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Description of Statistical Computation

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, 04 Dec 2009 06:55:02 -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/t12599349690rdck1lau54y43p.htm/, Retrieved Sun, 28 Apr 2024 16:26:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63527, Retrieved Sun, 28 Apr 2024 16:26:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

Tree of Dependent Computations

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] [SHWWS9klasmeth3] [2009-12-01 20:04:49] [a66d3a79ef9e5308cd94a469bc5ca464]
-   PD        [Structural Time Series Models] [WS9-StructuralTime] [2009-12-04 13:55:02] [30970b478e356ce7f8c2e9fca280b230] [Current]
-    D          [Structural Time Series Models] [] [2010-12-07 09:55:06] [fb3a7008aea9486db3846dc25434607b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10.9
10
9.2
9.2
9.5
9.6
9.5
9.1
8.9
9
10.1
10.3
10.2
9.6
9.2
9.3
9.4
9.4
9.2
9
9
9
9.8
10
9.8
9.3
9
9
9.1
9.1
9.1
9.2
8.8
8.3
8.4
8.1
7.7
7.9
7.9
8
7.9
7.6
7.1
6.8
6.5
6.9
8.2
8.7
8.3
7.9
7.5
7.8
8.3
8.4
8.2
7.7
7.2
7.3
8.1
8.5

Tables (Output of Computation)





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=63527&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=63527&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63527&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
110.910.9000
21010.0068639334534-0.893703333928057-0.00686393345337242-2.23724651119606
39.29.19347489643745-0.8151493862598820.006525103562549730.198063228633983
49.29.18528063755083-0.03057805087704560.01471936244916871.95600560631568
59.59.49745394277710.3028223478027860.002546057222893030.831574902032834
69.69.605338419217560.113190193086188-0.0053384192175581-0.472975904468953
79.59.50315508458129-0.0963187489799362-0.00315508458128987-0.52255205140714
89.19.10468616282954-0.390241135587732-0.00468616282954316-0.733093985570277
98.98.89520750271395-0.2144008534428830.004792497286053930.438576505473098
1098.995008252002780.09124498845539630.004991747997223240.762334338967635
1110.110.08283682051421.060691414548300.01716317948582092.41796942438089
1210.310.32025371583000.259834810733633-0.0202537158299776-1.99747683752032
1310.210.2031878890508-0.106739136546773-0.00318788905084199-0.914533314160683
149.69.60644603896359-0.58354598789031-0.00644603896358816-1.19263321334968
159.29.19912884314424-0.4150529309692980.0008711568557642290.423495729103994
169.39.282230633544650.06018291510075860.01776936645534781.18443429535372
179.49.39684360887790.1121096890340250.003156391122100810.129523187198850
189.49.408927668143080.0166713248807178-0.00892766814307458-0.238039610665634
199.29.19996595311868-0.1986118742986263.40468813197327e-05-0.536954217243037
2099.00465525663785-0.19546214671726-0.004655256637848570.0078559729471201
2198.97897837142493-0.03346609158620430.02102162857507210.404046575722709
2299.032773264141790.0497916645295179-0.03277326414178940.207659447582270
239.89.745057106968740.6818936679496210.05494289303126251.57657342739812
241010.03612756482030.309002379720886-0.036127564820333-0.930057138880089
259.89.80310866158167-0.208058298903148-0.00310866158166938-1.29002097004471
269.39.30744631952414-0.482573049766388-0.00744631952414323-0.685783395991991
2799.00952632854252-0.308339324125258-0.009526328542515820.436606585672933
2898.97692222521665-0.04814740045221870.02307777478334970.648553124215936
299.19.094113455231970.1077978042390790.005886544768023960.388980472756534
309.19.104205341384440.0156252119531483-0.00420534138444017-0.229894430129386
