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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 computationWed, 29 Dec 2010 16:29:51 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293640311t8hvquxexwtcrik.htm/, Retrieved Fri, 03 May 2024 09:02:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116960, Retrieved Fri, 03 May 2024 09:02:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords3.2 - VRM
Estimated Impact132

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [Variantie reducti...] [2010-12-27 13:54:14] [8e0d27d3447b6ae48398467ddbde7cca]
- R       [Variance Reduction Matrix] [paper blog 6] [2010-12-29 16:29:51] [e88a7df0ec81b188ca860df63016b196] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.3
8.2
8.1
8
8.1
8.1
8
7.8
7.7
7.7
7.7
7.6
7.5
7.3
7.2
7.1
7.2
7.2
7.2
6.9
6.8
6.8
6.8
6.9
7
7.2
7.2
7.2
7
7
7.2
7.4
7.8
8
7.8
7.8
7.9
7.9
8
8
8
8
8.2
8.4
8.6
8.6
8.5
8.5
8.4
8.4
8.4
8.5
8.6
8.6
8.6
8.6
8.6
8.5
8.4
8.4
8.3
8.3
8.3
8.6
8.8
8.8
8.5
8.1
7.9
8
8.4
8.5
8.5
8.4
8.3
8.3
8.2
8.1
8.1
8.2
8.2
8.2
8.1
8.1
8
7.8
7.7
7.7
7.7
7.7
7.7
7.5
7.4
7.3
7.4
7.4
7.3
7.3
7.1
7
6.5
6.3
6.3
6.5
6.6
6.5
6.3
6.3
6.3
6.5
6.7
6.7
6.7
6.8
6.7
6.8
6.8
7
7
7.2
7.4
7.6
7.8
7.9
8.1
8.3
8.5
8.7
8.8
8.9
9
9
9.1
9.1
9.1
9.2
9.4
9.4
9.3
9.4
9.4
9.5
9.5
9.4
9.4
9.4
9.3
9.3
9.3
9.3
9.3
9.2
9.1
9.1
9.1
9.1
9.2
9.2
9.2
9.3
9.4
9.4
9.5
9.6
9.7
9.7
9.8
9.9
9.9
9.9
9.8
9.8
9.7
9.7
9.6
9.6
9.6
9.6
9.6
9.7
9.7
9.7
9.7
9.8
9.8
9.8
9.8
9.9
9.9
9.8
9.7
9.6
9.6
9.5
9.3
9.2
9
8.9
8.7
8.5
8.4
8.2
8.1
7.9
7.8
7.6
7.5
7.4
7.2
7.2
7.1
7
7
6.9
6.8
6.7
6.7
6.6
6.6
6.5
6.5
6.4
6.4
6.4
6.4
6.3
6.4
6.4
6.4
6.4
6.4
6.4
6.4
6.5
6.5
6.6
6.6
6.6
6.7
6.7
6.8

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116960&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116960&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116960&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)1.20566355463347Range3.6Trim Var.0.947569397427217
V(Y[t],d=1,D=0)0.0146469665271967Range0.9Trim Var.0.00675992348158774
V(Y[t],d=2,D=0)0.0145371119158961Range0.700000000000001Trim Var.0.00786858974358973
V(Y[t],d=3,D=0)0.0362445484522923Range1.30000000000000Trim Var.0.0219811320754717
V(Y[t],d=0,D=1)0.658562016394699Range3.9Trim Var.0.397460545193687
V(Y[t],d=1,D=1)0.0272849524692789Range1Trim Var.0.0132490582439873
V(Y[t],d=2,D=1)0.0282293087988773Range0.800000000000002Trim Var.0.0156278894472362
V(Y[t],d=3,D=1)0.0669333333333334Range1.50000000000000Trim Var.0.0378840796019900
V(Y[t],d=0,D=2)0.837403567161632Range3.9Trim Var.0.572162811815608
V(Y[t],d=1,D=2)0.0786012058570199Range2Trim Var.0.0391623488773748
V(Y[t],d=2,D=2)0.0856974570745492Range1.50000000000000Trim Var.0.0474656084656086
V(Y[t],d=3,D=2)0.199053135009434Range2.80000000000000Trim Var.0.112805137434555

