Free Statistics

of Irreproducible Research!

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 computationMon, 12 Dec 2011 11:31:39 -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/2011/Dec/12/t13237075159gyyp4i480uowe2.htm/, Retrieved Fri, 03 May 2024 13:48:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154106, Retrieved Fri, 03 May 2024 13:48:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS10 Regression ...] [2011-12-12 15:56:34] [d3c56829b3e69baec30b0d469b5d7237]
- RMPD      [Structural Time Series Models] [Structural Time S...] [2011-12-12 16:31:39] [82ceb5b481b3a9ad89a8151bb4a3670f] [Current]
Feedback Forum

Post a new message
Dataseries X:
1.35
1.91
1.31
1.19
1.3
1.14
1.1
1.02
1.11
1.18
1.24
1.36
1.29
1.73
1.41
1.15
1.31
1.15
1.08
1.1
1.14
1.24
1.33
1.49
1.38
1.96
1.36
1.24
1.35
1.23
1.09
1.08
1.33
1.35
1.38
1.5
1.47
2.09
1.52
1.29
1.52
1.27
1.35
1.29
1.41
1.39
1.45
1.53
1.45
2.11
1.53
1.38
1.54
1.35
1.29
1.33
1.47
1.47
1.54
1.59
1.5
2
1.51
1.4
1.62
1.44
1.29
1.28
1.4
1.39
1.46
1.49




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154106&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154106&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
11.351.35000
21.911.790218314968890.02088156425939760.1197816850311133.39722315344247
31.311.520914517981020.00752345076568155-0.210914517981024-2.89396230043741
41.191.284679447432310.00181942425331031-0.0946794474323141-2.78664026456425
51.31.2572505815460.001492880535842030.0427494184540022-0.338805434252571
61.141.180540457694640.000854351434070408-0.0405404576946381-0.905239204052314
71.11.118790245384580.000327413122677772-0.0187902453845779-0.724007386640269
81.021.04838699913059-0.000310254928890694-0.0283869991305913-0.817530173258391
91.111.07515089223152-5.84219990801256e-050.03484910776848020.312865682169147
101.181.14350171150230.0005823865653250970.03649828849770050.790469092776312
111.241.213021682415350.001227834615935590.02697831758465390.796526020262743
121.361.313457708131970.002155873367873930.04654229186802731.14619782460677
131.291.39306104795842-0.00021027853618634-0.103061047958420.980913312189165
141.731.436283469629032.73511963285213e-050.2937165303709740.491364216816028
151.411.501941328314860.00148988653037453-0.09194132831485940.700799334943802
161.151.35647022696756-0.00150272305728734-0.206470226967558-1.64728827166166
171.311.27049350191668-0.002644478990199560.0395064980833158-0.971615884967316
181.151.19504496000207-0.00329906783551835-0.0450449600020661-0.841069006619217
191.081.1105815456399-0.00389989262054471-0.0305815456398983-0.937511320941156
201.11.11313789418744-0.00385209317927523-0.01313789418744040.0745363430010047
211.141.1222225609323-0.003749966904328930.01777743906770250.149316962397917
221.241.18729424170494-0.0031967451836250.0527057582950590.794327952401985
231.331.2882951339885-0.00251376796862470.04170486601150451.20169053432618
241.491.41335157114653-0.002287918833978180.07664842885347321.47292933974515
251.381.48282633265411-0.00262567101434111-0.1028263326541070.844672145638163
261.961.60646760704617-0.00209324225211530.3535323929538271.44610029463602
271.361.49293559195104-0.00362425312571275-0.132935591951041-1.23580002102466
281.241.43041568699909-0.00456822292745102-0.190415686999086-0.660836768950929
291.351.33153589425745-0.005847153961616580.0184641057425512-1.07822019829124
301.231.26428891715511-0.00648688571531531-0.0342889171551126-0.707329126595636
311.091.17022398965457-0.00723354281007352-0.0802239896545687-1.01045452141955
321.081.1155033756589-0.00760728963870048-0.0355033756588949-0.547913049782186
331.331.23865254425769-0.006601047872346560.09134745574230761.50829019071171
