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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 computationSun, 19 Dec 2010 09:00:25 +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/19/t129274914435nokj4pilt22e0.htm/, Retrieved Sun, 05 May 2024 07:26:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112235, Retrieved Sun, 05 May 2024 07:26:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
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Dataseries X:
3111
3995
5245
5588
10681
10516
7496
9935
10249
6271
3616
3724
2886
3318
4166
6401
9209
9820
7470
8207
9564
5309
3385
3706
2733
3045
3449
5542
10072
9418
7516
7840
10081
4956
3641
3970
2931
3170
3889
4850
8037
12370
6712
7297
10613
5184
3506
3810
2692
3073
3713
4555
7807
10869
9682
7704
9826
5456
3677
3431
2765
3483
3445
6081
8767
9407
6551
12480
9530
5960
3252
3717
2642
2989
3607
5366
8898
9435
7328
8594
11349
5797
3621
3851




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112235&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]4 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=112235&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112235&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
131113111000
239953612.8827132778488.485601609452139.6419794182420.620189542035713
352455006.45705586261081.2711499928189.10848137015120.572527068887661
455885743.84885630487862.125116581925-99.9421133704833-0.215281102215225
5106819642.940233469042812.7211173582559.0318018873491.86347004929820
61051611121.04724207871955.49656800710-396.734039870185-0.814360226394706
774968825.7241469518-777.585402586055-665.404896527415-2.59759688442302
899359281.9559906364716.2985863715707460.0759247737710.754510604173224
91024910056.1528182779503.99951020950474.31627652311110.463476299882457
1062717365.66708819162-1551.39659789881-595.132195777487-1.95328656788902
1136164025.62514647689-2702.15611401201-129.949019976698-1.09359249350097
1237243044.82566983615-1594.71609317863410.0264870834641.05242470700327
1328862486.99169218527-943.350248229637240.7124384721270.678557196445312
1433182905.40921354793-135.699925167198247.0070930576140.726065431327129
1541663737.89494658164444.853435525258296.4568896921170.56071052595083
1664016377.463067975721806.62825228939-285.1313620209201.29363633827189
1792098490.685317756021996.4021415759675.7958503378950.178869417356362
1898209763.73784502511549.84297605897157.021975453824-0.423405146846143
1974709186.05330072487234.051729130800-1418.05246931372-1.25048210857331
2082078276.98282280701-473.93103701909490.3921004331875-0.672827970709785
2195648563.54853643257-2.75179268249906893.7290215418470.447757498171943
2253096381.09526547425-1353.07287452423-766.23079770436-1.2832538466505
2333853992.71691558194-1994.03137885859-462.53688099803-0.609117291309971
2437062985.20391654292-1384.49182222250582.7031038171110.58013292969877
2527332482.90683865989-839.434633585993125.3260020192490.532934471486548
2630452683.53531007380-213.210724730631226.3035511622680.583363512011349
2734493405.59943920103346.098657533172-80.46201155133740.534857700407542
2855425401.354976141611352.49251879154-84.14864810444850.960372141135581
29100728652.615965505182513.150791292491162.848930101811.09755123053373
3094189541.189572140841523.1164154425895.9834983456691-0.938194337190754
3175169353.0419373926479.62237999601-1605.19275633586-0.991419371310645
3278408410.24716402446-389.062976705659-377.104253989255-0.825579731308563
33100818419.58764217559-145.5975115763171607.286874475110.231364888819556
3449566223.907636558-1398.15090957433-989.438051854332-1.19033085292967
3536414325.72522003029-1703.32713323455-616.880953781665-0.290064996583784
3639703238.19767537723-1327.96454301442648.263888171350.357711172156726
3729312703.31359699742-843.190162248884119.2157520696480.465387849269072
3831702829.66577232126-258.374032740858212.5872993252360.550849466717508
