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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 computationTue, 29 Nov 2011 08:34:40 -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/29/t1322574233s4uxleflda4k4np.htm/, Retrieved Tue, 16 Apr 2024 06:43:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=148349, Retrieved Tue, 16 Apr 2024 06:43:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Structural Time Series Models] [WS8] [2011-11-29 13:34:40] [84449ea5bbe6e767918d59f07903f9b5] [Current]
- R  D    [Structural Time Series Models] [WS 8 ] [2011-12-02 11:19:04] [d3c56829b3e69baec30b0d469b5d7237]
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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 time3 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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148349&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148349&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148349&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 time3 seconds
R Server'AstonUniversity' @ aston.wessa.net







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13737000
23034.0671084760339-0.315647737500083-0.315647736320408-0.291557373297855
34739.2754264703310.1130180120017950.1130180120017950.57810722881681
43537.59882463034260.00609094586439640.00609094586439598-0.194826689186506
53034.6319916400494-0.138701841110739-0.138701841110739-0.332719356911607
64337.82933131253030.0002856516264097270.0002856516264100220.380389931704606
78254.73456191674750.626347560649850.626347560649851.95163537398228
84049.27553922269060.420159264532240.420159264532242-0.708355606356589
94748.51401168778450.3827646183940170.382764618394018-0.138315762523038
101937.44183723315850.03933647497451450.0393364749745142-1.34590443367781
115242.94931818651550.1961707171182210.1961707171182220.644219296450611
1213678.09342468130521.161409346688531.161409346688534.12554662749494
138082.40512861785380.803640420746045-8.840044631976940.530695413896699
144265.05161545700970.05427328660639480.0542732981022025-1.77265793824513
155460.6291425254912-0.0882433292423381-0.088243329242337-0.486103540348744
166662.6908737099899-0.0332820707490223-0.03328207074902090.244989298840089
178169.6599818114650.117306981495030.1173069814950350.817044164758606
186367.1730867886630.0681274595688330.0681274595688366-0.307662324045754
1913793.4364575658740.5179943287419570.5179943287419663.11620636654481
207285.53136822940480.3829471393965430.382947139396555-1.00607626215171
2110793.66083811767050.5010775977159280.5010775977159380.927553150780204
225880.458127375830.299769566648340.299769566648349-1.64344369296849
233663.92819502264090.05958395749464170.059583957494641-2.02042796957818
245259.4900688308256-0.00312603544044859-0.00312603544044848-0.540347393016699
257965.1690061520987-0.2969582088444433.266540283721520.816165033317329
267769.9327176486847-0.163315949974213-0.1633159385581080.539272846595519
275463.696620041007-0.286952604417844-0.286952604417842-0.689459782018152
288471.3619353098259-0.156428679699605-0.1564286796996010.929872415242651
294862.5185472672936-0.277017597853295-0.27701759785329-1.03081304004356
309674.9979214739793-0.120646336742614-0.1206463367426051.52551248940031
318377.9573560185148-0.0861220386810155-0.08612203868100750.369942031906162
326673.4756554423252-0.132363326923305-0.132363326923294-0.529281926642959
336168.7937784991393-0.178117805613167-0.178117805613164-0.548659556966716
345362.8678232937318-0.234077738159332-0.234077738159328-0.693843430170238
353050.5756045911645-0.348675408541567-0.348675408541574-1.45649856734107
367459.2017176271318-0.264959388325418-0.2649593883254181.08452208749375
376961.1893275433039-0.3403712407839153.744083637234950.306763104211526
385960.2507045257848-0.351987470897095-0.351987459254997-0.066247760813739
394253.1654629114096-0.452754200831145-0.452754200831148-0.780050187683922
406557.5263499877294-0.394569172192065-0.3945691721920640.569637432311457
417062.1044147443621-0.343605637696904-0.3436056376968960.594750078816598
