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of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationSat, 26 Nov 2011 09:29:17 -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/26/t1322317807f97g1q92rd8cw1o.htm/, Retrieved Sun, 05 Feb 2023 14:01:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147409, Retrieved Sun, 05 Feb 2023 14:01:23 +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] [WS 8 - Structural...] [2011-11-26 14:29:17] [01668b8db120351c61467eadc96b2965] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'AstonUniversity' @ aston.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 & 2 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147409&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147409&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147409&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'AstonUniversity' @ aston.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
197009700000
290819420.75450143316-4.57323719029182-334.807899115417-1.94834013632446
390849148.23690533508-22.2229146322506-61.4760003105638-1.35940558228141
497439343.3410893296-8.81350400055626396.8025127559631.35792880524648
585879095.71792616632-19.3898286098834-504.931356723109-1.70068400570002
697319271.8098096163-13.6789709574988455.8751655862041.44995580907109
795639434.3419938712-10.0335857183998125.6125544010451.31903750930236
899989698.05321184868-5.3287449179581295.195070007962.05149393903093
994379674.89339629866-5.61986801785221-237.5839449585-0.133514733832356
10100389807.9599625221-3.34218991531362227.6365196976751.03724816845382
1199189889.1118146233-1.9365078703504827.42554736427880.6314277490997
1292529667.92152658138-5.59569782119897-412.128213795466-1.63791142714891
1397379552.09646360713-3.60297502605207186.867956364579-0.867993514739218
1490359409.60408920573-2.67806138231115-372.145429557391-1.07958564974153
1591339311.8670562481-4.16396481957125-177.363252235036-0.67266590846052
1694879154.7049266868-8.33657128828147334.584414763756-1.05065058009594
1787009193.58255742851-6.97670755254194-494.306170224580.333375757119034
1896279253.45661914782-5.30619486369144372.477365362720.487314530858606
1989479171.22218329847-6.8489039452291-222.963887319412-0.570816284511288
2092839097.5437699252-7.92764326966444186.562643494913-0.499537504676475
2188299104.99880734835-7.72006152946614-276.2547673069470.115256870510946
2299479330.401327745-5.08068646725834612.711965117631.74690752842199
2396289424.0075377992-4.23375213024213202.3451698412920.739029481362515
2493189518.233353789-3.76373048259059-201.8812643851330.738235804272518
2596059459.67914841382-3.82810473375604146.243003672187-0.412983398291401
2686409244.05520062057-4.5614664474363-600.521714378761-1.58335108754847
2792149214.62719890013-4.81019516280528-0.22464114649487-0.181371770740218
2895679224.516761558-4.57400113307158342.2514175185350.105411069767361
2985479155.49379595095-5.80539090009781-607.480281767522-0.463548212929267
3091858982.94359899634-8.98969150139358204.715084781184-1.21612242545158
3194709195.95005020216-5.17614871686062270.4524616261721.64058433173774
3291239183.67764068775-5.27922954825366-60.5613089813522-0.0528669508714306
3392789382.19726346708-2.87328272037568-107.5597203617461.5236769346327
34101709524.09469487191-1.54036934419529643.5094489717251.08334525580578
3594349459.83031998424-1.96703685693058-24.7907015091494-0.469402423735074
3696559554.2111387519-1.4917666453109799.19044662038230.721117644780371
3794299426.44024351576-2.041437435279764.65246249873495-0.94390669372978
3887399354.04484078152-2.44741155897752-613.887181820166-0.52257735361842
3995529400.49104181577-2.02045921379527150.7150491745590.359346060548004
4096879364.46944021814-2.42468218856774323.075912981341-0.247865446690069