3938893983.96087908827587.470541468975-281.2113749386110.80579458235679
4048505264.870704534891007.32103940934-508.0443843702750.400751969095235
4180376489.296340828281139.221107834061518.629427954060.125042567003883
421237010742.69214211413026.787240388091211.709764157761.78898381848514
4367129759.16905103896596.752782097315-2510.72086596432-2.30796268443930
4472978350.23046327393-620.07791911936-784.324438665361-1.15641550144057
45106138200.4942155982-334.5231211994072349.401243054060.271369112718619
4651846521.2315631491-1150.72649051446-1156.85866327522-0.775666692127054
4735064464.29799100156-1700.20885484243-836.850789054728-0.522438247213406
4838103100.71491926352-1496.13658083475664.1187088751280.194467277053269
4926922453.83481865599-980.292614557232123.8325955261450.49209310630182
5030732725.90165906752-225.489507274648181.6853907198000.713900863345525
5137133750.94333846790524.035897149407-202.7316056051610.713021622804519
5245555079.657518978691009.39869587636-631.9791201777560.462930348129642
5378076832.955271630721459.66748888408874.8682437300420.427520908671153
54108698613.179046822881653.409497741532213.234633229480.183710129756517
55968211385.65698820432329.24534408472-1852.439508158120.641722926439211
56770410059.3187176853119.888134200352-1868.38793580562-2.09946329941921
5798267831.79311468685-1299.717723122502307.13586034375-1.34911383900893
5854566306.45038806238-1436.13364121289-820.378547042119-0.129651744926658
5936774602.27645309638-1598.09125948034-889.562088178773-0.154025322410853
6034312917.08268668110-1650.74161669649525.54055657661-0.0501450902934945
6127652439.02982233213-941.20474857984169.3897104679170.675208864974534
6234833036.25713096270-15.0678939724961243.5851302637760.877526374428028
6334453670.20234969692374.101696986122-310.7213580023810.370012529483473
6460816194.042783553051668.03785286121-398.5347660679241.23314967659956
6587678163.634817334551850.10197382981563.2797749329820.173014768934893
6694078214.341749782764.5572702297961431.15646748956-1.02988108971288
6765517852.619582537685.7453517588755-1152.38354595725-0.644468027014579
681248012141.25525629462619.53841609243-218.8703459516392.40746254299608
6995309351.01383181355-643.280446847622897.279008057147-3.10085235725312
7059607006.63878269136-1669.13408875546-820.769986925271-0.975110854149383
7132524349.17660043382-2265.00816273368-965.921265147465-0.566765490942552
7237173029.0424727204-1694.97407893364562.3061554714140.542600600387632
7326422429.10663139549-1034.1257213004867.3748505505730.628219483552403
7429892565.09861081663-330.488691603592269.5012465763330.667305291446075
7536073909.19209567202673.674180482145-522.8067076830060.95451709240853
7653665516.295154385071234.74618543120-273.9244597382830.534382161751387
7788987618.759903898141757.663349909551164.146331046780.497148175427193
7894358432.836908484951189.201349915791127.04301909470-0.539563338112475
7973289519.740077827561127.63291556398-2178.21376343124-0.0584518175544107
8085948327.30874113942-268.547958961703573.672040946175-1.32641207593223
81113499351.0037102388509.2893074522631826.895030554440.739233760317641
8257977312.85060997764-1024.07333385544-1178.46392321495-1.45770336996176
8336215075.71319716074-1754.31216075792-1293.96449646832-0.69459643242334
8438513410.20838367394-1700.81546488972429.0114756520040.0508986538457064

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 3111 & 3111 & 0 & 0 & 0 \tabularnewline
2 & 3995 & 3612.8827132778 & 488.485601609452 & 139.641979418242 & 0.620189542035713 \tabularnewline
3 & 5245 & 5006.4570558626 & 1081.27114999281 & 89.1084813701512 & 0.572527068887661 \tabularnewline
4 & 5588 & 5743.84885630487 & 862.125116581925 & -99.9421133704833 & -0.215281102215225 \tabularnewline
5 & 10681 & 9642.94023346904 & 2812.7211173582 & 559.031801887349 & 1.86347004929820 \tabularnewline
6 & 10516 & 11121.0472420787 & 1955.49656800710 & -396.734039870185 & -0.814360226394706 \tabularnewline