4210076.1680236737669-0.212868231685964-0.2128682316859481.73293648060448
436371.2077588624377-0.25236439589097-0.252364395890961-0.572834756068961
4410583.7167841105625-0.152464750570409-0.1524647505703891.54266391675115
458283.0393996995518-0.156402944758214-0.156402944758199-0.0635256836864092
468182.2413689887265-0.161074338587761-0.161074338587742-0.077703576459442
477579.5098014787918-0.179386208079512-0.1793862080795-0.311435530670776
4810287.8171010606124-0.119908842039537-0.1199088420395151.02853430095686
4912198.1776818910438-0.3793096856764164.172406525710041.3895631762375
509898.0252746803681-0.375826136746588-0.3758261201126940.0256752910698797
517689.5513253984587-0.471636483978675-0.471636483978645-0.949061239410507
527784.7142597572061-0.513397331351034-0.513397331351008-0.520171727227506
536376.467998024928-0.576211519011183-0.576211519011155-0.929148216472264
543761.6153898382439-0.679051355188246-0.67905135518823-1.72302404623255
553551.5421477424259-0.741189245074906-0.741189245074901-1.13660581313807
562340.746673744239-0.803861372835162-0.803861372835162-1.2182308723515
574040.2625459617518-0.80194846900267-0.8019484690026760.0387744119577759
582935.8754052045394-0.822793626493745-0.822793626493749-0.435015168551769
593736.0806288000063-0.816936799470949-0.8169367994709580.124779242374854
605141.4056602182784-0.78246431340705-0.782464313407050.745672597690209
612030.8153768097132-0.589909227948346.4890014994434-1.27958596447687
622829.5928933586227-0.597939928050207-0.597939919793418-0.072578703769454
631323.1643183786699-0.654977397708129-0.654977397708155-0.688443432908597
642222.5639431676384-0.654545206872247-0.654545206872270.00653549948649092
652523.306533897525-0.645145843610734-0.6451458436107520.168379159219014
661319.3067220526953-0.665180436527215-0.665180436527237-0.405776123705453
671617.9072739612615-0.669211175383106-0.66921117538313-0.0889978055465433
681315.9132663235983-0.676071446853364-0.676071446853388-0.160764798113387
691615.7710514423186-0.673415469238442-0.673415469238470.0648302152090737
701716.0530129299213-0.66879187280381-0.6687918728038310.116070035945486
71913.2647382320071-0.678852256618217-0.678852256618247-0.257571298809853
721714.4742987156403-0.670013551996105-0.670013551996130.229532515330214
732515.108449005177-0.691230650519997.603537148631370.168326295903008
741414.5224713499744-0.690091592759751-0.6900915857156420.0121933502720044
75811.8921752169747-0.70626982441512-0.706269824415147-0.230287013996705
7679.88411969105073-0.715057226921945-0.715057226921972-0.156306743909773
77109.7449910114781-0.711750639831308-0.711750639831330.0695565556643066
7878.54205051946086-0.714255870683049-0.714255870683071-0.059506993644035
79108.89933968733015-0.709228707951349-0.7092287079513720.130042000680163
8036.5278635413411-0.71659042961966-0.716590429619683-0.201933369589595

\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 & 34.0671084760339 & -0.315647737500083 & -0.315647736320408 & -0.291557373297855 \tabularnewline
3 & 47 & 39.275426470331 & 0.113018012001795 & 0.113018012001795 & 0.57810722881681 \tabularnewline
4 & 35 & 37.5988246303426 & 0.0060909458643964 & 0.00609094586439598 & -0.194826689186506 \tabularnewline
5 & 30 & 34.6319916400494 & -0.138701841110739 & -0.138701841110739 & -0.332719356911607 \tabularnewline
6 & 43 & 37.8293313125303 & 0.000285651626409727 & 0.000285651626410022 & 0.380389931704606 \tabularnewline
7 & 82 & 54.7345619167475 & 0.62634756064985 & 0.62634756064985 & 1.95163537398228 \tabularnewline
8 & 40 & 49.2755392226906 & 0.42015926453224 & 0.420159264532242 & -0.708355606356589 \tabularnewline
9 & 47 & 48.5140116877845 & 0.382764618394017 & 0.382764618394018 & -0.138315762523038 \tabularnewline
10 & 19 & 37.4418372331585 & 0.0393364749745145 & 0.0393364749745142 & -1.34590443367781 \tabularnewline
11 & 52 & 42.9493181865155 & 0.196170717118221 & 0.196170717118222 & 0.644219296450611 \tabularnewline
12 & 136 & 78.0934246813052 & 1.16140934668853 & 1.16140934668853 & 4.12554662749494 \tabularnewline
