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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 13:17:38 -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/t12599579270sn5baasod7iqjo.htm/, Retrieved Sun, 28 Apr 2024 06:23:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64127, Retrieved Sun, 28 Apr 2024 06:23:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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]
- R PD      [Structural Time Series Models] [Structurele tijdr...] [2009-12-04 20:17:38] [e458b4e05bf28a297f8af8d9f96e59d6] [Current]
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Dataseries X:
96.2
96.8
109.9
88
91.1
106.4
68.6
100.1
108
106
108.6
91.5
99.2
98
96.6
102.8
96.9
110
70.5
101.9
109.6
107.8
113
93.8
108
102.8
116.3
89.2
106.7
112.1
74.2
108.8
111.5
118.8
118.9
97.6
116.4
107.9
121.2
97.9
113.4
117.6
79.6
115.9
115.7
129.1
123.3
96.7
121.2
118.2
102.1
125.4
116.7
121.3
85.3
114.2
124.4
131
118.3
99.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64127&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]1 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=64127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64127&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
196.296.2000
296.896.1618718674227-0.01268651095021980.4909716939275250.0860215771604872
3109.997.85649995775220.4155931652753959.040132202297521.88304573603355
48897.41626376869660.243871635195932-8.77666105585891-0.467456494185525
591.195.9179594030788-0.0478297635721764-3.649505879799-0.896635600476273
6106.497.40005383637490.172409028051757.826655400085140.887236300630742
768.693.0761071637469-0.39705910807483-20.3647862701227-3.02520531147466
8100.192.4915161993549-0.4183555356052147.81037705660163-0.144907068863579
910894.2436324968791-0.19369015337755411.06538092055611.89289485129042
1010696.30971860455420.02267565113163196.530860819378112.18718107318629
11108.698.55894734766680.2225480055171906.593444507699422.35656974113319
1291.598.79599611061650.223783704608746-7.320484510490120.0165692232562231
1399.299.19465554210750.23879406333943-0.3613112245083510.251425387781672
149899.50639217472670.244841122722130-1.656664628099650.103254253493782
1596.698.53117893974470.1481765460677760.429629625185758-1.64136365338194
16102.899.1773741371270.1861622720361822.683684156690780.658413845685761
1796.999.60746631271510.204282120150003-3.171057642075540.326128408301993
1811099.80588492624970.20385309049448410.2055444122462-0.00803834350682169
1970.599.38958068031280.158724877253652-27.643454931696-0.874814263939484
20101.999.2149106966140.1344157932600703.37457341488604-0.482935013575488
21109.699.4748421620350.1436360191431879.85923919824280.185851046967658
22107.8100.0193409778830.1734183388867636.915635100661740.603463025216093
23113100.8428760175410.22241446536253210.73663346855190.989712451552453
2493.8101.3326439817840.242913984386474-8.12096338665070.409803892376178
25108102.1660373601420.2891733666116084.529309226310140.910942360346007
26102.8102.7460208765570.312140769626003-0.588907298223940.448987937159778
27116.3104.2517718077460.4064259236445149.4198818620651.83654106272736
2889.2103.7470718499220.33460597814422-12.5487334222503-1.39746185411897
29106.7104.1569400124410.3405292750782332.378354330584550.115256056455591
30112.1104.2625508270130.3220441200015028.35104105603378-0.359528535384603
3174.2104.2929262544310.299068910279756-29.4558214110489-0.446067606127425
