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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationTue, 02 Dec 2008 06:55:29 -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/2008/Dec/02/t1228226218wqhd9gi0qc1kgpb.htm/, Retrieved Sun, 19 May 2024 12:42:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27798, Retrieved Sun, 19 May 2024 12:42:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact219

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] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Spectral Analysis] [q6] [2008-12-02 13:19:49] [74be16979710d4c4e7c6647856088456]
F RMPD    [Cross Correlation Function] [q7] [2008-12-02 13:34:43] [7ab42b4673454531c59df48fbb842b60]
F   PD      [Cross Correlation Function] [q8] [2008-12-02 13:49:39] [7ab42b4673454531c59df48fbb842b60]
F RM D          [Variance Reduction Matrix] [q8] [2008-12-02 13:55:29] [074508d5a5a3592082de3e836d27af7d] [Current]
- RMP             [Standard Deviation-Mean Plot] [q9] [2008-12-07 11:33:42] [1b742211e88d1643c42c5773474321b2]
Feedback Forum
2008-12-07 11:26:55 [Kelly Deckx] [reply] 
Ik heb de tabel juist geinterpreteerd.
2008-12-07 11:35:23 [Kelly Deckx] [reply] 
om lamda te vinden heb ik de volgende berekening gemaakt: http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/07/t1228649646uizgjroopgyei6z.htm
deze is Lambda -2.88324302118657
2008-12-07 11:38:36 [Kelly Deckx] [reply] 
2008-12-07 21:05:37 [Jasmine Hendrikx] [reply] 
Evaluatie Q8:
Om deze vraag op te lossen, heeft de student gebruik gemaakt van de VRM. Dit is inderdaad een methode om de vraag op te lossen. Het klopt inderdaad dat men naar de combinatie van d en D moet kijken met de kleinste variantie en deze komt in dit geval overeen met d=1 en D=0. Er moet dus maar één keer niet-seizoenaal gedifferentieerd worden. Maar eigenlijk is dit slechts één methode en zou je ook nog gebruik moeten maken van de ACF en van Spectral Analysis om de resultaten uit de VRM te controleren. Het is namelijk zo dat deze niet altijd overeenkomen en wanneer de VRM en de ACF een verschillend resultaat geven, zou men eerder geneigd moeten zijn om de ACF te gebruiken. Je zou dus ook van de andere methodes gebruik moeten maken ter controle.

Ook is de optimale lambda niet berekend. Deze zou je kunnen berekenen via de methode Standard Deviation – Mean plot. Zo zou je de variantie kunnen stabiliseren om zo de tijdreeks meer stationair te maken.
2008-12-08 19:34:48 [Koen Van Baelen] [reply] 
Correct. Maar om helemaal correct te zijn moet de student ook de ACF en de spectraalanalyse berekenen. DE optimale lambda moet ook nog berekend worden via de SMP-methode.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9762.12
10124.63
10540.05
10601.61
10323.73
10418.4
10092.96
10364.91
10152.09
10032.8
10204.59
10001.6
10411.75
10673.38
10539.51
10723.78
10682.06
10283.19
10377.18
10486.64
10545.38
10554.27
10532.54
10324.31
10695.25
10827.81
10872.48
10971.19
11145.65
11234.68
11333.88
10997.97
11036.89
11257.35
11533.59
11963.12
12185.15
12377.62
12512.89
12631.48
12268.53
12754.8
13407.75
13480.21
13673.28
13239.71
13557.69
13901.28
13200.58
13406.97
12538.12
12419.57
12193.88
12656.63
12812.48
12056.67
11322.38
11530.75
11114.08

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' @ 193.190.124.10:1001

\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' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27798&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' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27798&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' @ 193.190.124.10:1001






Variance Reduction Matrix
V(Y[t],d=0,D=0)1348085.48540818Range4139.16Trim Var.1069207.37354100
V(Y[t],d=1,D=0)107214.974080581Range1521.8Trim Var.64999.3191603319
V(Y[t],d=2,D=0)213539.775001755Range2017.9Trim Var.129091.203244314
V(Y[t],d=3,D=0)650056.730943864Range3933.71Trim Var.390494.126578817
V(Y[t],d=0,D=1)1204862.47948363Range5080Trim Var.557006.614635122
V(Y[t],d=1,D=1)176110.797465121Range1646.06Trim Var.107191.318411282
V(Y[t],d=2,D=1)341527.267696768Range2945.89000000001Trim Var.188755.177314305
V(Y[t],d=3,D=1)991104.863369768Range4730.91Trim Var.507753.505490185
V(Y[t],d=0,D=2)3361514.65228689Range7132.17Trim Var.2089685.60808559
V(Y[t],d=1,D=2)414114.045201426Range2532.61Trim Var.284275.621340345
V(Y[t],d=2,D=2)754702.434420455Range4449.84000000002Trim Var.401952.386465023
V(Y[t],d=3,D=2)1958770.66338992Range6672.19000000003Trim Var.997298.71053968

