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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, 20 Dec 2011 15:25:09 -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/Dec/20/t1324412722gn5uvi75qbc15ft.htm/, Retrieved Mon, 06 May 2024 06:24:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158238, Retrieved Mon, 06 May 2024 06:24:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Correlatie tussen...] [2007-11-03 21:44:17] [0b2d8ed757c467aee7199cdee05779c9]
- RMPD  [(Partial) Autocorrelation Function] [WS 8 01] [2009-11-21 08:59:55] [6e4e01d7eb22a9f33d58ebb35753a195]
- RMPD    [Variance Reduction Matrix] [Paper VRM] [2010-12-21 12:23:34] [a9e130f95bad0a0597234e75c6380c5a]
-    D        [Variance Reduction Matrix] [] [2011-12-20 20:25:09] [3b32143baae8ca4a077b118800e50af3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103.7
103.75
103.85
104.02
104.13
104.17
104.18
104.2
104.5
104.78
104.88
104.89
104.9
104.95
105.24
105.35
105.44
105.46
105.47
105.48
105.75
106.1
106.19
106.23
106.24
106.25
106.35
106.48
106.52
106.55
106.55
106.56
106.89
107.09
107.24
107.28
107.3
107.31
107.47
107.35
107.31
107.32
107.32
107.34
107.53
107.72
107.75
107.79
107.81
107.9
107.8
107.86
107.8
107.74
107.75
107.83
107.8
107.81
107.86
107.83

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'AstonUniversity' @ aston.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 & 1 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158238&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158238&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158238&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 time1 seconds
R Server'AstonUniversity' @ aston.wessa.net






Variance Reduction Matrix
V(Y[t],d=0,D=0)1.83426042372881Range4.2Trim Var.1.52923553459119
V(Y[t],d=1,D=0)0.0106517241379308Range0.469999999999999Trim Var.0.00626798642533914
V(Y[t],d=2,D=0)0.0141840290381123Range0.599999999999994Trim Var.0.00739709653091992
V(Y[t],d=3,D=0)0.0340107769423556Range0.810000000000002Trim Var.0.0198491764705881
V(Y[t],d=0,D=1)0.15122109929078Range1.35000000000001Trim Var.0.108664634146341
V(Y[t],d=1,D=1)0.0086515263644773Range0.450000000000003Trim Var.0.0038578048780485
V(Y[t],d=2,D=1)0.021919855072464Range0.780000000000015Trim Var.0.0106307692307693
V(Y[t],d=3,D=1)0.0703740404040423Range1.42000000000004Trim Var.0.0341572199730094
V(Y[t],d=0,D=2)0.0323532539682536Range0.929999999999993Trim Var.0.0152426411290323
V(Y[t],d=1,D=2)0.0235137815126051Range0.810000000000016Trim Var.0.0101556989247306
V(Y[t],d=2,D=2)0.0685537433155097Range1.27000000000005Trim Var.0.0363581609195394
V(Y[t],d=3,D=2)0.229724621212131Range2.27000000000011Trim Var.0.128638177339901

