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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 computationThu, 03 Dec 2009 10:48:33 -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/03/t1259862545padc3lf1dbrnoye.htm/, Retrieved Thu, 28 Mar 2024 10:48:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62985, Retrieved Thu, 28 Mar 2024 10:48:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
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]
- R  D      [Structural Time Series Models] [] [2009-12-03 17:48:33] [0f1f1142419956a95ff6f880845f2408] [Current]
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Dataseries X:
115.47
103.34
102.60
100.69
105.67
123.61
113.08
106.46
123.38
109.87
95.74
123.06
123.39
120.28
115.33
110.4
114.49
132.03
123.16
118.82
128.32
112.24
104.53
132.57
122.52
131.8
124.55
120.96
122.6
145.52
118.57
134.25
136.7
121.37
111.63
134.42
137.65
137.86
119.77
130.69
128.28
147.45
128.42
136.9
143.95
135.64
122.48
136.83
153.04
142.71
123.46
144.37
146.15
147.61
158.51
147.4
165.05
154.64
126.2
157.36
154.15
123.21
113.07
110.45
113.57
122.44
114.93
111.85
126.04
121.34




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=62985&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=62985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62985&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
1115.47115.47000
2103.34107.327958564947-0.325179553534179-3.98795856494674-1.31384091634919
3102.6103.335702428143-0.531108494473806-0.735702428143354-0.626994004952887
4100.69101.477800279429-0.582071727486263-0.78780027942943-0.277578314861533
5105.67103.358480085691-0.5305688204010642.311519914308920.54103236586135
6123.61114.176046780436-0.3868933491131949.433953219564432.50565761915007
7113.08115.999701237838-0.363708685345868-2.91970123783790.487784846962114
8106.46111.601457500004-0.406517997994906-5.14145750000427-0.889343162668956
9123.38116.778244224018-0.3446805518181836.60175577598171.22986577501193
10109.87114.426950837446-0.367363045269992-4.55695083744614-0.441883426953570
1195.74104.474675073961-0.47567205218093-8.73467507396095-2.11065283575416
12123.06111.968804389118-0.38652258447178411.09119561088231.75506234544260
13123.39115.761408280578-0.4981893593515777.628591719422420.987641954012402
14120.28119.227540796823-0.5061999676160211.052459203177070.884476715828104
15115.33118.010039970474-0.520238922243998-2.68003997047408-0.144853501871608
16110.4115.656618777612-0.564656549838987-5.25661877761206-0.381198650923356
17114.49116.273380986845-0.541489489472847-1.783380986844870.255282991059217
18132.03119.676897405235-0.48740034234750812.35310259476490.866253538290777
19123.16122.752227021661-0.4518219744451560.4077729783390690.785280587709519
20118.82124.872475979880-0.429886337941507-6.052475979880470.56717228913698
21128.32122.470496177201-0.4461615345243735.84950382279913-0.434773043057902
22112.24117.393782083926-0.482434263437948-5.153782083926-1.02038976027460
23104.53116.47949329434-0.48496253223978-11.9494932943399-0.095073714820492
24132.57119.789924425355-0.47978343909462512.78007557464530.83685261990826
25122.52118.619278274337-0.4772479438178943.90072172566312-0.154085010070662
26131.8123.770050157323-0.4681738367146768.029949842676971.23641175510898
27124.55125.958475991532-0.440029360964334-1.408475991532180.565287481436237
28120.96127.091100751783-0.415541955356167-6.13110075178260.333279162886685
29122.6127.257181742023-0.406460463823326-4.657181742022720.125298946680323
30145.52131.126861504700-0.35045639865690314.39313849530040.934480534758247
