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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationThu, 10 Dec 2009 11:55:45 -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/10/t1260471386pb54r8x8wn4ny88.htm/, Retrieved Fri, 26 Apr 2024 20:14:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65726, Retrieved Fri, 26 Apr 2024 20:14:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D    [Structural Time Series Models] [WS 9 Structural T...] [2009-12-04 14:23:59] [83058a88a37d754675a5cd22dab372fc]
-    D        [Structural Time Series Models] [WS 9 Review 3 Str...] [2009-12-10 18:55:45] [eba9f01697e64705b70041e6f338cb22] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881
293.299

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65726&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=65726&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65726&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1283.042283.042000
2276.687279.643099949226-2.92361682846477-2.95609994922578-1.60260866952763
3277.915276.140559463114-3.261756285662101.77444053688619-0.125865216510943
4277.128275.559759426404-1.655331587442981.568240573595940.625674700662030
5277.103276.338883741368-0.1800070069982450.7641162586320770.536882429936485
6275.037275.60117321584-0.51149442481712-0.564173215840185-0.120727948617283
7270.15271.60358070987-2.58377139671973-1.45358070987009-0.761464920927339
8267.14267.303320934953-3.60804292510288-0.163320934952686-0.376027613640751
9264.993264.329183977815-3.229421931724690.6638160221847230.138895032605294
10287.259278.8965049642217.398469971225168.362495035778573.89920571124566
11291.186291.46110007156110.4826558430896-0.2751000715613011.13156009519430
12292.3295.8636214428266.85348046703138-3.56362144282566-1.33148594559240
13288.186292.2557464057330.659880772553535-4.06974640573311-2.31104249620351
14281.477286.251855201344-3.29403221142526-4.77485520134415-1.46225990329933
15282.656281.892745223988-3.907207723376370.763254776012354-0.222981966516256
16280.19279.177391289139-3.226419614219551.012608710860940.253176943380808
17280.408278.155724777872-1.948906809138892.252275222127570.472296549723932
18276.836275.44314718552-2.39018212684141.39285281447971-0.161077836539959
19275.216273.85075963422-1.931878592898961.365240365779750.167747256630069
20274.352274.118593889419-0.6669679969701730.2334061105807570.464367161811322
21271.311277.0051249135041.38092983527905-5.694124913504050.751506378766255
22289.802282.6167871706813.820324191085767.185212829318670.894942062131855
23290.726288.3959708104114.948723451482852.330029189589490.414038742564155
24292.3292.353305969474.37838610119883-0.0533059694697586-0.209592664946352
25278.506285.316536319926-2.19633091444993-6.81053631992616-2.42343573801538
26269.826276.256467579431-6.14771431710252-6.43046757943118-1.45003376242421
27265.861266.634641590441-8.12879440977871-0.773641590440527-0.724380148669477
28269.034265.086978257636-4.39244385315573.94702174236371.37611885262853
29264.176261.384315302726-3.998641439438732.791684697273780.145243433947237
30255.198254.689111649302-5.540849076981730.508888350698388-0.565358817642569
31253.353251.237668901975-4.349738274929762.115331098025210.435929106413505
32246.057247.567923625875-3.96258950990762-1.510923625875260.141949478481996
33235.372243.730269033852-3.89139064501295-8.35826903385190.0261260507276766
34258.556248.9556873653651.307153142766919.600312634635081.90760936506891
