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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, 02 Dec 2008 13:27:54 -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/t1228249777ptwhv8rg0eyshz7.htm/, Retrieved Fri, 17 May 2024 06:38:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28381, Retrieved Fri, 17 May 2024 06:38:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Variance Reduction Matrix] [Non sts Q8 VRM ind] [2008-12-02 20:27:54] [c5d6d05aee6be5527ac4a30a8c3b8fe5] [Current]
Feedback Forum
2008-12-08 13:53:19 [Katja van Hek] [reply
De kleinste waarde bij VRM is 88.5977937853107 met een getrimde variantie van 28.6747169811321 bij d=1 en D=0. Bij deze waarden wordt de tijdreeks dus stationair gemaakt.
2008-12-08 16:58:22 [Jonas Janssens] [reply
In orde
2008-12-08 19:47:39 [5faab2fc6fb120339944528a32d48a04] [reply
De differentiatie die nodig is om de meeste volatiliteit te verklaren hoort bij lijn 2.
2008-12-09 22:57:05 [Gert-Jan Geudens] [reply
Correct. We willen nog graag even vermelden dat de resultaten van de autocorrelatie functie en deze variance reduction matrix, niet altijd gelijk zijn. In dit geval kiezen we voor de autocorrelatie aangezien de methode van de variance reduction matrix te ruw is.

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Dataseries X:
109,1
111,4
114,1
121,8
127,6
129,9
128
123,5
124
127,4
127,6
128,4
131,4
135,1
134
144,5
147,3
150,9
148,7
141,4
138,9
139,8
145,6
147,9
148,5
151,1
157,5
167,5
172,3
173,5
187,5
205,5
195,1
204,5
204,5
201,7
207
206,6
210,6
211,1
215
223,9
238,2
238,9
229,6
232,2
222,1
221,6
227,3
221
213,6
243,4
253,8
265,3
268,2
268,5
266,9
268,4
250,8
231,2
192




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28381&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28381&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28381&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Variance Reduction Matrix
V(Y[t],d=0,D=0)2412.37843169399Range159.4Trim Var.1859.81669811321
V(Y[t],d=1,D=0)88.5977937853107Range69Trim Var.28.6747169811321
V(Y[t],d=2,D=0)97.4130917592052Range65.6Trim Var.41.0766835994194
V(Y[t],d=3,D=0)265.712692075015Range104.8Trim Var.112.881945701357
V(Y[t],d=0,D=1)334.687253401361Range100Trim Var.177.337763012182
V(Y[t],d=1,D=1)117.747234042553Range74.2Trim Var.27.0558710801394
V(Y[t],d=2,D=1)156.619990749306Range73.9Trim Var.64.4491219512195
V(Y[t],d=3,D=1)412.594106280193Range113.1Trim Var.151.666916666667
V(Y[t],d=0,D=2)861.616111111111Range107.9Trim Var.641.479545454545
V(Y[t],d=1,D=2)257.205142857143Range84.1Trim Var.93.0267338709679
V(Y[t],d=2,D=2)413.910050420168Range84.6Trim Var.230.255569892473
V(Y[t],d=3,D=2)1154.88264705882Range157.5Trim Var.588.830298850574

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 2412.37843169399 & Range & 159.4 & Trim Var. & 1859.81669811321 \tabularnewline
V(Y[t],d=1,D=0) & 88.5977937853107 & Range & 69 & Trim Var. & 28.6747169811321 \tabularnewline
V(Y[t],d=2,D=0) & 97.4130917592052 & Range & 65.6 & Trim Var. & 41.0766835994194 \tabularnewline
V(Y[t],d=3,D=0) & 265.712692075015 & Range & 104.8 & Trim Var. & 112.881945701357 \tabularnewline
V(Y[t],d=0,D=1) & 334.687253401361 & Range & 100 & Trim Var. & 177.337763012182 \tabularnewline
V(Y[t],d=1,D=1) & 117.747234042553 & Range & 74.2 & Trim Var. & 27.0558710801394 \tabularnewline
V(Y[t],d=2,D=1) & 156.619990749306 & Range & 73.9 & Trim Var. & 64.4491219512195 \tabularnewline
V(Y[t],d=3,D=1) & 412.594106280193 & Range & 113.1 & Trim Var. & 151.666916666667 \tabularnewline
V(Y[t],d=0,D=2) & 861.616111111111 & Range & 107.9 & Trim Var. & 641.479545454545 \tabularnewline
V(Y[t],d=1,D=2) & 257.205142857143 & Range & 84.1 & Trim Var. & 93.0267338709679 \tabularnewline
V(Y[t],d=2,D=2) & 413.910050420168 & Range & 84.6 & Trim Var. & 230.255569892473 \tabularnewline
V(Y[t],d=3,D=2) & 1154.88264705882 & Range & 157.5 & Trim Var. & 588.830298850574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28381&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]2412.37843169399[/C][C]Range[/C][C]159.4[/C][C]Trim Var.[/C][C]1859.81669811321[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]88.5977937853107[/C][C]Range[/C][C]69[/C][C]Trim Var.[/C][C]28.6747169811321[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]97.4130917592052[/C][C]Range[/C][C]65.6[/C][C]Trim Var.[/C][C]41.0766835994194[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]265.712692075015[/C][C]Range[/C][C]104.8[/C][C]Trim Var.[/C][C]112.881945701357[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]334.687253401361[/C][C]Range[/C][C]100[/C][C]Trim Var.[/C][C]177.337763012182[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]117.747234042553[/C][C]Range[/C][C]74.2[/C][C]Trim Var.[/C][C]27.0558710801394[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]156.619990749306[/C][C]Range[/C][C]73.9[/C][C]Trim Var.[/C][C]64.4491219512195[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]412.594106280193[/C][C]Range[/C][C]113.1[/C][C]Trim Var.[/C][C]151.666916666667[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]861.616111111111[/C][C]Range[/C][C]107.9[/C][C]Trim Var.[/C][C]641.479545454545[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]257.205142857143[/C][C]Range[/C][C]84.1[/C][C]Trim Var.[/C][C]93.0267338709679[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]413.910050420168[/C][C]Range[/C][C]84.6[/C][C]Trim Var.[/C][C]230.255569892473[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1154.88264705882[/C][C]Range[/C][C]157.5[/C][C]Trim Var.[/C][C]588.830298850574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28381&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28381&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)2412.37843169399Range159.4Trim Var.1859.81669811321
V(Y[t],d=1,D=0)88.5977937853107Range69Trim Var.28.6747169811321
V(Y[t],d=2,D=0)97.4130917592052Range65.6Trim Var.41.0766835994194
V(Y[t],d=3,D=0)265.712692075015Range104.8Trim Var.112.881945701357
V(Y[t],d=0,D=1)334.687253401361Range100Trim Var.177.337763012182
V(Y[t],d=1,D=1)117.747234042553Range74.2Trim Var.27.0558710801394
V(Y[t],d=2,D=1)156.619990749306Range73.9Trim Var.64.4491219512195
V(Y[t],d=3,D=1)412.594106280193Range113.1Trim Var.151.666916666667
V(Y[t],d=0,D=2)861.616111111111Range107.9Trim Var.641.479545454545
V(Y[t],d=1,D=2)257.205142857143Range84.1Trim Var.93.0267338709679
V(Y[t],d=2,D=2)413.910050420168Range84.6Trim Var.230.255569892473
V(Y[t],d=3,D=2)1154.88264705882Range157.5Trim Var.588.830298850574



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')