13 & 80 & 82.4051286178538 & 0.803640420746045 & -8.84004463197694 & 0.530695413896699 \tabularnewline
14 & 42 & 65.0516154570097 & 0.0542732866063948 & 0.0542732981022025 & -1.77265793824513 \tabularnewline
15 & 54 & 60.6291425254912 & -0.0882433292423381 & -0.088243329242337 & -0.486103540348744 \tabularnewline
16 & 66 & 62.6908737099899 & -0.0332820707490223 & -0.0332820707490209 & 0.244989298840089 \tabularnewline
17 & 81 & 69.659981811465 & 0.11730698149503 & 0.117306981495035 & 0.817044164758606 \tabularnewline
18 & 63 & 67.173086788663 & 0.068127459568833 & 0.0681274595688366 & -0.307662324045754 \tabularnewline
19 & 137 & 93.436457565874 & 0.517994328741957 & 0.517994328741966 & 3.11620636654481 \tabularnewline
20 & 72 & 85.5313682294048 & 0.382947139396543 & 0.382947139396555 & -1.00607626215171 \tabularnewline
21 & 107 & 93.6608381176705 & 0.501077597715928 & 0.501077597715938 & 0.927553150780204 \tabularnewline
22 & 58 & 80.45812737583 & 0.29976956664834 & 0.299769566648349 & -1.64344369296849 \tabularnewline
23 & 36 & 63.9281950226409 & 0.0595839574946417 & 0.059583957494641 & -2.02042796957818 \tabularnewline
24 & 52 & 59.4900688308256 & -0.00312603544044859 & -0.00312603544044848 & -0.540347393016699 \tabularnewline
25 & 79 & 65.1690061520987 & -0.296958208844443 & 3.26654028372152 & 0.816165033317329 \tabularnewline
26 & 77 & 69.9327176486847 & -0.163315949974213 & -0.163315938558108 & 0.539272846595519 \tabularnewline
27 & 54 & 63.696620041007 & -0.286952604417844 & -0.286952604417842 & -0.689459782018152 \tabularnewline
28 & 84 & 71.3619353098259 & -0.156428679699605 & -0.156428679699601 & 0.929872415242651 \tabularnewline
29 & 48 & 62.5185472672936 & -0.277017597853295 & -0.27701759785329 & -1.03081304004356 \tabularnewline
30 & 96 & 74.9979214739793 & -0.120646336742614 & -0.120646336742605 & 1.52551248940031 \tabularnewline
31 & 83 & 77.9573560185148 & -0.0861220386810155 & -0.0861220386810075 & 0.369942031906162 \tabularnewline
32 & 66 & 73.4756554423252 & -0.132363326923305 & -0.132363326923294 & -0.529281926642959 \tabularnewline
33 & 61 & 68.7937784991393 & -0.178117805613167 & -0.178117805613164 & -0.548659556966716 \tabularnewline
34 & 53 & 62.8678232937318 & -0.234077738159332 & -0.234077738159328 & -0.693843430170238 \tabularnewline
35 & 30 & 50.5756045911645 & -0.348675408541567 & -0.348675408541574 & -1.45649856734107 \tabularnewline
36 & 74 & 59.2017176271318 & -0.264959388325418 & -0.264959388325418 & 1.08452208749375 \tabularnewline
37 & 69 & 61.1893275433039 & -0.340371240783915 & 3.74408363723495 & 0.306763104211526 \tabularnewline
38 & 59 & 60.2507045257848 & -0.351987470897095 & -0.351987459254997 & -0.066247760813739 \tabularnewline
39 & 42 & 53.1654629114096 & -0.452754200831145 & -0.452754200831148 & -0.780050187683922 \tabularnewline
40 & 65 & 57.5263499877294 & -0.394569172192065 & -0.394569172192064 & 0.569637432311457 \tabularnewline
41 & 70 & 62.1044147443621 & -0.343605637696904 & -0.343605637696896 & 0.594750078816598 \tabularnewline
42 & 100 & 76.1680236737669 & -0.212868231685964 & -0.212868231685948 & 1.73293648060448 \tabularnewline
43 & 63 & 71.2077588624377 & -0.25236439589097 & -0.252364395890961 & -0.572834756068961 \tabularnewline
44 & 105 & 83.7167841105625 & -0.152464750570409 & -0.152464750570389 & 1.54266391675115 \tabularnewline
45 & 82 & 83.0393996995518 & -0.156402944758214 & -0.156402944758199 & -0.0635256836864092 \tabularnewline
46 & 81 & 82.2413689887265 & -0.161074338587761 & -0.161074338587742 & -0.077703576459442 \tabularnewline
47 & 75 & 79.5098014787918 & -0.179386208079512 & -0.1793862080795 & -0.311435530670776 \tabularnewline
48 & 102 & 87.8171010606124 & -0.119908842039537 & -0.119908842039515 & 1.02853430095686 \tabularnewline
49 & 121 & 98.1776818910438 & -0.379309685676416 & 4.17240652571004 & 1.3895631762375 \tabularnewline
50 & 98 & 98.0252746803681 & -0.375826136746588 & -0.375826120112694 & 0.0256752910698797 \tabularnewline
51 & 76 & 89.5513253984587 & -0.471636483978675 & -0.471636483978645 & -0.949061239410507 \tabularnewline
52 & 77 & 84.7142597572061 & -0.513397331351034 & -0.513397331351008 & -0.520171727227506 \tabularnewline