341.351.31668757566524-0.006000755024501620.03331242433475530.975607469627651
351.381.37468622065249-0.005659010675688960.005313779347512350.737021990799474
361.51.4350608351173-0.005480070029549550.06493916488269930.76178906356518
371.471.5436357795116-0.0052879316918133-0.07363577951159851.32047278277853
382.091.63550964999385-0.004740405338813710.454490350006151.11094901125649
391.521.6368188547619-0.00467505014101754-0.1168188547619050.0680710469543522
401.291.51918038374923-0.00617110939634237-0.22918038374923-1.27379613625988
411.521.47237546382421-0.006688636186417830.0476245361757924-0.463374276697789
421.271.34339257930631-0.00803092376069269-0.0733925793063115-1.40523653860362
431.351.36966798589351-0.00770873056881062-0.01966798589351120.395286592257381
441.291.3786794087494-0.00756834120383169-0.08867940874940.192747906629204
451.411.36518133515694-0.007613925932375320.0448186648430612-0.0683286464113474
461.391.37617073857872-0.007488078312640570.01382926142128350.21416427112852
471.451.43927578502388-0.007105492996726260.01072421497611930.812154725697178
481.531.49435429379885-0.006845091063539620.03564570620115110.716030125317521
491.451.53897178362511-0.0066209228651986-0.08897178362511420.592353371352604
502.111.60278175801385-0.006149106762284420.5072182419861480.804628448516588
511.531.60229520736634-0.00609373850194805-0.07229520736633870.0641189162051376
521.381.5871839522167-0.006199247198695-0.207183952216701-0.102095660240673
531.541.5021553365934-0.007137776755518740.037844663406597-0.898507069342562
541.351.45435205663728-0.00758508479762459-0.104352056637275-0.466435487573247
551.291.36671704543709-0.00837040268923069-0.0767170454370863-0.921033913466359
561.331.38246079526867-0.00815904441607177-0.0524607952686740.277658305559019
571.471.41394674192391-0.007847787304963560.05605325807609010.456277291651119
581.471.45967839999219-0.007477791981595090.01032160000780750.616110417894833
591.541.52146976875163-0.00706546938643060.01853023124837360.796167040192301
601.591.56328658977214-0.006801725110967490.02671341022786450.561937798927378
611.51.59936612338976-0.0065516869107464-0.0993661233897590.492286674235943
6221.54130085446287-0.006933543683577080.458699145537125-0.58841563366209
631.511.54860411554113-0.00679950425492595-0.03860411554112910.161736350219995
641.41.55752166846644-0.00662897046850284-0.1575216684664430.178452226212818
651.621.55405287879038-0.006593397724362070.06594712120961910.0360309034726005
661.441.52709914210742-0.00681347115217159-0.0870991421074194-0.233268378513849
671.291.4387920147465-0.00762412742908874-0.148792014746504-0.936389462034217
681.281.37991011796557-0.00808572581181639-0.0999101179655657-0.589463278237579
691.41.36872550997732-0.008110828802286170.0312744900226844-0.0356235388189369
701.391.39152405462652-0.00788654047641351-0.001524054626521320.355070799160776
711.461.43360670777083-0.007557740804608970.02639329222916850.573813597086181
721.491.45933484903642-0.007346934163931990.03066515096357860.382158844710064

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1.35 & 1.35 & 0 & 0 & 0 \tabularnewline
2 & 1.91 & 1.79021831496889 & 0.0208815642593976 & 0.119781685031113 & 3.39722315344247 \tabularnewline
3 & 1.31 & 1.52091451798102 & 0.00752345076568155 & -0.210914517981024 & -2.89396230043741 \tabularnewline
4 & 1.19 & 1.28467944743231 & 0.00181942425331031 & -0.0946794474323141 & -2.78664026456425 \tabularnewline
5 & 1.3 & 1.257250581546 & 0.00149288053584203 & 0.0427494184540022 & -0.338805434252571 \tabularnewline
6 & 1.14 & 1.18054045769464 & 0.000854351434070408 & -0.0405404576946381 & -0.905239204052314 \tabularnewline
7 & 1.1 & 1.11879024538458 & 0.000327413122677772 & -0.0187902453845779 & -0.724007386640269 \tabularnewline