35260.993256.2905796337624.743209584889714.702420366237711.26134079557061
36254.663253.6670854754680.5434433757649970.995914524532434-1.54329364666324
37250.643253.6415451898880.218748236195865-2.99854518988786-0.119323540000477
38243.422250.621368195597-1.62730637310546-7.1993681955969-0.676709885244777
39247.105249.983560617877-1.06592851870523-2.878560617876720.205616875082688
40248.541246.003758049731-2.715433789500172.53724195026863-0.606230501450016
41245.039241.424549571363-3.773240818555483.61445042863657-0.389366177369497
42237.08236.939737742876-4.177867997847960.140262257124481-0.148538252076744
43237.085233.95956975557-3.497760217385893.125430244430200.249143942575550
44225.554228.694666217751-4.49947570767945-3.14066621775122-0.367107800009838
45226.839234.7025602421641.45475009444517-7.863560242164462.18424387096914
46247.934239.9354098737833.596314379497167.998590126217230.78606664695112
47248.333241.8379337502432.63589526214666.49506624975652-0.352698905034626
48246.969245.1634259079073.02717145061961.805574092092910.143736203847724
49245.098247.5747278062962.67755544773851-2.47672780629557-0.128339536551380
50246.263252.6886531322274.05883059350544-6.425653132227420.506263793426672
51255.765257.3126339091064.37833178738536-1.547633909106070.117099126771383
52264.319261.0293907431144.004784449170483.28960925688568-0.137195330543637
53268.347264.2459993166803.559073015377364.10100068332029-0.163895920974508
54273.046271.0336843974025.387645423661912.012315602598470.671488106668861
55273.963272.7624158320253.316603795045821.20058416797530-0.75918263216
56267.43275.1943227759542.81665594756149-7.76432277595415-0.183216475363602
57271.993280.1860834017454.04463347747718-8.193083401744680.450382121364642
58292.71284.2117927849524.033949639436758.4982072150479-0.00392200071173293
59295.881289.0685614333174.498771025024026.812438566682930.170729131214369
60293.299292.1353367761303.689146571897521.16366322386956-0.2973494716982

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 283.042 & 283.042 & 0 & 0 & 0 \tabularnewline
2 & 276.687 & 279.643099949226 & -2.92361682846477 & -2.95609994922578 & -1.60260866952763 \tabularnewline
3 & 277.915 & 276.140559463114 & -3.26175628566210 & 1.77444053688619 & -0.125865216510943 \tabularnewline
4 & 277.128 & 275.559759426404 & -1.65533158744298 & 1.56824057359594 & 0.625674700662030 \tabularnewline
5 & 277.103 & 276.338883741368 & -0.180007006998245 & 0.764116258632077 & 0.536882429936485 \tabularnewline
6 & 275.037 & 275.60117321584 & -0.51149442481712 & -0.564173215840185 & -0.120727948617283 \tabularnewline
7 & 270.15 & 271.60358070987 & -2.58377139671973 & -1.45358070987009 & -0.761464920927339 \tabularnewline
8 & 267.14 & 267.303320934953 & -3.60804292510288 & -0.163320934952686 & -0.376027613640751 \tabularnewline
9 & 264.993 & 264.329183977815 & -3.22942193172469 & 0.663816022184723 & 0.138895032605294 \tabularnewline
10 & 287.259 & 278.896504964221 & 7.39846997122516 & 8.36249503577857 & 3.89920571124566 \tabularnewline
11 & 291.186 & 291.461100071561 & 10.4826558430896 & -0.275100071561301 & 1.13156009519430 \tabularnewline
12 & 292.3 & 295.863621442826 & 6.85348046703138 & -3.56362144282566 & -1.33148594559240 \tabularnewline
13 & 288.186 & 292.255746405733 & 0.659880772553535 & -4.06974640573311 & -2.31104249620351 \tabularnewline
14 & 281.477 & 286.251855201344 & -3.29403221142526 & -4.77485520134415 & -1.46225990329933 \tabularnewline
