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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 09:02:52 -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/t1259942693kapxohb7eitp7zq.htm/, Retrieved Sun, 28 Apr 2024 04:13:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63818, Retrieved Sun, 28 Apr 2024 04:13:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
-    D      [Structural Time Series Models] [WS 9, Populair mo...] [2009-12-04 16:02:52] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataseries X:
95.1
97
112.7
102.9
97.4
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
99.8
116.8
115.7
99.4
94.3




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=63818&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=63818&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63818&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
195.195.1000
29795.6172457653417-0.0179137176742521.382754234658300.355983429664942
3112.7103.5100616272320.5340923194878279.189938372768072.67772412401643
4102.9105.4251076176920.641816735585187-2.52510761769160.524277643898397
597.4103.3551962666260.460294618783866-5.95519626662574-1.24098426251121
6111.4105.2457309838250.5332805092316346.15426901617550.718438978345372
787.4100.4666726477180.329844159680205-13.0666726477184-2.75926564762998
896.897.672784523050.234102247883548-0.872784523050115-1.63863215627200
9114.1101.6933542373650.33663223555950012.40664576263471.98989980491086
10110.3105.7755021953270.4338824502431264.524497804672641.96626962392249
11103.9106.6795445621540.446107526520085-2.77954456215430.246334626336542
12101.6105.4605103913420.402164682467852-3.86051039134245-0.870790832135682
1394.6103.1481338389920.405379607507195-8.54813383899242-1.47301299519722
1495.9101.7544218949180.402709771371073-5.8544218949178-0.984415504282895
15104.799.36325425671940.3504626435315585.33674574328056-1.42367769403096
16102.899.74685114918540.3515346366261283.053148850814630.0159369830328408
1798.1100.8773749058810.381386334477444-2.777374905880530.374405975970588
18113.9102.1867256387730.41703308506737911.71327436122680.458675404508563
1980.9100.2017226910660.332036963937143-19.3017226910665-1.21903554616217
2095.799.86950202548030.311129466062295-4.16950202548029-0.342395782714928
21113.2100.9466128778360.33244735500771612.25338712216360.397518723956748
22105.9101.3508794106910.3342171266034764.549120589309240.0373398207251376
23108.8103.9135043927720.3824801656873094.886495607228411.15844417296230
24102.3104.6535146927400.38919806339569-2.353514692740420.185972813927420
2599105.2332420319010.392389111935568-6.233242031901200.0993119002920132
26100.7105.4438996787980.389169397687618-4.74389967879805-0.0942976122775373
27115.5107.1978436013670.4184584285606638.302156398633390.696945241968241
28100.7105.0995509225060.353254968432263-4.39955092250612-1.2629212179917
29109.9106.7846600190470.3921723827496673.115339980952940.663873067526193
30114.6105.7236340082280.3475939529422338.87636599177186-0.728312784310215
3185.4105.2238029187730.321815495199988-19.8238029187726-0.429238487890929
32100.5105.3473614133830.316066024321495-4.84736141338333-0.101341346478198
33114.8104.9920693048840.2979295982350339.80793069511591-0.344952271055414
34116.5107.6207835211470.3560500977776328.879216478853141.19994015450840
35112.9108.7960802632850.3749855708085064.103919736715070.421988022066659
36102108.0023795876820.349450759817781-6.00237958768213-0.602100114128637
37106109.1546314242050.366654233734571-3.154631424205370.413329357891361
38105.3109.9425999863250.375926629927034-4.642599986325140.216233565197655
39118.8110.0093481000030.3686922251984028.79065189999748-0.157728262228629
40106.1110.3609998361840.368264442911986-4.26099983618425-0.0086377960010636
41109.3108.9180627572430.3201346141313540.381937242756751-0.915032123650648
42117.2108.2489784937810.2929931740147278.95102150621929-0.500264487858976
4392.5109.4459128121500.317924742269318-16.94591281214980.458898932602473
44104.2109.9983290249230.324280203971745-5.798329024923360.119539579511164
45112.5108.6401796146860.2801547188721153.8598203853136-0.860136310712448
