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Description of Statistical Computation

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, 03 Dec 2009 10:39:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/03/t1259862056lm289erx5ptthsi.htm/, Retrieved Fri, 26 Apr 2024 21:24:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62969, Retrieved Fri, 26 Apr 2024 21:24:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Structural Time Series Models] [] [2009-11-27 15:02:30] [b98453cac15ba1066b407e146608df68]
- R  D      [Structural Time Series Models] [] [2009-12-03 17:39:51] [429631dabc57c2ce83a6344a979b9063] [Current]
-   PD        [Structural Time Series Models] [] [2009-12-04 12:33:37] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
115.6
111.9
107
107.1
100.6
99.2
108.4
103
99.8
115
90.8
95.9
114.4
108.2
112.6
109.1
105
105
118.5
103.7
112.5
116.6
96.6
101.9
116.5
119.3
115.4
108.5
111.5
108.8
121.8
109.6
112.2
119.6
104.1
105.3
115
124.1
116.8
107.5
115.6
116.2
116.3
119
111.9
118.6
106.9
103.2
118.6
118.7
102.8
100.6
94.9
94.5
102.9
95.3
92.5
102.7
91.5
89.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62969&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62969&T=0

As an alternative you can also use a QR Code:  
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'Gwilym Jenkins' @ 72.249.127.135






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1115.6115.6000
2111.9113.687741698322-0.0471880947713023-1.78774169832153-0.694616887019448
3107109.650650696342-0.300420268999158-2.65065069634248-1.17986137036417
4107.1107.814489771174-0.385595156621494-0.714489771174473-0.566908339019792
5100.6104.419200030583-0.496982609736158-3.81920003058255-1.23743256303235
699.2101.419839936339-0.555269108731592-2.21983993633902-1.05663906033247
7108.4103.566241016991-0.5103825257635024.833758983008891.14565084555901
8103103.965582784848-0.497212433639811-0.965582784848050.385656753962141
999.8102.390220127020-0.512656541279543-2.59022012702034-0.456517879969998
10115107.274609641902-0.4335473393926177.725390358097932.28293493881863
1190.8101.772904482027-0.508966894946389-10.9729044820271-2.14266125805436
1295.997.8872412315243-0.559236776159608-1.98724123152428-1.42734437175178
13114.4101.586003629562-0.64449499104738712.81399637043791.89998440734161
14108.2104.014112406986-0.6637328438335764.185887593013891.34566828907464
15112.6108.473969780654-0.5797237998376164.126030219345772.03765011748638
16109.1109.091962759106-0.5478029283863140.008037240894005930.467741863725307
17105108.696170420485-0.543763716981717-3.696170420485330.0614092171368355
18105108.610425413051-0.533763963761378-3.610425413050610.190500847750431
19118.5110.708530927764-0.4893427936529317.791469072236351.10937591968876
20103.7108.723510441766-0.509550736680363-5.02351044176596-0.633337479110186
21112.5111.240773259540-0.4745497971530291.259226740459841.28309305009180
22116.6109.208001436983-0.4900836080454647.39199856301667-0.66028082431665
2396.6107.514220213086-0.499127164182574-10.9142202130862-0.509582683146522
