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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 computationTue, 01 Dec 2009 05:57:06 -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/01/t1259672261b01yyua39rsv6kk.htm/, Retrieved Thu, 25 Apr 2024 21:49:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62022, Retrieved Thu, 25 Apr 2024 21:49:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-   PD      [Structural Time Series Models] [] [2009-12-01 12:57:06] [c60887983b0820a525cba943a935572d] [Current]
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Dataseries X:
149
139
135
130
127
122
117
112
113
149
157
157
147
137
132
125
123
117
114
111
112
144
150
149
134
123
116
117
111
105
102
95
93
124
130
124
115
106
105
105
101
95
93
84
87
116
120
117
109
105
107
109
109
108
107
99
103
131
137
135




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=62022&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=62022&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62022&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
1149149000
2139140.642680576515-8.49307815026848-1.64268057651493-1.53765266125616
3135133.500074333951-7.484795962701341.499925666049080.179646588980101
4130129.283276243248-4.925184883095340.7167237567524830.429256154743507
5127126.651382555049-3.166310018431370.3486174449509740.291079352906523
6122122.311849936321-4.06655159085912-0.311849936321065-0.149789544700316
7117117.091867312654-4.95319531857533-0.0918673126539773-0.147350847235532
8112111.898546268554-5.137800748278450.101453731446092-0.030678983366815
9113111.824958833281-1.245114312629271.175041166719070.646919143568898
10149142.44671925255123.24742380852756.553280747448914.07033839229334
11157160.10421869560618.9510630761748-3.10421869560569-0.713999477056228
12157160.5196668679784.70448911649624-3.51966686797777-2.36759559411703
13147149.423130932804-7.41655389196125-2.42313093280427-2.01924575973931
14137138.651364472095-9.99854288348633-1.65136447209467-0.435433688410373
15132130.534993534140-8.610431846345181.465006465860460.230491923985758
16125124.486759690037-6.70924648256850.5132403099631740.320020400182524
17123121.875694581931-3.669063826847661.124305418069350.503847616814496
18117117.154745667397-4.44511553052076-0.154745667396741-0.128904542228427
19114112.241921807902-4.790630408280971.75807819209767-0.0574491052755323
20111109.914705067346-2.969364295436931.085294932654010.302662382626359
21112116.9762422998244.44713746808584-4.976242299824131.23251262198667
22144135.03976506988814.51411010455578.960234930111661.67305690975416
23150150.23711739365715.0191564854999-0.2371173936565920.0839323489125045
24149152.1177354974485.3147578699753-3.11773549744823-1.61305750864612
25134139.248256960133-8.11523536092009-5.24825696013339-2.2378636708975
26123125.537574114383-12.2528618936276-2.53757411438341-0.68856124472217
27116114.622774908123-11.27519938775731.377225091876970.162261823446907
28117114.934190035589-2.808870452216212.065809964411281.41518045008513
29111110.122835175705-4.276669184022110.877164824294993-0.24393608941861
30105104.312497403644-5.396705631594060.687502596355746-0.185893147778276
3110299.421513904895-5.027526349847932.578486095105020.0613714544182754
329595.7214539580626-4.05752352454881-0.7214539580626080.161219869519527
3393100.4143621895542.33841008833955-7.41436218955441.06288312950126
