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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationFri, 04 Dec 2009 15:45:35 -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/04/t12599667697p4lsr6xcxhkbej.htm/, Retrieved Sun, 28 Apr 2024 03:10:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64198, Retrieved Sun, 28 Apr 2024 03:10:30 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65
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] [] [2009-12-04 22:45:35] [7cc673c2b3a8ab442a3ec6ca430f2445] [Current]
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Dataseries X:
102.80 
118.72 
119.01 
118.61 
120.43 
111.83 
116.79 
131.71 
120.57 
117.83 
130.80 
107.46 
112.09 
129.47 
119.72 
134.81 
135.80 
129.27 
126.94 
153.45 
121.86 
133.47 
135.34 
117.10 
120.65 
132.49 
137.60 
138.69 
125.53 
133.09 
129.08 
145.94 
129.07 
139.69 
142.09 
137.29 
127.03 
137.25 
156.87 
150.89 
139.14 
158.30 
149.00 
158.36 
168.06 
153.38 
173.86 
162.47 
145.17 
168.89 
166.64 
140.07 
128.84 
123.40 
120.30 
129.66 
118.12 
113.91 
131.09 
119.14 
115.33




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64198&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]3 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=64198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64198&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1102.8102.8000
2118.72114.7820345326040.5447487312076293.937965467395911.5016766394609
3119.01118.5741368086360.7059074207108520.4358631913636830.495975304070011
4118.61118.9000913175160.695319232732628-0.290091317515946-0.0681451771864634
5120.43119.613538736120.6955644387625090.8164612638800880.00332412911584292
6111.83115.0160641127540.648418697856438-3.18606411275436-0.970778696005901
7116.79115.0254312810530.6429501852060621.7645687189466-0.117094027564651
8131.71124.4593796320540.722804864643187.250620367946181.60967624902769
9120.57123.6150596632300.708074067210482-3.04505966323035-0.286868722278071
10117.83119.8907201014320.666215185027466-2.06072010143239-0.81133923800776
11130.8125.3282825543350.7108975485425525.471717445664720.87339948572819
12107.46115.7116067893590.615255630219125-8.2516067893593-1.89050218102496
13112.09116.5830584237000.607490411114577-4.493058423699810.0510342840132408
14129.47122.7659320738760.6192694249419196.704067926123781.01190798118922
15119.72121.9107228278360.588250625414513-2.19072282783557-0.248991663922578
16134.81129.0659635257720.7272754738944575.744036474228321.15623667828975
17135.8132.2125307019970.7626973064584083.587469298002720.439502884174452
18129.27133.3467728748740.76617471395629-4.076772874874190.0680170372315233
19126.94131.3258466296450.746672121187336-4.38584662964456-0.510570456047832
20153.45138.9195426019820.79167388529234714.53045739801821.25387560409885
21121.86131.8913865981530.737887976018393-10.0313865981526-1.43166863199556
22133.47133.6315712632710.74475163822905-0.1615712632710320.183481242284209
23135.34129.5453079067260.7200591023336325.79469209327393-0.883680351298068
24117.1127.0945289154160.719273405955426-9.9945289154161-0.580831637602893