319.19.09777943334338-0.005177212650813930.00222056665661839-0.0518849250734059
329.29.20578174117140.101591825367637-0.005781741171401290.266300705894962
338.88.78437779453467-0.3917805696617960.0156222054653325-1.23055731007511
348.38.3691357868653-0.413913168086644-0.0691357868652932-0.055202582996755
358.48.32552987174606-0.06457707496927260.07447012825394310.871306220113875
368.18.12699836736927-0.190941601484591-0.0269983673692684-0.315175885342798
377.77.69528993229173-0.4180295431244880.00471006770826683-0.566605392522188
387.97.883673903476850.1541813443011700.01632609652315401.42817202858971
397.97.928427816097290.0516615994181745-0.0284278160972864-0.256407404154038
4087.990095624653490.06104469441629930.00990437534651030.0233944117440882
417.97.89369007918744-0.0864953128488720.00630992081255707-0.367995665153523
427.67.60737337609937-0.273778118114413-0.00737337609937432-0.467119686453087
437.17.12790678002376-0.466564350772447-0.0279067800237604-0.48084252403469
446.86.76997639806375-0.3647460803623580.03002360193625340.253952669001719
456.56.46898313845856-0.3049934569028790.03101686154143850.149033529440635
466.96.963943335791060.444770777441391-0.06394333579105561.87004355104455
478.28.100086722316751.092771500617050.0999132776832451.61623101977772
488.78.720299459743260.64988173331435-0.0202994597432559-1.10464896675928
498.38.37337275074385-0.284241898090932-0.0733727507438535-2.33081906302693
507.97.88034989676338-0.479931048052000.0196501032366244-0.488182428801203
517.57.52415620682437-0.364474447190549-0.02415620682437440.288448810613073
527.87.760586109360870.1968210745178060.03941389063913121.39976634310803
538.38.27675467257120.494896693158930.02324532742879470.743416939198634
548.48.412750037905770.159842837590559-0.0127500379057717-0.83569808624464
558.28.24662740361231-0.144476337426449-0.046627403612313-0.759024029954874
567.77.66111285180186-0.5562223403278090.038887148198135-1.02696712124741
577.27.18912411420272-0.4775838640040370.01087588579727880.196138158351528
587.37.39379470662250.159356857009129-0.09379470662249721.58864218902277
598.17.986242129162610.5636842985896060.1137578708373851.00846642172225
608.58.472987495870250.4918605848578090.0270125041297530-0.179142885955574

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 10.9 & 10.9 & 0 & 0 & 0 \tabularnewline
2 & 10 & 10.0068639334534 & -0.893703333928057 & -0.00686393345337242 & -2.23724651119606 \tabularnewline
3 & 9.2 & 9.19347489643745 & -0.815149386259882 & 0.00652510356254973 & 0.198063228633983 \tabularnewline
4 & 9.2 & 9.18528063755083 & -0.0305780508770456 & 0.0147193624491687 & 1.95600560631568 \tabularnewline
5 & 9.5 & 9.4974539427771 & 0.302822347802786 & 0.00254605722289303 & 0.831574902032834 \tabularnewline
6 & 9.6 & 9.60533841921756 & 0.113190193086188 & -0.0053384192175581 & -0.472975904468953 \tabularnewline
7 & 9.5 & 9.50315508458129 & -0.0963187489799362 & -0.00315508458128987 & -0.52255205140714 \tabularnewline
8 & 9.1 & 9.10468616282954 & -0.390241135587732 & -0.00468616282954316 & -0.733093985570277 \tabularnewline
9 & 8.9 & 8.89520750271395 & -0.214400853442883 & 0.00479249728605393 & 0.438576505473098 \tabularnewline
10 & 9 & 8.99500825200278 & 0.0912449884553963 & 0.00499174799722324 & 0.762334338967635 \tabularnewline
11 & 10.1 & 10.0828368205142 & 1.06069141454830 & 0.0171631794858209 & 2.41796942438089 \tabularnewline
12 & 10.3 & 10.3202537158300 & 0.259834810733633 & -0.0202537158299776 & -1.99747683752032 \tabularnewline
