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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_rwalk.wasp
Title produced by softwareLaw of Averages
Date of computationWed, 26 Nov 2008 09:36:25 -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/26/t1227717434cq1mjo3p3qc63s0.htm/, Retrieved Sat, 18 May 2024 18:03:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25654, Retrieved Sat, 18 May 2024 18:03:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact210

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] [Q3 t7] [2008-11-26 16:36:25] [fb0ffb935e9c1a725d69519be28b148f] [Current]
F           [Law of Averages] [question 3 Varian...] [2008-12-01 22:12:15] [c29178f7f550574a75dc881e636e0923]
Feedback Forum
2008-12-08 19:08:11 [Hidde Van Kerckhoven] [reply] 
Hier vond ik enkel de berekeningen, en geen interpretatie of antwoord.

In de tabel moet je op zoek gaan naar de kleinste variantie. Deze wordt gevonden bij d=1, D=0. De variantie is daar 1.00084506362122.

Daarna werken we verder met de d en D bij onze kleinst gevonden variantie.
d= gewone differentiatie
D= seizoenale differentiatie

Deze gegevens zullen we nodig hebben om de tijdreeks stationair te maken.

In dit geval moeten we dus 1 keer gewoon differentiëren (d=1) en helemaal niet seizoenaal differentiëren (D=0). Dan houd je de variantie zo klein mogelijk.
2008-12-08 19:14:19 [Hidde Van Kerckhoven] [reply] 
Dit is een verkeerde comment voor iemand anders, de student heeft de opdracht goed gemaakt..
2008-12-08 20:01:25 [Michaël De Kuyer] [reply] 
Deze vraag is correct beantwoord.

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25654&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25654&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25654&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'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)69.296496993988Range38Trim Var.43.9593985883987
V(Y[t],d=1,D=0)1.00181085061690Range2Trim Var.NA
V(Y[t],d=2,D=0)2.02010456312170Range4Trim Var.0
V(Y[t],d=3,D=0)5.91935483870968Range8Trim Var.2.80727676711568
V(Y[t],d=0,D=1)9.0650015147945Range16Trim Var.3.67385774846915
V(Y[t],d=1,D=1)1.95060038363712Range4Trim Var.0
V(Y[t],d=2,D=1)3.88451911246871Range8Trim Var.2.27083197176655
V(Y[t],d=3,D=1)11.2973672999915Range16Trim Var.6.38702052711897
V(Y[t],d=0,D=2)18.1808934099956Range26Trim Var.9.23981588032221
V(Y[t],d=1,D=2)5.84808349988896Range8Trim Var.2.51947482903776
V(Y[t],d=2,D=2)11.4080159855844Range16Trim Var.6.25051789757672
V(Y[t],d=3,D=2)32.7879743433547Range32Trim Var.19.8991940672118

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 69.296496993988 & Range & 38 & Trim Var. & 43.9593985883987 \tabularnewline
V(Y[t],d=1,D=0) & 1.00181085061690 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.02010456312170 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.91935483870968 & Range & 8 & Trim Var. & 2.80727676711568 \tabularnewline
V(Y[t],d=0,D=1) & 9.0650015147945 & Range & 16 & Trim Var. & 3.67385774846915 \tabularnewline
V(Y[t],d=1,D=1) & 1.95060038363712 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.88451911246871 & Range & 8 & Trim Var. & 2.27083197176655 \tabularnewline
V(Y[t],d=3,D=1) & 11.2973672999915 & Range & 16 & Trim Var. & 6.38702052711897 \tabularnewline
V(Y[t],d=0,D=2) & 18.1808934099956 & Range & 26 & Trim Var. & 9.23981588032221 \tabularnewline
V(Y[t],d=1,D=2) & 5.84808349988896 & Range & 8 & Trim Var. & 2.51947482903776 \tabularnewline
V(Y[t],d=2,D=2) & 11.4080159855844 & Range & 16 & Trim Var. & 6.25051789757672 \tabularnewline
V(Y[t],d=3,D=2) & 32.7879743433547 & Range & 32 & Trim Var. & 19.8991940672118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25654&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]69.296496993988[/C][C]Range[/C][C]38[/C][C]Trim Var.[/C][C]43.9593985883987[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00181085061690[/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]2.02010456312170[/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]5.91935483870968[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.80727676711568[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]9.0650015147945[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]3.67385774846915[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.95060038363712[/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]3.88451911246871[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.27083197176655[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]11.2973672999915[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.38702052711897[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]18.1808934099956[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]9.23981588032221[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.84808349988896[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.51947482903776[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]11.4080159855844[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.25051789757672[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]32.7879743433547[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]19.8991940672118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25654&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25654&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)69.296496993988Range38Trim Var.43.9593985883987
V(Y[t],d=1,D=0)1.00181085061690Range2Trim Var.NA
V(Y[t],d=2,D=0)2.02010456312170Range4Trim Var.0
V(Y[t],d=3,D=0)5.91935483870968Range8Trim Var.2.80727676711568
V(Y[t],d=0,D=1)9.0650015147945Range16Trim Var.3.67385774846915
V(Y[t],d=1,D=1)1.95060038363712Range4Trim Var.0
V(Y[t],d=2,D=1)3.88451911246871Range8Trim Var.2.27083197176655
V(Y[t],d=3,D=1)11.2973672999915Range16Trim Var.6.38702052711897
V(Y[t],d=0,D=2)18.1808934099956Range26Trim Var.9.23981588032221
V(Y[t],d=1,D=2)5.84808349988896Range8Trim Var.2.51947482903776
V(Y[t],d=2,D=2)11.4080159855844Range16Trim Var.6.25051789757672
V(Y[t],d=3,D=2)32.7879743433547Range32Trim Var.19.8991940672118


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')