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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationSun, 27 Nov 2011 05:13:01 -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/27/t13223887975q76zjxmr9yob1j.htm/, Retrieved Fri, 26 Apr 2024 13:55:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147456, Retrieved Fri, 26 Apr 2024 13:55:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Structural Time Series Models] [HPC Retail Sales] [2008-03-06 16:52:55] [74be16979710d4c4e7c6647856088456]
- R  D    [Structural Time Series Models] [HPC Retail Sales] [2008-03-08 11:33:35] [74be16979710d4c4e7c6647856088456]
- RM D        [Structural Time Series Models] [B4] [2011-11-27 10:13:01] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
39923931
35810356.5
35492936.3
38937434.1
40059102.8
37708710.2
41570965.7
36333563
34181220.1
42593543.9
43119727.6
38497690.9
45473273.4
38399780.4
38882302.6
44051120.6
41677559.9
40699203.5
44150027.6
38225518.7
35447405.7
43075518.3
42302792
39743541.7
44670641.2
37123384
37668266.4
46117528.8
42273156.4
39404153.2
45799994.5
38602505.2
39454830.1
47427901.4
46497980.9
45057149.4
50615569.2
43033396.2
46013056.5
54222266.3
46417306.4
51046271.8
51201279.6
43475288.7
44968981.1
53939345.4
54549319.7
54072107.3
58434230.1
51158751
50039368
57872617.4
51642978.8
54534465.9
56094697.8
48189983.1
47492381
52987449.1
55719803.5
53922860.5
54931231.9

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'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147456&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147456&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147456&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'Herman Ole Andreas Wold' @ wold.wessa.net






Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
13992393139923931000
235810356.537764212.219464-57894.8477786084-1953855.71946401-1.85324194406532
335492936.335948409.7974761-172407.791926949-455473.497476138-1.24371674890468
438937434.136954121.956554-103298.4208269391983312.143446021.03837901669124
540059102.838500315.8447563-32194.50391752151558786.955243711.60720442533046
637708710.238574540.6934185-28714.4404800565-865830.493418540.10583441533859
741570965.739875012.84729679922.540880732791695952.852703311.32163570838708
83633356338728523.0406538-24227.0460371257-2394960.0406538-1.14559149959861
934181220.136485098.2718056-94335.025723006-2303878.1718056-2.18954178727766
1042593543.938496705.8282835-23001.83127192634096838.07171652.07029376189452
1143119727.641029752.053404768695.62066209072089975.546595292.50522129747492
1238497690.940645361.214418151685.5488044167-2147670.31441813-0.442949349072746
1345473273.441907485.139787565708.67356298223565788.260212541.23962204349156
1438399780.441375903.822314250557.7875903929-2976123.42231425-0.601223837079506
1538882302.640875491.519005626412.6741238436-1993188.91900557-0.505473163855573
1644051120.641746437.801687670016.14834305972304682.798312430.763300234590609
1741677559.941375061.592192147151.5522825132302498.307807874-0.411868396130859
1840699203.541504897.614962151207.5174906838-805694.1149621020.0789599868772808
1944150027.641461468.805732346804.24300278722688558.79426767-0.0910122903019198
2038225518.740795893.38121114570.1158616406-2570374.68121097-0.685045385652672
