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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, 24 Nov 2009 13:46:29 -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/24/t125909564004s2fbgt2bt7mn3.htm/, Retrieved Mon, 26 Feb 2024 04:50:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59274, Retrieved Mon, 26 Feb 2024 04:50:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
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]
F    D          [Variance Reduction Matrix] [ws 8] [2009-11-24 20:46:29] [f7d3e79b917995ba1c8c80042fc22ef9] [Current]
-    D            [Variance Reduction Matrix] [WS8 VRM] [2009-11-27 15:39:14] [37a8d600db9abe09a2528d150ccff095]
- RMPD            [Spectral Analysis] [spectrum d=1 en D=1] [2009-12-08 18:53:31] [cd6314e7e707a6546bd4604c9d1f2b69]
- RMPD            [Spectral Analysis] [spectrum d=2 en D=1] [2009-12-08 18:55:13] [cd6314e7e707a6546bd4604c9d1f2b69]
- RM D            [Mean Plot] [verbetering] [2009-12-09 21:09:27] [f5d341d4bbba73282fc6e80153a6d315]
-    D              [Mean Plot] [verbetering] [2009-12-10 16:01:19] [f5d341d4bbba73282fc6e80153a6d315]
-    D                [Mean Plot] [verbetering] [2009-12-10 17:38:18] [f5d341d4bbba73282fc6e80153a6d315]
-    D                [Mean Plot] [verbetering] [2009-12-11 09:16:10] [f5d341d4bbba73282fc6e80153a6d315]
Feedback Forum
2009-12-08 19:55:18 [Joris Van Mol] [reply
Bij deze methode zou het echter nuttig geweest zijn om ook de spectraalanalyse er eventjes bij te nemen om eventueel uitsluitsel te geven over deze differentiatie. Ik heb dit even voor jouw gedaan en heb de uitkomsten hier geblogd:
Spectrum d=1 en D=1

http://www.freestatistics.org/blog/index.php?v=date/2009/Dec/08/t1260298450kf8i2alpqlye926.htm/

spectrum d=2 en D=1

http://www.freestatistics.org/blog/index.php?v=date/2009/Dec/08/t1260298560m9o5ffosedxe09y.htm/

Ik zou aan de hand van deze output durven stellen dat d=2 en D=1 de beste mogelijkheid is omdat bij deze differentiatie het cumulatief periodogram het best binnen het 95% betrouwbaarheidsinterval ligt.

Post a new message
Dataseries X:
9,3
9,3
8,7
8,2
8,3
8,5
8,6
8,5
8,2
8,1
7,9
8,6
8,7
8,7
8,5
8,4
8,5
8,7
8,7
8,6
8,5
8,3
8
8,2
8,1
8,1
8
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
6,4
6,1
6,5
7,7
7,9
7,5
6,9
6,6
6,9
7,7
8
8
7,7
7,3
7,4
8,1




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=59274&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=59274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59274&T=0

As an alternative you can also use a 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)0.501387005649717Range3.2Trim Var.0.300261224489796
V(Y[t],d=1,D=0)0.111303331385155Range1.8Trim Var.0.0417411764705882
V(Y[t],d=2,D=0)0.145641258318209Range1.9Trim Var.0.0881749622926093
V(Y[t],d=3,D=0)0.282763157894737Range2.8Trim Var.0.120031372549020
V(Y[t],d=0,D=1)0.48040780141844Range2.9Trim Var.0.282346109175378
V(Y[t],d=1,D=1)0.102580943570768Range1.5Trim Var.0.0442439024390244
V(Y[t],d=2,D=1)0.0912125603864735Range1.40000000000000Trim Var.0.0491025641025642
V(Y[t],d=3,D=1)0.143Range1.7Trim Var.0.0741430499325237
V(Y[t],d=0,D=2)1.21139682539683Range4Trim Var.0.804475806451613
V(Y[t],d=1,D=2)0.271731092436975Range2.5Trim Var.0.132129032258064
V(Y[t],d=2,D=2)0.210374331550802Range2.1Trim Var.0.11351724137931
V(Y[t],d=3,D=2)0.357178030303029Range2.60000000000000Trim Var.0.184630541871921

