][C]0.169692280945363[/C][/ROW]
[ROW][C]36[/C][C]19.8[/C][C]19.7046967814650[/C][C]-0.387176308674755[/C][C]0.0953032185349647[/C][C]-0.407945161905656[/C][/ROW]
[ROW][C]37[/C][C]19.5[/C][C]21.1297265954815[/C][C]1.02794309669007[/C][C]-1.62972659548154[/C][C]1.48271640034508[/C][/ROW]
[ROW][C]38[/C][C]23.1[/C][C]22.8684619366577[/C][C]1.55189471247967[/C][C]0.231538063342316[/C][C]0.52597406976628[/C][/ROW]
[ROW][C]39[/C][C]23.5[/C][C]23.4066347629893[/C][C]0.758802522538638[/C][C]0.0933652370107018[/C][C]-0.80197510590265[/C][/ROW]
[ROW][C]40[/C][C]23.5[/C][C]23.3331567444107[/C][C]0.110218177337289[/C][C]0.166843255589294[/C][C]-0.658890555859676[/C][/ROW]
[ROW][C]41[/C][C]22.9[/C][C]22.7583142710786[/C][C]-0.42402720431889[/C][C]0.141685728921434[/C][C]-0.54418400017232[/C][/ROW]
[ROW][C]42[/C][C]21.9[/C][C]21.7890760967952[/C][C]-0.849307098078393[/C][C]0.110923903204748[/C][C]-0.433209393622026[/C][/ROW]
[ROW][C]43[/C][C]21.5[/C][C]21.3069770570496[/C][C]-0.56287853957346[/C][C]0.193022942950386[/C][C]0.29176916548235[/C][/ROW]
[ROW][C]44[/C][C]20.5[/C][C]20.3555221305564[/C][C]-0.865972774956012[/C][C]0.144477869443552[/C][C]-0.308745570150847[/C][/ROW]
[ROW][C]45[/C][C]20.2[/C][C]20.0275731302723[/C][C]-0.44630778574107[/C][C]0.172426869727690[/C][C]0.427489865582739[/C][/ROW]
[ROW][C]46[/C][C]19.4[/C][C]19.2670945002758[/C][C]-0.691371828148299[/C][C]0.132905499724190[/C][C]-0.249633473705347[/C][/ROW]
[ROW][C]47[/C][C]19.2[/C][C]18.9559171978162[/C][C]-0.394785120192152[/C][C]0.244082802183789[/C][C]0.302121020156532[/C][/ROW]
[ROW][C]48[/C][C]18.8[/C][C]18.7431486512696[/C][C]-0.252924954882053[/C][C]0.0568513487303511[/C][C]0.144553995138398[/C][/ROW]
[ROW][C]49[/C][C]18.8[/C][C]20.4309765259120[/C][C]1.2595856319138[/C][C]-1.63097652591196[/C][C]1.56953414634139[/C][/ROW]
[ROW][C]50[/C][C]22.6[/C][C]22.3442638188545[/C][C]1.74804911047923[/C][C]0.255736181145520[/C][C]0.492583844589775[/C][/ROW]
[ROW][C]51[/C][C]23.3[/C][C]23.2200128706681[/C][C]1.06846113927952[/C][C]0.0799871293318805[/C][C]-0.689780782450303[/C][/ROW]
[ROW][C]52[/C][C]23[/C][C]22.8526861167906[/C][C]-0.0471980932328169[/C][C]0.147313883209369[/C][C]-1.13333102016186[/C][/ROW]
[ROW][C]53[/C][C]21.4[/C][C]21.2679155967925[/C][C]-1.24258392127100[/C][C]0.132084403207512[/C][C]-1.21760144990606[/C][/ROW]
[ROW][C]54[/C][C]19.9[/C][C]19.8029482758241[/C][C]-1.41552605262346[/C][C]0.0970517241758727[/C][C]-0.176166552709623[/C][/ROW]
[ROW][C]55[/C][C]18.8[/C][C]18.5801599358129[/C][C]-1.26564078420919[/C][C]0.21984006418706[/C][C]0.152679956194470[/C][/ROW]
[ROW][C]56[/C][C]18.6[/C][C]18.4499206974265[/C][C]-0.382693695003875[/C][C]0.150079302573451[/C][C]0.899410046192465[/C][/ROW]
[ROW][C]57[/C][C]18.4[/C][C]18.2165947665365[/C][C]-0.266537358924606[/C][C]0.183405233463466[/C][C]0.118322133278772[/C][/ROW]
[ROW][C]58[/C][C]18.6[/C][C]18.4651228591248[/C][C]0.134023237565158[/C][C]0.134877140875249[/C][C]0.408029936691651[/C][/ROW]
[ROW][C]59[/C][C]19.9[/C][C]19.6074242264006[/C][C]0.918177833591412[/C][C]0.292575773599369[/C][C]0.798786383587909[/C][/ROW]
[ROW][C]60[/C][C]19.2[/C][C]19.2338836305732[/C][C]-0.085342739094769[/C][C]-0.0338836305732321[/C][C]-1.02281489359146[/C][/ROW]
[ROW][C]61[/C][C]18.4[/C][C]20.0825764786328[/C][C]0.641142144687673[/C][C]-1.68257647863281[/C][C]0.749424269891704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64046&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
114.914.9000
218.618.49636633637121.959522008949630.1036336636288482.67574541083026
319.119.00179993658950.7891890667212250.0982000634105242-1.09926465742992
418.818.6893326054695-0.08197203010778480.110667394530525-0.88351553292114
518.218.0852634657129-0.4954024087138590.114736534287079-0.421087081668346
61817.8804873802303-0.2651803929172770.1195126197696780.234513407124899
71918.87503957465050.7328057231108060.1249604253494991.01659379848851
820.720.57900404500881.502191784235420.1209959549912070.783731637440376
921.221.09268287475930.7190539880751380.107317125240685-0.797739789429887
