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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 computationThu, 01 Dec 2011 13:17:13 -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/Dec/01/t1322763521g7eif8q7ba2rfc7.htm/, Retrieved Fri, 29 Mar 2024 13:22:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149926, Retrieved Fri, 29 Mar 2024 13:22:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [Workshop 8] [2011-12-01 18:17:13] [8ccb8599b802845e9be7b9afcc742f78] [Current]
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Dataseries X:
37
30
47
35
30
43
82
40
47
19
52
136
80
42
54
66
81
63
137
72
107
58
36
52
79
77
54
84
48
96
83
66
61
53
30
74
69
59
42
65
70
100
63
105
82
81
75
102
121
98
76
77
63
37
35
23
40
29
37
51
20
28
13
22
25
13
16
13
16
17
9
17
25
14
8
7
10
7
10
3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149926&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149926&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149926&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'Gertrude Mary Cox' @ cox.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13737000
23034.0671084694471-0.315647737615294-0.315647736436525-0.29155737328711
34739.27542647557430.1130180117798760.1130180117798770.578107229266551
43537.5988246307670.006090945675757280.00609094567575816-0.194826689466057
53034.6319916358277-0.138701841058417-0.138701841058416-0.332719357043247
64337.82933131505270.0002856515040436710.0002856515040440340.380389932032702
78254.7345619448940.6263475594597230.6263475594597231.95163537461613
84049.2755392300110.4201592633198430.420159263319843-0.708355607757247
94748.51401169008830.3827646171725980.382764617172598-0.138315762789636
101937.44183721511950.03933647415291990.0393364741529204-1.34590443414074
115242.94931818434980.1961707163522130.1961707163522130.64421929754689
1213678.0934247395061.161409345122011.161409345122014.1255466290521
138082.40512866353960.803640420704622-8.840044631516140.530695410998642
144265.05161545328630.0542732851163690.0542732966009278-1.77265794052499
155460.6291425159306-0.0882433306326506-0.0882433306326515-0.486103540212186
166662.6908737073002-0.0332820720582505-0.03328207205824610.244989299468995
178169.65998182120790.1173069800575420.1173069800575430.817044165274097
186367.17308678990040.06812745814740260.0681274581474005-0.307662324455832
1913793.43645761212780.5179943267189190.5179943267189193.11620636764878
207285.53136824289070.382947137297380.382947137297377-1.0060762643699
2110793.66083813942770.501077595418470.5010775954184710.927553150663626
225880.45812736423170.2997695644849930.299769564484998-1.64344369426388
233663.92819498443060.05958395566638230.0595839556663851-2.02042796950647
245259.4900687982678-0.00312603699070098-0.00312603699070027-0.540347391309787
257965.1690061337992-0.2969582110498273.266540307993380.816165034142767
267769.9327176463266-0.163315951792059-0.1633159403867160.539272847706658
275463.6966200288003-0.286952606199976-0.286952606199978-0.689459781963792
288471.3619353156005-0.156428681461017-0.1564286814610110.929872416244485
294862.5185472551475-0.277017599502774-0.277017599502772-1.03081304047755
309674.9979214887972-0.120646338515087-0.120646338515091.52551249058199
318377.9573560329193-0.0861220405457666-0.08612204054576710.369942031517822
326673.475655442873-0.132363328770627-0.132363328770631-0.529281927289174
336168.7937784906781-0.178117807392957-0.178117807392956-0.548659556997052
345362.8678232773988-0.234077739831739-0.234077739831734-0.693843429845988
353050.5756045582557-0.348675410022505-0.348675410022502-1.45649856690811
367459.2017176226624-0.264959389765092-0.2649593897650911.08452208944176
376961.1893275385438-0.3403712422255923.744083653103810.306763103842066
385960.2507045215378-0.351987472293371-0.351987460661899-0.0662477604940434
394253.165462896579-0.452754202205243-0.452754202205246-0.780050187605597
406557.5263499867393-0.394569173516563-0.3945691735165590.569637433276139
417062.104414752382-0.343605639057002-0.3436056390569990.594750079167233
4210076.1680237047132-0.212868233221141-0.2128682332211411.7329364809099
