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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 computationSun, 06 Dec 2009 08:13:07 -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/06/t1260112435zt73r4i51ttopcz.htm/, Retrieved Sun, 05 May 2024 22:14:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64429, Retrieved Sun, 05 May 2024 22:14:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113

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] [workshop 9 - ad h...] [2009-12-04 10:30:46] [f1a50df816abcbb519e7637ff6b72fa0]
-   PD        [Structural Time Series Models] [WS9] [2009-12-06 15:13:07] [48076ccf082563ab8a2c81e57fdb5364] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10414,9
12476,8
12384,6
12266,7
12919,9
11497,3
12142
13919,4
12656,8
12034,1
13199,7
10881,3
11301,2
13643,9
12517
13981,1
14275,7
13435
13565,7
16216,3
12970
14079,9
14235
12213,4
12581
14130,4
14210,8
14378,5
13142,8
13714,7
13621,9
15379,8
13306,3
14391,2
14909,9
14025,4
12951,2
14344,3
16093,4
15413,6
14705,7
15972,8
16241,4
16626,4
17136,2
15622,9
18003,9
16136,1
14423,7
16789,4
16782,2
14133,8
12607
12004,5
12175,4
13268
12299,3
11800,6
13873,3
12269,6

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=64429&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=64429&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64429&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
110414.910414.9000
212476.811986.714315715872.5907689086392490.0856842842261.88245541158710
312384.612380.823944324688.35663536594893.776055675442020.477124864408427
412266.712339.915337674784.8151765382335-73.2153376746813-0.223696855269900
512919.912644.253930193387.901746369392275.6460698067410.387151487804389
611497.311992.210189924580.275256871552-494.910189924496-1.30430890953452
71214211941.597905408478.899919702437200.402094591628-0.230417083179349
813919.413068.488958231990.8541930763192850.9110417681021.84310385129774
912656.812986.508843667488.7717232752276-329.708843667419-0.303787261758424
1012034.112382.273631185180.1613984544392-348.173631185062-1.21762916846940
1113199.712732.659747135283.5895961605094467.0402528648300.47463707088919
1210881.311673.167730607268.8171446801262-791.867730607161-2.00710807886463
1311301.211845.090758469966.231334918307-543.8907584699130.197176874366809
1413643.912708.077564580972.3358651041639935.8224354190671.38177730067491
151251712741.004216636171.3305927723695-224.004216636068-0.0638419287637921
1613981.113533.676468729689.53097523124447.4235312703691.22053362873885
1714275.713752.149780594591.9948813251609523.550219405510.224593727141872
181343513938.645451449093.3697424019842-503.6454514489630.165577341149965
1913565.713924.797370132691.9965374392374-359.097370132605-0.187841860858894
2016216.314701.0829616665100.7593592143571515.217038333491.19798727220817
211297013927.111373034289.0286972613423-957.111373034246-1.53061688811245
2214079.914125.293305609690.5226842878062-45.39330560958340.190925715701640
231423513703.074878459384.1538649568585531.925121540712-0.895830588701286
