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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 12:44:14 -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/t1228160698w2ku88f634lhdfv.htm/, Retrieved Sun, 05 May 2024 18:04:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27260, Retrieved Sun, 05 May 2024 18:04:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsNon stationary Time series , Q3
Estimated Impact186

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] [loïqueverhasselt] [2008-12-01 19:44:14] [6440ec5a21e5d35520cb2ae6b4b70e45] [Current]
Feedback Forum
2008-12-06 12:16:41 [Loïque Verhasselt] [reply] 
Q3: Correcte berekening met een juiste output gegeven en juiste conclusie.
2008-12-07 08:54:15 [Gert-Jan Geudens] [reply] 
Het antwoord is correct al willen we nog snel even vermelden dat we van niet-seizonaal differentiëren spreken wanneer we d gelijkstellen aan 1.
2008-12-09 20:33:37 [Gert-Jan Geudens] [reply] 
Ter aanvulling op onze vorige post willen we hier nog even vermelden dat we in het geval van outliers nog naar de getrimde variantie kunnen kijken. Hier is gebruik gemaakt van een logaritme waardoor de 5% hoogste en laagste gegevens verwijderd zijn, waardoor het vertekende effect van outliers ook verdwenen is.

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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 time1 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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27260&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27260&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27260&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'George Udny Yule' @ 72.249.76.132






Variance Reduction Matrix
V(Y[t],d=0,D=0)17.9608657314629Range23Trim Var.9.75269656026045
V(Y[t],d=1,D=0)1.00084506362122Range2Trim Var.NA
V(Y[t],d=2,D=0)1.95571824521426Range4Trim Var.0
V(Y[t],d=3,D=0)5.75806451612903Range8Trim Var.2.70113324128881
V(Y[t],d=0,D=1)7.71124650755714Range16Trim Var.2.43065928405872
V(Y[t],d=1,D=1)2.12338918886945Range4Trim Var.0
V(Y[t],d=2,D=1)4.26390055576768Range8Trim Var.2.30510975834717
V(Y[t],d=3,D=1)12.7603305785124Range16Trim Var.6.82991526159725
V(Y[t],d=0,D=2)20.3367005749668Range26Trim Var.11.1924930491196
V(Y[t],d=1,D=2)6.54006662225183Range8Trim Var.2.80759013282732
V(Y[t],d=2,D=2)13.3699610172969Range16Trim Var.7.02227535694164
V(Y[t],d=3,D=2)40.2033181639015Range28Trim Var.23.2965526333136

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 17.9608657314629 & Range & 23 & Trim Var. & 9.75269656026045 \tabularnewline
V(Y[t],d=1,D=0) & 1.00084506362122 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.95571824521426 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.75806451612903 & Range & 8 & Trim Var. & 2.70113324128881 \tabularnewline
V(Y[t],d=0,D=1) & 7.71124650755714 & Range & 16 & Trim Var. & 2.43065928405872 \tabularnewline
V(Y[t],d=1,D=1) & 2.12338918886945 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.26390055576768 & Range & 8 & Trim Var. & 2.30510975834717 \tabularnewline
V(Y[t],d=3,D=1) & 12.7603305785124 & Range & 16 & Trim Var. & 6.82991526159725 \tabularnewline
V(Y[t],d=0,D=2) & 20.3367005749668 & Range & 26 & Trim Var. & 11.1924930491196 \tabularnewline
V(Y[t],d=1,D=2) & 6.54006662225183 & Range & 8 & Trim Var. & 2.80759013282732 \tabularnewline
V(Y[t],d=2,D=2) & 13.3699610172969 & Range & 16 & Trim Var. & 7.02227535694164 \tabularnewline
V(Y[t],d=3,D=2) & 40.2033181639015 & Range & 28 & Trim Var. & 23.2965526333136 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27260&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]17.9608657314629[/C][C]Range[/C][C]23[/C][C]Trim Var.[/C][C]9.75269656026045[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00084506362122[/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]1.95571824521426[/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]5.75806451612903[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.70113324128881[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]7.71124650755714[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]2.43065928405872[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.12338918886945[/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]4.26390055576768[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.30510975834717[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.7603305785124[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.82991526159725[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]20.3367005749668[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]11.1924930491196[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.54006662225183[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.80759013282732[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]13.3699610172969[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.02227535694164[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]40.2033181639015[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]23.2965526333136[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27260&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27260&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)17.9608657314629Range23Trim Var.9.75269656026045
V(Y[t],d=1,D=0)1.00084506362122Range2Trim Var.NA
V(Y[t],d=2,D=0)1.95571824521426Range4Trim Var.0
V(Y[t],d=3,D=0)5.75806451612903Range8Trim Var.2.70113324128881
V(Y[t],d=0,D=1)7.71124650755714Range16Trim Var.2.43065928405872
V(Y[t],d=1,D=1)2.12338918886945Range4Trim Var.0
V(Y[t],d=2,D=1)4.26390055576768Range8Trim Var.2.30510975834717
V(Y[t],d=3,D=1)12.7603305785124Range16Trim Var.6.82991526159725
V(Y[t],d=0,D=2)20.3367005749668Range26Trim Var.11.1924930491196
V(Y[t],d=1,D=2)6.54006662225183Range8Trim Var.2.80759013282732
V(Y[t],d=2,D=2)13.3699610172969Range16Trim Var.7.02227535694164
V(Y[t],d=3,D=2)40.2033181639015Range28Trim Var.23.2965526333136


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')