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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 computationThu, 03 Dec 2009 13:11:45 -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/03/t1259871174uu1144d3bfixbcn.htm/, Retrieved Tue, 23 Apr 2024 06:41:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63107, Retrieved Tue, 23 Apr 2024 06:41:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
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]
- R PD      [Structural Time Series Models] [] [2009-12-03 20:11:45] [ed082d38031561faed979d8cebfeba4d] [Current]
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Dataseries X:
1915
1843
1761
2858
3968
5061
4661
4269
3857
3568
3274
2987
1683
1381
1071
2772
4485
6181
5479
4782
4067
3489
2903
2330
1736
1483
1242
2334
3423
4523
3986
3462
2908
2575
2237
1904
1610
1251
941
2450
3946
5409
4741
4069
3539
3189
2960
2704
1697
1598
1456
2316
3083
4158
3469
2892
2578
2233
1947
2049




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=63107&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=63107&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63107&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
119151915000
218431843.99431779336-71.0878572278306-0.994317793362496-0.118782434738233
317611760.73098327906-82.79602661632550.269016720944476-0.0197979134286433
428582822.078522541981008.5146958664635.92147745802351.80930312201797
539683973.665316975581145.05557395077-5.665316975581360.226651816858512
650615064.750010769451093.53921250168-3.75001076945432-0.0855081585733372
746614707.95024201491-290.809005748789-46.9502420149081-2.29777874671134
842694262.81579709793-438.1115372845176.18420290207231-0.244496699778275
938573855.57711371601-408.6435649790291.422886283987230.048911732379963
1035683565.2986173083-295.6647058406612.701382691700440.187525346612445
1132743275.62819664525-289.943191544671-1.628196645254960.00949672318951281
1229872987.82371629392-287.901802766272-0.8237162939170270.00338835195504231
1316831713.56712235695-1229.09328966190-30.5671223569538-1.56288797789183
1413811310.58847923549-440.18825673155270.41152076451261.31544286775096
1510711115.78963292764-211.956642260241-44.78963292764470.382814720271252
1627722656.377270821581417.3902456518115.6227291784162.70100818871470
1744854522.435732912381834.19199689547-37.4357329123780.69191532764831
1861816141.509304928091634.2847825375639.4906950719122-0.331810694409317
1954795624.42982228918-364.968921181233-145.429822289177-3.3184186995403
2047824775.8528407719-814.373495587366.14715922809708-0.745933775605698
2140674056.26948079706-726.28765935037410.73051920294500.14620723998589
2234893478.66689042194-588.1183820288310.33310957805540.229337083254824
2329032933.57001990285-548.139156145035-30.57001990285070.0663586288894683
2423302275.39758623601-650.38812330166954.6024137639919-0.169715967477611
2517361789.52878030154-497.551705537405-53.52878030154090.253817080946218
2614831358.69512767047-435.517824043713124.3048723295330.103160040904261
2712421360.81582933842-34.3595457358069-118.8158293384190.669222722740359
2823342212.03858623321779.028281892274121.9614137667881.34931369578257
2934233503.316934503721248.84227706923-80.3169345037240.779813126068055
3045234377.37502345757904.978553551751145.624976542429-0.570766233674772
3139864155.86452813121-128.625373867469-169.864528131208-1.71560135414889
