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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 computationMon, 28 Nov 2011 11:51:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/28/t1322499112u8wb2u0a9qmsmf0.htm/, Retrieved Fri, 29 Mar 2024 08:48:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147863, Retrieved Fri, 29 Mar 2024 08:48:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [WS8 Structural Ti...] [2011-11-28 16:51:25] [2a6d487209befbc7c5ce02a41ecac161] [Current]
- R  D    [Structural Time Series Models] [] [2011-12-23 16:55:08] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
2564
2820
3508
3088
3299
2939
3320
3418
3604
3495
4163
4882
2211
3260
2992
2425
2707
3244
3965
3315
3333
3583
4021
4904
2252
2952
3573
3048
3059
2731
3563
3092
3478
3478
4308
5029
2075
3264
3308
3688
3136
2824
3644
4694
2914
3686
4358
5587
2265
3685
3754
3708
3210
3517
3905
3670
4221
4404
5086
5725




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147863&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147863&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147863&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'George Udny Yule' @ yule.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
125642564000
228202688.986806885545.8342669813934688.9692422097570.472416131686939
335083112.632312699834.7129801987174290.677469019561.36695517730431
430883164.1155971688835.6344650368863-81.38387364316830.0652859974972406
532993228.5924485425936.737271075286560.13737533201120.122365022937456
629393123.4718601243432.8146950138256-131.665761053962-0.619065441513733
733203182.0713326708233.3943973261943128.1920018210540.113470242694466
834183288.8382432868234.8853517740372101.3342030347440.323629896983065
936043437.2354158164837.0951707521399123.6644916577330.50091344922011
1034953489.8426254041737.3909365006509-0.7339086355250090.0684543353739025
1141633772.2577869004641.9908371487397297.6746344867621.08128410417434
1248824276.9585754337850.5427029797793429.2588197924012.04204759199085
1322113838.527455469170.9729311553288-1430.60717068855-2.4500323425465
1432603633.4545959250870.0598178172495-274.01456308583-1.21720301789067
1529923226.0372041891457.2558770687019-83.7542773668122-1.90869055168757
1624252869.1598009104744.4796877056911-312.827017030491-1.68546500288601
1727072697.4633575643338.674620994351481.5796183489982-0.916294892914144
1832442935.2521221257342.9362801121882240.0544522065330.865488304210428
1939653346.3370742704249.2862145275451489.4816029089271.61850666607996
2033153416.5723152077349.5962296188218-108.9766152016870.0925144592117822
2133333399.4627707567348.6960481950842-42.8172580233257-0.295065466048118
2235833561.9104114412250.1074995486412-19.29037365393680.503393614110412
2340213739.2883475257651.4081511772866236.4528205244290.56321503089917
2449043948.6041438738752.1861590525839898.955787400260.700636118301852
2522523797.0051894048953.467886033518-1470.55941153713-0.92746169658979
2629523433.4901104590351.9648941529332-334.044033340193-1.83962287567979
2735733367.3606373241850.1943665346493245.126282801367-0.499170473224281
2830483340.6136213054748.6444264224157-267.297314074438-0.32316376969706
2930593278.8221668194246.4085284029649-182.907527084312-0.471376910609375
3027313064.7442826461941.7228797028525-244.832886921233-1.13099168717222
3135633052.3880156857240.8945243312262529.345793239231-0.237387746568945