2412213.413329.353563492280.3432287081576-1115.95356349221-0.800748573406247
251258113385.506611132380.2682096002647-804.506611132293-0.0430312598770271
2614130.413349.483878182579.0380971620759780.916121817504-0.202217411286757
2714210.814169.688464344093.353215728640641.11153565597951.24768770824706
2814378.514204.849555746592.0660325364289173.650444253494-0.0987037239538626
2913142.813396.474871522473.6069523699097-253.674871522432-1.55528192363082
3013714.713804.033423737579.5750058156826-89.33342373750370.581671395862051
3113621.914104.301620432383.135461377311-482.4016204323380.385061806367694
3215379.813837.988627256377.72823704352521541.81137274374-0.609634025418624
3313306.314057.39338263479.8944191666793-751.0933826340060.247031098944685
3414391.214162.376264371380.2658792324204228.8237356286610.043700264628799
3514909.914201.523493423379.7120937970591708.376506576667-0.0715417360164041
3614025.414746.522663946085.101425860396-721.122663945960.810877852226727
3712951.214325.987849703879.5118819278745-1374.78784970377-0.884126284977143
3814344.314045.076344067874.3961269745379299.22365593219-0.623801523786547
3916093.415076.173763751392.00444584841281017.226236248721.63101952750475
4015413.615123.153866418491.0729228982325290.446133581597-0.076764230726259
4114705.715158.317183641189.9184089430259-452.617183641073-0.0962566641610976
4215972.815719.938114166899.0908809869327252.8618858331720.818087098189793
4316241.416295.4495183719107.76611090318-54.04951837188940.828436312909904
4416626.415768.689538478296.7491641021483857.710461521787-1.10354942394755
4517136.216911.7955241907114.280870189783224.4044758092681.81838504209820
4615622.916268.3266729907102.110495617744-645.426672990685-1.31530223531364
4718003.916858.9634181646109.5193834284501144.936581835400.847482504137743
4816136.116829.3958356651107.508803639363-693.295835665049-0.241547565718181
4914423.716274.807296587597.7434799940832-1851.1072965875-1.15008810602703
5016789.416592.4406258495101.36544608688196.9593741504870.379705018855491
5116782.216189.025714056791.9210257845975593.174285943283-0.864714889736485
5214133.814882.593156534763.6402016941964-748.793156534742-2.39275906912631
531260713877.981766978441.6667644730760-1270.98176697844-1.83763748476646
5412004.512686.805829823816.9183970773988-682.305829823762-2.13245739655853
5512175.412214.48628025627.48460512561205-39.0862802561969-0.848444374173807
561326812485.164554186512.3610931170309782.8354458134580.456544099362177
5712299.312152.45658662566.1963179923449146.843413374378-0.598043717279071
5811800.612391.647797828610.2124111880962-591.047797828550.403398419545134
5913873.312545.856622533712.61451961440261327.443377466300.249254179390781
6012269.612653.769272548814.1809859140995-384.1692725487890.165072824014381

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 10414.9 & 10414.9 & 0 & 0 & 0 \tabularnewline
2 & 12476.8 & 11986.7143157158 & 72.5907689086392 & 490.085684284226 & 1.88245541158710 \tabularnewline
3 & 12384.6 & 12380.8239443246 & 88.3566353659489 & 3.77605567544202 & 0.477124864408427 \tabularnewline