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.501387005649717 & Range & 3.2 & Trim Var. & 0.300261224489796 \tabularnewline
V(Y[t],d=1,D=0) & 0.111303331385155 & Range & 1.8 & Trim Var. & 0.0417411764705882 \tabularnewline
V(Y[t],d=2,D=0) & 0.145641258318209 & Range & 1.9 & Trim Var. & 0.0881749622926093 \tabularnewline
V(Y[t],d=3,D=0) & 0.282763157894737 & Range & 2.8 & Trim Var. & 0.120031372549020 \tabularnewline
V(Y[t],d=0,D=1) & 0.48040780141844 & Range & 2.9 & Trim Var. & 0.282346109175378 \tabularnewline
V(Y[t],d=1,D=1) & 0.102580943570768 & Range & 1.5 & Trim Var. & 0.0442439024390244 \tabularnewline
V(Y[t],d=2,D=1) & 0.0912125603864735 & Range & 1.40000000000000 & Trim Var. & 0.0491025641025642 \tabularnewline
V(Y[t],d=3,D=1) & 0.143 & Range & 1.7 & Trim Var. & 0.0741430499325237 \tabularnewline
V(Y[t],d=0,D=2) & 1.21139682539683 & Range & 4 & Trim Var. & 0.804475806451613 \tabularnewline
V(Y[t],d=1,D=2) & 0.271731092436975 & Range & 2.5 & Trim Var. & 0.132129032258064 \tabularnewline
V(Y[t],d=2,D=2) & 0.210374331550802 & Range & 2.1 & Trim Var. & 0.11351724137931 \tabularnewline
V(Y[t],d=3,D=2) & 0.357178030303029 & Range & 2.60000000000000 & Trim Var. & 0.184630541871921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59274&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.501387005649717[/C][C]Range[/C][C]3.2[/C][C]Trim Var.[/C][C]0.300261224489796[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.111303331385155[/C][C]Range[/C][C]1.8[/C][C]Trim Var.[/C][C]0.0417411764705882[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.145641258318209[/C][C]Range[/C][C]1.9[/C][C]Trim Var.[/C][C]0.0881749622926093[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.282763157894737[/C][C]Range[/C][C]2.8[/C][C]Trim Var.[/C][C]0.120031372549020[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.48040780141844[/C][C]Range[/C][C]2.9[/C][C]Trim Var.[/C][C]0.282346109175378[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.102580943570768[/C][C]Range[/C][C]1.5[/C][C]Trim Var.[/C][C]0.0442439024390244[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0912125603864735[/C][C]Range[/C][C]1.40000000000000[/C][C]Trim Var.[/C][C]0.0491025641025642[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.143[/C][C]Range[/C][C]1.7[/C][C]Trim Var.[/C][C]0.0741430499325237[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1.21139682539683[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0.804475806451613[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.271731092436975[/C][C]Range[/C][C]2.5[/C][C]Trim Var.[/C][C]0.132129032258064[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.210374331550802[/C][C]Range[/C][C]2.1[/C][C]Trim Var.[/C][C]0.11351724137931[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.357178030303029[/C][C]Range[/C][C]2.60000000000000[/C][C]Trim Var.[/C][C]0.184630541871921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59274&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Variance Reduction Matrix
V(Y[t],d=0,D=0)0.501387005649717Range3.2Trim Var.0.300261224489796
V(Y[t],d=1,D=0)0.111303331385155Range1.8Trim Var.0.0417411764705882
V(Y[t],d=2,D=0)0.145641258318209Range1.9Trim Var.0.0881749622926093
V(Y[t],d=3,D=0)0.282763157894737Range2.8Trim Var.0.120031372549020
V(Y[t],d=0,D=1)0.48040780141844Range2.9Trim Var.0.282346109175378
V(Y[t],d=1,D=1)0.102580943570768Range1.5Trim Var.0.0442439024390244
V(Y[t],d=2,D=1)0.0912125603864735Range1.40000000000000Trim Var.0.0491025641025642
V(Y[t],d=3,D=1)0.143Range1.7Trim Var.0.0741430499325237
V(Y[t],d=0,D=2)1.21139682539683Range4Trim Var.0.804475806451613
V(Y[t],d=1,D=2)0.271731092436975Range2.5Trim Var.0.132129032258064
V(Y[t],d=2,D=2)0.210374331550802Range2.1Trim Var.0.11351724137931
V(Y[t],d=3,D=2)0.357178030303029Range2.60000000000000Trim Var.0.184630541871921



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')