46122.4109.9000557701530.30485973485720112.49994422984700.501577874270787
47113.3110.0755762280610.3017148533132483.22442377193928-0.0662506501165228
48100109.2060262067490.273938130054238-9.2060262067488-0.599998337377564
49110.7110.4483175901230.2967250122735250.2516824098770660.495801751623415
50112.8112.8624980849980.347070434402659-0.06249808499815671.08249484577425
51109.8109.8799750419130.265999384561075-0.0799750419126175-1.69792094268066
52117.3112.1356507426380.3158673219980375.164349257361861.01199085651005
53109.1111.8126277355600.299438038773849-2.71262773556041-0.324450755077545
54115.9110.4939366319220.2571323165408355.40606336807807-0.821824416022757
5596110.8414351453850.259506739129008-14.84143514538460.0459592510074398
5699.8109.2329027353120.21071436643755-9.4329027353118-0.951746156028206
57116.8110.122693160560.2282056760226966.677306839440040.346469066656222
58115.7108.3444766965770.177429441639467.35552330342254-1.02451795269354
5999.4104.1609222656280.0688897057410682-4.76092226562805-2.22758842502274
6094.3103.0749195601550.0404780215439526-8.77491956015468-0.589954652290969

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 95.1 & 95.1 & 0 & 0 & 0 \tabularnewline
2 & 97 & 95.6172457653417 & -0.017913717674252 & 1.38275423465830 & 0.355983429664942 \tabularnewline
3 & 112.7 & 103.510061627232 & 0.534092319487827 & 9.18993837276807 & 2.67772412401643 \tabularnewline
4 & 102.9 & 105.425107617692 & 0.641816735585187 & -2.5251076176916 & 0.524277643898397 \tabularnewline
5 & 97.4 & 103.355196266626 & 0.460294618783866 & -5.95519626662574 & -1.24098426251121 \tabularnewline
6 & 111.4 & 105.245730983825 & 0.533280509231634 & 6.1542690161755 & 0.718438978345372 \tabularnewline
7 & 87.4 & 100.466672647718 & 0.329844159680205 & -13.0666726477184 & -2.75926564762998 \tabularnewline
8 & 96.8 & 97.67278452305 & 0.234102247883548 & -0.872784523050115 & -1.63863215627200 \tabularnewline
9 & 114.1 & 101.693354237365 & 0.336632235559500 & 12.4066457626347 & 1.98989980491086 \tabularnewline
10 & 110.3 & 105.775502195327 & 0.433882450243126 & 4.52449780467264 & 1.96626962392249 \tabularnewline
11 & 103.9 & 106.679544562154 & 0.446107526520085 & -2.7795445621543 & 0.246334626336542 \tabularnewline
12 & 101.6 & 105.460510391342 & 0.402164682467852 & -3.86051039134245 & -0.870790832135682 \tabularnewline
13 & 94.6 & 103.148133838992 & 0.405379607507195 & -8.54813383899242 & -1.47301299519722 \tabularnewline
14 & 95.9 & 101.754421894918 & 0.402709771371073 & -5.8544218949178 & -0.984415504282895 \tabularnewline
15 & 104.7 & 99.3632542567194 & 0.350462643531558 & 5.33674574328056 & -1.42367769403096 \tabularnewline
16 & 102.8 & 99.7468511491854 & 0.351534636626128 & 3.05314885081463 & 0.0159369830328408 \tabularnewline
17 & 98.1 & 100.877374905881 & 0.381386334477444 & -2.77737490588053 & 0.374405975970588 \tabularnewline
18 & 113.9 & 102.186725638773 & 0.417033085067379 & 11.7132743612268 & 0.458675404508563 \tabularnewline
19 & 80.9 & 100.201722691066 & 0.332036963937143 & -19.3017226910665 & -1.21903554616217 \tabularnewline
20 & 95.7 & 99.8695020254803 & 0.311129466062295 & -4.16950202548029 & -0.342395782714928 \tabularnewline
21 & 113.2 & 100.946612877836 & 0.332447355007716 & 12.2533871221636 & 0.397518723956748 \tabularnewline
22 & 105.9 & 101.350879410691 & 0.334217126603476 & 4.54912058930924 & 0.0373398207251376 \tabularnewline
23 & 108.8 & 103.913504392772 & 0.382480165687309 & 4.88649560722841 & 1.15844417296230 \tabularnewline
24 & 102.3 & 104.653514692740 & 0.38919806339569 & -2.35351469274042 & 0.185972813927420 \tabularnewline
25 & 99 & 105.233242031901 & 0.392389111935568 & -6.23324203190120 & 0.0993119002920132 \tabularnewline
26 & 100.7 & 105.443899678798 & 0.389169397687618 & -4.74389967879805 & -0.0942976122775373 \tabularnewline
27 & 115.5 & 107.197843601367 & 0.418458428560663 & 8.30215639863339 & 0.696945241968241 \tabularnewline
28 & 100.7 & 105.099550922506 & 0.353254968432263 & -4.39955092250612 & -1.2629212179917 \tabularnewline
