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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 computationSun, 17 Nov 2013 13:51:51 -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/2013/Nov/17/t1384714343nsazclclu1awn9y.htm/, Retrieved Mon, 29 Apr 2024 01:36:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225851, Retrieved Mon, 29 Apr 2024 01:36:33 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [structural time s...] [2013-11-17 18:51:51] [dbceeb23fcf622ba260f793fe955ad62] [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 time5 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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225851&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]5 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=225851&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225851&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 time5 seconds
R Server'George Udny Yule' @ yule.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13737000
23034.0671084703555-0.3156477376112-0.31564773643273-0.291557373576176
34739.27542647477640.1130180118117980.1130180118117970.578107229778994
43537.5988246306990.006090945702644680.00609094570264402-0.194826689618108
53034.631991636474-0.138701841066129-0.138701841066129-0.332719357356007
64337.82933131465770.0002856515194773920.0002856515194769740.38038993236368
78254.73456194051740.6263475596176760.6263475596176781.9516353764764
84049.27553922886790.4201592634907980.420159263490799-0.708355608247964
94748.51401168972340.3827646173471510.382764617347152-0.138315762885229
101937.44183721792290.03933647428201690.0393364742820169-1.34590443541795
115242.94931818468270.1961707164651320.1961707164651320.644219298022962
1213678.09342473043161.161409345313381.161409345313384.12554663295133
138082.40512865631270.803640420685588-8.840044631305260.530695411965217
144265.05161545387360.05427328535014410.0542732968314112-1.77265794194507
155460.6291425174319-0.0882433304102541-0.0882433304102529-0.486103540722062
166662.6908737077265-0.0332820718503202-0.03328207185032130.244989299615891
178169.65998181969510.1173069802810390.117306980281040.817044166013044
186367.173086789710.06812745836974320.0681274583697453-0.307662324700843
1913793.43645760493280.5179943270226050.517994327022613.11620637060327
207285.53136824078990.3829471376166420.382947137616644-1.0060762650332
2110793.66083813604020.5010775957655770.5010775957655790.927553151613082
225880.45812736602990.299769564816570.299769564816569-1.64344369571002
233663.92819499036720.05958395595203010.0595839559520303-2.02042797154379
245259.4900688033274-0.00312603674670147-0.00312603674670232-0.540347392117158
257965.1690061366869-0.2969582106980823.266540304127870.816165034835556
267769.9327176467141-0.163315951503842-0.1633159401016430.539272848072828
275463.6966200307163-0.286952605915262-0.286952605915258-0.689459782665461
288471.3619353147134-0.156428681182113-0.1564286811821150.929872417020118
294862.5185472570446-0.277017599239218-0.277017599239215-1.03081304144508
309674.9979214865008-0.120646338235284-0.1206463382352821.5255124919275
318377.9573560306857-0.0861220402522869-0.08612204025228170.369942031948574
326673.475655442792-0.132363328479108-0.132363328479106-0.529281927720197
336168.7937784919967-0.178117807111202-0.178117807111201-0.548659557543251
345362.8678232799406-0.23407773956578-0.23407773956578-0.693843430592962
353050.5756045633735-0.348675409784445-0.348675409784444-1.45649856843702
367459.2017176233615-0.264959389534895-0.2649593895348961.08452209022597
376961.1893275393182-0.3403712419945313.744083650565180.306763104209787
385960.2507045222226-0.351987472069535-0.351987460441162-0.0662477606121289
394253.1654628989029-0.452754201983734-0.452754201983729-0.780050188402
406557.5263499869063-0.394569173303685-0.3945691733036870.569637433696047
417062.1044147511454-0.343605638839358-0.3436056388393540.594750079708083
4210076.1680236999117-0.212868232978044-0.2128682329780431.73293648259962
436371.2077588711463-0.252364397212901-0.252364397212896-0.572834757853432
