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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationSat, 16 Nov 2013 05:12:21 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/16/t1384596813vpbg9wi741hpnv8.htm/, Retrieved Mon, 06 May 2024 04:06:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225546, Retrieved Mon, 06 May 2024 04:06:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [ws8] [2013-11-16 10:12:21] [16986792796a040c0e2998a7aab14aa2] [Current]
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Dataseries X:
0.7869
0.7439
0.7492
0.7804
0.7678
0.7573
0.7337
0.7136
0.7107
0.7015
0.6874
0.6754
0.6713
0.6849
0.7003
0.7309
0.7364
0.7439
0.7928
0.8188
0.784
0.7746
0.7677
0.7197
0.7304
0.7567
0.749
0.7328
0.7142
0.6927
0.6974
0.6953
0.699
0.6971
0.7246
0.7301
0.736
0.7585
0.7756
0.7564
0.7568
0.7593
0.779
0.7978
0.8125
0.8075
0.7781
0.771
0.7796
0.763
0.7531
0.7473
0.7707
0.7684
0.7702
0.759
0.7649
0.7508
0.7494
0.7334




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225546&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225546&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225546&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 time4 seconds
R Server'George Udny Yule' @ yule.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
10.78690.7869000
20.74390.746135999755881-0.00223599975200609-0.00223599975588088-1.32371222575477
30.74920.751405975852319-0.00220597585231925-0.002205975852319250.406749088151219
40.78040.78247341247092-0.00207341247092002-0.002073412470920021.80307305072687
50.76780.769915019531613-0.00211501953161296-0.00211501953161295-0.568172467598937
60.75730.759448031262074-0.00214803126207398-0.00214803126207398-0.45258278099855
70.73370.735932156620367-0.00223215662036687-0.00223215662036687-1.1578878388629
80.71360.715901952875877-0.00230195287587714-0.00230195287587713-0.964439110236784
90.71070.713004279907233-0.00230427990723307-0.00230427990723307-0.0322805849897944
100.70150.703831007501569-0.00233100750156936-0.00233100750156936-0.372210741690534
110.68740.689776447622097-0.0023764476220965-0.0023764476220965-0.635260461994926
120.67540.677813461281072-0.00241346128107235-0.00241346128107235-0.519458900045856
130.67130.654485303058006-0.001528608813299780.0168146969419939-1.36924117813922
140.68490.685637454494975-0.000737454509602873-0.0007374544949749211.50665657511759
150.70030.701008166935126-0.000708166935125493-0.0007081669351254920.871953139684908
160.73090.731551449244372-0.000651449244372451-0.0006514492443724461.69167320504931
170.73640.737040325466935-0.000640325466935135-0.0006403254669351350.332381610092796
180.74390.744525631739422-0.000625631739422126-0.0006256317394221250.439847392335327
190.79280.793336396371566-0.000536396371566198-0.0005363963715661912.67603003724994
200.81880.819288669042444-0.000488669042443915-0.000488669042443911.43384963597756
210.7840.78455026927436-0.000550269274359495-0.000550269274359493-1.8539581745372
220.77460.775166129006021-0.000566129006021159-0.000566129006021162-0.47818188933928
230.76770.768277459722777-0.000577459722776902-0.000577459722776902-0.342241742298012
240.71970.720362142826044-0.000662142826043734-0.000662142826043734-2.56241385302898
250.73040.722509398262243-0.0007173274319616330.007890601737756980.167550380726554
