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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 computationMon, 21 Dec 2009 02:08:31 -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/21/t126138725591saxannm69576p.htm/, Retrieved Sun, 05 May 2024 13:44:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70075, Retrieved Sun, 05 May 2024 13:44:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [] [2009-12-21 09:08:31] [479db4778e5b462dda1f74ecdd6ccff3] [Current]
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Dataseries X:
43.9
51
51.9
54.3
50.3
57.2
48.8
41.1
58
63
53.8
54.7
55.5
56.1
69.6
69.4
57.2
68
53.3
47.9
60.8
61.7
57.8
51.4
50.5
48.1
58.7
54
56.1
60.4
51.2
50.7
56.4
53.3
52.6
47.7
49.5
48.5
55.3
49.8
57.4
64.6
53
41.5
55.9
58.4
53.5
50.6
58.5
49.1
61.1
52.3
58.4
65.5
61.7
45.1
52.1
59.3
57.9
45
64.9
63.8
69.4
71.1
62.9
73.5
62.7
51.9
73.3
66.7
62.5
70.3




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=70075&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=70075&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70075&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
143.943.9000
25147.04793873982470.242466833752151.361277533335641.26074627467546
351.949.53420445731520.4129879699542430.7649555587114330.838804156120813
454.351.74513526056920.5162125067377411.069821939520450.750065638183191
550.351.44394791805920.480574326747510-0.411927839400074-0.361439218912688
657.253.71367442944990.5438902856392091.823537768366260.812245665555612
748.852.11434549575410.47805805650679-1.28726242350551-0.985070826735972
841.147.7605329490350.342811600505252-2.04994527836049-2.23456079808479
95851.28736047476340.4265338468938183.659912246049871.47734413378979
106356.21455493948080.5396885426222032.458302895170022.09221944455839
1153.855.87338493644260.518301384147634-1.22496659807875-0.409975474867575
1254.755.45247307807140.4961558284273030.153305781609470-0.437501565988379
1355.557.21379362676420.403544336590115-3.099344845756270.742458763455314
1456.157.07713071975010.392992277629068-0.525075027860392-0.234573078051621
1569.662.07357064900030.5523145058736023.98237866941861.90134065412892
1669.465.21301626496490.6340581847465272.077581616219011.12295731789267
1757.263.31561650404260.568944095229283-3.94398964904352-1.14258144736441
186864.05692737539860.5725797565367373.79110406391450.0793874532872976
1953.360.46092474488550.496871557211125-3.43639356611302-1.93847418203173
2047.956.89532277457940.429988951815727-5.34213010193577-1.89781595802939
2160.857.23000387211720.4285123575235823.65597926641452-0.0446256332810679
2261.758.0787993937150.4347330659158893.241320495445870.197037454541535
2357.858.6654798900170.436790180774811-1.00311996129540.0713087354670697
2451.456.52058616977730.415703256923539-2.76442592757245-1.21607328704858
2550.555.66028773438430.441615487726866-3.93064842701421-0.647276116576455
2648.154.0173991153430.423337757501751-4.09893107506617-0.95745141010759
2758.754.59182229148140.4265584602467473.984898338012320.0661858454190987
285453.56273122104930.3937061238742521.63883702357988-0.646869717969869
2956.155.87234042100940.431978256949423-1.399380918822310.871665033531613
3060.455.90375415211210.4251806742073334.84354048535129-0.185180304537702
3151.255.12562491635740.40753460907217-2.86987058899475-0.561221199723659
3250.755.59167948890040.408304050185515-4.943349351736210.0274188324302579
3356.454.94601479011250.3955129877220592.38777761910336-0.495000302771469
3453.353.32547511596630.3730807857578811.76481470901880-0.948101104512396
