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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 computationSat, 28 Nov 2009 07:18:36 -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/28/t12594180432k4prt1q3os36sa.htm/, Retrieved Fri, 03 May 2024 12:54:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61473, Retrieved Fri, 03 May 2024 12:54:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS8 VRM
Estimated Impact117

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]
F    D          [Variance Reduction Matrix] [WS8 VRM] [2009-11-28 14:18:36] [85defb7a20869746625978e6577e6e44] [Current]
Feedback Forum
2009-12-05 15:20:19 [f1e24346ff4ab8a20729561498ad5c34] [reply] 
In de Variance Reduction Matrix moeten we zoeken naar de kleinste variantie en dan het bijhorende model aflezen. Dit zou ons het juiste model moeten geven, maar dit is niet altijd juist omdat het beïnvloed kan worden door outliers

Trimmed Variance is een robuuste variantie omdat de kleinste 5% en de grootste 5% waarden worden weggelaten. Wanneer de reeks vol met outliers zit, dus ook nog buiten de 5% kleinste en 5% grootste waarden dan kijken we best naar Range. De Range geeft de hoogte van de boxplot tot op de ‘whiskers’.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
683
1099
1124
1136
2374
4354
3341
4428
2066
1310
1031
1123
729
936
1005
1146
2515
3577
2911
4241
1972
1310
957
1062
747
924
948
1301
2373
3265
3698
3621
2054
1326
837
1260
779
980
1008
1218
2278
3000
3584
3718
2153
1428
990
1256
742
964
1037
1201
1863
3251
3380
3630
2308
1218
899
1228
836
959
1163
1071
1958
3813
4001
3823
2306
1351
1066
1124
797
1094
1110
1195
2321
3576
3145
5487
2225
1618
1122
1435

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61473&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]2 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=61473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61473&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)1347715.89027539Range4804Trim Var.953020.216956683
V(Y[t],d=1,D=0)858487.398765795Range5604Trim Var.380169.469178082
V(Y[t],d=2,D=0)1657259.15582656Range8377Trim Var.560597.408255086
V(Y[t],d=3,D=0)5231804.85277778Range16636Trim Var.1409967.63742455
V(Y[t],d=0,D=1)103859.718309859Range2520Trim Var.22124.4561011905
V(Y[t],d=1,D=1)250443.884507042Range4265Trim Var.44950.3824884793
V(Y[t],d=2,D=1)806088.439751553Range7404Trim Var.139237.197514543
V(Y[t],d=3,D=1)2769710.13938619Range13762Trim Var.448453.493989071
V(Y[t],d=0,D=2)183210.066666667Range2948Trim Var.54168.3759608665
V(Y[t],d=1,D=2)442942.943892461Range4598Trim Var.142576.323657475
V(Y[t],d=2,D=2)1365728.75408348Range8124Trim Var.423817.739064857
V(Y[t],d=3,D=2)4559299.71491228Range14385Trim Var.1658166.27372549

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 1347715.89027539 & Range & 4804 & Trim Var. & 953020.216956683 \tabularnewline
V(Y[t],d=1,D=0) & 858487.398765795 & Range & 5604 & Trim Var. & 380169.469178082 \tabularnewline
V(Y[t],d=2,D=0) & 1657259.15582656 & Range & 8377 & Trim Var. & 560597.408255086 \tabularnewline
V(Y[t],d=3,D=0) & 5231804.85277778 & Range & 16636 & Trim Var. & 1409967.63742455 \tabularnewline
V(Y[t],d=0,D=1) & 103859.718309859 & Range & 2520 & Trim Var. & 22124.4561011905 \tabularnewline
V(Y[t],d=1,D=1) & 250443.884507042 & Range & 4265 & Trim Var. & 44950.3824884793 \tabularnewline
V(Y[t],d=2,D=1) & 806088.439751553 & Range & 7404 & Trim Var. & 139237.197514543 \tabularnewline
V(Y[t],d=3,D=1) & 2769710.13938619 & Range & 13762 & Trim Var. & 448453.493989071 \tabularnewline
V(Y[t],d=0,D=2) & 183210.066666667 & Range & 2948 & Trim Var. & 54168.3759608665 \tabularnewline
V(Y[t],d=1,D=2) & 442942.943892461 & Range & 4598 & Trim Var. & 142576.323657475 \tabularnewline
V(Y[t],d=2,D=2) & 1365728.75408348 & Range & 8124 & Trim Var. & 423817.739064857 \tabularnewline
V(Y[t],d=3,D=2) & 4559299.71491228 & Range & 14385 & Trim Var. & 1658166.27372549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61473&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]1347715.89027539[/C][C]Range[/C][C]4804[/C][C]Trim Var.[/C][C]953020.216956683[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]858487.398765795[/C][C]Range[/C][C]5604[/C][C]Trim Var.[/C][C]380169.469178082[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1657259.15582656[/C][C]Range[/C][C]8377[/C][C]Trim Var.[/C][C]560597.408255086[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]5231804.85277778[/C][C]Range[/C][C]16636[/C][C]Trim Var.[/C][C]1409967.63742455[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]103859.718309859[/C][C]Range[/C][C]2520[/C][C]Trim Var.[/C][C]22124.4561011905[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]250443.884507042[/C][C]Range[/C][C]4265[/C][C]Trim Var.[/C][C]44950.3824884793[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]806088.439751553[/C][C]Range[/C][C]7404[/C][C]Trim Var.[/C][C]139237.197514543[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]2769710.13938619[/C][C]Range[/C][C]13762[/C][C]Trim Var.[/C][C]448453.493989071[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]183210.066666667[/C][C]Range[/C][C]2948[/C][C]Trim Var.[/C][C]54168.3759608665[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]442942.943892461[/C][C]Range[/C][C]4598[/C][C]Trim Var.[/C][C]142576.323657475[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]1365728.75408348[/C][C]Range[/C][C]8124[/C][C]Trim Var.[/C][C]423817.739064857[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]4559299.71491228[/C][C]Range[/C][C]14385[/C][C]Trim Var.[/C][C]1658166.27372549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61473&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)1347715.89027539Range4804Trim Var.953020.216956683
V(Y[t],d=1,D=0)858487.398765795Range5604Trim Var.380169.469178082
V(Y[t],d=2,D=0)1657259.15582656Range8377Trim Var.560597.408255086
V(Y[t],d=3,D=0)5231804.85277778Range16636Trim Var.1409967.63742455
V(Y[t],d=0,D=1)103859.718309859Range2520Trim Var.22124.4561011905
V(Y[t],d=1,D=1)250443.884507042Range4265Trim Var.44950.3824884793
V(Y[t],d=2,D=1)806088.439751553Range7404Trim Var.139237.197514543
V(Y[t],d=3,D=1)2769710.13938619Range13762Trim Var.448453.493989071
V(Y[t],d=0,D=2)183210.066666667Range2948Trim Var.54168.3759608665
V(Y[t],d=1,D=2)442942.943892461Range4598Trim Var.142576.323657475
V(Y[t],d=2,D=2)1365728.75408348Range8124Trim Var.423817.739064857
V(Y[t],d=3,D=2)4559299.71491228Range14385Trim Var.1658166.27372549


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')