260.75670.756872470563609-0.00017247058482704-0.0001724705636090351.72454553041177
270.7490.749181316094911-0.000181316094911377-0.000181316094911379-0.40686441918394
280.73280.73300011736645-0.000200117366450224-0.00020011736645022-0.865813560054639
290.71420.714421688154256-0.000221688154255562-0.000221688154255562-0.994518585613282
300.69270.692946604209421-0.000246604209421344-0.000246604209421344-1.15009924598243
310.69740.697640818707621-0.000240818707621405-0.0002408187076213980.26736564972654
320.69530.695542990648309-0.000242990648308766-0.000242990648308758-0.100489453601121
330.6990.69923838972589-0.000238389725890167-0.0002383897258901660.213120284613318
340.69710.697340326334535-0.000240326334534743-0.000240326334534746-0.0898107875978445
350.72460.724808032591365-0.000208032591364801-0.0002080325913648021.4993782159894
360.73010.730301395344393-0.000201395344392947-0.0002013953443929480.308522164441061
370.7360.733330173367193-0.0002427115139788040.002669826632806990.186579394046411
380.75850.7584526956307564.73043441938195e-054.73043692436312e-051.28148757095803
390.77560.7755378801083236.21198916769271e-056.21198916769267e-050.921842802517331
400.75640.7563546006980734.53993019270361e-054.53993019270403e-05-1.04128130928401
410.75680.7567542931519424.57068480578948e-054.57068480578959e-050.0191691890795843
420.75930.7592521663815024.78336184984232e-054.78336184984231e-050.13267550638397
430.7790.7789351515192746.48484807260447e-056.48484807260531e-051.06236783617025
440.79780.7977189446412168.10553587838535e-058.10553587838624e-051.01279571185497
450.81250.8124063094257779.36905742233795e-059.36905742233798e-050.79027970331684
460.80750.8074107081221878.9291877813414e-058.92918778134106e-05-0.275357891976856
470.77810.778036151859136.38481408701324e-056.38481408701315e-05-1.5941510692015
480.7710.7709423275901275.76724098733491e-055.7672409873348e-05-0.387268045349062
490.77960.779362138956073-2.16237333923239e-050.0002378610439268180.475242770355299
500.7630.763167931012384-0.000167931033632242-0.000167931012383852-0.829533231151349
510.75310.75327463817954-0.000174638179540145-0.000174638179540146-0.526148141734986
520.74730.747478512395589-0.000178512395589405-0.000178512395589401-0.304125914857712
530.77070.770862284926969-0.00016228492696876-0.0001622849269687581.2747337118529
540.76840.768563755157387-0.000163755157387367-0.000163755157387367-0.115572096728545
550.77020.770362405497513-0.00016240549751338-0.0001624054975133730.106167261268881
560.7590.759169986262811-0.000169986262811236-0.000169986262811229-0.59672991904359
570.76490.765065820177611-0.000165820177610778-0.0001658201776107780.328164190548219
580.75080.750975377228046-0.000175377228045572-0.000175377228045575-0.753329533446021
590.74940.749576216585651-0.000176216585651206-0.000176216585651207-0.0662073211480212
600.73340.733587054793245-0.000187054793245193-0.000187054793245195-0.855488384148068

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 0.7869 & 0.7869 & 0 & 0 & 0 \tabularnewline
2 & 0.7439 & 0.746135999755881 & -0.00223599975200609 & -0.00223599975588088 & -1.32371222575477 \tabularnewline
3 & 0.7492 & 0.751405975852319 & -0.00220597585231925 & -0.00220597585231925 & 0.406749088151219 \tabularnewline
