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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationThu, 03 Dec 2009 11:24:15 -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/03/t1259864807l40oh1x97ipc41m.htm/, Retrieved Thu, 28 Mar 2024 19:26:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63031, Retrieved Thu, 28 Mar 2024 19:26:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
- R  D      [Structural Time Series Models] [] [2009-12-03 18:24:15] [bcaf453a09027aa0f995cb78bdc3c98a] [Current]
-    D        [Structural Time Series Models] [Structural time s...] [2009-12-04 19:54:39] [3dd791303389e75e672968b227170a72]
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Dataseries X:
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7
7.1
7.2
7.1
6.9
7
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8
8.1
8




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=63031&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=63031&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63031&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
18.18.1000
27.77.70255673257361-0.397654567698998-0.00255673257360823-1.23520546066601
37.57.49489669492155-0.2111740787412860.005103305078452880.582804441865597
47.67.595696455606710.09341601431732360.004303544393293100.942920929924098
57.87.79938252427580.2011210083670810.0006174757242067750.333533634956366
67.87.803465929480660.00865740305028759-0.00346592948065967-0.596001298891416
77.87.79928988768668-0.003878002934147250.0007101123133146-0.0388183381561296
87.57.50446420003691-0.288068718009973-0.00446420003691253-0.880052173394744
97.57.49427947229257-0.01663819059797730.005720527707431570.840537754486445
107.17.10690986218564-0.378759894815383-0.00690986218564131-1.12138073420551
117.57.486306851966820.361789854606670.01369314803317522.29325724487263
127.57.508356990086410.0299401183446609-0.00835699008640986-1.02763766038116
137.67.597897153891640.08814746178665070.002102846108358770.180289630740346
147.77.701555883064650.103302538349973-0.001555883064649190.0470458683674718
157.77.703294116031760.00558305067796482-0.00329411603175623-0.304688822394776
167.97.895264147816550.1844904407027310.004735852183454670.553686999538775
178.18.095239377235540.199355412593050.004760622764456960.046034554943113
188.28.21044875057380.118569235237501-0.0104487505738044-0.2501699501135
198.28.19142007994629-0.0135331867334410.00857992005370687-0.409081071479970
208.28.216203597292540.0232530085837302-0.01620359729253660.113915653261319
217.97.88396454635163-0.3180396957562770.0160354536483722-1.05687965998974
227.37.33390409274867-0.540792910621740-0.0339040927486718-0.689798930240939
236.96.87381767498871-0.4633098148824010.02618232501128940.239941586523382
246.66.60463692943745-0.276935581305291-0.004636929437446130.57714468085457
256.76.690910334486570.07171186837333750.009089665513431841.07992154152149
266.96.891131573585930.1951292190648330.00886842641407570.382752681603925
2777.013863558781970.126330843643383-0.0138635587819723-0.213991760031918
287.17.098845366395620.08705080921757720.00115463360437555-0.121568169407592
297.27.193943450574230.09469323456823540.006056549425774480.0236676906645659
307.17.1089902381741-0.0759408952527884-0.00899023817409676-0.52840113790303
316.96.90412059679866-0.198401705913208-0.00412059679866323-0.379224050060212
3276.997053729035450.07831495852428460.002946270964552270.856907296032036
336.86.78538678111635-0.1971164727446180.0146132188836501-0.852927344943819
346.46.4376234193637-0.340204553727664-0.0376234193636979-0.443100252369668