15 & 282.656 & 281.892745223988 & -3.90720772337637 & 0.763254776012354 & -0.222981966516256 \tabularnewline
16 & 280.19 & 279.177391289139 & -3.22641961421955 & 1.01260871086094 & 0.253176943380808 \tabularnewline
17 & 280.408 & 278.155724777872 & -1.94890680913889 & 2.25227522212757 & 0.472296549723932 \tabularnewline
18 & 276.836 & 275.44314718552 & -2.3901821268414 & 1.39285281447971 & -0.161077836539959 \tabularnewline
19 & 275.216 & 273.85075963422 & -1.93187859289896 & 1.36524036577975 & 0.167747256630069 \tabularnewline
20 & 274.352 & 274.118593889419 & -0.666967996970173 & 0.233406110580757 & 0.464367161811322 \tabularnewline
21 & 271.311 & 277.005124913504 & 1.38092983527905 & -5.69412491350405 & 0.751506378766255 \tabularnewline
22 & 289.802 & 282.616787170681 & 3.82032419108576 & 7.18521282931867 & 0.894942062131855 \tabularnewline
23 & 290.726 & 288.395970810411 & 4.94872345148285 & 2.33002918958949 & 0.414038742564155 \tabularnewline
24 & 292.3 & 292.35330596947 & 4.37838610119883 & -0.0533059694697586 & -0.209592664946352 \tabularnewline
25 & 278.506 & 285.316536319926 & -2.19633091444993 & -6.81053631992616 & -2.42343573801538 \tabularnewline
26 & 269.826 & 276.256467579431 & -6.14771431710252 & -6.43046757943118 & -1.45003376242421 \tabularnewline
27 & 265.861 & 266.634641590441 & -8.12879440977871 & -0.773641590440527 & -0.724380148669477 \tabularnewline
28 & 269.034 & 265.086978257636 & -4.3924438531557 & 3.9470217423637 & 1.37611885262853 \tabularnewline
29 & 264.176 & 261.384315302726 & -3.99864143943873 & 2.79168469727378 & 0.145243433947237 \tabularnewline
30 & 255.198 & 254.689111649302 & -5.54084907698173 & 0.508888350698388 & -0.565358817642569 \tabularnewline
31 & 253.353 & 251.237668901975 & -4.34973827492976 & 2.11533109802521 & 0.435929106413505 \tabularnewline
32 & 246.057 & 247.567923625875 & -3.96258950990762 & -1.51092362587526 & 0.141949478481996 \tabularnewline
33 & 235.372 & 243.730269033852 & -3.89139064501295 & -8.3582690338519 & 0.0261260507276766 \tabularnewline
34 & 258.556 & 248.955687365365 & 1.30715314276691 & 9.60031263463508 & 1.90760936506891 \tabularnewline
35 & 260.993 & 256.290579633762 & 4.74320958488971 & 4.70242036623771 & 1.26134079557061 \tabularnewline
36 & 254.663 & 253.667085475468 & 0.543443375764997 & 0.995914524532434 & -1.54329364666324 \tabularnewline
37 & 250.643 & 253.641545189888 & 0.218748236195865 & -2.99854518988786 & -0.119323540000477 \tabularnewline
38 & 243.422 & 250.621368195597 & -1.62730637310546 & -7.1993681955969 & -0.676709885244777 \tabularnewline
39 & 247.105 & 249.983560617877 & -1.06592851870523 & -2.87856061787672 & 0.205616875082688 \tabularnewline
40 & 248.541 & 246.003758049731 & -2.71543378950017 & 2.53724195026863 & -0.606230501450016 \tabularnewline
41 & 245.039 & 241.424549571363 & -3.77324081855548 & 3.61445042863657 & -0.389366177369497 \tabularnewline
42 & 237.08 & 236.939737742876 & -4.17786799784796 & 0.140262257124481 & -0.148538252076744 \tabularnewline
43 & 237.085 & 233.95956975557 & -3.49776021738589 & 3.12543024443020 & 0.249143942575550 \tabularnewline
44 & 225.554 & 228.694666217751 & -4.49947570767945 & -3.14066621775122 & -0.367107800009838 \tabularnewline
45 & 226.839 & 234.702560242164 & 1.45475009444517 & -7.86356024216446 & 2.18424387096914 \tabularnewline
46 & 247.934 & 239.935409873783 & 3.59631437949716 & 7.99859012621723 & 0.78606664695112 \tabularnewline
47 & 248.333 & 241.837933750243 & 2.6358952621466 & 6.49506624975652 & -0.352698905034626 \tabularnewline