2135447405.740087680.7244315-17903.9775265402-4640275.02443146-0.693755189948033
2243075518.340047160.6342335-18917.84570778673028357.66576647-0.0216552787517687
234230279240093501.6254371-16030.00702287412209290.374562910.0623233682017234
2439743541.741071009.637419826670.8817180106-1327467.937419770.949029140712565
2544670641.241138556.702765428385.73008217363532084.497234550.0393294040947425
263712338440682536.0372986949.13087806987-3559152.03729801-0.464005451023557
2737668266.440333540.1152299-10210.432063439-2665273.71522994-0.333632714324788
2846117528.841642662.308672957248.55375900024474866.491327151.22155976872588
2942273156.442071291.3426776639.9400545626201865.0573299960.345997910813962
3039404153.241279121.866183331499.2873061095-1874968.66618326-0.818714514947036
3145799994.541627487.089787647706.08419861484172507.410212430.300503292012853
3238602505.241357456.701189831716.4622773175-2754951.50118984-0.301559062257929
3339454830.142481001.181576285939.051352114-3026171.081576241.03417650398185
3447427901.443673800.7693417140313.3312811253754100.630658331.04585903022611
3546497980.944462871.6083534171887.0648739042035109.291646620.612326506236152
3645057149.445456212.9789765211643.252279911-399063.5789764910.776526212660306
3750615569.246326514.6135749243635.4358974294289054.586425150.624200424141396
3843033396.246779164.1438904253952.453348665-3745767.94389040.197671430199356
3946013056.548097867.6807554307687.395462165-2084811.180755390.999452279212932
4054222266.349180243.3901151347508.2215808565042022.909884930.723063073349865
4146417306.448082361.2004475272502.877843418-1665054.80044752-1.35061080696061
4251046271.850049710.6689866360543.203387068996561.1310133631.59187290676426
4351201279.649411695.9315502308905.3667416911789583.66844977-0.941599811432611
4443475288.748454074.4131848243863.042604128-4978785.71318479-1.19500993389012
4544968981.148518681.6679876234723.68800331-3549700.56798757-0.168843956715849
4653939345.449590910.6050402277156.3925306144348434.794959840.787351144899301
4754549319.751429844.6345038355937.7175187793119475.065496151.46765739889849
4854072107.353440229.2838497439273.225794151631878.016150291.55689862580915
4958434230.154492803.8893548470237.7496141633941426.210645230.577989387141991
505115875155355702.542122490176.366940516-4196951.542121990.36974594030579
515003936854442361.636581418397.857716298-4402993.63658099-1.31739573786496
5257872617.453498220.998854348288.5511302044374396.40114604-1.27544571449968
5351642978.853607976.1183359335970.972756208-1964997.3183359-0.223315876869508
5454534465.953344504.1169016304991.3476640111189961.78309843-0.562531381386994
5556094697.853520184.0612224298317.5003539072574513.73877756-0.121613171977881
5648189983.153653034.0745802289800.287754456-5463050.97458017-0.155675981124342
574749238153156392.0113508249445.323354573-5664011.01135079-0.739060004431014
5852987449.151860707.2251987170384.7333414481126741.87480127-1.45019517491265
5955719803.552458651.3154453192219.5656852353261152.184554680.401209250886882
6053922860.553151488.9295885217770.973413839771371.5704115030.470199543410154
6154931231.952360032.7825103166211.7156857132571199.11748966-0.948788842947677

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 39923931 & 39923931 & 0 & 0 & 0 \tabularnewline
