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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationTue, 02 Dec 2008 08:05:11 -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/02/t1228230414wkj00gs8odc8yhp.htm/, Retrieved Fri, 17 May 2024 02:39:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27921, Retrieved Fri, 17 May 2024 02:39:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Cross Correlation Function] [Non-stationary ti...] [2008-12-02 14:37:27] [6c955a33a02d5e30e404487434e7a5c9]
F RMPD  [Standard Deviation-Mean Plot] [non stationary ti...] [2008-12-02 14:58:33] [6c955a33a02d5e30e404487434e7a5c9]
F    D    [Standard Deviation-Mean Plot] [non stationary ti...] [2008-12-02 15:02:16] [6c955a33a02d5e30e404487434e7a5c9]
F RM D        [Variance Reduction Matrix] [non stationary ti...] [2008-12-02 15:05:11] [a57a97ff9690154d18ed2c72b6ae351a] [Current]
Feedback Forum
2008-12-04 11:02:47 [Steven Vercammen] [reply] 
Dit klopt.
De variantie reductie matrix geeft de varianties weer na differentiatie, optimaal is dat de variantie zo klein mogelijk is omdat we dan zoveel mogelijk kunnen verklaren. De variantie is hier optimaal bij d=1 en D=0.
2008-12-07 10:08:16 [Käthe Vanderheggen] [reply] 
Dit is juist opgelost. We bekomen voor d verschillende waarden bij X en Y.
2008-12-08 18:55:40 [Koen Van Baelen] [reply] 
Correct, hier valt niks aan toe te voegen

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.84
2.78
2.63
2.54
2.56
2.19
2.09
2.06
2.08
2.05
2.03
2.04
2.03
2.01
2.01
2.01
2.01
2.01
2.01
2.02
2.02
2.03
2.05
2.08
2.07
2.06
2.05
2.05
2.05
2.05
2.05
2.06
2.06
2.07
2.07
2.30
2.31
2.31
2.53
2.58
2.59
2.73
2.82
3.00
3.04
3.23
3.32
3.49
3.57
3.56
3.72
3.82
3.82
3.98
4.06
4.08
4.19
4.16
4.17
4.21
4.21
4.17
4.19
4.25
4.25
4.20
4.33
4.41
4.56

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=27921&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=27921&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27921&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)0.811642966751918Range2.55Trim Var.0.69139758015729
V(Y[t],d=1,D=0)0.00783722563652326Range0.6Trim Var.0.00301861581920904
V(Y[t],d=2,D=0)0.0107218453188602Range0.66Trim Var.0.00434143775569843
V(Y[t],d=3,D=0)0.0329848018648019Range1.16Trim Var.0.0144043557168784
V(Y[t],d=0,D=1)0.322630451127820Range2.07Trim Var.0.230337510204082
V(Y[t],d=1,D=1)0.0100606493506493Range0.59Trim Var.0.00431595918367346
V(Y[t],d=2,D=1)0.0164333333333333Range0.68Trim Var.0.00861360544217684
V(Y[t],d=3,D=1)0.0525091893780572Range1.16Trim Var.0.0268393173758864
V(Y[t],d=0,D=2)0.406724545454545Range2.26Trim Var.0.299279892037787
V(Y[t],d=1,D=2)0.0221096723044397Range0.64Trim Var.0.0116453058321479
V(Y[t],d=2,D=2)0.038547619047619Range0.79Trim Var.0.023163063063063
V(Y[t],d=3,D=2)0.119796515679442Range1.47000000000000Trim Var.0.0718558730158727