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 1.20566355463347 & Range & 3.6 & Trim Var. & 0.947569397427217 \tabularnewline
V(Y[t],d=1,D=0) & 0.0146469665271967 & Range & 0.9 & Trim Var. & 0.00675992348158774 \tabularnewline
V(Y[t],d=2,D=0) & 0.0145371119158961 & Range & 0.700000000000001 & Trim Var. & 0.00786858974358973 \tabularnewline
V(Y[t],d=3,D=0) & 0.0362445484522923 & Range & 1.30000000000000 & Trim Var. & 0.0219811320754717 \tabularnewline
V(Y[t],d=0,D=1) & 0.658562016394699 & Range & 3.9 & Trim Var. & 0.397460545193687 \tabularnewline
V(Y[t],d=1,D=1) & 0.0272849524692789 & Range & 1 & Trim Var. & 0.0132490582439873 \tabularnewline
V(Y[t],d=2,D=1) & 0.0282293087988773 & Range & 0.800000000000002 & Trim Var. & 0.0156278894472362 \tabularnewline
V(Y[t],d=3,D=1) & 0.0669333333333334 & Range & 1.50000000000000 & Trim Var. & 0.0378840796019900 \tabularnewline
V(Y[t],d=0,D=2) & 0.837403567161632 & Range & 3.9 & Trim Var. & 0.572162811815608 \tabularnewline
V(Y[t],d=1,D=2) & 0.0786012058570199 & Range & 2 & Trim Var. & 0.0391623488773748 \tabularnewline
V(Y[t],d=2,D=2) & 0.0856974570745492 & Range & 1.50000000000000 & Trim Var. & 0.0474656084656086 \tabularnewline
V(Y[t],d=3,D=2) & 0.199053135009434 & Range & 2.80000000000000 & Trim Var. & 0.112805137434555 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116960&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]1.20566355463347[/C][C]Range[/C][C]3.6[/C][C]Trim Var.[/C][C]0.947569397427217[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0146469665271967[/C][C]Range[/C][C]0.9[/C][C]Trim Var.[/C][C]0.00675992348158774[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.0145371119158961[/C][C]Range[/C][C]0.700000000000001[/C][C]Trim Var.[/C][C]0.00786858974358973[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.0362445484522923[/C][C]Range[/C][C]1.30000000000000[/C][C]Trim Var.[/C][C]0.0219811320754717[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.658562016394699[/C][C]Range[/C][C]3.9[/C][C]Trim Var.[/C][C]0.397460545193687[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0272849524692789[/C][C]Range[/C][C]1[/C][C]Trim Var.[/C][C]0.0132490582439873[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0282293087988773[/C][C]Range[/C][C]0.800000000000002[/C][C]Trim Var.[/C][C]0.0156278894472362[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0669333333333334[/C][C]Range[/C][C]1.50000000000000[/C][C]Trim Var.[/C][C]0.0378840796019900[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.837403567161632[/C][C]Range[/C][C]3.9[/C][C]Trim Var.[/C][C]0.572162811815608[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.0786012058570199[/C][C]Range[/C][C]2[/C][C]Trim Var.[/C][C]0.0391623488773748[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.0856974570745492[/C][C]Range[/C][C]1.50000000000000[/C][C]Trim Var.[/C][C]0.0474656084656086[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.199053135009434[/C][C]Range[/C][C]2.80000000000000[/C][C]Trim Var.[/C][C]0.112805137434555[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116960&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116960&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)1.20566355463347Range3.6Trim Var.0.947569397427217
V(Y[t],d=1,D=0)0.0146469665271967Range0.9Trim Var.0.00675992348158774
V(Y[t],d=2,D=0)0.0145371119158961Range0.700000000000001Trim Var.0.00786858974358973
V(Y[t],d=3,D=0)0.0362445484522923Range1.30000000000000Trim Var.0.0219811320754717
V(Y[t],d=0,D=1)0.658562016394699Range3.9Trim Var.0.397460545193687
V(Y[t],d=1,D=1)0.0272849524692789Range1Trim Var.0.0132490582439873
V(Y[t],d=2,D=1)0.0282293087988773Range0.800000000000002Trim Var.0.0156278894472362
V(Y[t],d=3,D=1)0.0669333333333334Range1.50000000000000Trim Var.0.0378840796019900
V(Y[t],d=0,D=2)0.837403567161632Range3.9Trim Var.0.572162811815608
V(Y[t],d=1,D=2)0.0786012058570199Range2Trim Var.0.0391623488773748
V(Y[t],d=2,D=2)0.0856974570745492Range1.50000000000000Trim Var.0.0474656084656086
V(Y[t],d=3,D=2)0.199053135009434Range2.80000000000000Trim Var.0.112805137434555


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(myx,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')
bitmap(file='pic0.png')
op <- par(mfrow=c(2,2))
plot(x,type='l',xlab='time',ylab='value',main='d=0, D=0')
plot(diff(x,lag=1,differences=1),type='l',xlab='time',ylab='value',main='d=1, D=0')
plot(diff(x,lag=par1,differences=1),type='l',xlab='time',ylab='value',main='d=0, D=1')
plot(diff(diff(x,lag=1,differences=1),lag=par1,differences=1),type='l',xlab='time',ylab='value',main='d=1, D=1')
par(op)
dev.off()