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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationThu, 27 Nov 2008 15:46:51 -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/27/t122782606152rb5la1mii30vk.htm/, Retrieved Sun, 19 May 2024 08:52:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25941, Retrieved Sun, 19 May 2024 08:52:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
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 18:40:39] [b98453cac15ba1066b407e146608df68]
F       [Law of Averages] [Random Walk Simul...] [2008-11-27 19:45:04] [58bf45a666dc5198906262e8815a9722]
F RMPD    [Standard Deviation-Mean Plot] [Standard Deviatio...] [2008-11-27 22:08:29] [58bf45a666dc5198906262e8815a9722]
F RM          [Variance Reduction Matrix] [Variance Reductio...] [2008-11-27 22:46:51] [63db34dadd44fb018112addcdefe949f] [Current]
Feedback Forum
2008-12-04 15:33:27 [Matthieu Blondeau] [reply] 
Dit is correct. Met de VRM moet men de kleinste waarde kiezen om op deze manier te weten welke waarde we aan 'd' en 'D' moeten toekennen om zo de reeks stationair te maken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106
82
114
118
105
105
103
107
123
112
104
122
108
94
120
118
117
113
106
108
122
115
110
120
104
96
121
111
120
114
107
108
127
105
119
121
106
97
119
122
121
106
114
112
127
109
118
123
115
105
116
131
121
104
127
126
124
132
117
123

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25941&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25941&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25941&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'George Udny Yule' @ 72.249.76.132






Variance Reduction Matrix
V(Y[t],d=0,D=0)93.5819209039548Range50Trim Var.51.9396681749623
V(Y[t],d=1,D=0)169.656925774401Range56Trim Var.117.99854862119
V(Y[t],d=2,D=0)466.569872958258Range97Trim Var.319.192684766214
V(Y[t],d=3,D=0)1346.52694235589Range161Trim Var.933.545882352941
V(Y[t],d=0,D=1)37.354609929078Range33Trim Var.18.1004645760743
V(Y[t],d=1,D=1)90.6068455134135Range50Trim Var.40.5121794871795
V(Y[t],d=2,D=1)288.027536231884Range93Trim Var.142.871153846154
V(Y[t],d=3,D=1)951.537373737374Range168Trim Var.401.985155195682
V(Y[t],d=0,D=2)58.2563492063492Range32Trim Var.32.9182795698925
V(Y[t],d=1,D=2)124.986554621849Range46Trim Var.78.2731182795699
V(Y[t],d=2,D=2)403.632798573975Range78Trim Var.163.582010582011
V(Y[t],d=3,D=2)1387.62689393939Range154Trim Var.849.113300492611

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 93.5819209039548 & Range & 50 & Trim Var. & 51.9396681749623 \tabularnewline
V(Y[t],d=1,D=0) & 169.656925774401 & Range & 56 & Trim Var. & 117.99854862119 \tabularnewline
V(Y[t],d=2,D=0) & 466.569872958258 & Range & 97 & Trim Var. & 319.192684766214 \tabularnewline
V(Y[t],d=3,D=0) & 1346.52694235589 & Range & 161 & Trim Var. & 933.545882352941 \tabularnewline
V(Y[t],d=0,D=1) & 37.354609929078 & Range & 33 & Trim Var. & 18.1004645760743 \tabularnewline
V(Y[t],d=1,D=1) & 90.6068455134135 & Range & 50 & Trim Var. & 40.5121794871795 \tabularnewline
V(Y[t],d=2,D=1) & 288.027536231884 & Range & 93 & Trim Var. & 142.871153846154 \tabularnewline
V(Y[t],d=3,D=1) & 951.537373737374 & Range & 168 & Trim Var. & 401.985155195682 \tabularnewline
V(Y[t],d=0,D=2) & 58.2563492063492 & Range & 32 & Trim Var. & 32.9182795698925 \tabularnewline
V(Y[t],d=1,D=2) & 124.986554621849 & Range & 46 & Trim Var. & 78.2731182795699 \tabularnewline
V(Y[t],d=2,D=2) & 403.632798573975 & Range & 78 & Trim Var. & 163.582010582011 \tabularnewline
V(Y[t],d=3,D=2) & 1387.62689393939 & Range & 154 & Trim Var. & 849.113300492611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25941&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]93.5819209039548[/C][C]Range[/C][C]50[/C][C]Trim Var.[/C][C]51.9396681749623[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]169.656925774401[/C][C]Range[/C][C]56[/C][C]Trim Var.[/C][C]117.99854862119[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]466.569872958258[/C][C]Range[/C][C]97[/C][C]Trim Var.[/C][C]319.192684766214[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1346.52694235589[/C][C]Range[/C][C]161[/C][C]Trim Var.[/C][C]933.545882352941[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]37.354609929078[/C][C]Range[/C][C]33[/C][C]Trim Var.[/C][C]18.1004645760743[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]90.6068455134135[/C][C]Range[/C][C]50[/C][C]Trim Var.[/C][C]40.5121794871795[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]288.027536231884[/C][C]Range[/C][C]93[/C][C]Trim Var.[/C][C]142.871153846154[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]951.537373737374[/C][C]Range[/C][C]168[/C][C]Trim Var.[/C][C]401.985155195682[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]58.2563492063492[/C][C]Range[/C][C]32[/C][C]Trim Var.[/C][C]32.9182795698925[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]124.986554621849[/C][C]Range[/C][C]46[/C][C]Trim Var.[/C][C]78.2731182795699[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]403.632798573975[/C][C]Range[/C][C]78[/C][C]Trim Var.[/C][C]163.582010582011[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1387.62689393939[/C][C]Range[/C][C]154[/C][C]Trim Var.[/C][C]849.113300492611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25941&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25941&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)93.5819209039548Range50Trim Var.51.9396681749623
V(Y[t],d=1,D=0)169.656925774401Range56Trim Var.117.99854862119
V(Y[t],d=2,D=0)466.569872958258Range97Trim Var.319.192684766214
V(Y[t],d=3,D=0)1346.52694235589Range161Trim Var.933.545882352941
V(Y[t],d=0,D=1)37.354609929078Range33Trim Var.18.1004645760743
V(Y[t],d=1,D=1)90.6068455134135Range50Trim Var.40.5121794871795
V(Y[t],d=2,D=1)288.027536231884Range93Trim Var.142.871153846154
V(Y[t],d=3,D=1)951.537373737374Range168Trim Var.401.985155195682
V(Y[t],d=0,D=2)58.2563492063492Range32Trim Var.32.9182795698925
V(Y[t],d=1,D=2)124.986554621849Range46Trim Var.78.2731182795699
V(Y[t],d=2,D=2)403.632798573975Range78Trim Var.163.582010582011
V(Y[t],d=3,D=2)1387.62689393939Range154Trim Var.849.113300492611


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
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')