FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 13:53:21 -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/Nov/30/t1228078442dceit4hwj9lde4t.htm/, Retrieved Sun, 19 May 2024 08:51:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26723, Retrieved Sun, 19 May 2024 08:51:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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] [Non Stationary Ti...] [2008-11-30 20:53:21] [7957bb37a64ed417bbed8444b0b0ea8a] [Current]
Feedback Forum
2008-12-08 18:20:56 [Stéphanie Claes] [reply] 
De varianties geven aan hoe groot de spreiding is van de reeks.
Zoals we kunnen aflezen in de tabel gaan we 1x gewoon differentieren om de variantie zo klein mogelijk te maken (= random walk) => V(Y[t],d=1,D=0) 1.00168207901747.
We kijken hiervoor naar de waarde die het kleinste is (tweede kolom) omdat we het niet verklaarde deel zo klein mogelijk willen houden.

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=26723&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=26723&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26723&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)19.2374188376753Range22Trim Var.12.3006194059237
V(Y[t],d=1,D=0)1.00168207901747Range2Trim Var.NA
V(Y[t],d=2,D=0)2.02010456312170Range4Trim Var.0
V(Y[t],d=3,D=0)6.01612903225806Range8Trim Var.2.72374589611618
V(Y[t],d=0,D=1)9.43614299660013Range18Trim Var.3.96259426847662
V(Y[t],d=1,D=1)2.08228762643547Range4Trim Var.0
V(Y[t],d=2,D=1)4.27216494845361Range8Trim Var.2.42336629162405
V(Y[t],d=3,D=1)12.7519127545369Range16Trim Var.6.96941694169417
V(Y[t],d=0,D=2)23.3139141972579Range24Trim Var.12.2533239509648
V(Y[t],d=1,D=2)6.19402176326893Range8Trim Var.2.79223538501159
V(Y[t],d=2,D=2)12.5324840991606Range16Trim Var.6.67955722607561
V(Y[t],d=3,D=2)37.211703156914Range30Trim Var.21.7264329410609

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 19.2374188376753 & Range & 22 & Trim Var. & 12.3006194059237 \tabularnewline
V(Y[t],d=1,D=0) & 1.00168207901747 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.02010456312170 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.01612903225806 & Range & 8 & Trim Var. & 2.72374589611618 \tabularnewline
V(Y[t],d=0,D=1) & 9.43614299660013 & Range & 18 & Trim Var. & 3.96259426847662 \tabularnewline
V(Y[t],d=1,D=1) & 2.08228762643547 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.27216494845361 & Range & 8 & Trim Var. & 2.42336629162405 \tabularnewline
V(Y[t],d=3,D=1) & 12.7519127545369 & Range & 16 & Trim Var. & 6.96941694169417 \tabularnewline
V(Y[t],d=0,D=2) & 23.3139141972579 & Range & 24 & Trim Var. & 12.2533239509648 \tabularnewline
V(Y[t],d=1,D=2) & 6.19402176326893 & Range & 8 & Trim Var. & 2.79223538501159 \tabularnewline
V(Y[t],d=2,D=2) & 12.5324840991606 & Range & 16 & Trim Var. & 6.67955722607561 \tabularnewline
V(Y[t],d=3,D=2) & 37.211703156914 & Range & 30 & Trim Var. & 21.7264329410609 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26723&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]19.2374188376753[/C][C]Range[/C][C]22[/C][C]Trim Var.[/C][C]12.3006194059237[/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]2.02010456312170[/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]6.01612903225806[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.72374589611618[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]9.43614299660013[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]3.96259426847662[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.08228762643547[/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]4.27216494845361[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.42336629162405[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.7519127545369[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.96941694169417[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]23.3139141972579[/C][C]Range[/C][C]24[/C][C]Trim Var.[/C][C]12.2533239509648[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.19402176326893[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.79223538501159[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.5324840991606[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.67955722607561[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]37.211703156914[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]21.7264329410609[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26723&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26723&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)19.2374188376753Range22Trim Var.12.3006194059237
V(Y[t],d=1,D=0)1.00168207901747Range2Trim Var.NA
V(Y[t],d=2,D=0)2.02010456312170Range4Trim Var.0
V(Y[t],d=3,D=0)6.01612903225806Range8Trim Var.2.72374589611618
V(Y[t],d=0,D=1)9.43614299660013Range18Trim Var.3.96259426847662
V(Y[t],d=1,D=1)2.08228762643547Range4Trim Var.0
V(Y[t],d=2,D=1)4.27216494845361Range8Trim Var.2.42336629162405
V(Y[t],d=3,D=1)12.7519127545369Range16Trim Var.6.96941694169417
V(Y[t],d=0,D=2)23.3139141972579Range24Trim Var.12.2533239509648
V(Y[t],d=1,D=2)6.19402176326893Range8Trim Var.2.79223538501159
V(Y[t],d=2,D=2)12.5324840991606Range16Trim Var.6.67955722607561
V(Y[t],d=3,D=2)37.211703156914Range30Trim Var.21.7264329410609


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')