4 & 0.7804 & 0.78247341247092 & -0.00207341247092002 & -0.00207341247092002 & 1.80307305072687 \tabularnewline
5 & 0.7678 & 0.769915019531613 & -0.00211501953161296 & -0.00211501953161295 & -0.568172467598937 \tabularnewline
6 & 0.7573 & 0.759448031262074 & -0.00214803126207398 & -0.00214803126207398 & -0.45258278099855 \tabularnewline
7 & 0.7337 & 0.735932156620367 & -0.00223215662036687 & -0.00223215662036687 & -1.1578878388629 \tabularnewline
8 & 0.7136 & 0.715901952875877 & -0.00230195287587714 & -0.00230195287587713 & -0.964439110236784 \tabularnewline
9 & 0.7107 & 0.713004279907233 & -0.00230427990723307 & -0.00230427990723307 & -0.0322805849897944 \tabularnewline
10 & 0.7015 & 0.703831007501569 & -0.00233100750156936 & -0.00233100750156936 & -0.372210741690534 \tabularnewline
11 & 0.6874 & 0.689776447622097 & -0.0023764476220965 & -0.0023764476220965 & -0.635260461994926 \tabularnewline
12 & 0.6754 & 0.677813461281072 & -0.00241346128107235 & -0.00241346128107235 & -0.519458900045856 \tabularnewline
13 & 0.6713 & 0.654485303058006 & -0.00152860881329978 & 0.0168146969419939 & -1.36924117813922 \tabularnewline
14 & 0.6849 & 0.685637454494975 & -0.000737454509602873 & -0.000737454494974921 & 1.50665657511759 \tabularnewline
15 & 0.7003 & 0.701008166935126 & -0.000708166935125493 & -0.000708166935125492 & 0.871953139684908 \tabularnewline
16 & 0.7309 & 0.731551449244372 & -0.000651449244372451 & -0.000651449244372446 & 1.69167320504931 \tabularnewline
17 & 0.7364 & 0.737040325466935 & -0.000640325466935135 & -0.000640325466935135 & 0.332381610092796 \tabularnewline
18 & 0.7439 & 0.744525631739422 & -0.000625631739422126 & -0.000625631739422125 & 0.439847392335327 \tabularnewline
19 & 0.7928 & 0.793336396371566 & -0.000536396371566198 & -0.000536396371566191 & 2.67603003724994 \tabularnewline
20 & 0.8188 & 0.819288669042444 & -0.000488669042443915 & -0.00048866904244391 & 1.43384963597756 \tabularnewline
21 & 0.784 & 0.78455026927436 & -0.000550269274359495 & -0.000550269274359493 & -1.8539581745372 \tabularnewline
22 & 0.7746 & 0.775166129006021 & -0.000566129006021159 & -0.000566129006021162 & -0.47818188933928 \tabularnewline
23 & 0.7677 & 0.768277459722777 & -0.000577459722776902 & -0.000577459722776902 & -0.342241742298012 \tabularnewline
24 & 0.7197 & 0.720362142826044 & -0.000662142826043734 & -0.000662142826043734 & -2.56241385302898 \tabularnewline
25 & 0.7304 & 0.722509398262243 & -0.000717327431961633 & 0.00789060173775698 & 0.167550380726554 \tabularnewline
26 & 0.7567 & 0.756872470563609 & -0.00017247058482704 & -0.000172470563609035 & 1.72454553041177 \tabularnewline
27 & 0.749 & 0.749181316094911 & -0.000181316094911377 & -0.000181316094911379 & -0.40686441918394 \tabularnewline
28 & 0.7328 & 0.73300011736645 & -0.000200117366450224 & -0.00020011736645022 & -0.865813560054639 \tabularnewline
29 & 0.7142 & 0.714421688154256 & -0.000221688154255562 & -0.000221688154255562 & -0.994518585613282 \tabularnewline
30 & 0.6927 & 0.692946604209421 & -0.000246604209421344 & -0.000246604209421344 & -1.15009924598243 \tabularnewline
31 & 0.6974 & 0.697640818707621 & -0.000240818707621405 & -0.000240818707621398 & 0.26736564972654 \tabularnewline
32 & 0.6953 & 0.695542990648309 & -0.000242990648308766 & -0.000242990648308758 & -0.100489453601121 \tabularnewline