2 & 35810356.5 & 37764212.219464 & -57894.8477786084 & -1953855.71946401 & -1.85324194406532 \tabularnewline
3 & 35492936.3 & 35948409.7974761 & -172407.791926949 & -455473.497476138 & -1.24371674890468 \tabularnewline
4 & 38937434.1 & 36954121.956554 & -103298.420826939 & 1983312.14344602 & 1.03837901669124 \tabularnewline
5 & 40059102.8 & 38500315.8447563 & -32194.5039175215 & 1558786.95524371 & 1.60720442533046 \tabularnewline
6 & 37708710.2 & 38574540.6934185 & -28714.4404800565 & -865830.49341854 & 0.10583441533859 \tabularnewline
7 & 41570965.7 & 39875012.8472967 & 9922.54088073279 & 1695952.85270331 & 1.32163570838708 \tabularnewline
8 & 36333563 & 38728523.0406538 & -24227.0460371257 & -2394960.0406538 & -1.14559149959861 \tabularnewline
9 & 34181220.1 & 36485098.2718056 & -94335.025723006 & -2303878.1718056 & -2.18954178727766 \tabularnewline
10 & 42593543.9 & 38496705.8282835 & -23001.8312719263 & 4096838.0717165 & 2.07029376189452 \tabularnewline
11 & 43119727.6 & 41029752.0534047 & 68695.6206620907 & 2089975.54659529 & 2.50522129747492 \tabularnewline
12 & 38497690.9 & 40645361.2144181 & 51685.5488044167 & -2147670.31441813 & -0.442949349072746 \tabularnewline
13 & 45473273.4 & 41907485.1397875 & 65708.6735629822 & 3565788.26021254 & 1.23962204349156 \tabularnewline
14 & 38399780.4 & 41375903.8223142 & 50557.7875903929 & -2976123.42231425 & -0.601223837079506 \tabularnewline
15 & 38882302.6 & 40875491.5190056 & 26412.6741238436 & -1993188.91900557 & -0.505473163855573 \tabularnewline
16 & 44051120.6 & 41746437.8016876 & 70016.1483430597 & 2304682.79831243 & 0.763300234590609 \tabularnewline
17 & 41677559.9 & 41375061.5921921 & 47151.5522825132 & 302498.307807874 & -0.411868396130859 \tabularnewline
18 & 40699203.5 & 41504897.6149621 & 51207.5174906838 & -805694.114962102 & 0.0789599868772808 \tabularnewline
19 & 44150027.6 & 41461468.8057323 & 46804.2430027872 & 2688558.79426767 & -0.0910122903019198 \tabularnewline
20 & 38225518.7 & 40795893.381211 & 14570.1158616406 & -2570374.68121097 & -0.685045385652672 \tabularnewline
21 & 35447405.7 & 40087680.7244315 & -17903.9775265402 & -4640275.02443146 & -0.693755189948033 \tabularnewline
22 & 43075518.3 & 40047160.6342335 & -18917.8457077867 & 3028357.66576647 & -0.0216552787517687 \tabularnewline
23 & 42302792 & 40093501.6254371 & -16030.0070228741 & 2209290.37456291 & 0.0623233682017234 \tabularnewline
24 & 39743541.7 & 41071009.6374198 & 26670.8817180106 & -1327467.93741977 & 0.949029140712565 \tabularnewline
25 & 44670641.2 & 41138556.7027654 & 28385.7300821736 & 3532084.49723455 & 0.0393294040947425 \tabularnewline
26 & 37123384 & 40682536.037298 & 6949.13087806987 & -3559152.03729801 & -0.464005451023557 \tabularnewline
27 & 37668266.4 & 40333540.1152299 & -10210.432063439 & -2665273.71522994 & -0.333632714324788 \tabularnewline
28 & 46117528.8 & 41642662.3086729 & 57248.5537590002 & 4474866.49132715 & 1.22155976872588 \tabularnewline
29 & 42273156.4 & 42071291.34267 & 76639.9400545626 & 201865.057329996 & 0.345997910813962 \tabularnewline
30 & 39404153.2 & 41279121.8661833 & 31499.2873061095 & -1874968.66618326 & -0.818714514947036 \tabularnewline
31 & 45799994.5 & 41627487.0897876 & 47706.0841986148 & 4172507.41021243 & 0.300503292012853 \tabularnewline
