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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 computationThu, 04 Dec 2008 01:14:17 -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/04/t1228378520e252o6cb5ul5gbb.htm/, Retrieved Fri, 17 May 2024 06:16:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28911, Retrieved Fri, 17 May 2024 06:16:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsvrm1
Estimated Impact234

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] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [smp1] [2008-12-03 17:09:38] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D    [Standard Deviation-Mean Plot] [SMP2] [2008-12-03 17:13:00] [74be16979710d4c4e7c6647856088456]
- RM          [Variance Reduction Matrix] [vrm1] [2008-12-04 08:14:17] [783db4b4a0f63b73ca8b14666b7f4329] [Current]
- RMP           [(Partial) Autocorrelation Function] [acf] [2008-12-05 09:47:21] [74be16979710d4c4e7c6647856088456]
-   P             [(Partial) Autocorrelation Function] [acf] [2008-12-05 09:52:47] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   P               [(Partial) Autocorrelation Function] [] [2008-12-05 09:55:25] [74be16979710d4c4e7c6647856088456]
-   P               [(Partial) Autocorrelation Function] [acf ] [2008-12-05 09:55:25] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   P                 [(Partial) Autocorrelation Function] [acf] [2008-12-05 09:58:14] [fe7291e888d31b8c4db0b24d6c0f75c6]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.4
8.4
8.4
8.6
8.9
8.8
8.3
7.5
7.2
7.5
8.8
9.3
9.3
8.7
8.2
8.3
8.5
8.6
8.6
8.2
8.1
8
8.6
8.7
8.8
8.5
8.4
8.5
8.7
8.7
8.6
8.5
8.3
8.1
8.2
8.1
8.1
7.9
7.9
7.9
8
8
7.9
8
7.7
7.2
7.5
7.3
7
7
7
7.2
7.3
7.1
6.8
6.6
6.2
6.2
6.8
6.9

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28911&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28911&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'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)0.542983050847458Range3.1Trim Var.0.366572327044025
V(Y[t],d=1,D=0)0.103308007013442Range2.1Trim Var.0.0369254901960784
V(Y[t],d=2,D=0)0.131049606775560Range1.8Trim Var.0.0731632653061225
V(Y[t],d=3,D=0)0.273314536340852Range2.8Trim Var.0.135537254901961
V(Y[t],d=0,D=1)0.314255319148936Range2.4Trim Var.0.183118466898955
V(Y[t],d=1,D=1)0.078131359851989Range1.2Trim Var.0.0379892037786775
V(Y[t],d=2,D=1)0.0771642512077296Range1.2Trim Var.0.0447098515519569
V(Y[t],d=3,D=1)0.163858585858586Range1.5Trim Var.0.100499325236168
V(Y[t],d=0,D=2)0.211587301587301Range2Trim Var.0.137167338709677
V(Y[t],d=1,D=2)0.127277310924370Range1.50000000000000Trim Var.0.0716989247311827
V(Y[t],d=2,D=2)0.155231729055258Range1.70000000000000Trim Var.0.087367816091954
V(Y[t],d=3,D=2)0.422727272727272Range2.59999999999999Trim Var.0.241798029556651

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.542983050847458 & Range & 3.1 & Trim Var. & 0.366572327044025 \tabularnewline
V(Y[t],d=1,D=0) & 0.103308007013442 & Range & 2.1 & Trim Var. & 0.0369254901960784 \tabularnewline
V(Y[t],d=2,D=0) & 0.131049606775560 & Range & 1.8 & Trim Var. & 0.0731632653061225 \tabularnewline
V(Y[t],d=3,D=0) & 0.273314536340852 & Range & 2.8 & Trim Var. & 0.135537254901961 \tabularnewline
V(Y[t],d=0,D=1) & 0.314255319148936 & Range & 2.4 & Trim Var. & 0.183118466898955 \tabularnewline
V(Y[t],d=1,D=1) & 0.078131359851989 & Range & 1.2 & Trim Var. & 0.0379892037786775 \tabularnewline
V(Y[t],d=2,D=1) & 0.0771642512077296 & Range & 1.2 & Trim Var. & 0.0447098515519569 \tabularnewline
V(Y[t],d=3,D=1) & 0.163858585858586 & Range & 1.5 & Trim Var. & 0.100499325236168 \tabularnewline
V(Y[t],d=0,D=2) & 0.211587301587301 & Range & 2 & Trim Var. & 0.137167338709677 \tabularnewline
V(Y[t],d=1,D=2) & 0.127277310924370 & Range & 1.50000000000000 & Trim Var. & 0.0716989247311827 \tabularnewline
V(Y[t],d=2,D=2) & 0.155231729055258 & Range & 1.70000000000000 & Trim Var. & 0.087367816091954 \tabularnewline
V(Y[t],d=3,D=2) & 0.422727272727272 & Range & 2.59999999999999 & Trim Var. & 0.241798029556651 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28911&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.542983050847458[/C][C]Range[/C][C]3.1[/C][C]Trim Var.[/C][C]0.366572327044025[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.103308007013442[/C][C]Range[/C][C]2.1[/C][C]Trim Var.[/C][C]0.0369254901960784[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.131049606775560[/C][C]Range[/C][C]1.8[/C][C]Trim Var.[/C][C]0.0731632653061225[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.273314536340852[/C][C]Range[/C][C]2.8[/C][C]Trim Var.[/C][C]0.135537254901961[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.314255319148936[/C][C]Range[/C][C]2.4[/C][C]Trim Var.[/C][C]0.183118466898955[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.078131359851989[/C][C]Range[/C][C]1.2[/C][C]Trim Var.[/C][C]0.0379892037786775[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0771642512077296[/C][C]Range[/C][C]1.2[/C][C]Trim Var.[/C][C]0.0447098515519569[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.163858585858586[/C][C]Range[/C][C]1.5[/C][C]Trim Var.[/C][C]0.100499325236168[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.211587301587301[/C][C]Range[/C][C]2[/C][C]Trim Var.[/C][C]0.137167338709677[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.127277310924370[/C][C]Range[/C][C]1.50000000000000[/C][C]Trim Var.[/C][C]0.0716989247311827[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.155231729055258[/C][C]Range[/C][C]1.70000000000000[/C][C]Trim Var.[/C][C]0.087367816091954[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.422727272727272[/C][C]Range[/C][C]2.59999999999999[/C][C]Trim Var.[/C][C]0.241798029556651[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28911&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)0.542983050847458Range3.1Trim Var.0.366572327044025
V(Y[t],d=1,D=0)0.103308007013442Range2.1Trim Var.0.0369254901960784
V(Y[t],d=2,D=0)0.131049606775560Range1.8Trim Var.0.0731632653061225
V(Y[t],d=3,D=0)0.273314536340852Range2.8Trim Var.0.135537254901961
V(Y[t],d=0,D=1)0.314255319148936Range2.4Trim Var.0.183118466898955
V(Y[t],d=1,D=1)0.078131359851989Range1.2Trim Var.0.0379892037786775
V(Y[t],d=2,D=1)0.0771642512077296Range1.2Trim Var.0.0447098515519569
V(Y[t],d=3,D=1)0.163858585858586Range1.5Trim Var.0.100499325236168
V(Y[t],d=0,D=2)0.211587301587301Range2Trim Var.0.137167338709677
V(Y[t],d=1,D=2)0.127277310924370Range1.50000000000000Trim Var.0.0716989247311827
V(Y[t],d=2,D=2)0.155231729055258Range1.70000000000000Trim Var.0.087367816091954
V(Y[t],d=3,D=2)0.422727272727272Range2.59999999999999Trim Var.0.241798029556651


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')