33 & 0.699 & 0.69923838972589 & -0.000238389725890167 & -0.000238389725890166 & 0.213120284613318 \tabularnewline
34 & 0.6971 & 0.697340326334535 & -0.000240326334534743 & -0.000240326334534746 & -0.0898107875978445 \tabularnewline
35 & 0.7246 & 0.724808032591365 & -0.000208032591364801 & -0.000208032591364802 & 1.4993782159894 \tabularnewline
36 & 0.7301 & 0.730301395344393 & -0.000201395344392947 & -0.000201395344392948 & 0.308522164441061 \tabularnewline
37 & 0.736 & 0.733330173367193 & -0.000242711513978804 & 0.00266982663280699 & 0.186579394046411 \tabularnewline
38 & 0.7585 & 0.758452695630756 & 4.73043441938195e-05 & 4.73043692436312e-05 & 1.28148757095803 \tabularnewline
39 & 0.7756 & 0.775537880108323 & 6.21198916769271e-05 & 6.21198916769267e-05 & 0.921842802517331 \tabularnewline
40 & 0.7564 & 0.756354600698073 & 4.53993019270361e-05 & 4.53993019270403e-05 & -1.04128130928401 \tabularnewline
41 & 0.7568 & 0.756754293151942 & 4.57068480578948e-05 & 4.57068480578959e-05 & 0.0191691890795843 \tabularnewline
42 & 0.7593 & 0.759252166381502 & 4.78336184984232e-05 & 4.78336184984231e-05 & 0.13267550638397 \tabularnewline
43 & 0.779 & 0.778935151519274 & 6.48484807260447e-05 & 6.48484807260531e-05 & 1.06236783617025 \tabularnewline
44 & 0.7978 & 0.797718944641216 & 8.10553587838535e-05 & 8.10553587838624e-05 & 1.01279571185497 \tabularnewline
45 & 0.8125 & 0.812406309425777 & 9.36905742233795e-05 & 9.36905742233798e-05 & 0.79027970331684 \tabularnewline
46 & 0.8075 & 0.807410708122187 & 8.9291877813414e-05 & 8.92918778134106e-05 & -0.275357891976856 \tabularnewline
47 & 0.7781 & 0.77803615185913 & 6.38481408701324e-05 & 6.38481408701315e-05 & -1.5941510692015 \tabularnewline
48 & 0.771 & 0.770942327590127 & 5.76724098733491e-05 & 5.7672409873348e-05 & -0.387268045349062 \tabularnewline
49 & 0.7796 & 0.779362138956073 & -2.16237333923239e-05 & 0.000237861043926818 & 0.475242770355299 \tabularnewline
50 & 0.763 & 0.763167931012384 & -0.000167931033632242 & -0.000167931012383852 & -0.829533231151349 \tabularnewline
51 & 0.7531 & 0.75327463817954 & -0.000174638179540145 & -0.000174638179540146 & -0.526148141734986 \tabularnewline
52 & 0.7473 & 0.747478512395589 & -0.000178512395589405 & -0.000178512395589401 & -0.304125914857712 \tabularnewline
53 & 0.7707 & 0.770862284926969 & -0.00016228492696876 & -0.000162284926968758 & 1.2747337118529 \tabularnewline
54 & 0.7684 & 0.768563755157387 & -0.000163755157387367 & -0.000163755157387367 & -0.115572096728545 \tabularnewline
55 & 0.7702 & 0.770362405497513 & -0.00016240549751338 & -0.000162405497513373 & 0.106167261268881 \tabularnewline
56 & 0.759 & 0.759169986262811 & -0.000169986262811236 & -0.000169986262811229 & -0.59672991904359 \tabularnewline
57 & 0.7649 & 0.765065820177611 & -0.000165820177610778 & -0.000165820177610778 & 0.328164190548219 \tabularnewline
58 & 0.7508 & 0.750975377228046 & -0.000175377228045572 & -0.000175377228045575 & -0.753329533446021 \tabularnewline
59 & 0.7494 & 0.749576216585651 & -0.000176216585651206 & -0.000176216585651207 & -0.0662073211480212 \tabularnewline
60 & 0.7334 & 0.733587054793245 & -0.000187054793245193 & -0.000187054793245195 & -0.855488384148068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225546&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]0.7869[/C][C]0.7869[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]0.7439[/C][C]0.746135999755881[/C][C]-0.00223599975200609[/C][C]-0.00223599975588088[/C][C]-1.32371222575477[/C][/ROW]
