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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, 17 Dec 2009 04:59:50 -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/17/t126105190214p1ej0svayh7hj.htm/, Retrieved Tue, 30 Apr 2024 03:40:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68780, Retrieved Tue, 30 Apr 2024 03:40:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131

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       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:16:10] [b98453cac15ba1066b407e146608df68]
-    D        [(Partial) Autocorrelation Function] [ws8] [2009-11-24 20:12:27] [8b1aef4e7013bd33fbc2a5833375c5f5]
-    D          [(Partial) Autocorrelation Function] [SHw WS8] [2009-11-25 18:52:43] [af2352cd9a951bedd08ebe247d0de1a2]
- RMPD            [Bivariate Granger Causality] [] [2009-12-11 18:26:06] [09f192433169b2c787c4a71fde86e883]
- RMPD                [Variance Reduction Matrix] [] [2009-12-17 11:59:50] [71596e6a53ccce532e52aaf6113616ef] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1322.4
1089.2
1147.3
1196.4
1190.2
1146
1139.8
1045.6
1050.9
1117.3
1120
1052.1
1065.8
1092.5
1422
1367.5
1136.3
1293.7
1154.8
1206.7
1199
1265
1247.1
1116.5
1153.9
1077.4
1132.5
1058.8
1195.1
1263.4
1023.1
1141
1116.3
1135.6
1210.5
1230
1136.5
1068.7
1372.5
1049.9
1302.2
1305.9
1173.5
1277.4
1238.6
1508.6
1423.4
1375.1
1344.1
1287.5
1446.9
1451
1604.4
1501.5
1522.8
1328
1420.5
1648
1631.1
1396.6
1663.4
1283
1582.4
1785.2
1853.6
1994.1
2042.8
1586.1
1942.4
1763.6
1819.9
1836
1449.9
1513.3
1677.7
1494.4
1375.3
1577.7
1537.7
1356.6
1469.6

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=68780&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=68780&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68780&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)61894.0776512346Range1019.7Trim Var.38865.3377585513
V(Y[t],d=1,D=0)26527.6168607595Range813Trim Var.15067.7173395931
V(Y[t],d=2,D=0)76419.2771178189Range1460.2Trim Var.41488.4092273642
V(Y[t],d=3,D=0)250565.318434899Range2675.1Trim Var.127728.834296066
V(Y[t],d=0,D=1)52809.4613554987Range1027Trim Var.28275.282704918
V(Y[t],d=1,D=1)42024.7622563652Range1096.7Trim Var.24464.5381327684
V(Y[t],d=2,D=1)121918.197919493Range2000.2Trim Var.71011.5100409118
V(Y[t],d=3,D=1)402210.513118881Range3675.7Trim Var.214647.418705384
V(Y[t],d=0,D=2)126293.64268797Range1554.6Trim Var.71741.2612705882
V(Y[t],d=1,D=2)103122.295311688Range1718.3Trim Var.58366.4920653061
V(Y[t],d=2,D=2)292483.017811448Range3105.8Trim Var.153603.547287415
V(Y[t],d=3,D=2)937721.067327044Range5866.7Trim Var.483505.833896277

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 61894.0776512346 & Range & 1019.7 & Trim Var. & 38865.3377585513 \tabularnewline
V(Y[t],d=1,D=0) & 26527.6168607595 & Range & 813 & Trim Var. & 15067.7173395931 \tabularnewline
V(Y[t],d=2,D=0) & 76419.2771178189 & Range & 1460.2 & Trim Var. & 41488.4092273642 \tabularnewline
V(Y[t],d=3,D=0) & 250565.318434899 & Range & 2675.1 & Trim Var. & 127728.834296066 \tabularnewline
V(Y[t],d=0,D=1) & 52809.4613554987 & Range & 1027 & Trim Var. & 28275.282704918 \tabularnewline
V(Y[t],d=1,D=1) & 42024.7622563652 & Range & 1096.7 & Trim Var. & 24464.5381327684 \tabularnewline
V(Y[t],d=2,D=1) & 121918.197919493 & Range & 2000.2 & Trim Var. & 71011.5100409118 \tabularnewline
V(Y[t],d=3,D=1) & 402210.513118881 & Range & 3675.7 & Trim Var. & 214647.418705384 \tabularnewline
V(Y[t],d=0,D=2) & 126293.64268797 & Range & 1554.6 & Trim Var. & 71741.2612705882 \tabularnewline
V(Y[t],d=1,D=2) & 103122.295311688 & Range & 1718.3 & Trim Var. & 58366.4920653061 \tabularnewline
V(Y[t],d=2,D=2) & 292483.017811448 & Range & 3105.8 & Trim Var. & 153603.547287415 \tabularnewline
V(Y[t],d=3,D=2) & 937721.067327044 & Range & 5866.7 & Trim Var. & 483505.833896277 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68780&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]61894.0776512346[/C][C]Range[/C][C]1019.7[/C][C]Trim Var.[/C][C]38865.3377585513[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]26527.6168607595[/C][C]Range[/C][C]813[/C][C]Trim Var.[/C][C]15067.7173395931[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]76419.2771178189[/C][C]Range[/C][C]1460.2[/C][C]Trim Var.[/C][C]41488.4092273642[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]250565.318434899[/C][C]Range[/C][C]2675.1[/C][C]Trim Var.[/C][C]127728.834296066[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]52809.4613554987[/C][C]Range[/C][C]1027[/C][C]Trim Var.[/C][C]28275.282704918[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]42024.7622563652[/C][C]Range[/C][C]1096.7[/C][C]Trim Var.[/C][C]24464.5381327684[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]121918.197919493[/C][C]Range[/C][C]2000.2[/C][C]Trim Var.[/C][C]71011.5100409118[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]402210.513118881[/C][C]Range[/C][C]3675.7[/C][C]Trim Var.[/C][C]214647.418705384[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]126293.64268797[/C][C]Range[/C][C]1554.6[/C][C]Trim Var.[/C][C]71741.2612705882[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]103122.295311688[/C][C]Range[/C][C]1718.3[/C][C]Trim Var.[/C][C]58366.4920653061[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]292483.017811448[/C][C]Range[/C][C]3105.8[/C][C]Trim Var.[/C][C]153603.547287415[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]937721.067327044[/C][C]Range[/C][C]5866.7[/C][C]Trim Var.[/C][C]483505.833896277[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68780&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68780&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)61894.0776512346Range1019.7Trim Var.38865.3377585513
V(Y[t],d=1,D=0)26527.6168607595Range813Trim Var.15067.7173395931
V(Y[t],d=2,D=0)76419.2771178189Range1460.2Trim Var.41488.4092273642
V(Y[t],d=3,D=0)250565.318434899Range2675.1Trim Var.127728.834296066
V(Y[t],d=0,D=1)52809.4613554987Range1027Trim Var.28275.282704918
V(Y[t],d=1,D=1)42024.7622563652Range1096.7Trim Var.24464.5381327684
V(Y[t],d=2,D=1)121918.197919493Range2000.2Trim Var.71011.5100409118
V(Y[t],d=3,D=1)402210.513118881Range3675.7Trim Var.214647.418705384
V(Y[t],d=0,D=2)126293.64268797Range1554.6Trim Var.71741.2612705882
V(Y[t],d=1,D=2)103122.295311688Range1718.3Trim Var.58366.4920653061
V(Y[t],d=2,D=2)292483.017811448Range3105.8Trim Var.153603.547287415
V(Y[t],d=3,D=2)937721.067327044Range5866.7Trim Var.483505.833896277


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')