436371.2077588727417-0.252364397462025-0.252364397462025-0.5728347575006
4410583.7167841401997-0.152464752288948-0.152464752288951.54266391690678
458283.0393997167564-0.156402946545059-0.156402946545059-0.0635256848848505
468182.2413689978863-0.161074340406145-0.16107434040614-0.0777035770967434
477579.5098014792954-0.179386209892841-0.179386209892836-0.311435531014723
4810287.8171010762846-0.119908843904937-0.1199088439049371.02853430141167
4912198.1776819135165-0.3793096876394214.172406547319041.38956317503951
509898.0252746940557-0.375826138816472-0.3758261221980790.0256752902574835
517689.5513253918736-0.471636486113598-0.471636486113606-0.949061240119998
527784.7142597446661-0.513397333426553-0.513397333426542-0.520171726901932
536376.4679980024167-0.576211520976846-0.576211520976839-0.9291482160112
543761.6153897973109-0.67905135696167-0.679051356961675-1.72302404557671
553551.5421476988491-0.741189246660368-0.741189246660366-1.13660581148644
562340.7466736977311-0.803861374233485-0.803861374233488-1.21823087062035
574040.2625459327507-0.801948470293772-0.8019484702937720.0387744141898792
582935.8754051792646-0.822793627692819-0.822793627692814-0.435015167261759
593736.0806287857274-0.816936800617845-0.816936800617840.124779243659257
605141.4056602205176-0.782464314554694-0.7824643145546930.745672598662939
612030.8153767875455-0.589909228722396.48900150796436-1.2795859653687
622829.5928933436866-0.597939928717823-0.597939920467862-0.0725787027359042
631323.1643183586142-0.654977398330396-0.654977398330402-0.688443432385987
642222.5639431550462-0.654545207431502-0.65454520743150.00653550044434713
652523.306533892064-0.645145844144243-0.6451458441442380.168379159887243
661319.3067220429044-0.665180437022895-0.665180437022897-0.405776123536171
671617.9072739536116-0.669211175851742-0.66921117585174-0.0889978050882027
681315.9132663161887-0.676071447296902-0.676071447296905-0.160764797775511
691615.7710514385449-0.673415469669871-0.6734154696698710.0648302156025662
701716.0530129292305-0.668791873232214-0.6687918732322090.116070036189566
71913.264738227476-0.678852257032985-0.678852257032981-0.257571298817967
721714.4742987162362-0.670013552410672-0.6700135524106730.229532515641097
732515.1084490062226-0.6912306509397357.603537153253850.168326295734259
741414.5224713507189-0.69009159317888-0.6900915861402340.0121933502663462
75811.8921752137748-0.706269824838547-0.706269824838553-0.230287014060901
7679.88411968654295-0.715057227331262-0.715057227331259-0.156306743775596
77109.74499100961332-0.711750640232233-0.7117506402322280.0695565559252094
7878.54205051727032-0.714255871076029-0.714255871076031-0.0595069935421055
79108.89933968785801-0.70922870834541-0.7092287083454090.130042000853788
8036.52786353845009-0.716590430004107-0.716590430004109-0.201933369639052

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 37 & 37 & 0 & 0 & 0 \tabularnewline
2 & 30 & 34.0671084694471 & -0.315647737615294 & -0.315647736436525 & -0.29155737328711 \tabularnewline
3 & 47 & 39.2754264755743 & 0.113018011779876 & 0.113018011779877 & 0.578107229266551 \tabularnewline
4 & 35 & 37.598824630767 & 0.00609094567575728 & 0.00609094567575816 & -0.194826689466057 \tabularnewline
5 & 30 & 34.6319916358277 & -0.138701841058417 & -0.138701841058416 & -0.332719357043247 \tabularnewline
6 & 43 & 37.8293313150527 & 0.000285651504043671 & 0.000285651504044034 & 0.380389932032702 \tabularnewline
7 & 82 & 54.734561944894 & 0.626347559459723 & 0.626347559459723 & 1.95163537461613 \tabularnewline
8 & 40 & 49.275539230011 & 0.420159263319843 & 0.420159263319843 & -0.708355607757247 \tabularnewline
9 & 47 & 48.5140116900883 & 0.382764617172598 & 0.382764617172598 & -0.138315762789636 \tabularnewline
10 & 19 & 37.4418372151195 & 0.0393364741529199 & 0.0393364741529204 & -1.34590443414074 \tabularnewline
11 & 52 & 42.9493181843498 & 0.196170716352213 & 0.196170716352213 & 0.64421929754689 \tabularnewline
12 & 136 & 78.093424739506 & 1.16140934512201 & 1.16140934512201 & 4.1255466290521 \tabularnewline