53 & 63 & 76.467998024928 & -0.576211519011183 & -0.576211519011155 & -0.929148216472264 \tabularnewline
54 & 37 & 61.6153898382439 & -0.679051355188246 & -0.67905135518823 & -1.72302404623255 \tabularnewline
55 & 35 & 51.5421477424259 & -0.741189245074906 & -0.741189245074901 & -1.13660581313807 \tabularnewline
56 & 23 & 40.746673744239 & -0.803861372835162 & -0.803861372835162 & -1.2182308723515 \tabularnewline
57 & 40 & 40.2625459617518 & -0.80194846900267 & -0.801948469002676 & 0.0387744119577759 \tabularnewline
58 & 29 & 35.8754052045394 & -0.822793626493745 & -0.822793626493749 & -0.435015168551769 \tabularnewline
59 & 37 & 36.0806288000063 & -0.816936799470949 & -0.816936799470958 & 0.124779242374854 \tabularnewline
60 & 51 & 41.4056602182784 & -0.78246431340705 & -0.78246431340705 & 0.745672597690209 \tabularnewline
61 & 20 & 30.8153768097132 & -0.58990922794834 & 6.4890014994434 & -1.27958596447687 \tabularnewline
62 & 28 & 29.5928933586227 & -0.597939928050207 & -0.597939919793418 & -0.072578703769454 \tabularnewline
63 & 13 & 23.1643183786699 & -0.654977397708129 & -0.654977397708155 & -0.688443432908597 \tabularnewline
64 & 22 & 22.5639431676384 & -0.654545206872247 & -0.65454520687227 & 0.00653549948649092 \tabularnewline
65 & 25 & 23.306533897525 & -0.645145843610734 & -0.645145843610752 & 0.168379159219014 \tabularnewline
66 & 13 & 19.3067220526953 & -0.665180436527215 & -0.665180436527237 & -0.405776123705453 \tabularnewline
67 & 16 & 17.9072739612615 & -0.669211175383106 & -0.66921117538313 & -0.0889978055465433 \tabularnewline
68 & 13 & 15.9132663235983 & -0.676071446853364 & -0.676071446853388 & -0.160764798113387 \tabularnewline
69 & 16 & 15.7710514423186 & -0.673415469238442 & -0.67341546923847 & 0.0648302152090737 \tabularnewline
70 & 17 & 16.0530129299213 & -0.66879187280381 & -0.668791872803831 & 0.116070035945486 \tabularnewline
71 & 9 & 13.2647382320071 & -0.678852256618217 & -0.678852256618247 & -0.257571298809853 \tabularnewline
72 & 17 & 14.4742987156403 & -0.670013551996105 & -0.67001355199613 & 0.229532515330214 \tabularnewline
73 & 25 & 15.108449005177 & -0.69123065051999 & 7.60353714863137 & 0.168326295903008 \tabularnewline
74 & 14 & 14.5224713499744 & -0.690091592759751 & -0.690091585715642 & 0.0121933502720044 \tabularnewline
75 & 8 & 11.8921752169747 & -0.70626982441512 & -0.706269824415147 & -0.230287013996705 \tabularnewline
76 & 7 & 9.88411969105073 & -0.715057226921945 & -0.715057226921972 & -0.156306743909773 \tabularnewline
77 & 10 & 9.7449910114781 & -0.711750639831308 & -0.71175063983133 & 0.0695565556643066 \tabularnewline
78 & 7 & 8.54205051946086 & -0.714255870683049 & -0.714255870683071 & -0.059506993644035 \tabularnewline
79 & 10 & 8.89933968733015 & -0.709228707951349 & -0.709228707951372 & 0.130042000680163 \tabularnewline
80 & 3 & 6.5278635413411 & -0.71659042961966 & -0.716590429619683 & -0.201933369589595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=148349&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]34.0671084760339[/C][C]-0.315647737500083[/C][C]-0.315647736320408[/C][C]-0.291557373297855[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]39.275426470331[/C][C]0.113018012001795[/C][C]0.113018012001795[/C][C]0.57810722881681[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]37.5988246303426[/C][C]0.0060909458643964[/C][C]0.00609094586439598[/C][C]-0.194826689186506[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]34.6319916400494[/C][C]-0.138701841110739[/C][C]-0.138701841110739[/C][C]-0.332719356911607[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]37.8293313125303[/C][C]0.000285651626409727[/C][C]0.000285651626410022[/C][C]0.380389931704606[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]54.7345619167475[/C][C]0.62634756064985[/C][C]0.62634756064985[/C][C]1.95163537398228[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]49.2755392226906[/C][C]0.42015926453224[/C][C]0.420159264532242[/C][C]-0.708355606356589[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]48.5140