7 & 7496 & 8825.7241469518 & -777.585402586055 & -665.404896527415 & -2.59759688442302 \tabularnewline
8 & 9935 & 9281.95599063647 & 16.2985863715707 & 460.075924773771 & 0.754510604173224 \tabularnewline
9 & 10249 & 10056.1528182779 & 503.999510209504 & 74.3162765231111 & 0.463476299882457 \tabularnewline
10 & 6271 & 7365.66708819162 & -1551.39659789881 & -595.132195777487 & -1.95328656788902 \tabularnewline
11 & 3616 & 4025.62514647689 & -2702.15611401201 & -129.949019976698 & -1.09359249350097 \tabularnewline
12 & 3724 & 3044.82566983615 & -1594.71609317863 & 410.026487083464 & 1.05242470700327 \tabularnewline
13 & 2886 & 2486.99169218527 & -943.350248229637 & 240.712438472127 & 0.678557196445312 \tabularnewline
14 & 3318 & 2905.40921354793 & -135.699925167198 & 247.007093057614 & 0.726065431327129 \tabularnewline
15 & 4166 & 3737.89494658164 & 444.853435525258 & 296.456889692117 & 0.56071052595083 \tabularnewline
16 & 6401 & 6377.46306797572 & 1806.62825228939 & -285.131362020920 & 1.29363633827189 \tabularnewline
17 & 9209 & 8490.68531775602 & 1996.4021415759 & 675.795850337895 & 0.178869417356362 \tabularnewline
18 & 9820 & 9763.7378450251 & 1549.84297605897 & 157.021975453824 & -0.423405146846143 \tabularnewline
19 & 7470 & 9186.05330072487 & 234.051729130800 & -1418.05246931372 & -1.25048210857331 \tabularnewline
20 & 8207 & 8276.98282280701 & -473.931037019094 & 90.3921004331875 & -0.672827970709785 \tabularnewline
21 & 9564 & 8563.54853643257 & -2.75179268249906 & 893.729021541847 & 0.447757498171943 \tabularnewline
22 & 5309 & 6381.09526547425 & -1353.07287452423 & -766.23079770436 & -1.2832538466505 \tabularnewline
23 & 3385 & 3992.71691558194 & -1994.03137885859 & -462.53688099803 & -0.609117291309971 \tabularnewline
24 & 3706 & 2985.20391654292 & -1384.49182222250 & 582.703103817111 & 0.58013292969877 \tabularnewline
25 & 2733 & 2482.90683865989 & -839.434633585993 & 125.326002019249 & 0.532934471486548 \tabularnewline
26 & 3045 & 2683.53531007380 & -213.210724730631 & 226.303551162268 & 0.583363512011349 \tabularnewline
27 & 3449 & 3405.59943920103 & 346.098657533172 & -80.4620115513374 & 0.534857700407542 \tabularnewline
28 & 5542 & 5401.35497614161 & 1352.49251879154 & -84.1486481044485 & 0.960372141135581 \tabularnewline
29 & 10072 & 8652.61596550518 & 2513.15079129249 & 1162.84893010181 & 1.09755123053373 \tabularnewline
30 & 9418 & 9541.18957214084 & 1523.11641544258 & 95.9834983456691 & -0.938194337190754 \tabularnewline
31 & 7516 & 9353.0419373926 & 479.62237999601 & -1605.19275633586 & -0.991419371310645 \tabularnewline
32 & 7840 & 8410.24716402446 & -389.062976705659 & -377.104253989255 & -0.825579731308563 \tabularnewline
33 & 10081 & 8419.58764217559 & -145.597511576317 & 1607.28687447511 & 0.231364888819556 \tabularnewline
34 & 4956 & 6223.907636558 & -1398.15090957433 & -989.438051854332 & -1.19033085292967 \tabularnewline
35 & 3641 & 4325.72522003029 & -1703.32713323455 & -616.880953781665 & -0.290064996583784 \tabularnewline
36 & 3970 & 3238.19767537723 & -1327.96454301442 & 648.26388817135 & 0.357711172156726 \tabularnewline
37 & 2931 & 2703.31359699742 & -843.190162248884 & 119.215752069648 & 0.465387849269072 \tabularnewline
38 & 3170 & 2829.66577232126 & -258.374032740858 & 212.587299325236 & 0.550849466717508 \tabularnewline
39 & 3889 & 3983.96087908827 & 587.470541468975 & -281.211374938611 & 0.80579458235679 \tabularnewline
40 & 4850 & 5264.87070453489 & 1007.32103940934 & -508.044384370275 & 0.400751969095235 \tabularnewline
41 & 8037 & 6489.29634082828 & 1139.22110783406 & 1518.62942795406 & 0.125042567003883 \tabularnewline
42 & 12370 & 10742.6921421141 & 3026.78724038809 & 1211.70976415776 & 1.78898381848514 \tabularnewline
43 & 6712 & 9759.16905103896 & 596.752782097315 & -2510.72086596432 & -2.30796268443930 \tabularnewline
44 & 7297 & 8350.23046327393 & -620.07791911936 & -784.324438665361 & -1.15641550144057 \tabularnewline