13 & 80 & 82.4051286635396 & 0.803640420704622 & -8.84004463151614 & 0.530695410998642 \tabularnewline
14 & 42 & 65.0516154532863 & 0.054273285116369 & 0.0542732966009278 & -1.77265794052499 \tabularnewline
15 & 54 & 60.6291425159306 & -0.0882433306326506 & -0.0882433306326515 & -0.486103540212186 \tabularnewline
16 & 66 & 62.6908737073002 & -0.0332820720582505 & -0.0332820720582461 & 0.244989299468995 \tabularnewline
17 & 81 & 69.6599818212079 & 0.117306980057542 & 0.117306980057543 & 0.817044165274097 \tabularnewline
18 & 63 & 67.1730867899004 & 0.0681274581474026 & 0.0681274581474005 & -0.307662324455832 \tabularnewline
19 & 137 & 93.4364576121278 & 0.517994326718919 & 0.517994326718919 & 3.11620636764878 \tabularnewline
20 & 72 & 85.5313682428907 & 0.38294713729738 & 0.382947137297377 & -1.0060762643699 \tabularnewline
21 & 107 & 93.6608381394277 & 0.50107759541847 & 0.501077595418471 & 0.927553150663626 \tabularnewline
22 & 58 & 80.4581273642317 & 0.299769564484993 & 0.299769564484998 & -1.64344369426388 \tabularnewline
23 & 36 & 63.9281949844306 & 0.0595839556663823 & 0.0595839556663851 & -2.02042796950647 \tabularnewline
24 & 52 & 59.4900687982678 & -0.00312603699070098 & -0.00312603699070027 & -0.540347391309787 \tabularnewline
25 & 79 & 65.1690061337992 & -0.296958211049827 & 3.26654030799338 & 0.816165034142767 \tabularnewline
26 & 77 & 69.9327176463266 & -0.163315951792059 & -0.163315940386716 & 0.539272847706658 \tabularnewline
27 & 54 & 63.6966200288003 & -0.286952606199976 & -0.286952606199978 & -0.689459781963792 \tabularnewline
28 & 84 & 71.3619353156005 & -0.156428681461017 & -0.156428681461011 & 0.929872416244485 \tabularnewline
29 & 48 & 62.5185472551475 & -0.277017599502774 & -0.277017599502772 & -1.03081304047755 \tabularnewline
30 & 96 & 74.9979214887972 & -0.120646338515087 & -0.12064633851509 & 1.52551249058199 \tabularnewline
31 & 83 & 77.9573560329193 & -0.0861220405457666 & -0.0861220405457671 & 0.369942031517822 \tabularnewline
32 & 66 & 73.475655442873 & -0.132363328770627 & -0.132363328770631 & -0.529281927289174 \tabularnewline
33 & 61 & 68.7937784906781 & -0.178117807392957 & -0.178117807392956 & -0.548659556997052 \tabularnewline
34 & 53 & 62.8678232773988 & -0.234077739831739 & -0.234077739831734 & -0.693843429845988 \tabularnewline
35 & 30 & 50.5756045582557 & -0.348675410022505 & -0.348675410022502 & -1.45649856690811 \tabularnewline
36 & 74 & 59.2017176226624 & -0.264959389765092 & -0.264959389765091 & 1.08452208944176 \tabularnewline
37 & 69 & 61.1893275385438 & -0.340371242225592 & 3.74408365310381 & 0.306763103842066 \tabularnewline
38 & 59 & 60.2507045215378 & -0.351987472293371 & -0.351987460661899 & -0.0662477604940434 \tabularnewline
39 & 42 & 53.165462896579 & -0.452754202205243 & -0.452754202205246 & -0.780050187605597 \tabularnewline
40 & 65 & 57.5263499867393 & -0.394569173516563 & -0.394569173516559 & 0.569637433276139 \tabularnewline
41 & 70 & 62.104414752382 & -0.343605639057002 & -0.343605639056999 & 0.594750079167233 \tabularnewline
42 & 100 & 76.1680237047132 & -0.212868233221141 & -0.212868233221141 & 1.7329364809099 \tabularnewline
43 & 63 & 71.2077588727417 & -0.252364397462025 & -0.252364397462025 & -0.5728347575006 \tabularnewline
44 & 105 & 83.7167841401997 & -0.152464752288948 & -0.15246475228895 & 1.54266391690678 \tabularnewline
45 & 82 & 83.0393997167564 & -0.156402946545059 & -0.156402946545059 & -0.0635256848848505 \tabularnewline
46 & 81 & 82.2413689978863 & -0.161074340406145 & -0.16107434040614 & -0.0777035770967434 \tabularnewline
47 & 75 & 79.5098014792954 & -0.179386209892841 & -0.179386209892836 & -0.311435531014723 \tabularnewline
48 & 102 & 87.8171010762846 & -0.119908843904937 & -0.119908843904937 & 1.02853430141167 \tabularnewline
49 & 121 & 98.1776819135165 & -0.379309687639421 & 4.17240654731904 & 1.38956317503951 \tabularnewline
50 & 98 & 98.0252746940557 & -0.375826138816472 & -0.375826122198079 & 0.0256752902574835 \tabularnewline
51 & 76 & 89.5513253918736 & -0.471636486113598 & -0.471636486113606 & -0.949061240119998 \tabularnewline
52 & 77 & 84.7142597446661 & -0.513397333426553 & -0.513397333426542 & -0.520171726901932 \tabularnewline
