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 computationTue, 02 Dec 2008 12:57:15 -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/02/t1228247901bjjb4mzm87e340r.htm/, Retrieved Sun, 19 May 2024 09:22:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28302, Retrieved Sun, 19 May 2024 09:22:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsnon stationary time series
Estimated Impact177

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...] [2008-12-02 19:57:15] [74c7506a1ea162af3aa8be25bcd05d28] [Current]
Feedback Forum
2008-12-08 15:45:09 [6066575aa30c0611e452e930b1dff53d] [reply] 
Deze vraag werd ook zeer goed beantwoord. Er werd duidelijk vermeld dat D staat voor seizoenaal differentiëren, dit is omdat er eventueel seizoenaliteit in de tijdreeks zit en dat de kleine d staat voor het aantal keer dat er gedifferentieerd werd. Verder had men eventueel kunnen vermelden dat als ik een tijdreeks wil voorspellen, de variantie dan het risico voorstelt dat in de tijdreeks zit. We trachten dus de variantie zo klein mogelijk te maken. De kleinste variantie is 1 (bij d=1 en D=0). Dit is dus bij 1 keer differentiëren. Dit is logisch want we hebben het zo gesimuleerd.


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=28302&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=28302&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28302&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)65.3483927855711Range35Trim Var.36.6285684018020
V(Y[t],d=1,D=0)0.999492961827269Range2Trim Var.NA
V(Y[t],d=2,D=0)2.06839430155229Range4Trim Var.0
V(Y[t],d=3,D=0)6.29032258064516Range8Trim Var.2.69778706142342
V(Y[t],d=0,D=1)9.2772747163968Range16Trim Var.4.13930174563591
V(Y[t],d=1,D=1)1.99176954732510Range4Trim Var.0
V(Y[t],d=2,D=1)4.14843663824191Range8Trim Var.2.24878098914024
V(Y[t],d=3,D=1)12.4132231404959Range16Trim Var.6.63017467999902
V(Y[t],d=0,D=2)19.6881379920389Range26Trim Var.8.90306081675058
V(Y[t],d=1,D=2)5.9409282700422Range8Trim Var.2.59415226764863
V(Y[t],d=2,D=2)12.1775184877923Range16Trim Var.5.73262144881682
V(Y[t],d=3,D=2)35.8893825921812Range32Trim Var.20.9055127734996

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 65.3483927855711 & Range & 35 & Trim Var. & 36.6285684018020 \tabularnewline
V(Y[t],d=1,D=0) & 0.999492961827269 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.06839430155229 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.29032258064516 & Range & 8 & Trim Var. & 2.69778706142342 \tabularnewline
V(Y[t],d=0,D=1) & 9.2772747163968 & Range & 16 & Trim Var. & 4.13930174563591 \tabularnewline
V(Y[t],d=1,D=1) & 1.99176954732510 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.14843663824191 & Range & 8 & Trim Var. & 2.24878098914024 \tabularnewline
V(Y[t],d=3,D=1) & 12.4132231404959 & Range & 16 & Trim Var. & 6.63017467999902 \tabularnewline
V(Y[t],d=0,D=2) & 19.6881379920389 & Range & 26 & Trim Var. & 8.90306081675058 \tabularnewline
V(Y[t],d=1,D=2) & 5.9409282700422 & Range & 8 & Trim Var. & 2.59415226764863 \tabularnewline
V(Y[t],d=2,D=2) & 12.1775184877923 & Range & 16 & Trim Var. & 5.73262144881682 \tabularnewline
V(Y[t],d=3,D=2) & 35.8893825921812 & Range & 32 & Trim Var. & 20.9055127734996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28302&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]65.3483927855711[/C][C]Range[/C][C]35[/C][C]Trim Var.[/C][C]36.6285684018020[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.999492961827269[/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]2.06839430155229[/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]6.29032258064516[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.69778706142342[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]9.2772747163968[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]4.13930174563591[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.99176954732510[/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]4.14843663824191[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.24878098914024[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.4132231404959[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.63017467999902[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]19.6881379920389[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]8.90306081675058[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.9409282700422[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.59415226764863[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.1775184877923[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]5.73262144881682[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]35.8893825921812[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]20.9055127734996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28302&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28302&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)65.3483927855711Range35Trim Var.36.6285684018020
V(Y[t],d=1,D=0)0.999492961827269Range2Trim Var.NA
V(Y[t],d=2,D=0)2.06839430155229Range4Trim Var.0
V(Y[t],d=3,D=0)6.29032258064516Range8Trim Var.2.69778706142342
V(Y[t],d=0,D=1)9.2772747163968Range16Trim Var.4.13930174563591
V(Y[t],d=1,D=1)1.99176954732510Range4Trim Var.0
V(Y[t],d=2,D=1)4.14843663824191Range8Trim Var.2.24878098914024
V(Y[t],d=3,D=1)12.4132231404959Range16Trim Var.6.63017467999902
V(Y[t],d=0,D=2)19.6881379920389Range26Trim Var.8.90306081675058
V(Y[t],d=1,D=2)5.9409282700422Range8Trim Var.2.59415226764863
V(Y[t],d=2,D=2)12.1775184877923Range16Trim Var.5.73262144881682
V(Y[t],d=3,D=2)35.8893825921812Range32Trim Var.20.9055127734996


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')