32 & 38602505.2 & 41357456.7011898 & 31716.4622773175 & -2754951.50118984 & -0.301559062257929 \tabularnewline
33 & 39454830.1 & 42481001.1815762 & 85939.051352114 & -3026171.08157624 & 1.03417650398185 \tabularnewline
34 & 47427901.4 & 43673800.7693417 & 140313.331281125 & 3754100.63065833 & 1.04585903022611 \tabularnewline
35 & 46497980.9 & 44462871.6083534 & 171887.064873904 & 2035109.29164662 & 0.612326506236152 \tabularnewline
36 & 45057149.4 & 45456212.9789765 & 211643.252279911 & -399063.578976491 & 0.776526212660306 \tabularnewline
37 & 50615569.2 & 46326514.6135749 & 243635.435897429 & 4289054.58642515 & 0.624200424141396 \tabularnewline
38 & 43033396.2 & 46779164.1438904 & 253952.453348665 & -3745767.9438904 & 0.197671430199356 \tabularnewline
39 & 46013056.5 & 48097867.6807554 & 307687.395462165 & -2084811.18075539 & 0.999452279212932 \tabularnewline
40 & 54222266.3 & 49180243.3901151 & 347508.221580856 & 5042022.90988493 & 0.723063073349865 \tabularnewline
41 & 46417306.4 & 48082361.2004475 & 272502.877843418 & -1665054.80044752 & -1.35061080696061 \tabularnewline
42 & 51046271.8 & 50049710.6689866 & 360543.203387068 & 996561.131013363 & 1.59187290676426 \tabularnewline
43 & 51201279.6 & 49411695.9315502 & 308905.366741691 & 1789583.66844977 & -0.941599811432611 \tabularnewline
44 & 43475288.7 & 48454074.4131848 & 243863.042604128 & -4978785.71318479 & -1.19500993389012 \tabularnewline
45 & 44968981.1 & 48518681.6679876 & 234723.68800331 & -3549700.56798757 & -0.168843956715849 \tabularnewline
46 & 53939345.4 & 49590910.6050402 & 277156.392530614 & 4348434.79495984 & 0.787351144899301 \tabularnewline
47 & 54549319.7 & 51429844.6345038 & 355937.717518779 & 3119475.06549615 & 1.46765739889849 \tabularnewline
48 & 54072107.3 & 53440229.2838497 & 439273.225794151 & 631878.01615029 & 1.55689862580915 \tabularnewline
49 & 58434230.1 & 54492803.8893548 & 470237.749614163 & 3941426.21064523 & 0.577989387141991 \tabularnewline
50 & 51158751 & 55355702.542122 & 490176.366940516 & -4196951.54212199 & 0.36974594030579 \tabularnewline
51 & 50039368 & 54442361.636581 & 418397.857716298 & -4402993.63658099 & -1.31739573786496 \tabularnewline
52 & 57872617.4 & 53498220.998854 & 348288.551130204 & 4374396.40114604 & -1.27544571449968 \tabularnewline
53 & 51642978.8 & 53607976.1183359 & 335970.972756208 & -1964997.3183359 & -0.223315876869508 \tabularnewline
54 & 54534465.9 & 53344504.1169016 & 304991.347664011 & 1189961.78309843 & -0.562531381386994 \tabularnewline
55 & 56094697.8 & 53520184.0612224 & 298317.500353907 & 2574513.73877756 & -0.121613171977881 \tabularnewline
56 & 48189983.1 & 53653034.0745802 & 289800.287754456 & -5463050.97458017 & -0.155675981124342 \tabularnewline
57 & 47492381 & 53156392.0113508 & 249445.323354573 & -5664011.01135079 & -0.739060004431014 \tabularnewline
58 & 52987449.1 & 51860707.2251987 & 170384.733341448 & 1126741.87480127 & -1.45019517491265 \tabularnewline
59 & 55719803.5 & 52458651.3154453 & 192219.565685235 & 3261152.18455468 & 0.401209250886882 \tabularnewline
60 & 53922860.5 & 53151488.9295885 & 217770.973413839 & 771371.570411503 & 0.470199543410154 \tabularnewline