8 & 1.02 & 1.04838699913059 & -0.000310254928890694 & -0.0283869991305913 & -0.817530173258391 \tabularnewline
9 & 1.11 & 1.07515089223152 & -5.84219990801256e-05 & 0.0348491077684802 & 0.312865682169147 \tabularnewline
10 & 1.18 & 1.1435017115023 & 0.000582386565325097 & 0.0364982884977005 & 0.790469092776312 \tabularnewline
11 & 1.24 & 1.21302168241535 & 0.00122783461593559 & 0.0269783175846539 & 0.796526020262743 \tabularnewline
12 & 1.36 & 1.31345770813197 & 0.00215587336787393 & 0.0465422918680273 & 1.14619782460677 \tabularnewline
13 & 1.29 & 1.39306104795842 & -0.00021027853618634 & -0.10306104795842 & 0.980913312189165 \tabularnewline
14 & 1.73 & 1.43628346962903 & 2.73511963285213e-05 & 0.293716530370974 & 0.491364216816028 \tabularnewline
15 & 1.41 & 1.50194132831486 & 0.00148988653037453 & -0.0919413283148594 & 0.700799334943802 \tabularnewline
16 & 1.15 & 1.35647022696756 & -0.00150272305728734 & -0.206470226967558 & -1.64728827166166 \tabularnewline
17 & 1.31 & 1.27049350191668 & -0.00264447899019956 & 0.0395064980833158 & -0.971615884967316 \tabularnewline
18 & 1.15 & 1.19504496000207 & -0.00329906783551835 & -0.0450449600020661 & -0.841069006619217 \tabularnewline
19 & 1.08 & 1.1105815456399 & -0.00389989262054471 & -0.0305815456398983 & -0.937511320941156 \tabularnewline
20 & 1.1 & 1.11313789418744 & -0.00385209317927523 & -0.0131378941874404 & 0.0745363430010047 \tabularnewline
21 & 1.14 & 1.1222225609323 & -0.00374996690432893 & 0.0177774390677025 & 0.149316962397917 \tabularnewline
22 & 1.24 & 1.18729424170494 & -0.003196745183625 & 0.052705758295059 & 0.794327952401985 \tabularnewline
23 & 1.33 & 1.2882951339885 & -0.0025137679686247 & 0.0417048660115045 & 1.20169053432618 \tabularnewline
24 & 1.49 & 1.41335157114653 & -0.00228791883397818 & 0.0766484288534732 & 1.47292933974515 \tabularnewline
25 & 1.38 & 1.48282633265411 & -0.00262567101434111 & -0.102826332654107 & 0.844672145638163 \tabularnewline
26 & 1.96 & 1.60646760704617 & -0.0020932422521153 & 0.353532392953827 & 1.44610029463602 \tabularnewline
27 & 1.36 & 1.49293559195104 & -0.00362425312571275 & -0.132935591951041 & -1.23580002102466 \tabularnewline
28 & 1.24 & 1.43041568699909 & -0.00456822292745102 & -0.190415686999086 & -0.660836768950929 \tabularnewline
29 & 1.35 & 1.33153589425745 & -0.00584715396161658 & 0.0184641057425512 & -1.07822019829124 \tabularnewline
30 & 1.23 & 1.26428891715511 & -0.00648688571531531 & -0.0342889171551126 & -0.707329126595636 \tabularnewline
31 & 1.09 & 1.17022398965457 & -0.00723354281007352 & -0.0802239896545687 & -1.01045452141955 \tabularnewline
32 & 1.08 & 1.1155033756589 & -0.00760728963870048 & -0.0355033756588949 & -0.547913049782186 \tabularnewline
33 & 1.33 & 1.23865254425769 & -0.00660104787234656 & 0.0913474557423076 & 1.50829019071171 \tabularnewline
34 & 1.35 & 1.31668757566524 & -0.00600075502450162 & 0.0333124243347553 & 0.975607469627651 \tabularnewline
35 & 1.38 & 1.37468622065249 & -0.00565901067568896 & 0.00531377934751235 & 0.737021990799474 \tabularnewline
36 & 1.5 & 1.4350608351173 & -0.00548007002954955 & 0.0649391648826993 & 0.76178906356518 \tabularnewline
37 & 1.47 & 1.5436357795116 & -0.0052879316918133 & -0.0736357795115985 & 1.32047278277853 \tabularnewline
38 & 2.09 & 1.63550964999385 & -0.00474040533881371 & 0.45449035000615 & 1.11094901125649 \tabularnewline
39 & 1.52 & 1.6368188547619 & -0.00467505014101754 & -0.116818854761905 & 0.0680710469543522 \tabularnewline
40 & 1.29 & 1.51918038374923 & -0.00617110939634237 & -0.22918038374923 & -1.27379613625988 \tabularnewline
41 & 1.52 & 1.47237546382421 & -0.00668863618641783 & 0.0476245361757924 & -0.463374276697789 \tabularnewline