13 & 10.2 & 10.2031878890508 & -0.106739136546773 & -0.00318788905084199 & -0.914533314160683 \tabularnewline
14 & 9.6 & 9.60644603896359 & -0.58354598789031 & -0.00644603896358816 & -1.19263321334968 \tabularnewline
15 & 9.2 & 9.19912884314424 & -0.415052930969298 & 0.000871156855764229 & 0.423495729103994 \tabularnewline
16 & 9.3 & 9.28223063354465 & 0.0601829151007586 & 0.0177693664553478 & 1.18443429535372 \tabularnewline
17 & 9.4 & 9.3968436088779 & 0.112109689034025 & 0.00315639112210081 & 0.129523187198850 \tabularnewline
18 & 9.4 & 9.40892766814308 & 0.0166713248807178 & -0.00892766814307458 & -0.238039610665634 \tabularnewline
19 & 9.2 & 9.19996595311868 & -0.198611874298626 & 3.40468813197327e-05 & -0.536954217243037 \tabularnewline
20 & 9 & 9.00465525663785 & -0.19546214671726 & -0.00465525663784857 & 0.0078559729471201 \tabularnewline
21 & 9 & 8.97897837142493 & -0.0334660915862043 & 0.0210216285750721 & 0.404046575722709 \tabularnewline
22 & 9 & 9.03277326414179 & 0.0497916645295179 & -0.0327732641417894 & 0.207659447582270 \tabularnewline
23 & 9.8 & 9.74505710696874 & 0.681893667949621 & 0.0549428930312625 & 1.57657342739812 \tabularnewline
24 & 10 & 10.0361275648203 & 0.309002379720886 & -0.036127564820333 & -0.930057138880089 \tabularnewline
25 & 9.8 & 9.80310866158167 & -0.208058298903148 & -0.00310866158166938 & -1.29002097004471 \tabularnewline
26 & 9.3 & 9.30744631952414 & -0.482573049766388 & -0.00744631952414323 & -0.685783395991991 \tabularnewline
27 & 9 & 9.00952632854252 & -0.308339324125258 & -0.00952632854251582 & 0.436606585672933 \tabularnewline
28 & 9 & 8.97692222521665 & -0.0481474004522187 & 0.0230777747833497 & 0.648553124215936 \tabularnewline
29 & 9.1 & 9.09411345523197 & 0.107797804239079 & 0.00588654476802396 & 0.388980472756534 \tabularnewline
30 & 9.1 & 9.10420534138444 & 0.0156252119531483 & -0.00420534138444017 & -0.229894430129386 \tabularnewline
31 & 9.1 & 9.09777943334338 & -0.00517721265081393 & 0.00222056665661839 & -0.0518849250734059 \tabularnewline
32 & 9.2 & 9.2057817411714 & 0.101591825367637 & -0.00578174117140129 & 0.266300705894962 \tabularnewline
33 & 8.8 & 8.78437779453467 & -0.391780569661796 & 0.0156222054653325 & -1.23055731007511 \tabularnewline
34 & 8.3 & 8.3691357868653 & -0.413913168086644 & -0.0691357868652932 & -0.055202582996755 \tabularnewline
35 & 8.4 & 8.32552987174606 & -0.0645770749692726 & 0.0744701282539431 & 0.871306220113875 \tabularnewline
36 & 8.1 & 8.12699836736927 & -0.190941601484591 & -0.0269983673692684 & -0.315175885342798 \tabularnewline
37 & 7.7 & 7.69528993229173 & -0.418029543124488 & 0.00471006770826683 & -0.566605392522188 \tabularnewline
38 & 7.9 & 7.88367390347685 & 0.154181344301170 & 0.0163260965231540 & 1.42817202858971 \tabularnewline
39 & 7.9 & 7.92842781609729 & 0.0516615994181745 & -0.0284278160972864 & -0.256407404154038 \tabularnewline
40 & 8 & 7.99009562465349 & 0.0610446944162993 & 0.0099043753465103 & 0.0233944117440882 \tabularnewline
41 & 7.9 & 7.89369007918744 & -0.086495312848872 & 0.00630992081255707 & -0.367995665153523 \tabularnewline
42 & 7.6 & 7.60737337609937 & -0.273778118114413 & -0.00737337609937432 & -0.467119686453087 \tabularnewline
43 & 7.1 & 7.12790678002376 & -0.466564350772447 & -0.0279067800237604 & -0.48084252403469 \tabularnewline
44 & 6.8 & 6.76997639806375 & -0.364746080362358 & 0.0300236019362534 & 0.253952669001719 \tabularnewline
45 & 6.5 & 6.46898313845856 & -0.304993456902879 & 0.0310168615414385 & 0.149033529440635 \tabularnewline
46 & 6.9 & 6.96394333579106 & 0.444770777441391 & -0.0639433357910556 & 1.87004355104455 \tabularnewline
