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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 10:23:52 -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/t12280658618ajqosrml6duxvv.htm/, Retrieved Mon, 20 May 2024 07:01:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26632, Retrieved Mon, 20 May 2024 07:01:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149

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] [NSTS_Q3] [2008-11-30 17:23:52] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]
Feedback Forum
2008-12-08 16:59:13 [Sandra Hofmans] [reply] 
Goede conclusie. Ik kan hier nog bij vermelden dat als we kiezen voor d=1 dit betekent dat we 1 periode terug in de tijdreeks zullen gaan. D betekent dat we seizoenaal differentiëren, maar dat gebeurt hier inderdaad dus niet. Nog beter is om te kijken naar de getrimde variantie omdat hier de outliners zijn weggewerkt.
2008-12-08 19:06:40 [Lana Van Wesemael] [reply] 
Goed opgelost. Hier had ik nog kunnen bijschrijven dat als er gevaar is op outliers men best naar de getrimde variantie kan kijken. Deze geeft dan een getrouwer beeld. De uiterste waarden zijn immers getrimd waardoor de outliers niet meer aanwezig zijn.
2008-12-10 09:55:56 [Peter Van Doninck] [reply] 
Misschien kleine opmerking: kleine d staat voor het aantal keer dat we de lange termijntrend zuiveren. Het klopt dat de range bij d=1 en D=0 het kleinste is. Er is hier echter geen getrimde variantie berekend! Deze variantie is correcter, omdat ze de outliërs niet gaat meerekenen. Wanneer we van deze variantie gebruik maken, hebben we zowel voor d als D een waarde gelijk aan 1.

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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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26632&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26632&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26632&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'George Udny Yule' @ 72.249.76.132






Variance Reduction Matrix
V(Y[t],d=0,D=0)22.5666372745491Range22Trim Var.13.7666194683001
V(Y[t],d=1,D=0)1.00200400801603Range2Trim Var.NA
V(Y[t],d=2,D=0)1.90742850678367Range4Trim Var.0
V(Y[t],d=3,D=0)5.62903225806452Range8Trim Var.2.56821148825065
V(Y[t],d=0,D=1)15.5304642003568Range20Trim Var.8.24963106437927
V(Y[t],d=1,D=1)1.79396827811156Range4Trim Var.0
V(Y[t],d=2,D=1)3.48864282380892Range8Trim Var.0
V(Y[t],d=3,D=1)10.3139814262588Range16Trim Var.6.37034479037393
V(Y[t],d=0,D=2)27.5422202565237Range26Trim Var.14.7293765586035
V(Y[t],d=1,D=2)5.02107928047968Range8Trim Var.2.58442212387035
V(Y[t],d=2,D=2)9.97038385027787Range16Trim Var.6.29348033119335
V(Y[t],d=3,D=2)29.5169312358906Range30Trim Var.14.2660998181893

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 22.5666372745491 & Range & 22 & Trim Var. & 13.7666194683001 \tabularnewline
V(Y[t],d=1,D=0) & 1.00200400801603 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.90742850678367 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.62903225806452 & Range & 8 & Trim Var. & 2.56821148825065 \tabularnewline
V(Y[t],d=0,D=1) & 15.5304642003568 & Range & 20 & Trim Var. & 8.24963106437927 \tabularnewline
V(Y[t],d=1,D=1) & 1.79396827811156 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.48864282380892 & Range & 8 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=1) & 10.3139814262588 & Range & 16 & Trim Var. & 6.37034479037393 \tabularnewline
V(Y[t],d=0,D=2) & 27.5422202565237 & Range & 26 & Trim Var. & 14.7293765586035 \tabularnewline
V(Y[t],d=1,D=2) & 5.02107928047968 & Range & 8 & Trim Var. & 2.58442212387035 \tabularnewline
V(Y[t],d=2,D=2) & 9.97038385027787 & Range & 16 & Trim Var. & 6.29348033119335 \tabularnewline
V(Y[t],d=3,D=2) & 29.5169312358906 & Range & 30 & Trim Var. & 14.2660998181893 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26632&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]22.5666372745491[/C][C]Range[/C][C]22[/C][C]Trim Var.[/C][C]13.7666194683001[/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]1.90742850678367[/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]5.62903225806452[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.56821148825065[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]15.5304642003568[/C][C]Range[/C][C]20[/C][C]Trim Var.[/C][C]8.24963106437927[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.79396827811156[/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]3.48864282380892[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]0[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10.3139814262588[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.37034479037393[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]27.5422202565237[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]14.7293765586035[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.02107928047968[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.58442212387035[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]9.97038385027787[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.29348033119335[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]29.5169312358906[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]14.2660998181893[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26632&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26632&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)22.5666372745491Range22Trim Var.13.7666194683001
V(Y[t],d=1,D=0)1.00200400801603Range2Trim Var.NA
V(Y[t],d=2,D=0)1.90742850678367Range4Trim Var.0
V(Y[t],d=3,D=0)5.62903225806452Range8Trim Var.2.56821148825065
V(Y[t],d=0,D=1)15.5304642003568Range20Trim Var.8.24963106437927
V(Y[t],d=1,D=1)1.79396827811156Range4Trim Var.0
V(Y[t],d=2,D=1)3.48864282380892Range8Trim Var.0
V(Y[t],d=3,D=1)10.3139814262588Range16Trim Var.6.37034479037393
V(Y[t],d=0,D=2)27.5422202565237Range26Trim Var.14.7293765586035
V(Y[t],d=1,D=2)5.02107928047968Range8Trim Var.2.58442212387035
V(Y[t],d=2,D=2)9.97038385027787Range16Trim Var.6.29348033119335
V(Y[t],d=3,D=2)29.5169312358906Range30Trim Var.14.2660998181893


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')