42 & 1.27 & 1.34339257930631 & -0.00803092376069269 & -0.0733925793063115 & -1.40523653860362 \tabularnewline
43 & 1.35 & 1.36966798589351 & -0.00770873056881062 & -0.0196679858935112 & 0.395286592257381 \tabularnewline
44 & 1.29 & 1.3786794087494 & -0.00756834120383169 & -0.0886794087494 & 0.192747906629204 \tabularnewline
45 & 1.41 & 1.36518133515694 & -0.00761392593237532 & 0.0448186648430612 & -0.0683286464113474 \tabularnewline
46 & 1.39 & 1.37617073857872 & -0.00748807831264057 & 0.0138292614212835 & 0.21416427112852 \tabularnewline
47 & 1.45 & 1.43927578502388 & -0.00710549299672626 & 0.0107242149761193 & 0.812154725697178 \tabularnewline
48 & 1.53 & 1.49435429379885 & -0.00684509106353962 & 0.0356457062011511 & 0.716030125317521 \tabularnewline
49 & 1.45 & 1.53897178362511 & -0.0066209228651986 & -0.0889717836251142 & 0.592353371352604 \tabularnewline
50 & 2.11 & 1.60278175801385 & -0.00614910676228442 & 0.507218241986148 & 0.804628448516588 \tabularnewline
51 & 1.53 & 1.60229520736634 & -0.00609373850194805 & -0.0722952073663387 & 0.0641189162051376 \tabularnewline
52 & 1.38 & 1.5871839522167 & -0.006199247198695 & -0.207183952216701 & -0.102095660240673 \tabularnewline
53 & 1.54 & 1.5021553365934 & -0.00713777675551874 & 0.037844663406597 & -0.898507069342562 \tabularnewline
54 & 1.35 & 1.45435205663728 & -0.00758508479762459 & -0.104352056637275 & -0.466435487573247 \tabularnewline
55 & 1.29 & 1.36671704543709 & -0.00837040268923069 & -0.0767170454370863 & -0.921033913466359 \tabularnewline
56 & 1.33 & 1.38246079526867 & -0.00815904441607177 & -0.052460795268674 & 0.277658305559019 \tabularnewline
57 & 1.47 & 1.41394674192391 & -0.00784778730496356 & 0.0560532580760901 & 0.456277291651119 \tabularnewline
58 & 1.47 & 1.45967839999219 & -0.00747779198159509 & 0.0103216000078075 & 0.616110417894833 \tabularnewline
59 & 1.54 & 1.52146976875163 & -0.0070654693864306 & 0.0185302312483736 & 0.796167040192301 \tabularnewline
60 & 1.59 & 1.56328658977214 & -0.00680172511096749 & 0.0267134102278645 & 0.561937798927378 \tabularnewline
61 & 1.5 & 1.59936612338976 & -0.0065516869107464 & -0.099366123389759 & 0.492286674235943 \tabularnewline
62 & 2 & 1.54130085446287 & -0.00693354368357708 & 0.458699145537125 & -0.58841563366209 \tabularnewline
63 & 1.51 & 1.54860411554113 & -0.00679950425492595 & -0.0386041155411291 & 0.161736350219995 \tabularnewline
64 & 1.4 & 1.55752166846644 & -0.00662897046850284 & -0.157521668466443 & 0.178452226212818 \tabularnewline
65 & 1.62 & 1.55405287879038 & -0.00659339772436207 & 0.0659471212096191 & 0.0360309034726005 \tabularnewline
66 & 1.44 & 1.52709914210742 & -0.00681347115217159 & -0.0870991421074194 & -0.233268378513849 \tabularnewline
67 & 1.29 & 1.4387920147465 & -0.00762412742908874 & -0.148792014746504 & -0.936389462034217 \tabularnewline
68 & 1.28 & 1.37991011796557 & -0.00808572581181639 & -0.0999101179655657 & -0.589463278237579 \tabularnewline
69 & 1.4 & 1.36872550997732 & -0.00811082880228617 & 0.0312744900226844 & -0.0356235388189369 \tabularnewline
70 & 1.39 & 1.39152405462652 & -0.00788654047641351 & -0.00152405462652132 & 0.355070799160776 \tabularnewline
71 & 1.46 & 1.43360670777083 & -0.00755774080460897 & 0.0263932922291685 & 0.573813597086181 \tabularnewline
72 & 1.49 & 1.45933484903642 & -0.00734693416393199 & 0.0306651509635786 & 0.382158844710064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154106&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]1.35[/C][C]1.35[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1.91[/C][C]1.79021831496889[/C][C]0.0208815642593976[/C][C]0.119781685031113[/C][C]3.39722315344247[/C][/ROW]