48 & 246.969 & 245.163425907907 & 3.0271714506196 & 1.80557409209291 & 0.143736203847724 \tabularnewline
49 & 245.098 & 247.574727806296 & 2.67755544773851 & -2.47672780629557 & -0.128339536551380 \tabularnewline
50 & 246.263 & 252.688653132227 & 4.05883059350544 & -6.42565313222742 & 0.506263793426672 \tabularnewline
51 & 255.765 & 257.312633909106 & 4.37833178738536 & -1.54763390910607 & 0.117099126771383 \tabularnewline
52 & 264.319 & 261.029390743114 & 4.00478444917048 & 3.28960925688568 & -0.137195330543637 \tabularnewline
53 & 268.347 & 264.245999316680 & 3.55907301537736 & 4.10100068332029 & -0.163895920974508 \tabularnewline
54 & 273.046 & 271.033684397402 & 5.38764542366191 & 2.01231560259847 & 0.671488106668861 \tabularnewline
55 & 273.963 & 272.762415832025 & 3.31660379504582 & 1.20058416797530 & -0.75918263216 \tabularnewline
56 & 267.43 & 275.194322775954 & 2.81665594756149 & -7.76432277595415 & -0.183216475363602 \tabularnewline
57 & 271.993 & 280.186083401745 & 4.04463347747718 & -8.19308340174468 & 0.450382121364642 \tabularnewline
58 & 292.71 & 284.211792784952 & 4.03394963943675 & 8.4982072150479 & -0.00392200071173293 \tabularnewline
59 & 295.881 & 289.068561433317 & 4.49877102502402 & 6.81243856668293 & 0.170729131214369 \tabularnewline
60 & 293.299 & 292.135336776130 & 3.68914657189752 & 1.16366322386956 & -0.2973494716982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65726&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]283.042[/C][C]283.042[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]276.687[/C][C]279.643099949226[/C][C]-2.92361682846477[/C][C]-2.95609994922578[/C][C]-1.60260866952763[/C][/ROW]
[ROW][C]3[/C][C]277.915[/C][C]276.140559463114[/C][C]-3.26175628566210[/C][C]1.77444053688619[/C][C]-0.125865216510943[/C][/ROW]
[ROW][C]4[/C][C]277.128[/C][C]275.559759426404[/C][C]-1.65533158744298[/C][C]1.56824057359594[/C][C]0.625674700662030[/C][/ROW]
[ROW][C]5[/C][C]277.103[/C][C]276.338883741368[/C][C]-0.180007006998245[/C][C]0.764116258632077[/C][C]0.536882429936485[/C][/ROW]
[ROW][C]6[/C][C]275.037[/C][C]275.60117321584[/C][C]-0.51149442481712[/C][C]-0.564173215840185[/C][C]-0.120727948617283[/C][/ROW]
[ROW][C]7[/C][C]270.15[/C][C]271.60358070987[/C][C]-2.58377139671973[/C][C]-1.45358070987009[/C][C]-0.761464920927339[/C][/ROW]
[ROW][C]8[/C][C]267.14[/C][C]267.303320934953[/C][C]-3.60804292510288[/C][C]-0.163320934952686[/C][C]-0.376027613640751[/C][/ROW]
[ROW][C]9[/C][C]264.993[/C][C]264.329183977815[/C][C]-3.22942193172469[/C][C]0.663816022184723[/C][C]0.138895032605294[/C][/ROW]
[ROW][C]10[/C][C]287.259[/C][C]278.896504964221[/C][C]7.39846997122516[/C][C]8.36249503577857[/C][C]3.89920571124566[/C][/ROW]
[ROW][C]11[/C][C]291.186[/C][C]291.461100071561[/C][C]10.4826558430896[/C][C]-0.275100071561301[/C][C]1.13156009519430[/C][/ROW]
[ROW][C]12[/C][C]292.3[/C][C]295.863621442826[/C][C]6.85348046703138[/C][C]-3.56362144282566[/C][C]-1.33148594559240[/C][/ROW]
[ROW][C]13[/C][C]288.186[/C][C]292.255746405733[/C][C]0.659880772553535[/C][C]-4.06974640573311[/C][C]-2.31104249620351[/C][/ROW]
[ROW][C]14[/C][C]281.477[/C][C]286.251855201344[/C][C]-3.29403221142526[/C][C]-4.77485520134415[/C][C]-1.46225990329933[/C][/ROW]
[ROW][C]15[/C][C]282.656[/C][C]281.892745223988[/C][C]-3.90720772337637[/C][C]0.763254776012354[/C][C]-0.222981966516256[/C][/ROW]
[ROW][C]16[/C][C]280.19[/C][C]279.177391289139[/C][C]-3.22641961421955[/C][C]1.01260871086094[/C][C]0.253176943380808[/C][/ROW]