32108.8104.6316478966740.3021982678937744.081869506047180.0605569658307478
33111.5104.8196555119120.2931664859230856.92876961709984-0.173977527971904
34118.8105.7412179249790.34297800773442311.69641252906370.954345355142754
35118.9106.6020495053990.38410653053751611.17983855890760.783587309827782
3697.6107.2229126477730.402938249704697-10.13199770511310.357005752894727
37116.4108.1500644164290.444671667974347.12624733913550.788577828270827
38107.9108.8907224973970.468232998680826-1.624494781139550.444878281818144
39121.2109.5604126322430.48425792510836111.20830843060940.302742568850661
4097.9110.1634210186520.493695495040898-12.51766358217780.178470242512235
41113.4110.7145571735040.4982569349670442.562434325938850.0863503235913144
42117.6111.0842964984940.4880570575094016.79100019038001-0.193258835069785
4379.6111.3796273026880.472766923304771-31.3667180926021-0.289865666753695
44115.9111.8485144149660.4724591865291354.05979741397394-0.00583501386620065
45115.7112.1859549994260.461751701385173.80319938640297-0.202995190229790
46129.1113.0879661856280.49665980472217715.06971687325320.661574159950877
47123.3113.7274886026080.5079849182490849.2669313098240.214560500698876
4896.7113.8056989241910.473924118810532-16.1868955791862-0.64521022119424
49121.2114.1504566458750.4636898446312757.32562972000344-0.193895446725384
50118.2115.0037273343080.49455245583642.363459091968760.584900465992555
51102.1113.7042952368850.352442535840324-7.76838182324776-2.69398427703633
52125.4115.2314964299800.4455012487691397.65614657275211.76441185270405
53116.7115.9712750722410.468813522502930.09929200299976280.442045513572269
54121.3116.3828445349910.4642784700663915.03960894674692-0.0859985382032313
5585.3116.8160029999180.461812925503101-31.4494268200874-0.0467563664472438
56114.2116.7035322016260.416312728777668-1.27488356153166-0.862879213363571
57124.4117.2075347602430.423260395031037.004857874768830.131756650380654
58131117.4994880721700.41285712996408313.781422607914-0.197284711475872
59118.3117.0786530884230.3468075064900443.00478326692709-1.25253114634782
6099.6116.9272193684480.307335914110379-16.2614276120992-0.748532767038808

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 96.2 & 96.2 & 0 & 0 & 0 \tabularnewline
2 & 96.8 & 96.1618718674227 & -0.0126865109502198 & 0.490971693927525 & 0.0860215771604872 \tabularnewline
3 & 109.9 & 97.8564999577522 & 0.415593165275395 & 9.04013220229752 & 1.88304573603355 \tabularnewline
4 & 88 & 97.4162637686966 & 0.243871635195932 & -8.77666105585891 & -0.467456494185525 \tabularnewline
5 & 91.1 & 95.9179594030788 & -0.0478297635721764 & -3.649505879799 & -0.896635600476273 \tabularnewline
6 & 106.4 & 97.4000538363749 & 0.17240902805175 & 7.82665540008514 & 0.887236300630742 \tabularnewline
7 & 68.6 & 93.0761071637469 & -0.39705910807483 & -20.3647862701227 & -3.02520531147466 \tabularnewline
8 & 100.1 & 92.4915161993549 & -0.418355535605214 & 7.81037705660163 & -0.144907068863579 \tabularnewline
9 & 108 & 94.2436324968791 & -0.193690153377554 & 11.0653809205561 & 1.89289485129042 \tabularnewline
10 & 106 & 96.3097186045542 & 0.0226756511316319 & 6.53086081937811 & 2.18718107318629 \tabularnewline
11 & 108.6 & 98.5589473476668 & 0.222548005517190 & 6.59344450769942 & 2.35656974113319 \tabularnewline
12 & 91.5 & 98.7959961106165 & 0.223783704608746 & -7.32048451049012 & 0.0165692232562231 \tabularnewline
