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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 computationTue, 29 Nov 2011 09:37:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/29/t1322577496rx0fj5k1ki509vi.htm/, Retrieved Thu, 25 Apr 2024 15:31:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148432, Retrieved Thu, 25 Apr 2024 15:31:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
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Dataseries X:
3440
2678
2981
2260
2844
2546
2456
2295
2379
2479
2057
2280
2351
2276
2548
2311
2201
2725
2408
2139
1898
2539
2070
2063
2565
2442
2194
2798
2074
2628
2289
2154
2467
2137
1850
2075
1791
1755
2232
1952
1822
2522
2074
2366
2173
2094
1833
1858
2040
2133
2921
3252
3318
3554
2308
1621
1315
1501
1418
1657




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148432&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148432&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' @ jenkins.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
134403440000
226782762.56414932993-38.4294472339593-46.9341401117968-1.39131405326396
329812964.37507626266-34.4992507077458-15.22808970879870.789451657079431
422602404.32440720153-38.129490035434-72.4254750209391-1.749725409212
528442787.72446033554-35.6398295514112-1.466816871960821.40372919805685
625462616.9378630644-36.4310097501767-52.4248638002104-0.450039480693289
724562504.81093178292-36.872530185916-38.4418938105226-0.252063773282742
822952353.20398297726-37.5381650903244-42.487301633522-0.382055629024362
923792397.89048101585-37.0638681016879-30.15393610528270.273797988041371
1024792491.66534500014-36.3134786386894-30.58824786549140.435672166535127
1120572155.56171402632-38.0230365340732-57.4948202591086-0.998242927527417
1222802279.78261778632-37.1030841565549-22.00780433652940.540235111015348
1323512068.04211820913-28.5110781527977307.10864613802-0.701718303062675
1422762284.24379908962-22.5637636862289-33.23357607895160.698413804787742
1525482475.32365825642-20.349010701145.10954898521840.702148526187961
1623112406.49203042451-20.5401635967329-89.0668255781634-0.161272118088235
1722012219.49788754718-21.03583708787333.58910711340628-0.553787636648194
1827252647.62600927868-19.690708382049617.77878274235471.49426312176725
1924082473.88950709522-20.1575722969995-45.4519324923021-0.512462958019808
2021392218.34662426683-20.8713073138351-48.1179165394986-0.783050525100425
2118981967.15074142038-21.5692070632873-38.5934375522727-0.766217158281547
2225392418.82668439509-20.11919226628857.3875731326851.57439873596696
2320702184.37932005239-20.7934628034211-85.9432322054667-0.713119528459332
2420632075.51966556452-20.9287243953168-0.831287298463279-0.292761134142018
2525652251.96110297785-25.1411069055697286.4759373249340.715482782642285
2624422452.96731388075-21.9133121382752-36.87068401652240.693600769263683
2721942212.18293366375-23.79987252702089.49411147008802-0.718326994177994
2827982724.15801014681-21.94595882598714.283706506810191.78145325981994
2920742250.97325973203-22.9496444564121-118.279646038613-1.50093781396799
3026282514.0605321113-22.326396710816976.73589994717620.951346084893503
3122892360.7660850852-22.6202696194486-54.7334895868871-0.4355752957275
3221542208.93345591683-22.9131924424353-38.1297623731913-0.429732535470575
3324672470.62443235694-22.2616973688557-40.63616195007020.946550195717155