31118.57125.780747762463-0.402289786662204-7.210747762463-1.09901390072755
32134.25130.762629209823-0.35688258826473.487370790176851.18700408177217
33136.7130.254959961601-0.3579510478818376.44504003839928-0.0332481632645142
34121.37128.303595820502-0.366972286446859-6.93359582050245-0.351062524363421
35111.63126.473888893507-0.372345045912592-14.8438888935066-0.32201849472247
36134.42124.058720669928-0.37541632784131810.3612793300721-0.45012444939853
37137.65129.866061576178-0.3703017110646137.78393842382191.36299362686532
38137.86131.537550056016-0.3632098617097596.322449943983640.44618146357317
39119.77127.165199857200-0.394911294055877-7.39519985720027-0.863858541840704
40130.69131.518977027508-0.341231395126340-0.8289770275082191.01856100287634
41128.28133.561342767946-0.311735769256841-5.281342767946250.514873768675007
42147.45132.764725227951-0.31732575714190114.6852747720485-0.105771942779876
43128.42135.154076773928-0.290757182740712-6.73407677392850.594353827488041
44136.9134.33399253471-0.2949985298399112.56600746529012-0.116568172349279
45143.95134.995478364977-0.2889259611929678.954521635023310.210740649050774
46135.64138.190665341355-0.272397667199056-2.550665341355180.767201469201027
47122.48138.254498853333-0.271316509184752-15.77449885333320.0739973500996006
48136.83134.148506177838-0.2796685828180512.68149382216241-0.843764540139479
49153.04139.038125773193-0.26751658154099414.00187422680661.13538098247071
50142.71137.994579852830-0.2706964749693414.71542014717033-0.169431413360703
51123.46135.677102722450-0.284477601915333-12.2171027224504-0.443468572362297
52144.37140.449551607447-0.2388819779448253.920448392552881.09224676117646
53146.15146.527074125742-0.175029283663729-0.3770741257423171.36894574101198
54147.61141.454574985447-0.2236194892936936.15542501455256-1.06825724005665
55158.51151.972920945210-0.1284743382267036.537079054790462.35575479203730
56147.4150.672578421736-0.137142283222423-3.27257842173574-0.257732805261729
57165.05153.743011841665-0.11835259890356911.30698815833530.706005605617627
58154.64155.669447008316-0.109317000949693-1.029447008316320.449957575074511
59126.2148.838105752518-0.131296583785884-22.6381057525176-1.47852021154116
60157.36151.710423859536-0.123164876041085.64957614046420.66017908858174
61154.15146.374999541500-0.138966201662437.77500045849982-1.14322064543085
62123.21132.103879576503-0.199355267159524-8.8938795765034-3.08655763456848
63113.07128.24269740184-0.221396367090524-15.1726974018401-0.79601403941959
64110.45118.437305427123-0.294314662373635-7.98730542712287-2.07908279108819
65113.57114.372258390693-0.326413110977238-0.802258390692765-0.819523877149432
66122.44117.268916504714-0.2988394220085915.171083495286050.703454687289402
67114.93113.484551149028-0.3262867800233211.44544885097167-0.763837738536676
68111.85114.51460613273-0.317123253986082-2.664606132730060.297982879869803
69126.04114.776303062257-0.31394856380701011.26369693774310.127282440685429
70121.34116.877827379219-0.3035571408383294.462172620781380.531164098880861

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 115.47 & 115.47 & 0 & 0 & 0 \tabularnewline
2 & 103.34 & 107.327958564947 & -0.325179553534179 & -3.98795856494674 & -1.31384091634919 \tabularnewline
3 & 102.6 & 103.335702428143 & -0.531108494473806 & -0.735702428143354 & -0.626994004952887 \tabularnewline
4 & 100.69 & 101.477800279429 & -0.582071727486263 & -0.78780027942943 & -0.277578314861533 \tabularnewline
5 & 105.67 & 103.358480085691 & -0.530568820401064 & 2.31151991430892 & 0.54103236586135 \tabularnewline
