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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 03:26:42 -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/t12281272384yk5i97je0yrqsh.htm/, Retrieved Sun, 05 May 2024 19:45:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26853, Retrieved Sun, 05 May 2024 19:45:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189

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] [] [2008-12-01 10:26:42] [428345b1a3979ee2ad6751f9aac15fbb] [Current]
Feedback Forum
2008-12-05 19:29:22 [Bob Leysen] [reply] 
Niet juist, hieronder een correcte link.

http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/01/t1228087794b0hria4avwtppfs.htm

Bij het bestuderen van de autocorrelatiefunctie merken we dat de autocorrelatiecoëfficiënten ver boven het betrouwbaarheidsinterval liggen. Er is overal positieve autocorrelatie. We merken geen seizonaliteit, wel een lange termijn trend.
“Every random-walk series exhibits a pattern of slowly decreasing coefficients in its ACF, because of its non-stationarity.”
We zien dit hier dan ook zeer duidelijk, de autocorrelatiecoëfficiënten dalen omdat het een random-walk model is en dat is een niet stationaire tijdreeks. Er is bijgevolg geen seizonaliteit aanwezig.
Een stationaire tijdreeks is een tijdreeks waar het gemiddelde, de spreiding (variantie) en de ACF niet wijzigen in de tijd, dus constant blijven. Daarom is deze tijdreeks niet-stationair.
2008-12-08 20:35:36 [Dries Van Gheluwe] [reply] 
Het commentaar van de student hierboven is inderdaad correct, het is geen toeval en dat wordt gekenmerkt door het smal betrouwbaarheidsinterval waar alles ver bovenuit komt. (één van de kenmerken van langetermijnrente)

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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=26853&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=26853&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26853&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