47 & 8.2 & 8.10008672231675 & 1.09277150061705 & 0.099913277683245 & 1.61623101977772 \tabularnewline
48 & 8.7 & 8.72029945974326 & 0.64988173331435 & -0.0202994597432559 & -1.10464896675928 \tabularnewline
49 & 8.3 & 8.37337275074385 & -0.284241898090932 & -0.0733727507438535 & -2.33081906302693 \tabularnewline
50 & 7.9 & 7.88034989676338 & -0.47993104805200 & 0.0196501032366244 & -0.488182428801203 \tabularnewline
51 & 7.5 & 7.52415620682437 & -0.364474447190549 & -0.0241562068243744 & 0.288448810613073 \tabularnewline
52 & 7.8 & 7.76058610936087 & 0.196821074517806 & 0.0394138906391312 & 1.39976634310803 \tabularnewline
53 & 8.3 & 8.2767546725712 & 0.49489669315893 & 0.0232453274287947 & 0.743416939198634 \tabularnewline
54 & 8.4 & 8.41275003790577 & 0.159842837590559 & -0.0127500379057717 & -0.83569808624464 \tabularnewline
55 & 8.2 & 8.24662740361231 & -0.144476337426449 & -0.046627403612313 & -0.759024029954874 \tabularnewline
56 & 7.7 & 7.66111285180186 & -0.556222340327809 & 0.038887148198135 & -1.02696712124741 \tabularnewline
57 & 7.2 & 7.18912411420272 & -0.477583864004037 & 0.0108758857972788 & 0.196138158351528 \tabularnewline
58 & 7.3 & 7.3937947066225 & 0.159356857009129 & -0.0937947066224972 & 1.58864218902277 \tabularnewline
59 & 8.1 & 7.98624212916261 & 0.563684298589606 & 0.113757870837385 & 1.00846642172225 \tabularnewline
60 & 8.5 & 8.47298749587025 & 0.491860584857809 & 0.0270125041297530 & -0.179142885955574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63527&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]10.9[/C][C]10.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]10.0068639334534[/C][C]-0.893703333928057[/C][C]-0.00686393345337242[/C][C]-2.23724651119606[/C][/ROW]
[ROW][C]3[/C][C]9.2[/C][C]9.19347489643745[/C][C]-0.815149386259882[/C][C]0.00652510356254973[/C][C]0.198063228633983[/C][/ROW]
[ROW][C]4[/C][C]9.2[/C][C]9.18528063755083[/C][C]-0.0305780508770456[/C][C]0.0147193624491687[/C][C]1.95600560631568[/C][/ROW]
[ROW][C]5[/C][C]9.5[/C][C]9.4974539427771[/C][C]0.302822347802786[/C][C]0.00254605722289303[/C][C]0.831574902032834[/C][/ROW]
[ROW][C]6[/C][C]9.6[/C][C]9.60533841921756[/C][C]0.113190193086188[/C][C]-0.0053384192175581[/C][C]-0.472975904468953[/C][/ROW]
[ROW][C]7[/C][C]9.5[/C][C]9.50315508458129[/C][C]-0.0963187489799362[/C][C]-0.00315508458128987[/C][C]-0.52255205140714[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]9.10468616282954[/C][C]-0.390241135587732[/C][C]-0.00468616282954316[/C][C]-0.733093985570277[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.89520750271395[/C][C]-0.214400853442883[/C][C]0.00479249728605393[/C][C]0.438576505473098[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]8.99500825200278[/C][C]0.0912449884553963[/C][C]0.00499174799722324[/C][C]0.762334338967635[/C][/ROW]
[ROW][C]11[/C][C]10.1[/C][C]10.0828368205142[/C][C]1.06069141454830[/C][C]0.0171631794858209[/C][C]2.41796942438089[/C][/ROW]
[ROW][C]12[/C][C]10.3[/C][C]10.3202537158300[/C][C]0.259834810733633[/C][C]-0.0202537158299776[/C][C]-1.99747683752032[/C][/ROW]
[ROW][C]13[/C][C]10.2[/C][C]10.2031878890508[/C][C]-0.106739136546773[/C][C]-0.00318788905084199[/C][C]-0.914533314160683[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]9.60644603896359[/C][C]-0.58354598789031[/C][C]-0.00644603896358816[/C][C]-1.19263321334968[/C][/ROW]
[ROW][C]15[/C][C]9.2[/C][C]9.19912884314424[/C][C]-0.415052930969298[/C][C]0.000871156855764229[/C][C]0.423495729103994[/C][/ROW]
[ROW][C]16[/C][C]9.3[/C][C]9.28223063354465[/C][C]0.0601829151007586[/C][C]0.0177693664553478[/C][C]1.18443429535372[/C][/ROW]