13 & 99.2 & 99.1946555421075 & 0.23879406333943 & -0.361311224508351 & 0.251425387781672 \tabularnewline
14 & 98 & 99.5063921747267 & 0.244841122722130 & -1.65666462809965 & 0.103254253493782 \tabularnewline
15 & 96.6 & 98.5311789397447 & 0.148176546067776 & 0.429629625185758 & -1.64136365338194 \tabularnewline
16 & 102.8 & 99.177374137127 & 0.186162272036182 & 2.68368415669078 & 0.658413845685761 \tabularnewline
17 & 96.9 & 99.6074663127151 & 0.204282120150003 & -3.17105764207554 & 0.326128408301993 \tabularnewline
18 & 110 & 99.8058849262497 & 0.203853090494484 & 10.2055444122462 & -0.00803834350682169 \tabularnewline
19 & 70.5 & 99.3895806803128 & 0.158724877253652 & -27.643454931696 & -0.874814263939484 \tabularnewline
20 & 101.9 & 99.214910696614 & 0.134415793260070 & 3.37457341488604 & -0.482935013575488 \tabularnewline
21 & 109.6 & 99.474842162035 & 0.143636019143187 & 9.8592391982428 & 0.185851046967658 \tabularnewline
22 & 107.8 & 100.019340977883 & 0.173418338886763 & 6.91563510066174 & 0.603463025216093 \tabularnewline
23 & 113 & 100.842876017541 & 0.222414465362532 & 10.7366334685519 & 0.989712451552453 \tabularnewline
24 & 93.8 & 101.332643981784 & 0.242913984386474 & -8.1209633866507 & 0.409803892376178 \tabularnewline
25 & 108 & 102.166037360142 & 0.289173366611608 & 4.52930922631014 & 0.910942360346007 \tabularnewline
26 & 102.8 & 102.746020876557 & 0.312140769626003 & -0.58890729822394 & 0.448987937159778 \tabularnewline
27 & 116.3 & 104.251771807746 & 0.406425923644514 & 9.419881862065 & 1.83654106272736 \tabularnewline
28 & 89.2 & 103.747071849922 & 0.33460597814422 & -12.5487334222503 & -1.39746185411897 \tabularnewline
29 & 106.7 & 104.156940012441 & 0.340529275078233 & 2.37835433058455 & 0.115256056455591 \tabularnewline
30 & 112.1 & 104.262550827013 & 0.322044120001502 & 8.35104105603378 & -0.359528535384603 \tabularnewline
31 & 74.2 & 104.292926254431 & 0.299068910279756 & -29.4558214110489 & -0.446067606127425 \tabularnewline
32 & 108.8 & 104.631647896674 & 0.302198267893774 & 4.08186950604718 & 0.0605569658307478 \tabularnewline
33 & 111.5 & 104.819655511912 & 0.293166485923085 & 6.92876961709984 & -0.173977527971904 \tabularnewline
34 & 118.8 & 105.741217924979 & 0.342978007734423 & 11.6964125290637 & 0.954345355142754 \tabularnewline
35 & 118.9 & 106.602049505399 & 0.384106530537516 & 11.1798385589076 & 0.783587309827782 \tabularnewline
36 & 97.6 & 107.222912647773 & 0.402938249704697 & -10.1319977051131 & 0.357005752894727 \tabularnewline
37 & 116.4 & 108.150064416429 & 0.44467166797434 & 7.1262473391355 & 0.788577828270827 \tabularnewline
38 & 107.9 & 108.890722497397 & 0.468232998680826 & -1.62449478113955 & 0.444878281818144 \tabularnewline
39 & 121.2 & 109.560412632243 & 0.484257925108361 & 11.2083084306094 & 0.302742568850661 \tabularnewline
40 & 97.9 & 110.163421018652 & 0.493695495040898 & -12.5176635821778 & 0.178470242512235 \tabularnewline
41 & 113.4 & 110.714557173504 & 0.498256934967044 & 2.56243432593885 & 0.0863503235913144 \tabularnewline
42 & 117.6 & 111.084296498494 & 0.488057057509401 & 6.79100019038001 & -0.193258835069785 \tabularnewline
43 & 79.6 & 111.379627302688 & 0.472766923304771 & -31.3667180926021 & -0.289865666753695 \tabularnewline
44 & 115.9 & 111.848514414966 & 0.472459186529135 & 4.05979741397394 & -0.00583501386620065 \tabularnewline
45 & 115.7 & 112.185954999426 & 0.46175170138517 & 3.80319938640297 & -0.202995190229790 \tabularnewline