6 & 123.61 & 114.176046780436 & -0.386893349113194 & 9.43395321956443 & 2.50565761915007 \tabularnewline
7 & 113.08 & 115.999701237838 & -0.363708685345868 & -2.9197012378379 & 0.487784846962114 \tabularnewline
8 & 106.46 & 111.601457500004 & -0.406517997994906 & -5.14145750000427 & -0.889343162668956 \tabularnewline
9 & 123.38 & 116.778244224018 & -0.344680551818183 & 6.6017557759817 & 1.22986577501193 \tabularnewline
10 & 109.87 & 114.426950837446 & -0.367363045269992 & -4.55695083744614 & -0.441883426953570 \tabularnewline
11 & 95.74 & 104.474675073961 & -0.47567205218093 & -8.73467507396095 & -2.11065283575416 \tabularnewline
12 & 123.06 & 111.968804389118 & -0.386522584471784 & 11.0911956108823 & 1.75506234544260 \tabularnewline
13 & 123.39 & 115.761408280578 & -0.498189359351577 & 7.62859171942242 & 0.987641954012402 \tabularnewline
14 & 120.28 & 119.227540796823 & -0.506199967616021 & 1.05245920317707 & 0.884476715828104 \tabularnewline
15 & 115.33 & 118.010039970474 & -0.520238922243998 & -2.68003997047408 & -0.144853501871608 \tabularnewline
16 & 110.4 & 115.656618777612 & -0.564656549838987 & -5.25661877761206 & -0.381198650923356 \tabularnewline
17 & 114.49 & 116.273380986845 & -0.541489489472847 & -1.78338098684487 & 0.255282991059217 \tabularnewline
18 & 132.03 & 119.676897405235 & -0.487400342347508 & 12.3531025947649 & 0.866253538290777 \tabularnewline
19 & 123.16 & 122.752227021661 & -0.451821974445156 & 0.407772978339069 & 0.785280587709519 \tabularnewline
20 & 118.82 & 124.872475979880 & -0.429886337941507 & -6.05247597988047 & 0.56717228913698 \tabularnewline
21 & 128.32 & 122.470496177201 & -0.446161534524373 & 5.84950382279913 & -0.434773043057902 \tabularnewline
22 & 112.24 & 117.393782083926 & -0.482434263437948 & -5.153782083926 & -1.02038976027460 \tabularnewline
23 & 104.53 & 116.47949329434 & -0.48496253223978 & -11.9494932943399 & -0.095073714820492 \tabularnewline
24 & 132.57 & 119.789924425355 & -0.479783439094625 & 12.7800755746453 & 0.83685261990826 \tabularnewline
25 & 122.52 & 118.619278274337 & -0.477247943817894 & 3.90072172566312 & -0.154085010070662 \tabularnewline
26 & 131.8 & 123.770050157323 & -0.468173836714676 & 8.02994984267697 & 1.23641175510898 \tabularnewline
27 & 124.55 & 125.958475991532 & -0.440029360964334 & -1.40847599153218 & 0.565287481436237 \tabularnewline
28 & 120.96 & 127.091100751783 & -0.415541955356167 & -6.1311007517826 & 0.333279162886685 \tabularnewline
29 & 122.6 & 127.257181742023 & -0.406460463823326 & -4.65718174202272 & 0.125298946680323 \tabularnewline
30 & 145.52 & 131.126861504700 & -0.350456398656903 & 14.3931384953004 & 0.934480534758247 \tabularnewline
31 & 118.57 & 125.780747762463 & -0.402289786662204 & -7.210747762463 & -1.09901390072755 \tabularnewline
32 & 134.25 & 130.762629209823 & -0.3568825882647 & 3.48737079017685 & 1.18700408177217 \tabularnewline
33 & 136.7 & 130.254959961601 & -0.357951047881837 & 6.44504003839928 & -0.0332481632645142 \tabularnewline
34 & 121.37 & 128.303595820502 & -0.366972286446859 & -6.93359582050245 & -0.351062524363421 \tabularnewline
35 & 111.63 & 126.473888893507 & -0.372345045912592 & -14.8438888935066 & -0.32201849472247 \tabularnewline
36 & 134.42 & 124.058720669928 & -0.375416327841318 & 10.3612793300721 & -0.45012444939853 \tabularnewline
37 & 137.65 & 129.866061576178 & -0.370301711064613 & 7.7839384238219 & 1.36299362686532 \tabularnewline
38 & 137.86 & 131.537550056016 & -0.363209861709759 & 6.32244994398364 & 0.44618146357317 \tabularnewline