[ROW][C]17[/C][C]280.408[/C][C]278.155724777872[/C][C]-1.94890680913889[/C][C]2.25227522212757[/C][C]0.472296549723932[/C][/ROW]
[ROW][C]18[/C][C]276.836[/C][C]275.44314718552[/C][C]-2.3901821268414[/C][C]1.39285281447971[/C][C]-0.161077836539959[/C][/ROW]
[ROW][C]19[/C][C]275.216[/C][C]273.85075963422[/C][C]-1.93187859289896[/C][C]1.36524036577975[/C][C]0.167747256630069[/C][/ROW]
[ROW][C]20[/C][C]274.352[/C][C]274.118593889419[/C][C]-0.666967996970173[/C][C]0.233406110580757[/C][C]0.464367161811322[/C][/ROW]
[ROW][C]21[/C][C]271.311[/C][C]277.005124913504[/C][C]1.38092983527905[/C][C]-5.69412491350405[/C][C]0.751506378766255[/C][/ROW]
[ROW][C]22[/C][C]289.802[/C][C]282.616787170681[/C][C]3.82032419108576[/C][C]7.18521282931867[/C][C]0.894942062131855[/C][/ROW]
[ROW][C]23[/C][C]290.726[/C][C]288.395970810411[/C][C]4.94872345148285[/C][C]2.33002918958949[/C][C]0.414038742564155[/C][/ROW]
[ROW][C]24[/C][C]292.3[/C][C]292.35330596947[/C][C]4.37838610119883[/C][C]-0.0533059694697586[/C][C]-0.209592664946352[/C][/ROW]
[ROW][C]25[/C][C]278.506[/C][C]285.316536319926[/C][C]-2.19633091444993[/C][C]-6.81053631992616[/C][C]-2.42343573801538[/C][/ROW]
[ROW][C]26[/C][C]269.826[/C][C]276.256467579431[/C][C]-6.14771431710252[/C][C]-6.43046757943118[/C][C]-1.45003376242421[/C][/ROW]
[ROW][C]27[/C][C]265.861[/C][C]266.634641590441[/C][C]-8.12879440977871[/C][C]-0.773641590440527[/C][C]-0.724380148669477[/C][/ROW]
[ROW][C]28[/C][C]269.034[/C][C]265.086978257636[/C][C]-4.3924438531557[/C][C]3.9470217423637[/C][C]1.37611885262853[/C][/ROW]
[ROW][C]29[/C][C]264.176[/C][C]261.384315302726[/C][C]-3.99864143943873[/C][C]2.79168469727378[/C][C]0.145243433947237[/C][/ROW]
[ROW][C]30[/C][C]255.198[/C][C]254.689111649302[/C][C]-5.54084907698173[/C][C]0.508888350698388[/C][C]-0.565358817642569[/C][/ROW]
[ROW][C]31[/C][C]253.353[/C][C]251.237668901975[/C][C]-4.34973827492976[/C][C]2.11533109802521[/C][C]0.435929106413505[/C][/ROW]
[ROW][C]32[/C][C]246.057[/C][C]247.567923625875[/C][C]-3.96258950990762[/C][C]-1.51092362587526[/C][C]0.141949478481996[/C][/ROW]
[ROW][C]33[/C][C]235.372[/C][C]243.730269033852[/C][C]-3.89139064501295[/C][C]-8.3582690338519[/C][C]0.0261260507276766[/C][/ROW]
[ROW][C]34[/C][C]258.556[/C][C]248.955687365365[/C][C]1.30715314276691[/C][C]9.60031263463508[/C][C]1.90760936506891[/C][/ROW]
[ROW][C]35[/C][C]260.993[/C][C]256.290579633762[/C][C]4.74320958488971[/C][C]4.70242036623771[/C][C]1.26134079557061[/C][/ROW]
[ROW][C]36[/C][C]254.663[/C][C]253.667085475468[/C][C]0.543443375764997[/C][C]0.995914524532434[/C][C]-1.54329364666324[/C][/ROW]
[ROW][C]37[/C][C]250.643[/C][C]253.641545189888[/C][C]0.218748236195865[/C][C]-2.99854518988786[/C][C]-0.119323540000477[/C][/ROW]
[ROW][C]38[/C][C]243.422[/C][C]250.621368195597[/C][C]-1.62730637310546[/C][C]-7.1993681955969[/C][C]-0.676709885244777[/C][/ROW]
[ROW][C]39[/C][C]247.105[/C][C]249.983560617877[/C][C]-1.06592851870523[/C][C]-2.87856061787672[/C][C]0.205616875082688[/C][/ROW]
[ROW][C]40[/C][C]248.541[/C][C]246.003758049731[/C][C]-2.71543378950017[/C][C]2.53724195026863[/C][C]-0.606230501450016[/C][/ROW]
[ROW][C]41[/C][C]245.039[/C][C]241.424549571363[/C][C]-3.77324081855548[/C][C]3.61445042863657[/C][C]-0.389366177369497[/C][/ROW]
[ROW][C]42[/C][C]237.08[/C][C]236.939737742876[/C][C]-4.17786799784796[/C][C]0.140262257124481[/C][C]-0.148538252076744[/C][/ROW]
[ROW][C]43[/C][C]237.085[/C][C]233.95956975557[/C][C]-3.49776021738589[/C][C]3.12543024443020[/C][C]0.249143942575550[/C][/ROW]