3421372151.32247133335-22.965928202481124.3069111243238-0.988006462234519
3518501959.95931943273-23.3567652471696-88.0568966975063-0.560161006672859
3620752054.97404967379-23.36931268465584.624517321374570.393464503777829
3717911674.14579806934-18.9800974785316164.07393006388-1.24768538421856
3817551735.29130512994-18.228122471025910.11313968954750.253708454225406
3922322156.69835958469-14.919467949673220.28528881393411.44190297566575
4019521958.34032777381-15.548500099952117.1849692373889-0.60965079356734
4118221956.50352094609-15.5212724042376-136.2667936662580.0456053604617785
4225222320.04665030365-14.8338190485467153.2039443864921.26064779774233
4320742161.79346697747-15.1034813892902-69.3515063096079-0.476935765256992
4423662367.16672052163-14.677773554307-29.5154432385720.733176845708424
4521732220.4448125066-14.9401343132464-30.4669248707995-0.439121238667441
4620942076.11275821785-15.208346485103734.5248388961547-0.43035973448493
4718331951.5130644948-15.4029769696873-104.444512468925-0.36380150864198
4818581836.78014539064-15.316432244676933.9993628764732-0.330312926305672
4920401882.46949808194-15.7908761926556149.5759521706480.208900952876033
5021332115.76744584923-14.1459752645584-13.23826287593830.802183184181099
5129212713.74648949672-10.0854415322363130.9808431270962.00792147690468
5232523131.83412154732-8.6009240355239565.65557499327131.42225303490739
5333183426.50126617655-8.0125491177434-147.2469228356861.00862759333526
5435543399.44879818759-8.04355250002009156.984313200717-0.0633190157851033
5523082619.23373440008-9.33306628653656-212.570092359061-2.56771568250534
5616211824.25496038067-10.7054200617168-102.879054733754-2.61247732838662
5713151405.43200190844-11.4531088883506-38.2909543394421-1.35719268456175
5815011417.35509259598-11.409289695724480.6581114060150.0777477237420598
5914181477.44002527149-11.3128179888805-68.57661984204680.237716394275444
6016571592.24200574156-11.461681462380748.63232665469970.419580031660107

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 3440 & 3440 & 0 & 0 & 0 \tabularnewline
2 & 2678 & 2762.56414932993 & -38.4294472339593 & -46.9341401117968 & -1.39131405326396 \tabularnewline
3 & 2981 & 2964.37507626266 & -34.4992507077458 & -15.2280897087987 & 0.789451657079431 \tabularnewline
4 & 2260 & 2404.32440720153 & -38.129490035434 & -72.4254750209391 & -1.749725409212 \tabularnewline
5 & 2844 & 2787.72446033554 & -35.6398295514112 & -1.46681687196082 & 1.40372919805685 \tabularnewline
6 & 2546 & 2616.9378630644 & -36.4310097501767 & -52.4248638002104 & -0.450039480693289 \tabularnewline
7 & 2456 & 2504.81093178292 & -36.872530185916 & -38.4418938105226 & -0.252063773282742 \tabularnewline
8 & 2295 & 2353.20398297726 & -37.5381650903244 & -42.487301633522 & -0.382055629024362 \tabularnewline
9 & 2379 & 2397.89048101585 & -37.0638681016879 & -30.1539361052827 & 0.273797988041371 \tabularnewline
10 & 2479 & 2491.66534500014 & -36.3134786386894 & -30.5882478654914 & 0.435672166535127 \tabularnewline
11 & 2057 & 2155.56171402632 & -38.0230365340732 & -57.4948202591086 & -0.998242927527417 \tabularnewline
12 & 2280 & 2279.78261778632 & -37.1030841565549 & -22.0078043365294 & 0.540235111015348 \tabularnewline
13 & 2351 & 2068.04211820913 & -28.5110781527977 & 307.10864613802 & -0.701718303062675 \tabularnewline
14 & 2276 & 2284.24379908962 & -22.5637636862289 & -33.2335760789516 & 0.698413804787742 \tabularnewline