3230923124.5100956298441.3025363620043-43.41902747151630.13785605075501
3334783344.027077826243.304345633364871.44256535821530.788687210611701
3434783498.3597713988144.3444503760403-59.40880155564130.491709229442687
3543083773.7880616385545.9430604289252452.784261736971.0236450337574
3650293902.8719510906746.25375379869821096.737652272170.369015350546262
3720753717.2847956773945.9613380302551-1559.98982862948-1.03289926512793
3832643625.0526588321645.269308829779-312.732453656092-0.607492919550524
3933083378.9385958763842.144870927375328.339884094-1.25563288828053
4036883523.8131539074243.6396393694653129.6449417937050.439490886511976
4131363427.1015216671841.4341781499049-243.722285299431-0.604102283759902
4228243288.9243621714838.7418072375024-403.585762595622-0.78122120509899
4336443243.1995516275137.6114342036095429.966174530026-0.370607701515012
4446943860.3423918247144.3028133257992632.0051393132142.55656902760114
4529143651.7103489161141.8479712069255-649.294667015328-1.11878544756338
4636863722.971637535742.0779783193448-47.27976918681240.130226414491567
4743583818.1402306107242.3889190448354521.2229509465320.235146310610769
4855874037.4886116745643.12016926463241487.30956108770.784195333818664
4922653964.7069127865842.7005645737109-1659.00292630253-0.513064746474522
5036853926.0283105966642.2443700577053-212.703828591961-0.357468908702511
5137543867.2581448321241.3563382469758-78.5803481630634-0.439075052214935
5237083723.4643090740539.246162057430147.5390729433404-0.800616067300081
5332103584.9114365108237.0032147390706-314.39018538899-0.77058580916519
5435173725.7942835122438.3041358491662-244.3791809984490.453051915939582
5539053773.6486286299938.4145809555446128.05470214910.0419168883875953
5636703481.482596437535.054063056275303.345270951874-1.45753982574946
5742213980.1695199610239.038225991853379.11843567523132.04930944063129
5844044277.7392037587940.847115013456335.8668073303041.14397242838518
5950864478.2045458277841.7316058769289551.9111262054490.70654283449687
6057254408.6746366692441.22526027037661355.29172909539-0.492379300817483

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 2564 & 2564 & 0 & 0 & 0 \tabularnewline
2 & 2820 & 2688.98680688554 & 5.83426698139346 & 88.969242209757 & 0.472416131686939 \tabularnewline
3 & 3508 & 3112.6323126998 & 34.7129801987174 & 290.67746901956 & 1.36695517730431 \tabularnewline
4 & 3088 & 3164.11559716888 & 35.6344650368863 & -81.3838736431683 & 0.0652859974972406 \tabularnewline
5 & 3299 & 3228.59244854259 & 36.7372710752865 & 60.1373753320112 & 0.122365022937456 \tabularnewline
6 & 2939 & 3123.47186012434 & 32.8146950138256 & -131.665761053962 & -0.619065441513733 \tabularnewline
7 & 3320 & 3182.07133267082 & 33.3943973261943 & 128.192001821054 & 0.113470242694466 \tabularnewline
8 & 3418 & 3288.83824328682 & 34.8853517740372 & 101.334203034744 & 0.323629896983065 \tabularnewline
9 & 3604 & 3437.23541581648 & 37.0951707521399 & 123.664491657733 & 0.50091344922011 \tabularnewline
10 & 3495 & 3489.84262540417 & 37.3909365006509 & -0.733908635525009 & 0.0684543353739025 \tabularnewline
11 & 4163 & 3772.25778690046 & 41.9908371487397 & 297.674634486762 & 1.08128410417434 \tabularnewline
12 & 4882 & 4276.95857543378 & 50.5427029797793 & 429.258819792401 & 2.04204759199085 \tabularnewline
