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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 computationFri, 04 Dec 2009 09:24:48 -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/t12599439518e3p47r36wu8h03.htm/, Retrieved Sat, 27 Apr 2024 20:32:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63857, Retrieved Sat, 27 Apr 2024 20:32:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsShwWs9forcasting 3
Estimated Impact93
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] [WS9 forcasting3] [2009-12-04 16:24:48] [51108381f3361ca8af49c4f74052c840] [Current]
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Dataseries X:
58608
46865
51378
46235
47206
45382
41227
33795
31295
42625
33625
21538
56421
53152
53536
52408
41454
38271
35306
26414
31917
38030
27534
18387
50556
43901
48572
43899
37532
40357
35489
29027
34485
42598
30306
26451
47460
50104
61465
53726
39477
43895
31481
29896
33842
39120
33702
25094
51442
45594
52518
48564
41745
49585
32747
33379
35645
37034
35681
20972




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=63857&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=63857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63857&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
15860858608000
24686548584.9379904029-509.664544578213-1719.9379904029-1.27204206328442
35137850205.042224822-434.3352941555021172.957775178050.393769644303424
44623547804.3462748787-460.684285216939-1569.34627487873-0.392172174663292
54720647353.7239964808-460.61853900334-147.7239964807920.00200966596961679
64538246153.7100546491-465.168166428774-771.71005464915-0.147558518314190
74122742789.1855847462-484.654125511493-1562.18558474616-0.578387495493522
83379536325.6743939540-526.244140169106-2530.67439395404-1.19259271493680
93129532328.9552103247-550.376163195836-1033.95521032471-0.692251320353443
104262539422.958405332-497.6860079960253202.041594667981.52482127140166
113362536393.5947263576-514.990787467951-2768.59472635759-0.504989341621224
122153825852.1302826083-583.03278821948-4314.13028260826-1.99994089883873
135642138303.5780785076-1043.0549633275018117.42192149242.91330639525111
145315250964.0266942212-902.761193613772187.973305778762.58420172799584
155353653164.6369014816-839.254486689911371.3630985184350.580228632732212
165240854346.7066265974-811.405071496059-1938.706626597360.398484153120184
174145446231.1203651707-863.863940998545-4777.12036517067-1.45685911595468
183827140168.5475668324-887.865389580385-1897.54756683238-1.03712215386867
193530636039.8507688213-901.826368424839-733.850768821253-0.646205167285719
202641430342.0057706233-924.015339045934-3928.0057706233-0.956001214406773
213191732953.9800646936-906.324002127458-1036.980064693590.704852945762631
223803033523.3351590186-898.649904945774506.664840981350.294250759472956
232753429375.9853589609-912.07514943499-1841.98535896086-0.647534709631828
241838727329.2691683526-911.08154659004-8942.2691683526-0.226266504085735
255055633416.8554175915-978.448774351317139.14458240851.4367123671755
264390140130.2127994938-948.6101365214633770.78720050621.50762959654234
274857246427.8189105520-856.8206856336922144.181089448051.38751357950049
284389944902.9416636137-864.722643132322-1003.94166361372-0.131044411205806
293753241824.1279441617-882.042438391359-4292.12794416171-0.440454980280319
304035740763.2507218831-882.944362756164-406.250721883069-0.0356653790028225