24101.9106.906780162968-0.499520615158058-5.00678016296778-0.04591202920547
25116.5106.616023582576-0.4995420337053909.883976417423830.0890426185936181
26119.3111.167437954341-0.4859615943621358.132562045659312.13501455937932
27115.4112.460169168803-0.4685260066644142.939830831197350.732356600130681
28108.5111.149181881861-0.481860703171466-2.64918188186052-0.34176275406857
29111.5112.735400796103-0.444007120938674-1.235400796102520.844620551513454
30108.8113.365961531754-0.425049793984041-4.565961531753620.445604412221594
31121.8113.468199095062-0.4169572885873228.331800904938080.221313615354056
32109.6114.228788658551-0.40201038768555-4.628788658550790.497382907054865
33112.2112.356940498885-0.417020417771914-0.156940498884516-0.622230937044485
34119.6111.394492866566-0.4213213085861758.2055071334341-0.230922661251823
35104.1112.604607148882-0.412128282963062-8.504607148881870.690472793856605
36105.3112.430824121390-0.411222634577728-7.130824121389770.100889960881476
37115110.706514287640-0.4155474786204624.29348571235951-0.555210311432967
38124.1112.716362589507-0.40346227407031911.38363741049341.01863608328307
39116.8113.573622595872-0.3930154511588363.22637740412750.523525100593334
40107.5112.883286111940-0.396463177848309-5.38328611194034-0.122521932774575
41115.6114.524937754335-0.368714054443211.075062245665420.840941179444782
42116.2117.417426362713-0.323190776880803-1.217426362712771.35571372482436
43116.3114.219682076951-0.3604244450392312.08031792304885-1.20471851411286
44119116.731999858988-0.3283164346070792.268000141011631.21057249830677
45111.9115.315896545428-0.33824442662793-3.41589654542791-0.459554304134722
46118.6113.269026546096-0.3504322784366245.33097345390403-0.722327441597483
47106.9113.543308157550-0.347016451361964-6.64330815755040.264088344838222
48103.2112.195956954700-0.351478738505313-8.99595695469985-0.422650512881255
49118.6113.340949444540-0.3448076235230815.259050555459630.630972541030574
50118.7111.444858783450-0.3534590979994747.2551412165505-0.65103651598311
51102.8106.394057355863-0.388552057390045-3.5940573558635-1.95933030901455
52100.6105.77669223239-0.390713289251661-5.17669223239005-0.095040005492601
5394.9101.307677568354-0.434911326123989-6.40767756835446-1.69421194787425
5494.597.5523946791226-0.472422598881949-3.05239467912265-1.38471498365671
55102.998.448287977431-0.4575983873950814.451712022568970.573525871408617
5695.395.9559531014323-0.477327684353014-0.655953101432312-0.856299223544018
5792.594.9026439899448-0.482063745010377-2.40264398994485-0.242951611824349
58102.795.5966404765-0.4741320882503577.103359523499960.496520236397815
5991.596.4089929797308-0.466983318817869-4.908992979730840.543191702966212
6089.597.2059100330946-0.46077845252541-7.705910033094570.533312661209458

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 115.6 & 115.6 & 0 & 0 & 0 \tabularnewline
2 & 111.9 & 113.687741698322 & -0.0471880947713023 & -1.78774169832153 & -0.694616887019448 \tabularnewline
3 & 107 & 109.650650696342 & -0.300420268999158 & -2.65065069634248 & -1.17986137036417 \tabularnewline