3234623477.50155379676-633.02257755839-15.5015537967614-0.837212454578822
3329082895.71191154208-586.01572173247212.28808845791880.0780232446176738
3425752556.15594892908-359.88404854333718.84405107092480.375339447186656
3522372254.92944466363-306.063859348415-17.92944466363450.0893324696921452
3619041868.31048068351-379.96973831263435.6895193164873-0.122671607646575
3716101643.48892647039-237.656303453972-33.48892647038980.236367686330915
3812511137.84448956637-483.583488938503113.155510433633-0.408410331825218
399411081.90698047364-94.1583891589908-140.9069804736390.648005992213137
4024502312.196818808341114.45402849545137.803181191662.00602416300433
4139464015.432030566381651.00213730320-69.43203056637940.890443149671629
4254095226.227052132351249.74236455958182.772947867653-0.66605263897509
4347414952.31071987489-139.244484583548-211.310719874888-2.30546717433684
4440694111.01882682801-779.231723660451-42.0188268280059-1.06226887344331
4535393523.76325955734-604.22882645321815.23674044265650.290474512133344
4631893158.66780754494-386.23591861163330.33219245506220.361830642206055
4729602961.76516842858-213.639583286423-1.765168428584390.286481270971357
4827042701.5272080579-256.1133268127282.47279194210022-0.0705004546891371
4916971728.62859318626-909.41822328979-31.6285931862648-1.08506379466651
5015981424.10866382671-358.094960988995173.891336173290.915106245886558
5114561651.48212534320173.136180758787-195.4821253432030.882959536644735
5223162235.67392653673546.74631362066980.3260734632670.620274005397241
5330833160.34386444056889.878033750948-77.34386444056080.56940580779805
5441583880.08849755857735.391157317516277.911502441432-0.256435030215154
5534693665.69139932732-127.163398260822-196.691399327316-1.43168605919819
5628922968.73774976568-644.62138869767-76.7377497656808-0.858890513139222
5725782556.69797954254-433.40392700530421.30202045746050.350584462021206
5822332221.43373046111-344.27838612627211.56626953889400.147933212853412
5919471964.37640494239-265.069358169029-17.37640494238580.1314735334007
6020491945.14683438854-41.8454611812789103.8531656114570.370530377263508

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 1915 & 1915 & 0 & 0 & 0 \tabularnewline
2 & 1843 & 1843.99431779336 & -71.0878572278306 & -0.994317793362496 & -0.118782434738233 \tabularnewline
3 & 1761 & 1760.73098327906 & -82.7960266163255 & 0.269016720944476 & -0.0197979134286433 \tabularnewline
4 & 2858 & 2822.07852254198 & 1008.51469586646 & 35.9214774580235 & 1.80930312201797 \tabularnewline
5 & 3968 & 3973.66531697558 & 1145.05557395077 & -5.66531697558136 & 0.226651816858512 \tabularnewline
6 & 5061 & 5064.75001076945 & 1093.53921250168 & -3.75001076945432 & -0.0855081585733372 \tabularnewline
7 & 4661 & 4707.95024201491 & -290.809005748789 & -46.9502420149081 & -2.29777874671134 \tabularnewline
8 & 4269 & 4262.81579709793 & -438.111537284517 & 6.18420290207231 & -0.244496699778275 \tabularnewline
9 & 3857 & 3855.57711371601 & -408.643564979029 & 1.42288628398723 & 0.048911732379963 \tabularnewline
10 & 3568 & 3565.2986173083 & -295.664705840661 & 2.70138269170044 & 0.187525346612445 \tabularnewline
11 & 3274 & 3275.62819664525 & -289.943191544671 & -1.62819664525496 & 0.00949672318951281 \tabularnewline
12 & 2987 & 2987.82371629392 & -287.901802766272 & -0.823716293917027 & 0.00338835195504231 \tabularnewline
13 & 1683 & 1713.56712235695 & -1229.09328966190 & -30.5671223569538 & -1.56288797789183 \tabularnewline
