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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 computationTue, 07 Dec 2010 09:54:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/07/t12917157327hpg61obrcndksl.htm/, Retrieved Fri, 03 May 2024 17:18:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106103, Retrieved Fri, 03 May 2024 17:18:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Variance Reduction Matrix] [Identifying Integ...] [2009-11-22 12:29:54] [b98453cac15ba1066b407e146608df68]
-    D        [Variance Reduction Matrix] [vrm_WS8] [2009-11-24 20:34:29] [8b1aef4e7013bd33fbc2a5833375c5f5]
-   PD            [Variance Reduction Matrix] [paper timeserie VRM] [2010-12-07 09:54:07] [da925928e5a77063c5ecc7b801d712e1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
194.9
195.5
196
196.2
196.2
196.2
196.2
197
197.7
198
198.2
198.5
198.6
199.5
200
201.3
202.2
202.9
203.5
203.5
204
204.1
204.3
204.5
204.8
205.1
205.7
206.5
206.9
207.1
207.8
208
208.5
208.6
209
209.1
209.7
209.8
209.9
210
210.8
211.4
211.7
212
212.2
212.4
212.9
213.4
213.7
214
214.3
214.8
215
215.9
216.4
216.9
217.2
217.5
217.9
218.1
218.6
218.9
219.3
220.4
220.9
221
221.8
222
222.2
222.5
222.9
223.1

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106103&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106103&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106103&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Variance Reduction Matrix
V(Y[t],d=0,D=0)69.6828169014085Range28.2Trim Var.49.3179617486339
V(Y[t],d=1,D=0)0.0754205231388341Range1.30000000000001Trim Var.0.0454377880184341
V(Y[t],d=2,D=0)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=3,D=0)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=0,D=1)0.0754205231388341Range1.30000000000001Trim Var.0.0454377880184341
V(Y[t],d=1,D=1)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=2,D=1)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=3,D=1)1.3243261633012Range4.8000000000001Trim Var.0.894268361581956
V(Y[t],d=0,D=2)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=1,D=2)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=2,D=2)1.3243261633012Range4.8000000000001Trim Var.0.894268361581956
V(Y[t],d=3,D=2)4.78749434644978Range9.40000000000018Trim Var.3.23597895967285

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 69.6828169014085 & Range & 28.2 & Trim Var. & 49.3179617486339 \tabularnewline
V(Y[t],d=1,D=0) & 0.0754205231388341 & Range & 1.30000000000001 & Trim Var. & 0.0454377880184341 \tabularnewline
V(Y[t],d=2,D=0) & 0.128952380952385 & Range & 1.40000000000003 & Trim Var. & 0.0689378531073466 \tabularnewline
V(Y[t],d=3,D=0) & 0.385144927536247 & Range & 2.50000000000006 & Trim Var. & 0.238403954802271 \tabularnewline
V(Y[t],d=0,D=1) & 0.0754205231388341 & Range & 1.30000000000001 & Trim Var. & 0.0454377880184341 \tabularnewline
V(Y[t],d=1,D=1) & 0.128952380952385 & Range & 1.40000000000003 & Trim Var. & 0.0689378531073466 \tabularnewline
V(Y[t],d=2,D=1) & 0.385144927536247 & Range & 2.50000000000006 & Trim Var. & 0.238403954802271 \tabularnewline
V(Y[t],d=3,D=1) & 1.3243261633012 & Range & 4.8000000000001 & Trim Var. & 0.894268361581956 \tabularnewline
V(Y[t],d=0,D=2) & 0.128952380952385 & Range & 1.40000000000003 & Trim Var. & 0.0689378531073466 \tabularnewline
V(Y[t],d=1,D=2) & 0.385144927536247 & Range & 2.50000000000006 & Trim Var. & 0.238403954802271 \tabularnewline
V(Y[t],d=2,D=2) & 1.3243261633012 & Range & 4.8000000000001 & Trim Var. & 0.894268361581956 \tabularnewline
V(Y[t],d=3,D=2) & 4.78749434644978 & Range & 9.40000000000018 & Trim Var. & 3.23597895967285 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106103&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]69.6828169014085[/C][C]Range[/C][C]28.2[/C][C]Trim Var.[/C][C]49.3179617486339[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0754205231388341[/C][C]Range[/C][C]1.30000000000001[/C][C]Trim Var.[/C][C]0.0454377880184341[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.128952380952385[/C][C]Range[/C][C]1.40000000000003[/C][C]Trim Var.[/C][C]0.0689378531073466[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.385144927536247[/C][C]Range[/C][C]2.50000000000006[/C][C]Trim Var.[/C][C]0.238403954802271[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.0754205231388341[/C][C]Range[/C][C]1.30000000000001[/C][C]Trim Var.[/C][C]0.0454377880184341[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.128952380952385[/C][C]Range[/C][C]1.40000000000003[/C][C]Trim Var.[/C][C]0.0689378531073466[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.385144927536247[/C][C]Range[/C][C]2.50000000000006[/C][C]Trim Var.[/C][C]0.238403954802271[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]1.3243261633012[/C][C]Range[/C][C]4.8000000000001[/C][C]Trim Var.[/C][C]0.894268361581956[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.128952380952385[/C][C]Range[/C][C]1.40000000000003[/C][C]Trim Var.[/C][C]0.0689378531073466[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.385144927536247[/C][C]Range[/C][C]2.50000000000006[/C][C]Trim Var.[/C][C]0.238403954802271[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]1.3243261633012[/C][C]Range[/C][C]4.8000000000001[/C][C]Trim Var.[/C][C]0.894268361581956[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]4.78749434644978[/C][C]Range[/C][C]9.40000000000018[/C][C]Trim Var.[/C][C]3.23597895967285[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106103&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106103&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)69.6828169014085Range28.2Trim Var.49.3179617486339
V(Y[t],d=1,D=0)0.0754205231388341Range1.30000000000001Trim Var.0.0454377880184341
V(Y[t],d=2,D=0)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=3,D=0)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=0,D=1)0.0754205231388341Range1.30000000000001Trim Var.0.0454377880184341
V(Y[t],d=1,D=1)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=2,D=1)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=3,D=1)1.3243261633012Range4.8000000000001Trim Var.0.894268361581956
V(Y[t],d=0,D=2)0.128952380952385Range1.40000000000003Trim Var.0.0689378531073466
V(Y[t],d=1,D=2)0.385144927536247Range2.50000000000006Trim Var.0.238403954802271
V(Y[t],d=2,D=2)1.3243261633012Range4.8000000000001Trim Var.0.894268361581956
V(Y[t],d=3,D=2)4.78749434644978Range9.40000000000018Trim Var.3.23597895967285


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ;
Parameters (R input):
par1 = 1 ;
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')