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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 12:17:03 -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/t1228159073fw4cpvwsahajyq9.htm/, Retrieved Sun, 05 May 2024 19:15:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27206, Retrieved Sun, 05 May 2024 19:15:03 +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 18:05:16] [b98453cac15ba1066b407e146608df68]
F         [Law of Averages] [Q1 Non Stationary...] [2008-12-01 19:17:03] [70ba55c7ff8e068610dc28fc16e6d1e2] [Current]
-           [Law of Averages] [Q2 verbetering] [2008-12-07 15:42:21] [85134b6edb9973b9193450dd2306c65b]
-           [Law of Averages] [Q2 verbetering] [2008-12-07 15:42:11] [85134b6edb9973b9193450dd2306c65b]
Feedback Forum
2008-12-03 10:18:50 [Romina Machiels] [reply] 
Deze vraag werd correct beantwoord.
2008-12-06 15:35:55 [Kevin Engels] [reply] 
De student geeft hier een correct antwoord. De auto-correlatie is alleen positief en stijgt ver boven de betrouwbaarheidsintervallen uit. Dit is een duidelijk kenmerk van een lange termijntrend.
2008-12-08 19:47:02 [Ruben Jacobs] [reply] 
De autocorrelatie is positief en significant. Dit komt omdat ze boven de betrouwbaarheidsintervallen boven komt. Er is een duidelijke lange termijn trend waarneembaar.
2008-12-08 19:58:31 [Annelies Michiels] [reply] 
De student heeft inderdaad een juiste conclusie getrokken uit deze grafiek. Het gaat inderdaad om een lange termijn trend met een dalend verloop waarbij de lags significant verschillen van nul.

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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=27206&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=27206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27206&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 ;
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