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Author's title

Author*Unverified author*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 23 Dec 2011 11:55:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324659322yvu9qqnvqx65sbf.htm/, Retrieved Mon, 29 Apr 2024 23:30:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160581, Retrieved Mon, 29 Apr 2024 23:30:31 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Structural Time Series Models] [WS8 Structural Ti...] [2011-11-28 16:51:25] [9d4f280afcb4ecc352d7c6f913a0a151]
- R  D    [Structural Time Series Models] [] [2011-12-23 16:55:08] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
61
65
55
56
91
80
135
129
129
130
109
126
73
68
74
95
105
108
127
108
126
154
127
103
95
59
68
82
92
124
139
167
138
146
128
145
91
66
89
98
113
130
127
157
157
136
145
112
71
95
95
105
116
104
128
181
130
124
123
152




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160581&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160581&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160581&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'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
16161000
26564.62445682368650.2052716197506150.2128378613568650.109119641138924
35556.01052287017210.1035776960695080.00721029735808343-0.432157883190664
45655.89514307896310.1022914269167730.13049605341325-0.0107879697659396
59186.81000950117370.266835312558670.579509734288021.51832320992921
68080.6880832667880.2331597466118750.0605634640002911-0.314812663698123
7135128.4454143661230.4821987878299170.9855760699549752.34177880099216
8129128.7127566173550.4810788044560290.312420939093299-0.0105870623734985
9129128.5972148541330.4779849366687070.47269923325334-0.0293981994260454
10130129.4679057296550.4800108674566150.4860754605783790.0193502077999385
11109111.0271806368160.3829016555623870.190032091979335-0.932291848754337
12126123.8973551620080.4466643591891320.6393280650811180.615286243840574
137396.47993947504281.80904561691824-20.1928431563405-1.67017072403873
146869.54219410072481.074065883425150.896410735720657-1.19853607373444
157472.51284232899571.085326517902961.268507372507070.0930528806386038
169591.67526332581461.135450094219721.222585469485010.889801531348371
17105102.1994969339431.159148824145361.708370494610510.462160752547482
18108107.0167718449551.168340689140320.5577014583392460.180069821202527
19127123.2641777189521.206156846014381.981771779349840.742258998675457
20108109.181954782471.167907241839280.596444835583205-0.75256064067552
21126123.0248452154121.199546090076571.5007537875910.623916740616233
22154149.2202173813981.26191908283651.872173607396621.23040480719133
23127129.2462196011971.208275458133720.22402765828903-1.04536333663303
24103104.7439805831651.152417752068181.24701584392345-1.26531560500798
2595107.1176127941741.12465513665238-12.26046989291450.0664021343894195
265963.76742621024730.40030801509065-0.486926569884086-1.98304530867309
276866.96989119001650.4127523322406360.7088666500803750.137491205077427
288279.85166894262160.4362280501615670.7079525613224230.61358953182785
299289.76559632577170.4522179539537051.13933487058320.466395859288387
30124120.2145851606310.5030730494764890.3195999179406211.47612392603156
31139135.5082647812560.528177729644961.782844281537860.727835582420924