3552.652.96281376559430.3663319655543500.292384820102349-0.346396265326864
3647.751.91759483799280.360300557721662-2.95175299722619-0.667564961315599
3749.552.05010930703930.361659253801339-2.34098209502258-0.110589336348128
3848.552.36823190271370.361391120771481-3.83003906323649-0.0202617188999141
3955.352.0622202399860.3515206565216223.79866147425206-0.300867366572693
4049.851.11984393368620.329266842405976-0.235467901674758-0.584259757644702
4157.453.80168865136680.3677566479429911.596710989042131.07693451108175
4264.656.20076838889420.3972616161301246.644761579297640.941276089190515
435356.56445501955150.396833113957337-3.53515562036393-0.0156816421068382
4441.553.11732815013380.353128277250413-8.2418979766742-1.80360053532200
4555.952.69788912836080.3452298328077713.8830523635093-0.363403968629055
4658.453.96970612899510.3535169473822053.611479628499060.436450342765258
4753.553.78342644081390.3497831027883960.194895942929942-0.254555779513232
4850.653.8099300731610.34866390722899-2.92201788178038-0.152980773781150
4958.556.32949129988270.3476535532622430.2198588681296531.03626506214856
5049.155.50767736495490.341245570942051-5.38179407121510-0.546431061873394
5161.156.19791457779840.3451606248484294.604116692041850.159767017226671
5252.355.64082983627450.332837940992319-2.57537673913724-0.412051704598823
5358.456.44103389841360.3392389918349581.559424270936380.215107328161140
5465.557.36107751759080.3465821834261347.637126424116120.269732110002818
5561.759.7507616724560.3697225648145090.1688610279882750.955299266811731
5645.157.93932767345840.347745408073652-10.9275216672941-1.02427063400410
5752.154.73016776543410.3161437522129480.499294035600076-1.6745044760329
5859.354.77864563722120.3141255205370274.75747062226951-0.126183068522801
5957.955.94423438571710.3190239545892631.202862140376560.401830949908647
604553.75037897677640.310296283456105-6.52122851060423-1.18842426686792
6164.957.2024120294330.3164175163079434.903750793101871.48885659181867
6263.861.90439780346010.339482792982958-1.949124122607822.05277376906402
6369.463.57576421121960.3516157724563154.676810141919600.615212032284827
6471.167.67760698429660.3937046153508830.2121715036983241.72659235314474
6562.966.33132119613220.373432710832944-1.93647182756971-0.804579225143055
6673.566.42340582150920.3703133062459147.32007982348669-0.130947420279429
6762.764.77182299965960.349867923716709-0.309872141465174-0.946344253981577
6851.963.52436791451220.335455565307162-10.2251337672085-0.750541928905227
6973.366.67086081014050.3576130827241624.157876295100251.32396538337305
7066.765.63390733444520.3483885937956382.29513311893782-0.657694490440457
7162.564.0792924538770.3386036845919100.101185287133001-0.898314217644751
7270.369.20234571095990.356066973006593-3.135195576160292.26093228719687

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 43.9 & 43.9 & 0 & 0 & 0 \tabularnewline
2 & 51 & 47.0479387398247 & 0.24246683375215 & 1.36127753333564 & 1.26074627467546 \tabularnewline
3 & 51.9 & 49.5342044573152 & 0.412987969954243 & 0.764955558711433 & 0.838804156120813 \tabularnewline
4 & 54.3 & 51.7451352605692 & 0.516212506737741 & 1.06982193952045 & 0.750065638183191 \tabularnewline
5 & 50.3 & 51.4439479180592 & 0.480574326747510 & -0.411927839400074 & -0.361439218912688 \tabularnewline
6 & 57.2 & 53.7136744294499 & 0.543890285639209 & 1.82353776836626 & 0.812245665555612 \tabularnewline
7 & 48.8 & 52.1143454957541 & 0.47805805650679 & -1.28726242350551 & -0.985070826735972 \tabularnewline