4 & 107.1 & 107.814489771174 & -0.385595156621494 & -0.714489771174473 & -0.566908339019792 \tabularnewline
5 & 100.6 & 104.419200030583 & -0.496982609736158 & -3.81920003058255 & -1.23743256303235 \tabularnewline
6 & 99.2 & 101.419839936339 & -0.555269108731592 & -2.21983993633902 & -1.05663906033247 \tabularnewline
7 & 108.4 & 103.566241016991 & -0.510382525763502 & 4.83375898300889 & 1.14565084555901 \tabularnewline
8 & 103 & 103.965582784848 & -0.497212433639811 & -0.96558278484805 & 0.385656753962141 \tabularnewline
9 & 99.8 & 102.390220127020 & -0.512656541279543 & -2.59022012702034 & -0.456517879969998 \tabularnewline
10 & 115 & 107.274609641902 & -0.433547339392617 & 7.72539035809793 & 2.28293493881863 \tabularnewline
11 & 90.8 & 101.772904482027 & -0.508966894946389 & -10.9729044820271 & -2.14266125805436 \tabularnewline
12 & 95.9 & 97.8872412315243 & -0.559236776159608 & -1.98724123152428 & -1.42734437175178 \tabularnewline
13 & 114.4 & 101.586003629562 & -0.644494991047387 & 12.8139963704379 & 1.89998440734161 \tabularnewline
14 & 108.2 & 104.014112406986 & -0.663732843833576 & 4.18588759301389 & 1.34566828907464 \tabularnewline
15 & 112.6 & 108.473969780654 & -0.579723799837616 & 4.12603021934577 & 2.03765011748638 \tabularnewline
16 & 109.1 & 109.091962759106 & -0.547802928386314 & 0.00803724089400593 & 0.467741863725307 \tabularnewline
17 & 105 & 108.696170420485 & -0.543763716981717 & -3.69617042048533 & 0.0614092171368355 \tabularnewline
18 & 105 & 108.610425413051 & -0.533763963761378 & -3.61042541305061 & 0.190500847750431 \tabularnewline
19 & 118.5 & 110.708530927764 & -0.489342793652931 & 7.79146907223635 & 1.10937591968876 \tabularnewline
20 & 103.7 & 108.723510441766 & -0.509550736680363 & -5.02351044176596 & -0.633337479110186 \tabularnewline
21 & 112.5 & 111.240773259540 & -0.474549797153029 & 1.25922674045984 & 1.28309305009180 \tabularnewline
22 & 116.6 & 109.208001436983 & -0.490083608045464 & 7.39199856301667 & -0.66028082431665 \tabularnewline
23 & 96.6 & 107.514220213086 & -0.499127164182574 & -10.9142202130862 & -0.509582683146522 \tabularnewline
24 & 101.9 & 106.906780162968 & -0.499520615158058 & -5.00678016296778 & -0.04591202920547 \tabularnewline
25 & 116.5 & 106.616023582576 & -0.499542033705390 & 9.88397641742383 & 0.0890426185936181 \tabularnewline
26 & 119.3 & 111.167437954341 & -0.485961594362135 & 8.13256204565931 & 2.13501455937932 \tabularnewline
27 & 115.4 & 112.460169168803 & -0.468526006664414 & 2.93983083119735 & 0.732356600130681 \tabularnewline
28 & 108.5 & 111.149181881861 & -0.481860703171466 & -2.64918188186052 & -0.34176275406857 \tabularnewline
29 & 111.5 & 112.735400796103 & -0.444007120938674 & -1.23540079610252 & 0.844620551513454 \tabularnewline
30 & 108.8 & 113.365961531754 & -0.425049793984041 & -4.56596153175362 & 0.445604412221594 \tabularnewline
31 & 121.8 & 113.468199095062 & -0.416957288587322 & 8.33180090493808 & 0.221313615354056 \tabularnewline
32 & 109.6 & 114.228788658551 & -0.40201038768555 & -4.62878865855079 & 0.497382907054865 \tabularnewline