32167163.350832415660.5744794191342440.4932931394531991.34412086689162
33138141.2525160633430.536082264724569-0.63292561634512-1.1157115549719
34146144.051829518570.5399377577914141.686679190989160.111373211996395
35128130.1613420812570.51456709175623-0.49405386166747-0.710164794317556
36145142.5345848496620.5277632147343061.095091631585090.583356959137528
3791106.8909789692461.05111813986662-11.6849769013783-1.8986298485671
386670.85999759319210.615721395828865-1.09201796324212-1.7042658252378
398985.70072168092330.669805414098931.677824663135360.697826261100165
409895.81048521125960.6839288605240921.105337571558110.464474467015639
41113110.3047872593740.7017792460085621.108756121728970.679512386607997
42130127.4056507303480.7232966092972310.7105789686280760.80687424044125
43127126.3291102498090.7209220321285640.877635572052185-0.0885560307348267
44157151.9800680771790.7538005487150762.156242203163561.22661288624791
45157157.1635372624270.759646398239442-0.6723690904700540.217949856761477
46136136.9534875679250.7315385779775641.4552811296153-1.0317794605526
47145144.5523575206950.741127133693811-0.34121626002690.337922152567806
48112114.6242228959480.7253661973666410.899267413927599-1.50839400710255
497185.52974377889491.03400838308367-11.0797751880364-1.53641388657701
509594.84364140028391.10870488366984-0.7102888260044820.387244912772322
519593.47609308717921.100136450019941.80474565066924-0.121440728533384
52105102.5185406459661.110521761315461.573672798519970.390758504929991
53116113.52146183571.121087598069081.34757779050070.486700210729011
54104104.4092177583941.109903673409450.760669112951423-0.503464387901396
55128124.4435987057561.130824924330971.39297012663540.931051418985322
56181171.7226166804991.181891919695564.001770939455142.2704094941315
57130136.0139292927851.14092910432979-1.79663926049611-1.81495852236437
58124124.2033534285131.126142422407321.27724950176419-0.637214968882016
59123122.3765341065781.122663424593160.961057383468839-0.145296708302002
60152146.3431963116631.12537555254273.044906066784021.12345460436783

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 61 & 61 & 0 & 0 & 0 \tabularnewline
2 & 65 & 64.6244568236865 & 0.205271619750615 & 0.212837861356865 & 0.109119641138924 \tabularnewline
3 & 55 & 56.0105228701721 & 0.103577696069508 & 0.00721029735808343 & -0.432157883190664 \tabularnewline
4 & 56 & 55.8951430789631 & 0.102291426916773 & 0.13049605341325 & -0.0107879697659396 \tabularnewline
5 & 91 & 86.8100095011737 & 0.26683531255867 & 0.57950973428802 & 1.51832320992921 \tabularnewline
6 & 80 & 80.688083266788 & 0.233159746611875 & 0.0605634640002911 & -0.314812663698123 \tabularnewline
7 & 135 & 128.445414366123 & 0.482198787829917 & 0.985576069954975 & 2.34177880099216 \tabularnewline
8 & 129 & 128.712756617355 & 0.481078804456029 & 0.312420939093299 & -0.0105870623734985 \tabularnewline
9 & 129 & 128.597214854133 & 0.477984936668707 & 0.47269923325334 & -0.0293981994260454 \tabularnewline
10 & 130 & 129.467905729655 & 0.480010867456615 & 0.486075460578379 & 0.0193502077999385 \tabularnewline
11 & 109 & 111.027180636816 & 0.382901655562387 & 0.190032091979335 & -0.932291848754337 \tabularnewline
12 & 126 & 123.897355162008 & 0.446664359189132 & 0.639328065081118 & 0.615286243840574 \tabularnewline
13 & 73 & 96.4799394750428 & 1.80904561691824 & -20.1928431563405 & -1.67017072403873 \tabularnewline