34124114.35518157542610.81852104459499.644818424574431.40936625470719
35130127.55530123620812.55850167639592.444698763792050.289158446775954
36124125.7345792329272.06014739807857-1.73457923292679-1.74557285435788
37115119.776311469382-3.79779900091381-4.77631146938185-0.975257794511677
38106110.058781352843-8.12081463749695-4.05878135284279-0.718291414599892
39105105.904108687892-5.24063090231314-0.9041086878923760.478301449981572
40105102.157704601022-4.155874988942872.842295398978260.180798076324648
4110199.0789740558476-3.371940828325951.921025944152430.130375755907062
429593.937052234357-4.657855302292021.06294776564306-0.213476291706792
439389.62050641417-4.410142668126713.379493585830020.0411638873681076
448486.587003679845-3.41035569721649-2.587003679844960.166174772278906
458795.63734446383235.64313017181864-8.637344463832281.50454521176173
46116106.9758773311379.781082349591739.024122668863240.687688851854283
47120114.6922320255808.281584838502225.3077679744197-0.249204179611138
48117117.6759763261694.43523333482768-0.675976326169283-0.639585710530923
49109113.874904078211-1.54813087197722-4.87490407821138-0.995393229057181
50105110.472914701071-2.89384823860998-5.47291470107083-0.223548606989907
51107108.300245479617-2.37204396861045-1.300245479616670.0866803898783519
52109106.305202687956-2.099331418256182.694797312044120.0453975352294533
53109105.741816728978-0.986298992555223.258183271021780.185116680497438
54108105.870806544331-0.1786821185696572.129193455668750.134123914864716
55107103.829194688776-1.526328525335773.17080531122411-0.223899932731448
5699104.5464545304910.0971454867296635-5.54645453049080.269819606800327
57103112.1310423146815.51737273017742-9.131042314680780.900777278002992
58131121.3663701100878.208855791410829.633629889913440.447291391219030
59137130.3809431068258.79195569536496.619056893174950.0969124558026854
60135134.4981385315395.408949236581990.50186146846088-0.562506573840623

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 149 & 149 & 0 & 0 & 0 \tabularnewline
2 & 139 & 140.642680576515 & -8.49307815026848 & -1.64268057651493 & -1.53765266125616 \tabularnewline
3 & 135 & 133.500074333951 & -7.48479596270134 & 1.49992566604908 & 0.179646588980101 \tabularnewline
4 & 130 & 129.283276243248 & -4.92518488309534 & 0.716723756752483 & 0.429256154743507 \tabularnewline
5 & 127 & 126.651382555049 & -3.16631001843137 & 0.348617444950974 & 0.291079352906523 \tabularnewline
6 & 122 & 122.311849936321 & -4.06655159085912 & -0.311849936321065 & -0.149789544700316 \tabularnewline
7 & 117 & 117.091867312654 & -4.95319531857533 & -0.0918673126539773 & -0.147350847235532 \tabularnewline
8 & 112 & 111.898546268554 & -5.13780074827845 & 0.101453731446092 & -0.030678983366815 \tabularnewline
9 & 113 & 111.824958833281 & -1.24511431262927 & 1.17504116671907 & 0.646919143568898 \tabularnewline
10 & 149 & 142.446719252551 & 23.2474238085275 & 6.55328074744891 & 4.07033839229334 \tabularnewline
11 & 157 & 160.104218695606 & 18.9510630761748 & -3.10421869560569 & -0.713999477056228 \tabularnewline
12 & 157 & 160.519666867978 & 4.70448911649624 & -3.51966686797777 & -2.36759559411703 \tabularnewline
13 & 147 & 149.423130932804 & -7.41655389196125 & -2.42313093280427 & -2.01924575973931 \tabularnewline
14 & 137 & 138.651364472095 & -9.99854288348633 & -1.65136447209467 & -0.435433688410373 \tabularnewline