[ROW][C]3[/C][C]0.7492[/C][C]0.751405975852319[/C][C]-0.00220597585231925[/C][C]-0.00220597585231925[/C][C]0.406749088151219[/C][/ROW]
[ROW][C]4[/C][C]0.7804[/C][C]0.78247341247092[/C][C]-0.00207341247092002[/C][C]-0.00207341247092002[/C][C]1.80307305072687[/C][/ROW]
[ROW][C]5[/C][C]0.7678[/C][C]0.769915019531613[/C][C]-0.00211501953161296[/C][C]-0.00211501953161295[/C][C]-0.568172467598937[/C][/ROW]
[ROW][C]6[/C][C]0.7573[/C][C]0.759448031262074[/C][C]-0.00214803126207398[/C][C]-0.00214803126207398[/C][C]-0.45258278099855[/C][/ROW]
[ROW][C]7[/C][C]0.7337[/C][C]0.735932156620367[/C][C]-0.00223215662036687[/C][C]-0.00223215662036687[/C][C]-1.1578878388629[/C][/ROW]
[ROW][C]8[/C][C]0.7136[/C][C]0.715901952875877[/C][C]-0.00230195287587714[/C][C]-0.00230195287587713[/C][C]-0.964439110236784[/C][/ROW]
[ROW][C]9[/C][C]0.7107[/C][C]0.713004279907233[/C][C]-0.00230427990723307[/C][C]-0.00230427990723307[/C][C]-0.0322805849897944[/C][/ROW]
[ROW][C]10[/C][C]0.7015[/C][C]0.703831007501569[/C][C]-0.00233100750156936[/C][C]-0.00233100750156936[/C][C]-0.372210741690534[/C][/ROW]
[ROW][C]11[/C][C]0.6874[/C][C]0.689776447622097[/C][C]-0.0023764476220965[/C][C]-0.0023764476220965[/C][C]-0.635260461994926[/C][/ROW]
[ROW][C]12[/C][C]0.6754[/C][C]0.677813461281072[/C][C]-0.00241346128107235[/C][C]-0.00241346128107235[/C][C]-0.519458900045856[/C][/ROW]
[ROW][C]13[/C][C]0.6713[/C][C]0.654485303058006[/C][C]-0.00152860881329978[/C][C]0.0168146969419939[/C][C]-1.36924117813922[/C][/ROW]
[ROW][C]14[/C][C]0.6849[/C][C]0.685637454494975[/C][C]-0.000737454509602873[/C][C]-0.000737454494974921[/C][C]1.50665657511759[/C][/ROW]
[ROW][C]15[/C][C]0.7003[/C][C]0.701008166935126[/C][C]-0.000708166935125493[/C][C]-0.000708166935125492[/C][C]0.871953139684908[/C][/ROW]
[ROW][C]16[/C][C]0.7309[/C][C]0.731551449244372[/C][C]-0.000651449244372451[/C][C]-0.000651449244372446[/C][C]1.69167320504931[/C][/ROW]
[ROW][C]17[/C][C]0.7364[/C][C]0.737040325466935[/C][C]-0.000640325466935135[/C][C]-0.000640325466935135[/C][C]0.332381610092796[/C][/ROW]
[ROW][C]18[/C][C]0.7439[/C][C]0.744525631739422[/C][C]-0.000625631739422126[/C][C]-0.000625631739422125[/C][C]0.439847392335327[/C][/ROW]
[ROW][C]19[/C][C]0.7928[/C][C]0.793336396371566[/C][C]-0.000536396371566198[/C][C]-0.000536396371566191[/C][C]2.67603003724994[/C][/ROW]
[ROW][C]20[/C][C]0.8188[/C][C]0.819288669042444[/C][C]-0.000488669042443915[/C][C]-0.00048866904244391[/C][C]1.43384963597756[/C][/ROW]
[ROW][C]21[/C][C]0.784[/C][C]0.78455026927436[/C][C]-0.000550269274359495[/C][C]-0.000550269274359493[/C][C]-1.8539581745372[/C][/ROW]
[ROW][C]22[/C][C]0.7746[/C][C]0.775166129006021[/C][C]-0.000566129006021159[/C][C]-0.000566129006021162[/C][C]-0.47818188933928[/C][/ROW]
[ROW][C]23[/C][C]0.7677[/C][C]0.768277459722777[/C][C]-0.000577459722776902[/C][C]-0.000577459722776902[/C][C]-0.342241742298012[/C][/ROW]
[ROW][C]24[/C][C]0.7197[/C][C]0.720362142826044[/C][C]-0.000662142826043734[/C][C]-0.000662142826043734[/C][C]-2.56241385302898[/C][/ROW]
[ROW][C]25[/C][C]0.7304[/C][C]0.722509398262243[/C][C]-0.000717327431961633[/C][C]0.00789060173775698[/C][C]0.167550380726554[/C][/ROW]
