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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, 28 Dec 2010 23:01:27 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293577210ms4evp531ldj894.htm/, Retrieved Fri, 03 May 2024 10:33:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116579, Retrieved Fri, 03 May 2024 10:33:41 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [Paper Structural ...] [2010-12-28 23:01:27] [a2e464febd5f86100a78930292e787b9] [Current]
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Dataseries X:
1203
1319
1328
1260
1286
1274
1389
1255
1244
1336
1214
1239
1174
1061
1116
1123
1086
1074
965
1035
1016
941
1003
998
891
828
833
887
842
793
778
699
686
727
641
619
627
593
535
536
504
487
477
435
433
393
389
377
339
370
350
341
367
396
408
405
391
396
368
356




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 3 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116579&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116579&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116579&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 time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
112031203000
213191271.337682384616.050119749731035.457190472364032.13680144797764
313281301.55097452457.85966207519046.622853345446990.913744894947211
412601279.881714761335.566149485771585.72141857710112-1.15670593386498
512861282.703722586765.32738976339465.66448771565941-0.106850817282098
612741277.441777306294.294354161074125.50474629024856-0.405381880146181
713891337.1383248256410.22475628967936.145497358047132.08292772605223
812551293.153934769154.003314106407215.64681477531442-2.00599739860565
912441264.52003946440.06323981690729895.40232066141286-1.19232186340311
1013361301.464469489774.679565452029455.63053347174651.33400781316754
1112141251.24567703662-2.370180625624295.34761169860572-1.97077075598029
1212391240.29597316713-3.491985562542195.31061435031826-0.306261799708889
1311741235.13552694535-3.66968062081088-59.9134187155649-0.0707135415833912
1410611123.43141275073-18.50223372431682.18890309220458-3.2560081436769
1511161110.03442129365-17.81276222196572.266841203302560.175038143358962
1611231108.4224787782-15.62671076315872.385004388677410.569389273276767
1710861087.57968016565-16.33192685079682.36795896296858-0.183848843083739
1810741071.46639629124-16.30230840061262.368239824009270.00769841210980477
199651002.45965182791-23.45279496531692.35065343484883-1.85351910030537
2010351009.58023427643-19.3001508758742.347580524701371.0743638727477
2110161003.60567272486-17.48867297145442.343859660114540.468054865037274
22941959.052858598201-21.17006519725162.35290836771339-0.950378993977456
231003973.676343017156-16.29899676618412.341022537155491.25675633114193
24998979.209394297848-13.32689351923372.334357281580250.76650184259075
25891943.521224888383-16.265335219752-36.4529886950164-0.858695912553732
26828866.024541826562-24.59042548918672.12590025102563-1.95978073100647
27833835.381901686831-25.41189893631912.06964532461525-0.209382487492236
28887852.654868242657-19.60603216272322.242961829667631.49808194978023
29842836.86948644346-19.08568302643472.248626669830060.134274672713953
30793802.374060078955-21.18538699030852.24461182611986-0.541219401232844
31778778.089737729936-21.60767662323772.24549275534144-0.108779907139429