15 & 132 & 130.534993534140 & -8.61043184634518 & 1.46500646586046 & 0.230491923985758 \tabularnewline
16 & 125 & 124.486759690037 & -6.7092464825685 & 0.513240309963174 & 0.320020400182524 \tabularnewline
17 & 123 & 121.875694581931 & -3.66906382684766 & 1.12430541806935 & 0.503847616814496 \tabularnewline
18 & 117 & 117.154745667397 & -4.44511553052076 & -0.154745667396741 & -0.128904542228427 \tabularnewline
19 & 114 & 112.241921807902 & -4.79063040828097 & 1.75807819209767 & -0.0574491052755323 \tabularnewline
20 & 111 & 109.914705067346 & -2.96936429543693 & 1.08529493265401 & 0.302662382626359 \tabularnewline
21 & 112 & 116.976242299824 & 4.44713746808584 & -4.97624229982413 & 1.23251262198667 \tabularnewline
22 & 144 & 135.039765069888 & 14.5141101045557 & 8.96023493011166 & 1.67305690975416 \tabularnewline
23 & 150 & 150.237117393657 & 15.0191564854999 & -0.237117393656592 & 0.0839323489125045 \tabularnewline
24 & 149 & 152.117735497448 & 5.3147578699753 & -3.11773549744823 & -1.61305750864612 \tabularnewline
25 & 134 & 139.248256960133 & -8.11523536092009 & -5.24825696013339 & -2.2378636708975 \tabularnewline
26 & 123 & 125.537574114383 & -12.2528618936276 & -2.53757411438341 & -0.68856124472217 \tabularnewline
27 & 116 & 114.622774908123 & -11.2751993877573 & 1.37722509187697 & 0.162261823446907 \tabularnewline
28 & 117 & 114.934190035589 & -2.80887045221621 & 2.06580996441128 & 1.41518045008513 \tabularnewline
29 & 111 & 110.122835175705 & -4.27666918402211 & 0.877164824294993 & -0.24393608941861 \tabularnewline
30 & 105 & 104.312497403644 & -5.39670563159406 & 0.687502596355746 & -0.185893147778276 \tabularnewline
31 & 102 & 99.421513904895 & -5.02752634984793 & 2.57848609510502 & 0.0613714544182754 \tabularnewline
32 & 95 & 95.7214539580626 & -4.05752352454881 & -0.721453958062608 & 0.161219869519527 \tabularnewline
33 & 93 & 100.414362189554 & 2.33841008833955 & -7.4143621895544 & 1.06288312950126 \tabularnewline
34 & 124 & 114.355181575426 & 10.8185210445949 & 9.64481842457443 & 1.40936625470719 \tabularnewline
35 & 130 & 127.555301236208 & 12.5585016763959 & 2.44469876379205 & 0.289158446775954 \tabularnewline
36 & 124 & 125.734579232927 & 2.06014739807857 & -1.73457923292679 & -1.74557285435788 \tabularnewline
37 & 115 & 119.776311469382 & -3.79779900091381 & -4.77631146938185 & -0.975257794511677 \tabularnewline
38 & 106 & 110.058781352843 & -8.12081463749695 & -4.05878135284279 & -0.718291414599892 \tabularnewline
39 & 105 & 105.904108687892 & -5.24063090231314 & -0.904108687892376 & 0.478301449981572 \tabularnewline
40 & 105 & 102.157704601022 & -4.15587498894287 & 2.84229539897826 & 0.180798076324648 \tabularnewline
41 & 101 & 99.0789740558476 & -3.37194082832595 & 1.92102594415243 & 0.130375755907062 \tabularnewline
42 & 95 & 93.937052234357 & -4.65785530229202 & 1.06294776564306 & -0.213476291706792 \tabularnewline
43 & 93 & 89.62050641417 & -4.41014266812671 & 3.37949358583002 & 0.0411638873681076 \tabularnewline
44 & 84 & 86.587003679845 & -3.41035569721649 & -2.58700367984496 & 0.166174772278906 \tabularnewline
45 & 87 & 95.6373444638323 & 5.64313017181864 & -8.63734446383228 & 1.50454521176173 \tabularnewline
46 & 116 & 106.975877331137 & 9.78108234959173 & 9.02412266886324 & 0.687688851854283 \tabularnewline
47 & 120 & 114.692232025580 & 8.28158483850222 & 5.3077679744197 & -0.249204179611138 \tabularnewline