25120.65127.1953223530990.722912945226276-6.5453223530992-0.115157656244479
26132.49126.7650550458640.72086147232265.72494495413641-0.209927933061647
27137.6135.9955178945050.8184175056201691.604482105495221.49852549191873
28138.69136.3943044061910.8121932343058782.29569559380864-0.0743664294146008
29125.53128.9591815821180.704504396803709-3.42918158211757-1.48991514546401
30133.09132.5728363607360.7331469945559630.517163639264170.531001088891679
31129.08135.0463685113770.746179540549839-5.966368511376860.318647598837167
32145.94131.8496282769730.72114833805477314.0903717230267-0.722314456937508
33129.07135.4762916727580.737955989005935-6.406291672758330.532171132341931
34139.69137.1084385939370.7423186591068172.581561406062990.163656817599235
35142.09136.2437800803470.737583717696585.84621991965328-0.293861994751582
36137.29142.2346275158240.739370102049005-4.944627515823870.962092945867542
37127.03138.9986041059680.741899592003961-11.9686041059682-0.729692373070082
38137.25136.7524440213690.7332278508018480.497555978630793-0.542397067167401
39156.87146.1444202366870.8028692680899910.72557976331321.54729566526100
40150.89147.1007104355790.804557807251813.789289564421450.0274038640410396
41139.14145.7851478647670.78117756767927-6.64514786476734-0.382662049098098
42158.3152.5074777989690.8370451135278885.79252220103111.08207086121228
43149154.1796028082300.84337304623839-5.179602808230450.152748209699735
44158.36150.0578628664590.8127259312450718.30213713354111-0.909344153241108
45168.06163.0937763836570.8745020328128534.966223616343132.23825741702652
46153.38158.3260423723780.85298069557824-4.94604237237773-1.03229841976618
47173.86164.1596884823210.8644406648310469.700311517678640.910697185743786
48162.47165.9333177448390.865389121029962-3.463317744838590.166306046825845
49145.17161.6964633001590.859302748582286-16.5264633001589-0.932198631889592
50168.89167.2832530400110.8754479015933021.606746959989220.85760666420435
51166.64162.0339886962050.8354336788805794.60601130379523-1.10152246408654
52140.07147.4588182885670.700375631719434-7.3888182885675-2.76787906597551
53128.84140.8214673726550.631944763271959-11.9814673726553-1.32571240749699
54123.4127.7101997954700.514198440616602-4.31019979547042-2.50033714604736
55120.3124.7226610069330.488827737971778-4.42266100693287-0.639845405386887
56129.66125.4445254222250.4901982165032494.215474577775130.0426505253904493
57118.12118.1837077203280.454524092001214-0.0637077203279084-1.41857192826512
58113.91119.1425526793750.456216491416185-5.232552679374840.092236304904101
59131.09120.6475760619410.45854740133622810.44242393805920.191717538727549
60119.14121.0387437522050.458439809244851-1.89874375220497-0.0123122287647229
61115.33127.4485290874000.470400364217843-12.11852908740051.08536045198542

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 102.8 & 102.8 & 0 & 0 & 0 \tabularnewline
2 & 118.72 & 114.782034532604 & 0.544748731207629 & 3.93796546739591 & 1.5016766394609 \tabularnewline
3 & 119.01 & 118.574136808636 & 0.705907420710852 & 0.435863191363683 & 0.495975304070011 \tabularnewline
