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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 computationFri, 28 Nov 2008 03:49:27 -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/Nov/28/t1227869439v3f3d91tsnrux9o.htm/, Retrieved Sun, 19 May 2024 12:16:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25992, Retrieved Sun, 19 May 2024 12:16:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact192

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 18:31:28] [b98453cac15ba1066b407e146608df68]
F         [Law of Averages] [Taak 7 -Q3 (1)] [2008-11-28 10:49:27] [b23db733701c4d62df5e228d507c1c6a] [Current]
-   P       [Law of Averages] [Verbetering Q3] [2008-12-08 16:36:46] [2bd2ad6af3eef3a703e9ec23e39bd695]
Feedback Forum
2008-12-08 14:47:40 [Mehmet Yilmaz] [reply] 
De berekening is correct, maar er is geen conclusie terug te vinden.

conclusie:
VRM test verschillende differentie waarden op de reeks en toont vervolgens de bijhorende variantie. Een reeks benaderd het beste het stationaire karakter wanneer de variantie het kleinst is, maw wanneer de mean stationair is.

Uit de tabel blijkt dat bij d=1 en D=0 de variantie het kleinst is met 1.00181085061690.

d = het aantal keer dat de reeks niet-seizoenaal gedifferentieerd is.
D = het aantal keer dat de reeks seizoenaal gedifferentieer is.

Wanneer een niet-seizoenale random-walk niet-seizoenaal gedifferentieerd word, dan word deze stationair. Uit onze berekening blijkt dat we 1 maal niet-seizoenaal moeten differentiëren om de kleinste variantie te krijgen. Maw, onze reeks wordt stationair door niet-seizoenaal te differentiëren => onze reeks was dus om te beginnen niet-seizoenaal. Dit staaft onze stelling van geen seizoenaliteit in Q1 nog verder.
  2008-12-08 22:45:44 [Ken Van den Heuvel] [reply] 
Je verbetert hier weer mijn eigen berekening door de uitleg uit mijn document gewoonweg te kopiëren.

Als ik dan toch geen conclusie gaf in mijn document, waarom zie ik ze dan hier toch opnieuw verschijnen?

  2008-12-08 16:36:17 [Mehmet Yilmaz] [reply] 
Verkeerd geblogd.


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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 time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25992&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]4 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=25992&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25992&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)97.3762885771543Range35Trim Var.69.926781354051
V(Y[t],d=1,D=0)1.00181085061690Range2Trim Var.NA
V(Y[t],d=2,D=0)2.03620114259856Range4Trim Var.0
V(Y[t],d=3,D=0)6.03225806451613Range8Trim Var.2.77344741532977
V(Y[t],d=0,D=1)12.5475982091763Range18Trim Var.6.52554126801288
V(Y[t],d=1,D=1)2.20569371561842Range4Trim Var.0
V(Y[t],d=2,D=1)4.51132323618005Range8Trim Var.2.40916266096968
V(Y[t],d=3,D=1)13.4130697793303Range16Trim Var.7.26385073984532
V(Y[t],d=0,D=2)22.8610172490049Range28Trim Var.11.8616816547851
V(Y[t],d=1,D=2)6.82684432600489Range8Trim Var.2.86109998908019
V(Y[t],d=2,D=2)14.1479737022863Range16Trim Var.6.56300268096515
V(Y[t],d=3,D=2)42.8643351130541Range32Trim Var.23.8413024850043

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 97.3762885771543 & Range & 35 & Trim Var. & 69.926781354051 \tabularnewline
V(Y[t],d=1,D=0) & 1.00181085061690 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.03620114259856 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.03225806451613 & Range & 8 & Trim Var. & 2.77344741532977 \tabularnewline
V(Y[t],d=0,D=1) & 12.5475982091763 & Range & 18 & Trim Var. & 6.52554126801288 \tabularnewline
V(Y[t],d=1,D=1) & 2.20569371561842 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.51132323618005 & Range & 8 & Trim Var. & 2.40916266096968 \tabularnewline
V(Y[t],d=3,D=1) & 13.4130697793303 & Range & 16 & Trim Var. & 7.26385073984532 \tabularnewline
V(Y[t],d=0,D=2) & 22.8610172490049 & Range & 28 & Trim Var. & 11.8616816547851 \tabularnewline
V(Y[t],d=1,D=2) & 6.82684432600489 & Range & 8 & Trim Var. & 2.86109998908019 \tabularnewline
V(Y[t],d=2,D=2) & 14.1479737022863 & Range & 16 & Trim Var. & 6.56300268096515 \tabularnewline
V(Y[t],d=3,D=2) & 42.8643351130541 & Range & 32 & Trim Var. & 23.8413024850043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25992&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]97.3762885771543[/C][C]Range[/C][C]35[/C][C]Trim Var.[/C][C]69.926781354051[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00181085061690[/C][C]Range[/C][C]2[/C][C]Trim Var.[/C][C]NA[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]2.03620114259856[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]6.03225806451613[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.77344741532977[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]12.5475982091763[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]6.52554126801288[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.20569371561842[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]4.51132323618005[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.40916266096968[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]13.4130697793303[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.26385073984532[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]22.8610172490049[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]11.8616816547851[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.82684432600489[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.86109998908019[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]14.1479737022863[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.56300268096515[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]42.8643351130541[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]23.8413024850043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25992&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25992&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Variance Reduction Matrix
V(Y[t],d=0,D=0)97.3762885771543Range35Trim Var.69.926781354051
V(Y[t],d=1,D=0)1.00181085061690Range2Trim Var.NA
V(Y[t],d=2,D=0)2.03620114259856Range4Trim Var.0
V(Y[t],d=3,D=0)6.03225806451613Range8Trim Var.2.77344741532977
V(Y[t],d=0,D=1)12.5475982091763Range18Trim Var.6.52554126801288
V(Y[t],d=1,D=1)2.20569371561842Range4Trim Var.0
V(Y[t],d=2,D=1)4.51132323618005Range8Trim Var.2.40916266096968
V(Y[t],d=3,D=1)13.4130697793303Range16Trim Var.7.26385073984532
V(Y[t],d=0,D=2)22.8610172490049Range28Trim Var.11.8616816547851
V(Y[t],d=1,D=2)6.82684432600489Range8Trim Var.2.86109998908019
V(Y[t],d=2,D=2)14.1479737022863Range16Trim Var.6.56300268096515
V(Y[t],d=3,D=2)42.8643351130541Range32Trim Var.23.8413024850043


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
par1 <- as.numeric(12)
x <- as.array(b)
n <- length(x)
sx <- sort(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Reduction Matrix',6,TRUE)
a<-table.row.end(a)
for (bigd in 0:2) {
for (smalld in 0:3) {
mylabel <- 'V(Y[t],d='
mylabel <- paste(mylabel,as.character(smalld),sep='')
mylabel <- paste(mylabel,',D=',sep='')
mylabel <- paste(mylabel,as.character(bigd),sep='')
mylabel <- paste(mylabel,')',sep='')
a<-table.row.start(a)
a<-table.element(a,mylabel,header=TRUE)
myx <- x
if (smalld > 0) myx <- diff(x,lag=1,differences=smalld)
if (bigd > 0) myx <- diff(myx,lag=par1,differences=bigd)
a<-table.element(a,var(myx))
a<-table.element(a,'Range',header=TRUE)
a<-table.element(a,max(myx)-min(myx))
a<-table.element(a,'Trim Var.',header=TRUE)
smyx <- sort(myx)
sn <- length(smyx)
a<-table.element(a,var(smyx[smyx>quantile(smyx,0.05) & smyxa<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')