Free Statistics

of Irreproducible Research!

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 computationSun, 06 Dec 2009 13:39:30 -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/06/t1260132157g4t24votr7y8bs6.htm/, Retrieved Mon, 06 May 2024 07:42:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64504, Retrieved Mon, 06 May 2024 07:42:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
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] [review WS 9 struc...] [2009-12-06 20:39:30] [51d49d3536f6a59f2486a67bf50b2759] [Current]
Feedback Forum

Post a new message
Dataseries X:
2360
2214
2825
2355
2333
3016
2155
2172
2150
2533
2058
2160
2260
2498
2695
2799
2947
2930
2318
2540
2570
2669
2450
2842
3440
2678
2981
2260
2844
2546
2456
2295
2379
2479
2057
2280
2351
2276
2548
2311
2201
2725
2408
2139
1898
2537
2069
2063
2524
2437
2189
2793
2074
2622
2278
2144
2427
2139
1828
2072
1800
1758
2246
1987
1868
2514
2121




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=64504&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=64504&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64504&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
123602360000
222142312.07185528361-6.09728971493757-10.4814504587985-0.503823320444186
328252486.4795383012112.029899271306037.98448062315171.76515815669492
423552448.680991414288.04694318022202-8.2138867659148-0.497455912616607
523332411.583929124645.049685592303861.66131960561733-0.462897298598276
630162611.9583518059716.209939953366045.99227765837172.05073021813675
721552472.54685420778.33551524132652-25.2704786803007-1.6650203457865
821722375.620536251773.51007807127923-2.24116510491916-1.14262590815467
921502302.678939706940.278209027686111-4.37049592090061-0.839005633276679
1025332374.459844104453.1034212647229118.36994048464870.791198574481487
1120582276.91686153069-0.651484681150949-20.0272748430463-1.12072730933494
1221602237.80471816400-2.01665279708150-1.32577296413176-0.430376487549795
1322602234.98380115384-1.9258207451082227.2212796857331-0.0135582763221846
1424982333.399028678892.143573524625721.688578562051110.963772746803275
1526952448.393528907086.6033354170983754.54115043674431.13358629261098
1627992571.2479915895210.534525277192716.17979546394171.23318890829615
1729472703.1618996553114.100819070645713.82057884124011.33046031264561
1829302769.5666371984815.471376849335958.89281551093940.584648547189137
1923182646.9806247964112.1681906169619-56.9107432447787-1.56206758493857
2025402614.0271404329411.163354808685215.7596577450187-0.514552001978047
2125702606.6482836811310.77325380976290.488353433914017-0.212558377597882
2226692621.6383820348610.858006812817138.87808153530530.0485147752708763
2324502579.134100074899.8274210650839-21.4206433738035-0.615559654652054
2428422660.9479830956111.058961839481434.82572328876540.833855510537335
2534402886.324134603031.6738175798809451.1881229973342.97836615147476
2626782839.518777232060.562390348888481-73.4366766160979-0.517168659177117
2729812873.162104750391.4177428629831747.91682494648820.35405989640968
2822602691.71310925119-2.75118549557304-87.9583997277819-2.01893954347535
2928442730.57594456748-1.9200249989936633.1521730635340.469256678942312
3025462656.79381526752-3.2064103264151730.049394039541-0.820940676054125