13 & 2211 & 3838.5274554691 & 70.9729311553288 & -1430.60717068855 & -2.4500323425465 \tabularnewline
14 & 3260 & 3633.45459592508 & 70.0598178172495 & -274.01456308583 & -1.21720301789067 \tabularnewline
15 & 2992 & 3226.03720418914 & 57.2558770687019 & -83.7542773668122 & -1.90869055168757 \tabularnewline
16 & 2425 & 2869.15980091047 & 44.4796877056911 & -312.827017030491 & -1.68546500288601 \tabularnewline
17 & 2707 & 2697.46335756433 & 38.6746209943514 & 81.5796183489982 & -0.916294892914144 \tabularnewline
18 & 3244 & 2935.25212212573 & 42.9362801121882 & 240.054452206533 & 0.865488304210428 \tabularnewline
19 & 3965 & 3346.33707427042 & 49.2862145275451 & 489.481602908927 & 1.61850666607996 \tabularnewline
20 & 3315 & 3416.57231520773 & 49.5962296188218 & -108.976615201687 & 0.0925144592117822 \tabularnewline
21 & 3333 & 3399.46277075673 & 48.6960481950842 & -42.8172580233257 & -0.295065466048118 \tabularnewline
22 & 3583 & 3561.91041144122 & 50.1074995486412 & -19.2903736539368 & 0.503393614110412 \tabularnewline
23 & 4021 & 3739.28834752576 & 51.4081511772866 & 236.452820524429 & 0.56321503089917 \tabularnewline
24 & 4904 & 3948.60414387387 & 52.1861590525839 & 898.95578740026 & 0.700636118301852 \tabularnewline
25 & 2252 & 3797.00518940489 & 53.467886033518 & -1470.55941153713 & -0.92746169658979 \tabularnewline
26 & 2952 & 3433.49011045903 & 51.9648941529332 & -334.044033340193 & -1.83962287567979 \tabularnewline
27 & 3573 & 3367.36063732418 & 50.1943665346493 & 245.126282801367 & -0.499170473224281 \tabularnewline
28 & 3048 & 3340.61362130547 & 48.6444264224157 & -267.297314074438 & -0.32316376969706 \tabularnewline
29 & 3059 & 3278.82216681942 & 46.4085284029649 & -182.907527084312 & -0.471376910609375 \tabularnewline
30 & 2731 & 3064.74428264619 & 41.7228797028525 & -244.832886921233 & -1.13099168717222 \tabularnewline
31 & 3563 & 3052.38801568572 & 40.8945243312262 & 529.345793239231 & -0.237387746568945 \tabularnewline
32 & 3092 & 3124.51009562984 & 41.3025363620043 & -43.4190274715163 & 0.13785605075501 \tabularnewline
33 & 3478 & 3344.0270778262 & 43.3043456333648 & 71.4425653582153 & 0.788687210611701 \tabularnewline
34 & 3478 & 3498.35977139881 & 44.3444503760403 & -59.4088015556413 & 0.491709229442687 \tabularnewline
35 & 4308 & 3773.78806163855 & 45.9430604289252 & 452.78426173697 & 1.0236450337574 \tabularnewline
36 & 5029 & 3902.87195109067 & 46.2537537986982 & 1096.73765227217 & 0.369015350546262 \tabularnewline
37 & 2075 & 3717.28479567739 & 45.9613380302551 & -1559.98982862948 & -1.03289926512793 \tabularnewline
38 & 3264 & 3625.05265883216 & 45.269308829779 & -312.732453656092 & -0.607492919550524 \tabularnewline
39 & 3308 & 3378.93859587638 & 42.1448709273753 & 28.339884094 & -1.25563288828053 \tabularnewline
40 & 3688 & 3523.81315390742 & 43.6396393694653 & 129.644941793705 & 0.439490886511976 \tabularnewline
41 & 3136 & 3427.10152166718 & 41.4341781499049 & -243.722285299431 & -0.604102283759902 \tabularnewline
42 & 2824 & 3288.92436217148 & 38.7418072375024 & -403.585762595622 & -0.78122120509899 \tabularnewline
43 & 3644 & 3243.19955162751 & 37.6114342036095 & 429.966174530026 & -0.370607701515012 \tabularnewline
44 & 4694 & 3860.34239182471 & 44.3028133257992 & 632.005139313214 & 2.55656902760114 \tabularnewline
45 & 2914 & 3651.71034891611 & 41.8479712069255 & -649.294667015328 & -1.11878544756338 \tabularnewline
