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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 03:30:46 -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/t1259922746f5ctdztzagolw7y.htm/, Retrieved Sat, 27 Apr 2024 14:19:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63255, Retrieved Sat, 27 Apr 2024 14:19:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

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] [workshop 9 - ad h...] [2009-12-04 10:30:46] [a18540c86166a2b66550d1fef0503cc2] [Current]
-   PD        [Structural Time Series Models] [WS9] [2009-12-06 15:13:07] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D        [Structural Time Series Models] [workshop 9 - revi...] [2009-12-11 12:10:52] [f1a50df816abcbb519e7637ff6b72fa0]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8,6
8,5
8,3
7,8
7,8
8
8,6
8,9
8,9
8,6
8,3
8,3
8,3
8,4
8,5
8,4
8,6
8,5
8,5
8,4
8,5
8,5
8,5
8,5
8,5
8,5
8,5
8,5
8,6
8,4
8,1
8
8
8
8
7,9
7,8
7,8
7,9
8,1
8
7,6
7,3
7
6,8
7
7,1
7,2
7,1
6,9
6,7
6,7
6,6
6,9
7,3
7,5
7,3
7,1
6,9
7,1

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=63255&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=63255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63255&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
18.68.6000
28.58.50000000000887-0.100000000017739-8.86945200771742e-12-0.493762736911324
38.38.30000000003548-0.199999999822610-3.54778078943416e-11-0.493762736234302
47.87.79999999994235-0.55.76514377763299e-11-1.48128821030140
57.87.79999999994235-1.11022302462516e-165.76514377763296e-112.46881368418236
687.999999999942350.2000000000000005.76514377763292e-110.987525473672945
78.68.599999999942350.5999999999999995.76514377763293e-111.97505094734588
88.98.899999999942350.3000000000000005.76514377763294e-11-1.48128821050941
98.98.89999999994235-1.11022302462516e-165.76514377763291e-11-1.48128821050942
108.68.59999999994235-0.3000000000000015.76514377763293e-11-1.48128821050942
118.38.29999999994235-0.2999999999999995.76514377763292e-119.87428323542675e-15
128.38.299999999942355.55111512312578e-165.76514377763293e-111.48128821050941
138.38.300000000372524.30169198642956e-10-3.72516982404092e-102.12401246773705e-09
148.48.400000000496690.100000000283822-4.96689309632006e-100.493762735358418
158.58.499999999929050.0999999994323557.09556149734031e-11-4.20422810455555e-09
168.48.39999999986696-0.1000000000000011.33041778215245e-10-0.987525470147529
178.68.599999999866960.2000000000000001.33041778215244e-101.48128821050942
188.58.49999999986696-0.0999999999999991.33041778215242e-10-1.48128821050941
198.58.499999999866968.04911692853238e-161.33041778215242e-100.493762736836471
208.48.39999999986696-0.09999999999999861.33041778215242e-10-0.493762736836468
218.58.499999999866960.09999999999999921.33041778215242e-100.987525473672932
228.58.49999999986696-2.4980018054066e-161.33041778215242e-10-0.493762736836468
238.58.49999999986696-6.17170161844868e-181.33041778215242e-101.20294664523938e-15
248.58.499999999866962.37456777665654e-161.33041778215242e-101.20294664702626e-15
258.58.500000000603127.36164984307555e-10-6.03122725170291e-103.63490720240107e-09