3124562610.984367479-3.9061159190136-70.65184050281-0.490649390332296
3222952511.75830313100-5.3707336568113-26.8606337874650-1.1035133652903
3323792468.93353149858-5.91732909600921-14.9989100389308-0.435104908455723
3424792456.85838204709-6.0035792421302134.4979595261797-0.0717062016589342
3520572349.75968351681-7.3552746871455-89.3865954923687-1.17950114685758
3622802322.58955430596-7.56062708489063-2.41419156589651-0.232359517512022
3723512287.53802422025-6.91536097947537124.168319463426-0.357766165566186
3822762299.77167985541-6.63001852927053-60.02694728449220.213490258175437
3925482348.08905197103-5.5706070732494197.86007511504780.605719354125933
4023112365.36307795246-5.16722818613402-97.76074409381450.256817895904591
4122012301.40705814395-6.0858106723234113.4272814764400-0.671434143825295
4227252407.08488840604-4.5167353766538298.80740987142471.28949247609095
4324082424.60731875143-4.23249209061842-60.15349052693510.255911638474619
4421392345.53963502971-5.13679192547475-57.9302577934933-0.872537064377107
4518982211.79820162133-6.61512455961682-57.5591676010272-1.50353655325922
4625372277.96735580523-5.81304532325085113.6685308091220.852553327350742
4720692240.82572657119-6.13564051572941-109.098359702545-0.367629344962183
4820632182.91872516131-6.49488393213439-15.3831744507752-0.610944330033546
4925242235.98447140751-7.31405058614799160.8827573801080.749725942233221
5024372314.76552887464-6.4056679745918-43.93624760924440.98213554386673
5121892250.30058657887-7.2925730369801648.1170304956805-0.65160067825887
5227932428.3741042231-4.5892848565675910.49868504660052.10641698339533
5320742335.30614196911-5.74716880791852-89.7567989202613-1.01798149709272
5426222382.82207958888-5.11866050924947134.8389024519370.618033780480188
5522782362.96009068093-5.27856545732531-55.8860781174464-0.172007948654729
5621442304.28415280321-5.82119236018597-54.5299604488134-0.625171885663563
5724272353.07773814699-5.29364767372799-34.55385167043710.64091858745992
5821392255.44894550371-6.1434412646315567.3433823392528-1.08541249282018
5918282152.31441886992-6.95320847066517-130.736536972702-1.14223313958602
6020722127.95099970961-7.04066723065633-20.9175276022442-0.206202918854760
6118001994.25102727495-5.9999928865758470.3221695240682-1.56346128657037
6217581924.42350806582-6.51009920322493-41.9747155219494-0.737738361564623
6322462007.67258776075-5.3847069257033967.41856423862991.01983569746544
6419871993.53473924339-5.4937982258472310.2607411200347-0.10021392832232
6518681983.01384121028-5.55098586876803-105.255784181258-0.058124904806395
6625142091.10432427825-4.37882657175151200.3632020416061.32356497331593
6721212115.25863604306-4.10806931066608-50.46133559737480.333929708904055

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 2360 & 2360 & 0 & 0 & 0 \tabularnewline
2 & 2214 & 2312.07185528361 & -6.09728971493757 & -10.4814504587985 & -0.503823320444186 \tabularnewline
3 & 2825 & 2486.47953830121 & 12.0298992713060 & 37.9844806231517 & 1.76515815669492 \tabularnewline
4 & 2355 & 2448.68099141428 & 8.04694318022202 & -8.2138867659148 & -0.497455912616607 \tabularnewline
5 & 2333 & 2411.58392912464 & 5.04968559230386 & 1.66131960561733 & -0.462897298598276 \tabularnewline
6 & 3016 & 2611.95835180597 & 16.2099399533660 & 45.9922776583717 & 2.05073021813675 \tabularnewline