4 & 12266.7 & 12339.9153376747 & 84.8151765382335 & -73.2153376746813 & -0.223696855269900 \tabularnewline
5 & 12919.9 & 12644.2539301933 & 87.901746369392 & 275.646069806741 & 0.387151487804389 \tabularnewline
6 & 11497.3 & 11992.2101899245 & 80.275256871552 & -494.910189924496 & -1.30430890953452 \tabularnewline
7 & 12142 & 11941.5979054084 & 78.899919702437 & 200.402094591628 & -0.230417083179349 \tabularnewline
8 & 13919.4 & 13068.4889582319 & 90.8541930763192 & 850.911041768102 & 1.84310385129774 \tabularnewline
9 & 12656.8 & 12986.5088436674 & 88.7717232752276 & -329.708843667419 & -0.303787261758424 \tabularnewline
10 & 12034.1 & 12382.2736311851 & 80.1613984544392 & -348.173631185062 & -1.21762916846940 \tabularnewline
11 & 13199.7 & 12732.6597471352 & 83.5895961605094 & 467.040252864830 & 0.47463707088919 \tabularnewline
12 & 10881.3 & 11673.1677306072 & 68.8171446801262 & -791.867730607161 & -2.00710807886463 \tabularnewline
13 & 11301.2 & 11845.0907584699 & 66.231334918307 & -543.890758469913 & 0.197176874366809 \tabularnewline
14 & 13643.9 & 12708.0775645809 & 72.3358651041639 & 935.822435419067 & 1.38177730067491 \tabularnewline
15 & 12517 & 12741.0042166361 & 71.3305927723695 & -224.004216636068 & -0.0638419287637921 \tabularnewline
16 & 13981.1 & 13533.6764687296 & 89.53097523124 & 447.423531270369 & 1.22053362873885 \tabularnewline
17 & 14275.7 & 13752.1497805945 & 91.9948813251609 & 523.55021940551 & 0.224593727141872 \tabularnewline
18 & 13435 & 13938.6454514490 & 93.3697424019842 & -503.645451448963 & 0.165577341149965 \tabularnewline
19 & 13565.7 & 13924.7973701326 & 91.9965374392374 & -359.097370132605 & -0.187841860858894 \tabularnewline
20 & 16216.3 & 14701.0829616665 & 100.759359214357 & 1515.21703833349 & 1.19798727220817 \tabularnewline
21 & 12970 & 13927.1113730342 & 89.0286972613423 & -957.111373034246 & -1.53061688811245 \tabularnewline
22 & 14079.9 & 14125.2933056096 & 90.5226842878062 & -45.3933056095834 & 0.190925715701640 \tabularnewline
23 & 14235 & 13703.0748784593 & 84.1538649568585 & 531.925121540712 & -0.895830588701286 \tabularnewline
24 & 12213.4 & 13329.3535634922 & 80.3432287081576 & -1115.95356349221 & -0.800748573406247 \tabularnewline
25 & 12581 & 13385.5066111323 & 80.2682096002647 & -804.506611132293 & -0.0430312598770271 \tabularnewline
26 & 14130.4 & 13349.4838781825 & 79.0380971620759 & 780.916121817504 & -0.202217411286757 \tabularnewline
27 & 14210.8 & 14169.6884643440 & 93.3532157286406 & 41.1115356559795 & 1.24768770824706 \tabularnewline
28 & 14378.5 & 14204.8495557465 & 92.0660325364289 & 173.650444253494 & -0.0987037239538626 \tabularnewline
29 & 13142.8 & 13396.4748715224 & 73.6069523699097 & -253.674871522432 & -1.55528192363082 \tabularnewline
30 & 13714.7 & 13804.0334237375 & 79.5750058156826 & -89.3334237375037 & 0.581671395862051 \tabularnewline
31 & 13621.9 & 14104.3016204323 & 83.135461377311 & -482.401620432338 & 0.385061806367694 \tabularnewline
32 & 15379.8 & 13837.9886272563 & 77.7282370435252 & 1541.81137274374 & -0.609634025418624 \tabularnewline