14 & 68 & 69.5421941007248 & 1.07406588342515 & 0.896410735720657 & -1.19853607373444 \tabularnewline
15 & 74 & 72.5128423289957 & 1.08532651790296 & 1.26850737250707 & 0.0930528806386038 \tabularnewline
16 & 95 & 91.6752633258146 & 1.13545009421972 & 1.22258546948501 & 0.889801531348371 \tabularnewline
17 & 105 & 102.199496933943 & 1.15914882414536 & 1.70837049461051 & 0.462160752547482 \tabularnewline
18 & 108 & 107.016771844955 & 1.16834068914032 & 0.557701458339246 & 0.180069821202527 \tabularnewline
19 & 127 & 123.264177718952 & 1.20615684601438 & 1.98177177934984 & 0.742258998675457 \tabularnewline
20 & 108 & 109.18195478247 & 1.16790724183928 & 0.596444835583205 & -0.75256064067552 \tabularnewline
21 & 126 & 123.024845215412 & 1.19954609007657 & 1.500753787591 & 0.623916740616233 \tabularnewline
22 & 154 & 149.220217381398 & 1.2619190828365 & 1.87217360739662 & 1.23040480719133 \tabularnewline
23 & 127 & 129.246219601197 & 1.20827545813372 & 0.22402765828903 & -1.04536333663303 \tabularnewline
24 & 103 & 104.743980583165 & 1.15241775206818 & 1.24701584392345 & -1.26531560500798 \tabularnewline
25 & 95 & 107.117612794174 & 1.12465513665238 & -12.2604698929145 & 0.0664021343894195 \tabularnewline
26 & 59 & 63.7674262102473 & 0.40030801509065 & -0.486926569884086 & -1.98304530867309 \tabularnewline
27 & 68 & 66.9698911900165 & 0.412752332240636 & 0.708866650080375 & 0.137491205077427 \tabularnewline
28 & 82 & 79.8516689426216 & 0.436228050161567 & 0.707952561322423 & 0.61358953182785 \tabularnewline
29 & 92 & 89.7655963257717 & 0.452217953953705 & 1.1393348705832 & 0.466395859288387 \tabularnewline
30 & 124 & 120.214585160631 & 0.503073049476489 & 0.319599917940621 & 1.47612392603156 \tabularnewline
31 & 139 & 135.508264781256 & 0.52817772964496 & 1.78284428153786 & 0.727835582420924 \tabularnewline
32 & 167 & 163.35083241566 & 0.574479419134244 & 0.493293139453199 & 1.34412086689162 \tabularnewline
33 & 138 & 141.252516063343 & 0.536082264724569 & -0.63292561634512 & -1.1157115549719 \tabularnewline
34 & 146 & 144.05182951857 & 0.539937757791414 & 1.68667919098916 & 0.111373211996395 \tabularnewline
35 & 128 & 130.161342081257 & 0.51456709175623 & -0.49405386166747 & -0.710164794317556 \tabularnewline
36 & 145 & 142.534584849662 & 0.527763214734306 & 1.09509163158509 & 0.583356959137528 \tabularnewline
37 & 91 & 106.890978969246 & 1.05111813986662 & -11.6849769013783 & -1.8986298485671 \tabularnewline
38 & 66 & 70.8599975931921 & 0.615721395828865 & -1.09201796324212 & -1.7042658252378 \tabularnewline
39 & 89 & 85.7007216809233 & 0.66980541409893 & 1.67782466313536 & 0.697826261100165 \tabularnewline
40 & 98 & 95.8104852112596 & 0.683928860524092 & 1.10533757155811 & 0.464474467015639 \tabularnewline
41 & 113 & 110.304787259374 & 0.701779246008562 & 1.10875612172897 & 0.679512386607997 \tabularnewline
42 & 130 & 127.405650730348 & 0.723296609297231 & 0.710578968628076 & 0.80687424044125 \tabularnewline
43 & 127 & 126.329110249809 & 0.720922032128564 & 0.877635572052185 & -0.0885560307348267 \tabularnewline
44 & 157 & 151.980068077179 & 0.753800548715076 & 2.15624220316356 & 1.22661288624791 \tabularnewline
45 & 157 & 157.163537262427 & 0.759646398239442 & -0.672369090470054 & 0.217949856761477 \tabularnewline
46 & 136 & 136.953487567925 & 0.731538577977564 & 1.4552811296153 & -1.0317794605526 \tabularnewline