4410583.7167841355996-0.152464752018214-0.1524647520182121.54266391842919
458283.0393997140871-0.156402946263696-0.156402946263696-0.0635256847629037
468182.2413689964665-0.161074340119854-0.161074340119855-0.0777035770761698
477579.5098014792203-0.179386209607139-0.179386209607139-0.31143553127414
4810287.8171010738526-0.119908843611901-0.1199088436119021.02853430237211
4912198.1776819100549-0.3793096873283584.172406543901881.38956317662093
509898.0252746919512-0.375826138489055-0.3758261218751790.025675290407908
517689.5513253929136-0.471636485775251-0.471636485775241-0.949061240963247
527784.7142597466271-0.513397333097122-0.513397333097122-0.520171727475613
536376.4679980059243-0.576211520663933-0.576211520663926-0.929148217015964
543761.615389803679-0.679051356677668-0.679051356677665-1.7230240474079
553551.5421477056269-0.741189246405015-0.741189246405012-1.13660581288418
562340.7466737049638-0.803861374006648-0.803861374006646-1.21823087211232
574040.2625459372633-0.801948470083671-0.8019484700836710.0387744138807999
582935.8754051831982-0.822793627496868-0.82279362749687-0.435015167899654
593736.0806287879526-0.816936800430113-0.8169368004301130.124779243583684
605141.4056602201766-0.782464314367231-0.7824643143672330.745672599258534
612030.8153767910228-0.5899092285953626.48900150656896-1.27958596651033
622829.5928933460305-0.597939928607467-0.597939920359493-0.0725787029712286
631323.164318361747-0.654977398226755-0.654977398226748-0.688443433159342
642222.5639431570149-0.654545207337746-0.6545452073377480.00653550030070437
652523.3065338929219-0.645145844054628-0.6451458440546240.168379159951135
661319.3067220444332-0.665180436939046-0.665180436939046-0.405776123970482
671617.9072739548067-0.669211175772106-0.669211175772101-0.088997805249633
681315.9132663173458-0.67607144722115-0.676071447221147-0.160764797990155
691615.7710514391367-0.673415469596057-0.6734154695960570.0648302156055744
701716.053012929343-0.66879187315894-0.6687918731589420.116070036267202
71913.264738228185-0.678852256961783-0.678852256961783-0.25757129907588
721714.4742987161485-0.670013552339604-0.6700135523396050.229532515822176
732515.1084490060929-0.6912306508680177.603537152466540.168326295931945
741414.5224713506258-0.690091593107447-0.6900915860703860.0121933502775276
75811.8921752142877-0.706269824766449-0.706269824766442-0.230287014283483
7679.88411968725477-0.715057227261368-0.715057227261371-0.156306743954489
77109.74499100991185-0.711750640163717-0.7117506401637130.0695565559533541
7878.54205051761777-0.714255871008771-0.714255871008771-0.0595069936185826
79108.8993396877821-0.709228708278052-0.7092287082780480.130042000956359
8036.52786353890461-0.716590429938223-0.716590429938221-0.201933369834727

\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 & 34.0671084703555 & -0.3156477376112 & -0.31564773643273 & -0.291557373576176 \tabularnewline
3 & 47 & 39.2754264747764 & 0.113018011811798 & 0.113018011811797 & 0.578107229778994 \tabularnewline
4 & 35 & 37.598824630699 & 0.00609094570264468 & 0.00609094570264402 & -0.194826689618108 \tabularnewline
5 & 30 & 34.631991636474 & -0.138701841066129 & -0.138701841066129 & -0.332719357356007 \tabularnewline
6 & 43 & 37.8293313146577 & 0.000285651519477392 & 0.000285651519476974 & 0.38038993236368 \tabularnewline
7 & 82 & 54.7345619405174 & 0.626347559617676 & 0.626347559617678 & 1.9516353764764 \tabularnewline
8 & 40 & 49.2755392288679 & 0.420159263490798 & 0.420159263490799 & -0.708355608247964 \tabularnewline
9 & 47 & 48.5140116897234 & 0.382764617347151 & 0.382764617347152 & -0.138315762885229 \tabularnewline
10 & 19 & 37.4418372179229 & 0.0393364742820169 & 0.0393364742820169 & -1.34590443541795 \tabularnewline
11 & 52 & 42.9493181846827 & 0.196170716465132 & 0.196170716465132 & 0.644219298022962 \tabularnewline
12 & 136 & 78.0934247304316 & 1.16140934531338 & 1.16140934531338 & 4.12554663295133 \tabularnewline
13 & 80 & 82.4051286563127 & 0.803640420685588 & -8.84004463130526 & 0.530695411965217 \tabularnewline