313548936525.7569008564-896.556785469636-1036.75690085645-0.668955562719628
322902734374.6252373376-901.671471642585-5347.6252373376-0.250135534093303
333448534710.7656451186-896.324914837041-225.7656451185630.246795403591711
344259835638.5672728373-888.9720768700766959.432727162730.363642226426057
353030632973.4091794103-892.997122700565-2667.40917941035-0.353767296955076
362645135902.7571771173-897.679772250762-9451.757177117330.762914086713338
374746034480.4470718369-896.01570213714512979.5529281631-0.105505542268957
385010443690.6293039249-866.6060255633316413.370696075121.99274481311324
396146554438.1355718401-765.1857518865427026.864428159852.25481393352627
405372654934.685445738-752.958875811204-1208.68544573800.247508969644245
413947747494.804688781-804.039717124675-8017.80468878096-1.3271502119926
424389543872.7572856429-819.3393906846422.2427143570515-0.561605487155103
433148135689.9737784699-850.491330996406-4208.97377846985-1.46849604717061
442989634642.1171398979-851.255120155232-4746.11713989786-0.039360568384386
453384233952.3283679442-850.656173369913-110.3283679441750.0321958242143152
463912031894.9023735931-854.3343124471057225.09762640685-0.240480896787619
473370234985.8678363222-848.67724785133-1283.867836322190.785652712894692
482509435032.9968107998-849.189793993987-9938.996810799760.178674558220097
495144240022.8855657107-853.41965705273511419.11443428931.16592549649676
504559442548.601861417-843.9603914295723045.398138582970.667326304283528
515251844616.9790282741-824.5329012324947901.020971725880.569605284417077
524856446420.9116135769-803.458416163892143.088386423120.516459587979655
534174547626.0445810174-789.17687107765-5881.044581017410.398109049881885
544958547096.0212103142-787.7409151094032488.978789685790.0516056723984227
553274740183.8967620555-814.49686125548-7436.8967620555-1.22125710348825
563337937971.0969132160-819.71049747859-4592.09691321596-0.278881537276715
573564535860.1283654492-823.88421580436-215.128365449166-0.257444135514916
583703432297.8954527341-830.4892699849134736.10454726590-0.5455389907807
593568135139.625161979-826.312632648762541.3748380209870.73122187896231
602097233796.4508002892-826.333209373016-12824.4508002892-0.102996598724395

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 58608 & 58608 & 0 & 0 & 0 \tabularnewline
2 & 46865 & 48584.9379904029 & -509.664544578213 & -1719.9379904029 & -1.27204206328442 \tabularnewline
3 & 51378 & 50205.042224822 & -434.335294155502 & 1172.95777517805 & 0.393769644303424 \tabularnewline
4 & 46235 & 47804.3462748787 & -460.684285216939 & -1569.34627487873 & -0.392172174663292 \tabularnewline
5 & 47206 & 47353.7239964808 & -460.61853900334 & -147.723996480792 & 0.00200966596961679 \tabularnewline
6 & 45382 & 46153.7100546491 & -465.168166428774 & -771.71005464915 & -0.147558518314190 \tabularnewline
7 & 41227 & 42789.1855847462 & -484.654125511493 & -1562.18558474616 & -0.578387495493522 \tabularnewline
8 & 33795 & 36325.6743939540 & -526.244140169106 & -2530.67439395404 & -1.19259271493680 \tabularnewline
9 & 31295 & 32328.9552103247 & -550.376163195836 & -1033.95521032471 & -0.692251320353443 \tabularnewline
10 & 42625 & 39422.958405332 & -497.686007996025 & 3202.04159466798 & 1.52482127140166 \tabularnewline
11 & 33625 & 36393.5947263576 & -514.990787467951 & -2768.59472635759 & -0.504989341621224 \tabularnewline
12 & 21538 & 25852.1302826083 & -583.03278821948 & -4314.13028260826 & -1.99994089883873 \tabularnewline