268.58.50000000072731.24172245109551e-10-7.2729505355854e-10-3.02179209368127e-09
278.58.49999999986696-8.60337761463212e-101.33041777577645e-10-4.86114356290699e-09
288.58.499999999866966.17590652905404e-171.33041777310574e-104.24802757887323e-09
298.68.599999999866960.11.33041777310573e-100.493762736836471
308.48.39999999986696-0.1999999999999991.33041777310568e-10-1.48128821050941
318.18.09999999986696-0.31.33041777310569e-10-0.493762736836477
3287.99999999986696-0.09999999999999951.33041777310569e-100.987525473672945
3387.999999999866963.88578058618805e-161.33041777310569e-100.493762736836471
3487.999999999866966.3311121114754e-161.33041777310569e-101.20741358639838e-15
3587.99999999986696-1.05340561789470e-171.33041777310569e-10-3.17808048746968e-15
367.97.89999999986696-0.09999999999999941.33041777310568e-10-0.493762736836468
377.87.80000000060312-0.0999999992638356-6.03122720670524e-103.63490279468883e-09
387.87.800000001019995.76514266881212e-10-1.01998696898304e-090.493762735292731
397.97.899999999813740.09999999876714731.86258487235213e-100.493762728920703
408.18.099999999844780.1999999999999991.55215405469158e-100.493762742562531
4187.99999999984478-0.09999999999999951.55215405469157e-10-1.48128821050940
427.67.59999999984478-0.41.55215405469151e-10-1.48128821050942
437.37.29999999984478-0.3000000000000001.55215405469152e-100.493762736836474
4476.99999999984478-0.3000000000000001.55215405469152e-10-4.63031783277707e-17
456.86.79999999984478-0.21.55215405469152e-100.49376273683647
4676.999999999844780.2000000000000001.55215405469152e-101.97505094734589
477.17.099999999844780.09999999999999971.55215405469152e-10-0.493762736836474
487.27.199999999844780.1000000000000011.55215405469152e-104.33919089502976e-15
497.17.10000000065634-0.0999999991440984-6.56339402476774e-10-0.987525469643896
506.96.9000000010466-0.199999999769393-1.04659530960808e-09-0.493762739168496
516.76.69999999981374-0.2000000012328551.86258485943498e-10-7.22602864436647e-09
526.76.69999999987583-1.66533453693773e-161.24172323119779e-100.987525479037717
536.66.59999999987583-0.1000000000000001.24172323119780e-10-0.493762736836472
546.96.899999999875830.3000000000000011.24172323119773e-101.97505094734589
557.37.299999999875830.3999999999999991.24172323119774e-100.493762736836463
567.57.499999999875830.2000000000000001.24172323119774e-10-0.987525473672938
577.37.29999999987583-0.1999999999999991.24172323119774e-10-1.97505094734589
587.17.09999999987583-0.21.24172323119774e-10-2.66213956014833e-15
596.96.89999999987583-0.1999999999999991.24172323119774e-106.10884858401404e-15
607.17.099999999875830.1999999999999991.24172323119773e-101.97505094734588

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8.6 & 8.6 & 0 & 0 & 0 \tabularnewline
2 & 8.5 & 8.50000000000887 & -0.100000000017739 & -8.86945200771742e-12 & -0.493762736911324 \tabularnewline
3 & 8.3 & 8.30000000003548 & -0.199999999822610 & -3.54778078943416e-11 & -0.493762736234302 \tabularnewline
4 & 7.8 & 7.79999999994235 & -0.5 & 5.76514377763299e-11 & -1.48128821030140 \tabularnewline