356.76.653328408949950.1878152372767420.04667159105004871.63511749661135
366.66.62644266576581-0.0161097996663322-0.0264426657658141-0.631494753731989
376.46.4041331520194-0.211931831054026-0.0041331520194012-0.606586612876887
386.36.28361242048162-0.1250857262490950.01638757951837860.269124863321676
396.26.2095157389639-0.0769735396405751-0.009515738963900270.149396470400398
406.56.492041989792350.2624360747934890.007958010207649191.05063559813044
416.86.788113945529790.2941752828063280.01188605447021040.0982895948557093
426.86.805492778326820.0329419259274602-0.00549277832681824-0.808962585659557
436.46.44149526477237-0.341684370456125-0.0414952647723735-1.16010380687189
446.16.08413228524105-0.3564814608953290.0158677147589512-0.0458220897755935
455.85.7599093543658-0.3260367539095760.04009064563420630.0942779972191758
466.16.145367101869440.345454695558719-0.04536710186944042.07940472039105
477.27.128373263887870.9471647619295330.071626736112131.86331593617394
487.37.346155816979570.258814331735642-0.0461558169795662-2.1316170463579
496.96.93055891689458-0.377588104499472-0.030558916894576-1.97142932402068
506.16.0882733484193-0.8161922453486850.0117266515806964-1.35856658116138
515.85.80312939773405-0.31718610017511-0.003129397734050621.54791396820684
526.26.175680006169380.3314867709387260.02431999383061982.00833139546841
537.17.066520786204050.8572155448012770.03347921379595251.62799411243550
547.77.698998608530040.6459534105992130.0010013914699612-0.654221551016279
557.97.955997228818510.280310792972354-0.0559972288185112-1.13228288505511
567.77.68102995309995-0.2416809843777330.0189700469000546-1.61645008446091
577.47.37941566361595-0.2980212362113460.0205843363840541-0.174468623858556
587.57.580680764289830.171335087180122-0.08068076428983141.45345384493380
5987.904551457027670.3147275858507820.09544854297232670.444044005965626
608.18.128525438079110.229418842255042-0.0285254380791152-0.264176949136665
6188.00403422492856-0.103240101283659-0.0040342249285615-1.03050743528850

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 8.1 & 8.1 & 0 & 0 & 0 \tabularnewline
2 & 7.7 & 7.70255673257361 & -0.397654567698998 & -0.00255673257360823 & -1.23520546066601 \tabularnewline
3 & 7.5 & 7.49489669492155 & -0.211174078741286 & 0.00510330507845288 & 0.582804441865597 \tabularnewline
4 & 7.6 & 7.59569645560671 & 0.0934160143173236 & 0.00430354439329310 & 0.942920929924098 \tabularnewline
5 & 7.8 & 7.7993825242758 & 0.201121008367081 & 0.000617475724206775 & 0.333533634956366 \tabularnewline
6 & 7.8 & 7.80346592948066 & 0.00865740305028759 & -0.00346592948065967 & -0.596001298891416 \tabularnewline
7 & 7.8 & 7.79928988768668 & -0.00387800293414725 & 0.0007101123133146 & -0.0388183381561296 \tabularnewline
8 & 7.5 & 7.50446420003691 & -0.288068718009973 & -0.00446420003691253 & -0.880052173394744 \tabularnewline
9 & 7.5 & 7.49427947229257 & -0.0166381905979773 & 0.00572052770743157 & 0.840537754486445 \tabularnewline
10 & 7.1 & 7.10690986218564 & -0.378759894815383 & -0.00690986218564131 & -1.12138073420551 \tabularnewline
11 & 7.5 & 7.48630685196682 & 0.36178985460667 & 0.0136931480331752 & 2.29325724487263 \tabularnewline
12 & 7.5 & 7.50835699008641 & 0.0299401183446609 & -0.00835699008640986 & -1.02763766038116 \tabularnewline
13 & 7.6 & 7.59789715389164 & 0.0881474617866507 & 0.00210284610835877 & 0.180289630740346 \tabularnewline
14 & 7.7 & 7.70155588306465 & 0.103302538349973 & -0.00155588306464919 & 0.0470458683674718 \tabularnewline