7 & 2155 & 2472.5468542077 & 8.33551524132652 & -25.2704786803007 & -1.6650203457865 \tabularnewline
8 & 2172 & 2375.62053625177 & 3.51007807127923 & -2.24116510491916 & -1.14262590815467 \tabularnewline
9 & 2150 & 2302.67893970694 & 0.278209027686111 & -4.37049592090061 & -0.839005633276679 \tabularnewline
10 & 2533 & 2374.45984410445 & 3.10342126472291 & 18.3699404846487 & 0.791198574481487 \tabularnewline
11 & 2058 & 2276.91686153069 & -0.651484681150949 & -20.0272748430463 & -1.12072730933494 \tabularnewline
12 & 2160 & 2237.80471816400 & -2.01665279708150 & -1.32577296413176 & -0.430376487549795 \tabularnewline
13 & 2260 & 2234.98380115384 & -1.92582074510822 & 27.2212796857331 & -0.0135582763221846 \tabularnewline
14 & 2498 & 2333.39902867889 & 2.14357352462572 & 1.68857856205111 & 0.963772746803275 \tabularnewline
15 & 2695 & 2448.39352890708 & 6.60333541709837 & 54.5411504367443 & 1.13358629261098 \tabularnewline
16 & 2799 & 2571.24799158952 & 10.5345252771927 & 16.1797954639417 & 1.23318890829615 \tabularnewline
17 & 2947 & 2703.16189965531 & 14.1008190706457 & 13.8205788412401 & 1.33046031264561 \tabularnewline
18 & 2930 & 2769.56663719848 & 15.4713768493359 & 58.8928155109394 & 0.584648547189137 \tabularnewline
19 & 2318 & 2646.98062479641 & 12.1681906169619 & -56.9107432447787 & -1.56206758493857 \tabularnewline
20 & 2540 & 2614.02714043294 & 11.1633548086852 & 15.7596577450187 & -0.514552001978047 \tabularnewline
21 & 2570 & 2606.64828368113 & 10.7732538097629 & 0.488353433914017 & -0.212558377597882 \tabularnewline
22 & 2669 & 2621.63838203486 & 10.8580068128171 & 38.8780815353053 & 0.0485147752708763 \tabularnewline
23 & 2450 & 2579.13410007489 & 9.8274210650839 & -21.4206433738035 & -0.615559654652054 \tabularnewline
24 & 2842 & 2660.94798309561 & 11.0589618394814 & 34.8257232887654 & 0.833855510537335 \tabularnewline
25 & 3440 & 2886.32413460303 & 1.67381757988094 & 51.188122997334 & 2.97836615147476 \tabularnewline
26 & 2678 & 2839.51877723206 & 0.562390348888481 & -73.4366766160979 & -0.517168659177117 \tabularnewline
27 & 2981 & 2873.16210475039 & 1.41774286298317 & 47.9168249464882 & 0.35405989640968 \tabularnewline
28 & 2260 & 2691.71310925119 & -2.75118549557304 & -87.9583997277819 & -2.01893954347535 \tabularnewline
29 & 2844 & 2730.57594456748 & -1.92002499899366 & 33.152173063534 & 0.469256678942312 \tabularnewline
30 & 2546 & 2656.79381526752 & -3.20641032641517 & 30.049394039541 & -0.820940676054125 \tabularnewline
31 & 2456 & 2610.984367479 & -3.9061159190136 & -70.65184050281 & -0.490649390332296 \tabularnewline
32 & 2295 & 2511.75830313100 & -5.3707336568113 & -26.8606337874650 & -1.1035133652903 \tabularnewline
33 & 2379 & 2468.93353149858 & -5.91732909600921 & -14.9989100389308 & -0.435104908455723 \tabularnewline
34 & 2479 & 2456.85838204709 & -6.00357924213021 & 34.4979595261797 & -0.0717062016589342 \tabularnewline
35 & 2057 & 2349.75968351681 & -7.3552746871455 & -89.3865954923687 & -1.17950114685758 \tabularnewline
36 & 2280 & 2322.58955430596 & -7.56062708489063 & -2.41419156589651 & -0.232359517512022 \tabularnewline
37 & 2351 & 2287.53802422025 & -6.91536097947537 & 124.168319463426 & -0.357766165566186 \tabularnewline
38 & 2276 & 2299.77167985541 & -6.63001852927053 & -60.0269472844922 & 0.213490258175437 \tabularnewline