47 & 145 & 144.552357520695 & 0.741127133693811 & -0.3412162600269 & 0.337922152567806 \tabularnewline
48 & 112 & 114.624222895948 & 0.725366197366641 & 0.899267413927599 & -1.50839400710255 \tabularnewline
49 & 71 & 85.5297437788949 & 1.03400838308367 & -11.0797751880364 & -1.53641388657701 \tabularnewline
50 & 95 & 94.8436414002839 & 1.10870488366984 & -0.710288826004482 & 0.387244912772322 \tabularnewline
51 & 95 & 93.4760930871792 & 1.10013645001994 & 1.80474565066924 & -0.121440728533384 \tabularnewline
52 & 105 & 102.518540645966 & 1.11052176131546 & 1.57367279851997 & 0.390758504929991 \tabularnewline
53 & 116 & 113.5214618357 & 1.12108759806908 & 1.3475777905007 & 0.486700210729011 \tabularnewline
54 & 104 & 104.409217758394 & 1.10990367340945 & 0.760669112951423 & -0.503464387901396 \tabularnewline
55 & 128 & 124.443598705756 & 1.13082492433097 & 1.3929701266354 & 0.931051418985322 \tabularnewline
56 & 181 & 171.722616680499 & 1.18189191969556 & 4.00177093945514 & 2.2704094941315 \tabularnewline
57 & 130 & 136.013929292785 & 1.14092910432979 & -1.79663926049611 & -1.81495852236437 \tabularnewline
58 & 124 & 124.203353428513 & 1.12614242240732 & 1.27724950176419 & -0.637214968882016 \tabularnewline
59 & 123 & 122.376534106578 & 1.12266342459316 & 0.961057383468839 & -0.145296708302002 \tabularnewline
60 & 152 & 146.343196311663 & 1.1253755525427 & 3.04490606678402 & 1.12345460436783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160581&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]61[/C][C]61[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]65[/C][C]64.6244568236865[/C][C]0.205271619750615[/C][C]0.212837861356865[/C][C]0.109119641138924[/C][/ROW]
[ROW][C]3[/C][C]55[/C][C]56.0105228701721[/C][C]0.103577696069508[/C][C]0.00721029735808343[/C][C]-0.432157883190664[/C][/ROW]
[ROW][C]4[/C][C]56[/C][C]55.8951430789631[/C][C]0.102291426916773[/C][C]0.13049605341325[/C][C]-0.0107879697659396[/C][/ROW]
[ROW][C]5[/C][C]91[/C][C]86.8100095011737[/C][C]0.26683531255867[/C][C]0.57950973428802[/C][C]1.51832320992921[/C][/ROW]
[ROW][C]6[/C][C]80[/C][C]80.688083266788[/C][C]0.233159746611875[/C][C]0.0605634640002911[/C][C]-0.314812663698123[/C][/ROW]
[ROW][C]7[/C][C]135[/C][C]128.445414366123[/C][C]0.482198787829917[/C][C]0.985576069954975[/C][C]2.34177880099216[/C][/ROW]
[ROW][C]8[/C][C]129[/C][C]128.712756617355[/C][C]0.481078804456029[/C][C]0.312420939093299[/C][C]-0.0105870623734985[/C][/ROW]
[ROW][C]9[/C][C]129[/C][C]128.597214854133[/C][C]0.477984936668707[/C][C]0.47269923325334[/C][C]-0.0293981994260454[/C][/ROW]
[ROW][C]10[/C][C]130[/C][C]129.467905729655[/C][C]0.480010867456615[/C][C]0.486075460578379[/C][C]0.0193502077999385[/C][/ROW]
[ROW][C]11[/C][C]109[/C][C]111.027180636816[/C][C]0.382901655562387[/C][C]0.190032091979335[/C][C]-0.932291848754337[/C][/ROW]
[ROW][C]12[/C][C]126[/C][C]123.897355162008[/C][C]0.446664359189132[/C][C]0.639328065081118[/C][C]0.615286243840574[/C][/ROW]
[ROW][C]13[/C][C]73[/C][C]96.4799394750428[/C][C]1.80904561691824[/C][C]-20.1928431563405[/C][C]-1.67017072403873[/C][/ROW]
[ROW][C]14[/C][C]68[/C][C]69.5421941007248[/C][C]1.07406588342515[/C][C]0.896410735720657[/C][C]-1.19853607373444[/C][/ROW]
[ROW][C]15[/C][C]74[/C][C]72.5128423289957[/C][C]1.08532651790296[/C][C]1.26850737250707[/C][C]0.0930528806386038[/C][/ROW]