8 & 41.1 & 47.760532949035 & 0.342811600505252 & -2.04994527836049 & -2.23456079808479 \tabularnewline
9 & 58 & 51.2873604747634 & 0.426533846893818 & 3.65991224604987 & 1.47734413378979 \tabularnewline
10 & 63 & 56.2145549394808 & 0.539688542622203 & 2.45830289517002 & 2.09221944455839 \tabularnewline
11 & 53.8 & 55.8733849364426 & 0.518301384147634 & -1.22496659807875 & -0.409975474867575 \tabularnewline
12 & 54.7 & 55.4524730780714 & 0.496155828427303 & 0.153305781609470 & -0.437501565988379 \tabularnewline
13 & 55.5 & 57.2137936267642 & 0.403544336590115 & -3.09934484575627 & 0.742458763455314 \tabularnewline
14 & 56.1 & 57.0771307197501 & 0.392992277629068 & -0.525075027860392 & -0.234573078051621 \tabularnewline
15 & 69.6 & 62.0735706490003 & 0.552314505873602 & 3.9823786694186 & 1.90134065412892 \tabularnewline
16 & 69.4 & 65.2130162649649 & 0.634058184746527 & 2.07758161621901 & 1.12295731789267 \tabularnewline
17 & 57.2 & 63.3156165040426 & 0.568944095229283 & -3.94398964904352 & -1.14258144736441 \tabularnewline
18 & 68 & 64.0569273753986 & 0.572579756536737 & 3.7911040639145 & 0.0793874532872976 \tabularnewline
19 & 53.3 & 60.4609247448855 & 0.496871557211125 & -3.43639356611302 & -1.93847418203173 \tabularnewline
20 & 47.9 & 56.8953227745794 & 0.429988951815727 & -5.34213010193577 & -1.89781595802939 \tabularnewline
21 & 60.8 & 57.2300038721172 & 0.428512357523582 & 3.65597926641452 & -0.0446256332810679 \tabularnewline
22 & 61.7 & 58.078799393715 & 0.434733065915889 & 3.24132049544587 & 0.197037454541535 \tabularnewline
23 & 57.8 & 58.665479890017 & 0.436790180774811 & -1.0031199612954 & 0.0713087354670697 \tabularnewline
24 & 51.4 & 56.5205861697773 & 0.415703256923539 & -2.76442592757245 & -1.21607328704858 \tabularnewline
25 & 50.5 & 55.6602877343843 & 0.441615487726866 & -3.93064842701421 & -0.647276116576455 \tabularnewline
26 & 48.1 & 54.017399115343 & 0.423337757501751 & -4.09893107506617 & -0.95745141010759 \tabularnewline
27 & 58.7 & 54.5918222914814 & 0.426558460246747 & 3.98489833801232 & 0.0661858454190987 \tabularnewline
28 & 54 & 53.5627312210493 & 0.393706123874252 & 1.63883702357988 & -0.646869717969869 \tabularnewline
29 & 56.1 & 55.8723404210094 & 0.431978256949423 & -1.39938091882231 & 0.871665033531613 \tabularnewline
30 & 60.4 & 55.9037541521121 & 0.425180674207333 & 4.84354048535129 & -0.185180304537702 \tabularnewline
31 & 51.2 & 55.1256249163574 & 0.40753460907217 & -2.86987058899475 & -0.561221199723659 \tabularnewline
32 & 50.7 & 55.5916794889004 & 0.408304050185515 & -4.94334935173621 & 0.0274188324302579 \tabularnewline
33 & 56.4 & 54.9460147901125 & 0.395512987722059 & 2.38777761910336 & -0.495000302771469 \tabularnewline
34 & 53.3 & 53.3254751159663 & 0.373080785757881 & 1.76481470901880 & -0.948101104512396 \tabularnewline
35 & 52.6 & 52.9628137655943 & 0.366331965554350 & 0.292384820102349 & -0.346396265326864 \tabularnewline
36 & 47.7 & 51.9175948379928 & 0.360300557721662 & -2.95175299722619 & -0.667564961315599 \tabularnewline
37 & 49.5 & 52.0501093070393 & 0.361659253801339 & -2.34098209502258 & -0.110589336348128 \tabularnewline
38 & 48.5 & 52.3682319027137 & 0.361391120771481 & -3.83003906323649 & -0.0202617188999141 \tabularnewline
39 & 55.3 & 52.062220239986 & 0.351520656521622 & 3.79866147425206 & -0.300867366572693 \tabularnewline
40 & 49.8 & 51.1198439336862 & 0.329266842405976 & -0.235467901674758 & -0.584259757644702 \tabularnewline
41 & 57.4 & 53.8016886513668 & 0.367756647942991 & 1.59671098904213 & 1.07693451108175 \tabularnewline