29 & 109.9 & 106.784660019047 & 0.392172382749667 & 3.11533998095294 & 0.663873067526193 \tabularnewline
30 & 114.6 & 105.723634008228 & 0.347593952942233 & 8.87636599177186 & -0.728312784310215 \tabularnewline
31 & 85.4 & 105.223802918773 & 0.321815495199988 & -19.8238029187726 & -0.429238487890929 \tabularnewline
32 & 100.5 & 105.347361413383 & 0.316066024321495 & -4.84736141338333 & -0.101341346478198 \tabularnewline
33 & 114.8 & 104.992069304884 & 0.297929598235033 & 9.80793069511591 & -0.344952271055414 \tabularnewline
34 & 116.5 & 107.620783521147 & 0.356050097777632 & 8.87921647885314 & 1.19994015450840 \tabularnewline
35 & 112.9 & 108.796080263285 & 0.374985570808506 & 4.10391973671507 & 0.421988022066659 \tabularnewline
36 & 102 & 108.002379587682 & 0.349450759817781 & -6.00237958768213 & -0.602100114128637 \tabularnewline
37 & 106 & 109.154631424205 & 0.366654233734571 & -3.15463142420537 & 0.413329357891361 \tabularnewline
38 & 105.3 & 109.942599986325 & 0.375926629927034 & -4.64259998632514 & 0.216233565197655 \tabularnewline
39 & 118.8 & 110.009348100003 & 0.368692225198402 & 8.79065189999748 & -0.157728262228629 \tabularnewline
40 & 106.1 & 110.360999836184 & 0.368264442911986 & -4.26099983618425 & -0.0086377960010636 \tabularnewline
41 & 109.3 & 108.918062757243 & 0.320134614131354 & 0.381937242756751 & -0.915032123650648 \tabularnewline
42 & 117.2 & 108.248978493781 & 0.292993174014727 & 8.95102150621929 & -0.500264487858976 \tabularnewline
43 & 92.5 & 109.445912812150 & 0.317924742269318 & -16.9459128121498 & 0.458898932602473 \tabularnewline
44 & 104.2 & 109.998329024923 & 0.324280203971745 & -5.79832902492336 & 0.119539579511164 \tabularnewline
45 & 112.5 & 108.640179614686 & 0.280154718872115 & 3.8598203853136 & -0.860136310712448 \tabularnewline
46 & 122.4 & 109.900055770153 & 0.304859734857201 & 12.4999442298470 & 0.501577874270787 \tabularnewline
47 & 113.3 & 110.075576228061 & 0.301714853313248 & 3.22442377193928 & -0.0662506501165228 \tabularnewline
48 & 100 & 109.206026206749 & 0.273938130054238 & -9.2060262067488 & -0.599998337377564 \tabularnewline
49 & 110.7 & 110.448317590123 & 0.296725012273525 & 0.251682409877066 & 0.495801751623415 \tabularnewline
50 & 112.8 & 112.862498084998 & 0.347070434402659 & -0.0624980849981567 & 1.08249484577425 \tabularnewline
51 & 109.8 & 109.879975041913 & 0.265999384561075 & -0.0799750419126175 & -1.69792094268066 \tabularnewline
52 & 117.3 & 112.135650742638 & 0.315867321998037 & 5.16434925736186 & 1.01199085651005 \tabularnewline
53 & 109.1 & 111.812627735560 & 0.299438038773849 & -2.71262773556041 & -0.324450755077545 \tabularnewline
54 & 115.9 & 110.493936631922 & 0.257132316540835 & 5.40606336807807 & -0.821824416022757 \tabularnewline
55 & 96 & 110.841435145385 & 0.259506739129008 & -14.8414351453846 & 0.0459592510074398 \tabularnewline
56 & 99.8 & 109.232902735312 & 0.21071436643755 & -9.4329027353118 & -0.951746156028206 \tabularnewline
57 & 116.8 & 110.12269316056 & 0.228205676022696 & 6.67730683944004 & 0.346469066656222 \tabularnewline
58 & 115.7 & 108.344476696577 & 0.17742944163946 & 7.35552330342254 & -1.02451795269354 \tabularnewline
59 & 99.4 & 104.160922265628 & 0.0688897057410682 & -4.76092226562805 & -2.22758842502274 \tabularnewline
60 & 94.3 & 103.074919560155 & 0.0404780215439526 & -8.77491956015468 & -0.589954652290969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63818&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]95.1[/C][C]95.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]95.6172457653417[/C][C]-0.017913717674252[/C][C]1.38275423465830[/C][C]0.355983429664942[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]103.510061627232[/C][C]0.534092319487827[/C][C]9.18993837276807[/C][C]2.67772412401643[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]105.425107617692[/C][C]0.641816735585187[/C][C]-2.5251076176916[/C][C]0.524277643898397[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]103.355196266626[/C][C]0.460294618783866[/C][C]-5.95519626662574[/C][C]-1.24098426251121[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]105.245730983825[/C][C]0.533280509231634[/C][C]6.1542690161