FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 21 Dec 2010 13:54:38 +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/21/t1292939550gooqj6e0hck7zcj.htm/, Retrieved Sat, 11 May 2024 18:20:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113569, Retrieved Sat, 11 May 2024 18:20:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact126

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       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Variance Reduction Matrix] [Births] [2010-11-29 09:39:41] [b98453cac15ba1066b407e146608df68]
- R PD          [Variance Reduction Matrix] [WS9 3.3 VRM matrix ] [2010-12-07 10:25:28] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Variance Reduction Matrix] [Apple Inc - VRM] [2010-12-14 16:19:52] [afe9379cca749d06b3d6872e02cc47ed]
-    D                [Variance Reduction Matrix] [Paper - C&S VRM] [2010-12-21 13:54:38] [89d441ae0711e9b79b5d358f420c1317] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.31
105.63
106.02
105.85
106.57
106.48
106.60
106.75
106.69
106.69
106.93
107.21
107.88
108.84
108.96
109.52
108.45
108.67
108.96
108.76
107.85
108.78
107.51
108.83
111.54
111.74
112.04
111.74
111.81
111.86
114.23
114.80
115.17
115.11
114.43
114.66
115.11
117.74
118.18
118.56
117.63
117.71
117.46
117.37
117.34
117.09
116.65
116.71
116.82
117.33
117.95
123.53
124.91
125.99
126.29
125.68
125.52

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=113569&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=113569&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113569&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)35.6112107769424Range20.98Trim Var.26.4804266666667
V(Y[t],d=1,D=0)1.08977191558442Range6.85Trim Var.0.307263836734695
V(Y[t],d=2,D=0)1.82976686868687Range9.16Trim Var.0.693689200680274
V(Y[t],d=3,D=0)5.38239105520616Range14.01Trim Var.2.19565740248229
V(Y[t],d=0,D=1)5.91499767676768Range9.24Trim Var.3.96780499325236
V(Y[t],d=1,D=1)1.9372284883721Range7.82Trim Var.0.785975604551928
V(Y[t],d=2,D=1)3.62235980066448Range7.91000000000001Trim Var.2.07346696696699
V(Y[t],d=3,D=1)10.8354966318236Range13.7700000000000Trim Var.6.68390634920646
V(Y[t],d=0,D=2)14.7357320075758Range12.75Trim Var.11.0068935960591
V(Y[t],d=1,D=2)5.35182419354842Range9.22000000000001Trim Var.3.37216455026458
V(Y[t],d=2,D=2)11.2983045161292Range13.9600000000000Trim Var.7.53714871794883
V(Y[t],d=3,D=2)36.3487167816097Range21.7800000000001Trim Var.26.205127538462

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 35.6112107769424 & Range & 20.98 & Trim Var. & 26.4804266666667 \tabularnewline
V(Y[t],d=1,D=0) & 1.08977191558442 & Range & 6.85 & Trim Var. & 0.307263836734695 \tabularnewline
V(Y[t],d=2,D=0) & 1.82976686868687 & Range & 9.16 & Trim Var. & 0.693689200680274 \tabularnewline
V(Y[t],d=3,D=0) & 5.38239105520616 & Range & 14.01 & Trim Var. & 2.19565740248229 \tabularnewline
V(Y[t],d=0,D=1) & 5.91499767676768 & Range & 9.24 & Trim Var. & 3.96780499325236 \tabularnewline
V(Y[t],d=1,D=1) & 1.9372284883721 & Range & 7.82 & Trim Var. & 0.785975604551928 \tabularnewline
V(Y[t],d=2,D=1) & 3.62235980066448 & Range & 7.91000000000001 & Trim Var. & 2.07346696696699 \tabularnewline
V(Y[t],d=3,D=1) & 10.8354966318236 & Range & 13.7700000000000 & Trim Var. & 6.68390634920646 \tabularnewline
V(Y[t],d=0,D=2) & 14.7357320075758 & Range & 12.75 & Trim Var. & 11.0068935960591 \tabularnewline
V(Y[t],d=1,D=2) & 5.35182419354842 & Range & 9.22000000000001 & Trim Var. & 3.37216455026458 \tabularnewline
V(Y[t],d=2,D=2) & 11.2983045161292 & Range & 13.9600000000000 & Trim Var. & 7.53714871794883 \tabularnewline
V(Y[t],d=3,D=2) & 36.3487167816097 & Range & 21.7800000000001 & Trim Var. & 26.205127538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113569&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]35.6112107769424[/C][C]Range[/C][C]20.98[/C][C]Trim Var.[/C][C]26.4804266666667[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1.08977191558442[/C][C]Range[/C][C]6.85[/C][C]Trim Var.[/C][C]0.307263836734695[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1.82976686868687[/C][C]Range[/C][C]9.16[/C][C]Trim Var.[/C][C]0.693689200680274[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]5.38239105520616[/C][C]Range[/C][C]14.01[/C][C]Trim Var.[/C][C]2.19565740248229[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]5.91499767676768[/C][C]Range[/C][C]9.24[/C][C]Trim Var.[/C][C]3.96780499325236[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]1.9372284883721[/C][C]Range[/C][C]7.82[/C][C]Trim Var.[/C][C]0.785975604551928[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]3.62235980066448[/C][C]Range[/C][C]7.91000000000001[/C][C]Trim Var.[/C][C]2.07346696696699[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]10.8354966318236[/C][C]Range[/C][C]13.7700000000000[/C][C]Trim Var.[/C][C]6.68390634920646[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]14.7357320075758[/C][C]Range[/C][C]12.75[/C][C]Trim Var.[/C][C]11.0068935960591[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]5.35182419354842[/C][C]Range[/C][C]9.22000000000001[/C][C]Trim Var.[/C][C]3.37216455026458[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]11.2983045161292[/C][C]Range[/C][C]13.9600000000000[/C][C]Trim Var.[/C][C]7.53714871794883[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]36.3487167816097[/C][C]Range[/C][C]21.7800000000001[/C][C]Trim Var.[/C][C]26.205127538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113569&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113569&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)35.6112107769424Range20.98Trim Var.26.4804266666667
V(Y[t],d=1,D=0)1.08977191558442Range6.85Trim Var.0.307263836734695
V(Y[t],d=2,D=0)1.82976686868687Range9.16Trim Var.0.693689200680274
V(Y[t],d=3,D=0)5.38239105520616Range14.01Trim Var.2.19565740248229
V(Y[t],d=0,D=1)5.91499767676768Range9.24Trim Var.3.96780499325236
V(Y[t],d=1,D=1)1.9372284883721Range7.82Trim Var.0.785975604551928
V(Y[t],d=2,D=1)3.62235980066448Range7.91000000000001Trim Var.2.07346696696699
V(Y[t],d=3,D=1)10.8354966318236Range13.7700000000000Trim Var.6.68390634920646
V(Y[t],d=0,D=2)14.7357320075758Range12.75Trim Var.11.0068935960591
V(Y[t],d=1,D=2)5.35182419354842Range9.22000000000001Trim Var.3.37216455026458
V(Y[t],d=2,D=2)11.2983045161292Range13.9600000000000Trim Var.7.53714871794883
V(Y[t],d=3,D=2)36.3487167816097Range21.7800000000001Trim Var.26.205127538462


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')