15 & 2548 & 2475.32365825642 & -20.3490107011 & 45.1095489852184 & 0.702148526187961 \tabularnewline
16 & 2311 & 2406.49203042451 & -20.5401635967329 & -89.0668255781634 & -0.161272118088235 \tabularnewline
17 & 2201 & 2219.49788754718 & -21.0358370878733 & 3.58910711340628 & -0.553787636648194 \tabularnewline
18 & 2725 & 2647.62600927868 & -19.6907083820496 & 17.7787827423547 & 1.49426312176725 \tabularnewline
19 & 2408 & 2473.88950709522 & -20.1575722969995 & -45.4519324923021 & -0.512462958019808 \tabularnewline
20 & 2139 & 2218.34662426683 & -20.8713073138351 & -48.1179165394986 & -0.783050525100425 \tabularnewline
21 & 1898 & 1967.15074142038 & -21.5692070632873 & -38.5934375522727 & -0.766217158281547 \tabularnewline
22 & 2539 & 2418.82668439509 & -20.119192266288 & 57.387573132685 & 1.57439873596696 \tabularnewline
23 & 2070 & 2184.37932005239 & -20.7934628034211 & -85.9432322054667 & -0.713119528459332 \tabularnewline
24 & 2063 & 2075.51966556452 & -20.9287243953168 & -0.831287298463279 & -0.292761134142018 \tabularnewline
25 & 2565 & 2251.96110297785 & -25.1411069055697 & 286.475937324934 & 0.715482782642285 \tabularnewline
26 & 2442 & 2452.96731388075 & -21.9133121382752 & -36.8706840165224 & 0.693600769263683 \tabularnewline
27 & 2194 & 2212.18293366375 & -23.7998725270208 & 9.49411147008802 & -0.718326994177994 \tabularnewline
28 & 2798 & 2724.15801014681 & -21.9459588259871 & 4.28370650681019 & 1.78145325981994 \tabularnewline
29 & 2074 & 2250.97325973203 & -22.9496444564121 & -118.279646038613 & -1.50093781396799 \tabularnewline
30 & 2628 & 2514.0605321113 & -22.3263967108169 & 76.7358999471762 & 0.951346084893503 \tabularnewline
31 & 2289 & 2360.7660850852 & -22.6202696194486 & -54.7334895868871 & -0.4355752957275 \tabularnewline
32 & 2154 & 2208.93345591683 & -22.9131924424353 & -38.1297623731913 & -0.429732535470575 \tabularnewline
33 & 2467 & 2470.62443235694 & -22.2616973688557 & -40.6361619500702 & 0.946550195717155 \tabularnewline
34 & 2137 & 2151.32247133335 & -22.9659282024811 & 24.3069111243238 & -0.988006462234519 \tabularnewline
35 & 1850 & 1959.95931943273 & -23.3567652471696 & -88.0568966975063 & -0.560161006672859 \tabularnewline
36 & 2075 & 2054.97404967379 & -23.3693126846558 & 4.62451732137457 & 0.393464503777829 \tabularnewline
37 & 1791 & 1674.14579806934 & -18.9800974785316 & 164.07393006388 & -1.24768538421856 \tabularnewline
38 & 1755 & 1735.29130512994 & -18.2281224710259 & 10.1131396895475 & 0.253708454225406 \tabularnewline
39 & 2232 & 2156.69835958469 & -14.9194679496732 & 20.2852888139341 & 1.44190297566575 \tabularnewline
40 & 1952 & 1958.34032777381 & -15.5485000999521 & 17.1849692373889 & -0.60965079356734 \tabularnewline
41 & 1822 & 1956.50352094609 & -15.5212724042376 & -136.266793666258 & 0.0456053604617785 \tabularnewline
42 & 2522 & 2320.04665030365 & -14.8338190485467 & 153.203944386492 & 1.26064779774233 \tabularnewline
43 & 2074 & 2161.79346697747 & -15.1034813892902 & -69.3515063096079 & -0.476935765256992 \tabularnewline
44 & 2366 & 2367.16672052163 & -14.677773554307 & -29.515443238572 & 0.733176845708424 \tabularnewline
45 & 2173 & 2220.4448125066 & -14.9401343132464 & -30.4669248707995 & -0.439121238667441 \tabularnewline
46 & 2094 & 2076.11275821785 & -15.2083464851037 & 34.5248388961547 & -0.43035973448493 \tabularnewline