33 & 112.2 & 112.356940498885 & -0.417020417771914 & -0.156940498884516 & -0.622230937044485 \tabularnewline
34 & 119.6 & 111.394492866566 & -0.421321308586175 & 8.2055071334341 & -0.230922661251823 \tabularnewline
35 & 104.1 & 112.604607148882 & -0.412128282963062 & -8.50460714888187 & 0.690472793856605 \tabularnewline
36 & 105.3 & 112.430824121390 & -0.411222634577728 & -7.13082412138977 & 0.100889960881476 \tabularnewline
37 & 115 & 110.706514287640 & -0.415547478620462 & 4.29348571235951 & -0.555210311432967 \tabularnewline
38 & 124.1 & 112.716362589507 & -0.403462274070319 & 11.3836374104934 & 1.01863608328307 \tabularnewline
39 & 116.8 & 113.573622595872 & -0.393015451158836 & 3.2263774041275 & 0.523525100593334 \tabularnewline
40 & 107.5 & 112.883286111940 & -0.396463177848309 & -5.38328611194034 & -0.122521932774575 \tabularnewline
41 & 115.6 & 114.524937754335 & -0.36871405444321 & 1.07506224566542 & 0.840941179444782 \tabularnewline
42 & 116.2 & 117.417426362713 & -0.323190776880803 & -1.21742636271277 & 1.35571372482436 \tabularnewline
43 & 116.3 & 114.219682076951 & -0.360424445039231 & 2.08031792304885 & -1.20471851411286 \tabularnewline
44 & 119 & 116.731999858988 & -0.328316434607079 & 2.26800014101163 & 1.21057249830677 \tabularnewline
45 & 111.9 & 115.315896545428 & -0.33824442662793 & -3.41589654542791 & -0.459554304134722 \tabularnewline
46 & 118.6 & 113.269026546096 & -0.350432278436624 & 5.33097345390403 & -0.722327441597483 \tabularnewline
47 & 106.9 & 113.543308157550 & -0.347016451361964 & -6.6433081575504 & 0.264088344838222 \tabularnewline
48 & 103.2 & 112.195956954700 & -0.351478738505313 & -8.99595695469985 & -0.422650512881255 \tabularnewline
49 & 118.6 & 113.340949444540 & -0.344807623523081 & 5.25905055545963 & 0.630972541030574 \tabularnewline
50 & 118.7 & 111.444858783450 & -0.353459097999474 & 7.2551412165505 & -0.65103651598311 \tabularnewline
51 & 102.8 & 106.394057355863 & -0.388552057390045 & -3.5940573558635 & -1.95933030901455 \tabularnewline
52 & 100.6 & 105.77669223239 & -0.390713289251661 & -5.17669223239005 & -0.095040005492601 \tabularnewline
53 & 94.9 & 101.307677568354 & -0.434911326123989 & -6.40767756835446 & -1.69421194787425 \tabularnewline
54 & 94.5 & 97.5523946791226 & -0.472422598881949 & -3.05239467912265 & -1.38471498365671 \tabularnewline
55 & 102.9 & 98.448287977431 & -0.457598387395081 & 4.45171202256897 & 0.573525871408617 \tabularnewline
56 & 95.3 & 95.9559531014323 & -0.477327684353014 & -0.655953101432312 & -0.856299223544018 \tabularnewline
57 & 92.5 & 94.9026439899448 & -0.482063745010377 & -2.40264398994485 & -0.242951611824349 \tabularnewline
58 & 102.7 & 95.5966404765 & -0.474132088250357 & 7.10335952349996 & 0.496520236397815 \tabularnewline
59 & 91.5 & 96.4089929797308 & -0.466983318817869 & -4.90899297973084 & 0.543191702966212 \tabularnewline