32699722.407492963085-26.25077613337252.26239817256737-1.19578181891547
33686689.07115728401-27.21621407228582.26628532802501-0.248636281396004
34727697.728796472308-22.32846460171272.247324807239481.25886289734498
35641654.493149536096-25.17697164621272.25733621084937-0.733706977773668
36619622.143570050058-26.15420403371822.26035083650177-0.251730243543539
37627623.119983715367-22.5262429013667-15.86700562551881.01440217042801
38593595.262961825551-23.25004650414791.36456441868317-0.174843971626167
39535550.049067495107-26.23227921004981.21255232197458-0.762908927134954
40536530.065888275873-25.38200895364881.231188432586670.219357698894115
41504503.592791277614-25.53063615921161.23004198861049-0.0383354495839676
42487482.456536046761-24.93179888878031.230655114131870.154317811797882
43477467.930670726792-23.51366540250351.227923169483070.365272356578948
44435438.344535275654-24.3411865556071.23044217505364-0.21311716062041
45433424.138601946363-22.96010176902991.225901337618210.355687497801202
46393395.813615949552-23.69111554274491.22819474324193-0.188280842889908
47389381.049442065686-22.47477101860041.22475388318160.313308515487044
48377368.38596794487-21.13794012447821.221443220896570.344367634763589
49339349.859686094565-20.7869245209572-12.77632630431980.0962978215120284
50370352.373755865043-17.6215780851751.435511494954410.774890669983178
51350342.651197781017-16.54836569541871.479340866329610.274969712272711
52341333.747400642999-15.50792539591541.49758012693480.268359548560303
53367345.171387116108-11.83898427876081.520115642018830.946132259232507
54396368.194997151457-7.088097464903291.52382127600861.22423361118309
55408386.982298431636-3.561769978861171.518241204645560.908332395619856
56405394.863625713048-2.002357303564841.514382716136380.401638325218341
57391390.935708334571-2.26474998001141.51508210403931-0.0675824121439657
58396391.9873260262-1.812851911379471.513934052691610.116398959607753
59368376.662805628962-3.653952273590531.51814916260386-0.474258104875292
60356362.441685267873-5.093824076811891.52103418361381-0.370924786860383

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1203 & 1203 & 0 & 0 & 0 \tabularnewline
2 & 1319 & 1271.33768238461 & 6.05011974973103 & 5.45719047236403 & 2.13680144797764 \tabularnewline
3 & 1328 & 1301.5509745245 & 7.8596620751904 & 6.62285334544699 & 0.913744894947211 \tabularnewline
4 & 1260 & 1279.88171476133 & 5.56614948577158 & 5.72141857710112 & -1.15670593386498 \tabularnewline
5 & 1286 & 1282.70372258676 & 5.3273897633946 & 5.66448771565941 & -0.106850817282098 \tabularnewline
6 & 1274 & 1277.44177730629 & 4.29435416107412 & 5.50474629024856 & -0.405381880146181 \tabularnewline
7 & 1389 & 1337.13832482564 & 10.2247562896793 & 6.14549735804713 & 2.08292772605223 \tabularnewline
8 & 1255 & 1293.15393476915 & 4.00331410640721 & 5.64681477531442 & -2.00599739860565 \tabularnewline
9 & 1244 & 1264.5200394644 & 0.0632398169072989 & 5.40232066141286 & -1.19232186340311 \tabularnewline
10 & 1336 & 1301.46446948977 & 4.67956545202945 & 5.6305334717465 & 1.33400781316754 \tabularnewline
11 & 1214 & 1251.24567703662 & -2.37018062562429 & 5.34761169860572 & -1.97077075598029 \tabularnewline
12 & 1239 & 1240.29597316713 & -3.49198556254219 & 5.31061435031826 & -0.306261799708889 \tabularnewline
13 & 1174 & 1235.13552694535 & -3.66968062081088 & -59.9134187155649 & -0.0707135415833912 \tabularnewline