14 & 42 & 65.0516154538736 & 0.0542732853501441 & 0.0542732968314112 & -1.77265794194507 \tabularnewline
15 & 54 & 60.6291425174319 & -0.0882433304102541 & -0.0882433304102529 & -0.486103540722062 \tabularnewline
16 & 66 & 62.6908737077265 & -0.0332820718503202 & -0.0332820718503213 & 0.244989299615891 \tabularnewline
17 & 81 & 69.6599818196951 & 0.117306980281039 & 0.11730698028104 & 0.817044166013044 \tabularnewline
18 & 63 & 67.17308678971 & 0.0681274583697432 & 0.0681274583697453 & -0.307662324700843 \tabularnewline
19 & 137 & 93.4364576049328 & 0.517994327022605 & 0.51799432702261 & 3.11620637060327 \tabularnewline
20 & 72 & 85.5313682407899 & 0.382947137616642 & 0.382947137616644 & -1.0060762650332 \tabularnewline
21 & 107 & 93.6608381360402 & 0.501077595765577 & 0.501077595765579 & 0.927553151613082 \tabularnewline
22 & 58 & 80.4581273660299 & 0.29976956481657 & 0.299769564816569 & -1.64344369571002 \tabularnewline
23 & 36 & 63.9281949903672 & 0.0595839559520301 & 0.0595839559520303 & -2.02042797154379 \tabularnewline
24 & 52 & 59.4900688033274 & -0.00312603674670147 & -0.00312603674670232 & -0.540347392117158 \tabularnewline
25 & 79 & 65.1690061366869 & -0.296958210698082 & 3.26654030412787 & 0.816165034835556 \tabularnewline
26 & 77 & 69.9327176467141 & -0.163315951503842 & -0.163315940101643 & 0.539272848072828 \tabularnewline
27 & 54 & 63.6966200307163 & -0.286952605915262 & -0.286952605915258 & -0.689459782665461 \tabularnewline
28 & 84 & 71.3619353147134 & -0.156428681182113 & -0.156428681182115 & 0.929872417020118 \tabularnewline
29 & 48 & 62.5185472570446 & -0.277017599239218 & -0.277017599239215 & -1.03081304144508 \tabularnewline
30 & 96 & 74.9979214865008 & -0.120646338235284 & -0.120646338235282 & 1.5255124919275 \tabularnewline
31 & 83 & 77.9573560306857 & -0.0861220402522869 & -0.0861220402522817 & 0.369942031948574 \tabularnewline
32 & 66 & 73.475655442792 & -0.132363328479108 & -0.132363328479106 & -0.529281927720197 \tabularnewline
33 & 61 & 68.7937784919967 & -0.178117807111202 & -0.178117807111201 & -0.548659557543251 \tabularnewline
34 & 53 & 62.8678232799406 & -0.23407773956578 & -0.23407773956578 & -0.693843430592962 \tabularnewline
35 & 30 & 50.5756045633735 & -0.348675409784445 & -0.348675409784444 & -1.45649856843702 \tabularnewline
36 & 74 & 59.2017176233615 & -0.264959389534895 & -0.264959389534896 & 1.08452209022597 \tabularnewline
37 & 69 & 61.1893275393182 & -0.340371241994531 & 3.74408365056518 & 0.306763104209787 \tabularnewline
38 & 59 & 60.2507045222226 & -0.351987472069535 & -0.351987460441162 & -0.0662477606121289 \tabularnewline
39 & 42 & 53.1654628989029 & -0.452754201983734 & -0.452754201983729 & -0.780050188402 \tabularnewline
40 & 65 & 57.5263499869063 & -0.394569173303685 & -0.394569173303687 & 0.569637433696047 \tabularnewline
41 & 70 & 62.1044147511454 & -0.343605638839358 & -0.343605638839354 & 0.594750079708083 \tabularnewline
42 & 100 & 76.1680236999117 & -0.212868232978044 & -0.212868232978043 & 1.73293648259962 \tabularnewline
43 & 63 & 71.2077588711463 & -0.252364397212901 & -0.252364397212896 & -0.572834757853432 \tabularnewline
44 & 105 & 83.7167841355996 & -0.152464752018214 & -0.152464752018212 & 1.54266391842919 \tabularnewline
45 & 82 & 83.0393997140871 & -0.156402946263696 & -0.156402946263696 & -0.0635256847629037 \tabularnewline
46 & 81 & 82.2413689964665 & -0.161074340119854 & -0.161074340119855 & -0.0777035770761698 \tabularnewline
47 & 75 & 79.5098014792203 & -0.179386209607139 & -0.179386209607139 & -0.31143553127414 \tabularnewline
48 & 102 & 87.8171010738526 & -0.119908843611901 & -0.119908843611902 & 1.02853430237211 \tabularnewline
49 & 121 & 98.1776819100549 & -0.379309687328358 & 4.17240654390188 & 1.38956317662093 \tabularnewline
50 & 98 & 98.0252746919512 & -0.375826138489055 & -0.375826121875179 & 0.025675290407908 \tabularnewline
51 & 76 & 89.5513253929136 & -0.471636485775251 & -0.471636485775241 & -0.949061240963247 \tabularnewline
52 & 77 & 84.7142597466271 & -0.513397333097122 & -0.513397333097122 & -0.520171727475613 \tabularnewline
