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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 computationMon, 01 Dec 2008 08:05:11 -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/t1228143930gl7x2k9z2tsjbdt.htm/, Retrieved Sun, 05 May 2024 15:12:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26941, Retrieved Sun, 05 May 2024 15:12:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact231

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 18:05:16] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [Q2 Non Stationary...] [2008-12-01 13:35:01] [7a4703cb85a198d9845d72899eff0288]
F R         [Law of Averages] [Q2] [2008-12-01 15:05:11] [ee28d11f695cd3bc1f8bbd77ba77987a] [Current]
Feedback Forum
2008-12-08 14:43:27 [Mehmet Yilmaz] [reply] 
De berekening is correct.
Conclusie is minder.
Conclusie:
Op de grafiek zien we een zeer sterke autocorrelatie die licht afneemt.
Dit is logisch te verklaren:
Autocorrelatie wijst op een verband tussen data en zijn voorgaande data. Aangezien de gebruikte formule voor random-walk y(t) = y(t-1) + e(t) is, zien we dat random-walk gebaseerd is op het gebruik van voorgaande data + een random factor (gemiddeld 0) op nieuwe data te berekenen.
Het is dan ook niet verwonderlijk dat elke nieuwe waarde sterk in verband staat met voorgaande waarden met als enige verschil e(t).
2008-12-08 19:40:39 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
Correcte bereking, foute conclusie. De zacht afnemende autocorrolatie duid op een lange termijn trend. Hoewel er geen echte trend aanwezig is in random walks, maar de grafiek vertoond wel trend achtig gedrag (ook al is er geen trend aanwezig in random walks).
2008-12-08 20:19:19 [Annelies Michiels] [reply] 
Het is inderdaad een vreemde uitleg die de student geeft. Als we de autocorrelatie bekijken zien we een een dalende lange termijn trend. Al de lags die in deze autocorrelatie voorkomen zijn significant verschillend van 0.

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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'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=26941&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=26941&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26941&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


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
bitmap(file='pic1.png')
racf <- acf(b,n/10,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
racf