46 & 3686 & 3722.9716375357 & 42.0779783193448 & -47.2797691868124 & 0.130226414491567 \tabularnewline
47 & 4358 & 3818.14023061072 & 42.3889190448354 & 521.222950946532 & 0.235146310610769 \tabularnewline
48 & 5587 & 4037.48861167456 & 43.1201692646324 & 1487.3095610877 & 0.784195333818664 \tabularnewline
49 & 2265 & 3964.70691278658 & 42.7005645737109 & -1659.00292630253 & -0.513064746474522 \tabularnewline
50 & 3685 & 3926.02831059666 & 42.2443700577053 & -212.703828591961 & -0.357468908702511 \tabularnewline
51 & 3754 & 3867.25814483212 & 41.3563382469758 & -78.5803481630634 & -0.439075052214935 \tabularnewline
52 & 3708 & 3723.46430907405 & 39.2461620574301 & 47.5390729433404 & -0.800616067300081 \tabularnewline
53 & 3210 & 3584.91143651082 & 37.0032147390706 & -314.39018538899 & -0.77058580916519 \tabularnewline
54 & 3517 & 3725.79428351224 & 38.3041358491662 & -244.379180998449 & 0.453051915939582 \tabularnewline
55 & 3905 & 3773.64862862999 & 38.4145809555446 & 128.0547021491 & 0.0419168883875953 \tabularnewline
56 & 3670 & 3481.4825964375 & 35.054063056275 & 303.345270951874 & -1.45753982574946 \tabularnewline
57 & 4221 & 3980.16951996102 & 39.0382259918533 & 79.1184356752313 & 2.04930944063129 \tabularnewline
58 & 4404 & 4277.73920375879 & 40.8471150134563 & 35.866807330304 & 1.14397242838518 \tabularnewline
59 & 5086 & 4478.20454582778 & 41.7316058769289 & 551.911126205449 & 0.70654283449687 \tabularnewline
60 & 5725 & 4408.67463666924 & 41.2252602703766 & 1355.29172909539 & -0.492379300817483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147863&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]2564[/C][C]2564[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]2820[/C][C]2688.98680688554[/C][C]5.83426698139346[/C][C]88.969242209757[/C][C]0.472416131686939[/C][/ROW]
[ROW][C]3[/C][C]3508[/C][C]3112.6323126998[/C][C]34.7129801987174[/C][C]290.67746901956[/C][C]1.36695517730431[/C][/ROW]
[ROW][C]4[/C][C]3088[/C][C]3164.11559716888[/C][C]35.6344650368863[/C][C]-81.3838736431683[/C][C]0.0652859974972406[/C][/ROW]
[ROW][C]5[/C][C]3299[/C][C]3228.59244854259[/C][C]36.7372710752865[/C][C]60.1373753320112[/C][C]0.122365022937456[/C][/ROW]
[ROW][C]6[/C][C]2939[/C][C]3123.47186012434[/C][C]32.8146950138256[/C][C]-131.665761053962[/C][C]-0.619065441513733[/C][/ROW]
[ROW][C]7[/C][C]3320[/C][C]3182.07133267082[/C][C]33.3943973261943[/C][C]128.192001821054[/C][C]0.113470242694466[/C][/ROW]
[ROW][C]8[/C][C]3418[/C][C]3288.83824328682[/C][C]34.8853517740372[/C][C]101.334203034744[/C][C]0.323629896983065[/C][/ROW]
[ROW][C]9[/C][C]3604[/C][C]3437.23541581648[/C][C]37.0951707521399[/C][C]123.664491657733[/C][C]0.50091344922011[/C][/ROW]
[ROW][C]10[/C][C]3495[/C][C]3489.84262540417[/C][C]37.3909365006509[/C][C]-0.733908635525009[/C][C]0.0684543353739025[/C][/ROW]
[ROW][C]11[/C][C]4163[/C][C]3772.25778690046[/C][C]41.9908371487397[/C][C]297.674634486762[/C][C]1.08128410417434[/C][/ROW]
[ROW][C]12[/C][C]4882[/C][C]4276.95857543378[/C][C]50.5427029797793[/C][C]429.258819792401[/C][C]2.04204759199085[/C][/ROW]
[ROW][C]13[/C][C]2211[/C][C]3838.5274554691[/C][C]70.9729311553288[/C][C]-1430.60717068855[/C][C]-2.4500323425465[/C][/ROW]
[ROW][C]14[/C][C]3260[/C][C]3633.45459592508[/C][C]70.0598178172495[/C][C]-274.01456308583[/C][C]-1.21720301789067[/C][/ROW]
[ROW][C]15[/C][C]2992[/C][C]3226.03720418914[/C][C]57.2558770687019[/C][C]-83.7542773668122[/C][C]-1.90869055168757[/C][/ROW]