[ROW][C]3[/C][C]1.31[/C][C]1.52091451798102[/C][C]0.00752345076568155[/C][C]-0.210914517981024[/C][C]-2.89396230043741[/C][/ROW]
[ROW][C]4[/C][C]1.19[/C][C]1.28467944743231[/C][C]0.00181942425331031[/C][C]-0.0946794474323141[/C][C]-2.78664026456425[/C][/ROW]
[ROW][C]5[/C][C]1.3[/C][C]1.257250581546[/C][C]0.00149288053584203[/C][C]0.0427494184540022[/C][C]-0.338805434252571[/C][/ROW]
[ROW][C]6[/C][C]1.14[/C][C]1.18054045769464[/C][C]0.000854351434070408[/C][C]-0.0405404576946381[/C][C]-0.905239204052314[/C][/ROW]
[ROW][C]7[/C][C]1.1[/C][C]1.11879024538458[/C][C]0.000327413122677772[/C][C]-0.0187902453845779[/C][C]-0.724007386640269[/C][/ROW]
[ROW][C]8[/C][C]1.02[/C][C]1.04838699913059[/C][C]-0.000310254928890694[/C][C]-0.0283869991305913[/C][C]-0.817530173258391[/C][/ROW]
[ROW][C]9[/C][C]1.11[/C][C]1.07515089223152[/C][C]-5.84219990801256e-05[/C][C]0.0348491077684802[/C][C]0.312865682169147[/C][/ROW]
[ROW][C]10[/C][C]1.18[/C][C]1.1435017115023[/C][C]0.000582386565325097[/C][C]0.0364982884977005[/C][C]0.790469092776312[/C][/ROW]
[ROW][C]11[/C][C]1.24[/C][C]1.21302168241535[/C][C]0.00122783461593559[/C][C]0.0269783175846539[/C][C]0.796526020262743[/C][/ROW]
[ROW][C]12[/C][C]1.36[/C][C]1.31345770813197[/C][C]0.00215587336787393[/C][C]0.0465422918680273[/C][C]1.14619782460677[/C][/ROW]
[ROW][C]13[/C][C]1.29[/C][C]1.39306104795842[/C][C]-0.00021027853618634[/C][C]-0.10306104795842[/C][C]0.980913312189165[/C][/ROW]
[ROW][C]14[/C][C]1.73[/C][C]1.43628346962903[/C][C]2.73511963285213e-05[/C][C]0.293716530370974[/C][C]0.491364216816028[/C][/ROW]
[ROW][C]15[/C][C]1.41[/C][C]1.50194132831486[/C][C]0.00148988653037453[/C][C]-0.0919413283148594[/C][C]0.700799334943802[/C][/ROW]
[ROW][C]16[/C][C]1.15[/C][C]1.35647022696756[/C][C]-0.00150272305728734[/C][C]-0.206470226967558[/C][C]-1.64728827166166[/C][/ROW]
[ROW][C]17[/C][C]1.31[/C][C]1.27049350191668[/C][C]-0.00264447899019956[/C][C]0.0395064980833158[/C][C]-0.971615884967316[/C][/ROW]
[ROW][C]18[/C][C]1.15[/C][C]1.19504496000207[/C][C]-0.00329906783551835[/C][C]-0.0450449600020661[/C][C]-0.841069006619217[/C][/ROW]
[ROW][C]19[/C][C]1.08[/C][C]1.1105815456399[/C][C]-0.00389989262054471[/C][C]-0.0305815456398983[/C][C]-0.937511320941156[/C][/ROW]
[ROW][C]20[/C][C]1.1[/C][C]1.11313789418744[/C][C]-0.00385209317927523[/C][C]-0.0131378941874404[/C][C]0.0745363430010047[/C][/ROW]
[ROW][C]21[/C][C]1.14[/C][C]1.1222225609323[/C][C]-0.00374996690432893[/C][C]0.0177774390677025[/C][C]0.149316962397917[/C][/ROW]
[ROW][C]22[/C][C]1.24[/C][C]1.18729424170494[/C][C]-0.003196745183625[/C][C]0.052705758295059[/C][C]0.794327952401985[/C][/ROW]
[ROW][C]23[/C][C]1.33[/C][C]1.2882951339885[/C][C]-0.0025137679686247[/C][C]0.0417048660115045[/C][C]1.20169053432618[/C][/ROW]
[ROW][C]24[/C][C]1.49[/C][C]1.41335157114653[/C][C]-0.00228791883397818[/C][C]0.0766484288534732[/C][C]1.47292933974515[/C][/ROW]
[ROW][C]25[/C][C]1.38[/C][C]1.48282633265411[/C][C]-0.00262567101434111[/C][C]-0.102826332654107[/C][C]0.844672145638163[/C][/ROW]
[ROW][C]26[/C][C]1.96[/C][C]1.60646760704617[/C][C]-0.0020932422521153[/C][C]0.353532392953827[/C][C]1.44610029463602[/C][/ROW]
[ROW][C]27[/C][C]1.36[/C][C]1.49293559195104[/C][C]-0.00362425312571275[/C][C]-0.132935591951041[/C][C]-1.23580002102466[/C][/ROW]
[ROW][C]28[/C][C]1.24[/C][C]1.43041568699909[/C][C]-0.00456822292745102[/C][C]-0.190415686999086[/C][C]-0.660836768950929[/C][/ROW]
[ROW][C]29[/C][C]1.35[/C][C]1.33153589425745[/C][C]-0.00584715396161658[/C][C]0.0184641057425512[/C][C]-1.07822019829124[/C][/ROW]
[ROW][C]30[/C][C]1.23[/C][C]1.26428891715511[/C][C]-0.00648688571531531[/C][C]-0.0342889171551126[/C][C]-0.707329126595636[/C][/ROW]
[ROW][C]31[/C][C]1.09[/C][C]1.17022398965457[/C][C]-0.00723354281007352[/C][C]-0.0802239896545687[/C][C]-1.01045452141955[/C][/ROW]