13 & 56421 & 38303.5780785076 & -1043.05496332750 & 18117.4219214924 & 2.91330639525111 \tabularnewline
14 & 53152 & 50964.0266942212 & -902.76119361377 & 2187.97330577876 & 2.58420172799584 \tabularnewline
15 & 53536 & 53164.6369014816 & -839.254486689911 & 371.363098518435 & 0.580228632732212 \tabularnewline
16 & 52408 & 54346.7066265974 & -811.405071496059 & -1938.70662659736 & 0.398484153120184 \tabularnewline
17 & 41454 & 46231.1203651707 & -863.863940998545 & -4777.12036517067 & -1.45685911595468 \tabularnewline
18 & 38271 & 40168.5475668324 & -887.865389580385 & -1897.54756683238 & -1.03712215386867 \tabularnewline
19 & 35306 & 36039.8507688213 & -901.826368424839 & -733.850768821253 & -0.646205167285719 \tabularnewline
20 & 26414 & 30342.0057706233 & -924.015339045934 & -3928.0057706233 & -0.956001214406773 \tabularnewline
21 & 31917 & 32953.9800646936 & -906.324002127458 & -1036.98006469359 & 0.704852945762631 \tabularnewline
22 & 38030 & 33523.3351590186 & -898.64990494577 & 4506.66484098135 & 0.294250759472956 \tabularnewline
23 & 27534 & 29375.9853589609 & -912.07514943499 & -1841.98535896086 & -0.647534709631828 \tabularnewline
24 & 18387 & 27329.2691683526 & -911.08154659004 & -8942.2691683526 & -0.226266504085735 \tabularnewline
25 & 50556 & 33416.8554175915 & -978.4487743513 & 17139.1445824085 & 1.4367123671755 \tabularnewline
26 & 43901 & 40130.2127994938 & -948.610136521463 & 3770.7872005062 & 1.50762959654234 \tabularnewline
27 & 48572 & 46427.8189105520 & -856.820685633692 & 2144.18108944805 & 1.38751357950049 \tabularnewline
28 & 43899 & 44902.9416636137 & -864.722643132322 & -1003.94166361372 & -0.131044411205806 \tabularnewline
29 & 37532 & 41824.1279441617 & -882.042438391359 & -4292.12794416171 & -0.440454980280319 \tabularnewline
30 & 40357 & 40763.2507218831 & -882.944362756164 & -406.250721883069 & -0.0356653790028225 \tabularnewline
31 & 35489 & 36525.7569008564 & -896.556785469636 & -1036.75690085645 & -0.668955562719628 \tabularnewline
32 & 29027 & 34374.6252373376 & -901.671471642585 & -5347.6252373376 & -0.250135534093303 \tabularnewline
33 & 34485 & 34710.7656451186 & -896.324914837041 & -225.765645118563 & 0.246795403591711 \tabularnewline
34 & 42598 & 35638.5672728373 & -888.972076870076 & 6959.43272716273 & 0.363642226426057 \tabularnewline
35 & 30306 & 32973.4091794103 & -892.997122700565 & -2667.40917941035 & -0.353767296955076 \tabularnewline
36 & 26451 & 35902.7571771173 & -897.679772250762 & -9451.75717711733 & 0.762914086713338 \tabularnewline
37 & 47460 & 34480.4470718369 & -896.015702137145 & 12979.5529281631 & -0.105505542268957 \tabularnewline
38 & 50104 & 43690.6293039249 & -866.606025563331 & 6413.37069607512 & 1.99274481311324 \tabularnewline
39 & 61465 & 54438.1355718401 & -765.185751886542 & 7026.86442815985 & 2.25481393352627 \tabularnewline
40 & 53726 & 54934.685445738 & -752.958875811204 & -1208.6854457380 & 0.247508969644245 \tabularnewline
41 & 39477 & 47494.804688781 & -804.039717124675 & -8017.80468878096 & -1.3271502119926 \tabularnewline
42 & 43895 & 43872.7572856429 & -819.33939068464 & 22.2427143570515 & -0.561605487155103 \tabularnewline
43 & 31481 & 35689.9737784699 & -850.491330996406 & -4208.97377846985 & -1.46849604717061 \tabularnewline
44 & 29896 & 34642.1171398979 & -851.255120155232 & -4746.11713989786 & -0.039360568384386 \tabularnewline
45 & 33842 & 33952.3283679442 & -850.656173369913 & -110.328367944175 & 0.0321958242143152 \tabularnewline