39 & 119.77 & 127.165199857200 & -0.394911294055877 & -7.39519985720027 & -0.863858541840704 \tabularnewline
40 & 130.69 & 131.518977027508 & -0.341231395126340 & -0.828977027508219 & 1.01856100287634 \tabularnewline
41 & 128.28 & 133.561342767946 & -0.311735769256841 & -5.28134276794625 & 0.514873768675007 \tabularnewline
42 & 147.45 & 132.764725227951 & -0.317325757141901 & 14.6852747720485 & -0.105771942779876 \tabularnewline
43 & 128.42 & 135.154076773928 & -0.290757182740712 & -6.7340767739285 & 0.594353827488041 \tabularnewline
44 & 136.9 & 134.33399253471 & -0.294998529839911 & 2.56600746529012 & -0.116568172349279 \tabularnewline
45 & 143.95 & 134.995478364977 & -0.288925961192967 & 8.95452163502331 & 0.210740649050774 \tabularnewline
46 & 135.64 & 138.190665341355 & -0.272397667199056 & -2.55066534135518 & 0.767201469201027 \tabularnewline
47 & 122.48 & 138.254498853333 & -0.271316509184752 & -15.7744988533332 & 0.0739973500996006 \tabularnewline
48 & 136.83 & 134.148506177838 & -0.279668582818051 & 2.68149382216241 & -0.843764540139479 \tabularnewline
49 & 153.04 & 139.038125773193 & -0.267516581540994 & 14.0018742268066 & 1.13538098247071 \tabularnewline
50 & 142.71 & 137.994579852830 & -0.270696474969341 & 4.71542014717033 & -0.169431413360703 \tabularnewline
51 & 123.46 & 135.677102722450 & -0.284477601915333 & -12.2171027224504 & -0.443468572362297 \tabularnewline
52 & 144.37 & 140.449551607447 & -0.238881977944825 & 3.92044839255288 & 1.09224676117646 \tabularnewline
53 & 146.15 & 146.527074125742 & -0.175029283663729 & -0.377074125742317 & 1.36894574101198 \tabularnewline
54 & 147.61 & 141.454574985447 & -0.223619489293693 & 6.15542501455256 & -1.06825724005665 \tabularnewline
55 & 158.51 & 151.972920945210 & -0.128474338226703 & 6.53707905479046 & 2.35575479203730 \tabularnewline
56 & 147.4 & 150.672578421736 & -0.137142283222423 & -3.27257842173574 & -0.257732805261729 \tabularnewline
57 & 165.05 & 153.743011841665 & -0.118352598903569 & 11.3069881583353 & 0.706005605617627 \tabularnewline
58 & 154.64 & 155.669447008316 & -0.109317000949693 & -1.02944700831632 & 0.449957575074511 \tabularnewline
59 & 126.2 & 148.838105752518 & -0.131296583785884 & -22.6381057525176 & -1.47852021154116 \tabularnewline
60 & 157.36 & 151.710423859536 & -0.12316487604108 & 5.6495761404642 & 0.66017908858174 \tabularnewline
61 & 154.15 & 146.374999541500 & -0.13896620166243 & 7.77500045849982 & -1.14322064543085 \tabularnewline
62 & 123.21 & 132.103879576503 & -0.199355267159524 & -8.8938795765034 & -3.08655763456848 \tabularnewline
63 & 113.07 & 128.24269740184 & -0.221396367090524 & -15.1726974018401 & -0.79601403941959 \tabularnewline
64 & 110.45 & 118.437305427123 & -0.294314662373635 & -7.98730542712287 & -2.07908279108819 \tabularnewline
65 & 113.57 & 114.372258390693 & -0.326413110977238 & -0.802258390692765 & -0.819523877149432 \tabularnewline
66 & 122.44 & 117.268916504714 & -0.298839422008591 & 5.17108349528605 & 0.703454687289402 \tabularnewline
67 & 114.93 & 113.484551149028 & -0.326286780023321 & 1.44544885097167 & -0.763837738536676 \tabularnewline
68 & 111.85 & 114.51460613273 & -0.317123253986082 & -2.66460613273006 & 0.297982879869803 \tabularnewline
69 & 126.04 & 114.776303062257 & -0.313948563807010 & 11.2636969377431 & 0.127282440685429 \tabularnewline
70 & 121.34 & 116.877827379219 & -0.303557140838329 & 4.46217262078138 & 0.531164098880861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62985&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]115.47[/C][C]115.47[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]103.34[/C][C]107.327958564947[/C][C]-0.325179553534179[/C][C]-3.98795856494674[/C][C]-1.31384091634919[/C][/ROW]