[ROW][C]32[/C][C]1.08[/C][C]1.1155033756589[/C][C]-0.00760728963870048[/C][C]-0.0355033756588949[/C][C]-0.547913049782186[/C][/ROW]
[ROW][C]33[/C][C]1.33[/C][C]1.23865254425769[/C][C]-0.00660104787234656[/C][C]0.0913474557423076[/C][C]1.50829019071171[/C][/ROW]
[ROW][C]34[/C][C]1.35[/C][C]1.31668757566524[/C][C]-0.00600075502450162[/C][C]0.0333124243347553[/C][C]0.975607469627651[/C][/ROW]
[ROW][C]35[/C][C]1.38[/C][C]1.37468622065249[/C][C]-0.00565901067568896[/C][C]0.00531377934751235[/C][C]0.737021990799474[/C][/ROW]
[ROW][C]36[/C][C]1.5[/C][C]1.4350608351173[/C][C]-0.00548007002954955[/C][C]0.0649391648826993[/C][C]0.76178906356518[/C][/ROW]
[ROW][C]37[/C][C]1.47[/C][C]1.5436357795116[/C][C]-0.0052879316918133[/C][C]-0.0736357795115985[/C][C]1.32047278277853[/C][/ROW]
[ROW][C]38[/C][C]2.09[/C][C]1.63550964999385[/C][C]-0.00474040533881371[/C][C]0.45449035000615[/C][C]1.11094901125649[/C][/ROW]
[ROW][C]39[/C][C]1.52[/C][C]1.6368188547619[/C][C]-0.00467505014101754[/C][C]-0.116818854761905[/C][C]0.0680710469543522[/C][/ROW]
[ROW][C]40[/C][C]1.29[/C][C]1.51918038374923[/C][C]-0.00617110939634237[/C][C]-0.22918038374923[/C][C]-1.27379613625988[/C][/ROW]
[ROW][C]41[/C][C]1.52[/C][C]1.47237546382421[/C][C]-0.00668863618641783[/C][C]0.0476245361757924[/C][C]-0.463374276697789[/C][/ROW]
[ROW][C]42[/C][C]1.27[/C][C]1.34339257930631[/C][C]-0.00803092376069269[/C][C]-0.0733925793063115[/C][C]-1.40523653860362[/C][/ROW]
[ROW][C]43[/C][C]1.35[/C][C]1.36966798589351[/C][C]-0.00770873056881062[/C][C]-0.0196679858935112[/C][C]0.395286592257381[/C][/ROW]
[ROW][C]44[/C][C]1.29[/C][C]1.3786794087494[/C][C]-0.00756834120383169[/C][C]-0.0886794087494[/C][C]0.192747906629204[/C][/ROW]
[ROW][C]45[/C][C]1.41[/C][C]1.36518133515694[/C][C]-0.00761392593237532[/C][C]0.0448186648430612[/C][C]-0.0683286464113474[/C][/ROW]
[ROW][C]46[/C][C]1.39[/C][C]1.37617073857872[/C][C]-0.00748807831264057[/C][C]0.0138292614212835[/C][C]0.21416427112852[/C][/ROW]
[ROW][C]47[/C][C]1.45[/C][C]1.43927578502388[/C][C]-0.00710549299672626[/C][C]0.0107242149761193[/C][C]0.812154725697178[/C][/ROW]
[ROW][C]48[/C][C]1.53[/C][C]1.49435429379885[/C][C]-0.00684509106353962[/C][C]0.0356457062011511[/C][C]0.716030125317521[/C][/ROW]
[ROW][C]49[/C][C]1.45[/C][C]1.53897178362511[/C][C]-0.0066209228651986[/C][C]-0.0889717836251142[/C][C]0.592353371352604[/C][/ROW]
[ROW][C]50[/C][C]2.11[/C][C]1.60278175801385[/C][C]-0.00614910676228442[/C][C]0.507218241986148[/C][C]0.804628448516588[/C][/ROW]
[ROW][C]51[/C][C]1.53[/C][C]1.60229520736634[/C][C]-0.00609373850194805[/C][C]-0.0722952073663387[/C][C]0.0641189162051376[/C][/ROW]
[ROW][C]52[/C][C]1.38[/C][C]1.5871839522167[/C][C]-0.006199247198695[/C][C]-0.207183952216701[/C][C]-0.102095660240673[/C][/ROW]
[ROW][C]53[/C][C]1.54[/C][C]1.5021553365934[/C][C]-0.00713777675551874[/C][C]0.037844663406597[/C][C]-0.898507069342562[/C][/ROW]
[ROW][C]54[/C][C]1.35[/C][C]1.45435205663728[/C][C]-0.00758508479762459[/C][C]-0.104352056637275[/C][C]-0.466435487573247[/C][/ROW]
[ROW][C]55[/C][C]1.29[/C][C]1.36671704543709[/C][C]-0.00837040268923069[/C][C]-0.0767170454370863[/C][C]-0.921033913466359[/C][/ROW]
[ROW][C]56[/C][C]1.33[/C][C]1.38246079526867[/C][C]-0.00815904441607177[/C][C]-0.052460795268674[/C][C]0.277658305559019[/C][/ROW]
[ROW][C]57[/C][C]1.47[/C][C]1.41394674192391[/C][C]-0.00784778730496356[/C][C]0.0560532580760901[/C][C]0.456277291651119[/C][/ROW]
[ROW][C]58[/C][C]1.47[/C][C]1.45967839999219[/C][C]-0.00747779198159509[/C][C]0.0103216000078075[/C][C]0.616110417894833[/C][/ROW]
[ROW][C]59[/C][C]1.54[/C][C]1.52146976875163[/C][C]-0.0070654693864306[/C][C]0.0185302312483736[/C][C]0.796167040192301[/C][/ROW]