14 & 1061 & 1123.43141275073 & -18.5022337243168 & 2.18890309220458 & -3.2560081436769 \tabularnewline
15 & 1116 & 1110.03442129365 & -17.8127622219657 & 2.26684120330256 & 0.175038143358962 \tabularnewline
16 & 1123 & 1108.4224787782 & -15.6267107631587 & 2.38500438867741 & 0.569389273276767 \tabularnewline
17 & 1086 & 1087.57968016565 & -16.3319268507968 & 2.36795896296858 & -0.183848843083739 \tabularnewline
18 & 1074 & 1071.46639629124 & -16.3023084006126 & 2.36823982400927 & 0.00769841210980477 \tabularnewline
19 & 965 & 1002.45965182791 & -23.4527949653169 & 2.35065343484883 & -1.85351910030537 \tabularnewline
20 & 1035 & 1009.58023427643 & -19.300150875874 & 2.34758052470137 & 1.0743638727477 \tabularnewline
21 & 1016 & 1003.60567272486 & -17.4886729714544 & 2.34385966011454 & 0.468054865037274 \tabularnewline
22 & 941 & 959.052858598201 & -21.1700651972516 & 2.35290836771339 & -0.950378993977456 \tabularnewline
23 & 1003 & 973.676343017156 & -16.2989967661841 & 2.34102253715549 & 1.25675633114193 \tabularnewline
24 & 998 & 979.209394297848 & -13.3268935192337 & 2.33435728158025 & 0.76650184259075 \tabularnewline
25 & 891 & 943.521224888383 & -16.265335219752 & -36.4529886950164 & -0.858695912553732 \tabularnewline
26 & 828 & 866.024541826562 & -24.5904254891867 & 2.12590025102563 & -1.95978073100647 \tabularnewline
27 & 833 & 835.381901686831 & -25.4118989363191 & 2.06964532461525 & -0.209382487492236 \tabularnewline
28 & 887 & 852.654868242657 & -19.6060321627232 & 2.24296182966763 & 1.49808194978023 \tabularnewline
29 & 842 & 836.86948644346 & -19.0856830264347 & 2.24862666983006 & 0.134274672713953 \tabularnewline
30 & 793 & 802.374060078955 & -21.1853869903085 & 2.24461182611986 & -0.541219401232844 \tabularnewline
31 & 778 & 778.089737729936 & -21.6076766232377 & 2.24549275534144 & -0.108779907139429 \tabularnewline
32 & 699 & 722.407492963085 & -26.2507761333725 & 2.26239817256737 & -1.19578181891547 \tabularnewline
33 & 686 & 689.07115728401 & -27.2162140722858 & 2.26628532802501 & -0.248636281396004 \tabularnewline
34 & 727 & 697.728796472308 & -22.3284646017127 & 2.24732480723948 & 1.25886289734498 \tabularnewline
35 & 641 & 654.493149536096 & -25.1769716462127 & 2.25733621084937 & -0.733706977773668 \tabularnewline
36 & 619 & 622.143570050058 & -26.1542040337182 & 2.26035083650177 & -0.251730243543539 \tabularnewline
37 & 627 & 623.119983715367 & -22.5262429013667 & -15.8670056255188 & 1.01440217042801 \tabularnewline
38 & 593 & 595.262961825551 & -23.2500465041479 & 1.36456441868317 & -0.174843971626167 \tabularnewline
39 & 535 & 550.049067495107 & -26.2322792100498 & 1.21255232197458 & -0.762908927134954 \tabularnewline
40 & 536 & 530.065888275873 & -25.3820089536488 & 1.23118843258667 & 0.219357698894115 \tabularnewline
41 & 504 & 503.592791277614 & -25.5306361592116 & 1.23004198861049 & -0.0383354495839676 \tabularnewline
42 & 487 & 482.456536046761 & -24.9317988887803 & 1.23065511413187 & 0.154317811797882 \tabularnewline
43 & 477 & 467.930670726792 & -23.5136654025035 & 1.22792316948307 & 0.365272356578948 \tabularnewline
44 & 435 & 438.344535275654 & -24.341186555607 & 1.23044217505364 & -0.21311716062041 \tabularnewline
45 & 433 & 424.138601946363 & -22.9601017690299 & 1.22590133761821 & 0.355687497801202 \tabularnewline
46 & 393 & 395.813615949552 & -23.6911155427449 & 1.22819474324193 & -0.188280842889908 \tabularnewline