46 & 129.1 & 113.087966185628 & 0.496659804722177 & 15.0697168732532 & 0.661574159950877 \tabularnewline
47 & 123.3 & 113.727488602608 & 0.507984918249084 & 9.266931309824 & 0.214560500698876 \tabularnewline
48 & 96.7 & 113.805698924191 & 0.473924118810532 & -16.1868955791862 & -0.64521022119424 \tabularnewline
49 & 121.2 & 114.150456645875 & 0.463689844631275 & 7.32562972000344 & -0.193895446725384 \tabularnewline
50 & 118.2 & 115.003727334308 & 0.4945524558364 & 2.36345909196876 & 0.584900465992555 \tabularnewline
51 & 102.1 & 113.704295236885 & 0.352442535840324 & -7.76838182324776 & -2.69398427703633 \tabularnewline
52 & 125.4 & 115.231496429980 & 0.445501248769139 & 7.6561465727521 & 1.76441185270405 \tabularnewline
53 & 116.7 & 115.971275072241 & 0.46881352250293 & 0.0992920029997628 & 0.442045513572269 \tabularnewline
54 & 121.3 & 116.382844534991 & 0.464278470066391 & 5.03960894674692 & -0.0859985382032313 \tabularnewline
55 & 85.3 & 116.816002999918 & 0.461812925503101 & -31.4494268200874 & -0.0467563664472438 \tabularnewline
56 & 114.2 & 116.703532201626 & 0.416312728777668 & -1.27488356153166 & -0.862879213363571 \tabularnewline
57 & 124.4 & 117.207534760243 & 0.42326039503103 & 7.00485787476883 & 0.131756650380654 \tabularnewline
58 & 131 & 117.499488072170 & 0.412857129964083 & 13.781422607914 & -0.197284711475872 \tabularnewline
59 & 118.3 & 117.078653088423 & 0.346807506490044 & 3.00478326692709 & -1.25253114634782 \tabularnewline
60 & 99.6 & 116.927219368448 & 0.307335914110379 & -16.2614276120992 & -0.748532767038808 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64127&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]96.2[/C][C]96.2[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]96.8[/C][C]96.1618718674227[/C][C]-0.0126865109502198[/C][C]0.490971693927525[/C][C]0.0860215771604872[/C][/ROW]
[ROW][C]3[/C][C]109.9[/C][C]97.8564999577522[/C][C]0.415593165275395[/C][C]9.04013220229752[/C][C]1.88304573603355[/C][/ROW]
[ROW][C]4[/C][C]88[/C][C]97.4162637686966[/C][C]0.243871635195932[/C][C]-8.77666105585891[/C][C]-0.467456494185525[/C][/ROW]
[ROW][C]5[/C][C]91.1[/C][C]95.9179594030788[/C][C]-0.0478297635721764[/C][C]-3.649505879799[/C][C]-0.896635600476273[/C][/ROW]
[ROW][C]6[/C][C]106.4[/C][C]97.4000538363749[/C][C]0.17240902805175[/C][C]7.82665540008514[/C][C]0.887236300630742[/C][/ROW]
[ROW][C]7[/C][C]68.6[/C][C]93.0761071637469[/C][C]-0.39705910807483[/C][C]-20.3647862701227[/C][C]-3.02520531147466[/C][/ROW]
[ROW][C]8[/C][C]100.1[/C][C]92.4915161993549[/C][C]-0.418355535605214[/C][C]7.81037705660163[/C][C]-0.144907068863579[/C][/ROW]
[ROW][C]9[/C][C]108[/C][C]94.2436324968791[/C][C]-0.193690153377554[/C][C]11.0653809205561[/C][C]1.89289485129042[/C][/ROW]
[ROW][C]10[/C][C]106[/C][C]96.3097186045542[/C][C]0.0226756511316319[/C][C]6.53086081937811[/C][C]2.18718107318629[/C][/ROW]
[ROW][C]11[/C][C]108.6[/C][C]98.5589473476668[/C][C]0.222548005517190[/C][C]6.59344450769942[/C][C]2.35656974113319[/C][/ROW]
[ROW][C]12[/C][C]91.5[/C][C]98.7959961106165[/C][C]0.223783704608746[/C][C]-7.32048451049012[/C][C]0.0165692232562231[/C][/ROW]
[ROW][C]13[/C][C]99.2[/C][C]99.1946555421075[/C][C]0.23879406333943[/C][C]-0.361311224508351[/C][C]0.251425387781672[/C][/ROW]
[ROW][C]14[/C][C]98[/C][C]99.5063921747267[/C][C]0.244841122722130[/C][C]-1.65666462809965[/C][C]0.103254253493782[/C][/ROW]