116877845[/C][C]0.382764618394017[/C][C]0.382764618394018[/C][C]-0.138315762523038[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]37.4418372331585[/C][C]0.0393364749745145[/C][C]0.0393364749745142[/C][C]-1.34590443367781[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]42.9493181865155[/C][C]0.196170717118221[/C][C]0.196170717118222[/C][C]0.644219296450611[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]78.0934246813052[/C][C]1.16140934668853[/C][C]1.16140934668853[/C][C]4.12554662749494[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]82.4051286178538[/C][C]0.803640420746045[/C][C]-8.84004463197694[/C][C]0.530695413896699[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]65.0516154570097[/C][C]0.0542732866063948[/C][C]0.0542732981022025[/C][C]-1.77265793824513[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]60.6291425254912[/C][C]-0.0882433292423381[/C][C]-0.088243329242337[/C][C]-0.486103540348744[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]62.6908737099899[/C][C]-0.0332820707490223[/C][C]-0.0332820707490209[/C][C]0.244989298840089[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]69.659981811465[/C][C]0.11730698149503[/C][C]0.117306981495035[/C][C]0.817044164758606[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]67.173086788663[/C][C]0.068127459568833[/C][C]0.0681274595688366[/C][C]-0.307662324045754[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]93.436457565874[/C][C]0.517994328741957[/C][C]0.517994328741966[/C][C]3.11620636654481[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]85.5313682294048[/C][C]0.382947139396543[/C][C]0.382947139396555[/C][C]-1.00607626215171[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]93.6608381176705[/C][C]0.501077597715928[/C][C]0.501077597715938[/C][C]0.927553150780204[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]80.45812737583[/C][C]0.29976956664834[/C][C]0.299769566648349[/C][C]-1.64344369296849[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]63.9281950226409[/C][C]0.0595839574946417[/C][C]0.059583957494641[/C][C]-2.02042796957818[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]59.4900688308256[/C][C]-0.00312603544044859[/C][C]-0.00312603544044848[/C][C]-0.540347393016699[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]65.1690061520987[/C][C]-0.296958208844443[/C][C]3.26654028372152[/C][C]0.816165033317329[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]69.9327176486847[/C][C]-0.163315949974213[/C][C]-0.163315938558108[/C][C]0.539272846595519[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]63.696620041007[/C][C]-0.286952604417844[/C][C]-0.286952604417842[/C][C]-0.689459782018152[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]71.3619353098259[/C][C]-0.156428679699605[/C][C]-0.156428679699601[/C][C]0.929872415242651[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]62.5185472672936[/C][C]-0.277017597853295[/C][C]-0.27701759785329[/C][C]-1.03081304004356[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]74.9979214739793[/C][C]-0.120646336742614[/C][C]-0.120646336742605[/C][C]1.52551248940031[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]77.9573560185148[/C][C]-0.0861220386810155[/C][C]-0.0861220386810075[/C][C]0.369942031906162[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]73.4756554423252[/C][C]-0.132363326923305[/C][C]-0.132363326923294[/C][C]-0.529281926642959[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]68.7937784991393[/C][C]-0.178117805613167[/C][C]-0.178117805613164[/C][C]-0.548659556966716[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]62.8678232937318[/C][C]-0.234077738159332[/C][C]-0.234077738159328[/C][C]-0.693843430170238[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]50.5756045911645[/C][C]-0.348675408541567[/C][C]-0.348675408541574[/C][C]-1.45649856734107[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]59.2017176271318[/C][C]-0.264959388325418[/C][C]-0.264959388325418[/C][C]1.08452208749375[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]61.1893275433039[/C][C]-0.340371240783915[/C][C]3.74408363723495