[ROW][C]3[/C][C]102.6[/C][C]103.335702428143[/C][C]-0.531108494473806[/C][C]-0.735702428143354[/C][C]-0.626994004952887[/C][/ROW]
[ROW][C]4[/C][C]100.69[/C][C]101.477800279429[/C][C]-0.582071727486263[/C][C]-0.78780027942943[/C][C]-0.277578314861533[/C][/ROW]
[ROW][C]5[/C][C]105.67[/C][C]103.358480085691[/C][C]-0.530568820401064[/C][C]2.31151991430892[/C][C]0.54103236586135[/C][/ROW]
[ROW][C]6[/C][C]123.61[/C][C]114.176046780436[/C][C]-0.386893349113194[/C][C]9.43395321956443[/C][C]2.50565761915007[/C][/ROW]
[ROW][C]7[/C][C]113.08[/C][C]115.999701237838[/C][C]-0.363708685345868[/C][C]-2.9197012378379[/C][C]0.487784846962114[/C][/ROW]
[ROW][C]8[/C][C]106.46[/C][C]111.601457500004[/C][C]-0.406517997994906[/C][C]-5.14145750000427[/C][C]-0.889343162668956[/C][/ROW]
[ROW][C]9[/C][C]123.38[/C][C]116.778244224018[/C][C]-0.344680551818183[/C][C]6.6017557759817[/C][C]1.22986577501193[/C][/ROW]
[ROW][C]10[/C][C]109.87[/C][C]114.426950837446[/C][C]-0.367363045269992[/C][C]-4.55695083744614[/C][C]-0.441883426953570[/C][/ROW]
[ROW][C]11[/C][C]95.74[/C][C]104.474675073961[/C][C]-0.47567205218093[/C][C]-8.73467507396095[/C][C]-2.11065283575416[/C][/ROW]
[ROW][C]12[/C][C]123.06[/C][C]111.968804389118[/C][C]-0.386522584471784[/C][C]11.0911956108823[/C][C]1.75506234544260[/C][/ROW]
[ROW][C]13[/C][C]123.39[/C][C]115.761408280578[/C][C]-0.498189359351577[/C][C]7.62859171942242[/C][C]0.987641954012402[/C][/ROW]
[ROW][C]14[/C][C]120.28[/C][C]119.227540796823[/C][C]-0.506199967616021[/C][C]1.05245920317707[/C][C]0.884476715828104[/C][/ROW]
[ROW][C]15[/C][C]115.33[/C][C]118.010039970474[/C][C]-0.520238922243998[/C][C]-2.68003997047408[/C][C]-0.144853501871608[/C][/ROW]
[ROW][C]16[/C][C]110.4[/C][C]115.656618777612[/C][C]-0.564656549838987[/C][C]-5.25661877761206[/C][C]-0.381198650923356[/C][/ROW]
[ROW][C]17[/C][C]114.49[/C][C]116.273380986845[/C][C]-0.541489489472847[/C][C]-1.78338098684487[/C][C]0.255282991059217[/C][/ROW]
[ROW][C]18[/C][C]132.03[/C][C]119.676897405235[/C][C]-0.487400342347508[/C][C]12.3531025947649[/C][C]0.866253538290777[/C][/ROW]
[ROW][C]19[/C][C]123.16[/C][C]122.752227021661[/C][C]-0.451821974445156[/C][C]0.407772978339069[/C][C]0.785280587709519[/C][/ROW]
[ROW][C]20[/C][C]118.82[/C][C]124.872475979880[/C][C]-0.429886337941507[/C][C]-6.05247597988047[/C][C]0.56717228913698[/C][/ROW]
[ROW][C]21[/C][C]128.32[/C][C]122.470496177201[/C][C]-0.446161534524373[/C][C]5.84950382279913[/C][C]-0.434773043057902[/C][/ROW]
[ROW][C]22[/C][C]112.24[/C][C]117.393782083926[/C][C]-0.482434263437948[/C][C]-5.153782083926[/C][C]-1.02038976027460[/C][/ROW]
[ROW][C]23[/C][C]104.53[/C][C]116.47949329434[/C][C]-0.48496253223978[/C][C]-11.9494932943399[/C][C]-0.095073714820492[/C][/ROW]
[ROW][C]24[/C][C]132.57[/C][C]119.789924425355[/C][C]-0.479783439094625[/C][C]12.7800755746453[/C][C]0.83685261990826[/C][/ROW]
[ROW][C]25[/C][C]122.52[/C][C]118.619278274337[/C][C]-0.477247943817894[/C][C]3.90072172566312[/C][C]-0.154085010070662[/C][/ROW]
[ROW][C]26[/C][C]131.8[/C][C]123.770050157323[/C][C]-0.468173836714676[/C][C]8.02994984267697[/C][C]1.23641175510898[/C][/ROW]
[ROW][C]27[/C][C]124.55[/C][C]125.958475991532[/C][C]-0.440029360964334[/C][C]-1.40847599153218[/C][C]0.565287481436237[/C][/ROW]
[ROW][C]28[/C][C]120.96[/C][C]127.091100751783[/C][C]-0.415541955356167[/C][C]-6.1311007517826[/C][C]0.333279162886685[/C][/ROW]
[ROW][C]29[/C][C]122.6[/C][C]127.257181742023[/C][C]-0.406460463823326[/C][C]-4.65718174202272[/C][C]0.125298946680323[/C][/ROW]