33 & 13306.3 & 14057.393382634 & 79.8944191666793 & -751.093382634006 & 0.247031098944685 \tabularnewline
34 & 14391.2 & 14162.3762643713 & 80.2658792324204 & 228.823735628661 & 0.043700264628799 \tabularnewline
35 & 14909.9 & 14201.5234934233 & 79.7120937970591 & 708.376506576667 & -0.0715417360164041 \tabularnewline
36 & 14025.4 & 14746.5226639460 & 85.101425860396 & -721.12266394596 & 0.810877852226727 \tabularnewline
37 & 12951.2 & 14325.9878497038 & 79.5118819278745 & -1374.78784970377 & -0.884126284977143 \tabularnewline
38 & 14344.3 & 14045.0763440678 & 74.3961269745379 & 299.22365593219 & -0.623801523786547 \tabularnewline
39 & 16093.4 & 15076.1737637513 & 92.0044458484128 & 1017.22623624872 & 1.63101952750475 \tabularnewline
40 & 15413.6 & 15123.1538664184 & 91.0729228982325 & 290.446133581597 & -0.076764230726259 \tabularnewline
41 & 14705.7 & 15158.3171836411 & 89.9184089430259 & -452.617183641073 & -0.0962566641610976 \tabularnewline
42 & 15972.8 & 15719.9381141668 & 99.0908809869327 & 252.861885833172 & 0.818087098189793 \tabularnewline
43 & 16241.4 & 16295.4495183719 & 107.76611090318 & -54.0495183718894 & 0.828436312909904 \tabularnewline
44 & 16626.4 & 15768.6895384782 & 96.7491641021483 & 857.710461521787 & -1.10354942394755 \tabularnewline
45 & 17136.2 & 16911.7955241907 & 114.280870189783 & 224.404475809268 & 1.81838504209820 \tabularnewline
46 & 15622.9 & 16268.3266729907 & 102.110495617744 & -645.426672990685 & -1.31530223531364 \tabularnewline
47 & 18003.9 & 16858.9634181646 & 109.519383428450 & 1144.93658183540 & 0.847482504137743 \tabularnewline
48 & 16136.1 & 16829.3958356651 & 107.508803639363 & -693.295835665049 & -0.241547565718181 \tabularnewline
49 & 14423.7 & 16274.8072965875 & 97.7434799940832 & -1851.1072965875 & -1.15008810602703 \tabularnewline
50 & 16789.4 & 16592.4406258495 & 101.36544608688 & 196.959374150487 & 0.379705018855491 \tabularnewline
51 & 16782.2 & 16189.0257140567 & 91.9210257845975 & 593.174285943283 & -0.864714889736485 \tabularnewline
52 & 14133.8 & 14882.5931565347 & 63.6402016941964 & -748.793156534742 & -2.39275906912631 \tabularnewline
53 & 12607 & 13877.9817669784 & 41.6667644730760 & -1270.98176697844 & -1.83763748476646 \tabularnewline
54 & 12004.5 & 12686.8058298238 & 16.9183970773988 & -682.305829823762 & -2.13245739655853 \tabularnewline
55 & 12175.4 & 12214.4862802562 & 7.48460512561205 & -39.0862802561969 & -0.848444374173807 \tabularnewline
56 & 13268 & 12485.1645541865 & 12.3610931170309 & 782.835445813458 & 0.456544099362177 \tabularnewline
57 & 12299.3 & 12152.4565866256 & 6.1963179923449 & 146.843413374378 & -0.598043717279071 \tabularnewline
58 & 11800.6 & 12391.6477978286 & 10.2124111880962 & -591.04779782855 & 0.403398419545134 \tabularnewline
59 & 13873.3 & 12545.8566225337 & 12.6145196144026 & 1327.44337746630 & 0.249254179390781 \tabularnewline