15 & 7.7 & 7.70329411603176 & 0.00558305067796482 & -0.00329411603175623 & -0.304688822394776 \tabularnewline
16 & 7.9 & 7.89526414781655 & 0.184490440702731 & 0.00473585218345467 & 0.553686999538775 \tabularnewline
17 & 8.1 & 8.09523937723554 & 0.19935541259305 & 0.00476062276445696 & 0.046034554943113 \tabularnewline
18 & 8.2 & 8.2104487505738 & 0.118569235237501 & -0.0104487505738044 & -0.2501699501135 \tabularnewline
19 & 8.2 & 8.19142007994629 & -0.013533186733441 & 0.00857992005370687 & -0.409081071479970 \tabularnewline
20 & 8.2 & 8.21620359729254 & 0.0232530085837302 & -0.0162035972925366 & 0.113915653261319 \tabularnewline
21 & 7.9 & 7.88396454635163 & -0.318039695756277 & 0.0160354536483722 & -1.05687965998974 \tabularnewline
22 & 7.3 & 7.33390409274867 & -0.540792910621740 & -0.0339040927486718 & -0.689798930240939 \tabularnewline
23 & 6.9 & 6.87381767498871 & -0.463309814882401 & 0.0261823250112894 & 0.239941586523382 \tabularnewline
24 & 6.6 & 6.60463692943745 & -0.276935581305291 & -0.00463692943744613 & 0.57714468085457 \tabularnewline
25 & 6.7 & 6.69091033448657 & 0.0717118683733375 & 0.00908966551343184 & 1.07992154152149 \tabularnewline
26 & 6.9 & 6.89113157358593 & 0.195129219064833 & 0.0088684264140757 & 0.382752681603925 \tabularnewline
27 & 7 & 7.01386355878197 & 0.126330843643383 & -0.0138635587819723 & -0.213991760031918 \tabularnewline
28 & 7.1 & 7.09884536639562 & 0.0870508092175772 & 0.00115463360437555 & -0.121568169407592 \tabularnewline
29 & 7.2 & 7.19394345057423 & 0.0946932345682354 & 0.00605654942577448 & 0.0236676906645659 \tabularnewline
30 & 7.1 & 7.1089902381741 & -0.0759408952527884 & -0.00899023817409676 & -0.52840113790303 \tabularnewline
31 & 6.9 & 6.90412059679866 & -0.198401705913208 & -0.00412059679866323 & -0.379224050060212 \tabularnewline
32 & 7 & 6.99705372903545 & 0.0783149585242846 & 0.00294627096455227 & 0.856907296032036 \tabularnewline
33 & 6.8 & 6.78538678111635 & -0.197116472744618 & 0.0146132188836501 & -0.852927344943819 \tabularnewline
34 & 6.4 & 6.4376234193637 & -0.340204553727664 & -0.0376234193636979 & -0.443100252369668 \tabularnewline
35 & 6.7 & 6.65332840894995 & 0.187815237276742 & 0.0466715910500487 & 1.63511749661135 \tabularnewline
36 & 6.6 & 6.62644266576581 & -0.0161097996663322 & -0.0264426657658141 & -0.631494753731989 \tabularnewline
37 & 6.4 & 6.4041331520194 & -0.211931831054026 & -0.0041331520194012 & -0.606586612876887 \tabularnewline
38 & 6.3 & 6.28361242048162 & -0.125085726249095 & 0.0163875795183786 & 0.269124863321676 \tabularnewline
39 & 6.2 & 6.2095157389639 & -0.0769735396405751 & -0.00951573896390027 & 0.149396470400398 \tabularnewline
40 & 6.5 & 6.49204198979235 & 0.262436074793489 & 0.00795801020764919 & 1.05063559813044 \tabularnewline
41 & 6.8 & 6.78811394552979 & 0.294175282806328 & 0.0118860544702104 & 0.0982895948557093 \tabularnewline
42 & 6.8 & 6.80549277832682 & 0.0329419259274602 & -0.00549277832681824 & -0.808962585659557 \tabularnewline
43 & 6.4 & 6.44149526477237 & -0.341684370456125 & -0.0414952647723735 & -1.16010380687189 \tabularnewline
44 & 6.1 & 6.08413228524105 & -0.356481460895329 & 0.0158677147589512 & -0.0458220897755935 \tabularnewline
45 & 5.8 & 5.7599093543658 & -0.326036753909576 & 0.0400906456342063 & 0.0942779972191758 \tabularnewline
46 & 6.1 & 6.14536710186944 & 0.345454695558719 & -0.0453671018694404 & 2.07940472039105 \tabularnewline
47 & 7.2 & 7.12837326388787 & 0.947164761929533 & 0.07162673611213 & 1.86331593617394 \tabularnewline
48 & 7.3 & 7.34615581697957 & 0.258814331735642 & -0.0461558169795662 & -2.1316170463579 \tabularnewline