53 & 63 & 76.4679980024167 & -0.576211520976846 & -0.576211520976839 & -0.9291482160112 \tabularnewline
54 & 37 & 61.6153897973109 & -0.67905135696167 & -0.679051356961675 & -1.72302404557671 \tabularnewline
55 & 35 & 51.5421476988491 & -0.741189246660368 & -0.741189246660366 & -1.13660581148644 \tabularnewline
56 & 23 & 40.7466736977311 & -0.803861374233485 & -0.803861374233488 & -1.21823087062035 \tabularnewline
57 & 40 & 40.2625459327507 & -0.801948470293772 & -0.801948470293772 & 0.0387744141898792 \tabularnewline
58 & 29 & 35.8754051792646 & -0.822793627692819 & -0.822793627692814 & -0.435015167261759 \tabularnewline
59 & 37 & 36.0806287857274 & -0.816936800617845 & -0.81693680061784 & 0.124779243659257 \tabularnewline
60 & 51 & 41.4056602205176 & -0.782464314554694 & -0.782464314554693 & 0.745672598662939 \tabularnewline
61 & 20 & 30.8153767875455 & -0.58990922872239 & 6.48900150796436 & -1.2795859653687 \tabularnewline
62 & 28 & 29.5928933436866 & -0.597939928717823 & -0.597939920467862 & -0.0725787027359042 \tabularnewline
63 & 13 & 23.1643183586142 & -0.654977398330396 & -0.654977398330402 & -0.688443432385987 \tabularnewline
64 & 22 & 22.5639431550462 & -0.654545207431502 & -0.6545452074315 & 0.00653550044434713 \tabularnewline
65 & 25 & 23.306533892064 & -0.645145844144243 & -0.645145844144238 & 0.168379159887243 \tabularnewline
66 & 13 & 19.3067220429044 & -0.665180437022895 & -0.665180437022897 & -0.405776123536171 \tabularnewline
67 & 16 & 17.9072739536116 & -0.669211175851742 & -0.66921117585174 & -0.0889978050882027 \tabularnewline
68 & 13 & 15.9132663161887 & -0.676071447296902 & -0.676071447296905 & -0.160764797775511 \tabularnewline
69 & 16 & 15.7710514385449 & -0.673415469669871 & -0.673415469669871 & 0.0648302156025662 \tabularnewline
70 & 17 & 16.0530129292305 & -0.668791873232214 & -0.668791873232209 & 0.116070036189566 \tabularnewline
71 & 9 & 13.264738227476 & -0.678852257032985 & -0.678852257032981 & -0.257571298817967 \tabularnewline
72 & 17 & 14.4742987162362 & -0.670013552410672 & -0.670013552410673 & 0.229532515641097 \tabularnewline
73 & 25 & 15.1084490062226 & -0.691230650939735 & 7.60353715325385 & 0.168326295734259 \tabularnewline
74 & 14 & 14.5224713507189 & -0.69009159317888 & -0.690091586140234 & 0.0121933502663462 \tabularnewline
75 & 8 & 11.8921752137748 & -0.706269824838547 & -0.706269824838553 & -0.230287014060901 \tabularnewline
76 & 7 & 9.88411968654295 & -0.715057227331262 & -0.715057227331259 & -0.156306743775596 \tabularnewline
77 & 10 & 9.74499100961332 & -0.711750640232233 & -0.711750640232228 & 0.0695565559252094 \tabularnewline
78 & 7 & 8.54205051727032 & -0.714255871076029 & -0.714255871076031 & -0.0595069935421055 \tabularnewline
79 & 10 & 8.89933968785801 & -0.70922870834541 & -0.709228708345409 & 0.130042000853788 \tabularnewline
80 & 3 & 6.52786353845009 & -0.716590430004107 & -0.716590430004109 & -0.201933369639052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149926&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]37[/C][C]37[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]34.0671084694471[/C][C]-0.315647737615294[/C][C]-0.315647736436525[/C][C]-0.29155737328711[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]39.2754264755743[/C][C]0.113018011779876[/C][C]0.113018011779877[/C][C]0.578107229266551[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]37.598824630767[/C][C]0.00609094567575728[/C][C]0.00609094567575816[/C][C]-0.194826689466057[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]34.6319916358277[/C][C]-0.138701841058417[/C][C]-0.138701841058416[/C][C]-0.332719357043247[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]37.8293313150527[/C][C]0.000285651504043671[/C][C]0.000285651504044034[/C][C]0.380389932032702[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]54.734561944894[/C][C]0.626347559459723[/C][C]0.626347559459723[/C][C]1.95163537461613[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]49.275539230011[/C][C]0.420159263319843[/C][C]0.420159263319843[/C][C]-0.708355607757247[