46 & 39120 & 31894.9023735931 & -854.334312447105 & 7225.09762640685 & -0.240480896787619 \tabularnewline
47 & 33702 & 34985.8678363222 & -848.67724785133 & -1283.86783632219 & 0.785652712894692 \tabularnewline
48 & 25094 & 35032.9968107998 & -849.189793993987 & -9938.99681079976 & 0.178674558220097 \tabularnewline
49 & 51442 & 40022.8855657107 & -853.419657052735 & 11419.1144342893 & 1.16592549649676 \tabularnewline
50 & 45594 & 42548.601861417 & -843.960391429572 & 3045.39813858297 & 0.667326304283528 \tabularnewline
51 & 52518 & 44616.9790282741 & -824.532901232494 & 7901.02097172588 & 0.569605284417077 \tabularnewline
52 & 48564 & 46420.9116135769 & -803.45841616389 & 2143.08838642312 & 0.516459587979655 \tabularnewline
53 & 41745 & 47626.0445810174 & -789.17687107765 & -5881.04458101741 & 0.398109049881885 \tabularnewline
54 & 49585 & 47096.0212103142 & -787.740915109403 & 2488.97878968579 & 0.0516056723984227 \tabularnewline
55 & 32747 & 40183.8967620555 & -814.49686125548 & -7436.8967620555 & -1.22125710348825 \tabularnewline
56 & 33379 & 37971.0969132160 & -819.71049747859 & -4592.09691321596 & -0.278881537276715 \tabularnewline
57 & 35645 & 35860.1283654492 & -823.88421580436 & -215.128365449166 & -0.257444135514916 \tabularnewline
58 & 37034 & 32297.8954527341 & -830.489269984913 & 4736.10454726590 & -0.5455389907807 \tabularnewline
59 & 35681 & 35139.625161979 & -826.312632648762 & 541.374838020987 & 0.73122187896231 \tabularnewline
60 & 20972 & 33796.4508002892 & -826.333209373016 & -12824.4508002892 & -0.102996598724395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63857&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]58608[/C][C]58608[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]46865[/C][C]48584.9379904029[/C][C]-509.664544578213[/C][C]-1719.9379904029[/C][C]-1.27204206328442[/C][/ROW]
[ROW][C]3[/C][C]51378[/C][C]50205.042224822[/C][C]-434.335294155502[/C][C]1172.95777517805[/C][C]0.393769644303424[/C][/ROW]
[ROW][C]4[/C][C]46235[/C][C]47804.3462748787[/C][C]-460.684285216939[/C][C]-1569.34627487873[/C][C]-0.392172174663292[/C][/ROW]
[ROW][C]5[/C][C]47206[/C][C]47353.7239964808[/C][C]-460.61853900334[/C][C]-147.723996480792[/C][C]0.00200966596961679[/C][/ROW]
[ROW][C]6[/C][C]45382[/C][C]46153.7100546491[/C][C]-465.168166428774[/C][C]-771.71005464915[/C][C]-0.147558518314190[/C][/ROW]
[ROW][C]7[/C][C]41227[/C][C]42789.1855847462[/C][C]-484.654125511493[/C][C]-1562.18558474616[/C][C]-0.578387495493522[/C][/ROW]
[ROW][C]8[/C][C]33795[/C][C]36325.6743939540[/C][C]-526.244140169106[/C][C]-2530.67439395404[/C][C]-1.19259271493680[/C][/ROW]
[ROW][C]9[/C][C]31295[/C][C]32328.9552103247[/C][C]-550.376163195836[/C][C]-1033.95521032471[/C][C]-0.692251320353443[/C][/ROW]
[ROW][C]10[/C][C]42625[/C][C]39422.958405332[/C][C]-497.686007996025[/C][C]3202.04159466798[/C][C]1.52482127140166[/C][/ROW]
[ROW][C]11[/C][C]33625[/C][C]36393.5947263576[/C][C]-514.990787467951[/C][C]-2768.59472635759[/C][C]-0.504989341621224[/C][/ROW]
[ROW][C]12[/C][C]21538[/C][C]25852.1302826083[/C][C]-583.03278821948[/C][C]-4314.13028260826[/C][C]-1.99994089883873[/C][/ROW]
[ROW][C]13[/C][C]56421[/C][C]38303.5780785076[/C][C]-1043.05496332750[/C][C]18117.4219214924[/C][C]2.91330639525111[/C][/ROW]
[ROW][C]14[/C][C]53152[/C][C]50964.0266942212[/C][C]-902.76119361377[/C][C]2187.97330577876[/C][C]2.58420172799584[/C][/ROW]