45 & 10613 & 8200.4942155982 & -334.523121199407 & 2349.40124305406 & 0.271369112718619 \tabularnewline
46 & 5184 & 6521.2315631491 & -1150.72649051446 & -1156.85866327522 & -0.775666692127054 \tabularnewline
47 & 3506 & 4464.29799100156 & -1700.20885484243 & -836.850789054728 & -0.522438247213406 \tabularnewline
48 & 3810 & 3100.71491926352 & -1496.13658083475 & 664.118708875128 & 0.194467277053269 \tabularnewline
49 & 2692 & 2453.83481865599 & -980.292614557232 & 123.832595526145 & 0.49209310630182 \tabularnewline
50 & 3073 & 2725.90165906752 & -225.489507274648 & 181.685390719800 & 0.713900863345525 \tabularnewline
51 & 3713 & 3750.94333846790 & 524.035897149407 & -202.731605605161 & 0.713021622804519 \tabularnewline
52 & 4555 & 5079.65751897869 & 1009.39869587636 & -631.979120177756 & 0.462930348129642 \tabularnewline
53 & 7807 & 6832.95527163072 & 1459.66748888408 & 874.868243730042 & 0.427520908671153 \tabularnewline
54 & 10869 & 8613.17904682288 & 1653.40949774153 & 2213.23463322948 & 0.183710129756517 \tabularnewline
55 & 9682 & 11385.6569882043 & 2329.24534408472 & -1852.43950815812 & 0.641722926439211 \tabularnewline
56 & 7704 & 10059.3187176853 & 119.888134200352 & -1868.38793580562 & -2.09946329941921 \tabularnewline
57 & 9826 & 7831.79311468685 & -1299.71772312250 & 2307.13586034375 & -1.34911383900893 \tabularnewline
58 & 5456 & 6306.45038806238 & -1436.13364121289 & -820.378547042119 & -0.129651744926658 \tabularnewline
59 & 3677 & 4602.27645309638 & -1598.09125948034 & -889.562088178773 & -0.154025322410853 \tabularnewline
60 & 3431 & 2917.08268668110 & -1650.74161669649 & 525.54055657661 & -0.0501450902934945 \tabularnewline
61 & 2765 & 2439.02982233213 & -941.20474857984 & 169.389710467917 & 0.675208864974534 \tabularnewline
62 & 3483 & 3036.25713096270 & -15.0678939724961 & 243.585130263776 & 0.877526374428028 \tabularnewline
63 & 3445 & 3670.20234969692 & 374.101696986122 & -310.721358002381 & 0.370012529483473 \tabularnewline
64 & 6081 & 6194.04278355305 & 1668.03785286121 & -398.534766067924 & 1.23314967659956 \tabularnewline
65 & 8767 & 8163.63481733455 & 1850.10197382981 & 563.279774932982 & 0.173014768934893 \tabularnewline
66 & 9407 & 8214.341749782 & 764.557270229796 & 1431.15646748956 & -1.02988108971288 \tabularnewline
67 & 6551 & 7852.6195825376 & 85.7453517588755 & -1152.38354595725 & -0.644468027014579 \tabularnewline
68 & 12480 & 12141.2552562946 & 2619.53841609243 & -218.870345951639 & 2.40746254299608 \tabularnewline
69 & 9530 & 9351.01383181355 & -643.280446847622 & 897.279008057147 & -3.10085235725312 \tabularnewline
70 & 5960 & 7006.63878269136 & -1669.13408875546 & -820.769986925271 & -0.975110854149383 \tabularnewline
71 & 3252 & 4349.17660043382 & -2265.00816273368 & -965.921265147465 & -0.566765490942552 \tabularnewline
72 & 3717 & 3029.0424727204 & -1694.97407893364 & 562.306155471414 & 0.542600600387632 \tabularnewline
73 & 2642 & 2429.10663139549 & -1034.12572130048 & 67.374850550573 & 0.628219483552403 \tabularnewline
74 & 2989 & 2565.09861081663 & -330.488691603592 & 269.501246576333 & 0.667305291446075 \tabularnewline
75 & 3607 & 3909.19209567202 & 673.674180482145 & -522.806707683006 & 0.95451709240853 \tabularnewline
76 & 5366 & 5516.29515438507 & 1234.74618543120 & -273.924459738283 & 0.534382161751387 \tabularnewline
77 & 8898 & 7618.75990389814 & 1757.66334990955 & 1164.14633104678 & 0.497148175427193 \tabularnewline
78 & 9435 & 8432.83690848495 & 1189.20134991579 & 1127.04301909470 & -0.539563338112475 \tabularnewline
79 & 7328 & 9519.74007782756 & 1127.63291556398 & -2178.21376343124 & -0.0584518175544107 \tabularnewline
80 & 8594 & 8327.30874113942 & -268.547958961703 & 573.672040946175 & -1.32641207593223 \tabularnewline
81 & 11349 & 9351.0037102388 & 509.289307452263 & 1826.89503055444 & 0.739233760317641 \tabularnewline
82 & 5797 & 7312.85060997764 & -1024.07333385544 & -1178.46392321495 & -1.45770336996176 \tabularnewline