47 & 1833 & 1951.5130644948 & -15.4029769696873 & -104.444512468925 & -0.36380150864198 \tabularnewline
48 & 1858 & 1836.78014539064 & -15.3164322446769 & 33.9993628764732 & -0.330312926305672 \tabularnewline
49 & 2040 & 1882.46949808194 & -15.7908761926556 & 149.575952170648 & 0.208900952876033 \tabularnewline
50 & 2133 & 2115.76744584923 & -14.1459752645584 & -13.2382628759383 & 0.802183184181099 \tabularnewline
51 & 2921 & 2713.74648949672 & -10.0854415322363 & 130.980843127096 & 2.00792147690468 \tabularnewline
52 & 3252 & 3131.83412154732 & -8.60092403552395 & 65.6555749932713 & 1.42225303490739 \tabularnewline
53 & 3318 & 3426.50126617655 & -8.0125491177434 & -147.246922835686 & 1.00862759333526 \tabularnewline
54 & 3554 & 3399.44879818759 & -8.04355250002009 & 156.984313200717 & -0.0633190157851033 \tabularnewline
55 & 2308 & 2619.23373440008 & -9.33306628653656 & -212.570092359061 & -2.56771568250534 \tabularnewline
56 & 1621 & 1824.25496038067 & -10.7054200617168 & -102.879054733754 & -2.61247732838662 \tabularnewline
57 & 1315 & 1405.43200190844 & -11.4531088883506 & -38.2909543394421 & -1.35719268456175 \tabularnewline
58 & 1501 & 1417.35509259598 & -11.4092896957244 & 80.658111406015 & 0.0777477237420598 \tabularnewline
59 & 1418 & 1477.44002527149 & -11.3128179888805 & -68.5766198420468 & 0.237716394275444 \tabularnewline
60 & 1657 & 1592.24200574156 & -11.4616814623807 & 48.6323266546997 & 0.419580031660107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148432&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]3440[/C][C]3440[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2678[/C][C]2762.56414932993[/C][C]-38.4294472339593[/C][C]-46.9341401117968[/C][C]-1.39131405326396[/C][/ROW]
[ROW][C]3[/C][C]2981[/C][C]2964.37507626266[/C][C]-34.4992507077458[/C][C]-15.2280897087987[/C][C]0.789451657079431[/C][/ROW]
[ROW][C]4[/C][C]2260[/C][C]2404.32440720153[/C][C]-38.129490035434[/C][C]-72.4254750209391[/C][C]-1.749725409212[/C][/ROW]
[ROW][C]5[/C][C]2844[/C][C]2787.72446033554[/C][C]-35.6398295514112[/C][C]-1.46681687196082[/C][C]1.40372919805685[/C][/ROW]
[ROW][C]6[/C][C]2546[/C][C]2616.9378630644[/C][C]-36.4310097501767[/C][C]-52.4248638002104[/C][C]-0.450039480693289[/C][/ROW]
[ROW][C]7[/C][C]2456[/C][C]2504.81093178292[/C][C]-36.872530185916[/C][C]-38.4418938105226[/C][C]-0.252063773282742[/C][/ROW]
[ROW][C]8[/C][C]2295[/C][C]2353.20398297726[/C][C]-37.5381650903244[/C][C]-42.487301633522[/C][C]-0.382055629024362[/C][/ROW]
[ROW][C]9[/C][C]2379[/C][C]2397.89048101585[/C][C]-37.0638681016879[/C][C]-30.1539361052827[/C][C]0.273797988041371[/C][/ROW]
[ROW][C]10[/C][C]2479[/C][C]2491.66534500014[/C][C]-36.3134786386894[/C][C]-30.5882478654914[/C][C]0.435672166535127[/C][/ROW]
[ROW][C]11[/C][C]2057[/C][C]2155.56171402632[/C][C]-38.0230365340732[/C][C]-57.4948202591086[/C][C]-0.998242927527417[/C][/ROW]
[ROW][C]12[/C][C]2280[/C][C]2279.78261778632[/C][C]-37.1030841565549[/C][C]-22.0078043365294[/C][C]0.540235111015348[/C][/ROW]
[ROW][C]13[/C][C]2351[/C][C]2068.04211820913[/C][C]-28.5110781527977[/C][C]307.10864613802[/C][C]-0.701718303062675[/C][/ROW]
[ROW][C]14[/C][C]2276[/C][C]2284.24379908962[/C][C]-22.5637636862289[/C][C]-33.2335760789516[/C][C]0.698413804787742[/C][/ROW]
[ROW][C]15[/C][C]2548[/C][C]2475.32365825642[/C][C]-20.3490107011[/C][C]45.1095489852184[/C][C]0.702148526187961[/C][/ROW]