48 & 117 & 117.675976326169 & 4.43523333482768 & -0.675976326169283 & -0.639585710530923 \tabularnewline
49 & 109 & 113.874904078211 & -1.54813087197722 & -4.87490407821138 & -0.995393229057181 \tabularnewline
50 & 105 & 110.472914701071 & -2.89384823860998 & -5.47291470107083 & -0.223548606989907 \tabularnewline
51 & 107 & 108.300245479617 & -2.37204396861045 & -1.30024547961667 & 0.0866803898783519 \tabularnewline
52 & 109 & 106.305202687956 & -2.09933141825618 & 2.69479731204412 & 0.0453975352294533 \tabularnewline
53 & 109 & 105.741816728978 & -0.98629899255522 & 3.25818327102178 & 0.185116680497438 \tabularnewline
54 & 108 & 105.870806544331 & -0.178682118569657 & 2.12919345566875 & 0.134123914864716 \tabularnewline
55 & 107 & 103.829194688776 & -1.52632852533577 & 3.17080531122411 & -0.223899932731448 \tabularnewline
56 & 99 & 104.546454530491 & 0.0971454867296635 & -5.5464545304908 & 0.269819606800327 \tabularnewline
57 & 103 & 112.131042314681 & 5.51737273017742 & -9.13104231468078 & 0.900777278002992 \tabularnewline
58 & 131 & 121.366370110087 & 8.20885579141082 & 9.63362988991344 & 0.447291391219030 \tabularnewline
59 & 137 & 130.380943106825 & 8.7919556953649 & 6.61905689317495 & 0.0969124558026854 \tabularnewline
60 & 135 & 134.498138531539 & 5.40894923658199 & 0.50186146846088 & -0.562506573840623 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62022&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]149[/C][C]149[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]139[/C][C]140.642680576515[/C][C]-8.49307815026848[/C][C]-1.64268057651493[/C][C]-1.53765266125616[/C][/ROW]
[ROW][C]3[/C][C]135[/C][C]133.500074333951[/C][C]-7.48479596270134[/C][C]1.49992566604908[/C][C]0.179646588980101[/C][/ROW]
[ROW][C]4[/C][C]130[/C][C]129.283276243248[/C][C]-4.92518488309534[/C][C]0.716723756752483[/C][C]0.429256154743507[/C][/ROW]
[ROW][C]5[/C][C]127[/C][C]126.651382555049[/C][C]-3.16631001843137[/C][C]0.348617444950974[/C][C]0.291079352906523[/C][/ROW]
[ROW][C]6[/C][C]122[/C][C]122.311849936321[/C][C]-4.06655159085912[/C][C]-0.311849936321065[/C][C]-0.149789544700316[/C][/ROW]
[ROW][C]7[/C][C]117[/C][C]117.091867312654[/C][C]-4.95319531857533[/C][C]-0.0918673126539773[/C][C]-0.147350847235532[/C][/ROW]
[ROW][C]8[/C][C]112[/C][C]111.898546268554[/C][C]-5.13780074827845[/C][C]0.101453731446092[/C][C]-0.030678983366815[/C][/ROW]
[ROW][C]9[/C][C]113[/C][C]111.824958833281[/C][C]-1.24511431262927[/C][C]1.17504116671907[/C][C]0.646919143568898[/C][/ROW]
[ROW][C]10[/C][C]149[/C][C]142.446719252551[/C][C]23.2474238085275[/C][C]6.55328074744891[/C][C]4.07033839229334[/C][/ROW]
[ROW][C]11[/C][C]157[/C][C]160.104218695606[/C][C]18.9510630761748[/C][C]-3.10421869560569[/C][C]-0.713999477056228[/C][/ROW]
[ROW][C]12[/C][C]157[/C][C]160.519666867978[/C][C]4.70448911649624[/C][C]-3.51966686797777[/C][C]-2.36759559411703[/C][/ROW]
[ROW][C]13[/C][C]147[/C][C]149.423130932804[/C][C]-7.41655389196125[/C][C]-2.42313093280427[/C][C]-2.01924575973931[/C][/ROW]
[ROW][C]14[/C][C]137[/C][C]138.651364472095[/C][C]-9.99854288348633[/C][C]-1.65136447209467[/C][C]-0.435433688410373[/C][/ROW]
[ROW][C]15[/C][C]132[/C][C]130.534993534140[/C][C]-8.61043184634518[/C][C]1.46500646586046[/C][C]0.230491923985758[/C][/ROW]
[ROW][C]16[/C][C]125[/C][C]124.486759690037[/C][C]-6.7092464825685[/C][C]0.513240309963174[/C][C]0.320020400182524[/C][/ROW]
