FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationFri, 27 Nov 2009 04:16:35 -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/2009/Nov/27/t1259321044us4houhuui1zdi0.htm/, Retrieved Mon, 29 Apr 2024 19:55:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60575, Retrieved Mon, 29 Apr 2024 19:55:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Variance Reduction Matrix] [Identifying Integ...] [2009-11-22 12:29:54] [b98453cac15ba1066b407e146608df68]
-    D        [Variance Reduction Matrix] [variance reductio...] [2009-11-26 16:20:43] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D            [Variance Reduction Matrix] [Workshop8] [2009-11-27 11:16:35] [307139c5e328127f586f26d5bcc435d8] [Current]
-    D              [Variance Reduction Matrix] [methode2] [2009-12-12 10:41:15] [34b80aeb109c116fd63bf2eb7493a276]
-    D                [Variance Reduction Matrix] [methode 2] [2009-12-14 09:00:06] [34b80aeb109c116fd63bf2eb7493a276]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5.4
5.4
5.6
5.7
5.8
5.8
5.8
5.9
6.1
6.4
6.4
6.3
6.2
6.2
6.3
6.4
6.5
6.6
6.6
6.6
6.8
7
7.2
7.3
7.5
7.6
7.6
7.7
7.7
7.7
7.7
7.6
7.7
7.9
7.9
7.9
7.8
7.6
7.4
7
7
7.2
7.5
7.8
7.8
7.7
7.6
7.6
7.5
7.5
7.6
7.6
7.9
7.6
7.5
7.5
7.6
7.7
7.8
7.9
7.9

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=60575&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=60575&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60575&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)0.583699453551913Range2.5Trim Var.0.423749019607843
V(Y[t],d=1,D=0)0.0194209039548023Range0.700000000000001Trim Var.0.0097822931785196
V(Y[t],d=2,D=0)0.023448275862069Range1.00000000000000Trim Var.0.00960407239819005
V(Y[t],d=3,D=0)0.0573411978221418Range1.70000000000000Trim Var.0.0191843137254902
V(Y[t],d=0,D=1)0.322840136054422Range2.1Trim Var.0.216335656213705
V(Y[t],d=1,D=1)0.0463120567375887Range0.900000000000001Trim Var.0.0270673635307782
V(Y[t],d=2,D=1)0.0445605920444034Range1.30000000000000Trim Var.0.0140975609756098
V(Y[t],d=3,D=1)0.0931062801932371Range1.60000000000000Trim Var.0.040198717948718
V(Y[t],d=0,D=2)0.817132132132132Range3.6Trim Var.0.555795454545455
V(Y[t],d=1,D=2)0.143396825396826Range1.60000000000000Trim Var.0.0960887096774193
V(Y[t],d=2,D=2)0.125882352941177Range1.90000000000000Trim Var.0.0583225806451615
V(Y[t],d=3,D=2)0.251203208556151Range2.00000000000001Trim Var.0.165471264367817