[ROW][C]44[/C][C]225.554[/C][C]228.694666217751[/C][C]-4.49947570767945[/C][C]-3.14066621775122[/C][C]-0.367107800009838[/C][/ROW]
[ROW][C]45[/C][C]226.839[/C][C]234.702560242164[/C][C]1.45475009444517[/C][C]-7.86356024216446[/C][C]2.18424387096914[/C][/ROW]
[ROW][C]46[/C][C]247.934[/C][C]239.935409873783[/C][C]3.59631437949716[/C][C]7.99859012621723[/C][C]0.78606664695112[/C][/ROW]
[ROW][C]47[/C][C]248.333[/C][C]241.837933750243[/C][C]2.6358952621466[/C][C]6.49506624975652[/C][C]-0.352698905034626[/C][/ROW]
[ROW][C]48[/C][C]246.969[/C][C]245.163425907907[/C][C]3.0271714506196[/C][C]1.80557409209291[/C][C]0.143736203847724[/C][/ROW]
[ROW][C]49[/C][C]245.098[/C][C]247.574727806296[/C][C]2.67755544773851[/C][C]-2.47672780629557[/C][C]-0.128339536551380[/C][/ROW]
[ROW][C]50[/C][C]246.263[/C][C]252.688653132227[/C][C]4.05883059350544[/C][C]-6.42565313222742[/C][C]0.506263793426672[/C][/ROW]
[ROW][C]51[/C][C]255.765[/C][C]257.312633909106[/C][C]4.37833178738536[/C][C]-1.54763390910607[/C][C]0.117099126771383[/C][/ROW]
[ROW][C]52[/C][C]264.319[/C][C]261.029390743114[/C][C]4.00478444917048[/C][C]3.28960925688568[/C][C]-0.137195330543637[/C][/ROW]
[ROW][C]53[/C][C]268.347[/C][C]264.245999316680[/C][C]3.55907301537736[/C][C]4.10100068332029[/C][C]-0.163895920974508[/C][/ROW]
[ROW][C]54[/C][C]273.046[/C][C]271.033684397402[/C][C]5.38764542366191[/C][C]2.01231560259847[/C][C]0.671488106668861[/C][/ROW]
[ROW][C]55[/C][C]273.963[/C][C]272.762415832025[/C][C]3.31660379504582[/C][C]1.20058416797530[/C][C]-0.75918263216[/C][/ROW]
[ROW][C]56[/C][C]267.43[/C][C]275.194322775954[/C][C]2.81665594756149[/C][C]-7.76432277595415[/C][C]-0.183216475363602[/C][/ROW]
[ROW][C]57[/C][C]271.993[/C][C]280.186083401745[/C][C]4.04463347747718[/C][C]-8.19308340174468[/C][C]0.450382121364642[/C][/ROW]
[ROW][C]58[/C][C]292.71[/C][C]284.211792784952[/C][C]4.03394963943675[/C][C]8.4982072150479[/C][C]-0.00392200071173293[/C][/ROW]
[ROW][C]59[/C][C]295.881[/C][C]289.068561433317[/C][C]4.49877102502402[/C][C]6.81243856668293[/C][C]0.170729131214369[/C][/ROW]
[ROW][C]60[/C][C]293.299[/C][C]292.135336776130[/C][C]3.68914657189752[/C][C]1.16366322386956[/C][C]-0.2973494716982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65726&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65726&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1283.042283.042000
2276.687279.643099949226-2.92361682846477-2.95609994922578-1.60260866952763
3277.915276.140559463114-3.261756285662101.77444053688619-0.125865216510943
4277.128275.559759426404-1.655331587442981.568240573595940.625674700662030
5277.103276.338883741368-0.1800070069982450.7641162586320770.536882429936485
6275.037275.60117321584-0.51149442481712-0.564173215840185-0.120727948617283
7270.15271.60358070987-2.58377139671973-1.45358070987009-0.761464920927339
8267.14267.303320934953-3.60804292510288-0.163320934952686-0.376027613640751
9264.993264.329183977815-3.229421931724690.6638160221847230.138895032605294
10287.259278.8965049642217.398469971225168.362495035778573.89920571124566
11291.186291.46110007156110.4826558430896-0.2751000715613011.13156009519430
12292.3295.8636214428266.85348046703138-3.56362144282566-1.33148594559240
13288.186292.2557464057330.659880772553535-4.06974640573311-2.31104249620351
14281.477286.251855201344-3.29403221142526-4.77485520134415-1.46225990329933
15282.656281.892745223988-3.907207723376370.763254776012354-0.222981966516256
16280.19279.177391289139-3.226419614219551.012608710860940.253176943380808