[ROW][C]26[/C][C]0.7567[/C][C]0.756872470563609[/C][C]-0.00017247058482704[/C][C]-0.000172470563609035[/C][C]1.72454553041177[/C][/ROW]
[ROW][C]27[/C][C]0.749[/C][C]0.749181316094911[/C][C]-0.000181316094911377[/C][C]-0.000181316094911379[/C][C]-0.40686441918394[/C][/ROW]
[ROW][C]28[/C][C]0.7328[/C][C]0.73300011736645[/C][C]-0.000200117366450224[/C][C]-0.00020011736645022[/C][C]-0.865813560054639[/C][/ROW]
[ROW][C]29[/C][C]0.7142[/C][C]0.714421688154256[/C][C]-0.000221688154255562[/C][C]-0.000221688154255562[/C][C]-0.994518585613282[/C][/ROW]
[ROW][C]30[/C][C]0.6927[/C][C]0.692946604209421[/C][C]-0.000246604209421344[/C][C]-0.000246604209421344[/C][C]-1.15009924598243[/C][/ROW]
[ROW][C]31[/C][C]0.6974[/C][C]0.697640818707621[/C][C]-0.000240818707621405[/C][C]-0.000240818707621398[/C][C]0.26736564972654[/C][/ROW]
[ROW][C]32[/C][C]0.6953[/C][C]0.695542990648309[/C][C]-0.000242990648308766[/C][C]-0.000242990648308758[/C][C]-0.100489453601121[/C][/ROW]
[ROW][C]33[/C][C]0.699[/C][C]0.69923838972589[/C][C]-0.000238389725890167[/C][C]-0.000238389725890166[/C][C]0.213120284613318[/C][/ROW]
[ROW][C]34[/C][C]0.6971[/C][C]0.697340326334535[/C][C]-0.000240326334534743[/C][C]-0.000240326334534746[/C][C]-0.0898107875978445[/C][/ROW]
[ROW][C]35[/C][C]0.7246[/C][C]0.724808032591365[/C][C]-0.000208032591364801[/C][C]-0.000208032591364802[/C][C]1.4993782159894[/C][/ROW]
[ROW][C]36[/C][C]0.7301[/C][C]0.730301395344393[/C][C]-0.000201395344392947[/C][C]-0.000201395344392948[/C][C]0.308522164441061[/C][/ROW]
[ROW][C]37[/C][C]0.736[/C][C]0.733330173367193[/C][C]-0.000242711513978804[/C][C]0.00266982663280699[/C][C]0.186579394046411[/C][/ROW]
[ROW][C]38[/C][C]0.7585[/C][C]0.758452695630756[/C][C]4.73043441938195e-05[/C][C]4.73043692436312e-05[/C][C]1.28148757095803[/C][/ROW]
[ROW][C]39[/C][C]0.7756[/C][C]0.775537880108323[/C][C]6.21198916769271e-05[/C][C]6.21198916769267e-05[/C][C]0.921842802517331[/C][/ROW]
[ROW][C]40[/C][C]0.7564[/C][C]0.756354600698073[/C][C]4.53993019270361e-05[/C][C]4.53993019270403e-05[/C][C]-1.04128130928401[/C][/ROW]
[ROW][C]41[/C][C]0.7568[/C][C]0.756754293151942[/C][C]4.57068480578948e-05[/C][C]4.57068480578959e-05[/C][C]0.0191691890795843[/C][/ROW]
[ROW][C]42[/C][C]0.7593[/C][C]0.759252166381502[/C][C]4.78336184984232e-05[/C][C]4.78336184984231e-05[/C][C]0.13267550638397[/C][/ROW]
[ROW][C]43[/C][C]0.779[/C][C]0.778935151519274[/C][C]6.48484807260447e-05[/C][C]6.48484807260531e-05[/C][C]1.06236783617025[/C][/ROW]
[ROW][C]44[/C][C]0.7978[/C][C]0.797718944641216[/C][C]8.10553587838535e-05[/C][C]8.10553587838624e-05[/C][C]1.01279571185497[/C][/ROW]
[ROW][C]45[/C][C]0.8125[/C][C]0.812406309425777[/C][C]9.36905742233795e-05[/C][C]9.36905742233798e-05[/C][C]0.79027970331684[/C][/ROW]
[ROW][C]46[/C][C]0.8075[/C][C]0.807410708122187[/C][C]8.9291877813414e-05[/C][C]8.92918778134106e-05[/C][C]-0.275357891976856[/C][/ROW]
[ROW][C]47[/C][C]0.7781[/C][C]0.77803615185913[/C][C]6.38481408701324e-05[/C][C]6.38481408701315e-05[/C][C]-1.5941510692015[/C][/ROW]
[ROW][C]48[/C][C]0.771[/C][C]0.770942327590127[/C][C]5.76724098733491e-05[/C][C]5.7672409873348e-05[/C][C]-0.387268045349062[/C][/ROW]
[ROW][C]49[/C][C]0.7796[/C][C]0.779362138956073[/C][C]-2.16237333923239e-05[/C][C]0.000237861043926818[/C][C]0.475242770355299[/C][/ROW]