[/C][C]0.306763104211526[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]60.2507045257848[/C][C]-0.351987470897095[/C][C]-0.351987459254997[/C][C]-0.066247760813739[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]53.1654629114096[/C][C]-0.452754200831145[/C][C]-0.452754200831148[/C][C]-0.780050187683922[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]57.5263499877294[/C][C]-0.394569172192065[/C][C]-0.394569172192064[/C][C]0.569637432311457[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]62.1044147443621[/C][C]-0.343605637696904[/C][C]-0.343605637696896[/C][C]0.594750078816598[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]76.1680236737669[/C][C]-0.212868231685964[/C][C]-0.212868231685948[/C][C]1.73293648060448[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]71.2077588624377[/C][C]-0.25236439589097[/C][C]-0.252364395890961[/C][C]-0.572834756068961[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]83.7167841105625[/C][C]-0.152464750570409[/C][C]-0.152464750570389[/C][C]1.54266391675115[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]83.0393996995518[/C][C]-0.156402944758214[/C][C]-0.156402944758199[/C][C]-0.0635256836864092[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]82.2413689887265[/C][C]-0.161074338587761[/C][C]-0.161074338587742[/C][C]-0.077703576459442[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]79.5098014787918[/C][C]-0.179386208079512[/C][C]-0.1793862080795[/C][C]-0.311435530670776[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]87.8171010606124[/C][C]-0.119908842039537[/C][C]-0.119908842039515[/C][C]1.02853430095686[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]98.1776818910438[/C][C]-0.379309685676416[/C][C]4.17240652571004[/C][C]1.3895631762375[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]98.0252746803681[/C][C]-0.375826136746588[/C][C]-0.375826120112694[/C][C]0.0256752910698797[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]89.5513253984587[/C][C]-0.471636483978675[/C][C]-0.471636483978645[/C][C]-0.949061239410507[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]84.7142597572061[/C][C]-0.513397331351034[/C][C]-0.513397331351008[/C][C]-0.520171727227506[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]76.467998024928[/C][C]-0.576211519011183[/C][C]-0.576211519011155[/C][C]-0.929148216472264[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]61.6153898382439[/C][C]-0.679051355188246[/C][C]-0.67905135518823[/C][C]-1.72302404623255[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]51.5421477424259[/C][C]-0.741189245074906[/C][C]-0.741189245074901[/C][C]-1.13660581313807[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]40.746673744239[/C][C]-0.803861372835162[/C][C]-0.803861372835162[/C][C]-1.2182308723515[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]40.2625459617518[/C][C]-0.80194846900267[/C][C]-0.801948469002676[/C][C]0.0387744119577759[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.8754052045394[/C][C]-0.822793626493745[/C][C]-0.822793626493749[/C][C]-0.435015168551769[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]36.0806288000063[/C][C]-0.816936799470949[/C][C]-0.816936799470958[/C][C]0.124779242374854[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]41.4056602182784[/C][C]-0.78246431340705[/C][C]-0.78246431340705[/C][C]0.745672597690209[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]30.8153768097132[/C][C]-0.58990922794834[/C][C]6.4890014994434[/C][C]-1.27958596447687[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]29.5928933586227[/C][C]-0.597939928050207[/C][C]-0.597939919793418[/C][C]-0.072578703769454[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]23.1643183786699[/C][C]-0.654977397708129[/C][C]-0.654977397708155[/C][C]-0.688443432908597[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]22.5639431676384[/C][C]-0.654545206872247[/C][C]-0.65454520687227[/C][C]0.00653549948649092[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]23.306533897525[/C][C]-0.645145843610734[/C][C]-0.645145843610752[/C][C]0.168379159219014[