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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 15:50:54 -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/01/t12281719467qo9g67ja648su5.htm/, Retrieved Sun, 05 May 2024 16:01:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27493, Retrieved Sun, 05 May 2024 16:01:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ3 Non Stationary Time Series
Estimated Impact178

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 Stationary...] [2008-12-01 22:50:54] [9f72e095d5529918bf5b0810c01bf6ce] [Current]
Feedback Forum
2008-12-04 11:06:58 [] [reply] 
je antwoord is volledig, je moet inderdaad de kleinste variantie eruit pikken.
Rij 2 is hier inderdaad dan de beste.
2008-12-06 16:53:38 [a2386b643d711541400692649981f2dc] [reply] 
Goed antwoord! Je verklaart waarom je het VRM model gebruikt en je vertelt ook dat je de kleinste variantie moet nemen, aangezien bij deze het risico of de volatiliteit in de tijdreeks het kleinst zal zijn. Ook vertel je in welke gevallen je het getrimde VRM model zal gebruiken en geef je de bijhorende oplossing.
2008-12-08 23:08:43 [Jessica Alves Pires] [reply] 
Ik vind dat ik een volledig antwoord heb gegeven. Ik heb er niets op aan te merken. Ik heb de kleinste variantie genomen, ik zeg daar ook bij waarom: hoe kleiner de variantie, hoe kleiner het risico, hoe meer je kan verklaren en dus hoe beter het model. Ook vermeld ik nog de getrimde variantie.

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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=27493&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=27493&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27493&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)63.6725290581162Range29Trim Var.50.737840608644
V(Y[t],d=1,D=0)0.99949296182727Range2Trim Var.NA
V(Y[t],d=2,D=0)1.97987927565392Range4Trim Var.0
V(Y[t],d=3,D=0)5.83864477185695Range8Trim Var.2.70140258297459
V(Y[t],d=0,D=1)9.31679402161107Range16Trim Var.3.92796317606444
V(Y[t],d=1,D=1)1.92585832467192Range4Trim Var.0
V(Y[t],d=2,D=1)3.67008612277799Range8Trim Var.0.95940255160253
V(Y[t],d=3,D=1)10.4545284144160Range16Trim Var.6.30863485016648
V(Y[t],d=0,D=2)19.7296594427245Range24Trim Var.9.51747706860963
V(Y[t],d=1,D=2)5.67916500111037Range8Trim Var.2.59699079272401
V(Y[t],d=2,D=2)10.6975138491182Range16Trim Var.6.19930078189592
V(Y[t],d=3,D=2)30.2283656430286Range28Trim Var.16.4765683051851

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 63.6725290581162 & Range & 29 & Trim Var. & 50.737840608644 \tabularnewline
V(Y[t],d=1,D=0) & 0.99949296182727 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.97987927565392 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.83864477185695 & Range & 8 & Trim Var. & 2.70140258297459 \tabularnewline
V(Y[t],d=0,D=1) & 9.31679402161107 & Range & 16 & Trim Var. & 3.92796317606444 \tabularnewline
V(Y[t],d=1,D=1) & 1.92585832467192 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.67008612277799 & Range & 8 & Trim Var. & 0.95940255160253 \tabularnewline
V(Y[t],d=3,D=1) & 10.4545284144160 & Range & 16 & Trim Var. & 6.30863485016648 \tabularnewline
V(Y[t],d=0,D=2) & 19.7296594427245 & Range & 24 & Trim Var. & 9.51747706860963 \tabularnewline
V(Y[t],d=1,D=2) & 5.67916500111037 & Range & 8 & Trim Var. & 2.59699079272401 \tabularnewline
V(Y[t],d=2,D=2) & 10.6975138491182 & Range & 16 & Trim Var. & 6.19930078189592 \tabularnewline
V(Y[t],d=3,D=2) & 30.2283656430286 & Range & 28 & Trim Var. & 16.4765683051851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27493&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]63.6725290581162[/C][C]Range[/C][C]29[/C][C]Trim Var.[/C][C]50.737840608644[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.99949296182727[/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]1.97987927565392[/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]5.83864477185695[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.70140258297459[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]9.31679402161107[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]3.92796317606444[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.92585832467192[/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]3.67008612277799[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]0.95940255160253[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10.4545284144160[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.30863485016648[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]19.7296594427245[/C][C]Range[/C][C]24[/C][C]Trim Var.[/C][C]9.51747706860963[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.67916500111037[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.59699079272401[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]10.6975138491182[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.19930078189592[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]30.2283656430286[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]16.4765683051851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27493&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27493&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)63.6725290581162Range29Trim Var.50.737840608644
V(Y[t],d=1,D=0)0.99949296182727Range2Trim Var.NA
V(Y[t],d=2,D=0)1.97987927565392Range4Trim Var.0
V(Y[t],d=3,D=0)5.83864477185695Range8Trim Var.2.70140258297459
V(Y[t],d=0,D=1)9.31679402161107Range16Trim Var.3.92796317606444
V(Y[t],d=1,D=1)1.92585832467192Range4Trim Var.0
V(Y[t],d=2,D=1)3.67008612277799Range8Trim Var.0.95940255160253
V(Y[t],d=3,D=1)10.4545284144160Range16Trim Var.6.30863485016648
V(Y[t],d=0,D=2)19.7296594427245Range24Trim Var.9.51747706860963
V(Y[t],d=1,D=2)5.67916500111037Range8Trim Var.2.59699079272401
V(Y[t],d=2,D=2)10.6975138491182Range16Trim Var.6.19930078189592
V(Y[t],d=3,D=2)30.2283656430286Range28Trim Var.16.4765683051851


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 0.5 ;
Parameters (R input):
par1 = 500 ; par2 = 0.5 ;
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')