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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 computationSun, 27 Dec 2009 02:43:09 -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/Dec/27/t1261907059bfitjz28bg7x9te.htm/, Retrieved Thu, 02 May 2024 18:49:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70803, Retrieved Thu, 02 May 2024 18:49:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordspaper
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [VRM] [2009-12-23 10:57:13] [5e6d255681a7853beaa91b62357037a7]
-   P     [Variance Reduction Matrix] [VRM s=12] [2009-12-27 09:43:09] [b08f24ccf7d7e0757793cda532be96b3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
83.87
84.23
84.61
84.82
85.04
85.06
84.93
84.98
85.23
85.30
85.33
85.55
85.70
85.88
86.04
86.07
86.31
86.38
86.35
86.55
86.70
86.74
86.85
86.95
86.80
87.01
87.17
87.43
87.66
87.68
87.59
87.65
87.72
87.70
87.71
87.80
87.62
87.84
88.17
88.47
88.58
88.57
88.55
88.68
88.79
88.85
88.95
89.27
89.09
89.42
89.72
89.85
89.96
90.25
90.20
90.27
90.78
90.79
90.98
91.25
90.75
91.01
91.50
92.09
92.56
92.66
92.38
92.38
92.66
92.69
92.59
92.98
92.98
93.15
93.65
94.06
94.24
94.24
94.11
94.16
94.43
94.67
94.60
95.00
94.84
95.26
95.81
95.92
95.85
95.90
95.80
96.00
96.34
96.43
96.48
96.75
96.51
96.69
97.28
97.69
98.08
98.09
97.92
98.06
98.23
98.57
98.53
98.92
98.42
98.73
99.32
99.73
100.00
100.08
100.02
100.26
100.71
100.95
100.75
101.03
100.64
100.93
101.41
102.07
102.42
102.53
102.43
102.60
102.65
102.74
102.82
103.21
102.75
103.09
103.71
104.30
104.58
104.71
104.44
104.57
104.95
105.49
106.03
106.48
106.25
106.70
107.60
108.05
108.72
109.17
109.08
109.04
109.34
109.37
108.96
108.77
108.11
108.67
109.05
109.43
109.62
109.85
109.34
109.65
109.69
109.91
110.09

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=70803&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=70803&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70803&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)61.5677913931174Range26.22Trim Var.48.9561070187534
V(Y[t],d=1,D=0)0.064901234027017Range1.55999999999999Trim Var.0.0350605396212539
V(Y[t],d=2,D=0)0.111758558758315Range2.11Trim Var.0.0570987233249463
V(Y[t],d=3,D=0)0.312347104593745Range3.10000000000001Trim Var.0.141523703353803
V(Y[t],d=0,D=1)0.606142170087978Range4.3800Trim Var.0.261027192159316
V(Y[t],d=1,D=1)0.0495778966131911Range1.54000000000003Trim Var.0.0236990796572518
V(Y[t],d=2,D=1)0.0647804265565875Range1.39999999999998Trim Var.0.0400766316015456
V(Y[t],d=3,D=1)0.177584942488671Range2.45999999999998Trim Var.0.0987904520697158
V(Y[t],d=0,D=2)1.19262579533143Range7.01Trim Var.0.297982377202851
V(Y[t],d=1,D=2)0.141900973928680Range2.95000000000006Trim Var.0.0655563428571435
V(Y[t],d=2,D=2)0.175869675785207Range2.05999999999997Trim Var.0.108788877419354
V(Y[t],d=3,D=2)0.468372512846861Range3.48999999999994Trim Var.0.286378285714285