4190199439.2406026585-1.34112591070702-421.4739697943410.562504440206011
4296729526.31204832682-0.0428513508651429144.2684352673980.64814736397209
4392069354.37026517756-2.44559839392539-145.585843110317-1.2701001625453
4490699291.78863492691-3.18985192897205-221.806596474209-0.4470367361601
4597889527.2691993719-0.71589068617328256.8116731520041.78023707522975
46103129624.420828054410.100170390095405685.9671427460080.730999398129028
47101059836.120689942421.50193541449598265.3887170124081.58105311970829
4898639824.442724642681.4287332885863138.7747351140736-0.0984385370787065
4996569747.286481104871.00259473583613-89.9922998100579-0.585806296407066
5092959801.813267634571.34088482098732-507.690421639760.397302840410875
5199469816.202555516311.44476309446794129.5851444434310.0963122419887453
5297019677.003169722740.071461330048208726.270297346684-1.0336560688549
5390499569.03808422818-1.13203119590869-518.296349569519-0.793453922902278
54101909686.253809155680.255628143352628501.8345363654090.87166070665636
5597069795.083719329021.49968239644785-90.84590128563270.803256547155335
5697659964.84954253423.26806929279488-202.5947919059871.24994318954811
5798939928.37941113472.90356737883564-34.728420318324-0.295988750252327
5899949736.873560646941.39315731484946260.319591394797-1.45005227890209
59104339841.169848452442.07211549502545590.1377809939640.76785759012792
60100739924.92310693692.55428630773501146.7335003562740.609236964687423
611011210052.79970588893.2849971043270657.14225588673830.93330041507913
6292669964.623489790442.70209668546316-697.126316059047-0.67926581475193
6398209805.683571711381.5149502103494216.9512711711893-1.19658207327127
64100979863.912843145641.99306605262294232.166121683320.418809574824304
6591159841.341541796061.76455060490202-725.94329780538-0.181296573386226
66104119901.661191521872.33417542623351508.3887166237480.432784215057325
6796789895.09581224162.24852864652447-216.951027647743-0.0659462390872501
681040810131.5185574884.36911179243316272.6593023026991.73978933267392
691015310180.0046373444.73063532414807-27.72676481396870.328412380568315
701036810200.93838039144.84828292513222166.795868154950.120757261637749
711058110156.26751075264.52786575459365425.545517013987-0.369203429549534
721059710266.97906426625.16437023326159328.2774799771410.791437348961157
731068010388.57659975195.85381681877687289.5136304602470.866899581061993
74973810404.39924446935.91598737040549-666.5624232906760.0740933883024219
75955610104.99441281393.83319949892939-544.009399803566-2.26486452572958

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 9700 & 9700 & 0 & 0 & 0 \tabularnewline
2 & 9081 & 9420.75450143316 & -4.57323719029182 & -334.807899115417 & -1.94834013632446 \tabularnewline
3 & 9084 & 9148.23690533508 & -22.2229146322506 & -61.4760003105638 & -1.35940558228141 \tabularnewline
4 & 9743 & 9343.3410893296 & -8.81350400055626 & 396.802512755963 & 1.35792880524648 \tabularnewline
5 & 8587 & 9095.71792616632 & -19.3898286098834 & -504.931356723109 & -1.70068400570002 \tabularnewline
6 & 9731 & 9271.8098096163 & -13.6789709574988 & 455.875165586204 & 1.44995580907109 \tabularnewline
7 & 9563 & 9434.3419938712 & -10.0335857183998 & 125.612554401045 & 1.31903750930236 \tabularnewline
8 & 9998 & 9698.05321184868 & -5.3287449179581 & 295.19507000796 & 2.05149393903093 \tabularnewline
9 & 9437 & 9674.89339629866 & -5.61986801785221 & -237.5839449585 & -0.133514733832356 \tabularnewline
10 & 10038 & 9807.9599625221 & -3.34218991531362 & 227.636519697675 & 1.03724816845382 \tabularnewline
11 & 9918 & 9889.1118146233 & -1.93650787035048 & 27.4255473642788 & 0.6314277490997 \tabularnewline
12 & 9252 & 9667.92152658138 & -5.59569782119897 & -412.128213795466 & -1.63791142714891 \tabularnewline
