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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 09:40:09 +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/t1293615474sjlu8civvy998vi.htm/, Retrieved Fri, 03 May 2024 12:34:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116647, Retrieved Fri, 03 May 2024 12:34:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [Variance Reduction Matrix] [variantie reducti...] [2010-12-14 18:31:28] [d6e648f00513dd750579ba7880c5fbf5]
- R  D    [Variance Reduction Matrix] [] [2010-12-16 09:56:07] [58af523ef9b33032fd2497c80088399b]
-    D      [Variance Reduction Matrix] [] [2010-12-18 11:07:50] [58af523ef9b33032fd2497c80088399b]
-    D          [Variance Reduction Matrix] [] [2010-12-29 09:40:09] [a3cd012a7211edfe9ed4466e21aef6a6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
104.31
103.88
103.88
103.86
103.89
103.98
103.98
104.29
104.29
104.24
103.98
103.54
103.44
103.32
103.3
103.26
103.14
103.11
102.91
103.23
103.23
103.14
102.91
102.42
102.1
102.07
102.06
101.98
101.83
101.75
101.56
101.66
101.65
101.61
101.52
101.31
101.19
101.11
101.1
101.07
100.98
100.93
100.92
101.02
101.01
100.97
100.89
100.62
100.53
100.48
100.48
100.47
100.52
100.49
100.47
100.44

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116647&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116647&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116647&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Variance Reduction Matrix
V(Y[t],d=0,D=0)1.73341038961039Range3.87Trim Var.1.48987040816326
V(Y[t],d=1,D=0)0.0211961616161617Range0.810000000000002Trim Var.0.00739131205673766
V(Y[t],d=2,D=0)0.0281365478686232Range0.840000000000018Trim Var.0.0139829787234039
V(Y[t],d=3,D=0)0.0702297532656017Range1.53000000000003Trim Var.0.0311105457909329
V(Y[t],d=0,D=1)0.108442864693446Range1.13999999999999Trim Var.0.0748671408250362
V(Y[t],d=1,D=1)0.0122034330011075Range0.530000000000015Trim Var.0.00451861861861849
V(Y[t],d=2,D=1)0.0155948896631826Range0.64Trim Var.0.00662285714285684
V(Y[t],d=3,D=1)0.0377006097560986Range0.870000000000005Trim Var.0.0185610084033614
V(Y[t],d=0,D=2)0.262245060483871Range1.63Trim Var.0.20387447089947
V(Y[t],d=1,D=2)0.027101290322581Range0.760000000000005Trim Var.0.0139908831908831
V(Y[t],d=2,D=2)0.0351320689655181Range0.810000000000002Trim Var.0.0149404615384614
V(Y[t],d=3,D=2)0.0877733990147811Range1.33999999999999Trim Var.0.0326856666666663

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 1.73341038961039 & Range & 3.87 & Trim Var. & 1.48987040816326 \tabularnewline
V(Y[t],d=1,D=0) & 0.0211961616161617 & Range & 0.810000000000002 & Trim Var. & 0.00739131205673766 \tabularnewline
V(Y[t],d=2,D=0) & 0.0281365478686232 & Range & 0.840000000000018 & Trim Var. & 0.0139829787234039 \tabularnewline
V(Y[t],d=3,D=0) & 0.0702297532656017 & Range & 1.53000000000003 & Trim Var. & 0.0311105457909329 \tabularnewline
V(Y[t],d=0,D=1) & 0.108442864693446 & Range & 1.13999999999999 & Trim Var. & 0.0748671408250362 \tabularnewline
V(Y[t],d=1,D=1) & 0.0122034330011075 & Range & 0.530000000000015 & Trim Var. & 0.00451861861861849 \tabularnewline
V(Y[t],d=2,D=1) & 0.0155948896631826 & Range & 0.64 & Trim Var. & 0.00662285714285684 \tabularnewline
V(Y[t],d=3,D=1) & 0.0377006097560986 & Range & 0.870000000000005 & Trim Var. & 0.0185610084033614 \tabularnewline
V(Y[t],d=0,D=2) & 0.262245060483871 & Range & 1.63 & Trim Var. & 0.20387447089947 \tabularnewline
V(Y[t],d=1,D=2) & 0.027101290322581 & Range & 0.760000000000005 & Trim Var. & 0.0139908831908831 \tabularnewline
V(Y[t],d=2,D=2) & 0.0351320689655181 & Range & 0.810000000000002 & Trim Var. & 0.0149404615384614 \tabularnewline
V(Y[t],d=3,D=2) & 0.0877733990147811 & Range & 1.33999999999999 & Trim Var. & 0.0326856666666663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116647&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]1.73341038961039[/C][C]Range[/C][C]3.87[/C][C]Trim Var.[/C][C]1.48987040816326[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0211961616161617[/C][C]Range[/C][C]0.810000000000002[/C][C]Trim Var.[/C][C]0.00739131205673766[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.0281365478686232[/C][C]Range[/C][C]0.840000000000018[/C][C]Trim Var.[/C][C]0.0139829787234039[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.0702297532656017[/C][C]Range[/C][C]1.53000000000003[/C][C]Trim Var.[/C][C]0.0311105457909329[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.108442864693446[/C][C]Range[/C][C]1.13999999999999[/C][C]Trim Var.[/C][C]0.0748671408250362[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0122034330011075[/C][C]Range[/C][C]0.530000000000015[/C][C]Trim Var.[/C][C]0.00451861861861849[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0155948896631826[/C][C]Range[/C][C]0.64[/C][C]Trim Var.[/C][C]0.00662285714285684[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0377006097560986[/C][C]Range[/C][C]0.870000000000005[/C][C]Trim Var.[/C][C]0.0185610084033614[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.262245060483871[/C][C]Range[/C][C]1.63[/C][C]Trim Var.[/C][C]0.20387447089947[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.027101290322581[/C][C]Range[/C][C]0.760000000000005[/C][C]Trim Var.[/C][C]0.0139908831908831[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.0351320689655181[/C][C]Range[/C][C]0.810000000000002[/C][C]Trim Var.[/C][C]0.0149404615384614[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.0877733990147811[/C][C]Range[/C][C]1.33999999999999[/C][C]Trim Var.[/C][C]0.0326856666666663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116647&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116647&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)1.73341038961039Range3.87Trim Var.1.48987040816326
V(Y[t],d=1,D=0)0.0211961616161617Range0.810000000000002Trim Var.0.00739131205673766
V(Y[t],d=2,D=0)0.0281365478686232Range0.840000000000018Trim Var.0.0139829787234039
V(Y[t],d=3,D=0)0.0702297532656017Range1.53000000000003Trim Var.0.0311105457909329
V(Y[t],d=0,D=1)0.108442864693446Range1.13999999999999Trim Var.0.0748671408250362
V(Y[t],d=1,D=1)0.0122034330011075Range0.530000000000015Trim Var.0.00451861861861849
V(Y[t],d=2,D=1)0.0155948896631826Range0.64Trim Var.0.00662285714285684
V(Y[t],d=3,D=1)0.0377006097560986Range0.870000000000005Trim Var.0.0185610084033614
V(Y[t],d=0,D=2)0.262245060483871Range1.63Trim Var.0.20387447089947
V(Y[t],d=1,D=2)0.027101290322581Range0.760000000000005Trim Var.0.0139908831908831
V(Y[t],d=2,D=2)0.0351320689655181Range0.810000000000002Trim Var.0.0149404615384614
V(Y[t],d=3,D=2)0.0877733990147811Range1.33999999999999Trim Var.0.0326856666666663


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()