47 & 389 & 381.049442065686 & -22.4747710186004 & 1.2247538831816 & 0.313308515487044 \tabularnewline
48 & 377 & 368.38596794487 & -21.1379401244782 & 1.22144322089657 & 0.344367634763589 \tabularnewline
49 & 339 & 349.859686094565 & -20.7869245209572 & -12.7763263043198 & 0.0962978215120284 \tabularnewline
50 & 370 & 352.373755865043 & -17.621578085175 & 1.43551149495441 & 0.774890669983178 \tabularnewline
51 & 350 & 342.651197781017 & -16.5483656954187 & 1.47934086632961 & 0.274969712272711 \tabularnewline
52 & 341 & 333.747400642999 & -15.5079253959154 & 1.4975801269348 & 0.268359548560303 \tabularnewline
53 & 367 & 345.171387116108 & -11.8389842787608 & 1.52011564201883 & 0.946132259232507 \tabularnewline
54 & 396 & 368.194997151457 & -7.08809746490329 & 1.5238212760086 & 1.22423361118309 \tabularnewline
55 & 408 & 386.982298431636 & -3.56176997886117 & 1.51824120464556 & 0.908332395619856 \tabularnewline
56 & 405 & 394.863625713048 & -2.00235730356484 & 1.51438271613638 & 0.401638325218341 \tabularnewline
57 & 391 & 390.935708334571 & -2.2647499800114 & 1.51508210403931 & -0.0675824121439657 \tabularnewline
58 & 396 & 391.9873260262 & -1.81285191137947 & 1.51393405269161 & 0.116398959607753 \tabularnewline
59 & 368 & 376.662805628962 & -3.65395227359053 & 1.51814916260386 & -0.474258104875292 \tabularnewline
60 & 356 & 362.441685267873 & -5.09382407681189 & 1.52103418361381 & -0.370924786860383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116579&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]1203[/C][C]1203[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1319[/C][C]1271.33768238461[/C][C]6.05011974973103[/C][C]5.45719047236403[/C][C]2.13680144797764[/C][/ROW]
[ROW][C]3[/C][C]1328[/C][C]1301.5509745245[/C][C]7.8596620751904[/C][C]6.62285334544699[/C][C]0.913744894947211[/C][/ROW]
[ROW][C]4[/C][C]1260[/C][C]1279.88171476133[/C][C]5.56614948577158[/C][C]5.72141857710112[/C][C]-1.15670593386498[/C][/ROW]
[ROW][C]5[/C][C]1286[/C][C]1282.70372258676[/C][C]5.3273897633946[/C][C]5.66448771565941[/C][C]-0.106850817282098[/C][/ROW]
[ROW][C]6[/C][C]1274[/C][C]1277.44177730629[/C][C]4.29435416107412[/C][C]5.50474629024856[/C][C]-0.405381880146181[/C][/ROW]
[ROW][C]7[/C][C]1389[/C][C]1337.13832482564[/C][C]10.2247562896793[/C][C]6.14549735804713[/C][C]2.08292772605223[/C][/ROW]
[ROW][C]8[/C][C]1255[/C][C]1293.15393476915[/C][C]4.00331410640721[/C][C]5.64681477531442[/C][C]-2.00599739860565[/C][/ROW]
[ROW][C]9[/C][C]1244[/C][C]1264.5200394644[/C][C]0.0632398169072989[/C][C]5.40232066141286[/C][C]-1.19232186340311[/C][/ROW]
[ROW][C]10[/C][C]1336[/C][C]1301.46446948977[/C][C]4.67956545202945[/C][C]5.6305334717465[/C][C]1.33400781316754[/C][/ROW]
[ROW][C]11[/C][C]1214[/C][C]1251.24567703662[/C][C]-2.37018062562429[/C][C]5.34761169860572[/C][C]-1.97077075598029[/C][/ROW]
[ROW][C]12[/C][C]1239[/C][C]1240.29597316713[/C][C]-3.49198556254219[/C][C]5.31061435031826[/C][C]-0.306261799708889[/C][/ROW]
[ROW][C]13[/C][C]1174[/C][C]1235.13552694535[/C][C]-3.66968062081088[/C][C]-59.9134187155649[/C][C]-0.0707135415833912[/C][/ROW]
[ROW][C]14[/C][C]1061[/C][C]1123.43141275073[/C][C]-18.5022337243168[/C][C]2.18890309220458[/C][C]-3.2560081436769[/C][/ROW]
[ROW][C]15[/C][C]1116[/C][C]1110.03442129365[/C][C]-17.8127622219657[/C][C]2.26684120330256[/C][C]0.175038143358962[/C][/ROW]