[ROW][C]17[/C][C]123[/C][C]121.875694581931[/C][C]-3.66906382684766[/C][C]1.12430541806935[/C][C]0.503847616814496[/C][/ROW]
[ROW][C]18[/C][C]117[/C][C]117.154745667397[/C][C]-4.44511553052076[/C][C]-0.154745667396741[/C][C]-0.128904542228427[/C][/ROW]
[ROW][C]19[/C][C]114[/C][C]112.241921807902[/C][C]-4.79063040828097[/C][C]1.75807819209767[/C][C]-0.0574491052755323[/C][/ROW]
[ROW][C]20[/C][C]111[/C][C]109.914705067346[/C][C]-2.96936429543693[/C][C]1.08529493265401[/C][C]0.302662382626359[/C][/ROW]
[ROW][C]21[/C][C]112[/C][C]116.976242299824[/C][C]4.44713746808584[/C][C]-4.97624229982413[/C][C]1.23251262198667[/C][/ROW]
[ROW][C]22[/C][C]144[/C][C]135.039765069888[/C][C]14.5141101045557[/C][C]8.96023493011166[/C][C]1.67305690975416[/C][/ROW]
[ROW][C]23[/C][C]150[/C][C]150.237117393657[/C][C]15.0191564854999[/C][C]-0.237117393656592[/C][C]0.0839323489125045[/C][/ROW]
[ROW][C]24[/C][C]149[/C][C]152.117735497448[/C][C]5.3147578699753[/C][C]-3.11773549744823[/C][C]-1.61305750864612[/C][/ROW]
[ROW][C]25[/C][C]134[/C][C]139.248256960133[/C][C]-8.11523536092009[/C][C]-5.24825696013339[/C][C]-2.2378636708975[/C][/ROW]
[ROW][C]26[/C][C]123[/C][C]125.537574114383[/C][C]-12.2528618936276[/C][C]-2.53757411438341[/C][C]-0.68856124472217[/C][/ROW]
[ROW][C]27[/C][C]116[/C][C]114.622774908123[/C][C]-11.2751993877573[/C][C]1.37722509187697[/C][C]0.162261823446907[/C][/ROW]
[ROW][C]28[/C][C]117[/C][C]114.934190035589[/C][C]-2.80887045221621[/C][C]2.06580996441128[/C][C]1.41518045008513[/C][/ROW]
[ROW][C]29[/C][C]111[/C][C]110.122835175705[/C][C]-4.27666918402211[/C][C]0.877164824294993[/C][C]-0.24393608941861[/C][/ROW]
[ROW][C]30[/C][C]105[/C][C]104.312497403644[/C][C]-5.39670563159406[/C][C]0.687502596355746[/C][C]-0.185893147778276[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]99.421513904895[/C][C]-5.02752634984793[/C][C]2.57848609510502[/C][C]0.0613714544182754[/C][/ROW]
[ROW][C]32[/C][C]95[/C][C]95.7214539580626[/C][C]-4.05752352454881[/C][C]-0.721453958062608[/C][C]0.161219869519527[/C][/ROW]
[ROW][C]33[/C][C]93[/C][C]100.414362189554[/C][C]2.33841008833955[/C][C]-7.4143621895544[/C][C]1.06288312950126[/C][/ROW]
[ROW][C]34[/C][C]124[/C][C]114.355181575426[/C][C]10.8185210445949[/C][C]9.64481842457443[/C][C]1.40936625470719[/C][/ROW]
[ROW][C]35[/C][C]130[/C][C]127.555301236208[/C][C]12.5585016763959[/C][C]2.44469876379205[/C][C]0.289158446775954[/C][/ROW]
[ROW][C]36[/C][C]124[/C][C]125.734579232927[/C][C]2.06014739807857[/C][C]-1.73457923292679[/C][C]-1.74557285435788[/C][/ROW]
[ROW][C]37[/C][C]115[/C][C]119.776311469382[/C][C]-3.79779900091381[/C][C]-4.77631146938185[/C][C]-0.975257794511677[/C][/ROW]
[ROW][C]38[/C][C]106[/C][C]110.058781352843[/C][C]-8.12081463749695[/C][C]-4.05878135284279[/C][C]-0.718291414599892[/C][/ROW]
[ROW][C]39[/C][C]105[/C][C]105.904108687892[/C][C]-5.24063090231314[/C][C]-0.904108687892376[/C][C]0.478301449981572[/C][/ROW]
[ROW][C]40[/C][C]105[/C][C]102.157704601022[/C][C]-4.15587498894287[/C][C]2.84229539897826[/C][C]0.180798076324648[/C][/ROW]
[ROW][C]41[/C][C]101[/C][C]99.0789740558476[/C][C]-3.37194082832595[/C][C]1.92102594415243[/C][C]0.130375755907062[/C][/ROW]
[ROW][C]42[/C][C]95[/C][C]93.937052234357[/C][C]-4.65785530229202[/C][C]1.06294776564306[/C][C]-0.213476291706792[/C][/ROW]
[ROW][C]43[/C][C]93[/C][C]89.62050641417[/C][C]-4.41014266812671[/C][C]3.37949358583002[/C][C]0.0411638873681076[/C][/ROW]
