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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_rwalk.wasp
Title produced by softwareLaw of Averages
Date of computationFri, 28 Nov 2008 11:42:32 -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/t1227897823h5f8r9zp9osfb3t.htm/, Retrieved Sun, 19 May 2024 12:34:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26162, Retrieved Sun, 19 May 2024 12:34:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSeverijns Britt
Estimated Impact185

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] [non stationary ti...] [2008-11-28 18:42:32] [78308c9f3efc33d1da821bcd963df161] [Current]
Feedback Forum
2008-12-08 17:21:53 [Jessica Alves Pires] [reply] 
Juiste conclusie, je hebt de kleinste variantie genomen. Je zegt ook waarom een kleine variantie goed is. Je maakt echter geen onderscheid tussen seizoenaal en niet seizoenaal differentiëren. 'd' = het aantal keer dat we niet seizoenaal differentiëren.
2008-12-08 17:24:03 [Jessica Alves Pires] [reply] 
Ook had je iets kunnen zeggen over de getrimde variantie. Deze wordt gebruikt wanneer er veel outliers aanwezig zijn in een tijdreeks.
2008-12-08 19:48:03 [Vincent Dolhain] [reply] 
correct

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26162&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26162&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'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)19.0757354709419Range25Trim Var.10.4852732936845
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)1.95571824521426Range4Trim Var.0
V(Y[t],d=3,D=0)5.84675796715779Range8Trim Var.2.60635476042666
V(Y[t],d=0,D=1)13.152977412731Range20Trim Var.6.19746653267329
V(Y[t],d=1,D=1)2.12338918886945Range4Trim Var.0
V(Y[t],d=2,D=1)4.11544694752026Range8Trim Var.2.22432094556200
V(Y[t],d=3,D=1)12.5371730425151Range16Trim Var.6.71248013690258
V(Y[t],d=0,D=2)31.8038920831490Range28Trim Var.19.7084576330335
V(Y[t],d=1,D=2)6.50631134799023Range8Trim Var.2.88043257169978
V(Y[t],d=2,D=2)12.7695560253700Range16Trim Var.6.22581033859501
V(Y[t],d=3,D=2)39.2031031640807Range32Trim Var.22.163007518797

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 19.0757354709419 & Range & 25 & Trim Var. & 10.4852732936845 \tabularnewline
V(Y[t],d=1,D=0) & 1.00152111451819 & Range & 2 & Trim Var. & NA \tabularnewline
V(Y[t],d=2,D=0) & 1.95571824521426 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=3,D=0) & 5.84675796715779 & Range & 8 & Trim Var. & 2.60635476042666 \tabularnewline
V(Y[t],d=0,D=1) & 13.152977412731 & Range & 20 & Trim Var. & 6.19746653267329 \tabularnewline
V(Y[t],d=1,D=1) & 2.12338918886945 & Range & 4 & Trim Var. & 0 \tabularnewline
V(Y[t],d=2,D=1) & 4.11544694752026 & Range & 8 & Trim Var. & 2.22432094556200 \tabularnewline
V(Y[t],d=3,D=1) & 12.5371730425151 & Range & 16 & Trim Var. & 6.71248013690258 \tabularnewline
V(Y[t],d=0,D=2) & 31.8038920831490 & Range & 28 & Trim Var. & 19.7084576330335 \tabularnewline
V(Y[t],d=1,D=2) & 6.50631134799023 & Range & 8 & Trim Var. & 2.88043257169978 \tabularnewline
V(Y[t],d=2,D=2) & 12.7695560253700 & Range & 16 & Trim Var. & 6.22581033859501 \tabularnewline
V(Y[t],d=3,D=2) & 39.2031031640807 & Range & 32 & Trim Var. & 22.163007518797 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26162&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]19.0757354709419[/C][C]Range[/C][C]25[/C][C]Trim Var.[/C][C]10.4852732936845[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.00152111451819[/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]1.95571824521426[/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]5.84675796715779[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.60635476042666[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]13.152977412731[/C][C]Range[/C][C]20[/C][C]Trim Var.[/C][C]6.19746653267329[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.12338918886945[/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]4.11544694752026[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.22432094556200[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.5371730425151[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.71248013690258[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]31.8038920831490[/C][C]Range[/C][C]28[/C][C]Trim Var.[/C][C]19.7084576330335[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]6.50631134799023[/C][C]Range[/C][C]8[/C][C]Trim Var.[/C][C]2.88043257169978[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]12.7695560253700[/C][C]Range[/C][C]16[/C][C]Trim Var.[/C][C]6.22581033859501[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]39.2031031640807[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]22.163007518797[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26162&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)19.0757354709419Range25Trim Var.10.4852732936845
V(Y[t],d=1,D=0)1.00152111451819Range2Trim Var.NA
V(Y[t],d=2,D=0)1.95571824521426Range4Trim Var.0
V(Y[t],d=3,D=0)5.84675796715779Range8Trim Var.2.60635476042666
V(Y[t],d=0,D=1)13.152977412731Range20Trim Var.6.19746653267329
V(Y[t],d=1,D=1)2.12338918886945Range4Trim Var.0
V(Y[t],d=2,D=1)4.11544694752026Range8Trim Var.2.22432094556200
V(Y[t],d=3,D=1)12.5371730425151Range16Trim Var.6.71248013690258
V(Y[t],d=0,D=2)31.8038920831490Range28Trim Var.19.7084576330335
V(Y[t],d=1,D=2)6.50631134799023Range8Trim Var.2.88043257169978
V(Y[t],d=2,D=2)12.7695560253700Range16Trim Var.6.22581033859501
V(Y[t],d=3,D=2)39.2031031640807Range32Trim Var.22.163007518797


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')