[ROW][C]16[/C][C]2425[/C][C]2869.15980091047[/C][C]44.4796877056911[/C][C]-312.827017030491[/C][C]-1.68546500288601[/C][/ROW]
[ROW][C]17[/C][C]2707[/C][C]2697.46335756433[/C][C]38.6746209943514[/C][C]81.5796183489982[/C][C]-0.916294892914144[/C][/ROW]
[ROW][C]18[/C][C]3244[/C][C]2935.25212212573[/C][C]42.9362801121882[/C][C]240.054452206533[/C][C]0.865488304210428[/C][/ROW]
[ROW][C]19[/C][C]3965[/C][C]3346.33707427042[/C][C]49.2862145275451[/C][C]489.481602908927[/C][C]1.61850666607996[/C][/ROW]
[ROW][C]20[/C][C]3315[/C][C]3416.57231520773[/C][C]49.5962296188218[/C][C]-108.976615201687[/C][C]0.0925144592117822[/C][/ROW]
[ROW][C]21[/C][C]3333[/C][C]3399.46277075673[/C][C]48.6960481950842[/C][C]-42.8172580233257[/C][C]-0.295065466048118[/C][/ROW]
[ROW][C]22[/C][C]3583[/C][C]3561.91041144122[/C][C]50.1074995486412[/C][C]-19.2903736539368[/C][C]0.503393614110412[/C][/ROW]
[ROW][C]23[/C][C]4021[/C][C]3739.28834752576[/C][C]51.4081511772866[/C][C]236.452820524429[/C][C]0.56321503089917[/C][/ROW]
[ROW][C]24[/C][C]4904[/C][C]3948.60414387387[/C][C]52.1861590525839[/C][C]898.95578740026[/C][C]0.700636118301852[/C][/ROW]
[ROW][C]25[/C][C]2252[/C][C]3797.00518940489[/C][C]53.467886033518[/C][C]-1470.55941153713[/C][C]-0.92746169658979[/C][/ROW]
[ROW][C]26[/C][C]2952[/C][C]3433.49011045903[/C][C]51.9648941529332[/C][C]-334.044033340193[/C][C]-1.83962287567979[/C][/ROW]
[ROW][C]27[/C][C]3573[/C][C]3367.36063732418[/C][C]50.1943665346493[/C][C]245.126282801367[/C][C]-0.499170473224281[/C][/ROW]
[ROW][C]28[/C][C]3048[/C][C]3340.61362130547[/C][C]48.6444264224157[/C][C]-267.297314074438[/C][C]-0.32316376969706[/C][/ROW]
[ROW][C]29[/C][C]3059[/C][C]3278.82216681942[/C][C]46.4085284029649[/C][C]-182.907527084312[/C][C]-0.471376910609375[/C][/ROW]
[ROW][C]30[/C][C]2731[/C][C]3064.74428264619[/C][C]41.7228797028525[/C][C]-244.832886921233[/C][C]-1.13099168717222[/C][/ROW]
[ROW][C]31[/C][C]3563[/C][C]3052.38801568572[/C][C]40.8945243312262[/C][C]529.345793239231[/C][C]-0.237387746568945[/C][/ROW]
[ROW][C]32[/C][C]3092[/C][C]3124.51009562984[/C][C]41.3025363620043[/C][C]-43.4190274715163[/C][C]0.13785605075501[/C][/ROW]
[ROW][C]33[/C][C]3478[/C][C]3344.0270778262[/C][C]43.3043456333648[/C][C]71.4425653582153[/C][C]0.788687210611701[/C][/ROW]
[ROW][C]34[/C][C]3478[/C][C]3498.35977139881[/C][C]44.3444503760403[/C][C]-59.4088015556413[/C][C]0.491709229442687[/C][/ROW]
[ROW][C]35[/C][C]4308[/C][C]3773.78806163855[/C][C]45.9430604289252[/C][C]452.78426173697[/C][C]1.0236450337574[/C][/ROW]
[ROW][C]36[/C][C]5029[/C][C]3902.87195109067[/C][C]46.2537537986982[/C][C]1096.73765227217[/C][C]0.369015350546262[/C][/ROW]
[ROW][C]37[/C][C]2075[/C][C]3717.28479567739[/C][C]45.9613380302551[/C][C]-1559.98982862948[/C][C]-1.03289926512793[/C][/ROW]
[ROW][C]38[/C][C]3264[/C][C]3625.05265883216[/C][C]45.269308829779[/C][C]-312.732453656092[/C][C]-0.607492919550524[/C][/ROW]
[ROW][C]39[/C][C]3308[/C][C]3378.93859587638[/C][C]42.1448709273753[/C][C]28.339884094[/C][C]-1.25563288828053[/C][/ROW]
[ROW][C]40[/C][C]3688[/C][C]3523.81315390742[/C][C]43.6396393694653[/C][C]129.644941793705[/C][C]0.439490886511976[/C][/ROW]
[ROW][C]41[/C][C]3136[/C][C]3427.10152166718[/C][C]41.4341781499049[/C][C]-243.722285299431[/C][C]-0.604102283759902[/C][/ROW]
[ROW][C]42[/C][C]2824[/C][C]3288.92436217148[/C][C]38.7418072375024[/C][C]-403.585762595622[/C][C]-0.78122120509899[/C][/ROW]