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]48.5140116900883[/C][C]0.382764617172598[/C][C]0.382764617172598[/C][C]-0.138315762789636[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]37.4418372151195[/C][C]0.0393364741529199[/C][C]0.0393364741529204[/C][C]-1.34590443414074[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]42.9493181843498[/C][C]0.196170716352213[/C][C]0.196170716352213[/C][C]0.64421929754689[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]78.093424739506[/C][C]1.16140934512201[/C][C]1.16140934512201[/C][C]4.1255466290521[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]82.4051286635396[/C][C]0.803640420704622[/C][C]-8.84004463151614[/C][C]0.530695410998642[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]65.0516154532863[/C][C]0.054273285116369[/C][C]0.0542732966009278[/C][C]-1.77265794052499[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]60.6291425159306[/C][C]-0.0882433306326506[/C][C]-0.0882433306326515[/C][C]-0.486103540212186[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]62.6908737073002[/C][C]-0.0332820720582505[/C][C]-0.0332820720582461[/C][C]0.244989299468995[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]69.6599818212079[/C][C]0.117306980057542[/C][C]0.117306980057543[/C][C]0.817044165274097[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]67.1730867899004[/C][C]0.0681274581474026[/C][C]0.0681274581474005[/C][C]-0.307662324455832[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]93.4364576121278[/C][C]0.517994326718919[/C][C]0.517994326718919[/C][C]3.11620636764878[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]85.5313682428907[/C][C]0.38294713729738[/C][C]0.382947137297377[/C][C]-1.0060762643699[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]93.6608381394277[/C][C]0.50107759541847[/C][C]0.501077595418471[/C][C]0.927553150663626[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]80.4581273642317[/C][C]0.299769564484993[/C][C]0.299769564484998[/C][C]-1.64344369426388[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]63.9281949844306[/C][C]0.0595839556663823[/C][C]0.0595839556663851[/C][C]-2.02042796950647[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]59.4900687982678[/C][C]-0.00312603699070098[/C][C]-0.00312603699070027[/C][C]-0.540347391309787[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]65.1690061337992[/C][C]-0.296958211049827[/C][C]3.26654030799338[/C][C]0.816165034142767[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]69.9327176463266[/C][C]-0.163315951792059[/C][C]-0.163315940386716[/C][C]0.539272847706658[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]63.6966200288003[/C][C]-0.286952606199976[/C][C]-0.286952606199978[/C][C]-0.689459781963792[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]71.3619353156005[/C][C]-0.156428681461017[/C][C]-0.156428681461011[/C][C]0.929872416244485[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]62.5185472551475[/C][C]-0.277017599502774[/C][C]-0.277017599502772[/C][C]-1.03081304047755[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]74.9979214887972[/C][C]-0.120646338515087[/C][C]-0.12064633851509[/C][C]1.52551249058199[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]77.9573560329193[/C][C]-0.0861220405457666[/C][C]-0.0861220405457671[/C][C]0.369942031517822[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]73.475655442873[/C][C]-0.132363328770627[/C][C]-0.132363328770631[/C][C]-0.529281927289174[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]68.7937784906781[/C][C]-0.178117807392957[/C][C]-0.178117807392956[/C][C]-0.548659556997052[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]62.8678232773988[/C][C]-0.234077739831739[/C][C]-0.234077739831734[/C][C]-0.693843429845988[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]50.5756045582557[/C][C]-0.348675410022505[/C][C]-0.348675410022502[/C][C]-1.45649856690811[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]59.2017176226624[/C][C]-0.264959389765092[/C][C]-0.264959389765091[/C][C]1.08452208944176[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]61.1893275385438[/C][C]-0.340371242225592