[ROW][C]16[/C][C]95[/C][C]91.6752633258146[/C][C]1.13545009421972[/C][C]1.22258546948501[/C][C]0.889801531348371[/C][/ROW]
[ROW][C]17[/C][C]105[/C][C]102.199496933943[/C][C]1.15914882414536[/C][C]1.70837049461051[/C][C]0.462160752547482[/C][/ROW]
[ROW][C]18[/C][C]108[/C][C]107.016771844955[/C][C]1.16834068914032[/C][C]0.557701458339246[/C][C]0.180069821202527[/C][/ROW]
[ROW][C]19[/C][C]127[/C][C]123.264177718952[/C][C]1.20615684601438[/C][C]1.98177177934984[/C][C]0.742258998675457[/C][/ROW]
[ROW][C]20[/C][C]108[/C][C]109.18195478247[/C][C]1.16790724183928[/C][C]0.596444835583205[/C][C]-0.75256064067552[/C][/ROW]
[ROW][C]21[/C][C]126[/C][C]123.024845215412[/C][C]1.19954609007657[/C][C]1.500753787591[/C][C]0.623916740616233[/C][/ROW]
[ROW][C]22[/C][C]154[/C][C]149.220217381398[/C][C]1.2619190828365[/C][C]1.87217360739662[/C][C]1.23040480719133[/C][/ROW]
[ROW][C]23[/C][C]127[/C][C]129.246219601197[/C][C]1.20827545813372[/C][C]0.22402765828903[/C][C]-1.04536333663303[/C][/ROW]
[ROW][C]24[/C][C]103[/C][C]104.743980583165[/C][C]1.15241775206818[/C][C]1.24701584392345[/C][C]-1.26531560500798[/C][/ROW]
[ROW][C]25[/C][C]95[/C][C]107.117612794174[/C][C]1.12465513665238[/C][C]-12.2604698929145[/C][C]0.0664021343894195[/C][/ROW]
[ROW][C]26[/C][C]59[/C][C]63.7674262102473[/C][C]0.40030801509065[/C][C]-0.486926569884086[/C][C]-1.98304530867309[/C][/ROW]
[ROW][C]27[/C][C]68[/C][C]66.9698911900165[/C][C]0.412752332240636[/C][C]0.708866650080375[/C][C]0.137491205077427[/C][/ROW]
[ROW][C]28[/C][C]82[/C][C]79.8516689426216[/C][C]0.436228050161567[/C][C]0.707952561322423[/C][C]0.61358953182785[/C][/ROW]
[ROW][C]29[/C][C]92[/C][C]89.7655963257717[/C][C]0.452217953953705[/C][C]1.1393348705832[/C][C]0.466395859288387[/C][/ROW]
[ROW][C]30[/C][C]124[/C][C]120.214585160631[/C][C]0.503073049476489[/C][C]0.319599917940621[/C][C]1.47612392603156[/C][/ROW]
[ROW][C]31[/C][C]139[/C][C]135.508264781256[/C][C]0.52817772964496[/C][C]1.78284428153786[/C][C]0.727835582420924[/C][/ROW]
[ROW][C]32[/C][C]167[/C][C]163.35083241566[/C][C]0.574479419134244[/C][C]0.493293139453199[/C][C]1.34412086689162[/C][/ROW]
[ROW][C]33[/C][C]138[/C][C]141.252516063343[/C][C]0.536082264724569[/C][C]-0.63292561634512[/C][C]-1.1157115549719[/C][/ROW]
[ROW][C]34[/C][C]146[/C][C]144.05182951857[/C][C]0.539937757791414[/C][C]1.68667919098916[/C][C]0.111373211996395[/C][/ROW]
[ROW][C]35[/C][C]128[/C][C]130.161342081257[/C][C]0.51456709175623[/C][C]-0.49405386166747[/C][C]-0.710164794317556[/C][/ROW]
[ROW][C]36[/C][C]145[/C][C]142.534584849662[/C][C]0.527763214734306[/C][C]1.09509163158509[/C][C]0.583356959137528[/C][/ROW]
[ROW][C]37[/C][C]91[/C][C]106.890978969246[/C][C]1.05111813986662[/C][C]-11.6849769013783[/C][C]-1.8986298485671[/C][/ROW]
[ROW][C]38[/C][C]66[/C][C]70.8599975931921[/C][C]0.615721395828865[/C][C]-1.09201796324212[/C][C]-1.7042658252378[/C][/ROW]
[ROW][C]39[/C][C]89[/C][C]85.7007216809233[/C][C]0.66980541409893[/C][C]1.67782466313536[/C][C]0.697826261100165[/C][/ROW]
[ROW][C]40[/C][C]98[/C][C]95.8104852112596[/C][C]0.683928860524092[/C][C]1.10533757155811[/C][C]0.464474467015639[/C][/ROW]
[ROW][C]41[/C][C]113[/C][C]110.304787259374[/C][C]0.701779246008562[/C][C]1.10875612172897[/C][C]0.679512386607997[/C][/ROW]
[ROW][C]42[/C][C]130[/C][C]127.405650730348[/C][C]0.723296609297231[/C][C]0.710578968628076[/C][C]0.80687424044125[/C][/ROW]