42 & 64.6 & 56.2007683888942 & 0.397261616130124 & 6.64476157929764 & 0.941276089190515 \tabularnewline
43 & 53 & 56.5644550195515 & 0.396833113957337 & -3.53515562036393 & -0.0156816421068382 \tabularnewline
44 & 41.5 & 53.1173281501338 & 0.353128277250413 & -8.2418979766742 & -1.80360053532200 \tabularnewline
45 & 55.9 & 52.6978891283608 & 0.345229832807771 & 3.8830523635093 & -0.363403968629055 \tabularnewline
46 & 58.4 & 53.9697061289951 & 0.353516947382205 & 3.61147962849906 & 0.436450342765258 \tabularnewline
47 & 53.5 & 53.7834264408139 & 0.349783102788396 & 0.194895942929942 & -0.254555779513232 \tabularnewline
48 & 50.6 & 53.809930073161 & 0.34866390722899 & -2.92201788178038 & -0.152980773781150 \tabularnewline
49 & 58.5 & 56.3294912998827 & 0.347653553262243 & 0.219858868129653 & 1.03626506214856 \tabularnewline
50 & 49.1 & 55.5076773649549 & 0.341245570942051 & -5.38179407121510 & -0.546431061873394 \tabularnewline
51 & 61.1 & 56.1979145777984 & 0.345160624848429 & 4.60411669204185 & 0.159767017226671 \tabularnewline
52 & 52.3 & 55.6408298362745 & 0.332837940992319 & -2.57537673913724 & -0.412051704598823 \tabularnewline
53 & 58.4 & 56.4410338984136 & 0.339238991834958 & 1.55942427093638 & 0.215107328161140 \tabularnewline
54 & 65.5 & 57.3610775175908 & 0.346582183426134 & 7.63712642411612 & 0.269732110002818 \tabularnewline
55 & 61.7 & 59.750761672456 & 0.369722564814509 & 0.168861027988275 & 0.955299266811731 \tabularnewline
56 & 45.1 & 57.9393276734584 & 0.347745408073652 & -10.9275216672941 & -1.02427063400410 \tabularnewline
57 & 52.1 & 54.7301677654341 & 0.316143752212948 & 0.499294035600076 & -1.6745044760329 \tabularnewline
58 & 59.3 & 54.7786456372212 & 0.314125520537027 & 4.75747062226951 & -0.126183068522801 \tabularnewline
59 & 57.9 & 55.9442343857171 & 0.319023954589263 & 1.20286214037656 & 0.401830949908647 \tabularnewline
60 & 45 & 53.7503789767764 & 0.310296283456105 & -6.52122851060423 & -1.18842426686792 \tabularnewline
61 & 64.9 & 57.202412029433 & 0.316417516307943 & 4.90375079310187 & 1.48885659181867 \tabularnewline
62 & 63.8 & 61.9043978034601 & 0.339482792982958 & -1.94912412260782 & 2.05277376906402 \tabularnewline
63 & 69.4 & 63.5757642112196 & 0.351615772456315 & 4.67681014191960 & 0.615212032284827 \tabularnewline
64 & 71.1 & 67.6776069842966 & 0.393704615350883 & 0.212171503698324 & 1.72659235314474 \tabularnewline
65 & 62.9 & 66.3313211961322 & 0.373432710832944 & -1.93647182756971 & -0.804579225143055 \tabularnewline
66 & 73.5 & 66.4234058215092 & 0.370313306245914 & 7.32007982348669 & -0.130947420279429 \tabularnewline
67 & 62.7 & 64.7718229996596 & 0.349867923716709 & -0.309872141465174 & -0.946344253981577 \tabularnewline
68 & 51.9 & 63.5243679145122 & 0.335455565307162 & -10.2251337672085 & -0.750541928905227 \tabularnewline
69 & 73.3 & 66.6708608101405 & 0.357613082724162 & 4.15787629510025 & 1.32396538337305 \tabularnewline
70 & 66.7 & 65.6339073344452 & 0.348388593795638 & 2.29513311893782 & -0.657694490440457 \tabularnewline
71 & 62.5 & 64.079292453877 & 0.338603684591910 & 0.101185287133001 & -0.898314217644751 \tabularnewline
72 & 70.3 & 69.2023457109599 & 0.356066973006593 & -3.13519557616029 & 2.26093228719687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70075&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]43.9[/C][C]43.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]47.0479387398247[/C][C]0.24246683375215[/C][C]1.36127753333564[/C][C]1.26074627467546[/C][/ROW]
[ROW][C]3[/C][C]51.9[/C][C]49.5342044573152[/C][C]0.412987969954243[/C][C]0.764955558711433[/C][C]0.838804156120813[/C][/ROW]
