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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_rwalk.wasp
Title produced by softwareLaw of Averages
Date of computationMon, 01 Dec 2008 10:21:21 -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/01/t12281521053grv0mil0fgda0t.htm/, Retrieved Sun, 05 May 2024 12:16:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27010, Retrieved Sun, 05 May 2024 12:16:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact213

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:31:28] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [q3 / 7] [2008-11-30 17:25:28] [4300be8b33fd3dcdacd2aa9800ceba23]
F           [Law of Averages] [] [2008-12-01 17:21:21] [e8f764b122b426f433a1e1038b457077] [Current]
Feedback Forum
2008-12-06 09:30:20 [Siem Van Opstal] [reply] 
De student legt niet duidelijk uit waar het hier om gaat. VRM gaat trachten om de spreading van de tijdreeks te verkleinen door te differentiëren, d staat voor een gewone differentiatie tewijl D staat voor een seizonale differentiatie. De eerste kolom in de matric geeft aan hoe vaak er gewoon gedifferentieerd is en hoe vaak seizonaal gedifferentieerd. De 2e kolom geeft de variantie van onze tijdreeks weer, we moeten zoals eerder vermeld kijken naar de kleinste spreiding om een zo stationair mogelijke tijdreeks te bekomen, de optimale spreiding bekomen we bij 1.00197181511618, dus na 1 keer gewoon te differentiëren en geen enkele keer seizonaal
2008-12-07 16:46:18 [Chi-Kwong Man] [reply] 
In de eerste kolom van de variance reduction matrix vindt je de berekening van de variantie. De kleine 'd' staat voor differentiëren (lange termijn effect zuiveren, waardoor men een stabieler gemiddelde krijgt). Grote 'D' staat voor seizoenale differentiatie. V(Y[t],d=1,D=0) betekent dat men '1x' differentiërt. De tweede kolom geeft de variantie weer (de kleinste kan men vinden in de tweede rij (1.00084506362122)).

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Dataset

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=27010&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=27010&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27010&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)57.6857715430862Range29Trim Var.42.3393235781916
V(Y[t],d=1,D=0)1.00084506362122Range2Trim Var.NA
V(Y[t],d=2,D=0)2.09255533199195Range4Trim Var.0
V(Y[t],d=3,D=0)6.2257415460505Range8Trim Var.2.8500695817769
V(Y[t],d=0,D=1)8.41402699700407Range16Trim Var.2.56690636815183
V(Y[t],d=1,D=1)2.07380366905806Range4Trim Var.0
V(Y[t],d=2,D=1)4.51132323618005Range8Trim Var.2.24078331204768
V(Y[t],d=3,D=1)13.7024111783250Range16Trim Var.6.60112055896118
V(Y[t],d=0,D=2)16.7079876160991Range24Trim Var.6.70809145835801
V(Y[t],d=1,D=2)6.2869020652898Range8Trim Var.2.73195534797443
V(Y[t],d=2,D=2)13.8433912275537Range16Trim Var.6.57494792389946
V(Y[t],d=3,D=2)42.1778048518293Range32Trim Var.22.6713861973502

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 57.6857715430862 & Range & 29 & Trim Var. & 42.3393235781916 \tabularnewline
V(Y[t],d=1,D=0) & 1.00084506362122 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.09255533199195 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.2257415460505 & Range & 8 & Trim Var. & 2.8500695817769 \tabularnewline
V(Y[t],d=0,D=1) & 8.41402699700407 & Range & 16 & Trim Var. & 2.56690636815183 \tabularnewline
V(Y[t],d=1,D=1) & 2.07380366905806 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.51132323618005 & Range & 8 & Trim Var. & 2.24078331204768 \tabularnewline
V(Y[t],d=3,D=1) & 13.7024111783250 & Range & 16 & Trim Var. & 6.60112055896118 \tabularnewline
V(Y[t],d=0,D=2) & 16.7079876160991 & Range & 24 & Trim Var. & 6.70809145835801 \tabularnewline
V(Y[t],d=1,D=2) & 6.2869020652898 & Range & 8 & Trim Var. & 2.73195534797443 \tabularnewline
V(Y[t],d=2,D=2) & 13.8433912275537 & Range & 16 & Trim Var. & 6.57494792389946 \tabularnewline
V(Y[t],d=3,D=2) & 42.1778048518293 & Range & 32 & Trim Var. & 22.6713861973502 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27010&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]57.6857715430862[/C][C]Range[/C][C]29[/C][C]Trim Var.[/C][C]42.3393235781916[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00084506362122[/C][C]Range[/C][C]2[/C][C]Trim Var.[/C][C]NA[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]2.09255533199195[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]6.2257415460505[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.8500695817769[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]8.41402699700407[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]2.56690636815183[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.07380366905806[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]4.51132323618005[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.24078331204768[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]13.7024111783250[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.60112055896118[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]16.7079876160991[/C][C]Range[/C][C]24[/C][C]Trim Var.[/C][C]6.70809145835801[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.2869020652898[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.73195534797443[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]13.8433912275537[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.57494792389946[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]42.1778048518293[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]22.6713861973502[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27010&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27010&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)57.6857715430862Range29Trim Var.42.3393235781916
V(Y[t],d=1,D=0)1.00084506362122Range2Trim Var.NA
V(Y[t],d=2,D=0)2.09255533199195Range4Trim Var.0
V(Y[t],d=3,D=0)6.2257415460505Range8Trim Var.2.8500695817769
V(Y[t],d=0,D=1)8.41402699700407Range16Trim Var.2.56690636815183
V(Y[t],d=1,D=1)2.07380366905806Range4Trim Var.0
V(Y[t],d=2,D=1)4.51132323618005Range8Trim Var.2.24078331204768
V(Y[t],d=3,D=1)13.7024111783250Range16Trim Var.6.60112055896118
V(Y[t],d=0,D=2)16.7079876160991Range24Trim Var.6.70809145835801
V(Y[t],d=1,D=2)6.2869020652898Range8Trim Var.2.73195534797443
V(Y[t],d=2,D=2)13.8433912275537Range16Trim Var.6.57494792389946
V(Y[t],d=3,D=2)42.1778048518293Range32Trim Var.22.6713861973502


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 0.5 ;
Parameters (R input):
par1 = 500 ; par2 = 0.5 ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
n <- as.numeric(par1)
p <- as.numeric(par2)
heads=rbinom(n-1,1,p)
a=2*(heads)-1
b=diffinv(a,xi=0)
c=1:n
pheads=(diffinv(heads,xi=.5))/c
bitmap(file='test1.png')
op=par(mfrow=c(2,1))
plot(c,b,type='n',main='Law of Averages',xlab='Toss Number',ylab='Excess of Heads',lwd=2,cex.lab=1.5,cex.main=2)
lines(c,b,col='red')
lines(c,rep(0,n),col='black')
plot(c,pheads,type='n',xlab='Toss Number',ylab='Proportion of Heads',lwd=2,cex.lab=1.5)
lines(c,pheads,col='blue')
lines(c,rep(.5,n),col='black')
par(op)
dev.off()
b
par1 <- as.numeric(12)
x <- as.array(b)
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')