[ROW][C]16[/C][C]1123[/C][C]1108.4224787782[/C][C]-15.6267107631587[/C][C]2.38500438867741[/C][C]0.569389273276767[/C][/ROW]
[ROW][C]17[/C][C]1086[/C][C]1087.57968016565[/C][C]-16.3319268507968[/C][C]2.36795896296858[/C][C]-0.183848843083739[/C][/ROW]
[ROW][C]18[/C][C]1074[/C][C]1071.46639629124[/C][C]-16.3023084006126[/C][C]2.36823982400927[/C][C]0.00769841210980477[/C][/ROW]
[ROW][C]19[/C][C]965[/C][C]1002.45965182791[/C][C]-23.4527949653169[/C][C]2.35065343484883[/C][C]-1.85351910030537[/C][/ROW]
[ROW][C]20[/C][C]1035[/C][C]1009.58023427643[/C][C]-19.300150875874[/C][C]2.34758052470137[/C][C]1.0743638727477[/C][/ROW]
[ROW][C]21[/C][C]1016[/C][C]1003.60567272486[/C][C]-17.4886729714544[/C][C]2.34385966011454[/C][C]0.468054865037274[/C][/ROW]
[ROW][C]22[/C][C]941[/C][C]959.052858598201[/C][C]-21.1700651972516[/C][C]2.35290836771339[/C][C]-0.950378993977456[/C][/ROW]
[ROW][C]23[/C][C]1003[/C][C]973.676343017156[/C][C]-16.2989967661841[/C][C]2.34102253715549[/C][C]1.25675633114193[/C][/ROW]
[ROW][C]24[/C][C]998[/C][C]979.209394297848[/C][C]-13.3268935192337[/C][C]2.33435728158025[/C][C]0.76650184259075[/C][/ROW]
[ROW][C]25[/C][C]891[/C][C]943.521224888383[/C][C]-16.265335219752[/C][C]-36.4529886950164[/C][C]-0.858695912553732[/C][/ROW]
[ROW][C]26[/C][C]828[/C][C]866.024541826562[/C][C]-24.5904254891867[/C][C]2.12590025102563[/C][C]-1.95978073100647[/C][/ROW]
[ROW][C]27[/C][C]833[/C][C]835.381901686831[/C][C]-25.4118989363191[/C][C]2.06964532461525[/C][C]-0.209382487492236[/C][/ROW]
[ROW][C]28[/C][C]887[/C][C]852.654868242657[/C][C]-19.6060321627232[/C][C]2.24296182966763[/C][C]1.49808194978023[/C][/ROW]
[ROW][C]29[/C][C]842[/C][C]836.86948644346[/C][C]-19.0856830264347[/C][C]2.24862666983006[/C][C]0.134274672713953[/C][/ROW]
[ROW][C]30[/C][C]793[/C][C]802.374060078955[/C][C]-21.1853869903085[/C][C]2.24461182611986[/C][C]-0.541219401232844[/C][/ROW]
[ROW][C]31[/C][C]778[/C][C]778.089737729936[/C][C]-21.6076766232377[/C][C]2.24549275534144[/C][C]-0.108779907139429[/C][/ROW]
[ROW][C]32[/C][C]699[/C][C]722.407492963085[/C][C]-26.2507761333725[/C][C]2.26239817256737[/C][C]-1.19578181891547[/C][/ROW]
[ROW][C]33[/C][C]686[/C][C]689.07115728401[/C][C]-27.2162140722858[/C][C]2.26628532802501[/C][C]-0.248636281396004[/C][/ROW]
[ROW][C]34[/C][C]727[/C][C]697.728796472308[/C][C]-22.3284646017127[/C][C]2.24732480723948[/C][C]1.25886289734498[/C][/ROW]
[ROW][C]35[/C][C]641[/C][C]654.493149536096[/C][C]-25.1769716462127[/C][C]2.25733621084937[/C][C]-0.733706977773668[/C][/ROW]
[ROW][C]36[/C][C]619[/C][C]622.143570050058[/C][C]-26.1542040337182[/C][C]2.26035083650177[/C][C]-0.251730243543539[/C][/ROW]
[ROW][C]37[/C][C]627[/C][C]623.119983715367[/C][C]-22.5262429013667[/C][C]-15.8670056255188[/C][C]1.01440217042801[/C][/ROW]
[ROW][C]38[/C][C]593[/C][C]595.262961825551[/C][C]-23.2500465041479[/C][C]1.36456441868317[/C][C]-0.174843971626167[/C][/ROW]
[ROW][C]39[/C][C]535[/C][C]550.049067495107[/C][C]-26.2322792100498[/C][C]1.21255232197458[/C][C]-0.762908927134954[/C][/ROW]
[ROW][C]40[/C][C]536[/C][C]530.065888275873[/C][C]-25.3820089536488[/C][C]1.23118843258667[/C][C]0.219357698894115[/C][/ROW]
[ROW][C]41[/C][C]504[/C][C]503.592791277614[/C][C]-25.5306361592116[/C][C]1.23004198861049[/C][C]-0.0383354495839676[/C][/ROW]
[ROW][C]42[/C][C]487[/C][C]482.456536046761[/C][C]-24.9317988887803[/C][C]1.23065511413187[/C][C]0.154317811797882[/C][/ROW]