60 & 89.5 & 97.2059100330946 & -0.46077845252541 & -7.70591003309457 & 0.533312661209458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62969&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]115.6[/C][C]115.6[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]111.9[/C][C]113.687741698322[/C][C]-0.0471880947713023[/C][C]-1.78774169832153[/C][C]-0.694616887019448[/C][/ROW]
[ROW][C]3[/C][C]107[/C][C]109.650650696342[/C][C]-0.300420268999158[/C][C]-2.65065069634248[/C][C]-1.17986137036417[/C][/ROW]
[ROW][C]4[/C][C]107.1[/C][C]107.814489771174[/C][C]-0.385595156621494[/C][C]-0.714489771174473[/C][C]-0.566908339019792[/C][/ROW]
[ROW][C]5[/C][C]100.6[/C][C]104.419200030583[/C][C]-0.496982609736158[/C][C]-3.81920003058255[/C][C]-1.23743256303235[/C][/ROW]
[ROW][C]6[/C][C]99.2[/C][C]101.419839936339[/C][C]-0.555269108731592[/C][C]-2.21983993633902[/C][C]-1.05663906033247[/C][/ROW]
[ROW][C]7[/C][C]108.4[/C][C]103.566241016991[/C][C]-0.510382525763502[/C][C]4.83375898300889[/C][C]1.14565084555901[/C][/ROW]
[ROW][C]8[/C][C]103[/C][C]103.965582784848[/C][C]-0.497212433639811[/C][C]-0.96558278484805[/C][C]0.385656753962141[/C][/ROW]
[ROW][C]9[/C][C]99.8[/C][C]102.390220127020[/C][C]-0.512656541279543[/C][C]-2.59022012702034[/C][C]-0.456517879969998[/C][/ROW]
[ROW][C]10[/C][C]115[/C][C]107.274609641902[/C][C]-0.433547339392617[/C][C]7.72539035809793[/C][C]2.28293493881863[/C][/ROW]
[ROW][C]11[/C][C]90.8[/C][C]101.772904482027[/C][C]-0.508966894946389[/C][C]-10.9729044820271[/C][C]-2.14266125805436[/C][/ROW]
[ROW][C]12[/C][C]95.9[/C][C]97.8872412315243[/C][C]-0.559236776159608[/C][C]-1.98724123152428[/C][C]-1.42734437175178[/C][/ROW]
[ROW][C]13[/C][C]114.4[/C][C]101.586003629562[/C][C]-0.644494991047387[/C][C]12.8139963704379[/C][C]1.89998440734161[/C][/ROW]
[ROW][C]14[/C][C]108.2[/C][C]104.014112406986[/C][C]-0.663732843833576[/C][C]4.18588759301389[/C][C]1.34566828907464[/C][/ROW]
[ROW][C]15[/C][C]112.6[/C][C]108.473969780654[/C][C]-0.579723799837616[/C][C]4.12603021934577[/C][C]2.03765011748638[/C][/ROW]
[ROW][C]16[/C][C]109.1[/C][C]109.091962759106[/C][C]-0.547802928386314[/C][C]0.00803724089400593[/C][C]0.467741863725307[/C][/ROW]
[ROW][C]17[/C][C]105[/C][C]108.696170420485[/C][C]-0.543763716981717[/C][C]-3.69617042048533[/C][C]0.0614092171368355[/C][/ROW]
[ROW][C]18[/C][C]105[/C][C]108.610425413051[/C][C]-0.533763963761378[/C][C]-3.61042541305061[/C][C]0.190500847750431[/C][/ROW]
[ROW][C]19[/C][C]118.5[/C][C]110.708530927764[/C][C]-0.489342793652931[/C][C]7.79146907223635[/C][C]1.10937591968876[/C][/ROW]
[ROW][C]20[/C][C]103.7[/C][C]108.723510441766[/C][C]-0.509550736680363[/C][C]-5.02351044176596[/C][C]-0.633337479110186[/C][/ROW]
[ROW][C]21[/C][C]112.5[/C][C]111.240773259540[/C][C]-0.474549797153029[/C][C]1.25922674045984[/C][C]1.28309305009180[/C][/ROW]
[ROW][C]22[/C][C]116.6[/C][C]109.208001436983[/C][C]-0.490083608045464[/C][C]7.39199856301667[/C][C]-0.66028082431665[/C][/ROW]
[ROW][C]23[/C][C]96.6[/C][C]107.514220213086[/C][C]-0.499127164182574[/C][C]-10.9142202130862[/C][C]-0.509582683146522[/C][/ROW]
[ROW][C]24[/C][C]101.9[/C][C]106.906780162968[/C][C]-0.499520615158058[/C][C]-5.00678016296778[/C][C]-0.04591202920547[/C][/ROW]