53 & 63 & 76.4679980059243 & -0.576211520663933 & -0.576211520663926 & -0.929148217015964 \tabularnewline
54 & 37 & 61.615389803679 & -0.679051356677668 & -0.679051356677665 & -1.7230240474079 \tabularnewline
55 & 35 & 51.5421477056269 & -0.741189246405015 & -0.741189246405012 & -1.13660581288418 \tabularnewline
56 & 23 & 40.7466737049638 & -0.803861374006648 & -0.803861374006646 & -1.21823087211232 \tabularnewline
57 & 40 & 40.2625459372633 & -0.801948470083671 & -0.801948470083671 & 0.0387744138807999 \tabularnewline
58 & 29 & 35.8754051831982 & -0.822793627496868 & -0.82279362749687 & -0.435015167899654 \tabularnewline
59 & 37 & 36.0806287879526 & -0.816936800430113 & -0.816936800430113 & 0.124779243583684 \tabularnewline
60 & 51 & 41.4056602201766 & -0.782464314367231 & -0.782464314367233 & 0.745672599258534 \tabularnewline
61 & 20 & 30.8153767910228 & -0.589909228595362 & 6.48900150656896 & -1.27958596651033 \tabularnewline
62 & 28 & 29.5928933460305 & -0.597939928607467 & -0.597939920359493 & -0.0725787029712286 \tabularnewline
63 & 13 & 23.164318361747 & -0.654977398226755 & -0.654977398226748 & -0.688443433159342 \tabularnewline
64 & 22 & 22.5639431570149 & -0.654545207337746 & -0.654545207337748 & 0.00653550030070437 \tabularnewline
65 & 25 & 23.3065338929219 & -0.645145844054628 & -0.645145844054624 & 0.168379159951135 \tabularnewline
66 & 13 & 19.3067220444332 & -0.665180436939046 & -0.665180436939046 & -0.405776123970482 \tabularnewline
67 & 16 & 17.9072739548067 & -0.669211175772106 & -0.669211175772101 & -0.088997805249633 \tabularnewline
68 & 13 & 15.9132663173458 & -0.67607144722115 & -0.676071447221147 & -0.160764797990155 \tabularnewline
69 & 16 & 15.7710514391367 & -0.673415469596057 & -0.673415469596057 & 0.0648302156055744 \tabularnewline
70 & 17 & 16.053012929343 & -0.66879187315894 & -0.668791873158942 & 0.116070036267202 \tabularnewline
71 & 9 & 13.264738228185 & -0.678852256961783 & -0.678852256961783 & -0.25757129907588 \tabularnewline
72 & 17 & 14.4742987161485 & -0.670013552339604 & -0.670013552339605 & 0.229532515822176 \tabularnewline
73 & 25 & 15.1084490060929 & -0.691230650868017 & 7.60353715246654 & 0.168326295931945 \tabularnewline
74 & 14 & 14.5224713506258 & -0.690091593107447 & -0.690091586070386 & 0.0121933502775276 \tabularnewline
75 & 8 & 11.8921752142877 & -0.706269824766449 & -0.706269824766442 & -0.230287014283483 \tabularnewline
76 & 7 & 9.88411968725477 & -0.715057227261368 & -0.715057227261371 & -0.156306743954489 \tabularnewline
77 & 10 & 9.74499100991185 & -0.711750640163717 & -0.711750640163713 & 0.0695565559533541 \tabularnewline
78 & 7 & 8.54205051761777 & -0.714255871008771 & -0.714255871008771 & -0.0595069936185826 \tabularnewline
79 & 10 & 8.8993396877821 & -0.709228708278052 & -0.709228708278048 & 0.130042000956359 \tabularnewline
80 & 3 & 6.52786353890461 & -0.716590429938223 & -0.716590429938221 & -0.201933369834727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225851&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]34.0671084703555[/C][C]-0.3156477376112[/C][C]-0.31564773643273[/C][C]-0.291557373576176[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]39.2754264747764[/C][C]0.113018011811798[/C][C]0.113018011811797[/C][C]0.578107229778994[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]37.598824630699[/C][C]0.00609094570264468[/C][C]0.00609094570264402[/C][C]-0.194826689618108[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]34.631991636474[/C][C]-0.138701841066129[/C][C]-0.138701841066129[/C][C]-0.332719357356007[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]37.8293313146577[/C][C]0.000285651519477392[/C][C]0.000285651519476974[/C][C]0.38038993236368[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]54.7345619405174[/C][C]0.626347559617676[/C][C]0.626347559617678[/C][C]1.9516353764764[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]49.2755392288679[/C][C]0.420159263490798[/C][C]0.420159263490799[/C][C]-0.708355608247964[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]48.5140116897234[/C][C]0.382764617347151[/C][C]0.382764617347152[/C][C]-0.