FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_rwalk.wasp
Title produced by softwareLaw of Averages
Date of computationMon, 01 Dec 2008 14:52:07 -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/t1228168360c86rwbrq1cxvnnj.htm/, Retrieved Sun, 05 May 2024 12:59:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27439, Retrieved Sun, 05 May 2024 12:59:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact222

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...] [2008-12-01 21:52:07] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-12-04 12:05:15 [72e979bcc364082694890d2eccc1a66f] [reply] 
Ook deze vraag werd zeer goed beargumenteerd.
We gaan inderdaad op zoek naar de kleinste variantie om zo te bepalen hoe we moeten differentiëren.
2008-12-06 18:03:18 [Britt Severijns] [reply] 
correct. De variantie is het risico in de tijdreeks als men de tijdreeks wil voorspellen. Hoe kleiner de variantie hoe meer je kan verklaren.
2008-12-07 17:29:24 [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 dus niet.

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=27439&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=27439&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27439&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)364.378260521042Range62Trim Var.310.596711209652
V(Y[t],d=1,D=0)0.997625773635625Range2Trim Var.NA
V(Y[t],d=2,D=0)1.97181482469112Range4Trim Var.0
V(Y[t],d=3,D=0)5.75806451612903Range8Trim Var.2.77040043290043
V(Y[t],d=0,D=1)12.9134379102568Range20Trim Var.6.21820448877805
V(Y[t],d=1,D=1)1.95878013537151Range4Trim Var.0
V(Y[t],d=2,D=1)3.76082474226804Range8Trim Var.2.29099307159353
V(Y[t],d=3,D=1)10.9338672573912Range16Trim Var.6.18038683406854
V(Y[t],d=0,D=2)23.2166828836798Range24Trim Var.11.9612095565547
V(Y[t],d=1,D=2)5.9492071952032Range8Trim Var.2.49879844035967
V(Y[t],d=2,D=2)11.0528362815675Range16Trim Var.6.17890350243291
V(Y[t],d=3,D=2)31.7287418927151Range30Trim Var.17.0454673315433

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 364.378260521042 & Range & 62 & Trim Var. & 310.596711209652 \tabularnewline
V(Y[t],d=1,D=0) & 0.997625773635625 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.97181482469112 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.75806451612903 & Range & 8 & Trim Var. & 2.77040043290043 \tabularnewline
V(Y[t],d=0,D=1) & 12.9134379102568 & Range & 20 & Trim Var. & 6.21820448877805 \tabularnewline
V(Y[t],d=1,D=1) & 1.95878013537151 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.76082474226804 & Range & 8 & Trim Var. & 2.29099307159353 \tabularnewline
V(Y[t],d=3,D=1) & 10.9338672573912 & Range & 16 & Trim Var. & 6.18038683406854 \tabularnewline
V(Y[t],d=0,D=2) & 23.2166828836798 & Range & 24 & Trim Var. & 11.9612095565547 \tabularnewline
V(Y[t],d=1,D=2) & 5.9492071952032 & Range & 8 & Trim Var. & 2.49879844035967 \tabularnewline
V(Y[t],d=2,D=2) & 11.0528362815675 & Range & 16 & Trim Var. & 6.17890350243291 \tabularnewline
V(Y[t],d=3,D=2) & 31.7287418927151 & Range & 30 & Trim Var. & 17.0454673315433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27439&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]364.378260521042[/C][C]Range[/C][C]62[/C][C]Trim Var.[/C][C]310.596711209652[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.997625773635625[/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]1.97181482469112[/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]2.77040043290043[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]12.9134379102568[/C][C]Range[/C][C]20[/C][C]Trim Var.[/C][C]6.21820448877805[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.95878013537151[/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]3.76082474226804[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.29099307159353[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10.9338672573912[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.18038683406854[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]23.2166828836798[/C][C]Range[/C][C]24[/C][C]Trim Var.[/C][C]11.9612095565547[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.9492071952032[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.49879844035967[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]11.0528362815675[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.17890350243291[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]31.7287418927151[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]17.0454673315433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27439&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27439&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)364.378260521042Range62Trim Var.310.596711209652
V(Y[t],d=1,D=0)0.997625773635625Range2Trim Var.NA
V(Y[t],d=2,D=0)1.97181482469112Range4Trim Var.0
V(Y[t],d=3,D=0)5.75806451612903Range8Trim Var.2.77040043290043
V(Y[t],d=0,D=1)12.9134379102568Range20Trim Var.6.21820448877805
V(Y[t],d=1,D=1)1.95878013537151Range4Trim Var.0
V(Y[t],d=2,D=1)3.76082474226804Range8Trim Var.2.29099307159353
V(Y[t],d=3,D=1)10.9338672573912Range16Trim Var.6.18038683406854
V(Y[t],d=0,D=2)23.2166828836798Range24Trim Var.11.9612095565547
V(Y[t],d=1,D=2)5.9492071952032Range8Trim Var.2.49879844035967
V(Y[t],d=2,D=2)11.0528362815675Range16Trim Var.6.17890350243291
V(Y[t],d=3,D=2)31.7287418927151Range30Trim Var.17.0454673315433


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')