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 61.5677913931174 & Range & 26.22 & Trim Var. & 48.9561070187534 \tabularnewline
V(Y[t],d=1,D=0) & 0.064901234027017 & Range & 1.55999999999999 & Trim Var. & 0.0350605396212539 \tabularnewline
V(Y[t],d=2,D=0) & 0.111758558758315 & Range & 2.11 & Trim Var. & 0.0570987233249463 \tabularnewline
V(Y[t],d=3,D=0) & 0.312347104593745 & Range & 3.10000000000001 & Trim Var. & 0.141523703353803 \tabularnewline
V(Y[t],d=0,D=1) & 0.606142170087978 & Range & 4.3800 & Trim Var. & 0.261027192159316 \tabularnewline
V(Y[t],d=1,D=1) & 0.0495778966131911 & Range & 1.54000000000003 & Trim Var. & 0.0236990796572518 \tabularnewline
V(Y[t],d=2,D=1) & 0.0647804265565875 & Range & 1.39999999999998 & Trim Var. & 0.0400766316015456 \tabularnewline
V(Y[t],d=3,D=1) & 0.177584942488671 & Range & 2.45999999999998 & Trim Var. & 0.0987904520697158 \tabularnewline
V(Y[t],d=0,D=2) & 1.19262579533143 & Range & 7.01 & Trim Var. & 0.297982377202851 \tabularnewline
V(Y[t],d=1,D=2) & 0.141900973928680 & Range & 2.95000000000006 & Trim Var. & 0.0655563428571435 \tabularnewline
V(Y[t],d=2,D=2) & 0.175869675785207 & Range & 2.05999999999997 & Trim Var. & 0.108788877419354 \tabularnewline
V(Y[t],d=3,D=2) & 0.468372512846861 & Range & 3.48999999999994 & Trim Var. & 0.286378285714285 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70803&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]61.5677913931174[/C][C]Range[/C][C]26.22[/C][C]Trim Var.[/C][C]48.9561070187534[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.064901234027017[/C][C]Range[/C][C]1.55999999999999[/C][C]Trim Var.[/C][C]0.0350605396212539[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.111758558758315[/C][C]Range[/C][C]2.11[/C][C]Trim Var.[/C][C]0.0570987233249463[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.312347104593745[/C][C]Range[/C][C]3.10000000000001[/C][C]Trim Var.[/C][C]0.141523703353803[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.606142170087978[/C][C]Range[/C][C]4.3800[/C][C]Trim Var.[/C][C]0.261027192159316[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.0495778966131911[/C][C]Range[/C][C]1.54000000000003[/C][C]Trim Var.[/C][C]0.0236990796572518[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0647804265565875[/C][C]Range[/C][C]1.39999999999998[/C][C]Trim Var.[/C][C]0.0400766316015456[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.177584942488671[/C][C]Range[/C][C]2.45999999999998[/C][C]Trim Var.[/C][C]0.0987904520697158[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1.19262579533143[/C][C]Range[/C][C]7.01[/C][C]Trim Var.[/C][C]0.297982377202851[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.141900973928680[/C][C]Range[/C][C]2.95000000000006[/C][C]Trim Var.[/C][C]0.0655563428571435[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.175869675785207[/C][C]Range[/C][C]2.05999999999997[/C][C]Trim Var.[/C][C]0.108788877419354[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.468372512846861[/C][C]Range[/C][C]3.48999999999994[/C][C]Trim Var.[/C][C]0.286378285714285[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70803&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70803&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)61.5677913931174Range26.22Trim Var.48.9561070187534
V(Y[t],d=1,D=0)0.064901234027017Range1.55999999999999Trim Var.0.0350605396212539
V(Y[t],d=2,D=0)0.111758558758315Range2.11Trim Var.0.0570987233249463
V(Y[t],d=3,D=0)0.312347104593745Range3.10000000000001Trim Var.0.141523703353803
V(Y[t],d=0,D=1)0.606142170087978Range4.3800Trim Var.0.261027192159316
V(Y[t],d=1,D=1)0.0495778966131911Range1.54000000000003Trim Var.0.0236990796572518
V(Y[t],d=2,D=1)0.0647804265565875Range1.39999999999998Trim Var.0.0400766316015456
V(Y[t],d=3,D=1)0.177584942488671Range2.45999999999998Trim Var.0.0987904520697158
V(Y[t],d=0,D=2)1.19262579533143Range7.01Trim Var.0.297982377202851
V(Y[t],d=1,D=2)0.141900973928680Range2.95000000000006Trim Var.0.0655563428571435
V(Y[t],d=2,D=2)0.175869675785207Range2.05999999999997Trim Var.0.108788877419354
V(Y[t],d=3,D=2)0.468372512846861Range3.48999999999994Trim Var.0.286378285714285


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')