[ROW][C]15[/C][C]53536[/C][C]53164.6369014816[/C][C]-839.254486689911[/C][C]371.363098518435[/C][C]0.580228632732212[/C][/ROW]
[ROW][C]16[/C][C]52408[/C][C]54346.7066265974[/C][C]-811.405071496059[/C][C]-1938.70662659736[/C][C]0.398484153120184[/C][/ROW]
[ROW][C]17[/C][C]41454[/C][C]46231.1203651707[/C][C]-863.863940998545[/C][C]-4777.12036517067[/C][C]-1.45685911595468[/C][/ROW]
[ROW][C]18[/C][C]38271[/C][C]40168.5475668324[/C][C]-887.865389580385[/C][C]-1897.54756683238[/C][C]-1.03712215386867[/C][/ROW]
[ROW][C]19[/C][C]35306[/C][C]36039.8507688213[/C][C]-901.826368424839[/C][C]-733.850768821253[/C][C]-0.646205167285719[/C][/ROW]
[ROW][C]20[/C][C]26414[/C][C]30342.0057706233[/C][C]-924.015339045934[/C][C]-3928.0057706233[/C][C]-0.956001214406773[/C][/ROW]
[ROW][C]21[/C][C]31917[/C][C]32953.9800646936[/C][C]-906.324002127458[/C][C]-1036.98006469359[/C][C]0.704852945762631[/C][/ROW]
[ROW][C]22[/C][C]38030[/C][C]33523.3351590186[/C][C]-898.64990494577[/C][C]4506.66484098135[/C][C]0.294250759472956[/C][/ROW]
[ROW][C]23[/C][C]27534[/C][C]29375.9853589609[/C][C]-912.07514943499[/C][C]-1841.98535896086[/C][C]-0.647534709631828[/C][/ROW]
[ROW][C]24[/C][C]18387[/C][C]27329.2691683526[/C][C]-911.08154659004[/C][C]-8942.2691683526[/C][C]-0.226266504085735[/C][/ROW]
[ROW][C]25[/C][C]50556[/C][C]33416.8554175915[/C][C]-978.4487743513[/C][C]17139.1445824085[/C][C]1.4367123671755[/C][/ROW]
[ROW][C]26[/C][C]43901[/C][C]40130.2127994938[/C][C]-948.610136521463[/C][C]3770.7872005062[/C][C]1.50762959654234[/C][/ROW]
[ROW][C]27[/C][C]48572[/C][C]46427.8189105520[/C][C]-856.820685633692[/C][C]2144.18108944805[/C][C]1.38751357950049[/C][/ROW]
[ROW][C]28[/C][C]43899[/C][C]44902.9416636137[/C][C]-864.722643132322[/C][C]-1003.94166361372[/C][C]-0.131044411205806[/C][/ROW]
[ROW][C]29[/C][C]37532[/C][C]41824.1279441617[/C][C]-882.042438391359[/C][C]-4292.12794416171[/C][C]-0.440454980280319[/C][/ROW]
[ROW][C]30[/C][C]40357[/C][C]40763.2507218831[/C][C]-882.944362756164[/C][C]-406.250721883069[/C][C]-0.0356653790028225[/C][/ROW]
[ROW][C]31[/C][C]35489[/C][C]36525.7569008564[/C][C]-896.556785469636[/C][C]-1036.75690085645[/C][C]-0.668955562719628[/C][/ROW]
[ROW][C]32[/C][C]29027[/C][C]34374.6252373376[/C][C]-901.671471642585[/C][C]-5347.6252373376[/C][C]-0.250135534093303[/C][/ROW]
[ROW][C]33[/C][C]34485[/C][C]34710.7656451186[/C][C]-896.324914837041[/C][C]-225.765645118563[/C][C]0.246795403591711[/C][/ROW]
[ROW][C]34[/C][C]42598[/C][C]35638.5672728373[/C][C]-888.972076870076[/C][C]6959.43272716273[/C][C]0.363642226426057[/C][/ROW]
[ROW][C]35[/C][C]30306[/C][C]32973.4091794103[/C][C]-892.997122700565[/C][C]-2667.40917941035[/C][C]-0.353767296955076[/C][/ROW]
[ROW][C]36[/C][C]26451[/C][C]35902.7571771173[/C][C]-897.679772250762[/C][C]-9451.75717711733[/C][C]0.762914086713338[/C][/ROW]
[ROW][C]37[/C][C]47460[/C][C]34480.4470718369[/C][C]-896.015702137145[/C][C]12979.5529281631[/C][C]-0.105505542268957[/C][/ROW]
[ROW][C]38[/C][C]50104[/C][C]43690.6293039249[/C][C]-866.606025563331[/C][C]6413.37069607512[/C][C]1.99274481311324[/C][/ROW]
[ROW][C]39[/C][C]61465[/C][C]54438.1355718401[/C][C]-765.185751886542[/C][C]7026.86442815985[/C][C]2.25481393352627[/C][/ROW]
[ROW][C]40[/C][C]53726[/C][C]54934.685445738[/C][C]-752.958875811204[/C][C]-1208.6854457380[/C][C]0.247508969644245[/C][/ROW]
[ROW][C]41[/C][C]39477[/C][C]47494.804688781[/C][C]-804.039717124675[/C][C]-8017.80468878096[/C][C]-1.3271502119926[/C][/ROW]
[ROW][C]42[/C][C]43895[/C][C]43872.7572856429[/C][C]-819.33939068464[/C][C]22.2427143570515[/C][C]-0.561605487155103[/C][/ROW]