39 & 2548 & 2348.08905197103 & -5.57060707324941 & 97.8600751150478 & 0.605719354125933 \tabularnewline
40 & 2311 & 2365.36307795246 & -5.16722818613402 & -97.7607440938145 & 0.256817895904591 \tabularnewline
41 & 2201 & 2301.40705814395 & -6.08581067232341 & 13.4272814764400 & -0.671434143825295 \tabularnewline
42 & 2725 & 2407.08488840604 & -4.51673537665382 & 98.8074098714247 & 1.28949247609095 \tabularnewline
43 & 2408 & 2424.60731875143 & -4.23249209061842 & -60.1534905269351 & 0.255911638474619 \tabularnewline
44 & 2139 & 2345.53963502971 & -5.13679192547475 & -57.9302577934933 & -0.872537064377107 \tabularnewline
45 & 1898 & 2211.79820162133 & -6.61512455961682 & -57.5591676010272 & -1.50353655325922 \tabularnewline
46 & 2537 & 2277.96735580523 & -5.81304532325085 & 113.668530809122 & 0.852553327350742 \tabularnewline
47 & 2069 & 2240.82572657119 & -6.13564051572941 & -109.098359702545 & -0.367629344962183 \tabularnewline
48 & 2063 & 2182.91872516131 & -6.49488393213439 & -15.3831744507752 & -0.610944330033546 \tabularnewline
49 & 2524 & 2235.98447140751 & -7.31405058614799 & 160.882757380108 & 0.749725942233221 \tabularnewline
50 & 2437 & 2314.76552887464 & -6.4056679745918 & -43.9362476092444 & 0.98213554386673 \tabularnewline
51 & 2189 & 2250.30058657887 & -7.29257303698016 & 48.1170304956805 & -0.65160067825887 \tabularnewline
52 & 2793 & 2428.3741042231 & -4.58928485656759 & 10.4986850466005 & 2.10641698339533 \tabularnewline
53 & 2074 & 2335.30614196911 & -5.74716880791852 & -89.7567989202613 & -1.01798149709272 \tabularnewline
54 & 2622 & 2382.82207958888 & -5.11866050924947 & 134.838902451937 & 0.618033780480188 \tabularnewline
55 & 2278 & 2362.96009068093 & -5.27856545732531 & -55.8860781174464 & -0.172007948654729 \tabularnewline
56 & 2144 & 2304.28415280321 & -5.82119236018597 & -54.5299604488134 & -0.625171885663563 \tabularnewline
57 & 2427 & 2353.07773814699 & -5.29364767372799 & -34.5538516704371 & 0.64091858745992 \tabularnewline
58 & 2139 & 2255.44894550371 & -6.14344126463155 & 67.3433823392528 & -1.08541249282018 \tabularnewline
59 & 1828 & 2152.31441886992 & -6.95320847066517 & -130.736536972702 & -1.14223313958602 \tabularnewline
60 & 2072 & 2127.95099970961 & -7.04066723065633 & -20.9175276022442 & -0.206202918854760 \tabularnewline
61 & 1800 & 1994.25102727495 & -5.99999288657584 & 70.3221695240682 & -1.56346128657037 \tabularnewline
62 & 1758 & 1924.42350806582 & -6.51009920322493 & -41.9747155219494 & -0.737738361564623 \tabularnewline
63 & 2246 & 2007.67258776075 & -5.38470692570339 & 67.4185642386299 & 1.01983569746544 \tabularnewline
64 & 1987 & 1993.53473924339 & -5.49379822584723 & 10.2607411200347 & -0.10021392832232 \tabularnewline
65 & 1868 & 1983.01384121028 & -5.55098586876803 & -105.255784181258 & -0.058124904806395 \tabularnewline
66 & 2514 & 2091.10432427825 & -4.37882657175151 & 200.363202041606 & 1.32356497331593 \tabularnewline
67 & 2121 & 2115.25863604306 & -4.10806931066608 & -50.4613355973748 & 0.333929708904055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64504&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]2360[/C][C]2360[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2214[/C][C]2312.07185528361[/C][C]-6.09728971493757[/C][C]-10.4814504587985[/C][C]-0.503823320444186[/C][/ROW]