83 & 3621 & 5075.71319716074 & -1754.31216075792 & -1293.96449646832 & -0.69459643242334 \tabularnewline
84 & 3851 & 3410.20838367394 & -1700.81546488972 & 429.011475652004 & 0.0508986538457064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112235&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]3111[/C][C]3111[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]3995[/C][C]3612.8827132778[/C][C]488.485601609452[/C][C]139.641979418242[/C][C]0.620189542035713[/C][/ROW]
[ROW][C]3[/C][C]5245[/C][C]5006.4570558626[/C][C]1081.27114999281[/C][C]89.1084813701512[/C][C]0.572527068887661[/C][/ROW]
[ROW][C]4[/C][C]5588[/C][C]5743.84885630487[/C][C]862.125116581925[/C][C]-99.9421133704833[/C][C]-0.215281102215225[/C][/ROW]
[ROW][C]5[/C][C]10681[/C][C]9642.94023346904[/C][C]2812.7211173582[/C][C]559.031801887349[/C][C]1.86347004929820[/C][/ROW]
[ROW][C]6[/C][C]10516[/C][C]11121.0472420787[/C][C]1955.49656800710[/C][C]-396.734039870185[/C][C]-0.814360226394706[/C][/ROW]
[ROW][C]7[/C][C]7496[/C][C]8825.7241469518[/C][C]-777.585402586055[/C][C]-665.404896527415[/C][C]-2.59759688442302[/C][/ROW]
[ROW][C]8[/C][C]9935[/C][C]9281.95599063647[/C][C]16.2985863715707[/C][C]460.075924773771[/C][C]0.754510604173224[/C][/ROW]
[ROW][C]9[/C][C]10249[/C][C]10056.1528182779[/C][C]503.999510209504[/C][C]74.3162765231111[/C][C]0.463476299882457[/C][/ROW]
[ROW][C]10[/C][C]6271[/C][C]7365.66708819162[/C][C]-1551.39659789881[/C][C]-595.132195777487[/C][C]-1.95328656788902[/C][/ROW]
[ROW][C]11[/C][C]3616[/C][C]4025.62514647689[/C][C]-2702.15611401201[/C][C]-129.949019976698[/C][C]-1.09359249350097[/C][/ROW]
[ROW][C]12[/C][C]3724[/C][C]3044.82566983615[/C][C]-1594.71609317863[/C][C]410.026487083464[/C][C]1.05242470700327[/C][/ROW]
[ROW][C]13[/C][C]2886[/C][C]2486.99169218527[/C][C]-943.350248229637[/C][C]240.712438472127[/C][C]0.678557196445312[/C][/ROW]
[ROW][C]14[/C][C]3318[/C][C]2905.40921354793[/C][C]-135.699925167198[/C][C]247.007093057614[/C][C]0.726065431327129[/C][/ROW]
[ROW][C]15[/C][C]4166[/C][C]3737.89494658164[/C][C]444.853435525258[/C][C]296.456889692117[/C][C]0.56071052595083[/C][/ROW]
[ROW][C]16[/C][C]6401[/C][C]6377.46306797572[/C][C]1806.62825228939[/C][C]-285.131362020920[/C][C]1.29363633827189[/C][/ROW]
[ROW][C]17[/C][C]9209[/C][C]8490.68531775602[/C][C]1996.4021415759[/C][C]675.795850337895[/C][C]0.178869417356362[/C][/ROW]
[ROW][C]18[/C][C]9820[/C][C]9763.7378450251[/C][C]1549.84297605897[/C][C]157.021975453824[/C][C]-0.423405146846143[/C][/ROW]
[ROW][C]19[/C][C]7470[/C][C]9186.05330072487[/C][C]234.051729130800[/C][C]-1418.05246931372[/C][C]-1.25048210857331[/C][/ROW]
[ROW][C]20[/C][C]8207[/C][C]8276.98282280701[/C][C]-473.931037019094[/C][C]90.3921004331875[/C][C]-0.672827970709785[/C][/ROW]
[ROW][C]21[/C][C]9564[/C][C]8563.54853643257[/C][C]-2.75179268249906[/C][C]893.729021541847[/C][C]0.447757498171943[/C][/ROW]
[ROW][C]22[/C][C]5309[/C][C]6381.09526547425[/C][C]-1353.07287452423[/C][C]-766.23079770436[/C][C]-1.2832538466505[/C][/ROW]
[ROW][C]23[/C][C]3385[/C][C]3992.71691558194[/C][C]-1994.03137885859[/C][C]-462.53688099803[/C][C]-0.609117291309971[/C][/ROW]
[ROW][C]24[/C][C]3706[/C][C]2985.20391654292[/C][C]-1384.49182222250[/C][C]582.703103817111[/C][C]0.58013292969877[/C][/ROW]
[ROW][C]25[/C][C]2733[/C][C]2482.90683865989[/C][C]-839.434633585993[/C][C]125.326002019249[/C][C]0.532934471486548[/C][/ROW]
[ROW][C]26[/C][C]3045[/C][C]2683.53531007380[/C][C]-213.210724730631[/C][C]226.303551162268[/C][C]0.583363512011349[/C][/ROW]
[ROW][C]27[/C][C]3449[/C][C]3405.59943920103[/C][C]346.098657533172[/C][C]-80.4620115513374[/C][C]0.534857700407542[/C][/ROW]
[ROW][C]28[/C][C]5542[/C][C]5401.35497614161[/C][C]1352.49251879154[/C][C]-84.1486481044485[/C][C]0.960372141135581[/C][/ROW]
[ROW][C]29[/C][C]10072[/C][C]8652.61596550518[/C][C]2513.15079129249[/C][C]1162.84893010181[/C][C]1.09755123053373[/C][/ROW]