5 & 7.8 & 7.79999999994235 & -1.11022302462516e-16 & 5.76514377763296e-11 & 2.46881368418236 \tabularnewline
6 & 8 & 7.99999999994235 & 0.200000000000000 & 5.76514377763292e-11 & 0.987525473672945 \tabularnewline
7 & 8.6 & 8.59999999994235 & 0.599999999999999 & 5.76514377763293e-11 & 1.97505094734588 \tabularnewline
8 & 8.9 & 8.89999999994235 & 0.300000000000000 & 5.76514377763294e-11 & -1.48128821050941 \tabularnewline
9 & 8.9 & 8.89999999994235 & -1.11022302462516e-16 & 5.76514377763291e-11 & -1.48128821050942 \tabularnewline
10 & 8.6 & 8.59999999994235 & -0.300000000000001 & 5.76514377763293e-11 & -1.48128821050942 \tabularnewline
11 & 8.3 & 8.29999999994235 & -0.299999999999999 & 5.76514377763292e-11 & 9.87428323542675e-15 \tabularnewline
12 & 8.3 & 8.29999999994235 & 5.55111512312578e-16 & 5.76514377763293e-11 & 1.48128821050941 \tabularnewline
13 & 8.3 & 8.30000000037252 & 4.30169198642956e-10 & -3.72516982404092e-10 & 2.12401246773705e-09 \tabularnewline
14 & 8.4 & 8.40000000049669 & 0.100000000283822 & -4.96689309632006e-10 & 0.493762735358418 \tabularnewline
15 & 8.5 & 8.49999999992905 & 0.099999999432355 & 7.09556149734031e-11 & -4.20422810455555e-09 \tabularnewline
16 & 8.4 & 8.39999999986696 & -0.100000000000001 & 1.33041778215245e-10 & -0.987525470147529 \tabularnewline
17 & 8.6 & 8.59999999986696 & 0.200000000000000 & 1.33041778215244e-10 & 1.48128821050942 \tabularnewline
18 & 8.5 & 8.49999999986696 & -0.099999999999999 & 1.33041778215242e-10 & -1.48128821050941 \tabularnewline
19 & 8.5 & 8.49999999986696 & 8.04911692853238e-16 & 1.33041778215242e-10 & 0.493762736836471 \tabularnewline
20 & 8.4 & 8.39999999986696 & -0.0999999999999986 & 1.33041778215242e-10 & -0.493762736836468 \tabularnewline
21 & 8.5 & 8.49999999986696 & 0.0999999999999992 & 1.33041778215242e-10 & 0.987525473672932 \tabularnewline
22 & 8.5 & 8.49999999986696 & -2.4980018054066e-16 & 1.33041778215242e-10 & -0.493762736836468 \tabularnewline
23 & 8.5 & 8.49999999986696 & -6.17170161844868e-18 & 1.33041778215242e-10 & 1.20294664523938e-15 \tabularnewline
24 & 8.5 & 8.49999999986696 & 2.37456777665654e-16 & 1.33041778215242e-10 & 1.20294664702626e-15 \tabularnewline
25 & 8.5 & 8.50000000060312 & 7.36164984307555e-10 & -6.03122725170291e-10 & 3.63490720240107e-09 \tabularnewline
26 & 8.5 & 8.5000000007273 & 1.24172245109551e-10 & -7.2729505355854e-10 & -3.02179209368127e-09 \tabularnewline
27 & 8.5 & 8.49999999986696 & -8.60337761463212e-10 & 1.33041777577645e-10 & -4.86114356290699e-09 \tabularnewline
28 & 8.5 & 8.49999999986696 & 6.17590652905404e-17 & 1.33041777310574e-10 & 4.24802757887323e-09 \tabularnewline
29 & 8.6 & 8.59999999986696 & 0.1 & 1.33041777310573e-10 & 0.493762736836471 \tabularnewline
30 & 8.4 & 8.39999999986696 & -0.199999999999999 & 1.33041777310568e-10 & -1.48128821050941 \tabularnewline
31 & 8.1 & 8.09999999986696 & -0.3 & 1.33041777310569e-10 & -0.493762736836477 \tabularnewline
32 & 8 & 7.99999999986696 & -0.0999999999999995 & 1.33041777310569e-10 & 0.987525473672945 \tabularnewline
33 & 8 & 7.99999999986696 & 3.88578058618805e-16 & 1.33041777310569e-10 & 0.493762736836471 \tabularnewline