[ROW][C]3[/C][C]2825[/C][C]2486.47953830121[/C][C]12.0298992713060[/C][C]37.9844806231517[/C][C]1.76515815669492[/C][/ROW]
[ROW][C]4[/C][C]2355[/C][C]2448.68099141428[/C][C]8.04694318022202[/C][C]-8.2138867659148[/C][C]-0.497455912616607[/C][/ROW]
[ROW][C]5[/C][C]2333[/C][C]2411.58392912464[/C][C]5.04968559230386[/C][C]1.66131960561733[/C][C]-0.462897298598276[/C][/ROW]
[ROW][C]6[/C][C]3016[/C][C]2611.95835180597[/C][C]16.2099399533660[/C][C]45.9922776583717[/C][C]2.05073021813675[/C][/ROW]
[ROW][C]7[/C][C]2155[/C][C]2472.5468542077[/C][C]8.33551524132652[/C][C]-25.2704786803007[/C][C]-1.6650203457865[/C][/ROW]
[ROW][C]8[/C][C]2172[/C][C]2375.62053625177[/C][C]3.51007807127923[/C][C]-2.24116510491916[/C][C]-1.14262590815467[/C][/ROW]
[ROW][C]9[/C][C]2150[/C][C]2302.67893970694[/C][C]0.278209027686111[/C][C]-4.37049592090061[/C][C]-0.839005633276679[/C][/ROW]
[ROW][C]10[/C][C]2533[/C][C]2374.45984410445[/C][C]3.10342126472291[/C][C]18.3699404846487[/C][C]0.791198574481487[/C][/ROW]
[ROW][C]11[/C][C]2058[/C][C]2276.91686153069[/C][C]-0.651484681150949[/C][C]-20.0272748430463[/C][C]-1.12072730933494[/C][/ROW]
[ROW][C]12[/C][C]2160[/C][C]2237.80471816400[/C][C]-2.01665279708150[/C][C]-1.32577296413176[/C][C]-0.430376487549795[/C][/ROW]
[ROW][C]13[/C][C]2260[/C][C]2234.98380115384[/C][C]-1.92582074510822[/C][C]27.2212796857331[/C][C]-0.0135582763221846[/C][/ROW]
[ROW][C]14[/C][C]2498[/C][C]2333.39902867889[/C][C]2.14357352462572[/C][C]1.68857856205111[/C][C]0.963772746803275[/C][/ROW]
[ROW][C]15[/C][C]2695[/C][C]2448.39352890708[/C][C]6.60333541709837[/C][C]54.5411504367443[/C][C]1.13358629261098[/C][/ROW]
[ROW][C]16[/C][C]2799[/C][C]2571.24799158952[/C][C]10.5345252771927[/C][C]16.1797954639417[/C][C]1.23318890829615[/C][/ROW]
[ROW][C]17[/C][C]2947[/C][C]2703.16189965531[/C][C]14.1008190706457[/C][C]13.8205788412401[/C][C]1.33046031264561[/C][/ROW]
[ROW][C]18[/C][C]2930[/C][C]2769.56663719848[/C][C]15.4713768493359[/C][C]58.8928155109394[/C][C]0.584648547189137[/C][/ROW]
[ROW][C]19[/C][C]2318[/C][C]2646.98062479641[/C][C]12.1681906169619[/C][C]-56.9107432447787[/C][C]-1.56206758493857[/C][/ROW]
[ROW][C]20[/C][C]2540[/C][C]2614.02714043294[/C][C]11.1633548086852[/C][C]15.7596577450187[/C][C]-0.514552001978047[/C][/ROW]
[ROW][C]21[/C][C]2570[/C][C]2606.64828368113[/C][C]10.7732538097629[/C][C]0.488353433914017[/C][C]-0.212558377597882[/C][/ROW]
[ROW][C]22[/C][C]2669[/C][C]2621.63838203486[/C][C]10.8580068128171[/C][C]38.8780815353053[/C][C]0.0485147752708763[/C][/ROW]
[ROW][C]23[/C][C]2450[/C][C]2579.13410007489[/C][C]9.8274210650839[/C][C]-21.4206433738035[/C][C]-0.615559654652054[/C][/ROW]
[ROW][C]24[/C][C]2842[/C][C]2660.94798309561[/C][C]11.0589618394814[/C][C]34.8257232887654[/C][C]0.833855510537335[/C][/ROW]
[ROW][C]25[/C][C]3440[/C][C]2886.32413460303[/C][C]1.67381757988094[/C][C]51.188122997334[/C][C]2.97836615147476[/C][/ROW]
[ROW][C]26[/C][C]2678[/C][C]2839.51877723206[/C][C]0.562390348888481[/C][C]-73.4366766160979[/C][C]-0.517168659177117[/C][/ROW]
[ROW][C]27[/C][C]2981[/C][C]2873.16210475039[/C][C]1.41774286298317[/C][C]47.9168249464882[/C][C]0.35405989640968[/C][/ROW]
[ROW][C]28[/C][C]2260[/C][C]2691.71310925119[/C][C]-2.75118549557304[/C][C]-87.9583997277819[/C][C]-2.01893954347535[/C][/ROW]