[ROW][C]4[/C][C]54.3[/C][C]51.7451352605692[/C][C]0.516212506737741[/C][C]1.06982193952045[/C][C]0.750065638183191[/C][/ROW]
[ROW][C]5[/C][C]50.3[/C][C]51.4439479180592[/C][C]0.480574326747510[/C][C]-0.411927839400074[/C][C]-0.361439218912688[/C][/ROW]
[ROW][C]6[/C][C]57.2[/C][C]53.7136744294499[/C][C]0.543890285639209[/C][C]1.82353776836626[/C][C]0.812245665555612[/C][/ROW]
[ROW][C]7[/C][C]48.8[/C][C]52.1143454957541[/C][C]0.47805805650679[/C][C]-1.28726242350551[/C][C]-0.985070826735972[/C][/ROW]
[ROW][C]8[/C][C]41.1[/C][C]47.760532949035[/C][C]0.342811600505252[/C][C]-2.04994527836049[/C][C]-2.23456079808479[/C][/ROW]
[ROW][C]9[/C][C]58[/C][C]51.2873604747634[/C][C]0.426533846893818[/C][C]3.65991224604987[/C][C]1.47734413378979[/C][/ROW]
[ROW][C]10[/C][C]63[/C][C]56.2145549394808[/C][C]0.539688542622203[/C][C]2.45830289517002[/C][C]2.09221944455839[/C][/ROW]
[ROW][C]11[/C][C]53.8[/C][C]55.8733849364426[/C][C]0.518301384147634[/C][C]-1.22496659807875[/C][C]-0.409975474867575[/C][/ROW]
[ROW][C]12[/C][C]54.7[/C][C]55.4524730780714[/C][C]0.496155828427303[/C][C]0.153305781609470[/C][C]-0.437501565988379[/C][/ROW]
[ROW][C]13[/C][C]55.5[/C][C]57.2137936267642[/C][C]0.403544336590115[/C][C]-3.09934484575627[/C][C]0.742458763455314[/C][/ROW]
[ROW][C]14[/C][C]56.1[/C][C]57.0771307197501[/C][C]0.392992277629068[/C][C]-0.525075027860392[/C][C]-0.234573078051621[/C][/ROW]
[ROW][C]15[/C][C]69.6[/C][C]62.0735706490003[/C][C]0.552314505873602[/C][C]3.9823786694186[/C][C]1.90134065412892[/C][/ROW]
[ROW][C]16[/C][C]69.4[/C][C]65.2130162649649[/C][C]0.634058184746527[/C][C]2.07758161621901[/C][C]1.12295731789267[/C][/ROW]
[ROW][C]17[/C][C]57.2[/C][C]63.3156165040426[/C][C]0.568944095229283[/C][C]-3.94398964904352[/C][C]-1.14258144736441[/C][/ROW]
[ROW][C]18[/C][C]68[/C][C]64.0569273753986[/C][C]0.572579756536737[/C][C]3.7911040639145[/C][C]0.0793874532872976[/C][/ROW]
[ROW][C]19[/C][C]53.3[/C][C]60.4609247448855[/C][C]0.496871557211125[/C][C]-3.43639356611302[/C][C]-1.93847418203173[/C][/ROW]
[ROW][C]20[/C][C]47.9[/C][C]56.8953227745794[/C][C]0.429988951815727[/C][C]-5.34213010193577[/C][C]-1.89781595802939[/C][/ROW]
[ROW][C]21[/C][C]60.8[/C][C]57.2300038721172[/C][C]0.428512357523582[/C][C]3.65597926641452[/C][C]-0.0446256332810679[/C][/ROW]
[ROW][C]22[/C][C]61.7[/C][C]58.078799393715[/C][C]0.434733065915889[/C][C]3.24132049544587[/C][C]0.197037454541535[/C][/ROW]
[ROW][C]23[/C][C]57.8[/C][C]58.665479890017[/C][C]0.436790180774811[/C][C]-1.0031199612954[/C][C]0.0713087354670697[/C][/ROW]
[ROW][C]24[/C][C]51.4[/C][C]56.5205861697773[/C][C]0.415703256923539[/C][C]-2.76442592757245[/C][C]-1.21607328704858[/C][/ROW]
[ROW][C]25[/C][C]50.5[/C][C]55.6602877343843[/C][C]0.441615487726866[/C][C]-3.93064842701421[/C][C]-0.647276116576455[/C][/ROW]
[ROW][C]26[/C][C]48.1[/C][C]54.017399115343[/C][C]0.423337757501751[/C][C]-4.09893107506617[/C][C]-0.95745141010759[/C][/ROW]
[ROW][C]27[/C][C]58.7[/C][C]54.5918222914814[/C][C]0.426558460246747[/C][C]3.98489833801232[/C][C]0.0661858454190987[/C][/ROW]
[ROW][C]28[/C][C]54[/C][C]53.5627312210493[/C][C]0.393706123874252[/C][C]1.63883702357988[/C][C]-0.646869717969869[/C][/ROW]
[ROW][C]29[/C][C]56.1[/C][C]55.8723404210094[/C][C]0.431978256949423[/C][C]-1.39938091882231[/C][C]0.871665033531613[/C][/ROW]
[ROW][C]30[/C][C]60.4[/C][C]55.9037541521121[/C][C]0.425180674207333[/C][C]4.84354048535129[/C][C]-0.185180304537702[/C][/ROW]
[ROW][C]31[/C][C]51.2[/C][C]55.1256249163574[/C][C]0.40753460907217[/C][C]-2.86987058899475[/C][C]-0.561221199723659[/C][/ROW]