[ROW][C]60[/C][C]1.59[/C][C]1.56328658977214[/C][C]-0.00680172511096749[/C][C]0.0267134102278645[/C][C]0.561937798927378[/C][/ROW]
[ROW][C]61[/C][C]1.5[/C][C]1.59936612338976[/C][C]-0.0065516869107464[/C][C]-0.099366123389759[/C][C]0.492286674235943[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]1.54130085446287[/C][C]-0.00693354368357708[/C][C]0.458699145537125[/C][C]-0.58841563366209[/C][/ROW]
[ROW][C]63[/C][C]1.51[/C][C]1.54860411554113[/C][C]-0.00679950425492595[/C][C]-0.0386041155411291[/C][C]0.161736350219995[/C][/ROW]
[ROW][C]64[/C][C]1.4[/C][C]1.55752166846644[/C][C]-0.00662897046850284[/C][C]-0.157521668466443[/C][C]0.178452226212818[/C][/ROW]
[ROW][C]65[/C][C]1.62[/C][C]1.55405287879038[/C][C]-0.00659339772436207[/C][C]0.0659471212096191[/C][C]0.0360309034726005[/C][/ROW]
[ROW][C]66[/C][C]1.44[/C][C]1.52709914210742[/C][C]-0.00681347115217159[/C][C]-0.0870991421074194[/C][C]-0.233268378513849[/C][/ROW]
[ROW][C]67[/C][C]1.29[/C][C]1.4387920147465[/C][C]-0.00762412742908874[/C][C]-0.148792014746504[/C][C]-0.936389462034217[/C][/ROW]
[ROW][C]68[/C][C]1.28[/C][C]1.37991011796557[/C][C]-0.00808572581181639[/C][C]-0.0999101179655657[/C][C]-0.589463278237579[/C][/ROW]
[ROW][C]69[/C][C]1.4[/C][C]1.36872550997732[/C][C]-0.00811082880228617[/C][C]0.0312744900226844[/C][C]-0.0356235388189369[/C][/ROW]
[ROW][C]70[/C][C]1.39[/C][C]1.39152405462652[/C][C]-0.00788654047641351[/C][C]-0.00152405462652132[/C][C]0.355070799160776[/C][/ROW]
[ROW][C]71[/C][C]1.46[/C][C]1.43360670777083[/C][C]-0.00755774080460897[/C][C]0.0263932922291685[/C][C]0.573813597086181[/C][/ROW]
[ROW][C]72[/C][C]1.49[/C][C]1.45933484903642[/C][C]-0.00734693416393199[/C][C]0.0306651509635786[/C][C]0.382158844710064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154106&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
11.351.35000
21.911.790218314968890.02088156425939760.1197816850311133.39722315344247
31.311.520914517981020.00752345076568155-0.210914517981024-2.89396230043741
41.191.284679447432310.00181942425331031-0.0946794474323141-2.78664026456425
51.31.2572505815460.001492880535842030.0427494184540022-0.338805434252571
61.141.180540457694640.000854351434070408-0.0405404576946381-0.905239204052314
71.11.118790245384580.000327413122677772-0.0187902453845779-0.724007386640269
81.021.04838699913059-0.000310254928890694-0.0283869991305913-0.817530173258391
91.111.07515089223152-5.84219990801256e-050.03484910776848020.312865682169147
101.181.14350171150230.0005823865653250970.03649828849770050.790469092776312
111.241.213021682415350.001227834615935590.02697831758465390.796526020262743
121.361.313457708131970.002155873367873930.04654229186802731.14619782460677
131.291.39306104795842-0.00021027853618634-0.103061047958420.980913312189165
141.731.436283469629032.73511963285213e-050.2937165303709740.491364216816028
151.411.501941328314860.00148988653037453-0.09194132831485940.700799334943802
161.151.35647022696756-0.00150272305728734-0.206470226967558-1.64728827166166
171.311.27049350191668-0.002644478990199560.0395064980833158-0.971615884967316
181.151.19504496000207-0.00329906783551835-0.0450449600020661-0.841069006619217
191.081.1105815456399-0.00389989262054471-0.0305815456398983-0.937511320941156
201.11.11313789418744-0.00385209317927523-0.01313789418744040.0745363430010047
211.141.1222225609323-0.003749966904328930.01777743906770250.149316962397917
221.241.18729424170494-0.0031967451836250.0527057582950590.794327952401985
231.331.2882951339885-0.00251376796862470.04170486601150451.20169053432618
241.491.41335157114653-0.002287918833978180.07664842885347321.47292933974515
251.381.48282633265411-0.00262567101434111-0.1028263326541070.844672145638163
261.961.60646760704617-0.00209324225211530.3535323929538271.44610029463602