755[/C][C]0.718438978345372[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]100.466672647718[/C][C]0.329844159680205[/C][C]-13.0666726477184[/C][C]-2.75926564762998[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]97.67278452305[/C][C]0.234102247883548[/C][C]-0.872784523050115[/C][C]-1.63863215627200[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]101.693354237365[/C][C]0.336632235559500[/C][C]12.4066457626347[/C][C]1.98989980491086[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]105.775502195327[/C][C]0.433882450243126[/C][C]4.52449780467264[/C][C]1.96626962392249[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]106.679544562154[/C][C]0.446107526520085[/C][C]-2.7795445621543[/C][C]0.246334626336542[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]105.460510391342[/C][C]0.402164682467852[/C][C]-3.86051039134245[/C][C]-0.870790832135682[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]103.148133838992[/C][C]0.405379607507195[/C][C]-8.54813383899242[/C][C]-1.47301299519722[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]101.754421894918[/C][C]0.402709771371073[/C][C]-5.8544218949178[/C][C]-0.984415504282895[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]99.3632542567194[/C][C]0.350462643531558[/C][C]5.33674574328056[/C][C]-1.42367769403096[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]99.7468511491854[/C][C]0.351534636626128[/C][C]3.05314885081463[/C][C]0.0159369830328408[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]100.877374905881[/C][C]0.381386334477444[/C][C]-2.77737490588053[/C][C]0.374405975970588[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]102.186725638773[/C][C]0.417033085067379[/C][C]11.7132743612268[/C][C]0.458675404508563[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]100.201722691066[/C][C]0.332036963937143[/C][C]-19.3017226910665[/C][C]-1.21903554616217[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]99.8695020254803[/C][C]0.311129466062295[/C][C]-4.16950202548029[/C][C]-0.342395782714928[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]100.946612877836[/C][C]0.332447355007716[/C][C]12.2533871221636[/C][C]0.397518723956748[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]101.350879410691[/C][C]0.334217126603476[/C][C]4.54912058930924[/C][C]0.0373398207251376[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]103.913504392772[/C][C]0.382480165687309[/C][C]4.88649560722841[/C][C]1.15844417296230[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]104.653514692740[/C][C]0.38919806339569[/C][C]-2.35351469274042[/C][C]0.185972813927420[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]105.233242031901[/C][C]0.392389111935568[/C][C]-6.23324203190120[/C][C]0.0993119002920132[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]105.443899678798[/C][C]0.389169397687618[/C][C]-4.74389967879805[/C][C]-0.0942976122775373[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]107.197843601367[/C][C]0.418458428560663[/C][C]8.30215639863339[/C][C]0.696945241968241[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]105.099550922506[/C][C]0.353254968432263[/C][C]-4.39955092250612[/C][C]-1.2629212179917[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]106.784660019047[/C][C]0.392172382749667[/C][C]3.11533998095294[/C][C]0.663873067526193[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]105.723634008228[/C][C]0.347593952942233[/C][C]8.87636599177186[/C][C]-0.728312784310215[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]105.223802918773[/C][C]0.321815495199988[/C][C]-19.8238029187726[/C][C]-0.429238487890929[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]105.347361413383[/C][C]0.316066024321495[/C][C]-4.84736141338333[/C][C]-0.101341346478198[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]104.992069304884[/C][C]0.297929598235033[/C][C]9.80793069511591[/C][C]-0.344952271055414[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]107.620783521147[/C][C]0.356050097777632[/C][C]8.87921647885314[/C][C]1.19994015450840[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]108.796080263285[/C][C]0.374985570808506[/C][