[ROW][C]43[/C][C]3644[/C][C]3243.19955162751[/C][C]37.6114342036095[/C][C]429.966174530026[/C][C]-0.370607701515012[/C][/ROW]
[ROW][C]44[/C][C]4694[/C][C]3860.34239182471[/C][C]44.3028133257992[/C][C]632.005139313214[/C][C]2.55656902760114[/C][/ROW]
[ROW][C]45[/C][C]2914[/C][C]3651.71034891611[/C][C]41.8479712069255[/C][C]-649.294667015328[/C][C]-1.11878544756338[/C][/ROW]
[ROW][C]46[/C][C]3686[/C][C]3722.9716375357[/C][C]42.0779783193448[/C][C]-47.2797691868124[/C][C]0.130226414491567[/C][/ROW]
[ROW][C]47[/C][C]4358[/C][C]3818.14023061072[/C][C]42.3889190448354[/C][C]521.222950946532[/C][C]0.235146310610769[/C][/ROW]
[ROW][C]48[/C][C]5587[/C][C]4037.48861167456[/C][C]43.1201692646324[/C][C]1487.3095610877[/C][C]0.784195333818664[/C][/ROW]
[ROW][C]49[/C][C]2265[/C][C]3964.70691278658[/C][C]42.7005645737109[/C][C]-1659.00292630253[/C][C]-0.513064746474522[/C][/ROW]
[ROW][C]50[/C][C]3685[/C][C]3926.02831059666[/C][C]42.2443700577053[/C][C]-212.703828591961[/C][C]-0.357468908702511[/C][/ROW]
[ROW][C]51[/C][C]3754[/C][C]3867.25814483212[/C][C]41.3563382469758[/C][C]-78.5803481630634[/C][C]-0.439075052214935[/C][/ROW]
[ROW][C]52[/C][C]3708[/C][C]3723.46430907405[/C][C]39.2461620574301[/C][C]47.5390729433404[/C][C]-0.800616067300081[/C][/ROW]
[ROW][C]53[/C][C]3210[/C][C]3584.91143651082[/C][C]37.0032147390706[/C][C]-314.39018538899[/C][C]-0.77058580916519[/C][/ROW]
[ROW][C]54[/C][C]3517[/C][C]3725.79428351224[/C][C]38.3041358491662[/C][C]-244.379180998449[/C][C]0.453051915939582[/C][/ROW]
[ROW][C]55[/C][C]3905[/C][C]3773.64862862999[/C][C]38.4145809555446[/C][C]128.0547021491[/C][C]0.0419168883875953[/C][/ROW]
[ROW][C]56[/C][C]3670[/C][C]3481.4825964375[/C][C]35.054063056275[/C][C]303.345270951874[/C][C]-1.45753982574946[/C][/ROW]
[ROW][C]57[/C][C]4221[/C][C]3980.16951996102[/C][C]39.0382259918533[/C][C]79.1184356752313[/C][C]2.04930944063129[/C][/ROW]
[ROW][C]58[/C][C]4404[/C][C]4277.73920375879[/C][C]40.8471150134563[/C][C]35.866807330304[/C][C]1.14397242838518[/C][/ROW]
[ROW][C]59[/C][C]5086[/C][C]4478.20454582778[/C][C]41.7316058769289[/C][C]551.911126205449[/C][C]0.70654283449687[/C][/ROW]
[ROW][C]60[/C][C]5725[/C][C]4408.67463666924[/C][C]41.2252602703766[/C][C]1355.29172909539[/C][C]-0.492379300817483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147863&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147863&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
125642564000
228202688.986806885545.8342669813934688.9692422097570.472416131686939
335083112.632312699834.7129801987174290.677469019561.36695517730431
430883164.1155971688835.6344650368863-81.38387364316830.0652859974972406
532993228.5924485425936.737271075286560.13737533201120.122365022937456
629393123.4718601243432.8146950138256-131.665761053962-0.619065441513733
733203182.0713326708233.3943973261943128.1920018210540.113470242694466
834183288.8382432868234.8853517740372101.3342030347440.323629896983065
936043437.2354158164837.0951707521399123.6644916577330.50091344922011
1034953489.8426254041737.3909365006509-0.7339086355250090.0684543353739025
1141633772.2577869004641.9908371487397297.6746344867621.08128410417434
1248824276.9585754337850.5427029797793429.2588197924012.04204759199085
1322113838.527455469170.9729311553288-1430.60717068855-2.4500323425465
1432603633.4545959250870.0598178172495-274.01456308583-1.21720301789067