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.811642966751918 & Range & 2.55 & Trim Var. & 0.69139758015729 \tabularnewline
V(Y[t],d=1,D=0) & 0.00783722563652326 & Range & 0.6 & Trim Var. & 0.00301861581920904 \tabularnewline
V(Y[t],d=2,D=0) & 0.0107218453188602 & Range & 0.66 & Trim Var. & 0.00434143775569843 \tabularnewline
V(Y[t],d=3,D=0) & 0.0329848018648019 & Range & 1.16 & Trim Var. & 0.0144043557168784 \tabularnewline
V(Y[t],d=0,D=1) & 0.322630451127820 & Range & 2.07 & Trim Var. & 0.230337510204082 \tabularnewline
V(Y[t],d=1,D=1) & 0.0100606493506493 & Range & 0.59 & Trim Var. & 0.00431595918367346 \tabularnewline
V(Y[t],d=2,D=1) & 0.0164333333333333 & Range & 0.68 & Trim Var. & 0.00861360544217684 \tabularnewline
V(Y[t],d=3,D=1) & 0.0525091893780572 & Range & 1.16 & Trim Var. & 0.0268393173758864 \tabularnewline
V(Y[t],d=0,D=2) & 0.406724545454545 & Range & 2.26 & Trim Var. & 0.299279892037787 \tabularnewline
V(Y[t],d=1,D=2) & 0.0221096723044397 & Range & 0.64 & Trim Var. & 0.0116453058321479 \tabularnewline
V(Y[t],d=2,D=2) & 0.038547619047619 & Range & 0.79 & Trim Var. & 0.023163063063063 \tabularnewline
V(Y[t],d=3,D=2) & 0.119796515679442 & Range & 1.47000000000000 & Trim Var. & 0.0718558730158727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27921&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.811642966751918[/C][C]Range[/C][C]2.55[/C][C]Trim Var.[/C][C]0.69139758015729[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.00783722563652326[/C][C]Range[/C][C]0.6[/C][C]Trim Var.[/C][C]0.00301861581920904[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.0107218453188602[/C][C]Range[/C][C]0.66[/C][C]Trim Var.[/C][C]0.00434143775569843[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.0329848018648019[/C][C]Range[/C][C]1.16[/C][C]Trim Var.[/C][C]0.0144043557168784[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.322630451127820[/C][C]Range[/C][C]2.07[/C][C]Trim Var.[/C][C]0.230337510204082[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0100606493506493[/C][C]Range[/C][C]0.59[/C][C]Trim Var.[/C][C]0.00431595918367346[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0164333333333333[/C][C]Range[/C][C]0.68[/C][C]Trim Var.[/C][C]0.00861360544217684[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0525091893780572[/C][C]Range[/C][C]1.16[/C][C]Trim Var.[/C][C]0.0268393173758864[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.406724545454545[/C][C]Range[/C][C]2.26[/C][C]Trim Var.[/C][C]0.299279892037787[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.0221096723044397[/C][C]Range[/C][C]0.64[/C][C]Trim Var.[/C][C]0.0116453058321479[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.038547619047619[/C][C]Range[/C][C]0.79[/C][C]Trim Var.[/C][C]0.023163063063063[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.119796515679442[/C][C]Range[/C][C]1.47000000000000[/C][C]Trim Var.[/C][C]0.0718558730158727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27921&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27921&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)0.811642966751918Range2.55Trim Var.0.69139758015729
V(Y[t],d=1,D=0)0.00783722563652326Range0.6Trim Var.0.00301861581920904
V(Y[t],d=2,D=0)0.0107218453188602Range0.66Trim Var.0.00434143775569843
V(Y[t],d=3,D=0)0.0329848018648019Range1.16Trim Var.0.0144043557168784
V(Y[t],d=0,D=1)0.322630451127820Range2.07Trim Var.0.230337510204082
V(Y[t],d=1,D=1)0.0100606493506493Range0.59Trim Var.0.00431595918367346
V(Y[t],d=2,D=1)0.0164333333333333Range0.68Trim Var.0.00861360544217684
V(Y[t],d=3,D=1)0.0525091893780572Range1.16Trim Var.0.0268393173758864
V(Y[t],d=0,D=2)0.406724545454545Range2.26Trim Var.0.299279892037787
V(Y[t],d=1,D=2)0.0221096723044397Range0.64Trim Var.0.0116453058321479
V(Y[t],d=2,D=2)0.038547619047619Range0.79Trim Var.0.023163063063063
V(Y[t],d=3,D=2)0.119796515679442Range1.47000000000000Trim Var.0.0718558730158727


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')