34 & 8 & 7.99999999986696 & 6.3311121114754e-16 & 1.33041777310569e-10 & 1.20741358639838e-15 \tabularnewline
35 & 8 & 7.99999999986696 & -1.05340561789470e-17 & 1.33041777310569e-10 & -3.17808048746968e-15 \tabularnewline
36 & 7.9 & 7.89999999986696 & -0.0999999999999994 & 1.33041777310568e-10 & -0.493762736836468 \tabularnewline
37 & 7.8 & 7.80000000060312 & -0.0999999992638356 & -6.03122720670524e-10 & 3.63490279468883e-09 \tabularnewline
38 & 7.8 & 7.80000000101999 & 5.76514266881212e-10 & -1.01998696898304e-09 & 0.493762735292731 \tabularnewline
39 & 7.9 & 7.89999999981374 & 0.0999999987671473 & 1.86258487235213e-10 & 0.493762728920703 \tabularnewline
40 & 8.1 & 8.09999999984478 & 0.199999999999999 & 1.55215405469158e-10 & 0.493762742562531 \tabularnewline
41 & 8 & 7.99999999984478 & -0.0999999999999995 & 1.55215405469157e-10 & -1.48128821050940 \tabularnewline
42 & 7.6 & 7.59999999984478 & -0.4 & 1.55215405469151e-10 & -1.48128821050942 \tabularnewline
43 & 7.3 & 7.29999999984478 & -0.300000000000000 & 1.55215405469152e-10 & 0.493762736836474 \tabularnewline
44 & 7 & 6.99999999984478 & -0.300000000000000 & 1.55215405469152e-10 & -4.63031783277707e-17 \tabularnewline
45 & 6.8 & 6.79999999984478 & -0.2 & 1.55215405469152e-10 & 0.49376273683647 \tabularnewline
46 & 7 & 6.99999999984478 & 0.200000000000000 & 1.55215405469152e-10 & 1.97505094734589 \tabularnewline
47 & 7.1 & 7.09999999984478 & 0.0999999999999997 & 1.55215405469152e-10 & -0.493762736836474 \tabularnewline
48 & 7.2 & 7.19999999984478 & 0.100000000000001 & 1.55215405469152e-10 & 4.33919089502976e-15 \tabularnewline
49 & 7.1 & 7.10000000065634 & -0.0999999991440984 & -6.56339402476774e-10 & -0.987525469643896 \tabularnewline
50 & 6.9 & 6.9000000010466 & -0.199999999769393 & -1.04659530960808e-09 & -0.493762739168496 \tabularnewline
51 & 6.7 & 6.69999999981374 & -0.200000001232855 & 1.86258485943498e-10 & -7.22602864436647e-09 \tabularnewline
52 & 6.7 & 6.69999999987583 & -1.66533453693773e-16 & 1.24172323119779e-10 & 0.987525479037717 \tabularnewline
53 & 6.6 & 6.59999999987583 & -0.100000000000000 & 1.24172323119780e-10 & -0.493762736836472 \tabularnewline
54 & 6.9 & 6.89999999987583 & 0.300000000000001 & 1.24172323119773e-10 & 1.97505094734589 \tabularnewline
55 & 7.3 & 7.29999999987583 & 0.399999999999999 & 1.24172323119774e-10 & 0.493762736836463 \tabularnewline
56 & 7.5 & 7.49999999987583 & 0.200000000000000 & 1.24172323119774e-10 & -0.987525473672938 \tabularnewline
57 & 7.3 & 7.29999999987583 & -0.199999999999999 & 1.24172323119774e-10 & -1.97505094734589 \tabularnewline
58 & 7.1 & 7.09999999987583 & -0.2 & 1.24172323119774e-10 & -2.66213956014833e-15 \tabularnewline
59 & 6.9 & 6.89999999987583 & -0.199999999999999 & 1.24172323119774e-10 & 6.10884858401404e-15 \tabularnewline
60 & 7.1 & 7.09999999987583 & 0.199999999999999 & 1.24172323119773e-10 & 1.97505094734588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63255&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]8.6[/C][C]8.6[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]8.5[/C][C]8.50000000000887[/C][C]-0.100000000017739[/C][C]-8.86945200771742e-12[/C][C]-0.493762736911324[/C][/ROW]