[ROW][C]43[/C][C]31481[/C][C]35689.9737784699[/C][C]-850.491330996406[/C][C]-4208.97377846985[/C][C]-1.46849604717061[/C][/ROW]
[ROW][C]44[/C][C]29896[/C][C]34642.1171398979[/C][C]-851.255120155232[/C][C]-4746.11713989786[/C][C]-0.039360568384386[/C][/ROW]
[ROW][C]45[/C][C]33842[/C][C]33952.3283679442[/C][C]-850.656173369913[/C][C]-110.328367944175[/C][C]0.0321958242143152[/C][/ROW]
[ROW][C]46[/C][C]39120[/C][C]31894.9023735931[/C][C]-854.334312447105[/C][C]7225.09762640685[/C][C]-0.240480896787619[/C][/ROW]
[ROW][C]47[/C][C]33702[/C][C]34985.8678363222[/C][C]-848.67724785133[/C][C]-1283.86783632219[/C][C]0.785652712894692[/C][/ROW]
[ROW][C]48[/C][C]25094[/C][C]35032.9968107998[/C][C]-849.189793993987[/C][C]-9938.99681079976[/C][C]0.178674558220097[/C][/ROW]
[ROW][C]49[/C][C]51442[/C][C]40022.8855657107[/C][C]-853.419657052735[/C][C]11419.1144342893[/C][C]1.16592549649676[/C][/ROW]
[ROW][C]50[/C][C]45594[/C][C]42548.601861417[/C][C]-843.960391429572[/C][C]3045.39813858297[/C][C]0.667326304283528[/C][/ROW]
[ROW][C]51[/C][C]52518[/C][C]44616.9790282741[/C][C]-824.532901232494[/C][C]7901.02097172588[/C][C]0.569605284417077[/C][/ROW]
[ROW][C]52[/C][C]48564[/C][C]46420.9116135769[/C][C]-803.45841616389[/C][C]2143.08838642312[/C][C]0.516459587979655[/C][/ROW]
[ROW][C]53[/C][C]41745[/C][C]47626.0445810174[/C][C]-789.17687107765[/C][C]-5881.04458101741[/C][C]0.398109049881885[/C][/ROW]
[ROW][C]54[/C][C]49585[/C][C]47096.0212103142[/C][C]-787.740915109403[/C][C]2488.97878968579[/C][C]0.0516056723984227[/C][/ROW]
[ROW][C]55[/C][C]32747[/C][C]40183.8967620555[/C][C]-814.49686125548[/C][C]-7436.8967620555[/C][C]-1.22125710348825[/C][/ROW]
[ROW][C]56[/C][C]33379[/C][C]37971.0969132160[/C][C]-819.71049747859[/C][C]-4592.09691321596[/C][C]-0.278881537276715[/C][/ROW]
[ROW][C]57[/C][C]35645[/C][C]35860.1283654492[/C][C]-823.88421580436[/C][C]-215.128365449166[/C][C]-0.257444135514916[/C][/ROW]
[ROW][C]58[/C][C]37034[/C][C]32297.8954527341[/C][C]-830.489269984913[/C][C]4736.10454726590[/C][C]-0.5455389907807[/C][/ROW]
[ROW][C]59[/C][C]35681[/C][C]35139.625161979[/C][C]-826.312632648762[/C][C]541.374838020987[/C][C]0.73122187896231[/C][/ROW]
[ROW][C]60[/C][C]20972[/C][C]33796.4508002892[/C][C]-826.333209373016[/C][C]-12824.4508002892[/C][C]-0.102996598724395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63857&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
15860858608000
24686548584.9379904029-509.664544578213-1719.9379904029-1.27204206328442
35137850205.042224822-434.3352941555021172.957775178050.393769644303424
44623547804.3462748787-460.684285216939-1569.34627487873-0.392172174663292
54720647353.7239964808-460.61853900334-147.7239964807920.00200966596961679
64538246153.7100546491-465.168166428774-771.71005464915-0.147558518314190
74122742789.1855847462-484.654125511493-1562.18558474616-0.578387495493522
83379536325.6743939540-526.244140169106-2530.67439395404-1.19259271493680
93129532328.9552103247-550.376163195836-1033.95521032471-0.692251320353443
104262539422.958405332-497.6860079960253202.041594667981.52482127140166
113362536393.5947263576-514.990787467951-2768.59472635759-0.504989341621224
122153825852.1302826083-583.03278821948-4314.13028260826-1.99994089883873
135642138303.5780785076-1043.0549633275018117.42192149242.91330639525111
145315250964.0266942212-902.761193613772187.973305778762.58420172799584
