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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 15:50:13 -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/t1228172336xzpqpzcbamipuvw.htm/, Retrieved Sun, 05 May 2024 17:23:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27506, Retrieved Sun, 05 May 2024 17:23:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact196

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 / 7] [2008-11-30 17:25:28] [4300be8b33fd3dcdacd2aa9800ceba23]
F           [Law of Averages] [] [2008-12-01 22:50:13] [fa8b44cd657c07c6ee11bb2476ca3f8d] [Current]
Feedback Forum
2008-12-08 00:11:03 [df2ed12c9b09685cd516719b004050c5] [reply] 
Deze methode is zeer handig om een tijdreeks stationair te maken. We zoeken gewoon de kleinste variantie in de tabel, en de tabel zegt gewoon wat we moeten doen. Hier zien de d=1, D=0 staan bij de kleinste variantie 0.99385920435248. De kleine d duid op het niet-seizoenale, de grote D op het seizoenale. Er moet hier alleen iets gebeuren bij d. We zien dat we de tijdreeks 1x niet seizoenaal moeten differentiëren om de trend eruit te halen. Zo maken we de tijdreeks dus stationair
2008-12-08 18:06:11 [Peter Melgers] [reply] 
De variantie is dus zo klein mogelijk als we 1 keer gewoon gaan differentiëren.
Dit komt overeen met het random walk model.

Post a new message

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27506&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27506&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27506&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 time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)108.788681362725Range51Trim Var.65.2481516036155
V(Y[t],d=1,D=0)0.99385920435248Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93158953722334Range4Trim Var.0
V(Y[t],d=3,D=0)5.5241773219965Range8Trim Var.2.76248046395681
V(Y[t],d=0,D=1)10.1223280708251Range18Trim Var.3.86849156944387
V(Y[t],d=1,D=1)1.97503823695930Range4Trim Var.0
V(Y[t],d=2,D=1)3.99998302999448Range8Trim Var.2.40184737380999
V(Y[t],d=3,D=1)11.4958507284655Range16Trim Var.6.60785751702462
V(Y[t],d=0,D=2)17.1028748341442Range26Trim Var.8.26972568578554
V(Y[t],d=1,D=2)5.9323295580724Range8Trim Var.2.64770233889918
V(Y[t],d=2,D=2)12.2198553090517Range16Trim Var.7.11640419625089
V(Y[t],d=3,D=2)35.2372164689863Range30Trim Var.22.0375357286278

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 108.788681362725 & Range & 51 & Trim Var. & 65.2481516036155 \tabularnewline
V(Y[t],d=1,D=0) & 0.99385920435248 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.93158953722334 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.5241773219965 & Range & 8 & Trim Var. & 2.76248046395681 \tabularnewline
V(Y[t],d=0,D=1) & 10.1223280708251 & Range & 18 & Trim Var. & 3.86849156944387 \tabularnewline
V(Y[t],d=1,D=1) & 1.97503823695930 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 3.99998302999448 & Range & 8 & Trim Var. & 2.40184737380999 \tabularnewline
V(Y[t],d=3,D=1) & 11.4958507284655 & Range & 16 & Trim Var. & 6.60785751702462 \tabularnewline
V(Y[t],d=0,D=2) & 17.1028748341442 & Range & 26 & Trim Var. & 8.26972568578554 \tabularnewline
V(Y[t],d=1,D=2) & 5.9323295580724 & Range & 8 & Trim Var. & 2.64770233889918 \tabularnewline
V(Y[t],d=2,D=2) & 12.2198553090517 & Range & 16 & Trim Var. & 7.11640419625089 \tabularnewline
V(Y[t],d=3,D=2) & 35.2372164689863 & Range & 30 & Trim Var. & 22.0375357286278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27506&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]108.788681362725[/C][C]Range[/C][C]51[/C][C]Trim Var.[/C][C]65.2481516036155[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.99385920435248[/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]1.93158953722334[/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]5.5241773219965[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.76248046395681[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]10.1223280708251[/C][C]Range[/C][C]18[/C][C]Trim Var.[/C][C]3.86849156944387[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.97503823695930[/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]3.99998302999448[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.40184737380999[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]11.4958507284655[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.60785751702462[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]17.1028748341442[/C][C]Range[/C][C]26[/C][C]Trim Var.[/C][C]8.26972568578554[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.9323295580724[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.64770233889918[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.2198553090517[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]7.11640419625089[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]35.2372164689863[/C][C]Range[/C][C]30[/C][C]Trim Var.[/C][C]22.0375357286278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27506&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27506&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)108.788681362725Range51Trim Var.65.2481516036155
V(Y[t],d=1,D=0)0.99385920435248Range2Trim Var.NA
V(Y[t],d=2,D=0)1.93158953722334Range4Trim Var.0
V(Y[t],d=3,D=0)5.5241773219965Range8Trim Var.2.76248046395681
V(Y[t],d=0,D=1)10.1223280708251Range18Trim Var.3.86849156944387
V(Y[t],d=1,D=1)1.97503823695930Range4Trim Var.0
V(Y[t],d=2,D=1)3.99998302999448Range8Trim Var.2.40184737380999
V(Y[t],d=3,D=1)11.4958507284655Range16Trim Var.6.60785751702462
V(Y[t],d=0,D=2)17.1028748341442Range26Trim Var.8.26972568578554
V(Y[t],d=1,D=2)5.9323295580724Range8Trim Var.2.64770233889918
V(Y[t],d=2,D=2)12.2198553090517Range16Trim Var.7.11640419625089
V(Y[t],d=3,D=2)35.2372164689863Range30Trim Var.22.0375357286278


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')