[ROW][C]16[/C][C]2311[/C][C]2406.49203042451[/C][C]-20.5401635967329[/C][C]-89.0668255781634[/C][C]-0.161272118088235[/C][/ROW]
[ROW][C]17[/C][C]2201[/C][C]2219.49788754718[/C][C]-21.0358370878733[/C][C]3.58910711340628[/C][C]-0.553787636648194[/C][/ROW]
[ROW][C]18[/C][C]2725[/C][C]2647.62600927868[/C][C]-19.6907083820496[/C][C]17.7787827423547[/C][C]1.49426312176725[/C][/ROW]
[ROW][C]19[/C][C]2408[/C][C]2473.88950709522[/C][C]-20.1575722969995[/C][C]-45.4519324923021[/C][C]-0.512462958019808[/C][/ROW]
[ROW][C]20[/C][C]2139[/C][C]2218.34662426683[/C][C]-20.8713073138351[/C][C]-48.1179165394986[/C][C]-0.783050525100425[/C][/ROW]
[ROW][C]21[/C][C]1898[/C][C]1967.15074142038[/C][C]-21.5692070632873[/C][C]-38.5934375522727[/C][C]-0.766217158281547[/C][/ROW]
[ROW][C]22[/C][C]2539[/C][C]2418.82668439509[/C][C]-20.119192266288[/C][C]57.387573132685[/C][C]1.57439873596696[/C][/ROW]
[ROW][C]23[/C][C]2070[/C][C]2184.37932005239[/C][C]-20.7934628034211[/C][C]-85.9432322054667[/C][C]-0.713119528459332[/C][/ROW]
[ROW][C]24[/C][C]2063[/C][C]2075.51966556452[/C][C]-20.9287243953168[/C][C]-0.831287298463279[/C][C]-0.292761134142018[/C][/ROW]
[ROW][C]25[/C][C]2565[/C][C]2251.96110297785[/C][C]-25.1411069055697[/C][C]286.475937324934[/C][C]0.715482782642285[/C][/ROW]
[ROW][C]26[/C][C]2442[/C][C]2452.96731388075[/C][C]-21.9133121382752[/C][C]-36.8706840165224[/C][C]0.693600769263683[/C][/ROW]
[ROW][C]27[/C][C]2194[/C][C]2212.18293366375[/C][C]-23.7998725270208[/C][C]9.49411147008802[/C][C]-0.718326994177994[/C][/ROW]
[ROW][C]28[/C][C]2798[/C][C]2724.15801014681[/C][C]-21.9459588259871[/C][C]4.28370650681019[/C][C]1.78145325981994[/C][/ROW]
[ROW][C]29[/C][C]2074[/C][C]2250.97325973203[/C][C]-22.9496444564121[/C][C]-118.279646038613[/C][C]-1.50093781396799[/C][/ROW]
[ROW][C]30[/C][C]2628[/C][C]2514.0605321113[/C][C]-22.3263967108169[/C][C]76.7358999471762[/C][C]0.951346084893503[/C][/ROW]
[ROW][C]31[/C][C]2289[/C][C]2360.7660850852[/C][C]-22.6202696194486[/C][C]-54.7334895868871[/C][C]-0.4355752957275[/C][/ROW]
[ROW][C]32[/C][C]2154[/C][C]2208.93345591683[/C][C]-22.9131924424353[/C][C]-38.1297623731913[/C][C]-0.429732535470575[/C][/ROW]
[ROW][C]33[/C][C]2467[/C][C]2470.62443235694[/C][C]-22.2616973688557[/C][C]-40.6361619500702[/C][C]0.946550195717155[/C][/ROW]
[ROW][C]34[/C][C]2137[/C][C]2151.32247133335[/C][C]-22.9659282024811[/C][C]24.3069111243238[/C][C]-0.988006462234519[/C][/ROW]
[ROW][C]35[/C][C]1850[/C][C]1959.95931943273[/C][C]-23.3567652471696[/C][C]-88.0568966975063[/C][C]-0.560161006672859[/C][/ROW]
[ROW][C]36[/C][C]2075[/C][C]2054.97404967379[/C][C]-23.3693126846558[/C][C]4.62451732137457[/C][C]0.393464503777829[/C][/ROW]
[ROW][C]37[/C][C]1791[/C][C]1674.14579806934[/C][C]-18.9800974785316[/C][C]164.07393006388[/C][C]-1.24768538421856[/C][/ROW]
[ROW][C]38[/C][C]1755[/C][C]1735.29130512994[/C][C]-18.2281224710259[/C][C]10.1131396895475[/C][C]0.253708454225406[/C][/ROW]
[ROW][C]39[/C][C]2232[/C][C]2156.69835958469[/C][C]-14.9194679496732[/C][C]20.2852888139341[/C][C]1.44190297566575[/C][/ROW]
[ROW][C]40[/C][C]1952[/C][C]1958.34032777381[/C][C]-15.5485000999521[/C][C]17.1849692373889[/C][C]-0.60965079356734[/C][/ROW]
[ROW][C]41[/C][C]1822[/C][C]1956.50352094609[/C][C]-15.5212724042376[/C][C]-136.266793666258[/C][C]0.0456053604617785[/C][/ROW]
[ROW][C]42[/C][C]2522[/C][C]2320.04665030365[/C][C]-14.8338190485467[/C][C]153.203944386492[/C][C]1.26064779774233[/C][/ROW]