60 & 12269.6 & 12653.7692725488 & 14.1809859140995 & -384.169272548789 & 0.165072824014381 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64429&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]10414.9[/C][C]10414.9[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]12476.8[/C][C]11986.7143157158[/C][C]72.5907689086392[/C][C]490.085684284226[/C][C]1.88245541158710[/C][/ROW]
[ROW][C]3[/C][C]12384.6[/C][C]12380.8239443246[/C][C]88.3566353659489[/C][C]3.77605567544202[/C][C]0.477124864408427[/C][/ROW]
[ROW][C]4[/C][C]12266.7[/C][C]12339.9153376747[/C][C]84.8151765382335[/C][C]-73.2153376746813[/C][C]-0.223696855269900[/C][/ROW]
[ROW][C]5[/C][C]12919.9[/C][C]12644.2539301933[/C][C]87.901746369392[/C][C]275.646069806741[/C][C]0.387151487804389[/C][/ROW]
[ROW][C]6[/C][C]11497.3[/C][C]11992.2101899245[/C][C]80.275256871552[/C][C]-494.910189924496[/C][C]-1.30430890953452[/C][/ROW]
[ROW][C]7[/C][C]12142[/C][C]11941.5979054084[/C][C]78.899919702437[/C][C]200.402094591628[/C][C]-0.230417083179349[/C][/ROW]
[ROW][C]8[/C][C]13919.4[/C][C]13068.4889582319[/C][C]90.8541930763192[/C][C]850.911041768102[/C][C]1.84310385129774[/C][/ROW]
[ROW][C]9[/C][C]12656.8[/C][C]12986.5088436674[/C][C]88.7717232752276[/C][C]-329.708843667419[/C][C]-0.303787261758424[/C][/ROW]
[ROW][C]10[/C][C]12034.1[/C][C]12382.2736311851[/C][C]80.1613984544392[/C][C]-348.173631185062[/C][C]-1.21762916846940[/C][/ROW]
[ROW][C]11[/C][C]13199.7[/C][C]12732.6597471352[/C][C]83.5895961605094[/C][C]467.040252864830[/C][C]0.47463707088919[/C][/ROW]
[ROW][C]12[/C][C]10881.3[/C][C]11673.1677306072[/C][C]68.8171446801262[/C][C]-791.867730607161[/C][C]-2.00710807886463[/C][/ROW]
[ROW][C]13[/C][C]11301.2[/C][C]11845.0907584699[/C][C]66.231334918307[/C][C]-543.890758469913[/C][C]0.197176874366809[/C][/ROW]
[ROW][C]14[/C][C]13643.9[/C][C]12708.0775645809[/C][C]72.3358651041639[/C][C]935.822435419067[/C][C]1.38177730067491[/C][/ROW]
[ROW][C]15[/C][C]12517[/C][C]12741.0042166361[/C][C]71.3305927723695[/C][C]-224.004216636068[/C][C]-0.0638419287637921[/C][/ROW]
[ROW][C]16[/C][C]13981.1[/C][C]13533.6764687296[/C][C]89.53097523124[/C][C]447.423531270369[/C][C]1.22053362873885[/C][/ROW]
[ROW][C]17[/C][C]14275.7[/C][C]13752.1497805945[/C][C]91.9948813251609[/C][C]523.55021940551[/C][C]0.224593727141872[/C][/ROW]
[ROW][C]18[/C][C]13435[/C][C]13938.6454514490[/C][C]93.3697424019842[/C][C]-503.645451448963[/C][C]0.165577341149965[/C][/ROW]
[ROW][C]19[/C][C]13565.7[/C][C]13924.7973701326[/C][C]91.9965374392374[/C][C]-359.097370132605[/C][C]-0.187841860858894[/C][/ROW]
[ROW][C]20[/C][C]16216.3[/C][C]14701.0829616665[/C][C]100.759359214357[/C][C]1515.21703833349[/C][C]1.19798727220817[/C][/ROW]
[ROW][C]21[/C][C]12970[/C][C]13927.1113730342[/C][C]89.0286972613423[/C][C]-957.111373034246[/C][C]-1.53061688811245[/C][/ROW]
[ROW][C]22[/C][C]14079.9[/C][C]14125.2933056096[/C][C]90.5226842878062[/C][C]-45.3933056095834[/C][C]0.190925715701640[/C][/ROW]
[ROW][C]23[/C][C]14235[/C][C]13703.0748784593[/C][C]84.1538649568585[/C][C]531.925121540712[/C][C]-0.895830588701286[/C][/ROW]
[ROW][C]24[/C][C]12213.4[/C][C]13329.3535634922[/C][C]80.3432287081576[/C][C]-1115.95356349221[/C][C]-0.800748573406247[/C][/ROW]
[ROW][C]25[/C][C]12581[/C][C]13385.5066111323[/C][C]80.2682096002647[/C][C]-804.506611132293[/C][C]-0.0430312598770271[/C][/ROW]
