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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 12:10:53 -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/t12281586729htmclxudjinr5d.htm/, Retrieved Sun, 05 May 2024 14:47:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27192, Retrieved Sun, 05 May 2024 14:47:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146

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 18:40:39] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [] [2008-11-30 10:29:33] [a4ee3bef49b119f4bd2e925060c84f5e]
F           [Law of Averages] [] [2008-12-01 19:10:53] [db9a5fd0f9c3e1245d8075d8bb09236d] [Current]
Feedback Forum
2008-12-06 13:19:53 [90714a39acc78a7b2ecd294ecc6b2864] [reply] 
Het cumulatief periodogram is nuttig om het hele gedrag van het periodogram (en dus de dataset) te beschrijven. Het Spectrum geeft de aanwezigheid weer van onafhankelijke cyclische golven (sinusoïden). Hierdoor kunnen de sterkste en meest belangrijke cycli waargenomen worden. Het Cumulatief Periodogram relateert de cumulatieve intensiteit van de cyclische golven die in de tijdreeks aanwezig zijn aan hun periode.

Voor differentiatie kan je duidelijk zien dat het CP niet binnen de intervallijnen valt. Om het CP binnen de intervallijnen te doen vallen, kan je seizoenaal en niet-seizoenaal differentiëren.
2008-12-07 13:56:11 [Stijn Van de Velde] [reply] 
Juist, maar niet volledig. Zie de uitleg van de vorige student.
2008-12-08 18:28:45 [Bart Haemels] [reply] 
Juist

Bij 0.05 wordt 95%-98% van de gegevens verklaard. Het patroon is significant.
Wanneer we naar de Spectrum of simulated Random-Walk time kijken zien we een gelijkaardig verloop.

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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=27192&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=27192&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27192&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 ; 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
x <- b
bitmap(file='test1.png')
r <- spectrum(x,main='Raw Periodogram')
dev.off()
r
bitmap(file='test2.png')
cpgram(x,main='Cumulative Periodogram')
dev.off()