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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 computationTue, 02 Dec 2008 06:40:03 -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/02/t12282252966w4npxzsptpzkzo.htm/, Retrieved Sat, 18 May 2024 04:26:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27769, Retrieved Sat, 18 May 2024 04:26:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsk_vanderheggen
Estimated Impact180

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] [Non Stationary Ti...] [2008-12-02 13:40:03] [547f3960ab1cda94661cd6e0871d2c7b] [Current]
Feedback Forum
2008-12-06 11:30:49 [Britt Severijns] [reply] 
in orde.
2008-12-06 11:51:21 [Käthe Vanderheggen] [reply] 
Ik kan hier niets meer aan toevoegen.
2008-12-08 15:48:17 [Jessica Alves Pires] [reply] 
Je uitleg over de cumulative periodogram klopt. Je had er nog bij mogen zeggen dat de twee blauwe stippellijnen het Kolmogorov-Smirnov interval voorstellen. Je zegt ook niets over de seizoenaliteit. Er is geen trapfunctie aanwezig, dus is er geen sprake van seizoenaliteit.
Je hebt de raw periodogram niet besproken. Ook daarop kan men zien dat er sprake is van een langzaam dalend patroon, er is een lange termijn trend aanwezig.
2008-12-08 20:19:51 [Erik Geysen] [reply] 
De student heeft de juiste methode gebruikt. De lage frequentie wijst juist op een lange periode. Op de cumulative periodogram zien we dat we 80% van de tijdsreeks kunnen verklaren. Het is dan ook een snel stijgende lijn dat we zien. Er zijn geen trapjes die zouden kunnen wijzen op seizoenaliteit. Er is dus een lange termijnbeweging en geen seizoenaliteit.
Dit laatste is de student vergeten te vermelden.

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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'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27769&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27769&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27769&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'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()