61 & 54931231.9 & 52360032.7825103 & 166211.715685713 & 2571199.11748966 & -0.948788842947677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147456&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]39923931[/C][C]39923931[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]35810356.5[/C][C]37764212.219464[/C][C]-57894.8477786084[/C][C]-1953855.71946401[/C][C]-1.85324194406532[/C][/ROW]
[ROW][C]3[/C][C]35492936.3[/C][C]35948409.7974761[/C][C]-172407.791926949[/C][C]-455473.497476138[/C][C]-1.24371674890468[/C][/ROW]
[ROW][C]4[/C][C]38937434.1[/C][C]36954121.956554[/C][C]-103298.420826939[/C][C]1983312.14344602[/C][C]1.03837901669124[/C][/ROW]
[ROW][C]5[/C][C]40059102.8[/C][C]38500315.8447563[/C][C]-32194.5039175215[/C][C]1558786.95524371[/C][C]1.60720442533046[/C][/ROW]
[ROW][C]6[/C][C]37708710.2[/C][C]38574540.6934185[/C][C]-28714.4404800565[/C][C]-865830.49341854[/C][C]0.10583441533859[/C][/ROW]
[ROW][C]7[/C][C]41570965.7[/C][C]39875012.8472967[/C][C]9922.54088073279[/C][C]1695952.85270331[/C][C]1.32163570838708[/C][/ROW]
[ROW][C]8[/C][C]36333563[/C][C]38728523.0406538[/C][C]-24227.0460371257[/C][C]-2394960.0406538[/C][C]-1.14559149959861[/C][/ROW]
[ROW][C]9[/C][C]34181220.1[/C][C]36485098.2718056[/C][C]-94335.025723006[/C][C]-2303878.1718056[/C][C]-2.18954178727766[/C][/ROW]
[ROW][C]10[/C][C]42593543.9[/C][C]38496705.8282835[/C][C]-23001.8312719263[/C][C]4096838.0717165[/C][C]2.07029376189452[/C][/ROW]
[ROW][C]11[/C][C]43119727.6[/C][C]41029752.0534047[/C][C]68695.6206620907[/C][C]2089975.54659529[/C][C]2.50522129747492[/C][/ROW]
[ROW][C]12[/C][C]38497690.9[/C][C]40645361.2144181[/C][C]51685.5488044167[/C][C]-2147670.31441813[/C][C]-0.442949349072746[/C][/ROW]
[ROW][C]13[/C][C]45473273.4[/C][C]41907485.1397875[/C][C]65708.6735629822[/C][C]3565788.26021254[/C][C]1.23962204349156[/C][/ROW]
[ROW][C]14[/C][C]38399780.4[/C][C]41375903.8223142[/C][C]50557.7875903929[/C][C]-2976123.42231425[/C][C]-0.601223837079506[/C][/ROW]
[ROW][C]15[/C][C]38882302.6[/C][C]40875491.5190056[/C][C]26412.6741238436[/C][C]-1993188.91900557[/C][C]-0.505473163855573[/C][/ROW]
[ROW][C]16[/C][C]44051120.6[/C][C]41746437.8016876[/C][C]70016.1483430597[/C][C]2304682.79831243[/C][C]0.763300234590609[/C][/ROW]
[ROW][C]17[/C][C]41677559.9[/C][C]41375061.5921921[/C][C]47151.5522825132[/C][C]302498.307807874[/C][C]-0.411868396130859[/C][/ROW]
[ROW][C]18[/C][C]40699203.5[/C][C]41504897.6149621[/C][C]51207.5174906838[/C][C]-805694.114962102[/C][C]0.0789599868772808[/C][/ROW]
[ROW][C]19[/C][C]44150027.6[/C][C]41461468.8057323[/C][C]46804.2430027872[/C][C]2688558.79426767[/C][C]-0.0910122903019198[/C][/ROW]
[ROW][C]20[/C][C]38225518.7[/C][C]40795893.381211[/C][C]14570.1158616406[/C][C]-2570374.68121097[/C][C]-0.685045385652672[/C][/ROW]
[ROW][C]21[/C][C]35447405.7[/C][C]40087680.7244315[/C][C]-17903.9775265402[/C][C]-4640275.02443146[/C][C]-0.693755189948033[/C][/ROW]
[ROW][C]22[/C][C]43075518.3[/C][C]40047160.6342335[/C][C]-18917.8457077867[/C][C]3028357.66576647[/C][C]-0.0216552787517687[/C][/ROW]
[ROW][C]23[/C][C]42302792[/C][C]40093501.6254371[/C][C]-16030.0070228741[/C][C]2209290.37456291[/C][C]0.0623233682017234[/C][/ROW]
[ROW][C]24[/C][C]39743541.7[/C][C]41071009.6374198[/C][C]26670.8817180106[/C][C]-1327467.93741977[/C][C]0.949029140712565[/C][/ROW]
[ROW][C]25[/C][C]44670641.2[/C][C]41138556.7027654[/C][C]28385.7300821736[/C][C]3532084.49723455[/C][C]0.0393294040947425[/C][/ROW]