17280.408278.155724777872-1.948906809138892.252275222127570.472296549723932
18276.836275.44314718552-2.39018212684141.39285281447971-0.161077836539959
19275.216273.85075963422-1.931878592898961.365240365779750.167747256630069
20274.352274.118593889419-0.6669679969701730.2334061105807570.464367161811322
21271.311277.0051249135041.38092983527905-5.694124913504050.751506378766255
22289.802282.6167871706813.820324191085767.185212829318670.894942062131855
23290.726288.3959708104114.948723451482852.330029189589490.414038742564155
24292.3292.353305969474.37838610119883-0.0533059694697586-0.209592664946352
25278.506285.316536319926-2.19633091444993-6.81053631992616-2.42343573801538
26269.826276.256467579431-6.14771431710252-6.43046757943118-1.45003376242421
27265.861266.634641590441-8.12879440977871-0.773641590440527-0.724380148669477
28269.034265.086978257636-4.39244385315573.94702174236371.37611885262853
29264.176261.384315302726-3.998641439438732.791684697273780.145243433947237
30255.198254.689111649302-5.540849076981730.508888350698388-0.565358817642569
31253.353251.237668901975-4.349738274929762.115331098025210.435929106413505
32246.057247.567923625875-3.96258950990762-1.510923625875260.141949478481996
33235.372243.730269033852-3.89139064501295-8.35826903385190.0261260507276766
34258.556248.9556873653651.307153142766919.600312634635081.90760936506891
35260.993256.2905796337624.743209584889714.702420366237711.26134079557061
36254.663253.6670854754680.5434433757649970.995914524532434-1.54329364666324
37250.643253.6415451898880.218748236195865-2.99854518988786-0.119323540000477
38243.422250.621368195597-1.62730637310546-7.1993681955969-0.676709885244777
39247.105249.983560617877-1.06592851870523-2.878560617876720.205616875082688
40248.541246.003758049731-2.715433789500172.53724195026863-0.606230501450016
41245.039241.424549571363-3.773240818555483.61445042863657-0.389366177369497
42237.08236.939737742876-4.177867997847960.140262257124481-0.148538252076744
43237.085233.95956975557-3.497760217385893.125430244430200.249143942575550
44225.554228.694666217751-4.49947570767945-3.14066621775122-0.367107800009838
45226.839234.7025602421641.45475009444517-7.863560242164462.18424387096914
46247.934239.9354098737833.596314379497167.998590126217230.78606664695112
47248.333241.8379337502432.63589526214666.49506624975652-0.352698905034626
48246.969245.1634259079073.02717145061961.805574092092910.143736203847724
49245.098247.5747278062962.67755544773851-2.47672780629557-0.128339536551380
50246.263252.6886531322274.05883059350544-6.425653132227420.506263793426672
51255.765257.3126339091064.37833178738536-1.547633909106070.117099126771383
52264.319261.0293907431144.004784449170483.28960925688568-0.137195330543637
53268.347264.2459993166803.559073015377364.10100068332029-0.163895920974508
54273.046271.0336843974025.387645423661912.012315602598470.671488106668861
55273.963272.7624158320253.316603795045821.20058416797530-0.75918263216
56267.43275.1943227759542.81665594756149-7.76432277595415-0.183216475363602
57271.993280.1860834017454.04463347747718-8.193083401744680.450382121364642
58292.71284.2117927849524.033949639436758.4982072150479-0.00392200071173293
59295.881289.0685614333174.498771025024026.812438566682930.170729131214369
60293.299292.1353367761303.689146571897521.16366322386956-0.2973494716982


Figures (Output of Computation)

Input Parameters & R Code

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