[ROW][C]3[/C][C]8.3[/C][C]8.30000000003548[/C][C]-0.199999999822610[/C][C]-3.54778078943416e-11[/C][C]-0.493762736234302[/C][/ROW]
[ROW][C]4[/C][C]7.8[/C][C]7.79999999994235[/C][C]-0.5[/C][C]5.76514377763299e-11[/C][C]-1.48128821030140[/C][/ROW]
[ROW][C]5[/C][C]7.8[/C][C]7.79999999994235[/C][C]-1.11022302462516e-16[/C][C]5.76514377763296e-11[/C][C]2.46881368418236[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]7.99999999994235[/C][C]0.200000000000000[/C][C]5.76514377763292e-11[/C][C]0.987525473672945[/C][/ROW]
[ROW][C]7[/C][C]8.6[/C][C]8.59999999994235[/C][C]0.599999999999999[/C][C]5.76514377763293e-11[/C][C]1.97505094734588[/C][/ROW]
[ROW][C]8[/C][C]8.9[/C][C]8.89999999994235[/C][C]0.300000000000000[/C][C]5.76514377763294e-11[/C][C]-1.48128821050941[/C][/ROW]
[ROW][C]9[/C][C]8.9[/C][C]8.89999999994235[/C][C]-1.11022302462516e-16[/C][C]5.76514377763291e-11[/C][C]-1.48128821050942[/C][/ROW]
[ROW][C]10[/C][C]8.6[/C][C]8.59999999994235[/C][C]-0.300000000000001[/C][C]5.76514377763293e-11[/C][C]-1.48128821050942[/C][/ROW]
[ROW][C]11[/C][C]8.3[/C][C]8.29999999994235[/C][C]-0.299999999999999[/C][C]5.76514377763292e-11[/C][C]9.87428323542675e-15[/C][/ROW]
[ROW][C]12[/C][C]8.3[/C][C]8.29999999994235[/C][C]5.55111512312578e-16[/C][C]5.76514377763293e-11[/C][C]1.48128821050941[/C][/ROW]
[ROW][C]13[/C][C]8.3[/C][C]8.30000000037252[/C][C]4.30169198642956e-10[/C][C]-3.72516982404092e-10[/C][C]2.12401246773705e-09[/C][/ROW]
[ROW][C]14[/C][C]8.4[/C][C]8.40000000049669[/C][C]0.100000000283822[/C][C]-4.96689309632006e-10[/C][C]0.493762735358418[/C][/ROW]
[ROW][C]15[/C][C]8.5[/C][C]8.49999999992905[/C][C]0.099999999432355[/C][C]7.09556149734031e-11[/C][C]-4.20422810455555e-09[/C][/ROW]
[ROW][C]16[/C][C]8.4[/C][C]8.39999999986696[/C][C]-0.100000000000001[/C][C]1.33041778215245e-10[/C][C]-0.987525470147529[/C][/ROW]
[ROW][C]17[/C][C]8.6[/C][C]8.59999999986696[/C][C]0.200000000000000[/C][C]1.33041778215244e-10[/C][C]1.48128821050942[/C][/ROW]
[ROW][C]18[/C][C]8.5[/C][C]8.49999999986696[/C][C]-0.099999999999999[/C][C]1.33041778215242e-10[/C][C]-1.48128821050941[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.49999999986696[/C][C]8.04911692853238e-16[/C][C]1.33041778215242e-10[/C][C]0.493762736836471[/C][/ROW]
[ROW][C]20[/C][C]8.4[/C][C]8.39999999986696[/C][C]-0.0999999999999986[/C][C]1.33041778215242e-10[/C][C]-0.493762736836468[/C][/ROW]
[ROW][C]21[/C][C]8.5[/C][C]8.49999999986696[/C][C]0.0999999999999992[/C][C]1.33041778215242e-10[/C][C]0.987525473672932[/C][/ROW]
[ROW][C]22[/C][C]8.5[/C][C]8.49999999986696[/C][C]-2.4980018054066e-16[/C][C]1.33041778215242e-10[/C][C]-0.493762736836468[/C][/ROW]
[ROW][C]23[/C][C]8.5[/C][C]8.49999999986696[/C][C]-6.17170161844868e-18[/C][C]1.33041778215242e-10[/C][C]1.20294664523938e-15[/C][/ROW]
[ROW][C]24[/C][C]8.5[/C][C]8.49999999986696[/C][C]2.37456777665654e-16[/C][C]1.33041778215242e-10[/C][C]1.20294664702626e-15[/C][/ROW]
[ROW][C]25[/C][C]8.5[/C][C]8.50000000060312[/C][C]7.36164984307555e-10[/C][C]-6.03122725170291e-10[/C][C]3.63490720240107e-09[/C][/ROW]