[ROW][C]26[/C][C]14130.4[/C][C]13349.4838781825[/C][C]79.0380971620759[/C][C]780.916121817504[/C][C]-0.202217411286757[/C][/ROW]
[ROW][C]27[/C][C]14210.8[/C][C]14169.6884643440[/C][C]93.3532157286406[/C][C]41.1115356559795[/C][C]1.24768770824706[/C][/ROW]
[ROW][C]28[/C][C]14378.5[/C][C]14204.8495557465[/C][C]92.0660325364289[/C][C]173.650444253494[/C][C]-0.0987037239538626[/C][/ROW]
[ROW][C]29[/C][C]13142.8[/C][C]13396.4748715224[/C][C]73.6069523699097[/C][C]-253.674871522432[/C][C]-1.55528192363082[/C][/ROW]
[ROW][C]30[/C][C]13714.7[/C][C]13804.0334237375[/C][C]79.5750058156826[/C][C]-89.3334237375037[/C][C]0.581671395862051[/C][/ROW]
[ROW][C]31[/C][C]13621.9[/C][C]14104.3016204323[/C][C]83.135461377311[/C][C]-482.401620432338[/C][C]0.385061806367694[/C][/ROW]
[ROW][C]32[/C][C]15379.8[/C][C]13837.9886272563[/C][C]77.7282370435252[/C][C]1541.81137274374[/C][C]-0.609634025418624[/C][/ROW]
[ROW][C]33[/C][C]13306.3[/C][C]14057.393382634[/C][C]79.8944191666793[/C][C]-751.093382634006[/C][C]0.247031098944685[/C][/ROW]
[ROW][C]34[/C][C]14391.2[/C][C]14162.3762643713[/C][C]80.2658792324204[/C][C]228.823735628661[/C][C]0.043700264628799[/C][/ROW]
[ROW][C]35[/C][C]14909.9[/C][C]14201.5234934233[/C][C]79.7120937970591[/C][C]708.376506576667[/C][C]-0.0715417360164041[/C][/ROW]
[ROW][C]36[/C][C]14025.4[/C][C]14746.5226639460[/C][C]85.101425860396[/C][C]-721.12266394596[/C][C]0.810877852226727[/C][/ROW]
[ROW][C]37[/C][C]12951.2[/C][C]14325.9878497038[/C][C]79.5118819278745[/C][C]-1374.78784970377[/C][C]-0.884126284977143[/C][/ROW]
[ROW][C]38[/C][C]14344.3[/C][C]14045.0763440678[/C][C]74.3961269745379[/C][C]299.22365593219[/C][C]-0.623801523786547[/C][/ROW]
[ROW][C]39[/C][C]16093.4[/C][C]15076.1737637513[/C][C]92.0044458484128[/C][C]1017.22623624872[/C][C]1.63101952750475[/C][/ROW]
[ROW][C]40[/C][C]15413.6[/C][C]15123.1538664184[/C][C]91.0729228982325[/C][C]290.446133581597[/C][C]-0.076764230726259[/C][/ROW]
[ROW][C]41[/C][C]14705.7[/C][C]15158.3171836411[/C][C]89.9184089430259[/C][C]-452.617183641073[/C][C]-0.0962566641610976[/C][/ROW]
[ROW][C]42[/C][C]15972.8[/C][C]15719.9381141668[/C][C]99.0908809869327[/C][C]252.861885833172[/C][C]0.818087098189793[/C][/ROW]
[ROW][C]43[/C][C]16241.4[/C][C]16295.4495183719[/C][C]107.76611090318[/C][C]-54.0495183718894[/C][C]0.828436312909904[/C][/ROW]
[ROW][C]44[/C][C]16626.4[/C][C]15768.6895384782[/C][C]96.7491641021483[/C][C]857.710461521787[/C][C]-1.10354942394755[/C][/ROW]
[ROW][C]45[/C][C]17136.2[/C][C]16911.7955241907[/C][C]114.280870189783[/C][C]224.404475809268[/C][C]1.81838504209820[/C][/ROW]
[ROW][C]46[/C][C]15622.9[/C][C]16268.3266729907[/C][C]102.110495617744[/C][C]-645.426672990685[/C][C]-1.31530223531364[/C][/ROW]
[ROW][C]47[/C][C]18003.9[/C][C]16858.9634181646[/C][C]109.519383428450[/C][C]1144.93658183540[/C][C]0.847482504137743[/C][/ROW]
[ROW][C]48[/C][C]16136.1[/C][C]16829.3958356651[/C][C]107.508803639363[/C][C]-693.295835665049[/C][C]-0.241547565718181[/C][/ROW]