[ROW][C]25[/C][C]116.5[/C][C]106.616023582576[/C][C]-0.499542033705390[/C][C]9.88397641742383[/C][C]0.0890426185936181[/C][/ROW]
[ROW][C]26[/C][C]119.3[/C][C]111.167437954341[/C][C]-0.485961594362135[/C][C]8.13256204565931[/C][C]2.13501455937932[/C][/ROW]
[ROW][C]27[/C][C]115.4[/C][C]112.460169168803[/C][C]-0.468526006664414[/C][C]2.93983083119735[/C][C]0.732356600130681[/C][/ROW]
[ROW][C]28[/C][C]108.5[/C][C]111.149181881861[/C][C]-0.481860703171466[/C][C]-2.64918188186052[/C][C]-0.34176275406857[/C][/ROW]
[ROW][C]29[/C][C]111.5[/C][C]112.735400796103[/C][C]-0.444007120938674[/C][C]-1.23540079610252[/C][C]0.844620551513454[/C][/ROW]
[ROW][C]30[/C][C]108.8[/C][C]113.365961531754[/C][C]-0.425049793984041[/C][C]-4.56596153175362[/C][C]0.445604412221594[/C][/ROW]
[ROW][C]31[/C][C]121.8[/C][C]113.468199095062[/C][C]-0.416957288587322[/C][C]8.33180090493808[/C][C]0.221313615354056[/C][/ROW]
[ROW][C]32[/C][C]109.6[/C][C]114.228788658551[/C][C]-0.40201038768555[/C][C]-4.62878865855079[/C][C]0.497382907054865[/C][/ROW]
[ROW][C]33[/C][C]112.2[/C][C]112.356940498885[/C][C]-0.417020417771914[/C][C]-0.156940498884516[/C][C]-0.622230937044485[/C][/ROW]
[ROW][C]34[/C][C]119.6[/C][C]111.394492866566[/C][C]-0.421321308586175[/C][C]8.2055071334341[/C][C]-0.230922661251823[/C][/ROW]
[ROW][C]35[/C][C]104.1[/C][C]112.604607148882[/C][C]-0.412128282963062[/C][C]-8.50460714888187[/C][C]0.690472793856605[/C][/ROW]
[ROW][C]36[/C][C]105.3[/C][C]112.430824121390[/C][C]-0.411222634577728[/C][C]-7.13082412138977[/C][C]0.100889960881476[/C][/ROW]
[ROW][C]37[/C][C]115[/C][C]110.706514287640[/C][C]-0.415547478620462[/C][C]4.29348571235951[/C][C]-0.555210311432967[/C][/ROW]
[ROW][C]38[/C][C]124.1[/C][C]112.716362589507[/C][C]-0.403462274070319[/C][C]11.3836374104934[/C][C]1.01863608328307[/C][/ROW]
[ROW][C]39[/C][C]116.8[/C][C]113.573622595872[/C][C]-0.393015451158836[/C][C]3.2263774041275[/C][C]0.523525100593334[/C][/ROW]
[ROW][C]40[/C][C]107.5[/C][C]112.883286111940[/C][C]-0.396463177848309[/C][C]-5.38328611194034[/C][C]-0.122521932774575[/C][/ROW]
[ROW][C]41[/C][C]115.6[/C][C]114.524937754335[/C][C]-0.36871405444321[/C][C]1.07506224566542[/C][C]0.840941179444782[/C][/ROW]
[ROW][C]42[/C][C]116.2[/C][C]117.417426362713[/C][C]-0.323190776880803[/C][C]-1.21742636271277[/C][C]1.35571372482436[/C][/ROW]
[ROW][C]43[/C][C]116.3[/C][C]114.219682076951[/C][C]-0.360424445039231[/C][C]2.08031792304885[/C][C]-1.20471851411286[/C][/ROW]
[ROW][C]44[/C][C]119[/C][C]116.731999858988[/C][C]-0.328316434607079[/C][C]2.26800014101163[/C][C]1.21057249830677[/C][/ROW]
[ROW][C]45[/C][C]111.9[/C][C]115.315896545428[/C][C]-0.33824442662793[/C][C]-3.41589654542791[/C][C]-0.459554304134722[/C][/ROW]
[ROW][C]46[/C][C]118.6[/C][C]113.269026546096[/C][C]-0.350432278436624[/C][C]5.33097345390403[/C][C]-0.722327441597483[/C][/ROW]
[ROW][C]47[/C][C]106.9[/C][C]113.543308157550[/C][C]-0.347016451361964[/C][C]-6.6433081575504[/C][C]0.264088344838222[/C][/ROW]
[ROW][C]48[/C][C]103.2[/C][C]112.195956954700[/C][C]-0.351478738505313[/C][C]-8.99595695469985[/C][C]-0.422650512881255[/C][/ROW]
