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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationTue, 16 Dec 2008 11:36:11 -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/2008/Dec/16/t1229452740a3b46i7pwxfrbg7.htm/, Retrieved Wed, 15 May 2024 22:03:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34092, Retrieved Wed, 15 May 2024 22:03:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordspaper var red mat werk
Estimated Impact187

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Univariate Data Series] [Werkloosheids Bel...] [2008-12-03 18:39:41] [74be16979710d4c4e7c6647856088456]
-   PD    [Univariate Data Series] [Werkloosheids Bel...] [2008-12-03 18:47:55] [74be16979710d4c4e7c6647856088456]
-   PD      [Univariate Data Series] [Paper: tijdreeks ...] [2008-12-05 09:45:57] [74be16979710d4c4e7c6647856088456]
F RMPD        [Variance Reduction Matrix] [Paper: VRM werklo...] [2008-12-05 09:50:49] [74be16979710d4c4e7c6647856088456]
-                 [Variance Reduction Matrix] [paper var red mat...] [2008-12-16 18:36:11] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D              [Variance Reduction Matrix] [paper var red mat...] [2008-12-18 13:55:11] [5de5fb433ddcb9578e0fa830f795b7e9]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
95.20
95.00
94.00
92.20
91.00
91.20
103.40
105.00
104.60
103.80
101.80
102.40
103.80
103.40
102.00
101.80
100.20
101.40
113.80
116.00
115.60
113.00
109.40
111.00
112.40
112.20
111.00
108.80
107.40
108.60
118.80
122.20
122.60
122.20
118.80
119.00
118.20
117.80
116.80
114.60
113.40
113.80
124.20
125.80
125.60
122.40
119.00
119.40
118.60
118.00
116.00
114.80
114.60
114.60
124.00
125.20
124.00
117.60
113.20
111.40
112.20
109.80
106.40
105.20
102.20
99.80
111.00
113.00
108.40
105.40
102.00
102.80

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=34092&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=34092&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34092&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)79.7883489827856Range34.8Trim Var.54.3768154761905
V(Y[t],d=1,D=0)14.258092555332Range18.8Trim Var.5.84100358422939
V(Y[t],d=2,D=0)21.7870393374741Range24.2Trim Var.9.33851930195664
V(Y[t],d=3,D=0)54.2493776641092Range35.8Trim Var.22.7946229508197
V(Y[t],d=0,D=1)60.6170734463277Range26.6Trim Var.47.8194828791055
V(Y[t],d=1,D=1)2.04596142606663Range6.8Trim Var.1.17037707390648
V(Y[t],d=2,D=1)3.99622504537205Range11.0000000000000Trim Var.2.20609351432881
V(Y[t],d=3,D=1)12.2211278195489Range20.2Trim Var.6.86992941176473
V(Y[t],d=0,D=2)16.8558865248227Range17.4000000000000Trim Var.10.0902206736353
V(Y[t],d=1,D=2)4.98852913968547Range11.6000000000000Trim Var.2.35409756097560
V(Y[t],d=2,D=2)8.6377584541063Range14.4000000000000Trim Var.4.97641025641026
V(Y[t],d=3,D=2)25.786585858586Range26.2000000000001Trim Var.13.7880701754387