[ROW][C]29[/C][C]2844[/C][C]2730.57594456748[/C][C]-1.92002499899366[/C][C]33.152173063534[/C][C]0.469256678942312[/C][/ROW]
[ROW][C]30[/C][C]2546[/C][C]2656.79381526752[/C][C]-3.20641032641517[/C][C]30.049394039541[/C][C]-0.820940676054125[/C][/ROW]
[ROW][C]31[/C][C]2456[/C][C]2610.984367479[/C][C]-3.9061159190136[/C][C]-70.65184050281[/C][C]-0.490649390332296[/C][/ROW]
[ROW][C]32[/C][C]2295[/C][C]2511.75830313100[/C][C]-5.3707336568113[/C][C]-26.8606337874650[/C][C]-1.1035133652903[/C][/ROW]
[ROW][C]33[/C][C]2379[/C][C]2468.93353149858[/C][C]-5.91732909600921[/C][C]-14.9989100389308[/C][C]-0.435104908455723[/C][/ROW]
[ROW][C]34[/C][C]2479[/C][C]2456.85838204709[/C][C]-6.00357924213021[/C][C]34.4979595261797[/C][C]-0.0717062016589342[/C][/ROW]
[ROW][C]35[/C][C]2057[/C][C]2349.75968351681[/C][C]-7.3552746871455[/C][C]-89.3865954923687[/C][C]-1.17950114685758[/C][/ROW]
[ROW][C]36[/C][C]2280[/C][C]2322.58955430596[/C][C]-7.56062708489063[/C][C]-2.41419156589651[/C][C]-0.232359517512022[/C][/ROW]
[ROW][C]37[/C][C]2351[/C][C]2287.53802422025[/C][C]-6.91536097947537[/C][C]124.168319463426[/C][C]-0.357766165566186[/C][/ROW]
[ROW][C]38[/C][C]2276[/C][C]2299.77167985541[/C][C]-6.63001852927053[/C][C]-60.0269472844922[/C][C]0.213490258175437[/C][/ROW]
[ROW][C]39[/C][C]2548[/C][C]2348.08905197103[/C][C]-5.57060707324941[/C][C]97.8600751150478[/C][C]0.605719354125933[/C][/ROW]
[ROW][C]40[/C][C]2311[/C][C]2365.36307795246[/C][C]-5.16722818613402[/C][C]-97.7607440938145[/C][C]0.256817895904591[/C][/ROW]
[ROW][C]41[/C][C]2201[/C][C]2301.40705814395[/C][C]-6.08581067232341[/C][C]13.4272814764400[/C][C]-0.671434143825295[/C][/ROW]
[ROW][C]42[/C][C]2725[/C][C]2407.08488840604[/C][C]-4.51673537665382[/C][C]98.8074098714247[/C][C]1.28949247609095[/C][/ROW]
[ROW][C]43[/C][C]2408[/C][C]2424.60731875143[/C][C]-4.23249209061842[/C][C]-60.1534905269351[/C][C]0.255911638474619[/C][/ROW]
[ROW][C]44[/C][C]2139[/C][C]2345.53963502971[/C][C]-5.13679192547475[/C][C]-57.9302577934933[/C][C]-0.872537064377107[/C][/ROW]
[ROW][C]45[/C][C]1898[/C][C]2211.79820162133[/C][C]-6.61512455961682[/C][C]-57.5591676010272[/C][C]-1.50353655325922[/C][/ROW]
[ROW][C]46[/C][C]2537[/C][C]2277.96735580523[/C][C]-5.81304532325085[/C][C]113.668530809122[/C][C]0.852553327350742[/C][/ROW]
[ROW][C]47[/C][C]2069[/C][C]2240.82572657119[/C][C]-6.13564051572941[/C][C]-109.098359702545[/C][C]-0.367629344962183[/C][/ROW]
[ROW][C]48[/C][C]2063[/C][C]2182.91872516131[/C][C]-6.49488393213439[/C][C]-15.3831744507752[/C][C]-0.610944330033546[/C][/ROW]
[ROW][C]49[/C][C]2524[/C][C]2235.98447140751[/C][C]-7.31405058614799[/C][C]160.882757380108[/C][C]0.749725942233221[/C][/ROW]
[ROW][C]50[/C][C]2437[/C][C]2314.76552887464[/C][C]-6.4056679745918[/C][C]-43.9362476092444[/C][C]0.98213554386673[/C][/ROW]
[ROW][C]51[/C][C]2189[/C][C]2250.30058657887[/C][C]-7.29257303698016[/C][C]48.1170304956805[/C][C]-0.65160067825887[/C][/ROW]
[ROW][C]52[/C][C]2793[/C][C]2428.3741042231[/C][C]-4.58928485656759[/C][C]10.4986850466005[/C][C]2.10641698339533[/C][/ROW]
[ROW][C]53[/C][C]2074[/C][C]2335.30614196911[/C][C]-5.74716880791852[/C][C]-89.7567989202613[/C][C]-1.01798149709272[/C][/ROW]
[ROW][C]54[/C][C]2622[/C][C]2382.82207958888[/C][C]-5.11866050924947[/C][C]134.838902451937[/C][C]0.618033780480188[/C][/ROW]
[ROW][C]55[/C][C]2278[/C][C]2362.96009068093[/C][C]-5.27856545732531[/C][C]-55.8860781174464[/C][C]-0.172007948654729[/C][/ROW]