[ROW][C]30[/C][C]9418[/C][C]9541.18957214084[/C][C]1523.11641544258[/C][C]95.9834983456691[/C][C]-0.938194337190754[/C][/ROW]
[ROW][C]31[/C][C]7516[/C][C]9353.0419373926[/C][C]479.62237999601[/C][C]-1605.19275633586[/C][C]-0.991419371310645[/C][/ROW]
[ROW][C]32[/C][C]7840[/C][C]8410.24716402446[/C][C]-389.062976705659[/C][C]-377.104253989255[/C][C]-0.825579731308563[/C][/ROW]
[ROW][C]33[/C][C]10081[/C][C]8419.58764217559[/C][C]-145.597511576317[/C][C]1607.28687447511[/C][C]0.231364888819556[/C][/ROW]
[ROW][C]34[/C][C]4956[/C][C]6223.907636558[/C][C]-1398.15090957433[/C][C]-989.438051854332[/C][C]-1.19033085292967[/C][/ROW]
[ROW][C]35[/C][C]3641[/C][C]4325.72522003029[/C][C]-1703.32713323455[/C][C]-616.880953781665[/C][C]-0.290064996583784[/C][/ROW]
[ROW][C]36[/C][C]3970[/C][C]3238.19767537723[/C][C]-1327.96454301442[/C][C]648.26388817135[/C][C]0.357711172156726[/C][/ROW]
[ROW][C]37[/C][C]2931[/C][C]2703.31359699742[/C][C]-843.190162248884[/C][C]119.215752069648[/C][C]0.465387849269072[/C][/ROW]
[ROW][C]38[/C][C]3170[/C][C]2829.66577232126[/C][C]-258.374032740858[/C][C]212.587299325236[/C][C]0.550849466717508[/C][/ROW]
[ROW][C]39[/C][C]3889[/C][C]3983.96087908827[/C][C]587.470541468975[/C][C]-281.211374938611[/C][C]0.80579458235679[/C][/ROW]
[ROW][C]40[/C][C]4850[/C][C]5264.87070453489[/C][C]1007.32103940934[/C][C]-508.044384370275[/C][C]0.400751969095235[/C][/ROW]
[ROW][C]41[/C][C]8037[/C][C]6489.29634082828[/C][C]1139.22110783406[/C][C]1518.62942795406[/C][C]0.125042567003883[/C][/ROW]
[ROW][C]42[/C][C]12370[/C][C]10742.6921421141[/C][C]3026.78724038809[/C][C]1211.70976415776[/C][C]1.78898381848514[/C][/ROW]
[ROW][C]43[/C][C]6712[/C][C]9759.16905103896[/C][C]596.752782097315[/C][C]-2510.72086596432[/C][C]-2.30796268443930[/C][/ROW]
[ROW][C]44[/C][C]7297[/C][C]8350.23046327393[/C][C]-620.07791911936[/C][C]-784.324438665361[/C][C]-1.15641550144057[/C][/ROW]
[ROW][C]45[/C][C]10613[/C][C]8200.4942155982[/C][C]-334.523121199407[/C][C]2349.40124305406[/C][C]0.271369112718619[/C][/ROW]
[ROW][C]46[/C][C]5184[/C][C]6521.2315631491[/C][C]-1150.72649051446[/C][C]-1156.85866327522[/C][C]-0.775666692127054[/C][/ROW]
[ROW][C]47[/C][C]3506[/C][C]4464.29799100156[/C][C]-1700.20885484243[/C][C]-836.850789054728[/C][C]-0.522438247213406[/C][/ROW]
[ROW][C]48[/C][C]3810[/C][C]3100.71491926352[/C][C]-1496.13658083475[/C][C]664.118708875128[/C][C]0.194467277053269[/C][/ROW]
[ROW][C]49[/C][C]2692[/C][C]2453.83481865599[/C][C]-980.292614557232[/C][C]123.832595526145[/C][C]0.49209310630182[/C][/ROW]
[ROW][C]50[/C][C]3073[/C][C]2725.90165906752[/C][C]-225.489507274648[/C][C]181.685390719800[/C][C]0.713900863345525[/C][/ROW]
[ROW][C]51[/C][C]3713[/C][C]3750.94333846790[/C][C]524.035897149407[/C][C]-202.731605605161[/C][C]0.713021622804519[/C][/ROW]
[ROW][C]52[/C][C]4555[/C][C]5079.65751897869[/C][C]1009.39869587636[/C][C]-631.979120177756[/C][C]0.462930348129642[/C][/ROW]
[ROW][C]53[/C][C]7807[/C][C]6832.95527163072[/C][C]1459.66748888408[/C][C]874.868243730042[/C][C]0.427520908671153[/C][/ROW]
[ROW][C]54[/C][C]10869[/C][C]8613.17904682288[/C][C]1653.40949774153[/C][C]2213.23463322948[/C][C]0.183710129756517[/C][/ROW]
[ROW][C]55[/C][C]9682[/C][C]11385.6569882043[/C][C]2329.24534408472[/C][C]-1852.43950815812[/C][C]0.641722926439211[/C][/ROW]
[ROW][C]56[/C][C]7704[/C][C]10059.3187176853[/C][C]119.888134200352[/C][C]-1868.38793580562[/C][C]-2.09946329941921[/C][/ROW]
[ROW][C]57[/C][C]9826[/C][C]7831.79311468685[/C][C]-1299.71772312250[/C][C]2307.13586034375[/C][C]-1.34911383900893[/C][/ROW]
[ROW][C]58[/C][C]5456[/C][C]6306.45038806238[/C][C]-1436.13364121289[/C][C]-820.378547042119[/C][C]-0.129651744926658[/C][/ROW]
[ROW][C]59[/C][C]3677[/C][C]4602.27645309638[/C][C]-1598.09125948034[/C][C]-889.562088178773[/C][C]-0.154025322410853[/C][/ROW]
[ROW][C]60[/C][C]3431[/C][C]2917.08268668110[/C][C]-1650.74161669649[/C][C]525.54055657661[/C][C]-0.0501450902934945[/C][/ROW]
[ROW][C]61[/C][C]2765[/C][C]2439.02982233213[/C][C]-941.20474857984[/C][C]169.389710467917[/C][C]0.675208864974534[/C][/ROW]