[ROW][C]43[/C][C]2074[/C][C]2161.79346697747[/C][C]-15.1034813892902[/C][C]-69.3515063096079[/C][C]-0.476935765256992[/C][/ROW]
[ROW][C]44[/C][C]2366[/C][C]2367.16672052163[/C][C]-14.677773554307[/C][C]-29.515443238572[/C][C]0.733176845708424[/C][/ROW]
[ROW][C]45[/C][C]2173[/C][C]2220.4448125066[/C][C]-14.9401343132464[/C][C]-30.4669248707995[/C][C]-0.439121238667441[/C][/ROW]
[ROW][C]46[/C][C]2094[/C][C]2076.11275821785[/C][C]-15.2083464851037[/C][C]34.5248388961547[/C][C]-0.43035973448493[/C][/ROW]
[ROW][C]47[/C][C]1833[/C][C]1951.5130644948[/C][C]-15.4029769696873[/C][C]-104.444512468925[/C][C]-0.36380150864198[/C][/ROW]
[ROW][C]48[/C][C]1858[/C][C]1836.78014539064[/C][C]-15.3164322446769[/C][C]33.9993628764732[/C][C]-0.330312926305672[/C][/ROW]
[ROW][C]49[/C][C]2040[/C][C]1882.46949808194[/C][C]-15.7908761926556[/C][C]149.575952170648[/C][C]0.208900952876033[/C][/ROW]
[ROW][C]50[/C][C]2133[/C][C]2115.76744584923[/C][C]-14.1459752645584[/C][C]-13.2382628759383[/C][C]0.802183184181099[/C][/ROW]
[ROW][C]51[/C][C]2921[/C][C]2713.74648949672[/C][C]-10.0854415322363[/C][C]130.980843127096[/C][C]2.00792147690468[/C][/ROW]
[ROW][C]52[/C][C]3252[/C][C]3131.83412154732[/C][C]-8.60092403552395[/C][C]65.6555749932713[/C][C]1.42225303490739[/C][/ROW]
[ROW][C]53[/C][C]3318[/C][C]3426.50126617655[/C][C]-8.0125491177434[/C][C]-147.246922835686[/C][C]1.00862759333526[/C][/ROW]
[ROW][C]54[/C][C]3554[/C][C]3399.44879818759[/C][C]-8.04355250002009[/C][C]156.984313200717[/C][C]-0.0633190157851033[/C][/ROW]
[ROW][C]55[/C][C]2308[/C][C]2619.23373440008[/C][C]-9.33306628653656[/C][C]-212.570092359061[/C][C]-2.56771568250534[/C][/ROW]
[ROW][C]56[/C][C]1621[/C][C]1824.25496038067[/C][C]-10.7054200617168[/C][C]-102.879054733754[/C][C]-2.61247732838662[/C][/ROW]
[ROW][C]57[/C][C]1315[/C][C]1405.43200190844[/C][C]-11.4531088883506[/C][C]-38.2909543394421[/C][C]-1.35719268456175[/C][/ROW]
[ROW][C]58[/C][C]1501[/C][C]1417.35509259598[/C][C]-11.4092896957244[/C][C]80.658111406015[/C][C]0.0777477237420598[/C][/ROW]
[ROW][C]59[/C][C]1418[/C][C]1477.44002527149[/C][C]-11.3128179888805[/C][C]-68.5766198420468[/C][C]0.237716394275444[/C][/ROW]
[ROW][C]60[/C][C]1657[/C][C]1592.24200574156[/C][C]-11.4616814623807[/C][C]48.6323266546997[/C][C]0.419580031660107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148432&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
134403440000
226782762.56414932993-38.4294472339593-46.9341401117968-1.39131405326396
329812964.37507626266-34.4992507077458-15.22808970879870.789451657079431
422602404.32440720153-38.129490035434-72.4254750209391-1.749725409212
528442787.72446033554-35.6398295514112-1.466816871960821.40372919805685
625462616.9378630644-36.4310097501767-52.4248638002104-0.450039480693289
724562504.81093178292-36.872530185916-38.4418938105226-0.252063773282742
822952353.20398297726-37.5381650903244-42.487301633522-0.382055629024362
923792397.89048101585-37.0638681016879-30.15393610528270.273797988041371
1024792491.66534500014-36.3134786386894-30.58824786549140.435672166535127
1120572155.56171402632-38.0230365340732-57.4948202591086-0.998242927527417
1222802279.78261778632-37.1030841565549-22.00780433652940.540235111015348
1323512068.04211820913-28.5110781527977307.10864613802-0.701718303062675
1422762284.24379908962-22.5637636862289-33.23357607895160.698413804787742
1525482475.32365825642-20.349010701145.10954898521840.702148526187961
