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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationTue, 02 Dec 2008 09:36:54 -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/t1228235923f188nzn7749uvrz.htm/, Retrieved Fri, 17 May 2024 02:02:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28052, Retrieved Fri, 17 May 2024 02:02:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordshundrasmet
Estimated Impact176

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 17:50:19] [b98453cac15ba1066b407e146608df68]
F RMPD    [Variance Reduction Matrix] [workshop 4 Q3] [2008-12-02 16:36:54] [fb0a4305582623ea5408efbbf6f8b708] [Current]
Feedback Forum
2008-12-08 13:25:48 [Jessica Alves Pires] [reply] 
Goed, je hebt de kleinste variantie genomen. Je had er wel bij mogen zeggen dat indien er veel outliers aanwezig zijn in een tijdreeks, men best kijkt naar de getrimde variantie. Je zou bij jouw berekening dan komen aan een andere waarde voor d namelijk d=1, want de kleinste getrimde variantie bedraagt 0.0301234567901234. Goed dat je de volatiliteit vermeld en dat je zegt dat bij een kleine variantie meer verklaard kan worden.
2008-12-08 15:14:11 [Jessica Alves Pires] [reply] 
Twee taken die ik dien te verbeteren, hebben allebei dezelfde links en antwoorden. Aangezien de link en uitleg dezelfden zijn, verwijs ik naar bovenstaande opmerking.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5.5
5.3
5.2
5.3
5.3
5
4.8
4.9
5.3
6
6.2
6.4
6.4
6.4
6.2
6.1
6
5.9
6.2
6.2
6.4
6.8
6.9
7
7
6.9
6.7
6.6
6.5
6.4
6.5
6.5
6.6
6.7
6.8
7.2
7.6
7.6
7.3
6.4
6.1
6.3
7.1
7.5
7.4
7.1
6.8
6.9
7.2
7.4
7.3
6.9
6.9
6.8
7.1
7.2
7.1
7
6.9
7
7.4
7.5
7.5
7.4
7.3
7
6.7
6.5
6.5
6.5
6.6
6.8
6.9
6.9
6.8
6.8
6.5
6.1
6
5.9
5.8
5.9
5.9
6.2
6.3
6.2
6
5.8
5.5
5.5
5.7
5.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28052&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]0 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=28052&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28052&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)0.44673674151935Range2.8Trim Var.0.253667640376501
V(Y[t],d=1,D=0)0.0592112332112332Range1.7Trim Var.0.0301234567901234
V(Y[t],d=2,D=0)0.0580786516853932Range1.2Trim Var.0.0341708860759494
V(Y[t],d=3,D=0)0.109767620020429Range2.20000000000000Trim Var.0.0529470950989938
V(Y[t],d=0,D=1)0.372094936708861Range2.4Trim Var.0.274969818913481
V(Y[t],d=1,D=1)0.0649659201557936Range1.5Trim Var.0.0339047619047619
V(Y[t],d=2,D=1)0.0701298701298702Range1.30000000000000Trim Var.0.0402855924978687
V(Y[t],d=3,D=1)0.127098427887902Range2.20000000000000Trim Var.0.0729326513213983
V(Y[t],d=0,D=2)0.298375768217735Range2.5Trim Var.0.184455535390200
V(Y[t],d=1,D=2)0.164400723654455Range2.5Trim Var.0.0740327293980129
V(Y[t],d=2,D=2)0.179039627039627Range2.10000000000000Trim Var.0.105251058681186
V(Y[t],d=3,D=2)0.328711538461539Range3.1Trim Var.0.166773182957394