4 & 118.61 & 118.900091317516 & 0.695319232732628 & -0.290091317515946 & -0.0681451771864634 \tabularnewline
5 & 120.43 & 119.61353873612 & 0.695564438762509 & 0.816461263880088 & 0.00332412911584292 \tabularnewline
6 & 111.83 & 115.016064112754 & 0.648418697856438 & -3.18606411275436 & -0.970778696005901 \tabularnewline
7 & 116.79 & 115.025431281053 & 0.642950185206062 & 1.7645687189466 & -0.117094027564651 \tabularnewline
8 & 131.71 & 124.459379632054 & 0.72280486464318 & 7.25062036794618 & 1.60967624902769 \tabularnewline
9 & 120.57 & 123.615059663230 & 0.708074067210482 & -3.04505966323035 & -0.286868722278071 \tabularnewline
10 & 117.83 & 119.890720101432 & 0.666215185027466 & -2.06072010143239 & -0.81133923800776 \tabularnewline
11 & 130.8 & 125.328282554335 & 0.710897548542552 & 5.47171744566472 & 0.87339948572819 \tabularnewline
12 & 107.46 & 115.711606789359 & 0.615255630219125 & -8.2516067893593 & -1.89050218102496 \tabularnewline
13 & 112.09 & 116.583058423700 & 0.607490411114577 & -4.49305842369981 & 0.0510342840132408 \tabularnewline
14 & 129.47 & 122.765932073876 & 0.619269424941919 & 6.70406792612378 & 1.01190798118922 \tabularnewline
15 & 119.72 & 121.910722827836 & 0.588250625414513 & -2.19072282783557 & -0.248991663922578 \tabularnewline
16 & 134.81 & 129.065963525772 & 0.727275473894457 & 5.74403647422832 & 1.15623667828975 \tabularnewline
17 & 135.8 & 132.212530701997 & 0.762697306458408 & 3.58746929800272 & 0.439502884174452 \tabularnewline
18 & 129.27 & 133.346772874874 & 0.76617471395629 & -4.07677287487419 & 0.0680170372315233 \tabularnewline
19 & 126.94 & 131.325846629645 & 0.746672121187336 & -4.38584662964456 & -0.510570456047832 \tabularnewline
20 & 153.45 & 138.919542601982 & 0.791673885292347 & 14.5304573980182 & 1.25387560409885 \tabularnewline
21 & 121.86 & 131.891386598153 & 0.737887976018393 & -10.0313865981526 & -1.43166863199556 \tabularnewline
22 & 133.47 & 133.631571263271 & 0.74475163822905 & -0.161571263271032 & 0.183481242284209 \tabularnewline
23 & 135.34 & 129.545307906726 & 0.720059102333632 & 5.79469209327393 & -0.883680351298068 \tabularnewline
24 & 117.1 & 127.094528915416 & 0.719273405955426 & -9.9945289154161 & -0.580831637602893 \tabularnewline
25 & 120.65 & 127.195322353099 & 0.722912945226276 & -6.5453223530992 & -0.115157656244479 \tabularnewline
26 & 132.49 & 126.765055045864 & 0.7208614723226 & 5.72494495413641 & -0.209927933061647 \tabularnewline
27 & 137.6 & 135.995517894505 & 0.818417505620169 & 1.60448210549522 & 1.49852549191873 \tabularnewline
28 & 138.69 & 136.394304406191 & 0.812193234305878 & 2.29569559380864 & -0.0743664294146008 \tabularnewline
29 & 125.53 & 128.959181582118 & 0.704504396803709 & -3.42918158211757 & -1.48991514546401 \tabularnewline
30 & 133.09 & 132.572836360736 & 0.733146994555963 & 0.51716363926417 & 0.531001088891679 \tabularnewline
31 & 129.08 & 135.046368511377 & 0.746179540549839 & -5.96636851137686 & 0.318647598837167 \tabularnewline
32 & 145.94 & 131.849628276973 & 0.721148338054773 & 14.0903717230267 & -0.722314456937508 \tabularnewline
33 & 129.07 & 135.476291672758 & 0.737955989005935 & -6.40629167275833 & 0.532171132341931 \tabularnewline
