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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 11:51:15 -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/t1228157511mn00nu40u89nieq.htm/, Retrieved Sun, 05 May 2024 11:17:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27144, Retrieved Sun, 05 May 2024 11:17:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact223

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 18:51:15] [25d75405d700c34901b109463a9659f5] [Current]
Feedback Forum
2008-12-05 10:39:47 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct beantwoord. In de tweede rij is de variantie inderdaad het kleinst. Dit wijst erop dat wanneer we de reeks één keer differentiëren het lange termijn effect zal uitgezuiverd worden en de reeks stabieler wordt.
2008-12-08 18:46:46 [Jens Peeters] [reply] 
Correct antwoord.

Post a new message

Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27144&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]2 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=27144&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27144&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)26.4860761523046Range22Trim Var.18.4639558853845
V(Y[t],d=1,D=0)1.00168207901747Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93158953722334Range4Trim Var.0
V(Y[t],d=3,D=0)5.78224183812553Range8Trim Var.2.54891723857241
V(Y[t],d=0,D=1)11.018934931161Range16Trim Var.6.24943743607228
V(Y[t],d=1,D=1)2.03290491038609Range4Trim Var.0
V(Y[t],d=2,D=1)3.82673624368928Range8Trim Var.0.982857142857143
V(Y[t],d=3,D=1)11.6362954758456Range16Trim Var.7.05827920433182
V(Y[t],d=0,D=2)27.0136045997346Range30Trim Var.12.9427641884564
V(Y[t],d=1,D=2)6.22777703753053Range8Trim Var.2.64652688065189
V(Y[t],d=2,D=2)11.9154155627514Range16Trim Var.7.18030524927077
V(Y[t],d=3,D=2)37.0761099365751Range28Trim Var.21.9056359102244

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 26.4860761523046 & Range & 22 & Trim Var. & 18.4639558853845 \tabularnewline
V(Y[t],d=1,D=0) & 1.00168207901747 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.93158953722334 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.78224183812553 & Range & 8 & Trim Var. & 2.54891723857241 \tabularnewline
V(Y[t],d=0,D=1) & 11.018934931161 & Range & 16 & Trim Var. & 6.24943743607228 \tabularnewline
V(Y[t],d=1,D=1) & 2.03290491038609 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.82673624368928 & Range & 8 & Trim Var. & 0.982857142857143 \tabularnewline
V(Y[t],d=3,D=1) & 11.6362954758456 & Range & 16 & Trim Var. & 7.05827920433182 \tabularnewline
V(Y[t],d=0,D=2) & 27.0136045997346 & Range & 30 & Trim Var. & 12.9427641884564 \tabularnewline
V(Y[t],d=1,D=2) & 6.22777703753053 & Range & 8 & Trim Var. & 2.64652688065189 \tabularnewline
V(Y[t],d=2,D=2) & 11.9154155627514 & Range & 16 & Trim Var. & 7.18030524927077 \tabularnewline
V(Y[t],d=3,D=2) & 37.0761099365751 & Range & 28 & Trim Var. & 21.9056359102244 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27144&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]26.4860761523046[/C][C]Range[/C][C]22[/C][C]Trim Var.[/C][C]18.4639558853845[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00168207901747[/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]1.93158953722334[/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]5.78224183812553[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.54891723857241[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]11.018934931161[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.24943743607228[/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]3.82673624368928[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]0.982857142857143[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]11.6362954758456[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.05827920433182[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]27.0136045997346[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]12.9427641884564[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.22777703753053[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.64652688065189[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]11.9154155627514[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.18030524927077[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]37.0761099365751[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]21.9056359102244[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27144&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27144&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)26.4860761523046Range22Trim Var.18.4639558853845
V(Y[t],d=1,D=0)1.00168207901747Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93158953722334Range4Trim Var.0
V(Y[t],d=3,D=0)5.78224183812553Range8Trim Var.2.54891723857241
V(Y[t],d=0,D=1)11.018934931161Range16Trim Var.6.24943743607228
V(Y[t],d=1,D=1)2.03290491038609Range4Trim Var.0
V(Y[t],d=2,D=1)3.82673624368928Range8Trim Var.0.982857142857143
V(Y[t],d=3,D=1)11.6362954758456Range16Trim Var.7.05827920433182
V(Y[t],d=0,D=2)27.0136045997346Range30Trim Var.12.9427641884564
V(Y[t],d=1,D=2)6.22777703753053Range8Trim Var.2.64652688065189
V(Y[t],d=2,D=2)11.9154155627514Range16Trim Var.7.18030524927077
V(Y[t],d=3,D=2)37.0761099365751Range28Trim Var.21.9056359102244


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')