138315762885229[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]37.4418372179229[/C][C]0.0393364742820169[/C][C]0.0393364742820169[/C][C]-1.34590443541795[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]42.9493181846827[/C][C]0.196170716465132[/C][C]0.196170716465132[/C][C]0.644219298022962[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]78.0934247304316[/C][C]1.16140934531338[/C][C]1.16140934531338[/C][C]4.12554663295133[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]82.4051286563127[/C][C]0.803640420685588[/C][C]-8.84004463130526[/C][C]0.530695411965217[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]65.0516154538736[/C][C]0.0542732853501441[/C][C]0.0542732968314112[/C][C]-1.77265794194507[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]60.6291425174319[/C][C]-0.0882433304102541[/C][C]-0.0882433304102529[/C][C]-0.486103540722062[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]62.6908737077265[/C][C]-0.0332820718503202[/C][C]-0.0332820718503213[/C][C]0.244989299615891[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]69.6599818196951[/C][C]0.117306980281039[/C][C]0.11730698028104[/C][C]0.817044166013044[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]67.17308678971[/C][C]0.0681274583697432[/C][C]0.0681274583697453[/C][C]-0.307662324700843[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]93.4364576049328[/C][C]0.517994327022605[/C][C]0.51799432702261[/C][C]3.11620637060327[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]85.5313682407899[/C][C]0.382947137616642[/C][C]0.382947137616644[/C][C]-1.0060762650332[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]93.6608381360402[/C][C]0.501077595765577[/C][C]0.501077595765579[/C][C]0.927553151613082[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]80.4581273660299[/C][C]0.29976956481657[/C][C]0.299769564816569[/C][C]-1.64344369571002[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]63.9281949903672[/C][C]0.0595839559520301[/C][C]0.0595839559520303[/C][C]-2.02042797154379[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]59.4900688033274[/C][C]-0.00312603674670147[/C][C]-0.00312603674670232[/C][C]-0.540347392117158[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]65.1690061366869[/C][C]-0.296958210698082[/C][C]3.26654030412787[/C][C]0.816165034835556[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]69.9327176467141[/C][C]-0.163315951503842[/C][C]-0.163315940101643[/C][C]0.539272848072828[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]63.6966200307163[/C][C]-0.286952605915262[/C][C]-0.286952605915258[/C][C]-0.689459782665461[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]71.3619353147134[/C][C]-0.156428681182113[/C][C]-0.156428681182115[/C][C]0.929872417020118[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]62.5185472570446[/C][C]-0.277017599239218[/C][C]-0.277017599239215[/C][C]-1.03081304144508[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]74.9979214865008[/C][C]-0.120646338235284[/C][C]-0.120646338235282[/C][C]1.5255124919275[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]77.9573560306857[/C][C]-0.0861220402522869[/C][C]-0.0861220402522817[/C][C]0.369942031948574[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]73.475655442792[/C][C]-0.132363328479108[/C][C]-0.132363328479106[/C][C]-0.529281927720197[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]68.7937784919967[/C][C]-0.178117807111202[/C][C]-0.178117807111201[/C][C]-0.548659557543251[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]62.8678232799406[/C][C]-0.23407773956578[/C][C]-0.23407773956578[/C][C]-0.693843430592962[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]50.5756045633735[/C][C]-0.348675409784445[/C][C]-0.348675409784444[/C][C]-1.45649856843702[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]59.2017176233615[/C][C]-0.264959389534895[/C][C]-0.264959389534896[/C][C]1.08452209022597[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]61.1893275393182[/C][C]-0.340371241994531[/C][C]3.74408365056518[/C][C]0.306763104209787[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