13 & 9737 & 9552.09646360713 & -3.60297502605207 & 186.867956364579 & -0.867993514739218 \tabularnewline
14 & 9035 & 9409.60408920573 & -2.67806138231115 & -372.145429557391 & -1.07958564974153 \tabularnewline
15 & 9133 & 9311.8670562481 & -4.16396481957125 & -177.363252235036 & -0.67266590846052 \tabularnewline
16 & 9487 & 9154.7049266868 & -8.33657128828147 & 334.584414763756 & -1.05065058009594 \tabularnewline
17 & 8700 & 9193.58255742851 & -6.97670755254194 & -494.30617022458 & 0.333375757119034 \tabularnewline
18 & 9627 & 9253.45661914782 & -5.30619486369144 & 372.47736536272 & 0.487314530858606 \tabularnewline
19 & 8947 & 9171.22218329847 & -6.8489039452291 & -222.963887319412 & -0.570816284511288 \tabularnewline
20 & 9283 & 9097.5437699252 & -7.92764326966444 & 186.562643494913 & -0.499537504676475 \tabularnewline
21 & 8829 & 9104.99880734835 & -7.72006152946614 & -276.254767306947 & 0.115256870510946 \tabularnewline
22 & 9947 & 9330.401327745 & -5.08068646725834 & 612.71196511763 & 1.74690752842199 \tabularnewline
23 & 9628 & 9424.0075377992 & -4.23375213024213 & 202.345169841292 & 0.739029481362515 \tabularnewline
24 & 9318 & 9518.233353789 & -3.76373048259059 & -201.881264385133 & 0.738235804272518 \tabularnewline
25 & 9605 & 9459.67914841382 & -3.82810473375604 & 146.243003672187 & -0.412983398291401 \tabularnewline
26 & 8640 & 9244.05520062057 & -4.5614664474363 & -600.521714378761 & -1.58335108754847 \tabularnewline
27 & 9214 & 9214.62719890013 & -4.81019516280528 & -0.22464114649487 & -0.181371770740218 \tabularnewline
28 & 9567 & 9224.516761558 & -4.57400113307158 & 342.251417518535 & 0.105411069767361 \tabularnewline
29 & 8547 & 9155.49379595095 & -5.80539090009781 & -607.480281767522 & -0.463548212929267 \tabularnewline
30 & 9185 & 8982.94359899634 & -8.98969150139358 & 204.715084781184 & -1.21612242545158 \tabularnewline
31 & 9470 & 9195.95005020216 & -5.17614871686062 & 270.452461626172 & 1.64058433173774 \tabularnewline
32 & 9123 & 9183.67764068775 & -5.27922954825366 & -60.5613089813522 & -0.0528669508714306 \tabularnewline
33 & 9278 & 9382.19726346708 & -2.87328272037568 & -107.559720361746 & 1.5236769346327 \tabularnewline
34 & 10170 & 9524.09469487191 & -1.54036934419529 & 643.509448971725 & 1.08334525580578 \tabularnewline
35 & 9434 & 9459.83031998424 & -1.96703685693058 & -24.7907015091494 & -0.469402423735074 \tabularnewline
36 & 9655 & 9554.2111387519 & -1.49176664531097 & 99.1904466203823 & 0.721117644780371 \tabularnewline
37 & 9429 & 9426.44024351576 & -2.04143743527976 & 4.65246249873495 & -0.94390669372978 \tabularnewline
38 & 8739 & 9354.04484078152 & -2.44741155897752 & -613.887181820166 & -0.52257735361842 \tabularnewline
39 & 9552 & 9400.49104181577 & -2.02045921379527 & 150.715049174559 & 0.359346060548004 \tabularnewline
40 & 9687 & 9364.46944021814 & -2.42468218856774 & 323.075912981341 & -0.247865446690069 \tabularnewline
41 & 9019 & 9439.2406026585 & -1.34112591070702 & -421.473969794341 & 0.562504440206011 \tabularnewline
42 & 9672 & 9526.31204832682 & -0.0428513508651429 & 144.268435267398 & 0.64814736397209 \tabularnewline
43 & 9206 & 9354.37026517756 & -2.44559839392539 & -145.585843110317 & -1.2701001625453 \tabularnewline
44 & 9069 & 9291.78863492691 & -3.18985192897205 & -221.806596474209 & -0.4470367361601 \tabularnewline
45 & 9788 & 9527.2691993719 & -0.71589068617328 & 256.811673152004 & 1.78023707522975 \tabularnewline
46 & 10312 & 9624.42082805441 & 0.100170390095405 & 685.967142746008 & 0.730999398129028 \tabularnewline
47 & 10105 & 9836.12068994242 & 1.50193541449598 & 265.388717012408 & 1.58105311970829 \tabularnewline
48 & 9863 & 9824.44272464268 & 1.42873328858631 & 38.7747351140736 & -0.0984385370787065 \tabularnewline