[ROW][C]43[/C][C]127[/C][C]126.329110249809[/C][C]0.720922032128564[/C][C]0.877635572052185[/C][C]-0.0885560307348267[/C][/ROW]
[ROW][C]44[/C][C]157[/C][C]151.980068077179[/C][C]0.753800548715076[/C][C]2.15624220316356[/C][C]1.22661288624791[/C][/ROW]
[ROW][C]45[/C][C]157[/C][C]157.163537262427[/C][C]0.759646398239442[/C][C]-0.672369090470054[/C][C]0.217949856761477[/C][/ROW]
[ROW][C]46[/C][C]136[/C][C]136.953487567925[/C][C]0.731538577977564[/C][C]1.4552811296153[/C][C]-1.0317794605526[/C][/ROW]
[ROW][C]47[/C][C]145[/C][C]144.552357520695[/C][C]0.741127133693811[/C][C]-0.3412162600269[/C][C]0.337922152567806[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]114.624222895948[/C][C]0.725366197366641[/C][C]0.899267413927599[/C][C]-1.50839400710255[/C][/ROW]
[ROW][C]49[/C][C]71[/C][C]85.5297437788949[/C][C]1.03400838308367[/C][C]-11.0797751880364[/C][C]-1.53641388657701[/C][/ROW]
[ROW][C]50[/C][C]95[/C][C]94.8436414002839[/C][C]1.10870488366984[/C][C]-0.710288826004482[/C][C]0.387244912772322[/C][/ROW]
[ROW][C]51[/C][C]95[/C][C]93.4760930871792[/C][C]1.10013645001994[/C][C]1.80474565066924[/C][C]-0.121440728533384[/C][/ROW]
[ROW][C]52[/C][C]105[/C][C]102.518540645966[/C][C]1.11052176131546[/C][C]1.57367279851997[/C][C]0.390758504929991[/C][/ROW]
[ROW][C]53[/C][C]116[/C][C]113.5214618357[/C][C]1.12108759806908[/C][C]1.3475777905007[/C][C]0.486700210729011[/C][/ROW]
[ROW][C]54[/C][C]104[/C][C]104.409217758394[/C][C]1.10990367340945[/C][C]0.760669112951423[/C][C]-0.503464387901396[/C][/ROW]
[ROW][C]55[/C][C]128[/C][C]124.443598705756[/C][C]1.13082492433097[/C][C]1.3929701266354[/C][C]0.931051418985322[/C][/ROW]
[ROW][C]56[/C][C]181[/C][C]171.722616680499[/C][C]1.18189191969556[/C][C]4.00177093945514[/C][C]2.2704094941315[/C][/ROW]
[ROW][C]57[/C][C]130[/C][C]136.013929292785[/C][C]1.14092910432979[/C][C]-1.79663926049611[/C][C]-1.81495852236437[/C][/ROW]
[ROW][C]58[/C][C]124[/C][C]124.203353428513[/C][C]1.12614242240732[/C][C]1.27724950176419[/C][C]-0.637214968882016[/C][/ROW]
[ROW][C]59[/C][C]123[/C][C]122.376534106578[/C][C]1.12266342459316[/C][C]0.961057383468839[/C][C]-0.145296708302002[/C][/ROW]
[ROW][C]60[/C][C]152[/C][C]146.343196311663[/C][C]1.1253755525427[/C][C]3.04490606678402[/C][C]1.12345460436783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160581&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160581&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
16161000
26564.62445682368650.2052716197506150.2128378613568650.109119641138924
35556.01052287017210.1035776960695080.00721029735808343-0.432157883190664
45655.89514307896310.1022914269167730.13049605341325-0.0107879697659396
59186.81000950117370.266835312558670.579509734288021.51832320992921
68080.6880832667880.2331597466118750.0605634640002911-0.314812663698123
7135128.4454143661230.4821987878299170.9855760699549752.34177880099216
8129128.7127566173550.4810788044560290.312420939093299-0.0105870623734985
9129128.5972148541330.4779849366687070.47269923325334-0.0293981994260454
10130129.4679057296550.4800108674566150.4860754605783790.0193502077999385
11109111.0271806368160.3829016555623870.190032091979335-0.932291848754337
12126123.8973551620080.4466643591891320.6393280650811180.615286243840574
137396.47993947504281.80904561691824-20.1928431563405-1.67017072403873
146869.54219410072481.074065883425150.896410735720657-1.19853607373444