49 & 6.9 & 6.93055891689458 & -0.377588104499472 & -0.030558916894576 & -1.97142932402068 \tabularnewline
50 & 6.1 & 6.0882733484193 & -0.816192245348685 & 0.0117266515806964 & -1.35856658116138 \tabularnewline
51 & 5.8 & 5.80312939773405 & -0.31718610017511 & -0.00312939773405062 & 1.54791396820684 \tabularnewline
52 & 6.2 & 6.17568000616938 & 0.331486770938726 & 0.0243199938306198 & 2.00833139546841 \tabularnewline
53 & 7.1 & 7.06652078620405 & 0.857215544801277 & 0.0334792137959525 & 1.62799411243550 \tabularnewline
54 & 7.7 & 7.69899860853004 & 0.645953410599213 & 0.0010013914699612 & -0.654221551016279 \tabularnewline
55 & 7.9 & 7.95599722881851 & 0.280310792972354 & -0.0559972288185112 & -1.13228288505511 \tabularnewline
56 & 7.7 & 7.68102995309995 & -0.241680984377733 & 0.0189700469000546 & -1.61645008446091 \tabularnewline
57 & 7.4 & 7.37941566361595 & -0.298021236211346 & 0.0205843363840541 & -0.174468623858556 \tabularnewline
58 & 7.5 & 7.58068076428983 & 0.171335087180122 & -0.0806807642898314 & 1.45345384493380 \tabularnewline
59 & 8 & 7.90455145702767 & 0.314727585850782 & 0.0954485429723267 & 0.444044005965626 \tabularnewline
60 & 8.1 & 8.12852543807911 & 0.229418842255042 & -0.0285254380791152 & -0.264176949136665 \tabularnewline
61 & 8 & 8.00403422492856 & -0.103240101283659 & -0.0040342249285615 & -1.03050743528850 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63031&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]8.1[/C][C]8.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]7.7[/C][C]7.70255673257361[/C][C]-0.397654567698998[/C][C]-0.00255673257360823[/C][C]-1.23520546066601[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]7.49489669492155[/C][C]-0.211174078741286[/C][C]0.00510330507845288[/C][C]0.582804441865597[/C][/ROW]
[ROW][C]4[/C][C]7.6[/C][C]7.59569645560671[/C][C]0.0934160143173236[/C][C]0.00430354439329310[/C][C]0.942920929924098[/C][/ROW]
[ROW][C]5[/C][C]7.8[/C][C]7.7993825242758[/C][C]0.201121008367081[/C][C]0.000617475724206775[/C][C]0.333533634956366[/C][/ROW]
[ROW][C]6[/C][C]7.8[/C][C]7.80346592948066[/C][C]0.00865740305028759[/C][C]-0.00346592948065967[/C][C]-0.596001298891416[/C][/ROW]
[ROW][C]7[/C][C]7.8[/C][C]7.79928988768668[/C][C]-0.00387800293414725[/C][C]0.0007101123133146[/C][C]-0.0388183381561296[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.50446420003691[/C][C]-0.288068718009973[/C][C]-0.00446420003691253[/C][C]-0.880052173394744[/C][/ROW]
[ROW][C]9[/C][C]7.5[/C][C]7.49427947229257[/C][C]-0.0166381905979773[/C][C]0.00572052770743157[/C][C]0.840537754486445[/C][/ROW]
[ROW][C]10[/C][C]7.1[/C][C]7.10690986218564[/C][C]-0.378759894815383[/C][C]-0.00690986218564131[/C][C]-1.12138073420551[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.48630685196682[/C][C]0.36178985460667[/C][C]0.0136931480331752[/C][C]2.29325724487263[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.50835699008641[/C][C]0.0299401183446609[/C][C]-0.00835699008640986[/C][C]-1.02763766038116[/C][/ROW]
[ROW][C]13[/C][C]7.6[/C][C]7.59789715389164[/C][C]0.0881474617866507[/C][C]0.00210284610835877[/C][C]0.180289630740346[/C][/ROW]
[ROW][C]14[/C][C]7.7[/C][C]7.70155588306465[/C][C]0.103302538349973[/C][C]-0.00155588306464919[/C][C]0.0470458683674718[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.70329411603176[/C][C]0.00558305067796482[/C][C]-0.00329411603175623[/C][C]-0.304688822394776[/C][/ROW]
[ROW][C]16[/C][C]7.9[/C][C]7.89526414781655[/C][C]0.184490440702731[/C][C]0.00473585218345467[/C][C]0.553686999538775[/C][/ROW]