[ROW][C]56[/C][C]2144[/C][C]2304.28415280321[/C][C]-5.82119236018597[/C][C]-54.5299604488134[/C][C]-0.625171885663563[/C][/ROW]
[ROW][C]57[/C][C]2427[/C][C]2353.07773814699[/C][C]-5.29364767372799[/C][C]-34.5538516704371[/C][C]0.64091858745992[/C][/ROW]
[ROW][C]58[/C][C]2139[/C][C]2255.44894550371[/C][C]-6.14344126463155[/C][C]67.3433823392528[/C][C]-1.08541249282018[/C][/ROW]
[ROW][C]59[/C][C]1828[/C][C]2152.31441886992[/C][C]-6.95320847066517[/C][C]-130.736536972702[/C][C]-1.14223313958602[/C][/ROW]
[ROW][C]60[/C][C]2072[/C][C]2127.95099970961[/C][C]-7.04066723065633[/C][C]-20.9175276022442[/C][C]-0.206202918854760[/C][/ROW]
[ROW][C]61[/C][C]1800[/C][C]1994.25102727495[/C][C]-5.99999288657584[/C][C]70.3221695240682[/C][C]-1.56346128657037[/C][/ROW]
[ROW][C]62[/C][C]1758[/C][C]1924.42350806582[/C][C]-6.51009920322493[/C][C]-41.9747155219494[/C][C]-0.737738361564623[/C][/ROW]
[ROW][C]63[/C][C]2246[/C][C]2007.67258776075[/C][C]-5.38470692570339[/C][C]67.4185642386299[/C][C]1.01983569746544[/C][/ROW]
[ROW][C]64[/C][C]1987[/C][C]1993.53473924339[/C][C]-5.49379822584723[/C][C]10.2607411200347[/C][C]-0.10021392832232[/C][/ROW]
[ROW][C]65[/C][C]1868[/C][C]1983.01384121028[/C][C]-5.55098586876803[/C][C]-105.255784181258[/C][C]-0.058124904806395[/C][/ROW]
[ROW][C]66[/C][C]2514[/C][C]2091.10432427825[/C][C]-4.37882657175151[/C][C]200.363202041606[/C][C]1.32356497331593[/C][/ROW]
[ROW][C]67[/C][C]2121[/C][C]2115.25863604306[/C][C]-4.10806931066608[/C][C]-50.4613355973748[/C][C]0.333929708904055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64504&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64504&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
123602360000
222142312.07185528361-6.09728971493757-10.4814504587985-0.503823320444186
328252486.4795383012112.029899271306037.98448062315171.76515815669492
423552448.680991414288.04694318022202-8.2138867659148-0.497455912616607
523332411.583929124645.049685592303861.66131960561733-0.462897298598276
630162611.9583518059716.209939953366045.99227765837172.05073021813675
721552472.54685420778.33551524132652-25.2704786803007-1.6650203457865
821722375.620536251773.51007807127923-2.24116510491916-1.14262590815467
921502302.678939706940.278209027686111-4.37049592090061-0.839005633276679
1025332374.459844104453.1034212647229118.36994048464870.791198574481487
1120582276.91686153069-0.651484681150949-20.0272748430463-1.12072730933494
1221602237.80471816400-2.01665279708150-1.32577296413176-0.430376487549795
1322602234.98380115384-1.9258207451082227.2212796857331-0.0135582763221846
1424982333.399028678892.143573524625721.688578562051110.963772746803275
1526952448.393528907086.6033354170983754.54115043674431.13358629261098
1627992571.2479915895210.534525277192716.17979546394171.23318890829615
1729472703.1618996553114.100819070645713.82057884124011.33046031264561
1829302769.5666371984815.471376849335958.89281551093940.584648547189137
1923182646.9806247964112.1681906169619-56.9107432447787-1.56206758493857
2025402614.0271404329411.163354808685215.7596577450187-0.514552001978047
2125702606.6482836811310.77325380976290.488353433914017-0.212558377597882
2226692621.6383820348610.858006812817138.87808153530530.0485147752708763
2324502579.134100074899.8274210650839-21.4206433738035-0.615559654652054