[ROW][C]26[/C][C]37123384[/C][C]40682536.037298[/C][C]6949.13087806987[/C][C]-3559152.03729801[/C][C]-0.464005451023557[/C][/ROW]
[ROW][C]27[/C][C]37668266.4[/C][C]40333540.1152299[/C][C]-10210.432063439[/C][C]-2665273.71522994[/C][C]-0.333632714324788[/C][/ROW]
[ROW][C]28[/C][C]46117528.8[/C][C]41642662.3086729[/C][C]57248.5537590002[/C][C]4474866.49132715[/C][C]1.22155976872588[/C][/ROW]
[ROW][C]29[/C][C]42273156.4[/C][C]42071291.34267[/C][C]76639.9400545626[/C][C]201865.057329996[/C][C]0.345997910813962[/C][/ROW]
[ROW][C]30[/C][C]39404153.2[/C][C]41279121.8661833[/C][C]31499.2873061095[/C][C]-1874968.66618326[/C][C]-0.818714514947036[/C][/ROW]
[ROW][C]31[/C][C]45799994.5[/C][C]41627487.0897876[/C][C]47706.0841986148[/C][C]4172507.41021243[/C][C]0.300503292012853[/C][/ROW]
[ROW][C]32[/C][C]38602505.2[/C][C]41357456.7011898[/C][C]31716.4622773175[/C][C]-2754951.50118984[/C][C]-0.301559062257929[/C][/ROW]
[ROW][C]33[/C][C]39454830.1[/C][C]42481001.1815762[/C][C]85939.051352114[/C][C]-3026171.08157624[/C][C]1.03417650398185[/C][/ROW]
[ROW][C]34[/C][C]47427901.4[/C][C]43673800.7693417[/C][C]140313.331281125[/C][C]3754100.63065833[/C][C]1.04585903022611[/C][/ROW]
[ROW][C]35[/C][C]46497980.9[/C][C]44462871.6083534[/C][C]171887.064873904[/C][C]2035109.29164662[/C][C]0.612326506236152[/C][/ROW]
[ROW][C]36[/C][C]45057149.4[/C][C]45456212.9789765[/C][C]211643.252279911[/C][C]-399063.578976491[/C][C]0.776526212660306[/C][/ROW]
[ROW][C]37[/C][C]50615569.2[/C][C]46326514.6135749[/C][C]243635.435897429[/C][C]4289054.58642515[/C][C]0.624200424141396[/C][/ROW]
[ROW][C]38[/C][C]43033396.2[/C][C]46779164.1438904[/C][C]253952.453348665[/C][C]-3745767.9438904[/C][C]0.197671430199356[/C][/ROW]
[ROW][C]39[/C][C]46013056.5[/C][C]48097867.6807554[/C][C]307687.395462165[/C][C]-2084811.18075539[/C][C]0.999452279212932[/C][/ROW]
[ROW][C]40[/C][C]54222266.3[/C][C]49180243.3901151[/C][C]347508.221580856[/C][C]5042022.90988493[/C][C]0.723063073349865[/C][/ROW]
[ROW][C]41[/C][C]46417306.4[/C][C]48082361.2004475[/C][C]272502.877843418[/C][C]-1665054.80044752[/C][C]-1.35061080696061[/C][/ROW]
[ROW][C]42[/C][C]51046271.8[/C][C]50049710.6689866[/C][C]360543.203387068[/C][C]996561.131013363[/C][C]1.59187290676426[/C][/ROW]
[ROW][C]43[/C][C]51201279.6[/C][C]49411695.9315502[/C][C]308905.366741691[/C][C]1789583.66844977[/C][C]-0.941599811432611[/C][/ROW]
[ROW][C]44[/C][C]43475288.7[/C][C]48454074.4131848[/C][C]243863.042604128[/C][C]-4978785.71318479[/C][C]-1.19500993389012[/C][/ROW]
[ROW][C]45[/C][C]44968981.1[/C][C]48518681.6679876[/C][C]234723.68800331[/C][C]-3549700.56798757[/C][C]-0.168843956715849[/C][/ROW]
[ROW][C]46[/C][C]53939345.4[/C][C]49590910.6050402[/C][C]277156.392530614[/C][C]4348434.79495984[/C][C]0.787351144899301[/C][/ROW]
[ROW][C]47[/C][C]54549319.7[/C][C]51429844.6345038[/C][C]355937.717518779[/C][C]3119475.06549615[/C][C]1.46765739889849[/C][/ROW]
[ROW][C]48[/C][C]54072107.3[/C][C]53440229.2838497[/C][C]439273.225794151[/C][C]631878.01615029[/C][C]1.55689862580915[/C][/ROW]
[ROW][C]49[/C][C]58434230.1[/C][C]54492803.8893548[/C][C]470237.749614163[/C][C]3941426.21064523[/C][C]0.577989387141991[/C][/ROW]
[ROW][C]50[/C][C]51158751[/C][C]55355702.542122[/C][C]490176.366940516[/C][C]-4196951.54212199[/C][C]0.36974594030579[/C][/ROW]