49 & 9656 & 9747.28648110487 & 1.00259473583613 & -89.9922998100579 & -0.585806296407066 \tabularnewline
50 & 9295 & 9801.81326763457 & 1.34088482098732 & -507.69042163976 & 0.397302840410875 \tabularnewline
51 & 9946 & 9816.20255551631 & 1.44476309446794 & 129.585144443431 & 0.0963122419887453 \tabularnewline
52 & 9701 & 9677.00316972274 & 0.0714613300482087 & 26.270297346684 & -1.0336560688549 \tabularnewline
53 & 9049 & 9569.03808422818 & -1.13203119590869 & -518.296349569519 & -0.793453922902278 \tabularnewline
54 & 10190 & 9686.25380915568 & 0.255628143352628 & 501.834536365409 & 0.87166070665636 \tabularnewline
55 & 9706 & 9795.08371932902 & 1.49968239644785 & -90.8459012856327 & 0.803256547155335 \tabularnewline
56 & 9765 & 9964.8495425342 & 3.26806929279488 & -202.594791905987 & 1.24994318954811 \tabularnewline
57 & 9893 & 9928.3794111347 & 2.90356737883564 & -34.728420318324 & -0.295988750252327 \tabularnewline
58 & 9994 & 9736.87356064694 & 1.39315731484946 & 260.319591394797 & -1.45005227890209 \tabularnewline
59 & 10433 & 9841.16984845244 & 2.07211549502545 & 590.137780993964 & 0.76785759012792 \tabularnewline
60 & 10073 & 9924.9231069369 & 2.55428630773501 & 146.733500356274 & 0.609236964687423 \tabularnewline
61 & 10112 & 10052.7997058889 & 3.28499710432706 & 57.1422558867383 & 0.93330041507913 \tabularnewline
62 & 9266 & 9964.62348979044 & 2.70209668546316 & -697.126316059047 & -0.67926581475193 \tabularnewline
63 & 9820 & 9805.68357171138 & 1.51495021034942 & 16.9512711711893 & -1.19658207327127 \tabularnewline
64 & 10097 & 9863.91284314564 & 1.99306605262294 & 232.16612168332 & 0.418809574824304 \tabularnewline
65 & 9115 & 9841.34154179606 & 1.76455060490202 & -725.94329780538 & -0.181296573386226 \tabularnewline
66 & 10411 & 9901.66119152187 & 2.33417542623351 & 508.388716623748 & 0.432784215057325 \tabularnewline
67 & 9678 & 9895.0958122416 & 2.24852864652447 & -216.951027647743 & -0.0659462390872501 \tabularnewline
68 & 10408 & 10131.518557488 & 4.36911179243316 & 272.659302302699 & 1.73978933267392 \tabularnewline
69 & 10153 & 10180.004637344 & 4.73063532414807 & -27.7267648139687 & 0.328412380568315 \tabularnewline
70 & 10368 & 10200.9383803914 & 4.84828292513222 & 166.79586815495 & 0.120757261637749 \tabularnewline
71 & 10581 & 10156.2675107526 & 4.52786575459365 & 425.545517013987 & -0.369203429549534 \tabularnewline
72 & 10597 & 10266.9790642662 & 5.16437023326159 & 328.277479977141 & 0.791437348961157 \tabularnewline
73 & 10680 & 10388.5765997519 & 5.85381681877687 & 289.513630460247 & 0.866899581061993 \tabularnewline
74 & 9738 & 10404.3992444693 & 5.91598737040549 & -666.562423290676 & 0.0740933883024219 \tabularnewline
75 & 9556 & 10104.9944128139 & 3.83319949892939 & -544.009399803566 & -2.26486452572958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147409&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]9700[/C][C]9700[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]9420.75450143316[/C][C]-4.57323719029182[/C][C]-334.807899115417[/C][C]-1.94834013632446[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]9148.23690533508[/C][C]-22.2229146322506[/C][C]-61.4760003105638[/C][C]-1.35940558228141[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9343.3410893296[/C][C]-8.81350400055626[/C][C]396.802512755963[/C][C]1.35792880524648[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]9095.71792616632[/C][C]-19.3898286098834[/C][C]-504.931356723109[/C][C]-1.70068400570002[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9271.8098096163[/C][C]-13.6789709574988[/C][C]455.875165586204[/C][C]1.44995580907109[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9434.3419938712[/C][C]-10.0335857183998[/C][C]125.612554401045[/C][C]1.31903750930236[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9698.05321184868