[ROW][C]44[/C][C]84[/C][C]86.587003679845[/C][C]-3.41035569721649[/C][C]-2.58700367984496[/C][C]0.166174772278906[/C][/ROW]
[ROW][C]45[/C][C]87[/C][C]95.6373444638323[/C][C]5.64313017181864[/C][C]-8.63734446383228[/C][C]1.50454521176173[/C][/ROW]
[ROW][C]46[/C][C]116[/C][C]106.975877331137[/C][C]9.78108234959173[/C][C]9.02412266886324[/C][C]0.687688851854283[/C][/ROW]
[ROW][C]47[/C][C]120[/C][C]114.692232025580[/C][C]8.28158483850222[/C][C]5.3077679744197[/C][C]-0.249204179611138[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.675976326169[/C][C]4.43523333482768[/C][C]-0.675976326169283[/C][C]-0.639585710530923[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]113.874904078211[/C][C]-1.54813087197722[/C][C]-4.87490407821138[/C][C]-0.995393229057181[/C][/ROW]
[ROW][C]50[/C][C]105[/C][C]110.472914701071[/C][C]-2.89384823860998[/C][C]-5.47291470107083[/C][C]-0.223548606989907[/C][/ROW]
[ROW][C]51[/C][C]107[/C][C]108.300245479617[/C][C]-2.37204396861045[/C][C]-1.30024547961667[/C][C]0.0866803898783519[/C][/ROW]
[ROW][C]52[/C][C]109[/C][C]106.305202687956[/C][C]-2.09933141825618[/C][C]2.69479731204412[/C][C]0.0453975352294533[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]105.741816728978[/C][C]-0.98629899255522[/C][C]3.25818327102178[/C][C]0.185116680497438[/C][/ROW]
[ROW][C]54[/C][C]108[/C][C]105.870806544331[/C][C]-0.178682118569657[/C][C]2.12919345566875[/C][C]0.134123914864716[/C][/ROW]
[ROW][C]55[/C][C]107[/C][C]103.829194688776[/C][C]-1.52632852533577[/C][C]3.17080531122411[/C][C]-0.223899932731448[/C][/ROW]
[ROW][C]56[/C][C]99[/C][C]104.546454530491[/C][C]0.0971454867296635[/C][C]-5.5464545304908[/C][C]0.269819606800327[/C][/ROW]
[ROW][C]57[/C][C]103[/C][C]112.131042314681[/C][C]5.51737273017742[/C][C]-9.13104231468078[/C][C]0.900777278002992[/C][/ROW]
[ROW][C]58[/C][C]131[/C][C]121.366370110087[/C][C]8.20885579141082[/C][C]9.63362988991344[/C][C]0.447291391219030[/C][/ROW]
[ROW][C]59[/C][C]137[/C][C]130.380943106825[/C][C]8.7919556953649[/C][C]6.61905689317495[/C][C]0.0969124558026854[/C][/ROW]
[ROW][C]60[/C][C]135[/C][C]134.498138531539[/C][C]5.40894923658199[/C][C]0.50186146846088[/C][C]-0.562506573840623[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62022&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62022&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
1149149000
2139140.642680576515-8.49307815026848-1.64268057651493-1.53765266125616
3135133.500074333951-7.484795962701341.499925666049080.179646588980101
4130129.283276243248-4.925184883095340.7167237567524830.429256154743507
5127126.651382555049-3.166310018431370.3486174449509740.291079352906523
6122122.311849936321-4.06655159085912-0.311849936321065-0.149789544700316
7117117.091867312654-4.95319531857533-0.0918673126539773-0.147350847235532
8112111.898546268554-5.137800748278450.101453731446092-0.030678983366815
9113111.824958833281-1.245114312629271.175041166719070.646919143568898
10149142.44671925255123.24742380852756.553280747448914.07033839229334
11157160.10421869560618.9510630761748-3.10421869560569-0.713999477056228
12157160.5196668679784.70448911649624-3.51966686797777-2.36759559411703
13147149.423130932804-7.41655389196125-2.42313093280427-2.01924575973931
14137138.651364472095-9.99854288348633-1.65136447209467-0.435433688410373
15132130.534993534140-8.610431846345181.465006465860460.230491923985758
