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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 computationSun, 30 Nov 2008 16:29:17 -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/t1228087794b0hria4avwtppfs.htm/, Retrieved Sun, 05 May 2024 10:09:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26790, Retrieved Sun, 05 May 2024 10:09:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact326

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] [0831954c833179c36e9320daee0825b5] [Current]
F           [Law of Averages] [] [2008-12-01 09:36:06] [4c8dfb519edec2da3492d7e6be9a5685]
F           [Law of Averages] [Q2] [2008-12-01 17:27:15] [077ffec662d24c06be4c491541a44245]
F           [Law of Averages] [Q2] [2008-12-01 18:28:24] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
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]
F           [Law of Averages] [Q2] [2008-12-01 19:51:06] [547636b63517c1c2916a747d66b36ebf]
Feedback Forum
2008-12-05 18:10:48 [Bob Leysen] [reply] 
Bij het bestuderen van de autocorrelatiefunctie merken we dat de autocorrelatiecoëfficiënten ver boven het betrouwbaarheidsinterval liggen. Er is overal positieve autocorrelatie. We merken geen seizonaliteit, wel een lange termijn trend.
“Every random-walk series exhibits a pattern of slowly decreasing coefficients in its ACF, because of its non-stationarity.”
We zien dit hier dan ook zeer duidelijk, de autocorrelatiecoëfficiënten dalen omdat het een random-walk model is en dat is een niet stationaire tijdreeks. Er is bijgevolg geen seizonaliteit aanwezig.
Een stationaire tijdreeks is een tijdreeks waar het gemiddelde, de spreiding (variantie) en de ACF niet wijzigen in de tijd, dus constant blijven. Daarom is deze tijdreeks niet-stationair.

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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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26790&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26790&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001


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
bitmap(file='pic1.png')
racf <- acf(b,n/10,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
racf