[ROW][C]17[/C][C]9.4[/C][C]9.3968436088779[/C][C]0.112109689034025[/C][C]0.00315639112210081[/C][C]0.129523187198850[/C][/ROW]
[ROW][C]18[/C][C]9.4[/C][C]9.40892766814308[/C][C]0.0166713248807178[/C][C]-0.00892766814307458[/C][C]-0.238039610665634[/C][/ROW]
[ROW][C]19[/C][C]9.2[/C][C]9.19996595311868[/C][C]-0.198611874298626[/C][C]3.40468813197327e-05[/C][C]-0.536954217243037[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.00465525663785[/C][C]-0.19546214671726[/C][C]-0.00465525663784857[/C][C]0.0078559729471201[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]8.97897837142493[/C][C]-0.0334660915862043[/C][C]0.0210216285750721[/C][C]0.404046575722709[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.03277326414179[/C][C]0.0497916645295179[/C][C]-0.0327732641417894[/C][C]0.207659447582270[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]9.74505710696874[/C][C]0.681893667949621[/C][C]0.0549428930312625[/C][C]1.57657342739812[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.0361275648203[/C][C]0.309002379720886[/C][C]-0.036127564820333[/C][C]-0.930057138880089[/C][/ROW]
[ROW][C]25[/C][C]9.8[/C][C]9.80310866158167[/C][C]-0.208058298903148[/C][C]-0.00310866158166938[/C][C]-1.29002097004471[/C][/ROW]
[ROW][C]26[/C][C]9.3[/C][C]9.30744631952414[/C][C]-0.482573049766388[/C][C]-0.00744631952414323[/C][C]-0.685783395991991[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.00952632854252[/C][C]-0.308339324125258[/C][C]-0.00952632854251582[/C][C]0.436606585672933[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]8.97692222521665[/C][C]-0.0481474004522187[/C][C]0.0230777747833497[/C][C]0.648553124215936[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]9.09411345523197[/C][C]0.107797804239079[/C][C]0.00588654476802396[/C][C]0.388980472756534[/C][/ROW]
[ROW][C]30[/C][C]9.1[/C][C]9.10420534138444[/C][C]0.0156252119531483[/C][C]-0.00420534138444017[/C][C]-0.229894430129386[/C][/ROW]
[ROW][C]31[/C][C]9.1[/C][C]9.09777943334338[/C][C]-0.00517721265081393[/C][C]0.00222056665661839[/C][C]-0.0518849250734059[/C][/ROW]
[ROW][C]32[/C][C]9.2[/C][C]9.2057817411714[/C][C]0.101591825367637[/C][C]-0.00578174117140129[/C][C]0.266300705894962[/C][/ROW]
[ROW][C]33[/C][C]8.8[/C][C]8.78437779453467[/C][C]-0.391780569661796[/C][C]0.0156222054653325[/C][C]-1.23055731007511[/C][/ROW]
[ROW][C]34[/C][C]8.3[/C][C]8.3691357868653[/C][C]-0.413913168086644[/C][C]-0.0691357868652932[/C][C]-0.055202582996755[/C][/ROW]
[ROW][C]35[/C][C]8.4[/C][C]8.32552987174606[/C][C]-0.0645770749692726[/C][C]0.0744701282539431[/C][C]0.871306220113875[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.12699836736927[/C][C]-0.190941601484591[/C][C]-0.0269983673692684[/C][C]-0.315175885342798[/C][/ROW]
[ROW][C]37[/C][C]7.7[/C][C]7.69528993229173[/C][C]-0.418029543124488[/C][C]0.00471006770826683[/C][C]-0.566605392522188[/C][/ROW]
[ROW][C]38[/C][C]7.9[/C][C]7.88367390347685[/C][C]0.154181344301170[/C][C]0.0163260965231540[/C][C]1.42817202858971[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.92842781609729[/C][C]0.0516615994181745[/C][C]-0.0284278160972864[/C][C]-0.256407404154038[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]7.99009562465349[/C][C]0.0610446944162993[/C][C]0.0099043753465103[/C][C]0.0233944117440882[/C][/ROW]
[ROW][C]41[/C][C]7.9[/C][C]7.89369007918744[/C][C]-0.086495312848872[/C][C]0.00630992081255707[/C][C]-0.367995665153523[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]7.60737337609937[/C][C]-0.273778118114413[/C][C]-0.00737337609937432[/C][C]-0.467119686453087[/C][/ROW]