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.583699453551913 & Range & 2.5 & Trim Var. & 0.423749019607843 \tabularnewline
V(Y[t],d=1,D=0) & 0.0194209039548023 & Range & 0.700000000000001 & Trim Var. & 0.0097822931785196 \tabularnewline
V(Y[t],d=2,D=0) & 0.023448275862069 & Range & 1.00000000000000 & Trim Var. & 0.00960407239819005 \tabularnewline
V(Y[t],d=3,D=0) & 0.0573411978221418 & Range & 1.70000000000000 & Trim Var. & 0.0191843137254902 \tabularnewline
V(Y[t],d=0,D=1) & 0.322840136054422 & Range & 2.1 & Trim Var. & 0.216335656213705 \tabularnewline
V(Y[t],d=1,D=1) & 0.0463120567375887 & Range & 0.900000000000001 & Trim Var. & 0.0270673635307782 \tabularnewline
V(Y[t],d=2,D=1) & 0.0445605920444034 & Range & 1.30000000000000 & Trim Var. & 0.0140975609756098 \tabularnewline
V(Y[t],d=3,D=1) & 0.0931062801932371 & Range & 1.60000000000000 & Trim Var. & 0.040198717948718 \tabularnewline
V(Y[t],d=0,D=2) & 0.817132132132132 & Range & 3.6 & Trim Var. & 0.555795454545455 \tabularnewline
V(Y[t],d=1,D=2) & 0.143396825396826 & Range & 1.60000000000000 & Trim Var. & 0.0960887096774193 \tabularnewline
V(Y[t],d=2,D=2) & 0.125882352941177 & Range & 1.90000000000000 & Trim Var. & 0.0583225806451615 \tabularnewline
V(Y[t],d=3,D=2) & 0.251203208556151 & Range & 2.00000000000001 & Trim Var. & 0.165471264367817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60575&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.583699453551913[/C][C]Range[/C][C]2.5[/C][C]Trim Var.[/C][C]0.423749019607843[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0194209039548023[/C][C]Range[/C][C]0.700000000000001[/C][C]Trim Var.[/C][C]0.0097822931785196[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.023448275862069[/C][C]Range[/C][C]1.00000000000000[/C][C]Trim Var.[/C][C]0.00960407239819005[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.0573411978221418[/C][C]Range[/C][C]1.70000000000000[/C][C]Trim Var.[/C][C]0.0191843137254902[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.322840136054422[/C][C]Range[/C][C]2.1[/C][C]Trim Var.[/C][C]0.216335656213705[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0463120567375887[/C][C]Range[/C][C]0.900000000000001[/C][C]Trim Var.[/C][C]0.0270673635307782[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0445605920444034[/C][C]Range[/C][C]1.30000000000000[/C][C]Trim Var.[/C][C]0.0140975609756098[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0931062801932371[/C][C]Range[/C][C]1.60000000000000[/C][C]Trim Var.[/C][C]0.040198717948718[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.817132132132132[/C][C]Range[/C][C]3.6[/C][C]Trim Var.[/C][C]0.555795454545455[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.143396825396826[/C][C]Range[/C][C]1.60000000000000[/C][C]Trim Var.[/C][C]0.0960887096774193[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.125882352941177[/C][C]Range[/C][C]1.90000000000000[/C][C]Trim Var.[/C][C]0.0583225806451615[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.251203208556151[/C][C]Range[/C][C]2.00000000000001[/C][C]Trim Var.[/C][C]0.165471264367817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60575&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60575&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)0.583699453551913Range2.5Trim Var.0.423749019607843
V(Y[t],d=1,D=0)0.0194209039548023Range0.700000000000001Trim Var.0.0097822931785196
V(Y[t],d=2,D=0)0.023448275862069Range1.00000000000000Trim Var.0.00960407239819005
V(Y[t],d=3,D=0)0.0573411978221418Range1.70000000000000Trim Var.0.0191843137254902
V(Y[t],d=0,D=1)0.322840136054422Range2.1Trim Var.0.216335656213705
V(Y[t],d=1,D=1)0.0463120567375887Range0.900000000000001Trim Var.0.0270673635307782
V(Y[t],d=2,D=1)0.0445605920444034Range1.30000000000000Trim Var.0.0140975609756098
V(Y[t],d=3,D=1)0.0931062801932371Range1.60000000000000Trim Var.0.040198717948718
V(Y[t],d=0,D=2)0.817132132132132Range3.6Trim Var.0.555795454545455
V(Y[t],d=1,D=2)0.143396825396826Range1.60000000000000Trim Var.0.0960887096774193
V(Y[t],d=2,D=2)0.125882352941177Range1.90000000000000Trim Var.0.0583225806451615
V(Y[t],d=3,D=2)0.251203208556151Range2.00000000000001Trim Var.0.165471264367817


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')