[/C][C]3.74408365310381[/C][C]0.306763103842066[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]60.2507045215378[/C][C]-0.351987472293371[/C][C]-0.351987460661899[/C][C]-0.0662477604940434[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]53.165462896579[/C][C]-0.452754202205243[/C][C]-0.452754202205246[/C][C]-0.780050187605597[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]57.5263499867393[/C][C]-0.394569173516563[/C][C]-0.394569173516559[/C][C]0.569637433276139[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]62.104414752382[/C][C]-0.343605639057002[/C][C]-0.343605639056999[/C][C]0.594750079167233[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]76.1680237047132[/C][C]-0.212868233221141[/C][C]-0.212868233221141[/C][C]1.7329364809099[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]71.2077588727417[/C][C]-0.252364397462025[/C][C]-0.252364397462025[/C][C]-0.5728347575006[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]83.7167841401997[/C][C]-0.152464752288948[/C][C]-0.15246475228895[/C][C]1.54266391690678[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]83.0393997167564[/C][C]-0.156402946545059[/C][C]-0.156402946545059[/C][C]-0.0635256848848505[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]82.2413689978863[/C][C]-0.161074340406145[/C][C]-0.16107434040614[/C][C]-0.0777035770967434[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]79.5098014792954[/C][C]-0.179386209892841[/C][C]-0.179386209892836[/C][C]-0.311435531014723[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]87.8171010762846[/C][C]-0.119908843904937[/C][C]-0.119908843904937[/C][C]1.02853430141167[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]98.1776819135165[/C][C]-0.379309687639421[/C][C]4.17240654731904[/C][C]1.38956317503951[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]98.0252746940557[/C][C]-0.375826138816472[/C][C]-0.375826122198079[/C][C]0.0256752902574835[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]89.5513253918736[/C][C]-0.471636486113598[/C][C]-0.471636486113606[/C][C]-0.949061240119998[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]84.7142597446661[/C][C]-0.513397333426553[/C][C]-0.513397333426542[/C][C]-0.520171726901932[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]76.4679980024167[/C][C]-0.576211520976846[/C][C]-0.576211520976839[/C][C]-0.9291482160112[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]61.6153897973109[/C][C]-0.67905135696167[/C][C]-0.679051356961675[/C][C]-1.72302404557671[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]51.5421476988491[/C][C]-0.741189246660368[/C][C]-0.741189246660366[/C][C]-1.13660581148644[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]40.7466736977311[/C][C]-0.803861374233485[/C][C]-0.803861374233488[/C][C]-1.21823087062035[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]40.2625459327507[/C][C]-0.801948470293772[/C][C]-0.801948470293772[/C][C]0.0387744141898792[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.8754051792646[/C][C]-0.822793627692819[/C][C]-0.822793627692814[/C][C]-0.435015167261759[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]36.0806287857274[/C][C]-0.816936800617845[/C][C]-0.81693680061784[/C][C]0.124779243659257[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]41.4056602205176[/C][C]-0.782464314554694[/C][C]-0.782464314554693[/C][C]0.745672598662939[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]30.8153767875455[/C][C]-0.58990922872239[/C][C]6.48900150796436[/C][C]-1.2795859653687[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]29.5928933436866[/C][C]-0.597939928717823[/C][C]-0.597939920467862[/C][C]-0.0725787027359042[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]23.1643183586142[/C][C]-0.654977398330396[/C][C]-0.654977398330402[/C][C]-0.688443432385987[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]22.5639431550462[/C][C]-0.654545207431502[/C][C]-0.6545452074315[/C][C]0.00653550044434713[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]23.306533892064[/C][C]-0.645145844144243[/C][C]-0.645145844144238[/C][C]0.168379159887243[