[ROW][C]43[/C][C]7.1[/C][C]7.12790678002376[/C][C]-0.466564350772447[/C][C]-0.0279067800237604[/C][C]-0.48084252403469[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]6.76997639806375[/C][C]-0.364746080362358[/C][C]0.0300236019362534[/C][C]0.253952669001719[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]6.46898313845856[/C][C]-0.304993456902879[/C][C]0.0310168615414385[/C][C]0.149033529440635[/C][/ROW]
[ROW][C]46[/C][C]6.9[/C][C]6.96394333579106[/C][C]0.444770777441391[/C][C]-0.0639433357910556[/C][C]1.87004355104455[/C][/ROW]
[ROW][C]47[/C][C]8.2[/C][C]8.10008672231675[/C][C]1.09277150061705[/C][C]0.099913277683245[/C][C]1.61623101977772[/C][/ROW]
[ROW][C]48[/C][C]8.7[/C][C]8.72029945974326[/C][C]0.64988173331435[/C][C]-0.0202994597432559[/C][C]-1.10464896675928[/C][/ROW]
[ROW][C]49[/C][C]8.3[/C][C]8.37337275074385[/C][C]-0.284241898090932[/C][C]-0.0733727507438535[/C][C]-2.33081906302693[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]7.88034989676338[/C][C]-0.47993104805200[/C][C]0.0196501032366244[/C][C]-0.488182428801203[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.52415620682437[/C][C]-0.364474447190549[/C][C]-0.0241562068243744[/C][C]0.288448810613073[/C][/ROW]
[ROW][C]52[/C][C]7.8[/C][C]7.76058610936087[/C][C]0.196821074517806[/C][C]0.0394138906391312[/C][C]1.39976634310803[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]8.2767546725712[/C][C]0.49489669315893[/C][C]0.0232453274287947[/C][C]0.743416939198634[/C][/ROW]
[ROW][C]54[/C][C]8.4[/C][C]8.41275003790577[/C][C]0.159842837590559[/C][C]-0.0127500379057717[/C][C]-0.83569808624464[/C][/ROW]
[ROW][C]55[/C][C]8.2[/C][C]8.24662740361231[/C][C]-0.144476337426449[/C][C]-0.046627403612313[/C][C]-0.759024029954874[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.66111285180186[/C][C]-0.556222340327809[/C][C]0.038887148198135[/C][C]-1.02696712124741[/C][/ROW]
[ROW][C]57[/C][C]7.2[/C][C]7.18912411420272[/C][C]-0.477583864004037[/C][C]0.0108758857972788[/C][C]0.196138158351528[/C][/ROW]
[ROW][C]58[/C][C]7.3[/C][C]7.3937947066225[/C][C]0.159356857009129[/C][C]-0.0937947066224972[/C][C]1.58864218902277[/C][/ROW]
[ROW][C]59[/C][C]8.1[/C][C]7.98624212916261[/C][C]0.563684298589606[/C][C]0.113757870837385[/C][C]1.00846642172225[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]8.47298749587025[/C][C]0.491860584857809[/C][C]0.0270125041297530[/C][C]-0.179142885955574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63527&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63527&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
110.910.9000
21010.0068639334534-0.893703333928057-0.00686393345337242-2.23724651119606
39.29.19347489643745-0.8151493862598820.006525103562549730.198063228633983
49.29.18528063755083-0.03057805087704560.01471936244916871.95600560631568
59.59.49745394277710.3028223478027860.002546057222893030.831574902032834
69.69.605338419217560.113190193086188-0.0053384192175581-0.472975904468953
79.59.50315508458129-0.0963187489799362-0.00315508458128987-0.52255205140714
89.19.10468616282954-0.390241135587732-0.00468616282954316-0.733093985570277
98.98.89520750271395-0.2144008534428830.004792497286053930.438576505473098
1098.995008252002780.09124498845539630.004991747997223240.762334338967635
1110.110.08283682051421.060691414548300.01716317948582092.41796942438089
1210.310.32025371583000.259834810733633-0.0202537158299776-1.99747683752032
1310.210.2031878890508-0.106739136546773-0.00318788905084199-0.914533314160683
149.69.60644603896359-0.58354598789031-0.00644603896358816-1.19263321334968