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 1.83426042372881 & Range & 4.2 & Trim Var. & 1.52923553459119 \tabularnewline
V(Y[t],d=1,D=0) & 0.0106517241379308 & Range & 0.469999999999999 & Trim Var. & 0.00626798642533914 \tabularnewline
V(Y[t],d=2,D=0) & 0.0141840290381123 & Range & 0.599999999999994 & Trim Var. & 0.00739709653091992 \tabularnewline
V(Y[t],d=3,D=0) & 0.0340107769423556 & Range & 0.810000000000002 & Trim Var. & 0.0198491764705881 \tabularnewline
V(Y[t],d=0,D=1) & 0.15122109929078 & Range & 1.35000000000001 & Trim Var. & 0.108664634146341 \tabularnewline
V(Y[t],d=1,D=1) & 0.0086515263644773 & Range & 0.450000000000003 & Trim Var. & 0.0038578048780485 \tabularnewline
V(Y[t],d=2,D=1) & 0.021919855072464 & Range & 0.780000000000015 & Trim Var. & 0.0106307692307693 \tabularnewline
V(Y[t],d=3,D=1) & 0.0703740404040423 & Range & 1.42000000000004 & Trim Var. & 0.0341572199730094 \tabularnewline
V(Y[t],d=0,D=2) & 0.0323532539682536 & Range & 0.929999999999993 & Trim Var. & 0.0152426411290323 \tabularnewline
V(Y[t],d=1,D=2) & 0.0235137815126051 & Range & 0.810000000000016 & Trim Var. & 0.0101556989247306 \tabularnewline
V(Y[t],d=2,D=2) & 0.0685537433155097 & Range & 1.27000000000005 & Trim Var. & 0.0363581609195394 \tabularnewline
V(Y[t],d=3,D=2) & 0.229724621212131 & Range & 2.27000000000011 & Trim Var. & 0.128638177339901 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158238&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]1.83426042372881[/C][C]Range[/C][C]4.2[/C][C]Trim Var.[/C][C]1.52923553459119[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0106517241379308[/C][C]Range[/C][C]0.469999999999999[/C][C]Trim Var.[/C][C]0.00626798642533914[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.0141840290381123[/C][C]Range[/C][C]0.599999999999994[/C][C]Trim Var.[/C][C]0.00739709653091992[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.0340107769423556[/C][C]Range[/C][C]0.810000000000002[/C][C]Trim Var.[/C][C]0.0198491764705881[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.15122109929078[/C][C]Range[/C][C]1.35000000000001[/C][C]Trim Var.[/C][C]0.108664634146341[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0086515263644773[/C][C]Range[/C][C]0.450000000000003[/C][C]Trim Var.[/C][C]0.0038578048780485[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.021919855072464[/C][C]Range[/C][C]0.780000000000015[/C][C]Trim Var.[/C][C]0.0106307692307693[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0703740404040423[/C][C]Range[/C][C]1.42000000000004[/C][C]Trim Var.[/C][C]0.0341572199730094[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.0323532539682536[/C][C]Range[/C][C]0.929999999999993[/C][C]Trim Var.[/C][C]0.0152426411290323[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.0235137815126051[/C][C]Range[/C][C]0.810000000000016[/C][C]Trim Var.[/C][C]0.0101556989247306[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.0685537433155097[/C][C]Range[/C][C]1.27000000000005[/C][C]Trim Var.[/C][C]0.0363581609195394[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.229724621212131[/C][C]Range[/C][C]2.27000000000011[/C][C]Trim Var.[/C][C]0.128638177339901[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158238&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158238&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)1.83426042372881Range4.2Trim Var.1.52923553459119
V(Y[t],d=1,D=0)0.0106517241379308Range0.469999999999999Trim Var.0.00626798642533914
V(Y[t],d=2,D=0)0.0141840290381123Range0.599999999999994Trim Var.0.00739709653091992
V(Y[t],d=3,D=0)0.0340107769423556Range0.810000000000002Trim Var.0.0198491764705881
V(Y[t],d=0,D=1)0.15122109929078Range1.35000000000001Trim Var.0.108664634146341
V(Y[t],d=1,D=1)0.0086515263644773Range0.450000000000003Trim Var.0.0038578048780485
V(Y[t],d=2,D=1)0.021919855072464Range0.780000000000015Trim Var.0.0106307692307693
V(Y[t],d=3,D=1)0.0703740404040423Range1.42000000000004Trim Var.0.0341572199730094
V(Y[t],d=0,D=2)0.0323532539682536Range0.929999999999993Trim Var.0.0152426411290323
V(Y[t],d=1,D=2)0.0235137815126051Range0.810000000000016Trim Var.0.0101556989247306
V(Y[t],d=2,D=2)0.0685537433155097Range1.27000000000005Trim Var.0.0363581609195394
V(Y[t],d=3,D=2)0.229724621212131Range2.27000000000011Trim Var.0.128638177339901


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()