[ROW][C]49[/C][C]14423.7[/C][C]16274.8072965875[/C][C]97.7434799940832[/C][C]-1851.1072965875[/C][C]-1.15008810602703[/C][/ROW]
[ROW][C]50[/C][C]16789.4[/C][C]16592.4406258495[/C][C]101.36544608688[/C][C]196.959374150487[/C][C]0.379705018855491[/C][/ROW]
[ROW][C]51[/C][C]16782.2[/C][C]16189.0257140567[/C][C]91.9210257845975[/C][C]593.174285943283[/C][C]-0.864714889736485[/C][/ROW]
[ROW][C]52[/C][C]14133.8[/C][C]14882.5931565347[/C][C]63.6402016941964[/C][C]-748.793156534742[/C][C]-2.39275906912631[/C][/ROW]
[ROW][C]53[/C][C]12607[/C][C]13877.9817669784[/C][C]41.6667644730760[/C][C]-1270.98176697844[/C][C]-1.83763748476646[/C][/ROW]
[ROW][C]54[/C][C]12004.5[/C][C]12686.8058298238[/C][C]16.9183970773988[/C][C]-682.305829823762[/C][C]-2.13245739655853[/C][/ROW]
[ROW][C]55[/C][C]12175.4[/C][C]12214.4862802562[/C][C]7.48460512561205[/C][C]-39.0862802561969[/C][C]-0.848444374173807[/C][/ROW]
[ROW][C]56[/C][C]13268[/C][C]12485.1645541865[/C][C]12.3610931170309[/C][C]782.835445813458[/C][C]0.456544099362177[/C][/ROW]
[ROW][C]57[/C][C]12299.3[/C][C]12152.4565866256[/C][C]6.1963179923449[/C][C]146.843413374378[/C][C]-0.598043717279071[/C][/ROW]
[ROW][C]58[/C][C]11800.6[/C][C]12391.6477978286[/C][C]10.2124111880962[/C][C]-591.04779782855[/C][C]0.403398419545134[/C][/ROW]
[ROW][C]59[/C][C]13873.3[/C][C]12545.8566225337[/C][C]12.6145196144026[/C][C]1327.44337746630[/C][C]0.249254179390781[/C][/ROW]
[ROW][C]60[/C][C]12269.6[/C][C]12653.7692725488[/C][C]14.1809859140995[/C][C]-384.169272548789[/C][C]0.165072824014381[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64429&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64429&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
110414.910414.9000
212476.811986.714315715872.5907689086392490.0856842842261.88245541158710
312384.612380.823944324688.35663536594893.776055675442020.477124864408427
412266.712339.915337674784.8151765382335-73.2153376746813-0.223696855269900
512919.912644.253930193387.901746369392275.6460698067410.387151487804389
611497.311992.210189924580.275256871552-494.910189924496-1.30430890953452
71214211941.597905408478.899919702437200.402094591628-0.230417083179349
813919.413068.488958231990.8541930763192850.9110417681021.84310385129774
912656.812986.508843667488.7717232752276-329.708843667419-0.303787261758424
1012034.112382.273631185180.1613984544392-348.173631185062-1.21762916846940
1113199.712732.659747135283.5895961605094467.0402528648300.47463707088919
1210881.311673.167730607268.8171446801262-791.867730607161-2.00710807886463
1311301.211845.090758469966.231334918307-543.8907584699130.197176874366809
1413643.912708.077564580972.3358651041639935.8224354190671.38177730067491
151251712741.004216636171.3305927723695-224.004216636068-0.0638419287637921
1613981.113533.676468729689.53097523124447.4235312703691.22053362873885
1714275.713752.149780594591.9948813251609523.550219405510.224593727141872
181343513938.645451449093.3697424019842-503.6454514489630.165577341149965
1913565.713924.797370132691.9965374392374-359.097370132605-0.187841860858894
2016216.314701.0829616665100.7593592143571515.217038333491.19798727220817