[ROW][C]17[/C][C]8.1[/C][C]8.09523937723554[/C][C]0.19935541259305[/C][C]0.00476062276445696[/C][C]0.046034554943113[/C][/ROW]
[ROW][C]18[/C][C]8.2[/C][C]8.2104487505738[/C][C]0.118569235237501[/C][C]-0.0104487505738044[/C][C]-0.2501699501135[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.19142007994629[/C][C]-0.013533186733441[/C][C]0.00857992005370687[/C][C]-0.409081071479970[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.21620359729254[/C][C]0.0232530085837302[/C][C]-0.0162035972925366[/C][C]0.113915653261319[/C][/ROW]
[ROW][C]21[/C][C]7.9[/C][C]7.88396454635163[/C][C]-0.318039695756277[/C][C]0.0160354536483722[/C][C]-1.05687965998974[/C][/ROW]
[ROW][C]22[/C][C]7.3[/C][C]7.33390409274867[/C][C]-0.540792910621740[/C][C]-0.0339040927486718[/C][C]-0.689798930240939[/C][/ROW]
[ROW][C]23[/C][C]6.9[/C][C]6.87381767498871[/C][C]-0.463309814882401[/C][C]0.0261823250112894[/C][C]0.239941586523382[/C][/ROW]
[ROW][C]24[/C][C]6.6[/C][C]6.60463692943745[/C][C]-0.276935581305291[/C][C]-0.00463692943744613[/C][C]0.57714468085457[/C][/ROW]
[ROW][C]25[/C][C]6.7[/C][C]6.69091033448657[/C][C]0.0717118683733375[/C][C]0.00908966551343184[/C][C]1.07992154152149[/C][/ROW]
[ROW][C]26[/C][C]6.9[/C][C]6.89113157358593[/C][C]0.195129219064833[/C][C]0.0088684264140757[/C][C]0.382752681603925[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]7.01386355878197[/C][C]0.126330843643383[/C][C]-0.0138635587819723[/C][C]-0.213991760031918[/C][/ROW]
[ROW][C]28[/C][C]7.1[/C][C]7.09884536639562[/C][C]0.0870508092175772[/C][C]0.00115463360437555[/C][C]-0.121568169407592[/C][/ROW]
[ROW][C]29[/C][C]7.2[/C][C]7.19394345057423[/C][C]0.0946932345682354[/C][C]0.00605654942577448[/C][C]0.0236676906645659[/C][/ROW]
[ROW][C]30[/C][C]7.1[/C][C]7.1089902381741[/C][C]-0.0759408952527884[/C][C]-0.00899023817409676[/C][C]-0.52840113790303[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]6.90412059679866[/C][C]-0.198401705913208[/C][C]-0.00412059679866323[/C][C]-0.379224050060212[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]6.99705372903545[/C][C]0.0783149585242846[/C][C]0.00294627096455227[/C][C]0.856907296032036[/C][/ROW]
[ROW][C]33[/C][C]6.8[/C][C]6.78538678111635[/C][C]-0.197116472744618[/C][C]0.0146132188836501[/C][C]-0.852927344943819[/C][/ROW]
[ROW][C]34[/C][C]6.4[/C][C]6.4376234193637[/C][C]-0.340204553727664[/C][C]-0.0376234193636979[/C][C]-0.443100252369668[/C][/ROW]
[ROW][C]35[/C][C]6.7[/C][C]6.65332840894995[/C][C]0.187815237276742[/C][C]0.0466715910500487[/C][C]1.63511749661135[/C][/ROW]
[ROW][C]36[/C][C]6.6[/C][C]6.62644266576581[/C][C]-0.0161097996663322[/C][C]-0.0264426657658141[/C][C]-0.631494753731989[/C][/ROW]
[ROW][C]37[/C][C]6.4[/C][C]6.4041331520194[/C][C]-0.211931831054026[/C][C]-0.0041331520194012[/C][C]-0.606586612876887[/C][/ROW]
[ROW][C]38[/C][C]6.3[/C][C]6.28361242048162[/C][C]-0.125085726249095[/C][C]0.0163875795183786[/C][C]0.269124863321676[/C][/ROW]
[ROW][C]39[/C][C]6.2[/C][C]6.2095157389639[/C][C]-0.0769735396405751[/C][C]-0.00951573896390027[/C][C]0.149396470400398[/C][/ROW]
[ROW][C]40[/C][C]6.5[/C][C]6.49204198979235[/C][C]0.262436074793489[/C][C]0.00795801020764919[/C][C]1.05063559813044[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]6.78811394552979[/C][C]0.294175282806328[/C][C]0.0118860544702104[/C][C]0.0982895948557093[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]6.80549277832682[/C][C]0.0329419259274602[/C][C]-0.00549277832681824[/C][C]-0.808962585659557[/C][/ROW]
[ROW][C]43[/C][C]6.4[/C][C]6.44149526477237[/C][C]-0.341684370456125[/C][C]-0.0414952647723735[/C][C]-1.16010380687189[/C][/ROW]