[ROW][C]43[/C][C]477[/C][C]467.930670726792[/C][C]-23.5136654025035[/C][C]1.22792316948307[/C][C]0.365272356578948[/C][/ROW]
[ROW][C]44[/C][C]435[/C][C]438.344535275654[/C][C]-24.341186555607[/C][C]1.23044217505364[/C][C]-0.21311716062041[/C][/ROW]
[ROW][C]45[/C][C]433[/C][C]424.138601946363[/C][C]-22.9601017690299[/C][C]1.22590133761821[/C][C]0.355687497801202[/C][/ROW]
[ROW][C]46[/C][C]393[/C][C]395.813615949552[/C][C]-23.6911155427449[/C][C]1.22819474324193[/C][C]-0.188280842889908[/C][/ROW]
[ROW][C]47[/C][C]389[/C][C]381.049442065686[/C][C]-22.4747710186004[/C][C]1.2247538831816[/C][C]0.313308515487044[/C][/ROW]
[ROW][C]48[/C][C]377[/C][C]368.38596794487[/C][C]-21.1379401244782[/C][C]1.22144322089657[/C][C]0.344367634763589[/C][/ROW]
[ROW][C]49[/C][C]339[/C][C]349.859686094565[/C][C]-20.7869245209572[/C][C]-12.7763263043198[/C][C]0.0962978215120284[/C][/ROW]
[ROW][C]50[/C][C]370[/C][C]352.373755865043[/C][C]-17.621578085175[/C][C]1.43551149495441[/C][C]0.774890669983178[/C][/ROW]
[ROW][C]51[/C][C]350[/C][C]342.651197781017[/C][C]-16.5483656954187[/C][C]1.47934086632961[/C][C]0.274969712272711[/C][/ROW]
[ROW][C]52[/C][C]341[/C][C]333.747400642999[/C][C]-15.5079253959154[/C][C]1.4975801269348[/C][C]0.268359548560303[/C][/ROW]
[ROW][C]53[/C][C]367[/C][C]345.171387116108[/C][C]-11.8389842787608[/C][C]1.52011564201883[/C][C]0.946132259232507[/C][/ROW]
[ROW][C]54[/C][C]396[/C][C]368.194997151457[/C][C]-7.08809746490329[/C][C]1.5238212760086[/C][C]1.22423361118309[/C][/ROW]
[ROW][C]55[/C][C]408[/C][C]386.982298431636[/C][C]-3.56176997886117[/C][C]1.51824120464556[/C][C]0.908332395619856[/C][/ROW]
[ROW][C]56[/C][C]405[/C][C]394.863625713048[/C][C]-2.00235730356484[/C][C]1.51438271613638[/C][C]0.401638325218341[/C][/ROW]
[ROW][C]57[/C][C]391[/C][C]390.935708334571[/C][C]-2.2647499800114[/C][C]1.51508210403931[/C][C]-0.0675824121439657[/C][/ROW]
[ROW][C]58[/C][C]396[/C][C]391.9873260262[/C][C]-1.81285191137947[/C][C]1.51393405269161[/C][C]0.116398959607753[/C][/ROW]
[ROW][C]59[/C][C]368[/C][C]376.662805628962[/C][C]-3.65395227359053[/C][C]1.51814916260386[/C][C]-0.474258104875292[/C][/ROW]
[ROW][C]60[/C][C]356[/C][C]362.441685267873[/C][C]-5.09382407681189[/C][C]1.52103418361381[/C][C]-0.370924786860383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116579&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116579&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
112031203000
213191271.337682384616.050119749731035.457190472364032.13680144797764
313281301.55097452457.85966207519046.622853345446990.913744894947211
412601279.881714761335.566149485771585.72141857710112-1.15670593386498
512861282.703722586765.32738976339465.66448771565941-0.106850817282098
612741277.441777306294.294354161074125.50474629024856-0.405381880146181
713891337.1383248256410.22475628967936.145497358047132.08292772605223
812551293.153934769154.003314106407215.64681477531442-2.00599739860565
912441264.52003946440.06323981690729895.40232066141286-1.19232186340311
1013361301.464469489774.679565452029455.63053347174651.33400781316754
1112141251.24567703662-2.370180625624295.34761169860572-1.97077075598029
1212391240.29597316713-3.491985562542195.31061435031826-0.306261799708889
1311741235.13552694535-3.66968062081088-59.9134187155649-0.0707135415833912
1410611123.43141275073-18.50223372431682.18890309220458-3.2560081436769