[ROW][C]15[/C][C]96.6[/C][C]98.5311789397447[/C][C]0.148176546067776[/C][C]0.429629625185758[/C][C]-1.64136365338194[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]99.177374137127[/C][C]0.186162272036182[/C][C]2.68368415669078[/C][C]0.658413845685761[/C][/ROW]
[ROW][C]17[/C][C]96.9[/C][C]99.6074663127151[/C][C]0.204282120150003[/C][C]-3.17105764207554[/C][C]0.326128408301993[/C][/ROW]
[ROW][C]18[/C][C]110[/C][C]99.8058849262497[/C][C]0.203853090494484[/C][C]10.2055444122462[/C][C]-0.00803834350682169[/C][/ROW]
[ROW][C]19[/C][C]70.5[/C][C]99.3895806803128[/C][C]0.158724877253652[/C][C]-27.643454931696[/C][C]-0.874814263939484[/C][/ROW]
[ROW][C]20[/C][C]101.9[/C][C]99.214910696614[/C][C]0.134415793260070[/C][C]3.37457341488604[/C][C]-0.482935013575488[/C][/ROW]
[ROW][C]21[/C][C]109.6[/C][C]99.474842162035[/C][C]0.143636019143187[/C][C]9.8592391982428[/C][C]0.185851046967658[/C][/ROW]
[ROW][C]22[/C][C]107.8[/C][C]100.019340977883[/C][C]0.173418338886763[/C][C]6.91563510066174[/C][C]0.603463025216093[/C][/ROW]
[ROW][C]23[/C][C]113[/C][C]100.842876017541[/C][C]0.222414465362532[/C][C]10.7366334685519[/C][C]0.989712451552453[/C][/ROW]
[ROW][C]24[/C][C]93.8[/C][C]101.332643981784[/C][C]0.242913984386474[/C][C]-8.1209633866507[/C][C]0.409803892376178[/C][/ROW]
[ROW][C]25[/C][C]108[/C][C]102.166037360142[/C][C]0.289173366611608[/C][C]4.52930922631014[/C][C]0.910942360346007[/C][/ROW]
[ROW][C]26[/C][C]102.8[/C][C]102.746020876557[/C][C]0.312140769626003[/C][C]-0.58890729822394[/C][C]0.448987937159778[/C][/ROW]
[ROW][C]27[/C][C]116.3[/C][C]104.251771807746[/C][C]0.406425923644514[/C][C]9.419881862065[/C][C]1.83654106272736[/C][/ROW]
[ROW][C]28[/C][C]89.2[/C][C]103.747071849922[/C][C]0.33460597814422[/C][C]-12.5487334222503[/C][C]-1.39746185411897[/C][/ROW]
[ROW][C]29[/C][C]106.7[/C][C]104.156940012441[/C][C]0.340529275078233[/C][C]2.37835433058455[/C][C]0.115256056455591[/C][/ROW]
[ROW][C]30[/C][C]112.1[/C][C]104.262550827013[/C][C]0.322044120001502[/C][C]8.35104105603378[/C][C]-0.359528535384603[/C][/ROW]
[ROW][C]31[/C][C]74.2[/C][C]104.292926254431[/C][C]0.299068910279756[/C][C]-29.4558214110489[/C][C]-0.446067606127425[/C][/ROW]
[ROW][C]32[/C][C]108.8[/C][C]104.631647896674[/C][C]0.302198267893774[/C][C]4.08186950604718[/C][C]0.0605569658307478[/C][/ROW]
[ROW][C]33[/C][C]111.5[/C][C]104.819655511912[/C][C]0.293166485923085[/C][C]6.92876961709984[/C][C]-0.173977527971904[/C][/ROW]
[ROW][C]34[/C][C]118.8[/C][C]105.741217924979[/C][C]0.342978007734423[/C][C]11.6964125290637[/C][C]0.954345355142754[/C][/ROW]
[ROW][C]35[/C][C]118.9[/C][C]106.602049505399[/C][C]0.384106530537516[/C][C]11.1798385589076[/C][C]0.783587309827782[/C][/ROW]
[ROW][C]36[/C][C]97.6[/C][C]107.222912647773[/C][C]0.402938249704697[/C][C]-10.1319977051131[/C][C]0.357005752894727[/C][/ROW]
[ROW][C]37[/C][C]116.4[/C][C]108.150064416429[/C][C]0.44467166797434[/C][C]7.1262473391355[/C][C]0.788577828270827[/C][/ROW]
[ROW][C]38[/C][C]107.9[/C][C]108.890722497397[/C][C]0.468232998680826[/C][C]-1.62449478113955[/C][C]0.444878281818144[/C][/ROW]
[ROW][C]39[/C][C]121.2[/C][C]109.560412632243[/C][C]0.484257925108361[/C][C]11.2083084306094[/C][C]0.302742568850661[/C][/ROW]
[ROW][C]40[/C][C]97.9[/C][C]110.163421018652[/C][C]0.493695495040898[/C][C]-12.5176635821778[/C][C]0.178470242512235[/C][/ROW]
[ROW][C]41[/C][C]113.4[/C][C]110.714557173504[/C][C]0.498256934967044[/C][C]2.56243432593885[/C][C]0.0863503235913144[/C][/ROW]
[ROW][C]42[/C][C]117.6[/C][C]111.084296498494[/C][C]0.488057057509401[/C][C]6.79100019038001[/C][C]-0.193258835069785[/C][/ROW]
