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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 computationSun, 30 Nov 2008 16:57:20 -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/t122808947414v3r1d25y6cxj4.htm/, Retrieved Sun, 05 May 2024 09:38:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26794, Retrieved Sun, 05 May 2024 09:38:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

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 - non stationa...] [2008-11-30 23:57:20] [0831954c833179c36e9320daee0825b5] [Current]
Feedback Forum
2008-12-05 18:13:14 [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 0,9998792, 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'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=26794&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=26794&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26794&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)116.069482965932Range35Trim Var.95.6598852700542
V(Y[t],d=1,D=0)1.00110260682007Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93962166573740Range4Trim Var.0
V(Y[t],d=3,D=0)5.70967741935484Range8Trim Var.2.61730892786352
V(Y[t],d=0,D=1)12.1137610664153Range18Trim Var.6.44679091961354
V(Y[t],d=1,D=1)2.04115226337449Range4Trim Var.0
V(Y[t],d=2,D=1)3.92577319587629Range8Trim Var.0.989831944640587
V(Y[t],d=3,D=1)11.5289085797052Range16Trim Var.7.2564982945143
V(Y[t],d=0,D=2)24.1431402034498Range26Trim Var.13.5706508647685
V(Y[t],d=1,D=2)6.1687586053742Range8Trim Var.2.7471671388102
V(Y[t],d=2,D=2)11.6701902748414Range16Trim Var.6.88316831683168
V(Y[t],d=3,D=2)34.1355215537320Range28Trim Var.22.8008444918706

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 116.069482965932 & Range & 35 & Trim Var. & 95.6598852700542 \tabularnewline
V(Y[t],d=1,D=0) & 1.00110260682007 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.93962166573740 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.70967741935484 & Range & 8 & Trim Var. & 2.61730892786352 \tabularnewline
V(Y[t],d=0,D=1) & 12.1137610664153 & Range & 18 & Trim Var. & 6.44679091961354 \tabularnewline
V(Y[t],d=1,D=1) & 2.04115226337449 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.92577319587629 & Range & 8 & Trim Var. & 0.989831944640587 \tabularnewline
V(Y[t],d=3,D=1) & 11.5289085797052 & Range & 16 & Trim Var. & 7.2564982945143 \tabularnewline
V(Y[t],d=0,D=2) & 24.1431402034498 & Range & 26 & Trim Var. & 13.5706508647685 \tabularnewline
V(Y[t],d=1,D=2) & 6.1687586053742 & Range & 8 & Trim Var. & 2.7471671388102 \tabularnewline
V(Y[t],d=2,D=2) & 11.6701902748414 & Range & 16 & Trim Var. & 6.88316831683168 \tabularnewline
V(Y[t],d=3,D=2) & 34.1355215537320 & Range & 28 & Trim Var. & 22.8008444918706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26794&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]116.069482965932[/C][C]Range[/C][C]35[/C][C]Trim Var.[/C][C]95.6598852700542[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00110260682007[/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]1.93962166573740[/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]5.70967741935484[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.61730892786352[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]12.1137610664153[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]6.44679091961354[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.04115226337449[/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]3.92577319587629[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]0.989831944640587[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]11.5289085797052[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.2564982945143[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]24.1431402034498[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]13.5706508647685[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.1687586053742[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.7471671388102[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]11.6701902748414[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.88316831683168[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]34.1355215537320[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]22.8008444918706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26794&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26794&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)116.069482965932Range35Trim Var.95.6598852700542
V(Y[t],d=1,D=0)1.00110260682007Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93962166573740Range4Trim Var.0
V(Y[t],d=3,D=0)5.70967741935484Range8Trim Var.2.61730892786352
V(Y[t],d=0,D=1)12.1137610664153Range18Trim Var.6.44679091961354
V(Y[t],d=1,D=1)2.04115226337449Range4Trim Var.0
V(Y[t],d=2,D=1)3.92577319587629Range8Trim Var.0.989831944640587
V(Y[t],d=3,D=1)11.5289085797052Range16Trim Var.7.2564982945143
V(Y[t],d=0,D=2)24.1431402034498Range26Trim Var.13.5706508647685
V(Y[t],d=1,D=2)6.1687586053742Range8Trim Var.2.7471671388102
V(Y[t],d=2,D=2)11.6701902748414Range16Trim Var.6.88316831683168
V(Y[t],d=3,D=2)34.1355215537320Range28Trim Var.22.8008444918706


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')