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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 12:12:15 -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/Nov/30/t12280723637aqj9xu0klozudo.htm/, Retrieved Sun, 19 May 2024 12:15:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26697, Retrieved Sun, 19 May 2024 12:15:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198

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]
-       [Law of Averages] [Q3] [2008-11-27 21:13:23] [1e1d8320a8a1170c475bf6e4ce119de6]
F           [Law of Averages] [Q3] [2008-11-30 19:12:15] [b0654df83a8a0e1de3ceb7bf60f0d58f] [Current]
Feedback Forum
2008-12-04 15:31:36 [Olivier Uyttendaele] [reply] 
De variance Reduction matrix heb je nodig om de verschillende differentie waarden op de reeks te zoeken en toont je de bijhorende variatie.
Waar de variatie het kleinst is, heeft de reeks het beste stationaire karakter. Stationair betekent dat de lange termijn trend uit de zo klein mogelijk te maken zodoende zoveel mogelijk van de tijdreeks kunnen verklaren. Bedoeling hier is concreet gezegd om ‘d’ en ‘D’ te identificeren.

Je moet de tabel als volgt interpreteren:


Kolom 1:
Hier lees je af van wat de variatie berekend wordt.
d = 0 => het aantal keren dat we niet seizonaal differentiëren – LT trend eruit te halen
D = 0 => het aantal keren dat we wel seizonaal differentiëren

Kolom 2:
In deze kolom zal je de laagste waarde moeten zoeken. De laagste waarde staat in de 2de rij (1.00132795711906). Je zal hier dus niet seizonaal differentiëren aangezien d=1 en D=0, dit is de meest gunstige waarde.

Deze kolom toont in zekere mate de volatiliteit uit.

Kolom 4 + 5:
Dit is de getrimde variatie. 5% van de kleinste en grootste waarden worden
weggelaten en beïnvloeden bijgevolg het resultaat niet meer.
2008-12-07 16:28:34 [Steffi Van Isveldt] [reply] 
In je taak ontbrak de analyse van de grafieken en berekening. Hier boven vind je een goede analyse door een andere student gemaakt.

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'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=26697&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=26697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26697&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)30.2171382765531Range33Trim Var.12.8242887356883
V(Y[t],d=1,D=0)1.00132795711906Range2Trim Var.NA
V(Y[t],d=2,D=0)2.04426559356137Range4Trim Var.0
V(Y[t],d=3,D=0)6.08869345102875Range8Trim Var.2.78422554700337
V(Y[t],d=0,D=1)11.0187497896119Range18Trim Var.3.8076459634133
V(Y[t],d=1,D=1)2.09874853178526Range4Trim Var.0
V(Y[t],d=2,D=1)4.19792117432438Range8Trim Var.2.24839076107535
V(Y[t],d=3,D=1)12.4958677685950Range16Trim Var.6.19024949307943
V(Y[t],d=0,D=2)22.812472357364Range26Trim Var.12.6581377615860
V(Y[t],d=1,D=2)6.37129025094382Range8Trim Var.2.84722912319057
V(Y[t],d=2,D=2)12.3466516801813Range16Trim Var.6.54820703011278
V(Y[t],d=3,D=2)36.1016232486473Range30Trim Var.21.1648239372377

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 30.2171382765531 & Range & 33 & Trim Var. & 12.8242887356883 \tabularnewline
V(Y[t],d=1,D=0) & 1.00132795711906 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.04426559356137 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.08869345102875 & Range & 8 & Trim Var. & 2.78422554700337 \tabularnewline
V(Y[t],d=0,D=1) & 11.0187497896119 & Range & 18 & Trim Var. & 3.8076459634133 \tabularnewline
V(Y[t],d=1,D=1) & 2.09874853178526 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.19792117432438 & Range & 8 & Trim Var. & 2.24839076107535 \tabularnewline
V(Y[t],d=3,D=1) & 12.4958677685950 & Range & 16 & Trim Var. & 6.19024949307943 \tabularnewline
V(Y[t],d=0,D=2) & 22.812472357364 & Range & 26 & Trim Var. & 12.6581377615860 \tabularnewline
V(Y[t],d=1,D=2) & 6.37129025094382 & Range & 8 & Trim Var. & 2.84722912319057 \tabularnewline
V(Y[t],d=2,D=2) & 12.3466516801813 & Range & 16 & Trim Var. & 6.54820703011278 \tabularnewline
V(Y[t],d=3,D=2) & 36.1016232486473 & Range & 30 & Trim Var. & 21.1648239372377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26697&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]30.2171382765531[/C][C]Range[/C][C]33[/C][C]Trim Var.[/C][C]12.8242887356883[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00132795711906[/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]2.04426559356137[/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]6.08869345102875[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.78422554700337[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]11.0187497896119[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]3.8076459634133[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.09874853178526[/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]4.19792117432438[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.24839076107535[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.4958677685950[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.19024949307943[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]22.812472357364[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]12.6581377615860[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.37129025094382[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.84722912319057[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.3466516801813[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.54820703011278[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]36.1016232486473[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]21.1648239372377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26697&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)30.2171382765531Range33Trim Var.12.8242887356883
V(Y[t],d=1,D=0)1.00132795711906Range2Trim Var.NA
V(Y[t],d=2,D=0)2.04426559356137Range4Trim Var.0
V(Y[t],d=3,D=0)6.08869345102875Range8Trim Var.2.78422554700337
V(Y[t],d=0,D=1)11.0187497896119Range18Trim Var.3.8076459634133
V(Y[t],d=1,D=1)2.09874853178526Range4Trim Var.0
V(Y[t],d=2,D=1)4.19792117432438Range8Trim Var.2.24839076107535
V(Y[t],d=3,D=1)12.4958677685950Range16Trim Var.6.19024949307943
V(Y[t],d=0,D=2)22.812472357364Range26Trim Var.12.6581377615860
V(Y[t],d=1,D=2)6.37129025094382Range8Trim Var.2.84722912319057
V(Y[t],d=2,D=2)12.3466516801813Range16Trim Var.6.54820703011278
V(Y[t],d=3,D=2)36.1016232486473Range30Trim Var.21.1648239372377


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')