[/C][C]-5.3287449179581[/C][C]295.19507000796[/C][C]2.05149393903093[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9674.89339629866[/C][C]-5.61986801785221[/C][C]-237.5839449585[/C][C]-0.133514733832356[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9807.9599625221[/C][C]-3.34218991531362[/C][C]227.636519697675[/C][C]1.03724816845382[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9889.1118146233[/C][C]-1.93650787035048[/C][C]27.4255473642788[/C][C]0.6314277490997[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9667.92152658138[/C][C]-5.59569782119897[/C][C]-412.128213795466[/C][C]-1.63791142714891[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9552.09646360713[/C][C]-3.60297502605207[/C][C]186.867956364579[/C][C]-0.867993514739218[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]9409.60408920573[/C][C]-2.67806138231115[/C][C]-372.145429557391[/C][C]-1.07958564974153[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9311.8670562481[/C][C]-4.16396481957125[/C][C]-177.363252235036[/C][C]-0.67266590846052[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9154.7049266868[/C][C]-8.33657128828147[/C][C]334.584414763756[/C][C]-1.05065058009594[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]9193.58255742851[/C][C]-6.97670755254194[/C][C]-494.30617022458[/C][C]0.333375757119034[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9253.45661914782[/C][C]-5.30619486369144[/C][C]372.47736536272[/C][C]0.487314530858606[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9171.22218329847[/C][C]-6.8489039452291[/C][C]-222.963887319412[/C][C]-0.570816284511288[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9097.5437699252[/C][C]-7.92764326966444[/C][C]186.562643494913[/C][C]-0.499537504676475[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9104.99880734835[/C][C]-7.72006152946614[/C][C]-276.254767306947[/C][C]0.115256870510946[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9330.401327745[/C][C]-5.08068646725834[/C][C]612.71196511763[/C][C]1.74690752842199[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9424.0075377992[/C][C]-4.23375213024213[/C][C]202.345169841292[/C][C]0.739029481362515[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9518.233353789[/C][C]-3.76373048259059[/C][C]-201.881264385133[/C][C]0.738235804272518[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9459.67914841382[/C][C]-3.82810473375604[/C][C]146.243003672187[/C][C]-0.412983398291401[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]9244.05520062057[/C][C]-4.5614664474363[/C][C]-600.521714378761[/C][C]-1.58335108754847[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9214.62719890013[/C][C]-4.81019516280528[/C][C]-0.22464114649487[/C][C]-0.181371770740218[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9224.516761558[/C][C]-4.57400113307158[/C][C]342.251417518535[/C][C]0.105411069767361[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]9155.49379595095[/C][C]-5.80539090009781[/C][C]-607.480281767522[/C][C]-0.463548212929267[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]8982.94359899634[/C][C]-8.98969150139358[/C][C]204.715084781184[/C][C]-1.21612242545158[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9195.95005020216[/C][C]-5.17614871686062[/C][C]270.452461626172[/C][C]1.64058433173774[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9183.67764068775[/C][C]-5.27922954825366[/C][C]-60.5613089813522[/C][C]-0.0528669508714306[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9382.19726346708[/C][C]-2.87328272037568[/C][C]-107.559720361746[/C][C]1.5236769346327[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]9524.09469487191[/C][C]-1.54036934419529[/C][C]643.509448971725[/C][C]1.08334525580578[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