[ROW][C]43[/C][C]79.6[/C][C]111.379627302688[/C][C]0.472766923304771[/C][C]-31.3667180926021[/C][C]-0.289865666753695[/C][/ROW]
[ROW][C]44[/C][C]115.9[/C][C]111.848514414966[/C][C]0.472459186529135[/C][C]4.05979741397394[/C][C]-0.00583501386620065[/C][/ROW]
[ROW][C]45[/C][C]115.7[/C][C]112.185954999426[/C][C]0.46175170138517[/C][C]3.80319938640297[/C][C]-0.202995190229790[/C][/ROW]
[ROW][C]46[/C][C]129.1[/C][C]113.087966185628[/C][C]0.496659804722177[/C][C]15.0697168732532[/C][C]0.661574159950877[/C][/ROW]
[ROW][C]47[/C][C]123.3[/C][C]113.727488602608[/C][C]0.507984918249084[/C][C]9.266931309824[/C][C]0.214560500698876[/C][/ROW]
[ROW][C]48[/C][C]96.7[/C][C]113.805698924191[/C][C]0.473924118810532[/C][C]-16.1868955791862[/C][C]-0.64521022119424[/C][/ROW]
[ROW][C]49[/C][C]121.2[/C][C]114.150456645875[/C][C]0.463689844631275[/C][C]7.32562972000344[/C][C]-0.193895446725384[/C][/ROW]
[ROW][C]50[/C][C]118.2[/C][C]115.003727334308[/C][C]0.4945524558364[/C][C]2.36345909196876[/C][C]0.584900465992555[/C][/ROW]
[ROW][C]51[/C][C]102.1[/C][C]113.704295236885[/C][C]0.352442535840324[/C][C]-7.76838182324776[/C][C]-2.69398427703633[/C][/ROW]
[ROW][C]52[/C][C]125.4[/C][C]115.231496429980[/C][C]0.445501248769139[/C][C]7.6561465727521[/C][C]1.76441185270405[/C][/ROW]
[ROW][C]53[/C][C]116.7[/C][C]115.971275072241[/C][C]0.46881352250293[/C][C]0.0992920029997628[/C][C]0.442045513572269[/C][/ROW]
[ROW][C]54[/C][C]121.3[/C][C]116.382844534991[/C][C]0.464278470066391[/C][C]5.03960894674692[/C][C]-0.0859985382032313[/C][/ROW]
[ROW][C]55[/C][C]85.3[/C][C]116.816002999918[/C][C]0.461812925503101[/C][C]-31.4494268200874[/C][C]-0.0467563664472438[/C][/ROW]
[ROW][C]56[/C][C]114.2[/C][C]116.703532201626[/C][C]0.416312728777668[/C][C]-1.27488356153166[/C][C]-0.862879213363571[/C][/ROW]
[ROW][C]57[/C][C]124.4[/C][C]117.207534760243[/C][C]0.42326039503103[/C][C]7.00485787476883[/C][C]0.131756650380654[/C][/ROW]
[ROW][C]58[/C][C]131[/C][C]117.499488072170[/C][C]0.412857129964083[/C][C]13.781422607914[/C][C]-0.197284711475872[/C][/ROW]
[ROW][C]59[/C][C]118.3[/C][C]117.078653088423[/C][C]0.346807506490044[/C][C]3.00478326692709[/C][C]-1.25253114634782[/C][/ROW]
[ROW][C]60[/C][C]99.6[/C][C]116.927219368448[/C][C]0.307335914110379[/C][C]-16.2614276120992[/C][C]-0.748532767038808[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64127&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
196.296.2000
296.896.1618718674227-0.01268651095021980.4909716939275250.0860215771604872
3109.997.85649995775220.4155931652753959.040132202297521.88304573603355
48897.41626376869660.243871635195932-8.77666105585891-0.467456494185525
591.195.9179594030788-0.0478297635721764-3.649505879799-0.896635600476273
6106.497.40005383637490.172409028051757.826655400085140.887236300630742
768.693.0761071637469-0.39705910807483-20.3647862701227-3.02520531147466
8100.192.4915161993549-0.4183555356052147.81037705660163-0.144907068863579
910894.2436324968791-0.19369015337755411.06538092055611.89289485129042
1010696.30971860455420.02267565113163196.530860819378112.18718107318629
11108.698.55894734766680.2225480055171906.593444507699422.35656974113319
1291.598.79599611061650.223783704608746-7.320484510490120.0165692232562231
1399.299.19465554210750.23879406333943-0.3613112245083510.251425387781672
149899.50639217472670.244841122722130-1.656664628099650.103254253493782
1596.698.53117893974470.1481765460677760.429629625185758-1.64136365338194
16102.899.1773741371270.1861622720361822.683684156690780.658413845685761