[ROW][C]32[/C][C]50.7[/C][C]55.5916794889004[/C][C]0.408304050185515[/C][C]-4.94334935173621[/C][C]0.0274188324302579[/C][/ROW]
[ROW][C]33[/C][C]56.4[/C][C]54.9460147901125[/C][C]0.395512987722059[/C][C]2.38777761910336[/C][C]-0.495000302771469[/C][/ROW]
[ROW][C]34[/C][C]53.3[/C][C]53.3254751159663[/C][C]0.373080785757881[/C][C]1.76481470901880[/C][C]-0.948101104512396[/C][/ROW]
[ROW][C]35[/C][C]52.6[/C][C]52.9628137655943[/C][C]0.366331965554350[/C][C]0.292384820102349[/C][C]-0.346396265326864[/C][/ROW]
[ROW][C]36[/C][C]47.7[/C][C]51.9175948379928[/C][C]0.360300557721662[/C][C]-2.95175299722619[/C][C]-0.667564961315599[/C][/ROW]
[ROW][C]37[/C][C]49.5[/C][C]52.0501093070393[/C][C]0.361659253801339[/C][C]-2.34098209502258[/C][C]-0.110589336348128[/C][/ROW]
[ROW][C]38[/C][C]48.5[/C][C]52.3682319027137[/C][C]0.361391120771481[/C][C]-3.83003906323649[/C][C]-0.0202617188999141[/C][/ROW]
[ROW][C]39[/C][C]55.3[/C][C]52.062220239986[/C][C]0.351520656521622[/C][C]3.79866147425206[/C][C]-0.300867366572693[/C][/ROW]
[ROW][C]40[/C][C]49.8[/C][C]51.1198439336862[/C][C]0.329266842405976[/C][C]-0.235467901674758[/C][C]-0.584259757644702[/C][/ROW]
[ROW][C]41[/C][C]57.4[/C][C]53.8016886513668[/C][C]0.367756647942991[/C][C]1.59671098904213[/C][C]1.07693451108175[/C][/ROW]
[ROW][C]42[/C][C]64.6[/C][C]56.2007683888942[/C][C]0.397261616130124[/C][C]6.64476157929764[/C][C]0.941276089190515[/C][/ROW]
[ROW][C]43[/C][C]53[/C][C]56.5644550195515[/C][C]0.396833113957337[/C][C]-3.53515562036393[/C][C]-0.0156816421068382[/C][/ROW]
[ROW][C]44[/C][C]41.5[/C][C]53.1173281501338[/C][C]0.353128277250413[/C][C]-8.2418979766742[/C][C]-1.80360053532200[/C][/ROW]
[ROW][C]45[/C][C]55.9[/C][C]52.6978891283608[/C][C]0.345229832807771[/C][C]3.8830523635093[/C][C]-0.363403968629055[/C][/ROW]
[ROW][C]46[/C][C]58.4[/C][C]53.9697061289951[/C][C]0.353516947382205[/C][C]3.61147962849906[/C][C]0.436450342765258[/C][/ROW]
[ROW][C]47[/C][C]53.5[/C][C]53.7834264408139[/C][C]0.349783102788396[/C][C]0.194895942929942[/C][C]-0.254555779513232[/C][/ROW]
[ROW][C]48[/C][C]50.6[/C][C]53.809930073161[/C][C]0.34866390722899[/C][C]-2.92201788178038[/C][C]-0.152980773781150[/C][/ROW]
[ROW][C]49[/C][C]58.5[/C][C]56.3294912998827[/C][C]0.347653553262243[/C][C]0.219858868129653[/C][C]1.03626506214856[/C][/ROW]
[ROW][C]50[/C][C]49.1[/C][C]55.5076773649549[/C][C]0.341245570942051[/C][C]-5.38179407121510[/C][C]-0.546431061873394[/C][/ROW]
[ROW][C]51[/C][C]61.1[/C][C]56.1979145777984[/C][C]0.345160624848429[/C][C]4.60411669204185[/C][C]0.159767017226671[/C][/ROW]
[ROW][C]52[/C][C]52.3[/C][C]55.6408298362745[/C][C]0.332837940992319[/C][C]-2.57537673913724[/C][C]-0.412051704598823[/C][/ROW]
[ROW][C]53[/C][C]58.4[/C][C]56.4410338984136[/C][C]0.339238991834958[/C][C]1.55942427093638[/C][C]0.215107328161140[/C][/ROW]
[ROW][C]54[/C][C]65.5[/C][C]57.3610775175908[/C][C]0.346582183426134[/C][C]7.63712642411612[/C][C]0.269732110002818[/C][/ROW]
[ROW][C]55[/C][C]61.7[/C][C]59.750761672456[/C][C]0.369722564814509[/C][C]0.168861027988275[/C][C]0.955299266811731[/C][/ROW]
[ROW][C]56[/C][C]45.1[/C][C]57.9393276734584[/C][C]0.347745408073652[/C][C]-10.9275216672941[/C][C]-1.02427063400410[/C][/ROW]
[ROW][C]57[/C][C]52.1[/C][C]54.7301677654341[/C][C]0.316143752212948[/C][C]0.499294035600076[/C][C]-1.6745044760329[/C][/ROW]
[ROW][C]58[/C][C]59.3[/C][C]54.7786456372212[/C][C]0.314125520537027[/C][C]4.75747062226951[/C][C]-0.126183068522801[/C][/ROW]
[ROW][C]59[/C][C]57.9[/C][C]55.9442343857171[/C][C]0.319023954589263[/C][C]1.20286214037656[/C][C]0.401830949908647[/C][/ROW]