9459.83031998424[/C][C]-1.96703685693058[/C][C]-24.7907015091494[/C][C]-0.469402423735074[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9554.2111387519[/C][C]-1.49176664531097[/C][C]99.1904466203823[/C][C]0.721117644780371[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9426.44024351576[/C][C]-2.04143743527976[/C][C]4.65246249873495[/C][C]-0.94390669372978[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]9354.04484078152[/C][C]-2.44741155897752[/C][C]-613.887181820166[/C][C]-0.52257735361842[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9400.49104181577[/C][C]-2.02045921379527[/C][C]150.715049174559[/C][C]0.359346060548004[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9364.46944021814[/C][C]-2.42468218856774[/C][C]323.075912981341[/C][C]-0.247865446690069[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]9439.2406026585[/C][C]-1.34112591070702[/C][C]-421.473969794341[/C][C]0.562504440206011[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9526.31204832682[/C][C]-0.0428513508651429[/C][C]144.268435267398[/C][C]0.64814736397209[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9354.37026517756[/C][C]-2.44559839392539[/C][C]-145.585843110317[/C][C]-1.2701001625453[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9291.78863492691[/C][C]-3.18985192897205[/C][C]-221.806596474209[/C][C]-0.4470367361601[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9527.2691993719[/C][C]-0.71589068617328[/C][C]256.811673152004[/C][C]1.78023707522975[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]9624.42082805441[/C][C]0.100170390095405[/C][C]685.967142746008[/C][C]0.730999398129028[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]9836.12068994242[/C][C]1.50193541449598[/C][C]265.388717012408[/C][C]1.58105311970829[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9824.44272464268[/C][C]1.42873328858631[/C][C]38.7747351140736[/C][C]-0.0984385370787065[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9747.28648110487[/C][C]1.00259473583613[/C][C]-89.9922998100579[/C][C]-0.585806296407066[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9801.81326763457[/C][C]1.34088482098732[/C][C]-507.69042163976[/C][C]0.397302840410875[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9816.20255551631[/C][C]1.44476309446794[/C][C]129.585144443431[/C][C]0.0963122419887453[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9677.00316972274[/C][C]0.0714613300482087[/C][C]26.270297346684[/C][C]-1.0336560688549[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9569.03808422818[/C][C]-1.13203119590869[/C][C]-518.296349569519[/C][C]-0.793453922902278[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]9686.25380915568[/C][C]0.255628143352628[/C][C]501.834536365409[/C][C]0.87166070665636[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9795.08371932902[/C][C]1.49968239644785[/C][C]-90.8459012856327[/C][C]0.803256547155335[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9964.8495425342[/C][C]3.26806929279488[/C][C]-202.594791905987[/C][C]1.24994318954811[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9928.3794111347[/C][C]2.90356737883564[/C][C]-34.728420318324[/C][C]-0.295988750252327[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9736.87356064694[/C][C]1.39315731484946[/C][C]260.319591394797[/C][C]-1.45005227890209[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]9841.16984845244[/C][C]2.07211549502545[/C][C]590.137780993964[/C][C]0.76785759012792[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9924.9231069369[/C][C]2.55428630773501[/C][C]146.733500356274[/C][C]0.609236964687423[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10052.7997058889[/C][C]3.28499710432706[/C][C]57.1422558867383[/C][C]0.93330041507913[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