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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_rwalk.wasp
Title produced by softwareLaw of Averages
Date of computationTue, 02 Dec 2008 05:43:25 -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/t1228221842hkddtq0djtmtguz.htm/, Retrieved Sun, 19 May 2024 12:01:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27694, Retrieved Sun, 19 May 2024 12:01:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186

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] [Vincent Dolhain T...] [2008-12-02 12:43:25] [b7b5ae6cd293e6a1a87dd48715549281] [Current]
Feedback Forum
2008-12-08 16:11:23 [Jessica Alves Pires] [reply] 
Je conclusie is juist, je hebt de waarden genomen met de kleinste variantie. Je had er nog bij kunnen zeggen waarom. Men wil een kleine variantie want hoe kleiner de variantie, hoe kleiner het risico, hoe meer je kan verklaren en hoe beter het model. Ook had je nog iets kunnen zeggen over de getrimde variantie. Als er veel outliers aanwezig zijn in een tijdreeks, dan kijk je best naar 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=27694&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=27694&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27694&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)22.6989819639279Range21Trim Var.13.8037111141857
V(Y[t],d=1,D=0)1.00200400801603Range2Trim Var.NA
V(Y[t],d=2,D=0)1.9476861167002Range4Trim Var.0
V(Y[t],d=3,D=0)5.81449990264166Range8Trim Var.2.59921687507894
V(Y[t],d=0,D=1)13.0844413774531Range20Trim Var.6.5069306930693
V(Y[t],d=1,D=1)1.95046518112911Range4Trim Var.0
V(Y[t],d=2,D=1)3.67828263544186Range8Trim Var.0.910992796742875
V(Y[t],d=3,D=1)10.9338672573912Range16Trim Var.5.53059545358377
V(Y[t],d=0,D=2)30.7956302521008Range30Trim Var.14.2020252057988
V(Y[t],d=1,D=2)5.81432822562736Range8Trim Var.2.61915183422354
V(Y[t],d=2,D=2)10.8837030891785Range16Trim Var.6.26276427441392
V(Y[t],d=3,D=2)32.3978571684524Range28Trim Var.19.1414634146341

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 22.6989819639279 & Range & 21 & Trim Var. & 13.8037111141857 \tabularnewline
V(Y[t],d=1,D=0) & 1.00200400801603 & 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) & 5.81449990264166 & Range & 8 & Trim Var. & 2.59921687507894 \tabularnewline
V(Y[t],d=0,D=1) & 13.0844413774531 & Range & 20 & Trim Var. & 6.5069306930693 \tabularnewline
V(Y[t],d=1,D=1) & 1.95046518112911 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.67828263544186 & Range & 8 & Trim Var. & 0.910992796742875 \tabularnewline
V(Y[t],d=3,D=1) & 10.9338672573912 & Range & 16 & Trim Var. & 5.53059545358377 \tabularnewline
V(Y[t],d=0,D=2) & 30.7956302521008 & Range & 30 & Trim Var. & 14.2020252057988 \tabularnewline
V(Y[t],d=1,D=2) & 5.81432822562736 & Range & 8 & Trim Var. & 2.61915183422354 \tabularnewline
V(Y[t],d=2,D=2) & 10.8837030891785 & Range & 16 & Trim Var. & 6.26276427441392 \tabularnewline
V(Y[t],d=3,D=2) & 32.3978571684524 & Range & 28 & Trim Var. & 19.1414634146341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27694&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]22.6989819639279[/C][C]Range[/C][C]21[/C][C]Trim Var.[/C][C]13.8037111141857[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00200400801603[/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]5.81449990264166[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.59921687507894[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]13.0844413774531[/C][C]Range[/C][C]20[/C][C]Trim Var.[/C][C]6.5069306930693[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.95046518112911[/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]3.67828263544186[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]0.910992796742875[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10.9338672573912[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]5.53059545358377[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]30.7956302521008[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]14.2020252057988[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.81432822562736[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.61915183422354[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]10.8837030891785[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.26276427441392[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]32.3978571684524[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]19.1414634146341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27694&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27694&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)22.6989819639279Range21Trim Var.13.8037111141857
V(Y[t],d=1,D=0)1.00200400801603Range2Trim Var.NA
V(Y[t],d=2,D=0)1.9476861167002Range4Trim Var.0
V(Y[t],d=3,D=0)5.81449990264166Range8Trim Var.2.59921687507894
V(Y[t],d=0,D=1)13.0844413774531Range20Trim Var.6.5069306930693
V(Y[t],d=1,D=1)1.95046518112911Range4Trim Var.0
V(Y[t],d=2,D=1)3.67828263544186Range8Trim Var.0.910992796742875
V(Y[t],d=3,D=1)10.9338672573912Range16Trim Var.5.53059545358377
V(Y[t],d=0,D=2)30.7956302521008Range30Trim Var.14.2020252057988
V(Y[t],d=1,D=2)5.81432822562736Range8Trim Var.2.61915183422354
V(Y[t],d=2,D=2)10.8837030891785Range16Trim Var.6.26276427441392
V(Y[t],d=3,D=2)32.3978571684524Range28Trim Var.19.1414634146341


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')