[ROW][C]50[/C][C]0.763[/C][C]0.763167931012384[/C][C]-0.000167931033632242[/C][C]-0.000167931012383852[/C][C]-0.829533231151349[/C][/ROW]
[ROW][C]51[/C][C]0.7531[/C][C]0.75327463817954[/C][C]-0.000174638179540145[/C][C]-0.000174638179540146[/C][C]-0.526148141734986[/C][/ROW]
[ROW][C]52[/C][C]0.7473[/C][C]0.747478512395589[/C][C]-0.000178512395589405[/C][C]-0.000178512395589401[/C][C]-0.304125914857712[/C][/ROW]
[ROW][C]53[/C][C]0.7707[/C][C]0.770862284926969[/C][C]-0.00016228492696876[/C][C]-0.000162284926968758[/C][C]1.2747337118529[/C][/ROW]
[ROW][C]54[/C][C]0.7684[/C][C]0.768563755157387[/C][C]-0.000163755157387367[/C][C]-0.000163755157387367[/C][C]-0.115572096728545[/C][/ROW]
[ROW][C]55[/C][C]0.7702[/C][C]0.770362405497513[/C][C]-0.00016240549751338[/C][C]-0.000162405497513373[/C][C]0.106167261268881[/C][/ROW]
[ROW][C]56[/C][C]0.759[/C][C]0.759169986262811[/C][C]-0.000169986262811236[/C][C]-0.000169986262811229[/C][C]-0.59672991904359[/C][/ROW]
[ROW][C]57[/C][C]0.7649[/C][C]0.765065820177611[/C][C]-0.000165820177610778[/C][C]-0.000165820177610778[/C][C]0.328164190548219[/C][/ROW]
[ROW][C]58[/C][C]0.7508[/C][C]0.750975377228046[/C][C]-0.000175377228045572[/C][C]-0.000175377228045575[/C][C]-0.753329533446021[/C][/ROW]
[ROW][C]59[/C][C]0.7494[/C][C]0.749576216585651[/C][C]-0.000176216585651206[/C][C]-0.000176216585651207[/C][C]-0.0662073211480212[/C][/ROW]
[ROW][C]60[/C][C]0.7334[/C][C]0.733587054793245[/C][C]-0.000187054793245193[/C][C]-0.000187054793245195[/C][C]-0.855488384148068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225546&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225546&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
10.78690.7869000
20.74390.746135999755881-0.00223599975200609-0.00223599975588088-1.32371222575477
30.74920.751405975852319-0.00220597585231925-0.002205975852319250.406749088151219
40.78040.78247341247092-0.00207341247092002-0.002073412470920021.80307305072687
50.76780.769915019531613-0.00211501953161296-0.00211501953161295-0.568172467598937
60.75730.759448031262074-0.00214803126207398-0.00214803126207398-0.45258278099855
70.73370.735932156620367-0.00223215662036687-0.00223215662036687-1.1578878388629
80.71360.715901952875877-0.00230195287587714-0.00230195287587713-0.964439110236784
90.71070.713004279907233-0.00230427990723307-0.00230427990723307-0.0322805849897944
100.70150.703831007501569-0.00233100750156936-0.00233100750156936-0.372210741690534
110.68740.689776447622097-0.0023764476220965-0.0023764476220965-0.635260461994926
120.67540.677813461281072-0.00241346128107235-0.00241346128107235-0.519458900045856
130.67130.654485303058006-0.001528608813299780.0168146969419939-1.36924117813922
140.68490.685637454494975-0.000737454509602873-0.0007374544949749211.50665657511759
150.70030.701008166935126-0.000708166935125493-0.0007081669351254920.871953139684908
160.73090.731551449244372-0.000651449244372451-0.0006514492443724461.69167320504931
170.73640.737040325466935-0.000640325466935135-0.0006403254669351350.332381610092796
180.74390.744525631739422-0.000625631739422126-0.0006256317394221250.439847392335327
190.79280.793336396371566-0.000536396371566198-0.0005363963715661912.67603003724994
200.81880.819288669042444-0.000488669042443915-0.000488669042443911.43384963597756
210.7840.78455026927436-0.000550269274359495-0.000550269274359493-1.8539581745372