1796.999.60746631271510.204282120150003-3.171057642075540.326128408301993
1811099.80588492624970.20385309049448410.2055444122462-0.00803834350682169
1970.599.38958068031280.158724877253652-27.643454931696-0.874814263939484
20101.999.2149106966140.1344157932600703.37457341488604-0.482935013575488
21109.699.4748421620350.1436360191431879.85923919824280.185851046967658
22107.8100.0193409778830.1734183388867636.915635100661740.603463025216093
23113100.8428760175410.22241446536253210.73663346855190.989712451552453
2493.8101.3326439817840.242913984386474-8.12096338665070.409803892376178
25108102.1660373601420.2891733666116084.529309226310140.910942360346007
26102.8102.7460208765570.312140769626003-0.588907298223940.448987937159778
27116.3104.2517718077460.4064259236445149.4198818620651.83654106272736
2889.2103.7470718499220.33460597814422-12.5487334222503-1.39746185411897
29106.7104.1569400124410.3405292750782332.378354330584550.115256056455591
30112.1104.2625508270130.3220441200015028.35104105603378-0.359528535384603
3174.2104.2929262544310.299068910279756-29.4558214110489-0.446067606127425
32108.8104.6316478966740.3021982678937744.081869506047180.0605569658307478
33111.5104.8196555119120.2931664859230856.92876961709984-0.173977527971904
34118.8105.7412179249790.34297800773442311.69641252906370.954345355142754
35118.9106.6020495053990.38410653053751611.17983855890760.783587309827782
3697.6107.2229126477730.402938249704697-10.13199770511310.357005752894727
37116.4108.1500644164290.444671667974347.12624733913550.788577828270827
38107.9108.8907224973970.468232998680826-1.624494781139550.444878281818144
39121.2109.5604126322430.48425792510836111.20830843060940.302742568850661
4097.9110.1634210186520.493695495040898-12.51766358217780.178470242512235
41113.4110.7145571735040.4982569349670442.562434325938850.0863503235913144
42117.6111.0842964984940.4880570575094016.79100019038001-0.193258835069785
4379.6111.3796273026880.472766923304771-31.3667180926021-0.289865666753695
44115.9111.8485144149660.4724591865291354.05979741397394-0.00583501386620065
45115.7112.1859549994260.461751701385173.80319938640297-0.202995190229790
46129.1113.0879661856280.49665980472217715.06971687325320.661574159950877
47123.3113.7274886026080.5079849182490849.2669313098240.214560500698876
4896.7113.8056989241910.473924118810532-16.1868955791862-0.64521022119424
49121.2114.1504566458750.4636898446312757.32562972000344-0.193895446725384
50118.2115.0037273343080.49455245583642.363459091968760.584900465992555
51102.1113.7042952368850.352442535840324-7.76838182324776-2.69398427703633
52125.4115.2314964299800.4455012487691397.65614657275211.76441185270405
53116.7115.9712750722410.468813522502930.09929200299976280.442045513572269
54121.3116.3828445349910.4642784700663915.03960894674692-0.0859985382032313
5585.3116.8160029999180.461812925503101-31.4494268200874-0.0467563664472438
56114.2116.7035322016260.416312728777668-1.27488356153166-0.862879213363571
57124.4117.2075347602430.423260395031037.004857874768830.131756650380654
58131117.4994880721700.41285712996408313.781422607914-0.197284711475872
59118.3117.0786530884230.3468075064900443.00478326692709-1.25253114634782
6099.6116.9272193684480.307335914110379-16.2614276120992-0.748532767038808



Parameters (Session):
par1 = FALSE ; par2 = 0.0 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
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')