1020.720.5906039300775-0.2483750699643520.109396069922484-0.985467253946108
1119.619.4874950900653-0.925528564890050.112504909934693-0.689779358614831
1218.618.4825677346654-0.9884313919602320.117432265334611-0.0640756815651315
1318.719.58024503294320.63599789298586-0.8802450329432041.81717330558587
1423.823.63367046622592.961769532732860.1663295337741432.25596283857556
1524.924.78995533015261.531090069446190.110044669847427-1.4191781479967
1624.824.66723427672330.2306954318525040.132765723276674-1.32112552682345
1723.823.6597219728002-0.7438697023914680.140278027199789-0.992692153068098
1822.322.1596515174833-1.339164822299580.140348482516665-0.606393989794981
1921.721.5433064816194-0.7701366503384260.1566935183805790.579637914084658
2020.720.5478414789925-0.9475240771017230.152158521007479-0.180694901995688
2119.719.5526192416813-0.9850740145220.147380758318688-0.0382500747013373
2218.418.2556005298177-1.230650061156130.144399470182251-0.250154934121929
2317.417.2348323352030-1.065412435206250.1651676647970270.168318936995219
241716.8570602808589-0.5242304970442360.1429397191411270.551288476205146
251819.35464240978771.83813004492188-1.354642409787692.52028624841156
2623.823.59061285656513.562457804464020.2093871434348751.71548337349918
2725.525.40346663232822.186121325550860.0965333676718153-1.38271892324709
2825.625.47615489452880.5326958906039110.123845105471156-1.67984582071548
2923.723.5659805432428-1.380120507871080.134019456757183-1.94841302878142
302221.8612771969591-1.634321206657320.138722803040948-0.258940390083650
3121.321.1122099702114-0.9410267745205320.1877900297886230.706221206934525
3220.720.5393365378003-0.652703791446250.1606634621996730.293698904322016
3320.420.2341127210846-0.3805713428527560.1658872789154450.277206496272518
3420.320.1436842126631-0.1533403352038590.1563157873368670.231467846116482
3520.420.20303177991400.01324440590738850.1969682200860330.169692280945363
3619.819.7046967814650-0.3871763086747550.0953032185349647-0.407945161905656
3719.521.12972659548151.02794309669007-1.629726595481541.48271640034508
3823.122.86846193665771.551894712479670.2315380633423160.52597406976628
3923.523.40663476298930.7588025225386380.0933652370107018-0.80197510590265
4023.523.33315674441070.1102181773372890.166843255589294-0.658890555859676
4122.922.7583142710786-0.424027204318890.141685728921434-0.54418400017232
4221.921.7890760967952-0.8493070980783930.110923903204748-0.433209393622026
4321.521.3069770570496-0.562878539573460.1930229429503860.29176916548235
4420.520.3555221305564-0.8659727749560120.144477869443552-0.308745570150847
4520.220.0275731302723-0.446307785741070.1724268697276900.427489865582739
4619.419.2670945002758-0.6913718281482990.132905499724190-0.249633473705347
4719.218.9559171978162-0.3947851201921520.2440828021837890.302121020156532
4818.818.7431486512696-0.2529249548820530.05685134873035110.144553995138398
4918.820.43097652591201.2595856319138-1.630976525911961.56953414634139
5022.622.34426381885451.748049110479230.2557361811455200.492583844589775
5123.323.22001287066811.068461139279520.0799871293318805-0.689780782450303
522322.8526861167906-0.04719809323281690.147313883209369-1.13333102016186
5321.421.2679155967925-1.242583921271000.132084403207512-1.21760144990606
5419.919.8029482758241-1.415526052623460.0970517241758727-0.176166552709623
5518.818.5801599358129-1.265640784209190.219840064187060.152679956194470
5618.618.4499206974265-0.3826936950038750.1500793025734510.899410046192465
5718.418.2165947665365-0.2665373589246060.1834052334634660.118322133278772
5818.618.46512285912480.1340232375651580.1348771408752490.408029936691651
5919.919.60742422640060.9181778335914120.2925757735993690.798786383587909
6019.219.2338836305732-0.085342739094769-0.0338836305732321-1.02281489359146
6118.420.08257647863280.641142144687673-1.682576478632810.749424269891704



Parameters (Session):
par1 = 36 ; par2 = -1.8 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
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')