155353653164.6369014816-839.254486689911371.3630985184350.580228632732212
165240854346.7066265974-811.405071496059-1938.706626597360.398484153120184
174145446231.1203651707-863.863940998545-4777.12036517067-1.45685911595468
183827140168.5475668324-887.865389580385-1897.54756683238-1.03712215386867
193530636039.8507688213-901.826368424839-733.850768821253-0.646205167285719
202641430342.0057706233-924.015339045934-3928.0057706233-0.956001214406773
213191732953.9800646936-906.324002127458-1036.980064693590.704852945762631
223803033523.3351590186-898.649904945774506.664840981350.294250759472956
232753429375.9853589609-912.07514943499-1841.98535896086-0.647534709631828
241838727329.2691683526-911.08154659004-8942.2691683526-0.226266504085735
255055633416.8554175915-978.448774351317139.14458240851.4367123671755
264390140130.2127994938-948.6101365214633770.78720050621.50762959654234
274857246427.8189105520-856.8206856336922144.181089448051.38751357950049
284389944902.9416636137-864.722643132322-1003.94166361372-0.131044411205806
293753241824.1279441617-882.042438391359-4292.12794416171-0.440454980280319
304035740763.2507218831-882.944362756164-406.250721883069-0.0356653790028225
313548936525.7569008564-896.556785469636-1036.75690085645-0.668955562719628
322902734374.6252373376-901.671471642585-5347.6252373376-0.250135534093303
333448534710.7656451186-896.324914837041-225.7656451185630.246795403591711
344259835638.5672728373-888.9720768700766959.432727162730.363642226426057
353030632973.4091794103-892.997122700565-2667.40917941035-0.353767296955076
362645135902.7571771173-897.679772250762-9451.757177117330.762914086713338
374746034480.4470718369-896.01570213714512979.5529281631-0.105505542268957
385010443690.6293039249-866.6060255633316413.370696075121.99274481311324
396146554438.1355718401-765.1857518865427026.864428159852.25481393352627
405372654934.685445738-752.958875811204-1208.68544573800.247508969644245
413947747494.804688781-804.039717124675-8017.80468878096-1.3271502119926
424389543872.7572856429-819.3393906846422.2427143570515-0.561605487155103
433148135689.9737784699-850.491330996406-4208.97377846985-1.46849604717061
442989634642.1171398979-851.255120155232-4746.11713989786-0.039360568384386
453384233952.3283679442-850.656173369913-110.3283679441750.0321958242143152
463912031894.9023735931-854.3343124471057225.09762640685-0.240480896787619
473370234985.8678363222-848.67724785133-1283.867836322190.785652712894692
482509435032.9968107998-849.189793993987-9938.996810799760.178674558220097
495144240022.8855657107-853.41965705273511419.11443428931.16592549649676
504559442548.601861417-843.9603914295723045.398138582970.667326304283528
515251844616.9790282741-824.5329012324947901.020971725880.569605284417077
524856446420.9116135769-803.458416163892143.088386423120.516459587979655
534174547626.0445810174-789.17687107765-5881.044581017410.398109049881885
544958547096.0212103142-787.7409151094032488.978789685790.0516056723984227
553274740183.8967620555-814.49686125548-7436.8967620555-1.22125710348825
563337937971.0969132160-819.71049747859-4592.09691321596-0.278881537276715
573564535860.1283654492-823.88421580436-215.128365449166-0.257444135514916
583703432297.8954527341-830.4892699849134736.10454726590-0.5455389907807
593568135139.625161979-826.312632648762541.3748380209870.73122187896231
602097233796.4508002892-826.333209373016-12824.4508002892-0.102996598724395



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')