[ROW][C]30[/C][C]145.52[/C][C]131.126861504700[/C][C]-0.350456398656903[/C][C]14.3931384953004[/C][C]0.934480534758247[/C][/ROW]
[ROW][C]31[/C][C]118.57[/C][C]125.780747762463[/C][C]-0.402289786662204[/C][C]-7.210747762463[/C][C]-1.09901390072755[/C][/ROW]
[ROW][C]32[/C][C]134.25[/C][C]130.762629209823[/C][C]-0.3568825882647[/C][C]3.48737079017685[/C][C]1.18700408177217[/C][/ROW]
[ROW][C]33[/C][C]136.7[/C][C]130.254959961601[/C][C]-0.357951047881837[/C][C]6.44504003839928[/C][C]-0.0332481632645142[/C][/ROW]
[ROW][C]34[/C][C]121.37[/C][C]128.303595820502[/C][C]-0.366972286446859[/C][C]-6.93359582050245[/C][C]-0.351062524363421[/C][/ROW]
[ROW][C]35[/C][C]111.63[/C][C]126.473888893507[/C][C]-0.372345045912592[/C][C]-14.8438888935066[/C][C]-0.32201849472247[/C][/ROW]
[ROW][C]36[/C][C]134.42[/C][C]124.058720669928[/C][C]-0.375416327841318[/C][C]10.3612793300721[/C][C]-0.45012444939853[/C][/ROW]
[ROW][C]37[/C][C]137.65[/C][C]129.866061576178[/C][C]-0.370301711064613[/C][C]7.7839384238219[/C][C]1.36299362686532[/C][/ROW]
[ROW][C]38[/C][C]137.86[/C][C]131.537550056016[/C][C]-0.363209861709759[/C][C]6.32244994398364[/C][C]0.44618146357317[/C][/ROW]
[ROW][C]39[/C][C]119.77[/C][C]127.165199857200[/C][C]-0.394911294055877[/C][C]-7.39519985720027[/C][C]-0.863858541840704[/C][/ROW]
[ROW][C]40[/C][C]130.69[/C][C]131.518977027508[/C][C]-0.341231395126340[/C][C]-0.828977027508219[/C][C]1.01856100287634[/C][/ROW]
[ROW][C]41[/C][C]128.28[/C][C]133.561342767946[/C][C]-0.311735769256841[/C][C]-5.28134276794625[/C][C]0.514873768675007[/C][/ROW]
[ROW][C]42[/C][C]147.45[/C][C]132.764725227951[/C][C]-0.317325757141901[/C][C]14.6852747720485[/C][C]-0.105771942779876[/C][/ROW]
[ROW][C]43[/C][C]128.42[/C][C]135.154076773928[/C][C]-0.290757182740712[/C][C]-6.7340767739285[/C][C]0.594353827488041[/C][/ROW]
[ROW][C]44[/C][C]136.9[/C][C]134.33399253471[/C][C]-0.294998529839911[/C][C]2.56600746529012[/C][C]-0.116568172349279[/C][/ROW]
[ROW][C]45[/C][C]143.95[/C][C]134.995478364977[/C][C]-0.288925961192967[/C][C]8.95452163502331[/C][C]0.210740649050774[/C][/ROW]
[ROW][C]46[/C][C]135.64[/C][C]138.190665341355[/C][C]-0.272397667199056[/C][C]-2.55066534135518[/C][C]0.767201469201027[/C][/ROW]
[ROW][C]47[/C][C]122.48[/C][C]138.254498853333[/C][C]-0.271316509184752[/C][C]-15.7744988533332[/C][C]0.0739973500996006[/C][/ROW]
[ROW][C]48[/C][C]136.83[/C][C]134.148506177838[/C][C]-0.279668582818051[/C][C]2.68149382216241[/C][C]-0.843764540139479[/C][/ROW]
[ROW][C]49[/C][C]153.04[/C][C]139.038125773193[/C][C]-0.267516581540994[/C][C]14.0018742268066[/C][C]1.13538098247071[/C][/ROW]
[ROW][C]50[/C][C]142.71[/C][C]137.994579852830[/C][C]-0.270696474969341[/C][C]4.71542014717033[/C][C]-0.169431413360703[/C][/ROW]
[ROW][C]51[/C][C]123.46[/C][C]135.677102722450[/C][C]-0.284477601915333[/C][C]-12.2171027224504[/C][C]-0.443468572362297[/C][/ROW]
[ROW][C]52[/C][C]144.37[/C][C]140.449551607447[/C][C]-0.238881977944825[/C][C]3.92044839255288[/C][C]1.09224676117646[/C][/ROW]
[ROW][C]53[/C][C]146.15[/C][C]146.527074125742[/C][C]-0.175029283663729[/C][C]-0.377074125742317[/C][C]1.36894574101198[/C][/ROW]
[ROW][C]54[/C][C]147.61[/C][C]141.454574985447[/C][C]-0.223619489293693[/C][C]6.15542501455256[/C][C]-1.06825724005665[/C][/ROW]
[ROW][C]55[/C][C]158.51[/C][C]151.972920945210[/C][C]-0.128474338226703[/C][C]6.53707905479046[/C][C]2.35575479203730[/C][/ROW]
[ROW][C]56[/C][C]147.4[/C][C]150.672578421736[/C][C]-0.137142283222423[/C][C]-3.27257842173574[/C][C]-0.257732805261729[/C][/ROW]
[ROW][C]57[/C][C]165.05[/C][C]153.743011841665[/C][C]-0.118352598903569[/C][C]11.3069881583353[/C][C]0.706005605617627[/C][/ROW]