[ROW][C]60[/C][C]45[/C][C]53.7503789767764[/C][C]0.310296283456105[/C][C]-6.52122851060423[/C][C]-1.18842426686792[/C][/ROW]
[ROW][C]61[/C][C]64.9[/C][C]57.202412029433[/C][C]0.316417516307943[/C][C]4.90375079310187[/C][C]1.48885659181867[/C][/ROW]
[ROW][C]62[/C][C]63.8[/C][C]61.9043978034601[/C][C]0.339482792982958[/C][C]-1.94912412260782[/C][C]2.05277376906402[/C][/ROW]
[ROW][C]63[/C][C]69.4[/C][C]63.5757642112196[/C][C]0.351615772456315[/C][C]4.67681014191960[/C][C]0.615212032284827[/C][/ROW]
[ROW][C]64[/C][C]71.1[/C][C]67.6776069842966[/C][C]0.393704615350883[/C][C]0.212171503698324[/C][C]1.72659235314474[/C][/ROW]
[ROW][C]65[/C][C]62.9[/C][C]66.3313211961322[/C][C]0.373432710832944[/C][C]-1.93647182756971[/C][C]-0.804579225143055[/C][/ROW]
[ROW][C]66[/C][C]73.5[/C][C]66.4234058215092[/C][C]0.370313306245914[/C][C]7.32007982348669[/C][C]-0.130947420279429[/C][/ROW]
[ROW][C]67[/C][C]62.7[/C][C]64.7718229996596[/C][C]0.349867923716709[/C][C]-0.309872141465174[/C][C]-0.946344253981577[/C][/ROW]
[ROW][C]68[/C][C]51.9[/C][C]63.5243679145122[/C][C]0.335455565307162[/C][C]-10.2251337672085[/C][C]-0.750541928905227[/C][/ROW]
[ROW][C]69[/C][C]73.3[/C][C]66.6708608101405[/C][C]0.357613082724162[/C][C]4.15787629510025[/C][C]1.32396538337305[/C][/ROW]
[ROW][C]70[/C][C]66.7[/C][C]65.6339073344452[/C][C]0.348388593795638[/C][C]2.29513311893782[/C][C]-0.657694490440457[/C][/ROW]
[ROW][C]71[/C][C]62.5[/C][C]64.079292453877[/C][C]0.338603684591910[/C][C]0.101185287133001[/C][C]-0.898314217644751[/C][/ROW]
[ROW][C]72[/C][C]70.3[/C][C]69.2023457109599[/C][C]0.356066973006593[/C][C]-3.13519557616029[/C][C]2.26093228719687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70075&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70075&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
143.943.9000
25147.04793873982470.242466833752151.361277533335641.26074627467546
351.949.53420445731520.4129879699542430.7649555587114330.838804156120813
454.351.74513526056920.5162125067377411.069821939520450.750065638183191
550.351.44394791805920.480574326747510-0.411927839400074-0.361439218912688
657.253.71367442944990.5438902856392091.823537768366260.812245665555612
748.852.11434549575410.47805805650679-1.28726242350551-0.985070826735972
841.147.7605329490350.342811600505252-2.04994527836049-2.23456079808479
95851.28736047476340.4265338468938183.659912246049871.47734413378979
106356.21455493948080.5396885426222032.458302895170022.09221944455839
1153.855.87338493644260.518301384147634-1.22496659807875-0.409975474867575
1254.755.45247307807140.4961558284273030.153305781609470-0.437501565988379
1355.557.21379362676420.403544336590115-3.099344845756270.742458763455314
1456.157.07713071975010.392992277629068-0.525075027860392-0.234573078051621
1569.662.07357064900030.5523145058736023.98237866941861.90134065412892
1669.465.21301626496490.6340581847465272.077581616219011.12295731789267
1757.263.31561650404260.568944095229283-3.94398964904352-1.14258144736441
186864.05692737539860.5725797565367373.79110406391450.0793874532872976
1953.360.46092474488550.496871557211125-3.43639356611302-1.93847418203173
2047.956.89532277457940.429988951815727-5.34213010193577-1.89781595802939
2160.857.23000387211720.4285123575235823.65597926641452-0.0446256332810679
2261.758.0787993937150.4347330659158893.241320495445870.197037454541535
2357.858.6654798900170.436790180774811-1.00311996129540.0713087354670697
2451.456.52058616977730.415703256923539-2.76442592757245-1.21607328704858
2550.555.66028773438430.441615487726866-3.93064842701421-0.647276116576455