159.29.19912884314424-0.4150529309692980.0008711568557642290.423495729103994
169.39.282230633544650.06018291510075860.01776936645534781.18443429535372
179.49.39684360887790.1121096890340250.003156391122100810.129523187198850
189.49.408927668143080.0166713248807178-0.00892766814307458-0.238039610665634
199.29.19996595311868-0.1986118742986263.40468813197327e-05-0.536954217243037
2099.00465525663785-0.19546214671726-0.004655256637848570.0078559729471201
2198.97897837142493-0.03346609158620430.02102162857507210.404046575722709
2299.032773264141790.0497916645295179-0.03277326414178940.207659447582270
239.89.745057106968740.6818936679496210.05494289303126251.57657342739812
241010.03612756482030.309002379720886-0.036127564820333-0.930057138880089
259.89.80310866158167-0.208058298903148-0.00310866158166938-1.29002097004471
269.39.30744631952414-0.482573049766388-0.00744631952414323-0.685783395991991
2799.00952632854252-0.308339324125258-0.009526328542515820.436606585672933
2898.97692222521665-0.04814740045221870.02307777478334970.648553124215936
299.19.094113455231970.1077978042390790.005886544768023960.388980472756534
309.19.104205341384440.0156252119531483-0.00420534138444017-0.229894430129386
319.19.09777943334338-0.005177212650813930.00222056665661839-0.0518849250734059
329.29.20578174117140.101591825367637-0.005781741171401290.266300705894962
338.88.78437779453467-0.3917805696617960.0156222054653325-1.23055731007511
348.38.3691357868653-0.413913168086644-0.0691357868652932-0.055202582996755
358.48.32552987174606-0.06457707496927260.07447012825394310.871306220113875
368.18.12699836736927-0.190941601484591-0.0269983673692684-0.315175885342798
377.77.69528993229173-0.4180295431244880.00471006770826683-0.566605392522188
387.97.883673903476850.1541813443011700.01632609652315401.42817202858971
397.97.928427816097290.0516615994181745-0.0284278160972864-0.256407404154038
4087.990095624653490.06104469441629930.00990437534651030.0233944117440882
417.97.89369007918744-0.0864953128488720.00630992081255707-0.367995665153523
427.67.60737337609937-0.273778118114413-0.00737337609937432-0.467119686453087
437.17.12790678002376-0.466564350772447-0.0279067800237604-0.48084252403469
446.86.76997639806375-0.3647460803623580.03002360193625340.253952669001719
456.56.46898313845856-0.3049934569028790.03101686154143850.149033529440635
466.96.963943335791060.444770777441391-0.06394333579105561.87004355104455
478.28.100086722316751.092771500617050.0999132776832451.61623101977772
488.78.720299459743260.64988173331435-0.0202994597432559-1.10464896675928
498.38.37337275074385-0.284241898090932-0.0733727507438535-2.33081906302693
507.97.88034989676338-0.479931048052000.0196501032366244-0.488182428801203
517.57.52415620682437-0.364474447190549-0.02415620682437440.288448810613073
527.87.760586109360870.1968210745178060.03941389063913121.39976634310803
538.38.27675467257120.494896693158930.02324532742879470.743416939198634
548.48.412750037905770.159842837590559-0.0127500379057717-0.83569808624464
558.28.24662740361231-0.144476337426449-0.046627403612313-0.759024029954874
567.77.66111285180186-0.5562223403278090.038887148198135-1.02696712124741
577.27.18912411420272-0.4775838640040370.01087588579727880.196138158351528
587.37.39379470662250.159356857009129-0.09379470662249721.58864218902277
598.17.986242129162610.5636842985896060.1137578708373851.00846642172225
608.58.472987495870250.4918605848578090.0270125041297530-0.179142885955574


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 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')