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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 computationFri, 27 Nov 2009 04:51:01 -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/2009/Nov/27/t12593227164y1wcntb4j9w56q.htm/, Retrieved Mon, 29 Apr 2024 00:27:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60595, Retrieved Mon, 29 Apr 2024 00:27:38 +0000
QR Codes:

Original text written by user:Method 2: VRM
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

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] [Shw8: Method 2 VRM] [2009-11-27 11:51:01] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
-                 [Variance Reduction Matrix] [Shw8: Method 2 VRM] [2009-12-04 14:49:54] [1433a524809eda02c3198b3ae6eebb69]
-    D              [Variance Reduction Matrix] [verbetering workshop] [2009-12-06 12:41:50] [1433a524809eda02c3198b3ae6eebb69]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.7461
0.7775
0.7790
0.7744
0.7905
0.7719
0.7811
0.7557
0.7637
0.7595
0.7471
0.7615
0.7487
0.7389
0.7337
0.7510
0.7382
0.7159
0.7542
0.7636
0.7433
0.7658
0.7627
0.7480
0.7692
0.7850
0.7913
0.7720
0.7880
0.8070
0.8268
0.8244
0.8487
0.8572
0.8214
0.8827
0.9216
0.8865
0.8816
0.8884
0.9466
0.9180
0.9337
0.9559
0.9626
0.9434
0.8639
0.7996
0.6680
0.6572
0.6928
0.6438
0.6454
0.6873
0.7265
0.7912
0.8114
0.8281
0.8393

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60595&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]0 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=60595&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60595&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)0.00601454821741672Range0.3188Trim Var.0.00399052124092888
V(Y[t],d=1,D=0)0.00109538942528736Range0.1963Trim Var.0.000447622141779788
V(Y[t],d=2,D=0)0.00164836859649123Range0.2076Trim Var.0.000887985725490195
V(Y[t],d=3,D=0)0.00458948051948051Range0.3263Trim Var.0.00261599405714285
V(Y[t],d=0,D=1)0.0149717833209991Range0.4598Trim Var.0.0102730640487805
V(Y[t],d=1,D=1)0.00232612970048309Range0.2612Trim Var.0.000960899685897435
V(Y[t],d=2,D=1)0.00344674264646464Range0.291100000000000Trim Var.0.00163306956815115
V(Y[t],d=3,D=1)0.0084197694027484Range0.4183Trim Var.0.00487400157183499
V(Y[t],d=0,D=2)0.0356560435798319Range0.608Trim Var.0.0279904542365591
V(Y[t],d=1,D=2)0.00608778727272727Range0.336Trim Var.0.00320501840229885
V(Y[t],d=2,D=2)0.0103125100757576Range0.454Trim Var.0.00568807972906404
V(Y[t],d=3,D=2)0.0250921364112903Range0.5574Trim Var.0.0178683439550264

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.00601454821741672 & Range & 0.3188 & Trim Var. & 0.00399052124092888 \tabularnewline
V(Y[t],d=1,D=0) & 0.00109538942528736 & Range & 0.1963 & Trim Var. & 0.000447622141779788 \tabularnewline
V(Y[t],d=2,D=0) & 0.00164836859649123 & Range & 0.2076 & Trim Var. & 0.000887985725490195 \tabularnewline
V(Y[t],d=3,D=0) & 0.00458948051948051 & Range & 0.3263 & Trim Var. & 0.00261599405714285 \tabularnewline
V(Y[t],d=0,D=1) & 0.0149717833209991 & Range & 0.4598 & Trim Var. & 0.0102730640487805 \tabularnewline
V(Y[t],d=1,D=1) & 0.00232612970048309 & Range & 0.2612 & Trim Var. & 0.000960899685897435 \tabularnewline
V(Y[t],d=2,D=1) & 0.00344674264646464 & Range & 0.291100000000000 & Trim Var. & 0.00163306956815115 \tabularnewline
V(Y[t],d=3,D=1) & 0.0084197694027484 & Range & 0.4183 & Trim Var. & 0.00487400157183499 \tabularnewline
V(Y[t],d=0,D=2) & 0.0356560435798319 & Range & 0.608 & Trim Var. & 0.0279904542365591 \tabularnewline
V(Y[t],d=1,D=2) & 0.00608778727272727 & Range & 0.336 & Trim Var. & 0.00320501840229885 \tabularnewline
V(Y[t],d=2,D=2) & 0.0103125100757576 & Range & 0.454 & Trim Var. & 0.00568807972906404 \tabularnewline
V(Y[t],d=3,D=2) & 0.0250921364112903 & Range & 0.5574 & Trim Var. & 0.0178683439550264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60595&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.00601454821741672[/C][C]Range[/C][C]0.3188[/C][C]Trim Var.[/C][C]0.00399052124092888[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.00109538942528736[/C][C]Range[/C][C]0.1963[/C][C]Trim Var.[/C][C]0.000447622141779788[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.00164836859649123[/C][C]Range[/C][C]0.2076[/C][C]Trim Var.[/C][C]0.000887985725490195[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.00458948051948051[/C][C]Range[/C][C]0.3263[/C][C]Trim Var.[/C][C]0.00261599405714285[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.0149717833209991[/C][C]Range[/C][C]0.4598[/C][C]Trim Var.[/C][C]0.0102730640487805[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.00232612970048309[/C][C]Range[/C][C]0.2612[/C][C]Trim Var.[/C][C]0.000960899685897435[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.00344674264646464[/C][C]Range[/C][C]0.291100000000000[/C][C]Trim Var.[/C][C]0.00163306956815115[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.0084197694027484[/C][C]Range[/C][C]0.4183[/C][C]Trim Var.[/C][C]0.00487400157183499[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.0356560435798319[/C][C]Range[/C][C]0.608[/C][C]Trim Var.[/C][C]0.0279904542365591[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.00608778727272727[/C][C]Range[/C][C]0.336[/C][C]Trim Var.[/C][C]0.00320501840229885[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.0103125100757576[/C][C]Range[/C][C]0.454[/C][C]Trim Var.[/C][C]0.00568807972906404[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.0250921364112903[/C][C]Range[/C][C]0.5574[/C][C]Trim Var.[/C][C]0.0178683439550264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60595&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60595&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)0.00601454821741672Range0.3188Trim Var.0.00399052124092888
V(Y[t],d=1,D=0)0.00109538942528736Range0.1963Trim Var.0.000447622141779788
V(Y[t],d=2,D=0)0.00164836859649123Range0.2076Trim Var.0.000887985725490195
V(Y[t],d=3,D=0)0.00458948051948051Range0.3263Trim Var.0.00261599405714285
V(Y[t],d=0,D=1)0.0149717833209991Range0.4598Trim Var.0.0102730640487805
V(Y[t],d=1,D=1)0.00232612970048309Range0.2612Trim Var.0.000960899685897435
V(Y[t],d=2,D=1)0.00344674264646464Range0.291100000000000Trim Var.0.00163306956815115
V(Y[t],d=3,D=1)0.0084197694027484Range0.4183Trim Var.0.00487400157183499
V(Y[t],d=0,D=2)0.0356560435798319Range0.608Trim Var.0.0279904542365591
V(Y[t],d=1,D=2)0.00608778727272727Range0.336Trim Var.0.00320501840229885
V(Y[t],d=2,D=2)0.0103125100757576Range0.454Trim Var.0.00568807972906404
V(Y[t],d=3,D=2)0.0250921364112903Range0.5574Trim Var.0.0178683439550264


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')