1529923226.0372041891457.2558770687019-83.7542773668122-1.90869055168757
1624252869.1598009104744.4796877056911-312.827017030491-1.68546500288601
1727072697.4633575643338.674620994351481.5796183489982-0.916294892914144
1832442935.2521221257342.9362801121882240.0544522065330.865488304210428
1939653346.3370742704249.2862145275451489.4816029089271.61850666607996
2033153416.5723152077349.5962296188218-108.9766152016870.0925144592117822
2133333399.4627707567348.6960481950842-42.8172580233257-0.295065466048118
2235833561.9104114412250.1074995486412-19.29037365393680.503393614110412
2340213739.2883475257651.4081511772866236.4528205244290.56321503089917
2449043948.6041438738752.1861590525839898.955787400260.700636118301852
2522523797.0051894048953.467886033518-1470.55941153713-0.92746169658979
2629523433.4901104590351.9648941529332-334.044033340193-1.83962287567979
2735733367.3606373241850.1943665346493245.126282801367-0.499170473224281
2830483340.6136213054748.6444264224157-267.297314074438-0.32316376969706
2930593278.8221668194246.4085284029649-182.907527084312-0.471376910609375
3027313064.7442826461941.7228797028525-244.832886921233-1.13099168717222
3135633052.3880156857240.8945243312262529.345793239231-0.237387746568945
3230923124.5100956298441.3025363620043-43.41902747151630.13785605075501
3334783344.027077826243.304345633364871.44256535821530.788687210611701
3434783498.3597713988144.3444503760403-59.40880155564130.491709229442687
3543083773.7880616385545.9430604289252452.784261736971.0236450337574
3650293902.8719510906746.25375379869821096.737652272170.369015350546262
3720753717.2847956773945.9613380302551-1559.98982862948-1.03289926512793
3832643625.0526588321645.269308829779-312.732453656092-0.607492919550524
3933083378.9385958763842.144870927375328.339884094-1.25563288828053
4036883523.8131539074243.6396393694653129.6449417937050.439490886511976
4131363427.1015216671841.4341781499049-243.722285299431-0.604102283759902
4228243288.9243621714838.7418072375024-403.585762595622-0.78122120509899
4336443243.1995516275137.6114342036095429.966174530026-0.370607701515012
4446943860.3423918247144.3028133257992632.0051393132142.55656902760114
4529143651.7103489161141.8479712069255-649.294667015328-1.11878544756338
4636863722.971637535742.0779783193448-47.27976918681240.130226414491567
4743583818.1402306107242.3889190448354521.2229509465320.235146310610769
4855874037.4886116745643.12016926463241487.30956108770.784195333818664
4922653964.7069127865842.7005645737109-1659.00292630253-0.513064746474522
5036853926.0283105966642.2443700577053-212.703828591961-0.357468908702511
5137543867.2581448321241.3563382469758-78.5803481630634-0.439075052214935
5237083723.4643090740539.246162057430147.5390729433404-0.800616067300081
5332103584.9114365108237.0032147390706-314.39018538899-0.77058580916519
5435173725.7942835122438.3041358491662-244.3791809984490.453051915939582
5539053773.6486286299938.4145809555446128.05470214910.0419168883875953
5636703481.482596437535.054063056275303.345270951874-1.45753982574946
5742213980.1695199610239.038225991853379.11843567523132.04930944063129
5844044277.7392037587940.847115013456335.8668073303041.14397242838518
5950864478.2045458277841.7316058769289551.9111262054490.70654283449687
6057254408.6746366692441.22526027037661355.29172909539-0.492379300817483



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')