2428422660.9479830956111.058961839481434.82572328876540.833855510537335
2534402886.324134603031.6738175798809451.1881229973342.97836615147476
2626782839.518777232060.562390348888481-73.4366766160979-0.517168659177117
2729812873.162104750391.4177428629831747.91682494648820.35405989640968
2822602691.71310925119-2.75118549557304-87.9583997277819-2.01893954347535
2928442730.57594456748-1.9200249989936633.1521730635340.469256678942312
3025462656.79381526752-3.2064103264151730.049394039541-0.820940676054125
3124562610.984367479-3.9061159190136-70.65184050281-0.490649390332296
3222952511.75830313100-5.3707336568113-26.8606337874650-1.1035133652903
3323792468.93353149858-5.91732909600921-14.9989100389308-0.435104908455723
3424792456.85838204709-6.0035792421302134.4979595261797-0.0717062016589342
3520572349.75968351681-7.3552746871455-89.3865954923687-1.17950114685758
3622802322.58955430596-7.56062708489063-2.41419156589651-0.232359517512022
3723512287.53802422025-6.91536097947537124.168319463426-0.357766165566186
3822762299.77167985541-6.63001852927053-60.02694728449220.213490258175437
3925482348.08905197103-5.5706070732494197.86007511504780.605719354125933
4023112365.36307795246-5.16722818613402-97.76074409381450.256817895904591
4122012301.40705814395-6.0858106723234113.4272814764400-0.671434143825295
4227252407.08488840604-4.5167353766538298.80740987142471.28949247609095
4324082424.60731875143-4.23249209061842-60.15349052693510.255911638474619
4421392345.53963502971-5.13679192547475-57.9302577934933-0.872537064377107
4518982211.79820162133-6.61512455961682-57.5591676010272-1.50353655325922
4625372277.96735580523-5.81304532325085113.6685308091220.852553327350742
4720692240.82572657119-6.13564051572941-109.098359702545-0.367629344962183
4820632182.91872516131-6.49488393213439-15.3831744507752-0.610944330033546
4925242235.98447140751-7.31405058614799160.8827573801080.749725942233221
5024372314.76552887464-6.4056679745918-43.93624760924440.98213554386673
5121892250.30058657887-7.2925730369801648.1170304956805-0.65160067825887
5227932428.3741042231-4.5892848565675910.49868504660052.10641698339533
5320742335.30614196911-5.74716880791852-89.7567989202613-1.01798149709272
5426222382.82207958888-5.11866050924947134.8389024519370.618033780480188
5522782362.96009068093-5.27856545732531-55.8860781174464-0.172007948654729
5621442304.28415280321-5.82119236018597-54.5299604488134-0.625171885663563
5724272353.07773814699-5.29364767372799-34.55385167043710.64091858745992
5821392255.44894550371-6.1434412646315567.3433823392528-1.08541249282018
5918282152.31441886992-6.95320847066517-130.736536972702-1.14223313958602
6020722127.95099970961-7.04066723065633-20.9175276022442-0.206202918854760
6118001994.25102727495-5.9999928865758470.3221695240682-1.56346128657037
6217581924.42350806582-6.51009920322493-41.9747155219494-0.737738361564623
6322462007.67258776075-5.3847069257033967.41856423862991.01983569746544
6419871993.53473924339-5.4937982258472310.2607411200347-0.10021392832232
6518681983.01384121028-5.55098586876803-105.255784181258-0.058124904806395
6625142091.10432427825-4.37882657175151200.3632020416061.32356497331593
6721212115.25863604306-4.10806931066608-50.46133559737480.333929708904055



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')