34 & 139.69 & 137.108438593937 & 0.742318659106817 & 2.58156140606299 & 0.163656817599235 \tabularnewline
35 & 142.09 & 136.243780080347 & 0.73758371769658 & 5.84621991965328 & -0.293861994751582 \tabularnewline
36 & 137.29 & 142.234627515824 & 0.739370102049005 & -4.94462751582387 & 0.962092945867542 \tabularnewline
37 & 127.03 & 138.998604105968 & 0.741899592003961 & -11.9686041059682 & -0.729692373070082 \tabularnewline
38 & 137.25 & 136.752444021369 & 0.733227850801848 & 0.497555978630793 & -0.542397067167401 \tabularnewline
39 & 156.87 & 146.144420236687 & 0.80286926808999 & 10.7255797633132 & 1.54729566526100 \tabularnewline
40 & 150.89 & 147.100710435579 & 0.80455780725181 & 3.78928956442145 & 0.0274038640410396 \tabularnewline
41 & 139.14 & 145.785147864767 & 0.78117756767927 & -6.64514786476734 & -0.382662049098098 \tabularnewline
42 & 158.3 & 152.507477798969 & 0.837045113527888 & 5.7925222010311 & 1.08207086121228 \tabularnewline
43 & 149 & 154.179602808230 & 0.84337304623839 & -5.17960280823045 & 0.152748209699735 \tabularnewline
44 & 158.36 & 150.057862866459 & 0.812725931245071 & 8.30213713354111 & -0.909344153241108 \tabularnewline
45 & 168.06 & 163.093776383657 & 0.874502032812853 & 4.96622361634313 & 2.23825741702652 \tabularnewline
46 & 153.38 & 158.326042372378 & 0.85298069557824 & -4.94604237237773 & -1.03229841976618 \tabularnewline
47 & 173.86 & 164.159688482321 & 0.864440664831046 & 9.70031151767864 & 0.910697185743786 \tabularnewline
48 & 162.47 & 165.933317744839 & 0.865389121029962 & -3.46331774483859 & 0.166306046825845 \tabularnewline
49 & 145.17 & 161.696463300159 & 0.859302748582286 & -16.5264633001589 & -0.932198631889592 \tabularnewline
50 & 168.89 & 167.283253040011 & 0.875447901593302 & 1.60674695998922 & 0.85760666420435 \tabularnewline
51 & 166.64 & 162.033988696205 & 0.835433678880579 & 4.60601130379523 & -1.10152246408654 \tabularnewline
52 & 140.07 & 147.458818288567 & 0.700375631719434 & -7.3888182885675 & -2.76787906597551 \tabularnewline
53 & 128.84 & 140.821467372655 & 0.631944763271959 & -11.9814673726553 & -1.32571240749699 \tabularnewline
54 & 123.4 & 127.710199795470 & 0.514198440616602 & -4.31019979547042 & -2.50033714604736 \tabularnewline
55 & 120.3 & 124.722661006933 & 0.488827737971778 & -4.42266100693287 & -0.639845405386887 \tabularnewline
56 & 129.66 & 125.444525422225 & 0.490198216503249 & 4.21547457777513 & 0.0426505253904493 \tabularnewline
57 & 118.12 & 118.183707720328 & 0.454524092001214 & -0.0637077203279084 & -1.41857192826512 \tabularnewline
58 & 113.91 & 119.142552679375 & 0.456216491416185 & -5.23255267937484 & 0.092236304904101 \tabularnewline
59 & 131.09 & 120.647576061941 & 0.458547401336228 & 10.4424239380592 & 0.191717538727549 \tabularnewline
60 & 119.14 & 121.038743752205 & 0.458439809244851 & -1.89874375220497 & -0.0123122287647229 \tabularnewline
61 & 115.33 & 127.448529087400 & 0.470400364217843 & -12.1185290874005 & 1.08536045198542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64198&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]102.8[/C][C]102.8[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]118.72[/C][C]114.782034532604[/C][C]0.544748731207629[/C][C]3.93796546739591[/C][C]1.5016766394609[/C][/ROW]