C]4.10391973671507[/C][C]0.421988022066659[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]108.002379587682[/C][C]0.349450759817781[/C][C]-6.00237958768213[/C][C]-0.602100114128637[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]109.154631424205[/C][C]0.366654233734571[/C][C]-3.15463142420537[/C][C]0.413329357891361[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]109.942599986325[/C][C]0.375926629927034[/C][C]-4.64259998632514[/C][C]0.216233565197655[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]110.009348100003[/C][C]0.368692225198402[/C][C]8.79065189999748[/C][C]-0.157728262228629[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]110.360999836184[/C][C]0.368264442911986[/C][C]-4.26099983618425[/C][C]-0.0086377960010636[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]108.918062757243[/C][C]0.320134614131354[/C][C]0.381937242756751[/C][C]-0.915032123650648[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]108.248978493781[/C][C]0.292993174014727[/C][C]8.95102150621929[/C][C]-0.500264487858976[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]109.445912812150[/C][C]0.317924742269318[/C][C]-16.9459128121498[/C][C]0.458898932602473[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]109.998329024923[/C][C]0.324280203971745[/C][C]-5.79832902492336[/C][C]0.119539579511164[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]108.640179614686[/C][C]0.280154718872115[/C][C]3.8598203853136[/C][C]-0.860136310712448[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]109.900055770153[/C][C]0.304859734857201[/C][C]12.4999442298470[/C][C]0.501577874270787[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]110.075576228061[/C][C]0.301714853313248[/C][C]3.22442377193928[/C][C]-0.0662506501165228[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]109.206026206749[/C][C]0.273938130054238[/C][C]-9.2060262067488[/C][C]-0.599998337377564[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]110.448317590123[/C][C]0.296725012273525[/C][C]0.251682409877066[/C][C]0.495801751623415[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]112.862498084998[/C][C]0.347070434402659[/C][C]-0.0624980849981567[/C][C]1.08249484577425[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]109.879975041913[/C][C]0.265999384561075[/C][C]-0.0799750419126175[/C][C]-1.69792094268066[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]112.135650742638[/C][C]0.315867321998037[/C][C]5.16434925736186[/C][C]1.01199085651005[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]111.812627735560[/C][C]0.299438038773849[/C][C]-2.71262773556041[/C][C]-0.324450755077545[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]110.493936631922[/C][C]0.257132316540835[/C][C]5.40606336807807[/C][C]-0.821824416022757[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]110.841435145385[/C][C]0.259506739129008[/C][C]-14.8414351453846[/C][C]0.0459592510074398[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]109.232902735312[/C][C]0.21071436643755[/C][C]-9.4329027353118[/C][C]-0.951746156028206[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]110.12269316056[/C][C]0.228205676022696[/C][C]6.67730683944004[/C][C]0.346469066656222[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]108.344476696577[/C][C]0.17742944163946[/C][C]7.35552330342254[/C][C]-1.02451795269354[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]104.160922265628[/C][C]0.0688897057410682[/C][C]-4.76092226562805[/C][C]-2.22758842502274[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]103.074919560155[/C][C]0.0404780215439526[/C][C]-8.77491956015468[/C][C]-0.589954652290969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63818&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63818&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
195.195.1000
29795.6172457653417-0.0179137176742521.382754234658300.355983429664942
3112.7103.5100616272320.5340923194878279.189938372768072.67772412401643
4102.9105.4251076176920.641816735585187-2.52510761769160.524277643898397
597.4103.3551962666260.460294618783866-5.95519626662574-1.24098426251121