[ROW][C]58[/C][C]154.64[/C][C]155.669447008316[/C][C]-0.109317000949693[/C][C]-1.02944700831632[/C][C]0.449957575074511[/C][/ROW]
[ROW][C]59[/C][C]126.2[/C][C]148.838105752518[/C][C]-0.131296583785884[/C][C]-22.6381057525176[/C][C]-1.47852021154116[/C][/ROW]
[ROW][C]60[/C][C]157.36[/C][C]151.710423859536[/C][C]-0.12316487604108[/C][C]5.6495761404642[/C][C]0.66017908858174[/C][/ROW]
[ROW][C]61[/C][C]154.15[/C][C]146.374999541500[/C][C]-0.13896620166243[/C][C]7.77500045849982[/C][C]-1.14322064543085[/C][/ROW]
[ROW][C]62[/C][C]123.21[/C][C]132.103879576503[/C][C]-0.199355267159524[/C][C]-8.8938795765034[/C][C]-3.08655763456848[/C][/ROW]
[ROW][C]63[/C][C]113.07[/C][C]128.24269740184[/C][C]-0.221396367090524[/C][C]-15.1726974018401[/C][C]-0.79601403941959[/C][/ROW]
[ROW][C]64[/C][C]110.45[/C][C]118.437305427123[/C][C]-0.294314662373635[/C][C]-7.98730542712287[/C][C]-2.07908279108819[/C][/ROW]
[ROW][C]65[/C][C]113.57[/C][C]114.372258390693[/C][C]-0.326413110977238[/C][C]-0.802258390692765[/C][C]-0.819523877149432[/C][/ROW]
[ROW][C]66[/C][C]122.44[/C][C]117.268916504714[/C][C]-0.298839422008591[/C][C]5.17108349528605[/C][C]0.703454687289402[/C][/ROW]
[ROW][C]67[/C][C]114.93[/C][C]113.484551149028[/C][C]-0.326286780023321[/C][C]1.44544885097167[/C][C]-0.763837738536676[/C][/ROW]
[ROW][C]68[/C][C]111.85[/C][C]114.51460613273[/C][C]-0.317123253986082[/C][C]-2.66460613273006[/C][C]0.297982879869803[/C][/ROW]
[ROW][C]69[/C][C]126.04[/C][C]114.776303062257[/C][C]-0.313948563807010[/C][C]11.2636969377431[/C][C]0.127282440685429[/C][/ROW]
[ROW][C]70[/C][C]121.34[/C][C]116.877827379219[/C][C]-0.303557140838329[/C][C]4.46217262078138[/C][C]0.531164098880861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62985&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
1115.47115.47000
2103.34107.327958564947-0.325179553534179-3.98795856494674-1.31384091634919
3102.6103.335702428143-0.531108494473806-0.735702428143354-0.626994004952887
4100.69101.477800279429-0.582071727486263-0.78780027942943-0.277578314861533
5105.67103.358480085691-0.5305688204010642.311519914308920.54103236586135
6123.61114.176046780436-0.3868933491131949.433953219564432.50565761915007
7113.08115.999701237838-0.363708685345868-2.91970123783790.487784846962114
8106.46111.601457500004-0.406517997994906-5.14145750000427-0.889343162668956
9123.38116.778244224018-0.3446805518181836.60175577598171.22986577501193
10109.87114.426950837446-0.367363045269992-4.55695083744614-0.441883426953570
1195.74104.474675073961-0.47567205218093-8.73467507396095-2.11065283575416
12123.06111.968804389118-0.38652258447178411.09119561088231.75506234544260
13123.39115.761408280578-0.4981893593515777.628591719422420.987641954012402
14120.28119.227540796823-0.5061999676160211.052459203177070.884476715828104
15115.33118.010039970474-0.520238922243998-2.68003997047408-0.144853501871608
16110.4115.656618777612-0.564656549838987-5.25661877761206-0.381198650923356
17114.49116.273380986845-0.541489489472847-1.783380986844870.255282991059217
18132.03119.676897405235-0.48740034234750812.35310259476490.866253538290777
19123.16122.752227021661-0.4518219744451560.4077729783390690.785280587709519
20118.82124.872475979880-0.429886337941507-6.052475979880470.56717228913698
21128.32122.470496177201-0.4461615345243735.84950382279913-0.434773043057902
22112.24117.393782083926-0.482434263437948-5.153782083926-1.02038976027460
23104.53116.47949329434-0.48496253223978-11.9494932943399-0.095073714820492
24132.57119.789924425355-0.47978343909462512.78007557464530.83685261990826