14 & 1381 & 1310.58847923549 & -440.188256731552 & 70.4115207645126 & 1.31544286775096 \tabularnewline
15 & 1071 & 1115.78963292764 & -211.956642260241 & -44.7896329276447 & 0.382814720271252 \tabularnewline
16 & 2772 & 2656.37727082158 & 1417.3902456518 & 115.622729178416 & 2.70100818871470 \tabularnewline
17 & 4485 & 4522.43573291238 & 1834.19199689547 & -37.435732912378 & 0.69191532764831 \tabularnewline
18 & 6181 & 6141.50930492809 & 1634.28478253756 & 39.4906950719122 & -0.331810694409317 \tabularnewline
19 & 5479 & 5624.42982228918 & -364.968921181233 & -145.429822289177 & -3.3184186995403 \tabularnewline
20 & 4782 & 4775.8528407719 & -814.37349558736 & 6.14715922809708 & -0.745933775605698 \tabularnewline
21 & 4067 & 4056.26948079706 & -726.287659350374 & 10.7305192029450 & 0.14620723998589 \tabularnewline
22 & 3489 & 3478.66689042194 & -588.11838202883 & 10.3331095780554 & 0.229337083254824 \tabularnewline
23 & 2903 & 2933.57001990285 & -548.139156145035 & -30.5700199028507 & 0.0663586288894683 \tabularnewline
24 & 2330 & 2275.39758623601 & -650.388123301669 & 54.6024137639919 & -0.169715967477611 \tabularnewline
25 & 1736 & 1789.52878030154 & -497.551705537405 & -53.5287803015409 & 0.253817080946218 \tabularnewline
26 & 1483 & 1358.69512767047 & -435.517824043713 & 124.304872329533 & 0.103160040904261 \tabularnewline
27 & 1242 & 1360.81582933842 & -34.3595457358069 & -118.815829338419 & 0.669222722740359 \tabularnewline
28 & 2334 & 2212.03858623321 & 779.028281892274 & 121.961413766788 & 1.34931369578257 \tabularnewline
29 & 3423 & 3503.31693450372 & 1248.84227706923 & -80.316934503724 & 0.779813126068055 \tabularnewline
30 & 4523 & 4377.37502345757 & 904.978553551751 & 145.624976542429 & -0.570766233674772 \tabularnewline
31 & 3986 & 4155.86452813121 & -128.625373867469 & -169.864528131208 & -1.71560135414889 \tabularnewline
32 & 3462 & 3477.50155379676 & -633.02257755839 & -15.5015537967614 & -0.837212454578822 \tabularnewline
33 & 2908 & 2895.71191154208 & -586.015721732472 & 12.2880884579188 & 0.0780232446176738 \tabularnewline
34 & 2575 & 2556.15594892908 & -359.884048543337 & 18.8440510709248 & 0.375339447186656 \tabularnewline
35 & 2237 & 2254.92944466363 & -306.063859348415 & -17.9294446636345 & 0.0893324696921452 \tabularnewline
36 & 1904 & 1868.31048068351 & -379.969738312634 & 35.6895193164873 & -0.122671607646575 \tabularnewline
37 & 1610 & 1643.48892647039 & -237.656303453972 & -33.4889264703898 & 0.236367686330915 \tabularnewline
38 & 1251 & 1137.84448956637 & -483.583488938503 & 113.155510433633 & -0.408410331825218 \tabularnewline
39 & 941 & 1081.90698047364 & -94.1583891589908 & -140.906980473639 & 0.648005992213137 \tabularnewline
40 & 2450 & 2312.19681880834 & 1114.45402849545 & 137.80318119166 & 2.00602416300433 \tabularnewline
41 & 3946 & 4015.43203056638 & 1651.00213730320 & -69.4320305663794 & 0.890443149671629 \tabularnewline
42 & 5409 & 5226.22705213235 & 1249.74236455958 & 182.772947867653 & -0.66605263897509 \tabularnewline
43 & 4741 & 4952.31071987489 & -139.244484583548 & -211.310719874888 & -2.30546717433684 \tabularnewline
44 & 4069 & 4111.01882682801 & -779.231723660451 & -42.0188268280059 & -1.06226887344331 \tabularnewline
45 & 3539 & 3523.76325955734 & -604.228826453218 & 15.2367404426565 & 0.290474512133344 \tabularnewline
46 & 3189 & 3158.66780754494 & -386.235918611633 & 30.3321924550622 & 0.361830642206055 \tabularnewline