1623112406.49203042451-20.5401635967329-89.0668255781634-0.161272118088235
1722012219.49788754718-21.03583708787333.58910711340628-0.553787636648194
1827252647.62600927868-19.690708382049617.77878274235471.49426312176725
1924082473.88950709522-20.1575722969995-45.4519324923021-0.512462958019808
2021392218.34662426683-20.8713073138351-48.1179165394986-0.783050525100425
2118981967.15074142038-21.5692070632873-38.5934375522727-0.766217158281547
2225392418.82668439509-20.11919226628857.3875731326851.57439873596696
2320702184.37932005239-20.7934628034211-85.9432322054667-0.713119528459332
2420632075.51966556452-20.9287243953168-0.831287298463279-0.292761134142018
2525652251.96110297785-25.1411069055697286.4759373249340.715482782642285
2624422452.96731388075-21.9133121382752-36.87068401652240.693600769263683
2721942212.18293366375-23.79987252702089.49411147008802-0.718326994177994
2827982724.15801014681-21.94595882598714.283706506810191.78145325981994
2920742250.97325973203-22.9496444564121-118.279646038613-1.50093781396799
3026282514.0605321113-22.326396710816976.73589994717620.951346084893503
3122892360.7660850852-22.6202696194486-54.7334895868871-0.4355752957275
3221542208.93345591683-22.9131924424353-38.1297623731913-0.429732535470575
3324672470.62443235694-22.2616973688557-40.63616195007020.946550195717155
3421372151.32247133335-22.965928202481124.3069111243238-0.988006462234519
3518501959.95931943273-23.3567652471696-88.0568966975063-0.560161006672859
3620752054.97404967379-23.36931268465584.624517321374570.393464503777829
3717911674.14579806934-18.9800974785316164.07393006388-1.24768538421856
3817551735.29130512994-18.228122471025910.11313968954750.253708454225406
3922322156.69835958469-14.919467949673220.28528881393411.44190297566575
4019521958.34032777381-15.548500099952117.1849692373889-0.60965079356734
4118221956.50352094609-15.5212724042376-136.2667936662580.0456053604617785
4225222320.04665030365-14.8338190485467153.2039443864921.26064779774233
4320742161.79346697747-15.1034813892902-69.3515063096079-0.476935765256992
4423662367.16672052163-14.677773554307-29.5154432385720.733176845708424
4521732220.4448125066-14.9401343132464-30.4669248707995-0.439121238667441
4620942076.11275821785-15.208346485103734.5248388961547-0.43035973448493
4718331951.5130644948-15.4029769696873-104.444512468925-0.36380150864198
4818581836.78014539064-15.316432244676933.9993628764732-0.330312926305672
4920401882.46949808194-15.7908761926556149.5759521706480.208900952876033
5021332115.76744584923-14.1459752645584-13.23826287593830.802183184181099
5129212713.74648949672-10.0854415322363130.9808431270962.00792147690468
5232523131.83412154732-8.6009240355239565.65557499327131.42225303490739
5333183426.50126617655-8.0125491177434-147.2469228356861.00862759333526
5435543399.44879818759-8.04355250002009156.984313200717-0.0633190157851033
5523082619.23373440008-9.33306628653656-212.570092359061-2.56771568250534
5616211824.25496038067-10.7054200617168-102.879054733754-2.61247732838662
5713151405.43200190844-11.4531088883506-38.2909543394421-1.35719268456175
5815011417.35509259598-11.409289695724480.6581114060150.0777477237420598
5914181477.44002527149-11.3128179888805-68.57661984204680.237716394275444
6016571592.24200574156-11.461681462380748.63232665469970.419580031660107



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')