[ROW][C]26[/C][C]8.5[/C][C]8.5000000007273[/C][C]1.24172245109551e-10[/C][C]-7.2729505355854e-10[/C][C]-3.02179209368127e-09[/C][/ROW]
[ROW][C]27[/C][C]8.5[/C][C]8.49999999986696[/C][C]-8.60337761463212e-10[/C][C]1.33041777577645e-10[/C][C]-4.86114356290699e-09[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.49999999986696[/C][C]6.17590652905404e-17[/C][C]1.33041777310574e-10[/C][C]4.24802757887323e-09[/C][/ROW]
[ROW][C]29[/C][C]8.6[/C][C]8.59999999986696[/C][C]0.1[/C][C]1.33041777310573e-10[/C][C]0.493762736836471[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.39999999986696[/C][C]-0.199999999999999[/C][C]1.33041777310568e-10[/C][C]-1.48128821050941[/C][/ROW]
[ROW][C]31[/C][C]8.1[/C][C]8.09999999986696[/C][C]-0.3[/C][C]1.33041777310569e-10[/C][C]-0.493762736836477[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]7.99999999986696[/C][C]-0.0999999999999995[/C][C]1.33041777310569e-10[/C][C]0.987525473672945[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.99999999986696[/C][C]3.88578058618805e-16[/C][C]1.33041777310569e-10[/C][C]0.493762736836471[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]7.99999999986696[/C][C]6.3311121114754e-16[/C][C]1.33041777310569e-10[/C][C]1.20741358639838e-15[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]7.99999999986696[/C][C]-1.05340561789470e-17[/C][C]1.33041777310569e-10[/C][C]-3.17808048746968e-15[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]7.89999999986696[/C][C]-0.0999999999999994[/C][C]1.33041777310568e-10[/C][C]-0.493762736836468[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]7.80000000060312[/C][C]-0.0999999992638356[/C][C]-6.03122720670524e-10[/C][C]3.63490279468883e-09[/C][/ROW]
[ROW][C]38[/C][C]7.8[/C][C]7.80000000101999[/C][C]5.76514266881212e-10[/C][C]-1.01998696898304e-09[/C][C]0.493762735292731[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]7.89999999981374[/C][C]0.0999999987671473[/C][C]1.86258487235213e-10[/C][C]0.493762728920703[/C][/ROW]
[ROW][C]40[/C][C]8.1[/C][C]8.09999999984478[/C][C]0.199999999999999[/C][C]1.55215405469158e-10[/C][C]0.493762742562531[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]7.99999999984478[/C][C]-0.0999999999999995[/C][C]1.55215405469157e-10[/C][C]-1.48128821050940[/C][/ROW]
[ROW][C]42[/C][C]7.6[/C][C]7.59999999984478[/C][C]-0.4[/C][C]1.55215405469151e-10[/C][C]-1.48128821050942[/C][/ROW]
[ROW][C]43[/C][C]7.3[/C][C]7.29999999984478[/C][C]-0.300000000000000[/C][C]1.55215405469152e-10[/C][C]0.493762736836474[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]6.99999999984478[/C][C]-0.300000000000000[/C][C]1.55215405469152e-10[/C][C]-4.63031783277707e-17[/C][/ROW]
[ROW][C]45[/C][C]6.8[/C][C]6.79999999984478[/C][C]-0.2[/C][C]1.55215405469152e-10[/C][C]0.49376273683647[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.99999999984478[/C][C]0.200000000000000[/C][C]1.55215405469152e-10[/C][C]1.97505094734589[/C][/ROW]
[ROW][C]47[/C][C]7.1[/C][C]7.09999999984478[/C][C]0.0999999999999997[/C][C]1.55215405469152e-10[/C][C]-0.493762736836474[/C][/ROW]
[ROW][C]48[/C][C]7.2[/C][C]7.19999999984478[/C][C]0.100000000000001[/C][C]1.55215405469152e-10[/C][C]4.33919089502976e-15[/C][/ROW]