271.361.49293559195104-0.00362425312571275-0.132935591951041-1.23580002102466
281.241.43041568699909-0.00456822292745102-0.190415686999086-0.660836768950929
291.351.33153589425745-0.005847153961616580.0184641057425512-1.07822019829124
301.231.26428891715511-0.00648688571531531-0.0342889171551126-0.707329126595636
311.091.17022398965457-0.00723354281007352-0.0802239896545687-1.01045452141955
321.081.1155033756589-0.00760728963870048-0.0355033756588949-0.547913049782186
331.331.23865254425769-0.006601047872346560.09134745574230761.50829019071171
341.351.31668757566524-0.006000755024501620.03331242433475530.975607469627651
351.381.37468622065249-0.005659010675688960.005313779347512350.737021990799474
361.51.4350608351173-0.005480070029549550.06493916488269930.76178906356518
371.471.5436357795116-0.0052879316918133-0.07363577951159851.32047278277853
382.091.63550964999385-0.004740405338813710.454490350006151.11094901125649
391.521.6368188547619-0.00467505014101754-0.1168188547619050.0680710469543522
401.291.51918038374923-0.00617110939634237-0.22918038374923-1.27379613625988
411.521.47237546382421-0.006688636186417830.0476245361757924-0.463374276697789
421.271.34339257930631-0.00803092376069269-0.0733925793063115-1.40523653860362
431.351.36966798589351-0.00770873056881062-0.01966798589351120.395286592257381
441.291.3786794087494-0.00756834120383169-0.08867940874940.192747906629204
451.411.36518133515694-0.007613925932375320.0448186648430612-0.0683286464113474
461.391.37617073857872-0.007488078312640570.01382926142128350.21416427112852
471.451.43927578502388-0.007105492996726260.01072421497611930.812154725697178
481.531.49435429379885-0.006845091063539620.03564570620115110.716030125317521
491.451.53897178362511-0.0066209228651986-0.08897178362511420.592353371352604
502.111.60278175801385-0.006149106762284420.5072182419861480.804628448516588
511.531.60229520736634-0.00609373850194805-0.07229520736633870.0641189162051376
521.381.5871839522167-0.006199247198695-0.207183952216701-0.102095660240673
531.541.5021553365934-0.007137776755518740.037844663406597-0.898507069342562
541.351.45435205663728-0.00758508479762459-0.104352056637275-0.466435487573247
551.291.36671704543709-0.00837040268923069-0.0767170454370863-0.921033913466359
561.331.38246079526867-0.00815904441607177-0.0524607952686740.277658305559019
571.471.41394674192391-0.007847787304963560.05605325807609010.456277291651119
581.471.45967839999219-0.007477791981595090.01032160000780750.616110417894833
591.541.52146976875163-0.00706546938643060.01853023124837360.796167040192301
601.591.56328658977214-0.006801725110967490.02671341022786450.561937798927378
611.51.59936612338976-0.0065516869107464-0.0993661233897590.492286674235943
6221.54130085446287-0.006933543683577080.458699145537125-0.58841563366209
631.511.54860411554113-0.00679950425492595-0.03860411554112910.161736350219995
641.41.55752166846644-0.00662897046850284-0.1575216684664430.178452226212818
651.621.55405287879038-0.006593397724362070.06594712120961910.0360309034726005
661.441.52709914210742-0.00681347115217159-0.0870991421074194-0.233268378513849
671.291.4387920147465-0.00762412742908874-0.148792014746504-0.936389462034217
681.281.37991011796557-0.00808572581181639-0.0999101179655657-0.589463278237579
691.41.36872550997732-0.008110828802286170.0312744900226844-0.0356235388189369
701.391.39152405462652-0.00788654047641351-0.001524054626521320.355070799160776
711.461.43360670777083-0.007557740804608970.02639329222916850.573813597086181
721.491.45933484903642-0.007346934163931990.03066515096357860.382158844710064



Parameters (Session):
par1 = additive ; par2 = 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')