47 & 2960 & 2961.76516842858 & -213.639583286423 & -1.76516842858439 & 0.286481270971357 \tabularnewline
48 & 2704 & 2701.5272080579 & -256.113326812728 & 2.47279194210022 & -0.0705004546891371 \tabularnewline
49 & 1697 & 1728.62859318626 & -909.41822328979 & -31.6285931862648 & -1.08506379466651 \tabularnewline
50 & 1598 & 1424.10866382671 & -358.094960988995 & 173.89133617329 & 0.915106245886558 \tabularnewline
51 & 1456 & 1651.48212534320 & 173.136180758787 & -195.482125343203 & 0.882959536644735 \tabularnewline
52 & 2316 & 2235.67392653673 & 546.746313620669 & 80.326073463267 & 0.620274005397241 \tabularnewline
53 & 3083 & 3160.34386444056 & 889.878033750948 & -77.3438644405608 & 0.56940580779805 \tabularnewline
54 & 4158 & 3880.08849755857 & 735.391157317516 & 277.911502441432 & -0.256435030215154 \tabularnewline
55 & 3469 & 3665.69139932732 & -127.163398260822 & -196.691399327316 & -1.43168605919819 \tabularnewline
56 & 2892 & 2968.73774976568 & -644.62138869767 & -76.7377497656808 & -0.858890513139222 \tabularnewline
57 & 2578 & 2556.69797954254 & -433.403927005304 & 21.3020204574605 & 0.350584462021206 \tabularnewline
58 & 2233 & 2221.43373046111 & -344.278386126272 & 11.5662695388940 & 0.147933212853412 \tabularnewline
59 & 1947 & 1964.37640494239 & -265.069358169029 & -17.3764049423858 & 0.1314735334007 \tabularnewline
60 & 2049 & 1945.14683438854 & -41.8454611812789 & 103.853165611457 & 0.370530377263508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63107&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]1915[/C][C]1915[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1843[/C][C]1843.99431779336[/C][C]-71.0878572278306[/C][C]-0.994317793362496[/C][C]-0.118782434738233[/C][/ROW]
[ROW][C]3[/C][C]1761[/C][C]1760.73098327906[/C][C]-82.7960266163255[/C][C]0.269016720944476[/C][C]-0.0197979134286433[/C][/ROW]
[ROW][C]4[/C][C]2858[/C][C]2822.07852254198[/C][C]1008.51469586646[/C][C]35.9214774580235[/C][C]1.80930312201797[/C][/ROW]
[ROW][C]5[/C][C]3968[/C][C]3973.66531697558[/C][C]1145.05557395077[/C][C]-5.66531697558136[/C][C]0.226651816858512[/C][/ROW]
[ROW][C]6[/C][C]5061[/C][C]5064.75001076945[/C][C]1093.53921250168[/C][C]-3.75001076945432[/C][C]-0.0855081585733372[/C][/ROW]
[ROW][C]7[/C][C]4661[/C][C]4707.95024201491[/C][C]-290.809005748789[/C][C]-46.9502420149081[/C][C]-2.29777874671134[/C][/ROW]
[ROW][C]8[/C][C]4269[/C][C]4262.81579709793[/C][C]-438.111537284517[/C][C]6.18420290207231[/C][C]-0.244496699778275[/C][/ROW]
[ROW][C]9[/C][C]3857[/C][C]3855.57711371601[/C][C]-408.643564979029[/C][C]1.42288628398723[/C][C]0.048911732379963[/C][/ROW]
[ROW][C]10[/C][C]3568[/C][C]3565.2986173083[/C][C]-295.664705840661[/C][C]2.70138269170044[/C][C]0.187525346612445[/C][/ROW]
[ROW][C]11[/C][C]3274[/C][C]3275.62819664525[/C][C]-289.943191544671[/C][C]-1.62819664525496[/C][C]0.00949672318951281[/C][/ROW]
[ROW][C]12[/C][C]2987[/C][C]2987.82371629392[/C][C]-287.901802766272[/C][C]-0.823716293917027[/C][C]0.00338835195504231[/C][/ROW]
[ROW][C]13[/C][C]1683[/C][C]1713.56712235695[/C][C]-1229.09328966190[/C][C]-30.5671223569538[/C][C]-1.56288797789183[/C][/ROW]
[ROW][C]14[/C][C]1381[/C][C]1310.58847923549[/C][C]-440.188256731552[/C][C]70.4115207645126[/C][C]1.31544286775096[/C][/ROW]
[ROW][C]15[/C][C]1071[/C][C]1115.78963292764[/C][C]-211.956642260241[/C][C]-44.7896329276447[/C][C]0.382814720271252[/C][/ROW]