[ROW][C]49[/C][C]7.1[/C][C]7.10000000065634[/C][C]-0.0999999991440984[/C][C]-6.56339402476774e-10[/C][C]-0.987525469643896[/C][/ROW]
[ROW][C]50[/C][C]6.9[/C][C]6.9000000010466[/C][C]-0.199999999769393[/C][C]-1.04659530960808e-09[/C][C]-0.493762739168496[/C][/ROW]
[ROW][C]51[/C][C]6.7[/C][C]6.69999999981374[/C][C]-0.200000001232855[/C][C]1.86258485943498e-10[/C][C]-7.22602864436647e-09[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]6.69999999987583[/C][C]-1.66533453693773e-16[/C][C]1.24172323119779e-10[/C][C]0.987525479037717[/C][/ROW]
[ROW][C]53[/C][C]6.6[/C][C]6.59999999987583[/C][C]-0.100000000000000[/C][C]1.24172323119780e-10[/C][C]-0.493762736836472[/C][/ROW]
[ROW][C]54[/C][C]6.9[/C][C]6.89999999987583[/C][C]0.300000000000001[/C][C]1.24172323119773e-10[/C][C]1.97505094734589[/C][/ROW]
[ROW][C]55[/C][C]7.3[/C][C]7.29999999987583[/C][C]0.399999999999999[/C][C]1.24172323119774e-10[/C][C]0.493762736836463[/C][/ROW]
[ROW][C]56[/C][C]7.5[/C][C]7.49999999987583[/C][C]0.200000000000000[/C][C]1.24172323119774e-10[/C][C]-0.987525473672938[/C][/ROW]
[ROW][C]57[/C][C]7.3[/C][C]7.29999999987583[/C][C]-0.199999999999999[/C][C]1.24172323119774e-10[/C][C]-1.97505094734589[/C][/ROW]
[ROW][C]58[/C][C]7.1[/C][C]7.09999999987583[/C][C]-0.2[/C][C]1.24172323119774e-10[/C][C]-2.66213956014833e-15[/C][/ROW]
[ROW][C]59[/C][C]6.9[/C][C]6.89999999987583[/C][C]-0.199999999999999[/C][C]1.24172323119774e-10[/C][C]6.10884858401404e-15[/C][/ROW]
[ROW][C]60[/C][C]7.1[/C][C]7.09999999987583[/C][C]0.199999999999999[/C][C]1.24172323119773e-10[/C][C]1.97505094734588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63255&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
18.68.6000
28.58.50000000000887-0.100000000017739-8.86945200771742e-12-0.493762736911324
38.38.30000000003548-0.199999999822610-3.54778078943416e-11-0.493762736234302
47.87.79999999994235-0.55.76514377763299e-11-1.48128821030140
57.87.79999999994235-1.11022302462516e-165.76514377763296e-112.46881368418236
687.999999999942350.2000000000000005.76514377763292e-110.987525473672945
78.68.599999999942350.5999999999999995.76514377763293e-111.97505094734588
88.98.899999999942350.3000000000000005.76514377763294e-11-1.48128821050941
98.98.89999999994235-1.11022302462516e-165.76514377763291e-11-1.48128821050942
108.68.59999999994235-0.3000000000000015.76514377763293e-11-1.48128821050942
118.38.29999999994235-0.2999999999999995.76514377763292e-119.87428323542675e-15
128.38.299999999942355.55111512312578e-165.76514377763293e-111.48128821050941
138.38.300000000372524.30169198642956e-10-3.72516982404092e-102.12401246773705e-09
148.48.400000000496690.100000000283822-4.96689309632006e-100.493762735358418
158.58.499999999929050.0999999994323557.09556149734031e-11-4.20422810455555e-09
168.48.39999999986696-0.1000000000000011.33041778215245e-10-0.987525470147529
178.68.599999999866960.2000000000000001.33041778215244e-101.48128821050942
188.58.49999999986696-0.0999999999999991.33041778215242e-10-1.48128821050941
198.58.499999999866968.04911692853238e-161.33041778215242e-100.493762736836471
208.48.39999999986696-0.09999999999999861.33041778215242e-10-0.493762736836468