[ROW][C]3[/C][C]119.01[/C][C]118.574136808636[/C][C]0.705907420710852[/C][C]0.435863191363683[/C][C]0.495975304070011[/C][/ROW]
[ROW][C]4[/C][C]118.61[/C][C]118.900091317516[/C][C]0.695319232732628[/C][C]-0.290091317515946[/C][C]-0.0681451771864634[/C][/ROW]
[ROW][C]5[/C][C]120.43[/C][C]119.61353873612[/C][C]0.695564438762509[/C][C]0.816461263880088[/C][C]0.00332412911584292[/C][/ROW]
[ROW][C]6[/C][C]111.83[/C][C]115.016064112754[/C][C]0.648418697856438[/C][C]-3.18606411275436[/C][C]-0.970778696005901[/C][/ROW]
[ROW][C]7[/C][C]116.79[/C][C]115.025431281053[/C][C]0.642950185206062[/C][C]1.7645687189466[/C][C]-0.117094027564651[/C][/ROW]
[ROW][C]8[/C][C]131.71[/C][C]124.459379632054[/C][C]0.72280486464318[/C][C]7.25062036794618[/C][C]1.60967624902769[/C][/ROW]
[ROW][C]9[/C][C]120.57[/C][C]123.615059663230[/C][C]0.708074067210482[/C][C]-3.04505966323035[/C][C]-0.286868722278071[/C][/ROW]
[ROW][C]10[/C][C]117.83[/C][C]119.890720101432[/C][C]0.666215185027466[/C][C]-2.06072010143239[/C][C]-0.81133923800776[/C][/ROW]
[ROW][C]11[/C][C]130.8[/C][C]125.328282554335[/C][C]0.710897548542552[/C][C]5.47171744566472[/C][C]0.87339948572819[/C][/ROW]
[ROW][C]12[/C][C]107.46[/C][C]115.711606789359[/C][C]0.615255630219125[/C][C]-8.2516067893593[/C][C]-1.89050218102496[/C][/ROW]
[ROW][C]13[/C][C]112.09[/C][C]116.583058423700[/C][C]0.607490411114577[/C][C]-4.49305842369981[/C][C]0.0510342840132408[/C][/ROW]
[ROW][C]14[/C][C]129.47[/C][C]122.765932073876[/C][C]0.619269424941919[/C][C]6.70406792612378[/C][C]1.01190798118922[/C][/ROW]
[ROW][C]15[/C][C]119.72[/C][C]121.910722827836[/C][C]0.588250625414513[/C][C]-2.19072282783557[/C][C]-0.248991663922578[/C][/ROW]
[ROW][C]16[/C][C]134.81[/C][C]129.065963525772[/C][C]0.727275473894457[/C][C]5.74403647422832[/C][C]1.15623667828975[/C][/ROW]
[ROW][C]17[/C][C]135.8[/C][C]132.212530701997[/C][C]0.762697306458408[/C][C]3.58746929800272[/C][C]0.439502884174452[/C][/ROW]
[ROW][C]18[/C][C]129.27[/C][C]133.346772874874[/C][C]0.76617471395629[/C][C]-4.07677287487419[/C][C]0.0680170372315233[/C][/ROW]
[ROW][C]19[/C][C]126.94[/C][C]131.325846629645[/C][C]0.746672121187336[/C][C]-4.38584662964456[/C][C]-0.510570456047832[/C][/ROW]
[ROW][C]20[/C][C]153.45[/C][C]138.919542601982[/C][C]0.791673885292347[/C][C]14.5304573980182[/C][C]1.25387560409885[/C][/ROW]
[ROW][C]21[/C][C]121.86[/C][C]131.891386598153[/C][C]0.737887976018393[/C][C]-10.0313865981526[/C][C]-1.43166863199556[/C][/ROW]
[ROW][C]22[/C][C]133.47[/C][C]133.631571263271[/C][C]0.74475163822905[/C][C]-0.161571263271032[/C][C]0.183481242284209[/C][/ROW]
[ROW][C]23[/C][C]135.34[/C][C]129.545307906726[/C][C]0.720059102333632[/C][C]5.79469209327393[/C][C]-0.883680351298068[/C][/ROW]
[ROW][C]24[/C][C]117.1[/C][C]127.094528915416[/C][C]0.719273405955426[/C][C]-9.9945289154161[/C][C]-0.580831637602893[/C][/ROW]
[ROW][C]25[/C][C]120.65[/C][C]127.195322353099[/C][C]0.722912945226276[/C][C]-6.5453223530992[/C][C]-0.115157656244479[/C][/ROW]
