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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 11:22:17 -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/t1228156045rlepxokrc1egbp4.htm/, Retrieved Sun, 05 May 2024 10:16:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27098, Retrieved Sun, 05 May 2024 10:16:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181

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] [] [2008-12-01 18:22:17] [428345b1a3979ee2ad6751f9aac15fbb] [Current]
Feedback Forum
2008-12-05 19:30:49 [Bob Leysen] [reply] 
De 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 1.00152, dus na 1 keer gewoon te differentiëren en geen enkele keer seizonaal.

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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=27098&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=27098&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27098&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)49.2501963927856Range34Trim Var.33.0804267419663
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)1.9476861167002Range4Trim Var.0
V(Y[t],d=3,D=0)5.76611280586746Range8Trim Var.2.62483768476309
V(Y[t],d=0,D=1)13.0166627394217Range20Trim Var.6.39133927012397
V(Y[t],d=1,D=1)2.03290491038609Range4Trim Var.0
V(Y[t],d=2,D=1)4.20611768698825Range8Trim Var.2.37516118086189
V(Y[t],d=3,D=1)12.6694044474738Range16Trim Var.6.9276616550544
V(Y[t],d=0,D=2)21.45590446705Range24Trim Var.11.8988184622548
V(Y[t],d=1,D=2)6.13500333111259Range8Trim Var.2.6764763014763
V(Y[t],d=2,D=2)12.8625614401299Range16Trim Var.6.70478702149764
V(Y[t],d=3,D=2)38.7965385028846Range30Trim Var.22.1733709894919

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 49.2501963927856 & Range & 34 & Trim Var. & 33.0804267419663 \tabularnewline
V(Y[t],d=1,D=0) & 1.00152111451819 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.9476861167002 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.76611280586746 & Range & 8 & Trim Var. & 2.62483768476309 \tabularnewline
V(Y[t],d=0,D=1) & 13.0166627394217 & Range & 20 & Trim Var. & 6.39133927012397 \tabularnewline
V(Y[t],d=1,D=1) & 2.03290491038609 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.20611768698825 & Range & 8 & Trim Var. & 2.37516118086189 \tabularnewline
V(Y[t],d=3,D=1) & 12.6694044474738 & Range & 16 & Trim Var. & 6.9276616550544 \tabularnewline
V(Y[t],d=0,D=2) & 21.45590446705 & Range & 24 & Trim Var. & 11.8988184622548 \tabularnewline
V(Y[t],d=1,D=2) & 6.13500333111259 & Range & 8 & Trim Var. & 2.6764763014763 \tabularnewline
V(Y[t],d=2,D=2) & 12.8625614401299 & Range & 16 & Trim Var. & 6.70478702149764 \tabularnewline
V(Y[t],d=3,D=2) & 38.7965385028846 & Range & 30 & Trim Var. & 22.1733709894919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27098&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]49.2501963927856[/C][C]Range[/C][C]34[/C][C]Trim Var.[/C][C]33.0804267419663[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00152111451819[/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]1.9476861167002[/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]5.76611280586746[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.62483768476309[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]13.0166627394217[/C][C]Range[/C][C]20[/C][C]Trim Var.[/C][C]6.39133927012397[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.03290491038609[/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]4.20611768698825[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.37516118086189[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.6694044474738[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.9276616550544[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]21.45590446705[/C][C]Range[/C][C]24[/C][C]Trim Var.[/C][C]11.8988184622548[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.13500333111259[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.6764763014763[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.8625614401299[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.70478702149764[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]38.7965385028846[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]22.1733709894919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27098&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27098&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)49.2501963927856Range34Trim Var.33.0804267419663
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)1.9476861167002Range4Trim Var.0
V(Y[t],d=3,D=0)5.76611280586746Range8Trim Var.2.62483768476309
V(Y[t],d=0,D=1)13.0166627394217Range20Trim Var.6.39133927012397
V(Y[t],d=1,D=1)2.03290491038609Range4Trim Var.0
V(Y[t],d=2,D=1)4.20611768698825Range8Trim Var.2.37516118086189
V(Y[t],d=3,D=1)12.6694044474738Range16Trim Var.6.9276616550544
V(Y[t],d=0,D=2)21.45590446705Range24Trim Var.11.8988184622548
V(Y[t],d=1,D=2)6.13500333111259Range8Trim Var.2.6764763014763
V(Y[t],d=2,D=2)12.8625614401299Range16Trim Var.6.70478702149764
V(Y[t],d=3,D=2)38.7965385028846Range30Trim Var.22.1733709894919


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')