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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 07:54:26 -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/t1259938922wpfusn7jlltr6fy.htm/, Retrieved Sun, 28 Apr 2024 06:26:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63701, Retrieved Sun, 28 Apr 2024 06:26:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
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 9.9] [2009-12-04 14:54:26] [29af64a72952b0c5025d716b5179273f] [Current]
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Dataseries X:
95.1
97.0
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
99.0
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102.0
106.0
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100.0
110.7
112.8
109.8
117.3
109.1
115.9
96.0
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=63701&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=63701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63701&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=63701&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=63701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63701&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')