[ROW][C]26[/C][C]132.49[/C][C]126.765055045864[/C][C]0.7208614723226[/C][C]5.72494495413641[/C][C]-0.209927933061647[/C][/ROW]
[ROW][C]27[/C][C]137.6[/C][C]135.995517894505[/C][C]0.818417505620169[/C][C]1.60448210549522[/C][C]1.49852549191873[/C][/ROW]
[ROW][C]28[/C][C]138.69[/C][C]136.394304406191[/C][C]0.812193234305878[/C][C]2.29569559380864[/C][C]-0.0743664294146008[/C][/ROW]
[ROW][C]29[/C][C]125.53[/C][C]128.959181582118[/C][C]0.704504396803709[/C][C]-3.42918158211757[/C][C]-1.48991514546401[/C][/ROW]
[ROW][C]30[/C][C]133.09[/C][C]132.572836360736[/C][C]0.733146994555963[/C][C]0.51716363926417[/C][C]0.531001088891679[/C][/ROW]
[ROW][C]31[/C][C]129.08[/C][C]135.046368511377[/C][C]0.746179540549839[/C][C]-5.96636851137686[/C][C]0.318647598837167[/C][/ROW]
[ROW][C]32[/C][C]145.94[/C][C]131.849628276973[/C][C]0.721148338054773[/C][C]14.0903717230267[/C][C]-0.722314456937508[/C][/ROW]
[ROW][C]33[/C][C]129.07[/C][C]135.476291672758[/C][C]0.737955989005935[/C][C]-6.40629167275833[/C][C]0.532171132341931[/C][/ROW]
[ROW][C]34[/C][C]139.69[/C][C]137.108438593937[/C][C]0.742318659106817[/C][C]2.58156140606299[/C][C]0.163656817599235[/C][/ROW]
[ROW][C]35[/C][C]142.09[/C][C]136.243780080347[/C][C]0.73758371769658[/C][C]5.84621991965328[/C][C]-0.293861994751582[/C][/ROW]
[ROW][C]36[/C][C]137.29[/C][C]142.234627515824[/C][C]0.739370102049005[/C][C]-4.94462751582387[/C][C]0.962092945867542[/C][/ROW]
[ROW][C]37[/C][C]127.03[/C][C]138.998604105968[/C][C]0.741899592003961[/C][C]-11.9686041059682[/C][C]-0.729692373070082[/C][/ROW]
[ROW][C]38[/C][C]137.25[/C][C]136.752444021369[/C][C]0.733227850801848[/C][C]0.497555978630793[/C][C]-0.542397067167401[/C][/ROW]
[ROW][C]39[/C][C]156.87[/C][C]146.144420236687[/C][C]0.80286926808999[/C][C]10.7255797633132[/C][C]1.54729566526100[/C][/ROW]
[ROW][C]40[/C][C]150.89[/C][C]147.100710435579[/C][C]0.80455780725181[/C][C]3.78928956442145[/C][C]0.0274038640410396[/C][/ROW]
[ROW][C]41[/C][C]139.14[/C][C]145.785147864767[/C][C]0.78117756767927[/C][C]-6.64514786476734[/C][C]-0.382662049098098[/C][/ROW]
[ROW][C]42[/C][C]158.3[/C][C]152.507477798969[/C][C]0.837045113527888[/C][C]5.7925222010311[/C][C]1.08207086121228[/C][/ROW]
[ROW][C]43[/C][C]149[/C][C]154.179602808230[/C][C]0.84337304623839[/C][C]-5.17960280823045[/C][C]0.152748209699735[/C][/ROW]
[ROW][C]44[/C][C]158.36[/C][C]150.057862866459[/C][C]0.812725931245071[/C][C]8.30213713354111[/C][C]-0.909344153241108[/C][/ROW]
[ROW][C]45[/C][C]168.06[/C][C]163.093776383657[/C][C]0.874502032812853[/C][C]4.96622361634313[/C][C]2.23825741702652[/C][/ROW]
[ROW][C]46[/C][C]153.38[/C][C]158.326042372378[/C][C]0.85298069557824[/C][C]-4.94604237237773[/C][C]-1.03229841976618[/C][/ROW]
[ROW][C]47[/C][C]173.86[/C][C]164.159688482321[/C][C]0.864440664831046[/C][C]9.70031151767864[/C][C]0.910697185743786[/C][/ROW]
[ROW][C]48[/C][C]162.47[/C][C]165.933317744839[/C][C]0.865389121029962[/C][C]-3.46331774483859[/C][C]0.166306046825845[/C][/ROW]
[ROW][C]49[/C][C]145.17[/C][C]161.696463300159[/C][C]0.859302748582286[/C][C]-16.5264633001589[/C][C]-0.932198631889592[/C][/ROW]