218.58.499999999866960.09999999999999921.33041778215242e-100.987525473672932
228.58.49999999986696-2.4980018054066e-161.33041778215242e-10-0.493762736836468
238.58.49999999986696-6.17170161844868e-181.33041778215242e-101.20294664523938e-15
248.58.499999999866962.37456777665654e-161.33041778215242e-101.20294664702626e-15
258.58.500000000603127.36164984307555e-10-6.03122725170291e-103.63490720240107e-09
268.58.50000000072731.24172245109551e-10-7.2729505355854e-10-3.02179209368127e-09
278.58.49999999986696-8.60337761463212e-101.33041777577645e-10-4.86114356290699e-09
288.58.499999999866966.17590652905404e-171.33041777310574e-104.24802757887323e-09
298.68.599999999866960.11.33041777310573e-100.493762736836471
308.48.39999999986696-0.1999999999999991.33041777310568e-10-1.48128821050941
318.18.09999999986696-0.31.33041777310569e-10-0.493762736836477
3287.99999999986696-0.09999999999999951.33041777310569e-100.987525473672945
3387.999999999866963.88578058618805e-161.33041777310569e-100.493762736836471
3487.999999999866966.3311121114754e-161.33041777310569e-101.20741358639838e-15
3587.99999999986696-1.05340561789470e-171.33041777310569e-10-3.17808048746968e-15
367.97.89999999986696-0.09999999999999941.33041777310568e-10-0.493762736836468
377.87.80000000060312-0.0999999992638356-6.03122720670524e-103.63490279468883e-09
387.87.800000001019995.76514266881212e-10-1.01998696898304e-090.493762735292731
397.97.899999999813740.09999999876714731.86258487235213e-100.493762728920703
408.18.099999999844780.1999999999999991.55215405469158e-100.493762742562531
4187.99999999984478-0.09999999999999951.55215405469157e-10-1.48128821050940
427.67.59999999984478-0.41.55215405469151e-10-1.48128821050942
437.37.29999999984478-0.3000000000000001.55215405469152e-100.493762736836474
4476.99999999984478-0.3000000000000001.55215405469152e-10-4.63031783277707e-17
456.86.79999999984478-0.21.55215405469152e-100.49376273683647
4676.999999999844780.2000000000000001.55215405469152e-101.97505094734589
477.17.099999999844780.09999999999999971.55215405469152e-10-0.493762736836474
487.27.199999999844780.1000000000000011.55215405469152e-104.33919089502976e-15
497.17.10000000065634-0.0999999991440984-6.56339402476774e-10-0.987525469643896
506.96.9000000010466-0.199999999769393-1.04659530960808e-09-0.493762739168496
516.76.69999999981374-0.2000000012328551.86258485943498e-10-7.22602864436647e-09
526.76.69999999987583-1.66533453693773e-161.24172323119779e-100.987525479037717
536.66.59999999987583-0.1000000000000001.24172323119780e-10-0.493762736836472
546.96.899999999875830.3000000000000011.24172323119773e-101.97505094734589
557.37.299999999875830.3999999999999991.24172323119774e-100.493762736836463
567.57.499999999875830.2000000000000001.24172323119774e-10-0.987525473672938
577.37.29999999987583-0.1999999999999991.24172323119774e-10-1.97505094734589
587.17.09999999987583-0.21.24172323119774e-10-2.66213956014833e-15
596.96.89999999987583-0.1999999999999991.24172323119774e-106.10884858401404e-15
607.17.099999999875830.1999999999999991.24172323119773e-101.97505094734588


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')