]60.2507045222226[/C][C]-0.351987472069535[/C][C]-0.351987460441162[/C][C]-0.0662477606121289[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]53.1654628989029[/C][C]-0.452754201983734[/C][C]-0.452754201983729[/C][C]-0.780050188402[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]57.5263499869063[/C][C]-0.394569173303685[/C][C]-0.394569173303687[/C][C]0.569637433696047[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]62.1044147511454[/C][C]-0.343605638839358[/C][C]-0.343605638839354[/C][C]0.594750079708083[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]76.1680236999117[/C][C]-0.212868232978044[/C][C]-0.212868232978043[/C][C]1.73293648259962[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]71.2077588711463[/C][C]-0.252364397212901[/C][C]-0.252364397212896[/C][C]-0.572834757853432[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]83.7167841355996[/C][C]-0.152464752018214[/C][C]-0.152464752018212[/C][C]1.54266391842919[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]83.0393997140871[/C][C]-0.156402946263696[/C][C]-0.156402946263696[/C][C]-0.0635256847629037[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]82.2413689964665[/C][C]-0.161074340119854[/C][C]-0.161074340119855[/C][C]-0.0777035770761698[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]79.5098014792203[/C][C]-0.179386209607139[/C][C]-0.179386209607139[/C][C]-0.31143553127414[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]87.8171010738526[/C][C]-0.119908843611901[/C][C]-0.119908843611902[/C][C]1.02853430237211[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]98.1776819100549[/C][C]-0.379309687328358[/C][C]4.17240654390188[/C][C]1.38956317662093[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]98.0252746919512[/C][C]-0.375826138489055[/C][C]-0.375826121875179[/C][C]0.025675290407908[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]89.5513253929136[/C][C]-0.471636485775251[/C][C]-0.471636485775241[/C][C]-0.949061240963247[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]84.7142597466271[/C][C]-0.513397333097122[/C][C]-0.513397333097122[/C][C]-0.520171727475613[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]76.4679980059243[/C][C]-0.576211520663933[/C][C]-0.576211520663926[/C][C]-0.929148217015964[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]61.615389803679[/C][C]-0.679051356677668[/C][C]-0.679051356677665[/C][C]-1.7230240474079[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]51.5421477056269[/C][C]-0.741189246405015[/C][C]-0.741189246405012[/C][C]-1.13660581288418[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]40.7466737049638[/C][C]-0.803861374006648[/C][C]-0.803861374006646[/C][C]-1.21823087211232[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]40.2625459372633[/C][C]-0.801948470083671[/C][C]-0.801948470083671[/C][C]0.0387744138807999[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.8754051831982[/C][C]-0.822793627496868[/C][C]-0.82279362749687[/C][C]-0.435015167899654[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]36.0806287879526[/C][C]-0.816936800430113[/C][C]-0.816936800430113[/C][C]0.124779243583684[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]41.4056602201766[/C][C]-0.782464314367231[/C][C]-0.782464314367233[/C][C]0.745672599258534[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]30.8153767910228[/C][C]-0.589909228595362[/C][C]6.48900150656896[/C][C]-1.27958596651033[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]29.5928933460305[/C][C]-0.597939928607467[/C][C]-0.597939920359493[/C][C]-0.0725787029712286[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]23.164318361747[/C][C]-0.654977398226755[/C][C]-0.654977398226748[/C][C]-0.688443433159342[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]22.5639431570149[/C][C]-0.654545207337746[/C][C]-0.654545207337748[/C][C]0.00653550030070437[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]23.3065338929219[/C][C]-0.645145844054628[/C][C]-0.645145844054624[/C][C]0.168379159951135[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]19