6111.4105.2457309838250.5332805092316346.15426901617550.718438978345372
787.4100.4666726477180.329844159680205-13.0666726477184-2.75926564762998
896.897.672784523050.234102247883548-0.872784523050115-1.63863215627200
9114.1101.6933542373650.33663223555950012.40664576263471.98989980491086
10110.3105.7755021953270.4338824502431264.524497804672641.96626962392249
11103.9106.6795445621540.446107526520085-2.77954456215430.246334626336542
12101.6105.4605103913420.402164682467852-3.86051039134245-0.870790832135682
1394.6103.1481338389920.405379607507195-8.54813383899242-1.47301299519722
1495.9101.7544218949180.402709771371073-5.8544218949178-0.984415504282895
15104.799.36325425671940.3504626435315585.33674574328056-1.42367769403096
16102.899.74685114918540.3515346366261283.053148850814630.0159369830328408
1798.1100.8773749058810.381386334477444-2.777374905880530.374405975970588
18113.9102.1867256387730.41703308506737911.71327436122680.458675404508563
1980.9100.2017226910660.332036963937143-19.3017226910665-1.21903554616217
2095.799.86950202548030.311129466062295-4.16950202548029-0.342395782714928
21113.2100.9466128778360.33244735500771612.25338712216360.397518723956748
22105.9101.3508794106910.3342171266034764.549120589309240.0373398207251376
23108.8103.9135043927720.3824801656873094.886495607228411.15844417296230
24102.3104.6535146927400.38919806339569-2.353514692740420.185972813927420
2599105.2332420319010.392389111935568-6.233242031901200.0993119002920132
26100.7105.4438996787980.389169397687618-4.74389967879805-0.0942976122775373
27115.5107.1978436013670.4184584285606638.302156398633390.696945241968241
28100.7105.0995509225060.353254968432263-4.39955092250612-1.2629212179917
29109.9106.7846600190470.3921723827496673.115339980952940.663873067526193
30114.6105.7236340082280.3475939529422338.87636599177186-0.728312784310215
3185.4105.2238029187730.321815495199988-19.8238029187726-0.429238487890929
32100.5105.3473614133830.316066024321495-4.84736141338333-0.101341346478198
33114.8104.9920693048840.2979295982350339.80793069511591-0.344952271055414
34116.5107.6207835211470.3560500977776328.879216478853141.19994015450840
35112.9108.7960802632850.3749855708085064.103919736715070.421988022066659
36102108.0023795876820.349450759817781-6.00237958768213-0.602100114128637
37106109.1546314242050.366654233734571-3.154631424205370.413329357891361
38105.3109.9425999863250.375926629927034-4.642599986325140.216233565197655
39118.8110.0093481000030.3686922251984028.79065189999748-0.157728262228629
40106.1110.3609998361840.368264442911986-4.26099983618425-0.0086377960010636
41109.3108.9180627572430.3201346141313540.381937242756751-0.915032123650648
42117.2108.2489784937810.2929931740147278.95102150621929-0.500264487858976
4392.5109.4459128121500.317924742269318-16.94591281214980.458898932602473
44104.2109.9983290249230.324280203971745-5.798329024923360.119539579511164
45112.5108.6401796146860.2801547188721153.8598203853136-0.860136310712448
46122.4109.9000557701530.30485973485720112.49994422984700.501577874270787
47113.3110.0755762280610.3017148533132483.22442377193928-0.0662506501165228
48100109.2060262067490.273938130054238-9.2060262067488-0.599998337377564
49110.7110.4483175901230.2967250122735250.2516824098770660.495801751623415
50112.8112.8624980849980.347070434402659-0.06249808499815671.08249484577425
51109.8109.8799750419130.265999384561075-0.0799750419126175-1.69792094268066
52117.3112.1356507426380.3158673219980375.164349257361861.01199085651005
53109.1111.8126277355600.299438038773849-2.71262773556041-0.324450755077545
54115.9110.4939366319220.2571323165408355.40606336807807-0.821824416022757
5596110.8414351453850.259506739129008-14.84143514538460.0459592510074398
5699.8109.2329027353120.21071436643755-9.4329027353118-0.951746156028206
57116.8110.122693160560.2282056760226966.677306839440040.346469066656222
58115.7108.3444766965770.177429441639467.35552330342254-1.02451795269354
5999.4104.1609222656280.0688897057410682-4.76092226562805-2.22758842502274
6094.3103.0749195601550.0404780215439526-8.77491956015468-0.589954652290969



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')