[ROW][C]49[/C][C]118.6[/C][C]113.340949444540[/C][C]-0.344807623523081[/C][C]5.25905055545963[/C][C]0.630972541030574[/C][/ROW]
[ROW][C]50[/C][C]118.7[/C][C]111.444858783450[/C][C]-0.353459097999474[/C][C]7.2551412165505[/C][C]-0.65103651598311[/C][/ROW]
[ROW][C]51[/C][C]102.8[/C][C]106.394057355863[/C][C]-0.388552057390045[/C][C]-3.5940573558635[/C][C]-1.95933030901455[/C][/ROW]
[ROW][C]52[/C][C]100.6[/C][C]105.77669223239[/C][C]-0.390713289251661[/C][C]-5.17669223239005[/C][C]-0.095040005492601[/C][/ROW]
[ROW][C]53[/C][C]94.9[/C][C]101.307677568354[/C][C]-0.434911326123989[/C][C]-6.40767756835446[/C][C]-1.69421194787425[/C][/ROW]
[ROW][C]54[/C][C]94.5[/C][C]97.5523946791226[/C][C]-0.472422598881949[/C][C]-3.05239467912265[/C][C]-1.38471498365671[/C][/ROW]
[ROW][C]55[/C][C]102.9[/C][C]98.448287977431[/C][C]-0.457598387395081[/C][C]4.45171202256897[/C][C]0.573525871408617[/C][/ROW]
[ROW][C]56[/C][C]95.3[/C][C]95.9559531014323[/C][C]-0.477327684353014[/C][C]-0.655953101432312[/C][C]-0.856299223544018[/C][/ROW]
[ROW][C]57[/C][C]92.5[/C][C]94.9026439899448[/C][C]-0.482063745010377[/C][C]-2.40264398994485[/C][C]-0.242951611824349[/C][/ROW]
[ROW][C]58[/C][C]102.7[/C][C]95.5966404765[/C][C]-0.474132088250357[/C][C]7.10335952349996[/C][C]0.496520236397815[/C][/ROW]
[ROW][C]59[/C][C]91.5[/C][C]96.4089929797308[/C][C]-0.466983318817869[/C][C]-4.90899297973084[/C][C]0.543191702966212[/C][/ROW]
[ROW][C]60[/C][C]89.5[/C][C]97.2059100330946[/C][C]-0.46077845252541[/C][C]-7.70591003309457[/C][C]0.533312661209458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62969&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1115.6115.6000
2111.9113.687741698322-0.0471880947713023-1.78774169832153-0.694616887019448
3107109.650650696342-0.300420268999158-2.65065069634248-1.17986137036417
4107.1107.814489771174-0.385595156621494-0.714489771174473-0.566908339019792
5100.6104.419200030583-0.496982609736158-3.81920003058255-1.23743256303235
699.2101.419839936339-0.555269108731592-2.21983993633902-1.05663906033247
7108.4103.566241016991-0.5103825257635024.833758983008891.14565084555901
8103103.965582784848-0.497212433639811-0.965582784848050.385656753962141
999.8102.390220127020-0.512656541279543-2.59022012702034-0.456517879969998
10115107.274609641902-0.4335473393926177.725390358097932.28293493881863
1190.8101.772904482027-0.508966894946389-10.9729044820271-2.14266125805436
1295.997.8872412315243-0.559236776159608-1.98724123152428-1.42734437175178
13114.4101.586003629562-0.64449499104738712.81399637043791.89998440734161
14108.2104.014112406986-0.6637328438335764.185887593013891.34566828907464
15112.6108.473969780654-0.5797237998376164.126030219345772.03765011748638
16109.1109.091962759106-0.5478029283863140.008037240894005930.467741863725307
17105108.696170420485-0.543763716981717-3.696170420485330.0614092171368355
18105108.610425413051-0.533763963761378-3.610425413050610.190500847750431
19118.5110.708530927764-0.4893427936529317.791469072236351.10937591968876
20103.7108.723510441766-0.509550736680363-5.02351044176596-0.633337479110186
21112.5111.240773259540-0.4745497971530291.259226740459841.28309305009180
