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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 03:24:27 -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/t1228040901nxczeykqec4fzbz.htm/, Retrieved Thu, 23 May 2024 07:23:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26414, Retrieved Thu, 23 May 2024 07:23:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192

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] [] [2008-11-30 10:24:27] [19ef54504342c1b076371d395a2ab19f] [Current]
-           [Law of Averages] [Q3] [2008-11-30 18:04:14] [2b46c8b774ad566be9a33a8da3812a44]
F           [Law of Averages] [] [2008-12-01 19:09:36] [d134696a922d84037f02d49ded84b0bd]
Feedback Forum
2008-12-08 09:35:29 [256f97d8b7c07ed49f142eff724c6520] [reply] 
Een goede uitleg, maar we zouden extra moeten uitleggen dat je bij e onderste grafiek wel een trend kunt vaststellen. Hoe meer keer je de simulatie doet hoe dichter bij 0,5 de grafiek zal liggen. Hier kan je zeggen dat als je oneindig veel simulaties doet is de kans op kop gelijk aan munt.
Omwille dat dit een willekeurige datareeks is veranderen telkens de gegevens, en kan je hier geen seizoenaliteit waarnemen.

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 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=26414&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=26414&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26414&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)58.0444248496994Range38Trim Var.29.7369881879337
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)2.03620114259856Range4Trim Var.0
V(Y[t],d=3,D=0)6.18546764457714Range8Trim Var.2.66298375626918
V(Y[t],d=0,D=1)11.7456491735954Range18Trim Var.6.82520740993295
V(Y[t],d=1,D=1)2.16445695067643Range4Trim Var.0
V(Y[t],d=2,D=1)4.49482839081923Range8Trim Var.2.35579939173475
V(Y[t],d=3,D=1)13.6115532078044Range16Trim Var.6.62508324675671
V(Y[t],d=0,D=2)24.0504555506413Range28Trim Var.11.3401714861312
V(Y[t],d=1,D=2)6.72572063069065Range8Trim Var.2.75610550309345
V(Y[t],d=2,D=2)14.0295804676140Range16Trim Var.6.6223516795641
V(Y[t],d=3,D=2)42.211703156914Range30Trim Var.23.9411371237458

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 58.0444248496994 & Range & 38 & Trim Var. & 29.7369881879337 \tabularnewline
V(Y[t],d=1,D=0) & 1.00152111451819 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.03620114259856 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.18546764457714 & Range & 8 & Trim Var. & 2.66298375626918 \tabularnewline
V(Y[t],d=0,D=1) & 11.7456491735954 & Range & 18 & Trim Var. & 6.82520740993295 \tabularnewline
V(Y[t],d=1,D=1) & 2.16445695067643 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.49482839081923 & Range & 8 & Trim Var. & 2.35579939173475 \tabularnewline
V(Y[t],d=3,D=1) & 13.6115532078044 & Range & 16 & Trim Var. & 6.62508324675671 \tabularnewline
V(Y[t],d=0,D=2) & 24.0504555506413 & Range & 28 & Trim Var. & 11.3401714861312 \tabularnewline
V(Y[t],d=1,D=2) & 6.72572063069065 & Range & 8 & Trim Var. & 2.75610550309345 \tabularnewline
V(Y[t],d=2,D=2) & 14.0295804676140 & Range & 16 & Trim Var. & 6.6223516795641 \tabularnewline
V(Y[t],d=3,D=2) & 42.211703156914 & Range & 30 & Trim Var. & 23.9411371237458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26414&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]58.0444248496994[/C][C]Range[/C][C]38[/C][C]Trim Var.[/C][C]29.7369881879337[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00152111451819[/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]2.03620114259856[/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]6.18546764457714[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.66298375626918[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]11.7456491735954[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]6.82520740993295[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.16445695067643[/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]4.49482839081923[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.35579939173475[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]13.6115532078044[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.62508324675671[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]24.0504555506413[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]11.3401714861312[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.72572063069065[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.75610550309345[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]14.0295804676140[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.6223516795641[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]42.211703156914[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]23.9411371237458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26414&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26414&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)58.0444248496994Range38Trim Var.29.7369881879337
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)2.03620114259856Range4Trim Var.0
V(Y[t],d=3,D=0)6.18546764457714Range8Trim Var.2.66298375626918
V(Y[t],d=0,D=1)11.7456491735954Range18Trim Var.6.82520740993295
V(Y[t],d=1,D=1)2.16445695067643Range4Trim Var.0
V(Y[t],d=2,D=1)4.49482839081923Range8Trim Var.2.35579939173475
V(Y[t],d=3,D=1)13.6115532078044Range16Trim Var.6.62508324675671
V(Y[t],d=0,D=2)24.0504555506413Range28Trim Var.11.3401714861312
V(Y[t],d=1,D=2)6.72572063069065Range8Trim Var.2.75610550309345
V(Y[t],d=2,D=2)14.0295804676140Range16Trim Var.6.6223516795641
V(Y[t],d=3,D=2)42.211703156914Range30Trim Var.23.9411371237458


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')