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]19.3067220429044[/C][C]-0.665180437022895[/C][C]-0.665180437022897[/C][C]-0.405776123536171[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]17.9072739536116[/C][C]-0.669211175851742[/C][C]-0.66921117585174[/C][C]-0.0889978050882027[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]15.9132663161887[/C][C]-0.676071447296902[/C][C]-0.676071447296905[/C][C]-0.160764797775511[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.7710514385449[/C][C]-0.673415469669871[/C][C]-0.673415469669871[/C][C]0.0648302156025662[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]16.0530129292305[/C][C]-0.668791873232214[/C][C]-0.668791873232209[/C][C]0.116070036189566[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]13.264738227476[/C][C]-0.678852257032985[/C][C]-0.678852257032981[/C][C]-0.257571298817967[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]14.4742987162362[/C][C]-0.670013552410672[/C][C]-0.670013552410673[/C][C]0.229532515641097[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]15.1084490062226[/C][C]-0.691230650939735[/C][C]7.60353715325385[/C][C]0.168326295734259[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]14.5224713507189[/C][C]-0.69009159317888[/C][C]-0.690091586140234[/C][C]0.0121933502663462[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]11.8921752137748[/C][C]-0.706269824838547[/C][C]-0.706269824838553[/C][C]-0.230287014060901[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]9.88411968654295[/C][C]-0.715057227331262[/C][C]-0.715057227331259[/C][C]-0.156306743775596[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]9.74499100961332[/C][C]-0.711750640232233[/C][C]-0.711750640232228[/C][C]0.0695565559252094[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]8.54205051727032[/C][C]-0.714255871076029[/C][C]-0.714255871076031[/C][C]-0.0595069935421055[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]8.89933968785801[/C][C]-0.70922870834541[/C][C]-0.709228708345409[/C][C]0.130042000853788[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]6.52786353845009[/C][C]-0.716590430004107[/C][C]-0.716590430004109[/C][C]-0.201933369639052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149926&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149926&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
13737000
23034.0671084694471-0.315647737615294-0.315647736436525-0.29155737328711
34739.27542647557430.1130180117798760.1130180117798770.578107229266551
43537.5988246307670.006090945675757280.00609094567575816-0.194826689466057
53034.6319916358277-0.138701841058417-0.138701841058416-0.332719357043247
64337.82933131505270.0002856515040436710.0002856515040440340.380389932032702
78254.7345619448940.6263475594597230.6263475594597231.95163537461613
84049.2755392300110.4201592633198430.420159263319843-0.708355607757247
94748.51401169008830.3827646171725980.382764617172598-0.138315762789636
101937.44183721511950.03933647415291990.0393364741529204-1.34590443414074
115242.94931818434980.1961707163522130.1961707163522130.64421929754689
1213678.0934247395061.161409345122011.161409345122014.1255466290521
138082.40512866353960.803640420704622-8.840044631516140.530695410998642
144265.05161545328630.0542732851163690.0542732966009278-1.77265794052499
155460.6291425159306-0.0882433306326506-0.0882433306326515-0.486103540212186
166662.6908737073002-0.0332820720582505-0.03328207205824610.244989299468995
178169.65998182120790.1173069800575420.1173069800575430.817044165274097
186367.17308678990040.06812745814740260.0681274581474005-0.307662324455832
1913793.43645761212780.5179943267189190.5179943267189193.11620636764878
207285.53136824289070.382947137297380.382947137297377-1.0060762643699
2110793.66083813942770.501077595418470.5010775954184710.927553150663626
225880.45812736423170.2997695644849930.299769564484998-1.64344369426388
233663.92819498443060.05958395566638230.0595839556663851-2.02042796950647
245259.4900687982678-0.00312603699070098-0.00312603699070027-0.540347391309787
257965.1690061337992-0.2969582110498273.266540307993380.816165034142767