9964.62348979044[/C][C]2.70209668546316[/C][C]-697.126316059047[/C][C]-0.67926581475193[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9805.68357171138[/C][C]1.51495021034942[/C][C]16.9512711711893[/C][C]-1.19658207327127[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]9863.91284314564[/C][C]1.99306605262294[/C][C]232.16612168332[/C][C]0.418809574824304[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9841.34154179606[/C][C]1.76455060490202[/C][C]-725.94329780538[/C][C]-0.181296573386226[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]9901.66119152187[/C][C]2.33417542623351[/C][C]508.388716623748[/C][C]0.432784215057325[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9895.0958122416[/C][C]2.24852864652447[/C][C]-216.951027647743[/C][C]-0.0659462390872501[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]10131.518557488[/C][C]4.36911179243316[/C][C]272.659302302699[/C][C]1.73978933267392[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]10180.004637344[/C][C]4.73063532414807[/C][C]-27.7267648139687[/C][C]0.328412380568315[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10200.9383803914[/C][C]4.84828292513222[/C][C]166.79586815495[/C][C]0.120757261637749[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10156.2675107526[/C][C]4.52786575459365[/C][C]425.545517013987[/C][C]-0.369203429549534[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10266.9790642662[/C][C]5.16437023326159[/C][C]328.277479977141[/C][C]0.791437348961157[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10388.5765997519[/C][C]5.85381681877687[/C][C]289.513630460247[/C][C]0.866899581061993[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]10404.3992444693[/C][C]5.91598737040549[/C][C]-666.562423290676[/C][C]0.0740933883024219[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]10104.9944128139[/C][C]3.83319949892939[/C][C]-544.009399803566[/C][C]-2.26486452572958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147409&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147409&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
197009700000
290819420.75450143316-4.57323719029182-334.807899115417-1.94834013632446
390849148.23690533508-22.2229146322506-61.4760003105638-1.35940558228141
497439343.3410893296-8.81350400055626396.8025127559631.35792880524648
585879095.71792616632-19.3898286098834-504.931356723109-1.70068400570002
697319271.8098096163-13.6789709574988455.8751655862041.44995580907109
795639434.3419938712-10.0335857183998125.6125544010451.31903750930236
899989698.05321184868-5.3287449179581295.195070007962.05149393903093
994379674.89339629866-5.61986801785221-237.5839449585-0.133514733832356
10100389807.9599625221-3.34218991531362227.6365196976751.03724816845382
1199189889.1118146233-1.9365078703504827.42554736427880.6314277490997
1292529667.92152658138-5.59569782119897-412.128213795466-1.63791142714891
1397379552.09646360713-3.60297502605207186.867956364579-0.867993514739218
1490359409.60408920573-2.67806138231115-372.145429557391-1.07958564974153
1591339311.8670562481-4.16396481957125-177.363252235036-0.67266590846052
1694879154.7049266868-8.33657128828147334.584414763756-1.05065058009594
1787009193.58255742851-6.97670755254194-494.306170224580.333375757119034
1896279253.45661914782-5.30619486369144372.477365362720.487314530858606
1989479171.22218329847-6.8489039452291-222.963887319412-0.570816284511288
2092839097.5437699252-7.92764326966444186.562643494913-0.499537504676475
2188299104.99880734835-7.72006152946614-276.2547673069470.115256870510946
2299479330.401327745-5.08068646725834612.711965117631.74690752842199
2396289424.0075377992-4.23375213024213202.3451698412920.739029481362515
2493189518.233353789-3.76373048259059-201.8812643851330.738235804272518