[ROW][C]16[/C][C]2772[/C][C]2656.37727082158[/C][C]1417.3902456518[/C][C]115.622729178416[/C][C]2.70100818871470[/C][/ROW]
[ROW][C]17[/C][C]4485[/C][C]4522.43573291238[/C][C]1834.19199689547[/C][C]-37.435732912378[/C][C]0.69191532764831[/C][/ROW]
[ROW][C]18[/C][C]6181[/C][C]6141.50930492809[/C][C]1634.28478253756[/C][C]39.4906950719122[/C][C]-0.331810694409317[/C][/ROW]
[ROW][C]19[/C][C]5479[/C][C]5624.42982228918[/C][C]-364.968921181233[/C][C]-145.429822289177[/C][C]-3.3184186995403[/C][/ROW]
[ROW][C]20[/C][C]4782[/C][C]4775.8528407719[/C][C]-814.37349558736[/C][C]6.14715922809708[/C][C]-0.745933775605698[/C][/ROW]
[ROW][C]21[/C][C]4067[/C][C]4056.26948079706[/C][C]-726.287659350374[/C][C]10.7305192029450[/C][C]0.14620723998589[/C][/ROW]
[ROW][C]22[/C][C]3489[/C][C]3478.66689042194[/C][C]-588.11838202883[/C][C]10.3331095780554[/C][C]0.229337083254824[/C][/ROW]
[ROW][C]23[/C][C]2903[/C][C]2933.57001990285[/C][C]-548.139156145035[/C][C]-30.5700199028507[/C][C]0.0663586288894683[/C][/ROW]
[ROW][C]24[/C][C]2330[/C][C]2275.39758623601[/C][C]-650.388123301669[/C][C]54.6024137639919[/C][C]-0.169715967477611[/C][/ROW]
[ROW][C]25[/C][C]1736[/C][C]1789.52878030154[/C][C]-497.551705537405[/C][C]-53.5287803015409[/C][C]0.253817080946218[/C][/ROW]
[ROW][C]26[/C][C]1483[/C][C]1358.69512767047[/C][C]-435.517824043713[/C][C]124.304872329533[/C][C]0.103160040904261[/C][/ROW]
[ROW][C]27[/C][C]1242[/C][C]1360.81582933842[/C][C]-34.3595457358069[/C][C]-118.815829338419[/C][C]0.669222722740359[/C][/ROW]
[ROW][C]28[/C][C]2334[/C][C]2212.03858623321[/C][C]779.028281892274[/C][C]121.961413766788[/C][C]1.34931369578257[/C][/ROW]
[ROW][C]29[/C][C]3423[/C][C]3503.31693450372[/C][C]1248.84227706923[/C][C]-80.316934503724[/C][C]0.779813126068055[/C][/ROW]
[ROW][C]30[/C][C]4523[/C][C]4377.37502345757[/C][C]904.978553551751[/C][C]145.624976542429[/C][C]-0.570766233674772[/C][/ROW]
[ROW][C]31[/C][C]3986[/C][C]4155.86452813121[/C][C]-128.625373867469[/C][C]-169.864528131208[/C][C]-1.71560135414889[/C][/ROW]
[ROW][C]32[/C][C]3462[/C][C]3477.50155379676[/C][C]-633.02257755839[/C][C]-15.5015537967614[/C][C]-0.837212454578822[/C][/ROW]
[ROW][C]33[/C][C]2908[/C][C]2895.71191154208[/C][C]-586.015721732472[/C][C]12.2880884579188[/C][C]0.0780232446176738[/C][/ROW]
[ROW][C]34[/C][C]2575[/C][C]2556.15594892908[/C][C]-359.884048543337[/C][C]18.8440510709248[/C][C]0.375339447186656[/C][/ROW]
[ROW][C]35[/C][C]2237[/C][C]2254.92944466363[/C][C]-306.063859348415[/C][C]-17.9294446636345[/C][C]0.0893324696921452[/C][/ROW]
[ROW][C]36[/C][C]1904[/C][C]1868.31048068351[/C][C]-379.969738312634[/C][C]35.6895193164873[/C][C]-0.122671607646575[/C][/ROW]
[ROW][C]37[/C][C]1610[/C][C]1643.48892647039[/C][C]-237.656303453972[/C][C]-33.4889264703898[/C][C]0.236367686330915[/C][/ROW]
[ROW][C]38[/C][C]1251[/C][C]1137.84448956637[/C][C]-483.583488938503[/C][C]113.155510433633[/C][C]-0.408410331825218[/C][/ROW]
[ROW][C]39[/C][C]941[/C][C]1081.90698047364[/C][C]-94.1583891589908[/C][C]-140.906980473639[/C][C]0.648005992213137[/C][/ROW]
[ROW][C]40[/C][C]2450[/C][C]2312.19681880834[/C][C]1114.45402849545[/C][C]137.80318119166[/C][C]2.00602416300433[/C][/ROW]
[ROW][C]41[/C][C]3946[/C][C]4015.43203056638[/C][C]1651.00213730320[/C][C]-69.4320305663794[/C][C]0.890443149671629[/C][/ROW]
[ROW][C]42[/C][C]5409[/C][C]5226.22705213235[/C][C]1249.74236455958[/C][C]182.772947867653[/C][C]-0.66605263897509[/C][/ROW]