.3067220444332[/C][C]-0.665180436939046[/C][C]-0.665180436939046[/C][C]-0.405776123970482[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]17.9072739548067[/C][C]-0.669211175772106[/C][C]-0.669211175772101[/C][C]-0.088997805249633[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]15.9132663173458[/C][C]-0.67607144722115[/C][C]-0.676071447221147[/C][C]-0.160764797990155[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.7710514391367[/C][C]-0.673415469596057[/C][C]-0.673415469596057[/C][C]0.0648302156055744[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]16.053012929343[/C][C]-0.66879187315894[/C][C]-0.668791873158942[/C][C]0.116070036267202[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]13.264738228185[/C][C]-0.678852256961783[/C][C]-0.678852256961783[/C][C]-0.25757129907588[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]14.4742987161485[/C][C]-0.670013552339604[/C][C]-0.670013552339605[/C][C]0.229532515822176[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]15.1084490060929[/C][C]-0.691230650868017[/C][C]7.60353715246654[/C][C]0.168326295931945[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]14.5224713506258[/C][C]-0.690091593107447[/C][C]-0.690091586070386[/C][C]0.0121933502775276[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]11.8921752142877[/C][C]-0.706269824766449[/C][C]-0.706269824766442[/C][C]-0.230287014283483[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]9.88411968725477[/C][C]-0.715057227261368[/C][C]-0.715057227261371[/C][C]-0.156306743954489[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]9.74499100991185[/C][C]-0.711750640163717[/C][C]-0.711750640163713[/C][C]0.0695565559533541[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]8.54205051761777[/C][C]-0.714255871008771[/C][C]-0.714255871008771[/C][C]-0.0595069936185826[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]8.8993396877821[/C][C]-0.709228708278052[/C][C]-0.709228708278048[/C][C]0.130042000956359[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]6.52786353890461[/C][C]-0.716590429938223[/C][C]-0.716590429938221[/C][C]-0.201933369834727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225851&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225851&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
23034.0671084703555-0.3156477376112-0.31564773643273-0.291557373576176
34739.27542647477640.1130180118117980.1130180118117970.578107229778994
43537.5988246306990.006090945702644680.00609094570264402-0.194826689618108
53034.631991636474-0.138701841066129-0.138701841066129-0.332719357356007
64337.82933131465770.0002856515194773920.0002856515194769740.38038993236368
78254.73456194051740.6263475596176760.6263475596176781.9516353764764
84049.27553922886790.4201592634907980.420159263490799-0.708355608247964
94748.51401168972340.3827646173471510.382764617347152-0.138315762885229
101937.44183721792290.03933647428201690.0393364742820169-1.34590443541795
115242.94931818468270.1961707164651320.1961707164651320.644219298022962
1213678.09342473043161.161409345313381.161409345313384.12554663295133
138082.40512865631270.803640420685588-8.840044631305260.530695411965217
144265.05161545387360.05427328535014410.0542732968314112-1.77265794194507
155460.6291425174319-0.0882433304102541-0.0882433304102529-0.486103540722062
166662.6908737077265-0.0332820718503202-0.03328207185032130.244989299615891
178169.65998181969510.1173069802810390.117306980281040.817044166013044
186367.173086789710.06812745836974320.0681274583697453-0.307662324700843
1913793.43645760493280.5179943270226050.517994327022613.11620637060327
207285.53136824078990.3829471376166420.382947137616644-1.0060762650332
2110793.66083813604020.5010775957655770.5010775957655790.927553151613082
225880.45812736602990.299769564816570.299769564816569-1.64344369571002
233663.92819499036720.05958395595203010.0595839559520303-2.02042797154379
245259.4900688033274-0.00312603674670147-0.00312603674670232-0.540347392117158
257965.1690061366869-0.2969582106980823.266540304127870.816165034835556
267769.9327176467141-0.163315951503842-0.1633159401016430.539272848072828