2596059459.67914841382-3.82810473375604146.243003672187-0.412983398291401
2686409244.05520062057-4.5614664474363-600.521714378761-1.58335108754847
2792149214.62719890013-4.81019516280528-0.22464114649487-0.181371770740218
2895679224.516761558-4.57400113307158342.2514175185350.105411069767361
2985479155.49379595095-5.80539090009781-607.480281767522-0.463548212929267
3091858982.94359899634-8.98969150139358204.715084781184-1.21612242545158
3194709195.95005020216-5.17614871686062270.4524616261721.64058433173774
3291239183.67764068775-5.27922954825366-60.5613089813522-0.0528669508714306
3392789382.19726346708-2.87328272037568-107.5597203617461.5236769346327
34101709524.09469487191-1.54036934419529643.5094489717251.08334525580578
3594349459.83031998424-1.96703685693058-24.7907015091494-0.469402423735074
3696559554.2111387519-1.4917666453109799.19044662038230.721117644780371
3794299426.44024351576-2.041437435279764.65246249873495-0.94390669372978
3887399354.04484078152-2.44741155897752-613.887181820166-0.52257735361842
3995529400.49104181577-2.02045921379527150.7150491745590.359346060548004
4096879364.46944021814-2.42468218856774323.075912981341-0.247865446690069
4190199439.2406026585-1.34112591070702-421.4739697943410.562504440206011
4296729526.31204832682-0.0428513508651429144.2684352673980.64814736397209
4392069354.37026517756-2.44559839392539-145.585843110317-1.2701001625453
4490699291.78863492691-3.18985192897205-221.806596474209-0.4470367361601
4597889527.2691993719-0.71589068617328256.8116731520041.78023707522975
46103129624.420828054410.100170390095405685.9671427460080.730999398129028
47101059836.120689942421.50193541449598265.3887170124081.58105311970829
4898639824.442724642681.4287332885863138.7747351140736-0.0984385370787065
4996569747.286481104871.00259473583613-89.9922998100579-0.585806296407066
5092959801.813267634571.34088482098732-507.690421639760.397302840410875
5199469816.202555516311.44476309446794129.5851444434310.0963122419887453
5297019677.003169722740.071461330048208726.270297346684-1.0336560688549
5390499569.03808422818-1.13203119590869-518.296349569519-0.793453922902278
54101909686.253809155680.255628143352628501.8345363654090.87166070665636
5597069795.083719329021.49968239644785-90.84590128563270.803256547155335
5697659964.84954253423.26806929279488-202.5947919059871.24994318954811
5798939928.37941113472.90356737883564-34.728420318324-0.295988750252327
5899949736.873560646941.39315731484946260.319591394797-1.45005227890209
59104339841.169848452442.07211549502545590.1377809939640.76785759012792
60100739924.92310693692.55428630773501146.7335003562740.609236964687423
611011210052.79970588893.2849971043270657.14225588673830.93330041507913
6292669964.623489790442.70209668546316-697.126316059047-0.67926581475193
6398209805.683571711381.5149502103494216.9512711711893-1.19658207327127
64100979863.912843145641.99306605262294232.166121683320.418809574824304
6591159841.341541796061.76455060490202-725.94329780538-0.181296573386226
66104119901.661191521872.33417542623351508.3887166237480.432784215057325
6796789895.09581224162.24852864652447-216.951027647743-0.0659462390872501
681040810131.5185574884.36911179243316272.6593023026991.73978933267392
691015310180.0046373444.73063532414807-27.72676481396870.328412380568315
701036810200.93838039144.84828292513222166.795868154950.120757261637749
711058110156.26751075264.52786575459365425.545517013987-0.369203429549534
721059710266.97906426625.16437023326159328.2774799771410.791437348961157
731068010388.57659975195.85381681877687289.5136304602470.866899581061993
74973810404.39924446935.91598737040549-666.5624232906760.0740933883024219
75955610104.99441281393.83319949892939-544.009399803566-2.26486452572958



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')