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]19.3067220526953[/C][C]-0.665180436527215[/C][C]-0.665180436527237[/C][C]-0.405776123705453[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]17.9072739612615[/C][C]-0.669211175383106[/C][C]-0.66921117538313[/C][C]-0.0889978055465433[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]15.9132663235983[/C][C]-0.676071446853364[/C][C]-0.676071446853388[/C][C]-0.160764798113387[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.7710514423186[/C][C]-0.673415469238442[/C][C]-0.67341546923847[/C][C]0.0648302152090737[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]16.0530129299213[/C][C]-0.66879187280381[/C][C]-0.668791872803831[/C][C]0.116070035945486[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]13.2647382320071[/C][C]-0.678852256618217[/C][C]-0.678852256618247[/C][C]-0.257571298809853[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]14.4742987156403[/C][C]-0.670013551996105[/C][C]-0.67001355199613[/C][C]0.229532515330214[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]15.108449005177[/C][C]-0.69123065051999[/C][C]7.60353714863137[/C][C]0.168326295903008[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]14.5224713499744[/C][C]-0.690091592759751[/C][C]-0.690091585715642[/C][C]0.0121933502720044[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]11.8921752169747[/C][C]-0.70626982441512[/C][C]-0.706269824415147[/C][C]-0.230287013996705[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]9.88411969105073[/C][C]-0.715057226921945[/C][C]-0.715057226921972[/C][C]-0.156306743909773[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]9.7449910114781[/C][C]-0.711750639831308[/C][C]-0.71175063983133[/C][C]0.0695565556643066[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]8.54205051946086[/C][C]-0.714255870683049[/C][C]-0.714255870683071[/C][C]-0.059506993644035[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]8.89933968733015[/C][C]-0.709228707951349[/C][C]-0.709228707951372[/C][C]0.130042000680163[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]6.5278635413411[/C][C]-0.71659042961966[/C][C]-0.716590429619683[/C][C]-0.201933369589595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=148349&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=148349&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
23034.0671084760339-0.315647737500083-0.315647736320408-0.291557373297855
34739.2754264703310.1130180120017950.1130180120017950.57810722881681
43537.59882463034260.00609094586439640.00609094586439598-0.194826689186506
53034.6319916400494-0.138701841110739-0.138701841110739-0.332719356911607
64337.82933131253030.0002856516264097270.0002856516264100220.380389931704606
78254.73456191674750.626347560649850.626347560649851.95163537398228
84049.27553922269060.420159264532240.420159264532242-0.708355606356589
94748.51401168778450.3827646183940170.382764618394018-0.138315762523038
101937.44183723315850.03933647497451450.0393364749745142-1.34590443367781
115242.94931818651550.1961707171182210.1961707171182220.644219296450611
1213678.09342468130521.161409346688531.161409346688534.12554662749494
138082.40512861785380.803640420746045-8.840044631976940.530695413896699
144265.05161545700970.05427328660639480.0542732981022025-1.77265793824513
155460.6291425254912-0.0882433292423381-0.088243329242337-0.486103540348744
166662.6908737099899-0.0332820707490223-0.03328207074902090.244989298840089
178169.6599818114650.117306981495030.1173069814950350.817044164758606
186367.1730867886630.0681274595688330.0681274595688366-0.307662324045754
1913793.4364575658740.5179943287419570.5179943287419663.11620636654481
207285.53136822940480.3829471393965430.382947139396555-1.00607626215171
2110793.66083811767050.5010775977159280.5010775977159380.927553150780204
225880.458127375830.299769566648340.299769566648349-1.64344369296849
233663.92819502264090.05958395749464170.059583957494641-2.02042796957818
245259.4900688308256-0.00312603544044859-0.00312603544044848-0.540347393016699
257965.1690061520987-0.2969582088444433.266540283721520.816165033317329
