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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 13:13:15 -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/t1228248838mvy1602xt6hctag.htm/, Retrieved Sat, 18 May 2024 05:32:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28341, Retrieved Sat, 18 May 2024 05:32:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

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] [Q3] [2008-12-02 20:13:15] [52492148dbcac26917ed19e489351f79] [Current]
-           [Law of Averages] [] [2008-12-03 11:11:45] [74be16979710d4c4e7c6647856088456]
F           [Law of Averages] [] [2008-12-03 11:13:27] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-06 11:50:54 [Loïque Verhasselt] [reply] 
Q3: We krijgen hier alleen maar de output in link, geen interpretatie of conclusie.We kunnen aan de conclusie toevoegen dat het variantie reductiemodel alle varianties weergeeft afhankelijk van het aantal keer gewone of seizoenale differentiatie. Respectievelijk kleine d en grote D(d=0,D=0 is de oorspronkelijke tijdreeks). We gaan ons de vraag stellen:als ik de tijdreeks wil voorspellen, wat is dan de variantie?Variantie= risico in de tijdreeks, de volatiliteit= dus men wil er zoveel mogelijk van verklaren. Hoe meer men verklaart, hoe kleiner de variantie. Het is de bedoeling van de kleinste variantie te kiezen en zo te kijken welke differentiatie er nodig is. Hier is het d=1,D=0 met een variantie van ongeveer 1.

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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'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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28341&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28341&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28341&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)41.384625250501Range30Trim Var.26.5428750966744
V(Y[t],d=1,D=0)1.00197181511618Range2Trim Var.NA
V(Y[t],d=2,D=0)2.13278061945973Range4Trim Var.0
V(Y[t],d=3,D=0)6.51612903225806Range8Trim Var.2.78601328171718
V(Y[t],d=0,D=1)9.3715117649039Range16Trim Var.4.20387998554043
V(Y[t],d=1,D=1)1.9999830996865Range4Trim Var.0
V(Y[t],d=2,D=1)4.29689024648933Range8Trim Var.2.32231232622799
V(Y[t],d=3,D=1)13.3801482491267Range16Trim Var.6.55998945356272
V(Y[t],d=0,D=2)19.8479787704556Range28Trim Var.11.0676996864047
V(Y[t],d=1,D=2)6.00836775483011Range8Trim Var.2.72137458335289
V(Y[t],d=2,D=2)12.9386892177590Range16Trim Var.6.87401983694241
V(Y[t],d=3,D=2)40.4745046045795Range30Trim Var.22.6174382796597

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 41.384625250501 & Range & 30 & Trim Var. & 26.5428750966744 \tabularnewline
V(Y[t],d=1,D=0) & 1.00197181511618 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 2.13278061945973 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 6.51612903225806 & Range & 8 & Trim Var. & 2.78601328171718 \tabularnewline
V(Y[t],d=0,D=1) & 9.3715117649039 & Range & 16 & Trim Var. & 4.20387998554043 \tabularnewline
V(Y[t],d=1,D=1) & 1.9999830996865 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.29689024648933 & Range & 8 & Trim Var. & 2.32231232622799 \tabularnewline
V(Y[t],d=3,D=1) & 13.3801482491267 & Range & 16 & Trim Var. & 6.55998945356272 \tabularnewline
V(Y[t],d=0,D=2) & 19.8479787704556 & Range & 28 & Trim Var. & 11.0676996864047 \tabularnewline
V(Y[t],d=1,D=2) & 6.00836775483011 & Range & 8 & Trim Var. & 2.72137458335289 \tabularnewline
V(Y[t],d=2,D=2) & 12.9386892177590 & Range & 16 & Trim Var. & 6.87401983694241 \tabularnewline
V(Y[t],d=3,D=2) & 40.4745046045795 & Range & 30 & Trim Var. & 22.6174382796597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28341&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]41.384625250501[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]26.5428750966744[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00197181511618[/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]2.13278061945973[/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]6.51612903225806[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.78601328171718[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]9.3715117649039[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]4.20387998554043[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.9999830996865[/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]4.29689024648933[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.32231232622799[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]13.3801482491267[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.55998945356272[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]19.8479787704556[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]11.0676996864047[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.00836775483011[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.72137458335289[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.9386892177590[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.87401983694241[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]40.4745046045795[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]22.6174382796597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28341&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28341&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)41.384625250501Range30Trim Var.26.5428750966744
V(Y[t],d=1,D=0)1.00197181511618Range2Trim Var.NA
V(Y[t],d=2,D=0)2.13278061945973Range4Trim Var.0
V(Y[t],d=3,D=0)6.51612903225806Range8Trim Var.2.78601328171718
V(Y[t],d=0,D=1)9.3715117649039Range16Trim Var.4.20387998554043
V(Y[t],d=1,D=1)1.9999830996865Range4Trim Var.0
V(Y[t],d=2,D=1)4.29689024648933Range8Trim Var.2.32231232622799
V(Y[t],d=3,D=1)13.3801482491267Range16Trim Var.6.55998945356272
V(Y[t],d=0,D=2)19.8479787704556Range28Trim Var.11.0676996864047
V(Y[t],d=1,D=2)6.00836775483011Range8Trim Var.2.72137458335289
V(Y[t],d=2,D=2)12.9386892177590Range16Trim Var.6.87401983694241
V(Y[t],d=3,D=2)40.4745046045795Range30Trim Var.22.6174382796597


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
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')