1511161110.03442129365-17.81276222196572.266841203302560.175038143358962
1611231108.4224787782-15.62671076315872.385004388677410.569389273276767
1710861087.57968016565-16.33192685079682.36795896296858-0.183848843083739
1810741071.46639629124-16.30230840061262.368239824009270.00769841210980477
199651002.45965182791-23.45279496531692.35065343484883-1.85351910030537
2010351009.58023427643-19.3001508758742.347580524701371.0743638727477
2110161003.60567272486-17.48867297145442.343859660114540.468054865037274
22941959.052858598201-21.17006519725162.35290836771339-0.950378993977456
231003973.676343017156-16.29899676618412.341022537155491.25675633114193
24998979.209394297848-13.32689351923372.334357281580250.76650184259075
25891943.521224888383-16.265335219752-36.4529886950164-0.858695912553732
26828866.024541826562-24.59042548918672.12590025102563-1.95978073100647
27833835.381901686831-25.41189893631912.06964532461525-0.209382487492236
28887852.654868242657-19.60603216272322.242961829667631.49808194978023
29842836.86948644346-19.08568302643472.248626669830060.134274672713953
30793802.374060078955-21.18538699030852.24461182611986-0.541219401232844
31778778.089737729936-21.60767662323772.24549275534144-0.108779907139429
32699722.407492963085-26.25077613337252.26239817256737-1.19578181891547
33686689.07115728401-27.21621407228582.26628532802501-0.248636281396004
34727697.728796472308-22.32846460171272.247324807239481.25886289734498
35641654.493149536096-25.17697164621272.25733621084937-0.733706977773668
36619622.143570050058-26.15420403371822.26035083650177-0.251730243543539
37627623.119983715367-22.5262429013667-15.86700562551881.01440217042801
38593595.262961825551-23.25004650414791.36456441868317-0.174843971626167
39535550.049067495107-26.23227921004981.21255232197458-0.762908927134954
40536530.065888275873-25.38200895364881.231188432586670.219357698894115
41504503.592791277614-25.53063615921161.23004198861049-0.0383354495839676
42487482.456536046761-24.93179888878031.230655114131870.154317811797882
43477467.930670726792-23.51366540250351.227923169483070.365272356578948
44435438.344535275654-24.3411865556071.23044217505364-0.21311716062041
45433424.138601946363-22.96010176902991.225901337618210.355687497801202
46393395.813615949552-23.69111554274491.22819474324193-0.188280842889908
47389381.049442065686-22.47477101860041.22475388318160.313308515487044
48377368.38596794487-21.13794012447821.221443220896570.344367634763589
49339349.859686094565-20.7869245209572-12.77632630431980.0962978215120284
50370352.373755865043-17.6215780851751.435511494954410.774890669983178
51350342.651197781017-16.54836569541871.479340866329610.274969712272711
52341333.747400642999-15.50792539591541.49758012693480.268359548560303
53367345.171387116108-11.83898427876081.520115642018830.946132259232507
54396368.194997151457-7.088097464903291.52382127600861.22423361118309
55408386.982298431636-3.561769978861171.518241204645560.908332395619856
56405394.863625713048-2.002357303564841.514382716136380.401638325218341
57391390.935708334571-2.26474998001141.51508210403931-0.0675824121439657
58396391.9873260262-1.812851911379471.513934052691610.116398959607753
59368376.662805628962-3.653952273590531.51814916260386-0.474258104875292
60356362.441685267873-5.093824076811891.52103418361381-0.370924786860383



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')