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 79.7883489827856 & Range & 34.8 & Trim Var. & 54.3768154761905 \tabularnewline
V(Y[t],d=1,D=0) & 14.258092555332 & Range & 18.8 & Trim Var. & 5.84100358422939 \tabularnewline
V(Y[t],d=2,D=0) & 21.7870393374741 & Range & 24.2 & Trim Var. & 9.33851930195664 \tabularnewline
V(Y[t],d=3,D=0) & 54.2493776641092 & Range & 35.8 & Trim Var. & 22.7946229508197 \tabularnewline
V(Y[t],d=0,D=1) & 60.6170734463277 & Range & 26.6 & Trim Var. & 47.8194828791055 \tabularnewline
V(Y[t],d=1,D=1) & 2.04596142606663 & Range & 6.8 & Trim Var. & 1.17037707390648 \tabularnewline
V(Y[t],d=2,D=1) & 3.99622504537205 & Range & 11.0000000000000 & Trim Var. & 2.20609351432881 \tabularnewline
V(Y[t],d=3,D=1) & 12.2211278195489 & Range & 20.2 & Trim Var. & 6.86992941176473 \tabularnewline
V(Y[t],d=0,D=2) & 16.8558865248227 & Range & 17.4000000000000 & Trim Var. & 10.0902206736353 \tabularnewline
V(Y[t],d=1,D=2) & 4.98852913968547 & Range & 11.6000000000000 & Trim Var. & 2.35409756097560 \tabularnewline
V(Y[t],d=2,D=2) & 8.6377584541063 & Range & 14.4000000000000 & Trim Var. & 4.97641025641026 \tabularnewline
V(Y[t],d=3,D=2) & 25.786585858586 & Range & 26.2000000000001 & Trim Var. & 13.7880701754387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34092&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]79.7883489827856[/C][C]Range[/C][C]34.8[/C][C]Trim Var.[/C][C]54.3768154761905[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]14.258092555332[/C][C]Range[/C][C]18.8[/C][C]Trim Var.[/C][C]5.84100358422939[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]21.7870393374741[/C][C]Range[/C][C]24.2[/C][C]Trim Var.[/C][C]9.33851930195664[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]54.2493776641092[/C][C]Range[/C][C]35.8[/C][C]Trim Var.[/C][C]22.7946229508197[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]60.6170734463277[/C][C]Range[/C][C]26.6[/C][C]Trim Var.[/C][C]47.8194828791055[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2.04596142606663[/C][C]Range[/C][C]6.8[/C][C]Trim Var.[/C][C]1.17037707390648[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]3.99622504537205[/C][C]Range[/C][C]11.0000000000000[/C][C]Trim Var.[/C][C]2.20609351432881[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]12.2211278195489[/C][C]Range[/C][C]20.2[/C][C]Trim Var.[/C][C]6.86992941176473[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]16.8558865248227[/C][C]Range[/C][C]17.4000000000000[/C][C]Trim Var.[/C][C]10.0902206736353[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]4.98852913968547[/C][C]Range[/C][C]11.6000000000000[/C][C]Trim Var.[/C][C]2.35409756097560[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]8.6377584541063[/C][C]Range[/C][C]14.4000000000000[/C][C]Trim Var.[/C][C]4.97641025641026[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]25.786585858586[/C][C]Range[/C][C]26.2000000000001[/C][C]Trim Var.[/C][C]13.7880701754387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34092&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34092&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)79.7883489827856Range34.8Trim Var.54.3768154761905
V(Y[t],d=1,D=0)14.258092555332Range18.8Trim Var.5.84100358422939
V(Y[t],d=2,D=0)21.7870393374741Range24.2Trim Var.9.33851930195664
V(Y[t],d=3,D=0)54.2493776641092Range35.8Trim Var.22.7946229508197
V(Y[t],d=0,D=1)60.6170734463277Range26.6Trim Var.47.8194828791055
V(Y[t],d=1,D=1)2.04596142606663Range6.8Trim Var.1.17037707390648
V(Y[t],d=2,D=1)3.99622504537205Range11.0000000000000Trim Var.2.20609351432881
V(Y[t],d=3,D=1)12.2211278195489Range20.2Trim Var.6.86992941176473
V(Y[t],d=0,D=2)16.8558865248227Range17.4000000000000Trim Var.10.0902206736353
V(Y[t],d=1,D=2)4.98852913968547Range11.6000000000000Trim Var.2.35409756097560
V(Y[t],d=2,D=2)8.6377584541063Range14.4000000000000Trim Var.4.97641025641026
V(Y[t],d=3,D=2)25.786585858586Range26.2000000000001Trim Var.13.7880701754387


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')