25122.52118.619278274337-0.4772479438178943.90072172566312-0.154085010070662
26131.8123.770050157323-0.4681738367146768.029949842676971.23641175510898
27124.55125.958475991532-0.440029360964334-1.408475991532180.565287481436237
28120.96127.091100751783-0.415541955356167-6.13110075178260.333279162886685
29122.6127.257181742023-0.406460463823326-4.657181742022720.125298946680323
30145.52131.126861504700-0.35045639865690314.39313849530040.934480534758247
31118.57125.780747762463-0.402289786662204-7.210747762463-1.09901390072755
32134.25130.762629209823-0.35688258826473.487370790176851.18700408177217
33136.7130.254959961601-0.3579510478818376.44504003839928-0.0332481632645142
34121.37128.303595820502-0.366972286446859-6.93359582050245-0.351062524363421
35111.63126.473888893507-0.372345045912592-14.8438888935066-0.32201849472247
36134.42124.058720669928-0.37541632784131810.3612793300721-0.45012444939853
37137.65129.866061576178-0.3703017110646137.78393842382191.36299362686532
38137.86131.537550056016-0.3632098617097596.322449943983640.44618146357317
39119.77127.165199857200-0.394911294055877-7.39519985720027-0.863858541840704
40130.69131.518977027508-0.341231395126340-0.8289770275082191.01856100287634
41128.28133.561342767946-0.311735769256841-5.281342767946250.514873768675007
42147.45132.764725227951-0.31732575714190114.6852747720485-0.105771942779876
43128.42135.154076773928-0.290757182740712-6.73407677392850.594353827488041
44136.9134.33399253471-0.2949985298399112.56600746529012-0.116568172349279
45143.95134.995478364977-0.2889259611929678.954521635023310.210740649050774
46135.64138.190665341355-0.272397667199056-2.550665341355180.767201469201027
47122.48138.254498853333-0.271316509184752-15.77449885333320.0739973500996006
48136.83134.148506177838-0.2796685828180512.68149382216241-0.843764540139479
49153.04139.038125773193-0.26751658154099414.00187422680661.13538098247071
50142.71137.994579852830-0.2706964749693414.71542014717033-0.169431413360703
51123.46135.677102722450-0.284477601915333-12.2171027224504-0.443468572362297
52144.37140.449551607447-0.2388819779448253.920448392552881.09224676117646
53146.15146.527074125742-0.175029283663729-0.3770741257423171.36894574101198
54147.61141.454574985447-0.2236194892936936.15542501455256-1.06825724005665
55158.51151.972920945210-0.1284743382267036.537079054790462.35575479203730
56147.4150.672578421736-0.137142283222423-3.27257842173574-0.257732805261729
57165.05153.743011841665-0.11835259890356911.30698815833530.706005605617627
58154.64155.669447008316-0.109317000949693-1.029447008316320.449957575074511
59126.2148.838105752518-0.131296583785884-22.6381057525176-1.47852021154116
60157.36151.710423859536-0.123164876041085.64957614046420.66017908858174
61154.15146.374999541500-0.138966201662437.77500045849982-1.14322064543085
62123.21132.103879576503-0.199355267159524-8.8938795765034-3.08655763456848
63113.07128.24269740184-0.221396367090524-15.1726974018401-0.79601403941959
64110.45118.437305427123-0.294314662373635-7.98730542712287-2.07908279108819
65113.57114.372258390693-0.326413110977238-0.802258390692765-0.819523877149432
66122.44117.268916504714-0.2988394220085915.171083495286050.703454687289402
67114.93113.484551149028-0.3262867800233211.44544885097167-0.763837738536676
68111.85114.51460613273-0.317123253986082-2.664606132730060.297982879869803
69126.04114.776303062257-0.31394856380701011.26369693774310.127282440685429
70121.34116.877827379219-0.3035571408383294.462172620781380.531164098880861



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')