[ROW][C]44[/C][C]6.1[/C][C]6.08413228524105[/C][C]-0.356481460895329[/C][C]0.0158677147589512[/C][C]-0.0458220897755935[/C][/ROW]
[ROW][C]45[/C][C]5.8[/C][C]5.7599093543658[/C][C]-0.326036753909576[/C][C]0.0400906456342063[/C][C]0.0942779972191758[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]6.14536710186944[/C][C]0.345454695558719[/C][C]-0.0453671018694404[/C][C]2.07940472039105[/C][/ROW]
[ROW][C]47[/C][C]7.2[/C][C]7.12837326388787[/C][C]0.947164761929533[/C][C]0.07162673611213[/C][C]1.86331593617394[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]7.34615581697957[/C][C]0.258814331735642[/C][C]-0.0461558169795662[/C][C]-2.1316170463579[/C][/ROW]
[ROW][C]49[/C][C]6.9[/C][C]6.93055891689458[/C][C]-0.377588104499472[/C][C]-0.030558916894576[/C][C]-1.97142932402068[/C][/ROW]
[ROW][C]50[/C][C]6.1[/C][C]6.0882733484193[/C][C]-0.816192245348685[/C][C]0.0117266515806964[/C][C]-1.35856658116138[/C][/ROW]
[ROW][C]51[/C][C]5.8[/C][C]5.80312939773405[/C][C]-0.31718610017511[/C][C]-0.00312939773405062[/C][C]1.54791396820684[/C][/ROW]
[ROW][C]52[/C][C]6.2[/C][C]6.17568000616938[/C][C]0.331486770938726[/C][C]0.0243199938306198[/C][C]2.00833139546841[/C][/ROW]
[ROW][C]53[/C][C]7.1[/C][C]7.06652078620405[/C][C]0.857215544801277[/C][C]0.0334792137959525[/C][C]1.62799411243550[/C][/ROW]
[ROW][C]54[/C][C]7.7[/C][C]7.69899860853004[/C][C]0.645953410599213[/C][C]0.0010013914699612[/C][C]-0.654221551016279[/C][/ROW]
[ROW][C]55[/C][C]7.9[/C][C]7.95599722881851[/C][C]0.280310792972354[/C][C]-0.0559972288185112[/C][C]-1.13228288505511[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.68102995309995[/C][C]-0.241680984377733[/C][C]0.0189700469000546[/C][C]-1.61645008446091[/C][/ROW]
[ROW][C]57[/C][C]7.4[/C][C]7.37941566361595[/C][C]-0.298021236211346[/C][C]0.0205843363840541[/C][C]-0.174468623858556[/C][/ROW]
[ROW][C]58[/C][C]7.5[/C][C]7.58068076428983[/C][C]0.171335087180122[/C][C]-0.0806807642898314[/C][C]1.45345384493380[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]7.90455145702767[/C][C]0.314727585850782[/C][C]0.0954485429723267[/C][C]0.444044005965626[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]8.12852543807911[/C][C]0.229418842255042[/C][C]-0.0285254380791152[/C][C]-0.264176949136665[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.00403422492856[/C][C]-0.103240101283659[/C][C]-0.0040342249285615[/C][C]-1.03050743528850[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63031&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63031&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
18.18.1000
27.77.70255673257361-0.397654567698998-0.00255673257360823-1.23520546066601
37.57.49489669492155-0.2111740787412860.005103305078452880.582804441865597
47.67.595696455606710.09341601431732360.004303544393293100.942920929924098
57.87.79938252427580.2011210083670810.0006174757242067750.333533634956366
67.87.803465929480660.00865740305028759-0.00346592948065967-0.596001298891416
77.87.79928988768668-0.003878002934147250.0007101123133146-0.0388183381561296
87.57.50446420003691-0.288068718009973-0.00446420003691253-0.880052173394744
97.57.49427947229257-0.01663819059797730.005720527707431570.840537754486445
107.17.10690986218564-0.378759894815383-0.00690986218564131-1.12138073420551
117.57.486306851966820.361789854606670.01369314803317522.29325724487263
127.57.508356990086410.0299401183446609-0.00835699008640986-1.02763766038116
137.67.597897153891640.08814746178665070.002102846108358770.180289630740346
147.77.701555883064650.103302538349973-0.001555883064649190.0470458683674718