2648.154.0173991153430.423337757501751-4.09893107506617-0.95745141010759
2758.754.59182229148140.4265584602467473.984898338012320.0661858454190987
285453.56273122104930.3937061238742521.63883702357988-0.646869717969869
2956.155.87234042100940.431978256949423-1.399380918822310.871665033531613
3060.455.90375415211210.4251806742073334.84354048535129-0.185180304537702
3151.255.12562491635740.40753460907217-2.86987058899475-0.561221199723659
3250.755.59167948890040.408304050185515-4.943349351736210.0274188324302579
3356.454.94601479011250.3955129877220592.38777761910336-0.495000302771469
3453.353.32547511596630.3730807857578811.76481470901880-0.948101104512396
3552.652.96281376559430.3663319655543500.292384820102349-0.346396265326864
3647.751.91759483799280.360300557721662-2.95175299722619-0.667564961315599
3749.552.05010930703930.361659253801339-2.34098209502258-0.110589336348128
3848.552.36823190271370.361391120771481-3.83003906323649-0.0202617188999141
3955.352.0622202399860.3515206565216223.79866147425206-0.300867366572693
4049.851.11984393368620.329266842405976-0.235467901674758-0.584259757644702
4157.453.80168865136680.3677566479429911.596710989042131.07693451108175
4264.656.20076838889420.3972616161301246.644761579297640.941276089190515
435356.56445501955150.396833113957337-3.53515562036393-0.0156816421068382
4441.553.11732815013380.353128277250413-8.2418979766742-1.80360053532200
4555.952.69788912836080.3452298328077713.8830523635093-0.363403968629055
4658.453.96970612899510.3535169473822053.611479628499060.436450342765258
4753.553.78342644081390.3497831027883960.194895942929942-0.254555779513232
4850.653.8099300731610.34866390722899-2.92201788178038-0.152980773781150
4958.556.32949129988270.3476535532622430.2198588681296531.03626506214856
5049.155.50767736495490.341245570942051-5.38179407121510-0.546431061873394
5161.156.19791457779840.3451606248484294.604116692041850.159767017226671
5252.355.64082983627450.332837940992319-2.57537673913724-0.412051704598823
5358.456.44103389841360.3392389918349581.559424270936380.215107328161140
5465.557.36107751759080.3465821834261347.637126424116120.269732110002818
5561.759.7507616724560.3697225648145090.1688610279882750.955299266811731
5645.157.93932767345840.347745408073652-10.9275216672941-1.02427063400410
5752.154.73016776543410.3161437522129480.499294035600076-1.6745044760329
5859.354.77864563722120.3141255205370274.75747062226951-0.126183068522801
5957.955.94423438571710.3190239545892631.202862140376560.401830949908647
604553.75037897677640.310296283456105-6.52122851060423-1.18842426686792
6164.957.2024120294330.3164175163079434.903750793101871.48885659181867
6263.861.90439780346010.339482792982958-1.949124122607822.05277376906402
6369.463.57576421121960.3516157724563154.676810141919600.615212032284827
6471.167.67760698429660.3937046153508830.2121715036983241.72659235314474
6562.966.33132119613220.373432710832944-1.93647182756971-0.804579225143055
6673.566.42340582150920.3703133062459147.32007982348669-0.130947420279429
6762.764.77182299965960.349867923716709-0.309872141465174-0.946344253981577
6851.963.52436791451220.335455565307162-10.2251337672085-0.750541928905227
6973.366.67086081014050.3576130827241624.157876295100251.32396538337305
7066.765.63390733444520.3483885937956382.29513311893782-0.657694490440457
7162.564.0792924538770.3386036845919100.101185287133001-0.898314217644751
7270.369.20234571095990.356066973006593-3.135195576160292.26093228719687



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')