22116.6109.208001436983-0.4900836080454647.39199856301667-0.66028082431665
2396.6107.514220213086-0.499127164182574-10.9142202130862-0.509582683146522
24101.9106.906780162968-0.499520615158058-5.00678016296778-0.04591202920547
25116.5106.616023582576-0.4995420337053909.883976417423830.0890426185936181
26119.3111.167437954341-0.4859615943621358.132562045659312.13501455937932
27115.4112.460169168803-0.4685260066644142.939830831197350.732356600130681
28108.5111.149181881861-0.481860703171466-2.64918188186052-0.34176275406857
29111.5112.735400796103-0.444007120938674-1.235400796102520.844620551513454
30108.8113.365961531754-0.425049793984041-4.565961531753620.445604412221594
31121.8113.468199095062-0.4169572885873228.331800904938080.221313615354056
32109.6114.228788658551-0.40201038768555-4.628788658550790.497382907054865
33112.2112.356940498885-0.417020417771914-0.156940498884516-0.622230937044485
34119.6111.394492866566-0.4213213085861758.2055071334341-0.230922661251823
35104.1112.604607148882-0.412128282963062-8.504607148881870.690472793856605
36105.3112.430824121390-0.411222634577728-7.130824121389770.100889960881476
37115110.706514287640-0.4155474786204624.29348571235951-0.555210311432967
38124.1112.716362589507-0.40346227407031911.38363741049341.01863608328307
39116.8113.573622595872-0.3930154511588363.22637740412750.523525100593334
40107.5112.883286111940-0.396463177848309-5.38328611194034-0.122521932774575
41115.6114.524937754335-0.368714054443211.075062245665420.840941179444782
42116.2117.417426362713-0.323190776880803-1.217426362712771.35571372482436
43116.3114.219682076951-0.3604244450392312.08031792304885-1.20471851411286
44119116.731999858988-0.3283164346070792.268000141011631.21057249830677
45111.9115.315896545428-0.33824442662793-3.41589654542791-0.459554304134722
46118.6113.269026546096-0.3504322784366245.33097345390403-0.722327441597483
47106.9113.543308157550-0.347016451361964-6.64330815755040.264088344838222
48103.2112.195956954700-0.351478738505313-8.99595695469985-0.422650512881255
49118.6113.340949444540-0.3448076235230815.259050555459630.630972541030574
50118.7111.444858783450-0.3534590979994747.2551412165505-0.65103651598311
51102.8106.394057355863-0.388552057390045-3.5940573558635-1.95933030901455
52100.6105.77669223239-0.390713289251661-5.17669223239005-0.095040005492601
5394.9101.307677568354-0.434911326123989-6.40767756835446-1.69421194787425
5494.597.5523946791226-0.472422598881949-3.05239467912265-1.38471498365671
55102.998.448287977431-0.4575983873950814.451712022568970.573525871408617
5695.395.9559531014323-0.477327684353014-0.655953101432312-0.856299223544018
5792.594.9026439899448-0.482063745010377-2.40264398994485-0.242951611824349
58102.795.5966404765-0.4741320882503577.103359523499960.496520236397815
5991.596.4089929797308-0.466983318817869-4.908992979730840.543191702966212
6089.597.2059100330946-0.46077845252541-7.705910033094570.533312661209458


Figures (Output of Computation)

Input Parameters & R Code

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')