16125124.486759690037-6.70924648256850.5132403099631740.320020400182524
17123121.875694581931-3.669063826847661.124305418069350.503847616814496
18117117.154745667397-4.44511553052076-0.154745667396741-0.128904542228427
19114112.241921807902-4.790630408280971.75807819209767-0.0574491052755323
20111109.914705067346-2.969364295436931.085294932654010.302662382626359
21112116.9762422998244.44713746808584-4.976242299824131.23251262198667
22144135.03976506988814.51411010455578.960234930111661.67305690975416
23150150.23711739365715.0191564854999-0.2371173936565920.0839323489125045
24149152.1177354974485.3147578699753-3.11773549744823-1.61305750864612
25134139.248256960133-8.11523536092009-5.24825696013339-2.2378636708975
26123125.537574114383-12.2528618936276-2.53757411438341-0.68856124472217
27116114.622774908123-11.27519938775731.377225091876970.162261823446907
28117114.934190035589-2.808870452216212.065809964411281.41518045008513
29111110.122835175705-4.276669184022110.877164824294993-0.24393608941861
30105104.312497403644-5.396705631594060.687502596355746-0.185893147778276
3110299.421513904895-5.027526349847932.578486095105020.0613714544182754
329595.7214539580626-4.05752352454881-0.7214539580626080.161219869519527
3393100.4143621895542.33841008833955-7.41436218955441.06288312950126
34124114.35518157542610.81852104459499.644818424574431.40936625470719
35130127.55530123620812.55850167639592.444698763792050.289158446775954
36124125.7345792329272.06014739807857-1.73457923292679-1.74557285435788
37115119.776311469382-3.79779900091381-4.77631146938185-0.975257794511677
38106110.058781352843-8.12081463749695-4.05878135284279-0.718291414599892
39105105.904108687892-5.24063090231314-0.9041086878923760.478301449981572
40105102.157704601022-4.155874988942872.842295398978260.180798076324648
4110199.0789740558476-3.371940828325951.921025944152430.130375755907062
429593.937052234357-4.657855302292021.06294776564306-0.213476291706792
439389.62050641417-4.410142668126713.379493585830020.0411638873681076
448486.587003679845-3.41035569721649-2.587003679844960.166174772278906
458795.63734446383235.64313017181864-8.637344463832281.50454521176173
46116106.9758773311379.781082349591739.024122668863240.687688851854283
47120114.6922320255808.281584838502225.3077679744197-0.249204179611138
48117117.6759763261694.43523333482768-0.675976326169283-0.639585710530923
49109113.874904078211-1.54813087197722-4.87490407821138-0.995393229057181
50105110.472914701071-2.89384823860998-5.47291470107083-0.223548606989907
51107108.300245479617-2.37204396861045-1.300245479616670.0866803898783519
52109106.305202687956-2.099331418256182.694797312044120.0453975352294533
53109105.741816728978-0.986298992555223.258183271021780.185116680497438
54108105.870806544331-0.1786821185696572.129193455668750.134123914864716
55107103.829194688776-1.526328525335773.17080531122411-0.223899932731448
5699104.5464545304910.0971454867296635-5.54645453049080.269819606800327
57103112.1310423146815.51737273017742-9.131042314680780.900777278002992
58131121.3663701100878.208855791410829.633629889913440.447291391219030
59137130.3809431068258.79195569536496.619056893174950.0969124558026854
60135134.4981385315395.408949236581990.50186146846088-0.562506573840623



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
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')