FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 02:36:06 -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/t12281241947ex502yyyg6pu58.htm/, Retrieved Sun, 05 May 2024 19:02:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26824, Retrieved Sun, 05 May 2024 19:02:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact268

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] [] [2008-12-01 09:36:06] [6d40a467de0f28bd2350f82ac9522c51] [Current]
Feedback Forum
2008-12-05 16:50:20 [Kristof Van Esbroeck] [reply] 
Correcte berekening en gebruik van de software.
De interpretatie kon echter beter.

We kunnen inderdaad een neerwaartse lange termijn trend opmaken uit de grafiek.
In de formule Yt = Yt-1 + et komt et overeen met de lange termijn trend en Yt komt overeen met de reeks. Zo kan dus verklaard worden dat de toekomst gerelateerd is aan het verleden gesommeerd met de trend op lange termijn.

We merken verder op dat alle waarden significant positief zijn.

Er kan dus, zoals aangegeven,geconcludeerd worden dat de toekomst gerelateerd is aan het verleden zonder dat de voorspelling op toeval berust.
2008-12-08 08:30:01 [Dorien Peeters] [reply] 
Ik ben het over een groot deel eens met de student, alleen mis ik soms toch nog wat extra informatie. Indien we naar de grafiek kijken kunnen we een neerwaartse trend vaststellen.
yt=de reeks
Yt=Yt-1+et=Lt-trend => de toekomst=verleden+LT trend.
Dus de student had het juist->we mogen dus besluiten dat er een verband is tussen de toekomst en het verleden(we moeten geen rekening houden met toeval)

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 time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26824&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]3 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=26824&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26824&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 time3 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