[ROW][C]43[/C][C]4741[/C][C]4952.31071987489[/C][C]-139.244484583548[/C][C]-211.310719874888[/C][C]-2.30546717433684[/C][/ROW]
[ROW][C]44[/C][C]4069[/C][C]4111.01882682801[/C][C]-779.231723660451[/C][C]-42.0188268280059[/C][C]-1.06226887344331[/C][/ROW]
[ROW][C]45[/C][C]3539[/C][C]3523.76325955734[/C][C]-604.228826453218[/C][C]15.2367404426565[/C][C]0.290474512133344[/C][/ROW]
[ROW][C]46[/C][C]3189[/C][C]3158.66780754494[/C][C]-386.235918611633[/C][C]30.3321924550622[/C][C]0.361830642206055[/C][/ROW]
[ROW][C]47[/C][C]2960[/C][C]2961.76516842858[/C][C]-213.639583286423[/C][C]-1.76516842858439[/C][C]0.286481270971357[/C][/ROW]
[ROW][C]48[/C][C]2704[/C][C]2701.5272080579[/C][C]-256.113326812728[/C][C]2.47279194210022[/C][C]-0.0705004546891371[/C][/ROW]
[ROW][C]49[/C][C]1697[/C][C]1728.62859318626[/C][C]-909.41822328979[/C][C]-31.6285931862648[/C][C]-1.08506379466651[/C][/ROW]
[ROW][C]50[/C][C]1598[/C][C]1424.10866382671[/C][C]-358.094960988995[/C][C]173.89133617329[/C][C]0.915106245886558[/C][/ROW]
[ROW][C]51[/C][C]1456[/C][C]1651.48212534320[/C][C]173.136180758787[/C][C]-195.482125343203[/C][C]0.882959536644735[/C][/ROW]
[ROW][C]52[/C][C]2316[/C][C]2235.67392653673[/C][C]546.746313620669[/C][C]80.326073463267[/C][C]0.620274005397241[/C][/ROW]
[ROW][C]53[/C][C]3083[/C][C]3160.34386444056[/C][C]889.878033750948[/C][C]-77.3438644405608[/C][C]0.56940580779805[/C][/ROW]
[ROW][C]54[/C][C]4158[/C][C]3880.08849755857[/C][C]735.391157317516[/C][C]277.911502441432[/C][C]-0.256435030215154[/C][/ROW]
[ROW][C]55[/C][C]3469[/C][C]3665.69139932732[/C][C]-127.163398260822[/C][C]-196.691399327316[/C][C]-1.43168605919819[/C][/ROW]
[ROW][C]56[/C][C]2892[/C][C]2968.73774976568[/C][C]-644.62138869767[/C][C]-76.7377497656808[/C][C]-0.858890513139222[/C][/ROW]
[ROW][C]57[/C][C]2578[/C][C]2556.69797954254[/C][C]-433.403927005304[/C][C]21.3020204574605[/C][C]0.350584462021206[/C][/ROW]
[ROW][C]58[/C][C]2233[/C][C]2221.43373046111[/C][C]-344.278386126272[/C][C]11.5662695388940[/C][C]0.147933212853412[/C][/ROW]
[ROW][C]59[/C][C]1947[/C][C]1964.37640494239[/C][C]-265.069358169029[/C][C]-17.3764049423858[/C][C]0.1314735334007[/C][/ROW]
[ROW][C]60[/C][C]2049[/C][C]1945.14683438854[/C][C]-41.8454611812789[/C][C]103.853165611457[/C][C]0.370530377263508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63107&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63107&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
119151915000
218431843.99431779336-71.0878572278306-0.994317793362496-0.118782434738233
317611760.73098327906-82.79602661632550.269016720944476-0.0197979134286433
428582822.078522541981008.5146958664635.92147745802351.80930312201797
539683973.665316975581145.05557395077-5.665316975581360.226651816858512
650615064.750010769451093.53921250168-3.75001076945432-0.0855081585733372
746614707.95024201491-290.809005748789-46.9502420149081-2.29777874671134
842694262.81579709793-438.1115372845176.18420290207231-0.244496699778275
938573855.57711371601-408.6435649790291.422886283987230.048911732379963
1035683565.2986173083-295.6647058406612.701382691700440.187525346612445
1132743275.62819664525-289.943191544671-1.628196645254960.00949672318951281
1229872987.82371629392-287.901802766272-0.8237162939170270.00338835195504231
1316831713.56712235695-1229.09328966190-30.5671223569538-1.56288797789183
1413811310.58847923549-440.18825673155270.41152076451261.31544286775096
1510711115.78963292764-211.956642260241-44.78963292764470.382814720271252