[ROW][C]51[/C][C]50039368[/C][C]54442361.636581[/C][C]418397.857716298[/C][C]-4402993.63658099[/C][C]-1.31739573786496[/C][/ROW]
[ROW][C]52[/C][C]57872617.4[/C][C]53498220.998854[/C][C]348288.551130204[/C][C]4374396.40114604[/C][C]-1.27544571449968[/C][/ROW]
[ROW][C]53[/C][C]51642978.8[/C][C]53607976.1183359[/C][C]335970.972756208[/C][C]-1964997.3183359[/C][C]-0.223315876869508[/C][/ROW]
[ROW][C]54[/C][C]54534465.9[/C][C]53344504.1169016[/C][C]304991.347664011[/C][C]1189961.78309843[/C][C]-0.562531381386994[/C][/ROW]
[ROW][C]55[/C][C]56094697.8[/C][C]53520184.0612224[/C][C]298317.500353907[/C][C]2574513.73877756[/C][C]-0.121613171977881[/C][/ROW]
[ROW][C]56[/C][C]48189983.1[/C][C]53653034.0745802[/C][C]289800.287754456[/C][C]-5463050.97458017[/C][C]-0.155675981124342[/C][/ROW]
[ROW][C]57[/C][C]47492381[/C][C]53156392.0113508[/C][C]249445.323354573[/C][C]-5664011.01135079[/C][C]-0.739060004431014[/C][/ROW]
[ROW][C]58[/C][C]52987449.1[/C][C]51860707.2251987[/C][C]170384.733341448[/C][C]1126741.87480127[/C][C]-1.45019517491265[/C][/ROW]
[ROW][C]59[/C][C]55719803.5[/C][C]52458651.3154453[/C][C]192219.565685235[/C][C]3261152.18455468[/C][C]0.401209250886882[/C][/ROW]
[ROW][C]60[/C][C]53922860.5[/C][C]53151488.9295885[/C][C]217770.973413839[/C][C]771371.570411503[/C][C]0.470199543410154[/C][/ROW]
[ROW][C]61[/C][C]54931231.9[/C][C]52360032.7825103[/C][C]166211.715685713[/C][C]2571199.11748966[/C][C]-0.948788842947677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147456&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147456&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
13992393139923931000
235810356.537764212.219464-57894.8477786084-1953855.71946401-1.85324194406532
335492936.335948409.7974761-172407.791926949-455473.497476138-1.24371674890468
438937434.136954121.956554-103298.4208269391983312.143446021.03837901669124
540059102.838500315.8447563-32194.50391752151558786.955243711.60720442533046
637708710.238574540.6934185-28714.4404800565-865830.493418540.10583441533859
741570965.739875012.84729679922.540880732791695952.852703311.32163570838708
83633356338728523.0406538-24227.0460371257-2394960.0406538-1.14559149959861
934181220.136485098.2718056-94335.025723006-2303878.1718056-2.18954178727766
1042593543.938496705.8282835-23001.83127192634096838.07171652.07029376189452
1143119727.641029752.053404768695.62066209072089975.546595292.50522129747492
1238497690.940645361.214418151685.5488044167-2147670.31441813-0.442949349072746
1345473273.441907485.139787565708.67356298223565788.260212541.23962204349156
1438399780.441375903.822314250557.7875903929-2976123.42231425-0.601223837079506
1538882302.640875491.519005626412.6741238436-1993188.91900557-0.505473163855573
1644051120.641746437.801687670016.14834305972304682.798312430.763300234590609
1741677559.941375061.592192147151.5522825132302498.307807874-0.411868396130859
1840699203.541504897.614962151207.5174906838-805694.1149621020.0789599868772808
1944150027.641461468.805732346804.24300278722688558.79426767-0.0910122903019198
2038225518.740795893.38121114570.1158616406-2570374.68121097-0.685045385652672
2135447405.740087680.7244315-17903.9775265402-4640275.02443146-0.693755189948033
2243075518.340047160.6342335-18917.84570778673028357.66576647-0.0216552787517687