267769.9327176463266-0.163315951792059-0.1633159403867160.539272847706658
275463.6966200288003-0.286952606199976-0.286952606199978-0.689459781963792
288471.3619353156005-0.156428681461017-0.1564286814610110.929872416244485
294862.5185472551475-0.277017599502774-0.277017599502772-1.03081304047755
309674.9979214887972-0.120646338515087-0.120646338515091.52551249058199
318377.9573560329193-0.0861220405457666-0.08612204054576710.369942031517822
326673.475655442873-0.132363328770627-0.132363328770631-0.529281927289174
336168.7937784906781-0.178117807392957-0.178117807392956-0.548659556997052
345362.8678232773988-0.234077739831739-0.234077739831734-0.693843429845988
353050.5756045582557-0.348675410022505-0.348675410022502-1.45649856690811
367459.2017176226624-0.264959389765092-0.2649593897650911.08452208944176
376961.1893275385438-0.3403712422255923.744083653103810.306763103842066
385960.2507045215378-0.351987472293371-0.351987460661899-0.0662477604940434
394253.165462896579-0.452754202205243-0.452754202205246-0.780050187605597
406557.5263499867393-0.394569173516563-0.3945691735165590.569637433276139
417062.104414752382-0.343605639057002-0.3436056390569990.594750079167233
4210076.1680237047132-0.212868233221141-0.2128682332211411.7329364809099
436371.2077588727417-0.252364397462025-0.252364397462025-0.5728347575006
4410583.7167841401997-0.152464752288948-0.152464752288951.54266391690678
458283.0393997167564-0.156402946545059-0.156402946545059-0.0635256848848505
468182.2413689978863-0.161074340406145-0.16107434040614-0.0777035770967434
477579.5098014792954-0.179386209892841-0.179386209892836-0.311435531014723
4810287.8171010762846-0.119908843904937-0.1199088439049371.02853430141167
4912198.1776819135165-0.3793096876394214.172406547319041.38956317503951
509898.0252746940557-0.375826138816472-0.3758261221980790.0256752902574835
517689.5513253918736-0.471636486113598-0.471636486113606-0.949061240119998
527784.7142597446661-0.513397333426553-0.513397333426542-0.520171726901932
536376.4679980024167-0.576211520976846-0.576211520976839-0.9291482160112
543761.6153897973109-0.67905135696167-0.679051356961675-1.72302404557671
553551.5421476988491-0.741189246660368-0.741189246660366-1.13660581148644
562340.7466736977311-0.803861374233485-0.803861374233488-1.21823087062035
574040.2625459327507-0.801948470293772-0.8019484702937720.0387744141898792
582935.8754051792646-0.822793627692819-0.822793627692814-0.435015167261759
593736.0806287857274-0.816936800617845-0.816936800617840.124779243659257
605141.4056602205176-0.782464314554694-0.7824643145546930.745672598662939
612030.8153767875455-0.589909228722396.48900150796436-1.2795859653687
622829.5928933436866-0.597939928717823-0.597939920467862-0.0725787027359042
631323.1643183586142-0.654977398330396-0.654977398330402-0.688443432385987
642222.5639431550462-0.654545207431502-0.65454520743150.00653550044434713
652523.306533892064-0.645145844144243-0.6451458441442380.168379159887243
661319.3067220429044-0.665180437022895-0.665180437022897-0.405776123536171
671617.9072739536116-0.669211175851742-0.66921117585174-0.0889978050882027
681315.9132663161887-0.676071447296902-0.676071447296905-0.160764797775511
691615.7710514385449-0.673415469669871-0.6734154696698710.0648302156025662
701716.0530129292305-0.668791873232214-0.6687918732322090.116070036189566
71913.264738227476-0.678852257032985-0.678852257032981-0.257571298817967
721714.4742987162362-0.670013552410672-0.6700135524106730.229532515641097
732515.1084490062226-0.6912306509397357.603537153253850.168326295734259
741414.5224713507189-0.69009159317888-0.6900915861402340.0121933502663462
75811.8921752137748-0.706269824838547-0.706269824838553-0.230287014060901
7679.88411968654295-0.715057227331262-0.715057227331259-0.156306743775596
77109.74499100961332-0.711750640232233-0.7117506402322280.0695565559252094
7878.54205051727032-0.714255871076029-0.714255871076031-0.0595069935421055
79108.89933968785801-0.70922870834541-0.7092287083454090.130042000853788
8036.52786353845009-0.716590430004107-0.716590430004109-0.201933369639052



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')