[ROW][C]50[/C][C]168.89[/C][C]167.283253040011[/C][C]0.875447901593302[/C][C]1.60674695998922[/C][C]0.85760666420435[/C][/ROW]
[ROW][C]51[/C][C]166.64[/C][C]162.033988696205[/C][C]0.835433678880579[/C][C]4.60601130379523[/C][C]-1.10152246408654[/C][/ROW]
[ROW][C]52[/C][C]140.07[/C][C]147.458818288567[/C][C]0.700375631719434[/C][C]-7.3888182885675[/C][C]-2.76787906597551[/C][/ROW]
[ROW][C]53[/C][C]128.84[/C][C]140.821467372655[/C][C]0.631944763271959[/C][C]-11.9814673726553[/C][C]-1.32571240749699[/C][/ROW]
[ROW][C]54[/C][C]123.4[/C][C]127.710199795470[/C][C]0.514198440616602[/C][C]-4.31019979547042[/C][C]-2.50033714604736[/C][/ROW]
[ROW][C]55[/C][C]120.3[/C][C]124.722661006933[/C][C]0.488827737971778[/C][C]-4.42266100693287[/C][C]-0.639845405386887[/C][/ROW]
[ROW][C]56[/C][C]129.66[/C][C]125.444525422225[/C][C]0.490198216503249[/C][C]4.21547457777513[/C][C]0.0426505253904493[/C][/ROW]
[ROW][C]57[/C][C]118.12[/C][C]118.183707720328[/C][C]0.454524092001214[/C][C]-0.0637077203279084[/C][C]-1.41857192826512[/C][/ROW]
[ROW][C]58[/C][C]113.91[/C][C]119.142552679375[/C][C]0.456216491416185[/C][C]-5.23255267937484[/C][C]0.092236304904101[/C][/ROW]
[ROW][C]59[/C][C]131.09[/C][C]120.647576061941[/C][C]0.458547401336228[/C][C]10.4424239380592[/C][C]0.191717538727549[/C][/ROW]
[ROW][C]60[/C][C]119.14[/C][C]121.038743752205[/C][C]0.458439809244851[/C][C]-1.89874375220497[/C][C]-0.0123122287647229[/C][/ROW]
[ROW][C]61[/C][C]115.33[/C][C]127.448529087400[/C][C]0.470400364217843[/C][C]-12.1185290874005[/C][C]1.08536045198542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64198&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
1102.8102.8000
2118.72114.7820345326040.5447487312076293.937965467395911.5016766394609
3119.01118.5741368086360.7059074207108520.4358631913636830.495975304070011
4118.61118.9000913175160.695319232732628-0.290091317515946-0.0681451771864634
5120.43119.613538736120.6955644387625090.8164612638800880.00332412911584292
6111.83115.0160641127540.648418697856438-3.18606411275436-0.970778696005901
7116.79115.0254312810530.6429501852060621.7645687189466-0.117094027564651
8131.71124.4593796320540.722804864643187.250620367946181.60967624902769
9120.57123.6150596632300.708074067210482-3.04505966323035-0.286868722278071
10117.83119.8907201014320.666215185027466-2.06072010143239-0.81133923800776
11130.8125.3282825543350.7108975485425525.471717445664720.87339948572819
12107.46115.7116067893590.615255630219125-8.2516067893593-1.89050218102496
13112.09116.5830584237000.607490411114577-4.493058423699810.0510342840132408
14129.47122.7659320738760.6192694249419196.704067926123781.01190798118922
15119.72121.9107228278360.588250625414513-2.19072282783557-0.248991663922578
16134.81129.0659635257720.7272754738944575.744036474228321.15623667828975
17135.8132.2125307019970.7626973064584083.587469298002720.439502884174452
18129.27133.3467728748740.76617471395629-4.076772874874190.0680170372315233
19126.94131.3258466296450.746672121187336-4.38584662964456-0.510570456047832
20153.45138.9195426019820.79167388529234714.53045739801821.25387560409885
21121.86131.8913865981530.737887976018393-10.0313865981526-1.43166863199556