234230279240093501.6254371-16030.00702287412209290.374562910.0623233682017234
2439743541.741071009.637419826670.8817180106-1327467.937419770.949029140712565
2544670641.241138556.702765428385.73008217363532084.497234550.0393294040947425
263712338440682536.0372986949.13087806987-3559152.03729801-0.464005451023557
2737668266.440333540.1152299-10210.432063439-2665273.71522994-0.333632714324788
2846117528.841642662.308672957248.55375900024474866.491327151.22155976872588
2942273156.442071291.3426776639.9400545626201865.0573299960.345997910813962
3039404153.241279121.866183331499.2873061095-1874968.66618326-0.818714514947036
3145799994.541627487.089787647706.08419861484172507.410212430.300503292012853
3238602505.241357456.701189831716.4622773175-2754951.50118984-0.301559062257929
3339454830.142481001.181576285939.051352114-3026171.081576241.03417650398185
3447427901.443673800.7693417140313.3312811253754100.630658331.04585903022611
3546497980.944462871.6083534171887.0648739042035109.291646620.612326506236152
3645057149.445456212.9789765211643.252279911-399063.5789764910.776526212660306
3750615569.246326514.6135749243635.4358974294289054.586425150.624200424141396
3843033396.246779164.1438904253952.453348665-3745767.94389040.197671430199356
3946013056.548097867.6807554307687.395462165-2084811.180755390.999452279212932
4054222266.349180243.3901151347508.2215808565042022.909884930.723063073349865
4146417306.448082361.2004475272502.877843418-1665054.80044752-1.35061080696061
4251046271.850049710.6689866360543.203387068996561.1310133631.59187290676426
4351201279.649411695.9315502308905.3667416911789583.66844977-0.941599811432611
4443475288.748454074.4131848243863.042604128-4978785.71318479-1.19500993389012
4544968981.148518681.6679876234723.68800331-3549700.56798757-0.168843956715849
4653939345.449590910.6050402277156.3925306144348434.794959840.787351144899301
4754549319.751429844.6345038355937.7175187793119475.065496151.46765739889849
4854072107.353440229.2838497439273.225794151631878.016150291.55689862580915
4958434230.154492803.8893548470237.7496141633941426.210645230.577989387141991
505115875155355702.542122490176.366940516-4196951.542121990.36974594030579
515003936854442361.636581418397.857716298-4402993.63658099-1.31739573786496
5257872617.453498220.998854348288.5511302044374396.40114604-1.27544571449968
5351642978.853607976.1183359335970.972756208-1964997.3183359-0.223315876869508
5454534465.953344504.1169016304991.3476640111189961.78309843-0.562531381386994
5556094697.853520184.0612224298317.5003539072574513.73877756-0.121613171977881
5648189983.153653034.0745802289800.287754456-5463050.97458017-0.155675981124342
574749238153156392.0113508249445.323354573-5664011.01135079-0.739060004431014
5852987449.151860707.2251987170384.7333414481126741.87480127-1.45019517491265
5955719803.552458651.3154453192219.5656852353261152.184554680.401209250886882
6053922860.553151488.9295885217770.973413839771371.5704115030.470199543410154
6154931231.952360032.7825103166211.7156857132571199.11748966-0.948788842947677


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$resid)
myfit <- mylevel+myseas
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time')
grid()
dev.off()
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='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')