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 1348085.48540818 & Range & 4139.16 & Trim Var. & 1069207.37354100 \tabularnewline
V(Y[t],d=1,D=0) & 107214.974080581 & Range & 1521.8 & Trim Var. & 64999.3191603319 \tabularnewline
V(Y[t],d=2,D=0) & 213539.775001755 & Range & 2017.9 & Trim Var. & 129091.203244314 \tabularnewline
V(Y[t],d=3,D=0) & 650056.730943864 & Range & 3933.71 & Trim Var. & 390494.126578817 \tabularnewline
V(Y[t],d=0,D=1) & 1204862.47948363 & Range & 5080 & Trim Var. & 557006.614635122 \tabularnewline
V(Y[t],d=1,D=1) & 176110.797465121 & Range & 1646.06 & Trim Var. & 107191.318411282 \tabularnewline
V(Y[t],d=2,D=1) & 341527.267696768 & Range & 2945.89000000001 & Trim Var. & 188755.177314305 \tabularnewline
V(Y[t],d=3,D=1) & 991104.863369768 & Range & 4730.91 & Trim Var. & 507753.505490185 \tabularnewline
V(Y[t],d=0,D=2) & 3361514.65228689 & Range & 7132.17 & Trim Var. & 2089685.60808559 \tabularnewline
V(Y[t],d=1,D=2) & 414114.045201426 & Range & 2532.61 & Trim Var. & 284275.621340345 \tabularnewline
V(Y[t],d=2,D=2) & 754702.434420455 & Range & 4449.84000000002 & Trim Var. & 401952.386465023 \tabularnewline
V(Y[t],d=3,D=2) & 1958770.66338992 & Range & 6672.19000000003 & Trim Var. & 997298.71053968 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27798&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]1348085.48540818[/C][C]Range[/C][C]4139.16[/C][C]Trim Var.[/C][C]1069207.37354100[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]107214.974080581[/C][C]Range[/C][C]1521.8[/C][C]Trim Var.[/C][C]64999.3191603319[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]213539.775001755[/C][C]Range[/C][C]2017.9[/C][C]Trim Var.[/C][C]129091.203244314[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]650056.730943864[/C][C]Range[/C][C]3933.71[/C][C]Trim Var.[/C][C]390494.126578817[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]1204862.47948363[/C][C]Range[/C][C]5080[/C][C]Trim Var.[/C][C]557006.614635122[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]176110.797465121[/C][C]Range[/C][C]1646.06[/C][C]Trim Var.[/C][C]107191.318411282[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]341527.267696768[/C][C]Range[/C][C]2945.89000000001[/C][C]Trim Var.[/C][C]188755.177314305[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]991104.863369768[/C][C]Range[/C][C]4730.91[/C][C]Trim Var.[/C][C]507753.505490185[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]3361514.65228689[/C][C]Range[/C][C]7132.17[/C][C]Trim Var.[/C][C]2089685.60808559[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]414114.045201426[/C][C]Range[/C][C]2532.61[/C][C]Trim Var.[/C][C]284275.621340345[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]754702.434420455[/C][C]Range[/C][C]4449.84000000002[/C][C]Trim Var.[/C][C]401952.386465023[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1958770.66338992[/C][C]Range[/C][C]6672.19000000003[/C][C]Trim Var.[/C][C]997298.71053968[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27798&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:

Variance Reduction Matrix
V(Y[t],d=0,D=0)1348085.48540818Range4139.16Trim Var.1069207.37354100
V(Y[t],d=1,D=0)107214.974080581Range1521.8Trim Var.64999.3191603319
V(Y[t],d=2,D=0)213539.775001755Range2017.9Trim Var.129091.203244314
V(Y[t],d=3,D=0)650056.730943864Range3933.71Trim Var.390494.126578817
V(Y[t],d=0,D=1)1204862.47948363Range5080Trim Var.557006.614635122
V(Y[t],d=1,D=1)176110.797465121Range1646.06Trim Var.107191.318411282
V(Y[t],d=2,D=1)341527.267696768Range2945.89000000001Trim Var.188755.177314305
V(Y[t],d=3,D=1)991104.863369768Range4730.91Trim Var.507753.505490185
V(Y[t],d=0,D=2)3361514.65228689Range7132.17Trim Var.2089685.60808559
V(Y[t],d=1,D=2)414114.045201426Range2532.61Trim Var.284275.621340345
V(Y[t],d=2,D=2)754702.434420455Range4449.84000000002Trim Var.401952.386465023
V(Y[t],d=3,D=2)1958770.66338992Range6672.19000000003Trim Var.997298.71053968


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
n <- length(x)
sx <- sort(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Reduction Matrix',6,TRUE)
a<-table.row.end(a)
for (bigd in 0:2) {
for (smalld in 0:3) {
mylabel <- 'V(Y[t],d='
mylabel <- paste(mylabel,as.character(smalld),sep='')
mylabel <- paste(mylabel,',D=',sep='')
mylabel <- paste(mylabel,as.character(bigd),sep='')
mylabel <- paste(mylabel,')',sep='')
a<-table.row.start(a)
a<-table.element(a,mylabel,header=TRUE)
myx <- x
if (smalld > 0) myx <- diff(x,lag=1,differences=smalld)
if (bigd > 0) myx <- diff(myx,lag=par1,differences=bigd)
a<-table.element(a,var(myx))
a<-table.element(a,'Range',header=TRUE)
a<-table.element(a,max(myx)-min(myx))
a<-table.element(a,'Trim Var.',header=TRUE)
smyx <- sort(myx)
sn <- length(smyx)
a<-table.element(a,var(smyx[smyx>quantile(smyx,0.05) & smyxa<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')