157472.51284232899571.085326517902961.268507372507070.0930528806386038
169591.67526332581461.135450094219721.222585469485010.889801531348371
17105102.1994969339431.159148824145361.708370494610510.462160752547482
18108107.0167718449551.168340689140320.5577014583392460.180069821202527
19127123.2641777189521.206156846014381.981771779349840.742258998675457
20108109.181954782471.167907241839280.596444835583205-0.75256064067552
21126123.0248452154121.199546090076571.5007537875910.623916740616233
22154149.2202173813981.26191908283651.872173607396621.23040480719133
23127129.2462196011971.208275458133720.22402765828903-1.04536333663303
24103104.7439805831651.152417752068181.24701584392345-1.26531560500798
2595107.1176127941741.12465513665238-12.26046989291450.0664021343894195
265963.76742621024730.40030801509065-0.486926569884086-1.98304530867309
276866.96989119001650.4127523322406360.7088666500803750.137491205077427
288279.85166894262160.4362280501615670.7079525613224230.61358953182785
299289.76559632577170.4522179539537051.13933487058320.466395859288387
30124120.2145851606310.5030730494764890.3195999179406211.47612392603156
31139135.5082647812560.528177729644961.782844281537860.727835582420924
32167163.350832415660.5744794191342440.4932931394531991.34412086689162
33138141.2525160633430.536082264724569-0.63292561634512-1.1157115549719
34146144.051829518570.5399377577914141.686679190989160.111373211996395
35128130.1613420812570.51456709175623-0.49405386166747-0.710164794317556
36145142.5345848496620.5277632147343061.095091631585090.583356959137528
3791106.8909789692461.05111813986662-11.6849769013783-1.8986298485671
386670.85999759319210.615721395828865-1.09201796324212-1.7042658252378
398985.70072168092330.669805414098931.677824663135360.697826261100165
409895.81048521125960.6839288605240921.105337571558110.464474467015639
41113110.3047872593740.7017792460085621.108756121728970.679512386607997
42130127.4056507303480.7232966092972310.7105789686280760.80687424044125
43127126.3291102498090.7209220321285640.877635572052185-0.0885560307348267
44157151.9800680771790.7538005487150762.156242203163561.22661288624791
45157157.1635372624270.759646398239442-0.6723690904700540.217949856761477
46136136.9534875679250.7315385779775641.4552811296153-1.0317794605526
47145144.5523575206950.741127133693811-0.34121626002690.337922152567806
48112114.6242228959480.7253661973666410.899267413927599-1.50839400710255
497185.52974377889491.03400838308367-11.0797751880364-1.53641388657701
509594.84364140028391.10870488366984-0.7102888260044820.387244912772322
519593.47609308717921.100136450019941.80474565066924-0.121440728533384
52105102.5185406459661.110521761315461.573672798519970.390758504929991
53116113.52146183571.121087598069081.34757779050070.486700210729011
54104104.4092177583941.109903673409450.760669112951423-0.503464387901396
55128124.4435987057561.130824924330971.39297012663540.931051418985322
56181171.7226166804991.181891919695564.001770939455142.2704094941315
57130136.0139292927851.14092910432979-1.79663926049611-1.81495852236437
58124124.2033534285131.126142422407321.27724950176419-0.637214968882016
59123122.3765341065781.122663424593160.961057383468839-0.145296708302002
60152146.3431963116631.12537555254273.044906066784021.12345460436783



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')