[ROW][C]62[/C][C]3483[/C][C]3036.25713096270[/C][C]-15.0678939724961[/C][C]243.585130263776[/C][C]0.877526374428028[/C][/ROW]
[ROW][C]63[/C][C]3445[/C][C]3670.20234969692[/C][C]374.101696986122[/C][C]-310.721358002381[/C][C]0.370012529483473[/C][/ROW]
[ROW][C]64[/C][C]6081[/C][C]6194.04278355305[/C][C]1668.03785286121[/C][C]-398.534766067924[/C][C]1.23314967659956[/C][/ROW]
[ROW][C]65[/C][C]8767[/C][C]8163.63481733455[/C][C]1850.10197382981[/C][C]563.279774932982[/C][C]0.173014768934893[/C][/ROW]
[ROW][C]66[/C][C]9407[/C][C]8214.341749782[/C][C]764.557270229796[/C][C]1431.15646748956[/C][C]-1.02988108971288[/C][/ROW]
[ROW][C]67[/C][C]6551[/C][C]7852.6195825376[/C][C]85.7453517588755[/C][C]-1152.38354595725[/C][C]-0.644468027014579[/C][/ROW]
[ROW][C]68[/C][C]12480[/C][C]12141.2552562946[/C][C]2619.53841609243[/C][C]-218.870345951639[/C][C]2.40746254299608[/C][/ROW]
[ROW][C]69[/C][C]9530[/C][C]9351.01383181355[/C][C]-643.280446847622[/C][C]897.279008057147[/C][C]-3.10085235725312[/C][/ROW]
[ROW][C]70[/C][C]5960[/C][C]7006.63878269136[/C][C]-1669.13408875546[/C][C]-820.769986925271[/C][C]-0.975110854149383[/C][/ROW]
[ROW][C]71[/C][C]3252[/C][C]4349.17660043382[/C][C]-2265.00816273368[/C][C]-965.921265147465[/C][C]-0.566765490942552[/C][/ROW]
[ROW][C]72[/C][C]3717[/C][C]3029.0424727204[/C][C]-1694.97407893364[/C][C]562.306155471414[/C][C]0.542600600387632[/C][/ROW]
[ROW][C]73[/C][C]2642[/C][C]2429.10663139549[/C][C]-1034.12572130048[/C][C]67.374850550573[/C][C]0.628219483552403[/C][/ROW]
[ROW][C]74[/C][C]2989[/C][C]2565.09861081663[/C][C]-330.488691603592[/C][C]269.501246576333[/C][C]0.667305291446075[/C][/ROW]
[ROW][C]75[/C][C]3607[/C][C]3909.19209567202[/C][C]673.674180482145[/C][C]-522.806707683006[/C][C]0.95451709240853[/C][/ROW]
[ROW][C]76[/C][C]5366[/C][C]5516.29515438507[/C][C]1234.74618543120[/C][C]-273.924459738283[/C][C]0.534382161751387[/C][/ROW]
[ROW][C]77[/C][C]8898[/C][C]7618.75990389814[/C][C]1757.66334990955[/C][C]1164.14633104678[/C][C]0.497148175427193[/C][/ROW]
[ROW][C]78[/C][C]9435[/C][C]8432.83690848495[/C][C]1189.20134991579[/C][C]1127.04301909470[/C][C]-0.539563338112475[/C][/ROW]
[ROW][C]79[/C][C]7328[/C][C]9519.74007782756[/C][C]1127.63291556398[/C][C]-2178.21376343124[/C][C]-0.0584518175544107[/C][/ROW]
[ROW][C]80[/C][C]8594[/C][C]8327.30874113942[/C][C]-268.547958961703[/C][C]573.672040946175[/C][C]-1.32641207593223[/C][/ROW]
[ROW][C]81[/C][C]11349[/C][C]9351.0037102388[/C][C]509.289307452263[/C][C]1826.89503055444[/C][C]0.739233760317641[/C][/ROW]
[ROW][C]82[/C][C]5797[/C][C]7312.85060997764[/C][C]-1024.07333385544[/C][C]-1178.46392321495[/C][C]-1.45770336996176[/C][/ROW]
[ROW][C]83[/C][C]3621[/C][C]5075.71319716074[/C][C]-1754.31216075792[/C][C]-1293.96449646832[/C][C]-0.69459643242334[/C][/ROW]
[ROW][C]84[/C][C]3851[/C][C]3410.20838367394[/C][C]-1700.81546488972[/C][C]429.011475652004[/C][C]0.0508986538457064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112235&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112235&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
131113111000
239953612.8827132778488.485601609452139.6419794182420.620189542035713
352455006.45705586261081.2711499928189.10848137015120.572527068887661
455885743.84885630487862.125116581925-99.9421133704833-0.215281102215225
5106819642.940233469042812.7211173582559.0318018873491.86347004929820
61051611121.04724207871955.49656800710-396.734039870185-0.814360226394706
774968825.7241469518-777.585402586055-665.404896527415-2.59759688442302
899359281.9559906364716.2985863715707460.0759247737710.754510604173224
91024910056.1528182779503.99951020950474.31627652311110.463476299882457
1062717365.66708819162-1551.39659789881-595.132195777487-1.95328656788902
1136164025.62514647689-2702.15611401201-129.949019976698-1.09359249350097
1237243044.82566983615-1594.71609317863410.0264870834641.05242470700327