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.44673674151935 & Range & 2.8 & Trim Var. & 0.253667640376501 \tabularnewline
V(Y[t],d=1,D=0) & 0.0592112332112332 & Range & 1.7 & Trim Var. & 0.0301234567901234 \tabularnewline
V(Y[t],d=2,D=0) & 0.0580786516853932 & Range & 1.2 & Trim Var. & 0.0341708860759494 \tabularnewline
V(Y[t],d=3,D=0) & 0.109767620020429 & Range & 2.20000000000000 & Trim Var. & 0.0529470950989938 \tabularnewline
V(Y[t],d=0,D=1) & 0.372094936708861 & Range & 2.4 & Trim Var. & 0.274969818913481 \tabularnewline
V(Y[t],d=1,D=1) & 0.0649659201557936 & Range & 1.5 & Trim Var. & 0.0339047619047619 \tabularnewline
V(Y[t],d=2,D=1) & 0.0701298701298702 & Range & 1.30000000000000 & Trim Var. & 0.0402855924978687 \tabularnewline
V(Y[t],d=3,D=1) & 0.127098427887902 & Range & 2.20000000000000 & Trim Var. & 0.0729326513213983 \tabularnewline
V(Y[t],d=0,D=2) & 0.298375768217735 & Range & 2.5 & Trim Var. & 0.184455535390200 \tabularnewline
V(Y[t],d=1,D=2) & 0.164400723654455 & Range & 2.5 & Trim Var. & 0.0740327293980129 \tabularnewline
V(Y[t],d=2,D=2) & 0.179039627039627 & Range & 2.10000000000000 & Trim Var. & 0.105251058681186 \tabularnewline
V(Y[t],d=3,D=2) & 0.328711538461539 & Range & 3.1 & Trim Var. & 0.166773182957394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28052&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.44673674151935[/C][C]Range[/C][C]2.8[/C][C]Trim Var.[/C][C]0.253667640376501[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0592112332112332[/C][C]Range[/C][C]1.7[/C][C]Trim Var.[/C][C]0.0301234567901234[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.0580786516853932[/C][C]Range[/C][C]1.2[/C][C]Trim Var.[/C][C]0.0341708860759494[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.109767620020429[/C][C]Range[/C][C]2.20000000000000[/C][C]Trim Var.[/C][C]0.0529470950989938[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.372094936708861[/C][C]Range[/C][C]2.4[/C][C]Trim Var.[/C][C]0.274969818913481[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0649659201557936[/C][C]Range[/C][C]1.5[/C][C]Trim Var.[/C][C]0.0339047619047619[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0701298701298702[/C][C]Range[/C][C]1.30000000000000[/C][C]Trim Var.[/C][C]0.0402855924978687[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.127098427887902[/C][C]Range[/C][C]2.20000000000000[/C][C]Trim Var.[/C][C]0.0729326513213983[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.298375768217735[/C][C]Range[/C][C]2.5[/C][C]Trim Var.[/C][C]0.184455535390200[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.164400723654455[/C][C]Range[/C][C]2.5[/C][C]Trim Var.[/C][C]0.0740327293980129[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.179039627039627[/C][C]Range[/C][C]2.10000000000000[/C][C]Trim Var.[/C][C]0.105251058681186[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.328711538461539[/C][C]Range[/C][C]3.1[/C][C]Trim Var.[/C][C]0.166773182957394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28052&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28052&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)0.44673674151935Range2.8Trim Var.0.253667640376501
V(Y[t],d=1,D=0)0.0592112332112332Range1.7Trim Var.0.0301234567901234
V(Y[t],d=2,D=0)0.0580786516853932Range1.2Trim Var.0.0341708860759494
V(Y[t],d=3,D=0)0.109767620020429Range2.20000000000000Trim Var.0.0529470950989938
V(Y[t],d=0,D=1)0.372094936708861Range2.4Trim Var.0.274969818913481
V(Y[t],d=1,D=1)0.0649659201557936Range1.5Trim Var.0.0339047619047619
V(Y[t],d=2,D=1)0.0701298701298702Range1.30000000000000Trim Var.0.0402855924978687
V(Y[t],d=3,D=1)0.127098427887902Range2.20000000000000Trim Var.0.0729326513213983
V(Y[t],d=0,D=2)0.298375768217735Range2.5Trim Var.0.184455535390200
V(Y[t],d=1,D=2)0.164400723654455Range2.5Trim Var.0.0740327293980129
V(Y[t],d=2,D=2)0.179039627039627Range2.10000000000000Trim Var.0.105251058681186
V(Y[t],d=3,D=2)0.328711538461539Range3.1Trim Var.0.166773182957394


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)
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')