220.77460.775166129006021-0.000566129006021159-0.000566129006021162-0.47818188933928
230.76770.768277459722777-0.000577459722776902-0.000577459722776902-0.342241742298012
240.71970.720362142826044-0.000662142826043734-0.000662142826043734-2.56241385302898
250.73040.722509398262243-0.0007173274319616330.007890601737756980.167550380726554
260.75670.756872470563609-0.00017247058482704-0.0001724705636090351.72454553041177
270.7490.749181316094911-0.000181316094911377-0.000181316094911379-0.40686441918394
280.73280.73300011736645-0.000200117366450224-0.00020011736645022-0.865813560054639
290.71420.714421688154256-0.000221688154255562-0.000221688154255562-0.994518585613282
300.69270.692946604209421-0.000246604209421344-0.000246604209421344-1.15009924598243
310.69740.697640818707621-0.000240818707621405-0.0002408187076213980.26736564972654
320.69530.695542990648309-0.000242990648308766-0.000242990648308758-0.100489453601121
330.6990.69923838972589-0.000238389725890167-0.0002383897258901660.213120284613318
340.69710.697340326334535-0.000240326334534743-0.000240326334534746-0.0898107875978445
350.72460.724808032591365-0.000208032591364801-0.0002080325913648021.4993782159894
360.73010.730301395344393-0.000201395344392947-0.0002013953443929480.308522164441061
370.7360.733330173367193-0.0002427115139788040.002669826632806990.186579394046411
380.75850.7584526956307564.73043441938195e-054.73043692436312e-051.28148757095803
390.77560.7755378801083236.21198916769271e-056.21198916769267e-050.921842802517331
400.75640.7563546006980734.53993019270361e-054.53993019270403e-05-1.04128130928401
410.75680.7567542931519424.57068480578948e-054.57068480578959e-050.0191691890795843
420.75930.7592521663815024.78336184984232e-054.78336184984231e-050.13267550638397
430.7790.7789351515192746.48484807260447e-056.48484807260531e-051.06236783617025
440.79780.7977189446412168.10553587838535e-058.10553587838624e-051.01279571185497
450.81250.8124063094257779.36905742233795e-059.36905742233798e-050.79027970331684
460.80750.8074107081221878.9291877813414e-058.92918778134106e-05-0.275357891976856
470.77810.778036151859136.38481408701324e-056.38481408701315e-05-1.5941510692015
480.7710.7709423275901275.76724098733491e-055.7672409873348e-05-0.387268045349062
490.77960.779362138956073-2.16237333923239e-050.0002378610439268180.475242770355299
500.7630.763167931012384-0.000167931033632242-0.000167931012383852-0.829533231151349
510.75310.75327463817954-0.000174638179540145-0.000174638179540146-0.526148141734986
520.74730.747478512395589-0.000178512395589405-0.000178512395589401-0.304125914857712
530.77070.770862284926969-0.00016228492696876-0.0001622849269687581.2747337118529
540.76840.768563755157387-0.000163755157387367-0.000163755157387367-0.115572096728545
550.77020.770362405497513-0.00016240549751338-0.0001624054975133730.106167261268881
560.7590.759169986262811-0.000169986262811236-0.000169986262811229-0.59672991904359
570.76490.765065820177611-0.000165820177610778-0.0001658201776107780.328164190548219
580.75080.750975377228046-0.000175377228045572-0.000175377228045575-0.753329533446021
590.74940.749576216585651-0.000176216585651206-0.000176216585651207-0.0662073211480212
600.73340.733587054793245-0.000187054793245193-0.000187054793245195-0.855488384148068



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')