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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 11:28:24 -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/t122815615155wut5ssx60h908.htm/, Retrieved Sun, 05 May 2024 11:07:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27104, Retrieved Sun, 05 May 2024 11:07:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact273

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 stationa...] [2008-11-30 23:29:17] [57850c80fd59ccfb28f882be994e814e]
F           [Law of Averages] [Q2] [2008-12-01 18:28:24] [14a75ec03b2c0d8ddd8b141a7b1594fd] [Current]
F             [Law of Averages] [Non stationary ti...] [2008-12-01 23:09:18] [cf9c64468d04c2c4dd548cc66b4e3677]
F             [Law of Averages] [] [2008-12-02 06:58:43] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-07 22:01:42 [Kenny Simons] [reply] 
Deze vraag heb ik juist berekend, maar mijn conclusies waren verkeerd.
Als we de grafiek van de autocorrelation bestuderen, zien we dat alle autocorrelaties positief en significant zijn, omdat ze buiten het betrouwbaarheidsinterval liggen (De stippelijnen zijn het betrouwbaarheidsinterval). Er is dus voorspelbaarheid op basis van het verleden dat niet aan het toeval te wijten is.
We zien ook een neerwaartse lange termijn trend in de grafiek. Het patroon in deze grafiek is zeer eenvoudig te verklaren door de formule Yt = Yt-1 + et. Yt is de tijdreeks, et is de lange termijn trend. Elk punt heeft dus een basis in het vorige (autocorrelatie).
De gevonden lange termijn trend kunnen we uit de tijdreeksen halen door differentiatie.

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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 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=27104&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=27104&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27104&T=0

As an alternative you can also use a QR Code:  
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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


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 500 ; par2 = 0.5 ;
Parameters (R input):
par1 = 500 ; par2 = 0.5 ;
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