275463.6966200307163-0.286952605915262-0.286952605915258-0.689459782665461
288471.3619353147134-0.156428681182113-0.1564286811821150.929872417020118
294862.5185472570446-0.277017599239218-0.277017599239215-1.03081304144508
309674.9979214865008-0.120646338235284-0.1206463382352821.5255124919275
318377.9573560306857-0.0861220402522869-0.08612204025228170.369942031948574
326673.475655442792-0.132363328479108-0.132363328479106-0.529281927720197
336168.7937784919967-0.178117807111202-0.178117807111201-0.548659557543251
345362.8678232799406-0.23407773956578-0.23407773956578-0.693843430592962
353050.5756045633735-0.348675409784445-0.348675409784444-1.45649856843702
367459.2017176233615-0.264959389534895-0.2649593895348961.08452209022597
376961.1893275393182-0.3403712419945313.744083650565180.306763104209787
385960.2507045222226-0.351987472069535-0.351987460441162-0.0662477606121289
394253.1654628989029-0.452754201983734-0.452754201983729-0.780050188402
406557.5263499869063-0.394569173303685-0.3945691733036870.569637433696047
417062.1044147511454-0.343605638839358-0.3436056388393540.594750079708083
4210076.1680236999117-0.212868232978044-0.2128682329780431.73293648259962
436371.2077588711463-0.252364397212901-0.252364397212896-0.572834757853432
4410583.7167841355996-0.152464752018214-0.1524647520182121.54266391842919
458283.0393997140871-0.156402946263696-0.156402946263696-0.0635256847629037
468182.2413689964665-0.161074340119854-0.161074340119855-0.0777035770761698
477579.5098014792203-0.179386209607139-0.179386209607139-0.31143553127414
4810287.8171010738526-0.119908843611901-0.1199088436119021.02853430237211
4912198.1776819100549-0.3793096873283584.172406543901881.38956317662093
509898.0252746919512-0.375826138489055-0.3758261218751790.025675290407908
517689.5513253929136-0.471636485775251-0.471636485775241-0.949061240963247
527784.7142597466271-0.513397333097122-0.513397333097122-0.520171727475613
536376.4679980059243-0.576211520663933-0.576211520663926-0.929148217015964
543761.615389803679-0.679051356677668-0.679051356677665-1.7230240474079
553551.5421477056269-0.741189246405015-0.741189246405012-1.13660581288418
562340.7466737049638-0.803861374006648-0.803861374006646-1.21823087211232
574040.2625459372633-0.801948470083671-0.8019484700836710.0387744138807999
582935.8754051831982-0.822793627496868-0.82279362749687-0.435015167899654
593736.0806287879526-0.816936800430113-0.8169368004301130.124779243583684
605141.4056602201766-0.782464314367231-0.7824643143672330.745672599258534
612030.8153767910228-0.5899092285953626.48900150656896-1.27958596651033
622829.5928933460305-0.597939928607467-0.597939920359493-0.0725787029712286
631323.164318361747-0.654977398226755-0.654977398226748-0.688443433159342
642222.5639431570149-0.654545207337746-0.6545452073377480.00653550030070437
652523.3065338929219-0.645145844054628-0.6451458440546240.168379159951135
661319.3067220444332-0.665180436939046-0.665180436939046-0.405776123970482
671617.9072739548067-0.669211175772106-0.669211175772101-0.088997805249633
681315.9132663173458-0.67607144722115-0.676071447221147-0.160764797990155
691615.7710514391367-0.673415469596057-0.6734154695960570.0648302156055744
701716.053012929343-0.66879187315894-0.6687918731589420.116070036267202
71913.264738228185-0.678852256961783-0.678852256961783-0.25757129907588
721714.4742987161485-0.670013552339604-0.6700135523396050.229532515822176
732515.1084490060929-0.6912306508680177.603537152466540.168326295931945
741414.5224713506258-0.690091593107447-0.6900915860703860.0121933502775276
75811.8921752142877-0.706269824766449-0.706269824766442-0.230287014283483
7679.88411968725477-0.715057227261368-0.715057227261371-0.156306743954489
77109.74499100991185-0.711750640163717-0.7117506401637130.0695565559533541
7878.54205051761777-0.714255871008771-0.714255871008771-0.0595069936185826
79108.8993396877821-0.709228708278052-0.7092287082780480.130042000956359
8036.52786353890461-0.716590429938223-0.716590429938221-0.201933369834727



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')