211297013927.111373034289.0286972613423-957.111373034246-1.53061688811245
2214079.914125.293305609690.5226842878062-45.39330560958340.190925715701640
231423513703.074878459384.1538649568585531.925121540712-0.895830588701286
2412213.413329.353563492280.3432287081576-1115.95356349221-0.800748573406247
251258113385.506611132380.2682096002647-804.506611132293-0.0430312598770271
2614130.413349.483878182579.0380971620759780.916121817504-0.202217411286757
2714210.814169.688464344093.353215728640641.11153565597951.24768770824706
2814378.514204.849555746592.0660325364289173.650444253494-0.0987037239538626
2913142.813396.474871522473.6069523699097-253.674871522432-1.55528192363082
3013714.713804.033423737579.5750058156826-89.33342373750370.581671395862051
3113621.914104.301620432383.135461377311-482.4016204323380.385061806367694
3215379.813837.988627256377.72823704352521541.81137274374-0.609634025418624
3313306.314057.39338263479.8944191666793-751.0933826340060.247031098944685
3414391.214162.376264371380.2658792324204228.8237356286610.043700264628799
3514909.914201.523493423379.7120937970591708.376506576667-0.0715417360164041
3614025.414746.522663946085.101425860396-721.122663945960.810877852226727
3712951.214325.987849703879.5118819278745-1374.78784970377-0.884126284977143
3814344.314045.076344067874.3961269745379299.22365593219-0.623801523786547
3916093.415076.173763751392.00444584841281017.226236248721.63101952750475
4015413.615123.153866418491.0729228982325290.446133581597-0.076764230726259
4114705.715158.317183641189.9184089430259-452.617183641073-0.0962566641610976
4215972.815719.938114166899.0908809869327252.8618858331720.818087098189793
4316241.416295.4495183719107.76611090318-54.04951837188940.828436312909904
4416626.415768.689538478296.7491641021483857.710461521787-1.10354942394755
4517136.216911.7955241907114.280870189783224.4044758092681.81838504209820
4615622.916268.3266729907102.110495617744-645.426672990685-1.31530223531364
4718003.916858.9634181646109.5193834284501144.936581835400.847482504137743
4816136.116829.3958356651107.508803639363-693.295835665049-0.241547565718181
4914423.716274.807296587597.7434799940832-1851.1072965875-1.15008810602703
5016789.416592.4406258495101.36544608688196.9593741504870.379705018855491
5116782.216189.025714056791.9210257845975593.174285943283-0.864714889736485
5214133.814882.593156534763.6402016941964-748.793156534742-2.39275906912631
531260713877.981766978441.6667644730760-1270.98176697844-1.83763748476646
5412004.512686.805829823816.9183970773988-682.305829823762-2.13245739655853
5512175.412214.48628025627.48460512561205-39.0862802561969-0.848444374173807
561326812485.164554186512.3610931170309782.8354458134580.456544099362177
5712299.312152.45658662566.1963179923449146.843413374378-0.598043717279071
5811800.612391.647797828610.2124111880962-591.047797828550.403398419545134
5913873.312545.856622533712.61451961440261327.443377466300.249254179390781
6012269.612653.769272548814.1809859140995-384.1692725487890.165072824014381


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')