22133.47133.6315712632710.74475163822905-0.1615712632710320.183481242284209
23135.34129.5453079067260.7200591023336325.79469209327393-0.883680351298068
24117.1127.0945289154160.719273405955426-9.9945289154161-0.580831637602893
25120.65127.1953223530990.722912945226276-6.5453223530992-0.115157656244479
26132.49126.7650550458640.72086147232265.72494495413641-0.209927933061647
27137.6135.9955178945050.8184175056201691.604482105495221.49852549191873
28138.69136.3943044061910.8121932343058782.29569559380864-0.0743664294146008
29125.53128.9591815821180.704504396803709-3.42918158211757-1.48991514546401
30133.09132.5728363607360.7331469945559630.517163639264170.531001088891679
31129.08135.0463685113770.746179540549839-5.966368511376860.318647598837167
32145.94131.8496282769730.72114833805477314.0903717230267-0.722314456937508
33129.07135.4762916727580.737955989005935-6.406291672758330.532171132341931
34139.69137.1084385939370.7423186591068172.581561406062990.163656817599235
35142.09136.2437800803470.737583717696585.84621991965328-0.293861994751582
36137.29142.2346275158240.739370102049005-4.944627515823870.962092945867542
37127.03138.9986041059680.741899592003961-11.9686041059682-0.729692373070082
38137.25136.7524440213690.7332278508018480.497555978630793-0.542397067167401
39156.87146.1444202366870.8028692680899910.72557976331321.54729566526100
40150.89147.1007104355790.804557807251813.789289564421450.0274038640410396
41139.14145.7851478647670.78117756767927-6.64514786476734-0.382662049098098
42158.3152.5074777989690.8370451135278885.79252220103111.08207086121228
43149154.1796028082300.84337304623839-5.179602808230450.152748209699735
44158.36150.0578628664590.8127259312450718.30213713354111-0.909344153241108
45168.06163.0937763836570.8745020328128534.966223616343132.23825741702652
46153.38158.3260423723780.85298069557824-4.94604237237773-1.03229841976618
47173.86164.1596884823210.8644406648310469.700311517678640.910697185743786
48162.47165.9333177448390.865389121029962-3.463317744838590.166306046825845
49145.17161.6964633001590.859302748582286-16.5264633001589-0.932198631889592
50168.89167.2832530400110.8754479015933021.606746959989220.85760666420435
51166.64162.0339886962050.8354336788805794.60601130379523-1.10152246408654
52140.07147.4588182885670.700375631719434-7.3888182885675-2.76787906597551
53128.84140.8214673726550.631944763271959-11.9814673726553-1.32571240749699
54123.4127.7101997954700.514198440616602-4.31019979547042-2.50033714604736
55120.3124.7226610069330.488827737971778-4.42266100693287-0.639845405386887
56129.66125.4445254222250.4901982165032494.215474577775130.0426505253904493
57118.12118.1837077203280.454524092001214-0.0637077203279084-1.41857192826512
58113.91119.1425526793750.456216491416185-5.232552679374840.092236304904101
59131.09120.6475760619410.45854740133622810.44242393805920.191717538727549
60119.14121.0387437522050.458439809244851-1.89874375220497-0.0123122287647229
61115.33127.4485290874000.470400364217843-12.11852908740051.08536045198542



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
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')