1328862486.99169218527-943.350248229637240.7124384721270.678557196445312
1433182905.40921354793-135.699925167198247.0070930576140.726065431327129
1541663737.89494658164444.853435525258296.4568896921170.56071052595083
1664016377.463067975721806.62825228939-285.1313620209201.29363633827189
1792098490.685317756021996.4021415759675.7958503378950.178869417356362
1898209763.73784502511549.84297605897157.021975453824-0.423405146846143
1974709186.05330072487234.051729130800-1418.05246931372-1.25048210857331
2082078276.98282280701-473.93103701909490.3921004331875-0.672827970709785
2195648563.54853643257-2.75179268249906893.7290215418470.447757498171943
2253096381.09526547425-1353.07287452423-766.23079770436-1.2832538466505
2333853992.71691558194-1994.03137885859-462.53688099803-0.609117291309971
2437062985.20391654292-1384.49182222250582.7031038171110.58013292969877
2527332482.90683865989-839.434633585993125.3260020192490.532934471486548
2630452683.53531007380-213.210724730631226.3035511622680.583363512011349
2734493405.59943920103346.098657533172-80.46201155133740.534857700407542
2855425401.354976141611352.49251879154-84.14864810444850.960372141135581
29100728652.615965505182513.150791292491162.848930101811.09755123053373
3094189541.189572140841523.1164154425895.9834983456691-0.938194337190754
3175169353.0419373926479.62237999601-1605.19275633586-0.991419371310645
3278408410.24716402446-389.062976705659-377.104253989255-0.825579731308563
33100818419.58764217559-145.5975115763171607.286874475110.231364888819556
3449566223.907636558-1398.15090957433-989.438051854332-1.19033085292967
3536414325.72522003029-1703.32713323455-616.880953781665-0.290064996583784
3639703238.19767537723-1327.96454301442648.263888171350.357711172156726
3729312703.31359699742-843.190162248884119.2157520696480.465387849269072
3831702829.66577232126-258.374032740858212.5872993252360.550849466717508
3938893983.96087908827587.470541468975-281.2113749386110.80579458235679
4048505264.870704534891007.32103940934-508.0443843702750.400751969095235
4180376489.296340828281139.221107834061518.629427954060.125042567003883
421237010742.69214211413026.787240388091211.709764157761.78898381848514
4367129759.16905103896596.752782097315-2510.72086596432-2.30796268443930
4472978350.23046327393-620.07791911936-784.324438665361-1.15641550144057
45106138200.4942155982-334.5231211994072349.401243054060.271369112718619
4651846521.2315631491-1150.72649051446-1156.85866327522-0.775666692127054
4735064464.29799100156-1700.20885484243-836.850789054728-0.522438247213406
4838103100.71491926352-1496.13658083475664.1187088751280.194467277053269
4926922453.83481865599-980.292614557232123.8325955261450.49209310630182
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55968211385.65698820432329.24534408472-1852.439508158120.641722926439211
56770410059.3187176853119.888134200352-1868.38793580562-2.09946329941921
5798267831.79311468685-1299.717723122502307.13586034375-1.34911383900893
5854566306.45038806238-1436.13364121289-820.378547042119-0.129651744926658
5936774602.27645309638-1598.09125948034-889.562088178773-0.154025322410853
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6127652439.02982233213-941.20474857984169.3897104679170.675208864974534
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6765517852.619582537685.7453517588755-1152.38354595725-0.644468027014579
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6995309351.01383181355-643.280446847622897.279008057147-3.10085235725312
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8085948327.30874113942-268.547958961703573.672040946175-1.32641207593223
81113499351.0037102388509.2893074522631826.895030554440.739233760317641
8257977312.85060997764-1024.07333385544-1178.46392321495-1.45770336996176
8336215075.71319716074-1754.31216075792-1293.96449646832-0.69459643242334
8438513410.20838367394-1700.81546488972429.0114756520040.0508986538457064



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')