267769.9327176486847-0.163315949974213-0.1633159385581080.539272846595519
275463.696620041007-0.286952604417844-0.286952604417842-0.689459782018152
288471.3619353098259-0.156428679699605-0.1564286796996010.929872415242651
294862.5185472672936-0.277017597853295-0.27701759785329-1.03081304004356
309674.9979214739793-0.120646336742614-0.1206463367426051.52551248940031
318377.9573560185148-0.0861220386810155-0.08612203868100750.369942031906162
326673.4756554423252-0.132363326923305-0.132363326923294-0.529281926642959
336168.7937784991393-0.178117805613167-0.178117805613164-0.548659556966716
345362.8678232937318-0.234077738159332-0.234077738159328-0.693843430170238
353050.5756045911645-0.348675408541567-0.348675408541574-1.45649856734107
367459.2017176271318-0.264959388325418-0.2649593883254181.08452208749375
376961.1893275433039-0.3403712407839153.744083637234950.306763104211526
385960.2507045257848-0.351987470897095-0.351987459254997-0.066247760813739
394253.1654629114096-0.452754200831145-0.452754200831148-0.780050187683922
406557.5263499877294-0.394569172192065-0.3945691721920640.569637432311457
417062.1044147443621-0.343605637696904-0.3436056376968960.594750078816598
4210076.1680236737669-0.212868231685964-0.2128682316859481.73293648060448
436371.2077588624377-0.25236439589097-0.252364395890961-0.572834756068961
4410583.7167841105625-0.152464750570409-0.1524647505703891.54266391675115
458283.0393996995518-0.156402944758214-0.156402944758199-0.0635256836864092
468182.2413689887265-0.161074338587761-0.161074338587742-0.077703576459442
477579.5098014787918-0.179386208079512-0.1793862080795-0.311435530670776
4810287.8171010606124-0.119908842039537-0.1199088420395151.02853430095686
4912198.1776818910438-0.3793096856764164.172406525710041.3895631762375
509898.0252746803681-0.375826136746588-0.3758261201126940.0256752910698797
517689.5513253984587-0.471636483978675-0.471636483978645-0.949061239410507
527784.7142597572061-0.513397331351034-0.513397331351008-0.520171727227506
536376.467998024928-0.576211519011183-0.576211519011155-0.929148216472264
543761.6153898382439-0.679051355188246-0.67905135518823-1.72302404623255
553551.5421477424259-0.741189245074906-0.741189245074901-1.13660581313807
562340.746673744239-0.803861372835162-0.803861372835162-1.2182308723515
574040.2625459617518-0.80194846900267-0.8019484690026760.0387744119577759
582935.8754052045394-0.822793626493745-0.822793626493749-0.435015168551769
593736.0806288000063-0.816936799470949-0.8169367994709580.124779242374854
605141.4056602182784-0.78246431340705-0.782464313407050.745672597690209
612030.8153768097132-0.589909227948346.4890014994434-1.27958596447687
622829.5928933586227-0.597939928050207-0.597939919793418-0.072578703769454
631323.1643183786699-0.654977397708129-0.654977397708155-0.688443432908597
642222.5639431676384-0.654545206872247-0.654545206872270.00653549948649092
652523.306533897525-0.645145843610734-0.6451458436107520.168379159219014
661319.3067220526953-0.665180436527215-0.665180436527237-0.405776123705453
671617.9072739612615-0.669211175383106-0.66921117538313-0.0889978055465433
681315.9132663235983-0.676071446853364-0.676071446853388-0.160764798113387
691615.7710514423186-0.673415469238442-0.673415469238470.0648302152090737
701716.0530129299213-0.66879187280381-0.6687918728038310.116070035945486
71913.2647382320071-0.678852256618217-0.678852256618247-0.257571298809853
721714.4742987156403-0.670013551996105-0.670013551996130.229532515330214
732515.108449005177-0.691230650519997.603537148631370.168326295903008
741414.5224713499744-0.690091592759751-0.6900915857156420.0121933502720044
75811.8921752169747-0.70626982441512-0.706269824415147-0.230287013996705
7679.88411969105073-0.715057226921945-0.715057226921972-0.156306743909773
77109.7449910114781-0.711750639831308-0.711750639831330.0695565556643066
7878.54205051946086-0.714255870683049-0.714255870683071-0.059506993644035
79108.89933968733015-0.709228707951349-0.7092287079513720.130042000680163
8036.5278635413411-0.71659042961966-0.716590429619683-0.201933369589595



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')