157.77.703294116031760.00558305067796482-0.00329411603175623-0.304688822394776
167.97.895264147816550.1844904407027310.004735852183454670.553686999538775
178.18.095239377235540.199355412593050.004760622764456960.046034554943113
188.28.21044875057380.118569235237501-0.0104487505738044-0.2501699501135
198.28.19142007994629-0.0135331867334410.00857992005370687-0.409081071479970
208.28.216203597292540.0232530085837302-0.01620359729253660.113915653261319
217.97.88396454635163-0.3180396957562770.0160354536483722-1.05687965998974
227.37.33390409274867-0.540792910621740-0.0339040927486718-0.689798930240939
236.96.87381767498871-0.4633098148824010.02618232501128940.239941586523382
246.66.60463692943745-0.276935581305291-0.004636929437446130.57714468085457
256.76.690910334486570.07171186837333750.009089665513431841.07992154152149
266.96.891131573585930.1951292190648330.00886842641407570.382752681603925
2777.013863558781970.126330843643383-0.0138635587819723-0.213991760031918
287.17.098845366395620.08705080921757720.00115463360437555-0.121568169407592
297.27.193943450574230.09469323456823540.006056549425774480.0236676906645659
307.17.1089902381741-0.0759408952527884-0.00899023817409676-0.52840113790303
316.96.90412059679866-0.198401705913208-0.00412059679866323-0.379224050060212
3276.997053729035450.07831495852428460.002946270964552270.856907296032036
336.86.78538678111635-0.1971164727446180.0146132188836501-0.852927344943819
346.46.4376234193637-0.340204553727664-0.0376234193636979-0.443100252369668
356.76.653328408949950.1878152372767420.04667159105004871.63511749661135
366.66.62644266576581-0.0161097996663322-0.0264426657658141-0.631494753731989
376.46.4041331520194-0.211931831054026-0.0041331520194012-0.606586612876887
386.36.28361242048162-0.1250857262490950.01638757951837860.269124863321676
396.26.2095157389639-0.0769735396405751-0.009515738963900270.149396470400398
406.56.492041989792350.2624360747934890.007958010207649191.05063559813044
416.86.788113945529790.2941752828063280.01188605447021040.0982895948557093
426.86.805492778326820.0329419259274602-0.00549277832681824-0.808962585659557
436.46.44149526477237-0.341684370456125-0.0414952647723735-1.16010380687189
446.16.08413228524105-0.3564814608953290.0158677147589512-0.0458220897755935
455.85.7599093543658-0.3260367539095760.04009064563420630.0942779972191758
466.16.145367101869440.345454695558719-0.04536710186944042.07940472039105
477.27.128373263887870.9471647619295330.071626736112131.86331593617394
487.37.346155816979570.258814331735642-0.0461558169795662-2.1316170463579
496.96.93055891689458-0.377588104499472-0.030558916894576-1.97142932402068
506.16.0882733484193-0.8161922453486850.0117266515806964-1.35856658116138
515.85.80312939773405-0.31718610017511-0.003129397734050621.54791396820684
526.26.175680006169380.3314867709387260.02431999383061982.00833139546841
537.17.066520786204050.8572155448012770.03347921379595251.62799411243550
547.77.698998608530040.6459534105992130.0010013914699612-0.654221551016279
557.97.955997228818510.280310792972354-0.0559972288185112-1.13228288505511
567.77.68102995309995-0.2416809843777330.0189700469000546-1.61645008446091
577.47.37941566361595-0.2980212362113460.0205843363840541-0.174468623858556
587.57.580680764289830.171335087180122-0.08068076428983141.45345384493380
5987.904551457027670.3147275858507820.09544854297232670.444044005965626
608.18.128525438079110.229418842255042-0.0285254380791152-0.264176949136665
6188.00403422492856-0.103240101283659-0.0040342249285615-1.03050743528850



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')