1627722656.377270821581417.3902456518115.6227291784162.70100818871470
1744854522.435732912381834.19199689547-37.4357329123780.69191532764831
1861816141.509304928091634.2847825375639.4906950719122-0.331810694409317
1954795624.42982228918-364.968921181233-145.429822289177-3.3184186995403
2047824775.8528407719-814.373495587366.14715922809708-0.745933775605698
2140674056.26948079706-726.28765935037410.73051920294500.14620723998589
2234893478.66689042194-588.1183820288310.33310957805540.229337083254824
2329032933.57001990285-548.139156145035-30.57001990285070.0663586288894683
2423302275.39758623601-650.38812330166954.6024137639919-0.169715967477611
2517361789.52878030154-497.551705537405-53.52878030154090.253817080946218
2614831358.69512767047-435.517824043713124.3048723295330.103160040904261
2712421360.81582933842-34.3595457358069-118.8158293384190.669222722740359
2823342212.03858623321779.028281892274121.9614137667881.34931369578257
2934233503.316934503721248.84227706923-80.3169345037240.779813126068055
3045234377.37502345757904.978553551751145.624976542429-0.570766233674772
3139864155.86452813121-128.625373867469-169.864528131208-1.71560135414889
3234623477.50155379676-633.02257755839-15.5015537967614-0.837212454578822
3329082895.71191154208-586.01572173247212.28808845791880.0780232446176738
3425752556.15594892908-359.88404854333718.84405107092480.375339447186656
3522372254.92944466363-306.063859348415-17.92944466363450.0893324696921452
3619041868.31048068351-379.96973831263435.6895193164873-0.122671607646575
3716101643.48892647039-237.656303453972-33.48892647038980.236367686330915
3812511137.84448956637-483.583488938503113.155510433633-0.408410331825218
399411081.90698047364-94.1583891589908-140.9069804736390.648005992213137
4024502312.196818808341114.45402849545137.803181191662.00602416300433
4139464015.432030566381651.00213730320-69.43203056637940.890443149671629
4254095226.227052132351249.74236455958182.772947867653-0.66605263897509
4347414952.31071987489-139.244484583548-211.310719874888-2.30546717433684
4440694111.01882682801-779.231723660451-42.0188268280059-1.06226887344331
4535393523.76325955734-604.22882645321815.23674044265650.290474512133344
4631893158.66780754494-386.23591861163330.33219245506220.361830642206055
4729602961.76516842858-213.639583286423-1.765168428584390.286481270971357
4827042701.5272080579-256.1133268127282.47279194210022-0.0705004546891371
4916971728.62859318626-909.41822328979-31.6285931862648-1.08506379466651
5015981424.10866382671-358.094960988995173.891336173290.915106245886558
5114561651.48212534320173.136180758787-195.4821253432030.882959536644735
5223162235.67392653673546.74631362066980.3260734632670.620274005397241
5330833160.34386444056889.878033750948-77.34386444056080.56940580779805
5441583880.08849755857735.391157317516277.911502441432-0.256435030215154
5534693665.69139932732-127.163398260822-196.691399327316-1.43168605919819
5628922968.73774976568-644.62138869767-76.7377497656808-0.858890513139222
5725782556.69797954254-433.40392700530421.30202045746050.350584462021206
5822332221.43373046111-344.27838612627211.56626953889400.147933212853412
5919471964.37640494239-265.069358169029-17.37640494238580.1314735334007
6020491945.14683438854-41.8454611812789103.8531656114570.370530377263508



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
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')