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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 computationMon, 08 Dec 2008 11:38:57 -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/08/t1228761611frgclurodgkqpmt.htm/, Retrieved Thu, 16 May 2024 05:57:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30672, Retrieved Thu, 16 May 2024 05:57:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191

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]
F RMP   [Variance Reduction Matrix] [step1.1] [2008-12-08 18:32:25] [922d8ae7bd2fd460a62d9020ccd4931a]
F    D      [Variance Reduction Matrix] [step2.1] [2008-12-08 18:38:57] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
Feedback Forum
2008-12-16 17:28:07 [Lana Van Wesemael] [reply] 
Goed besproken.
In de Variance Reductie matrix moeten we op zoek gaan naar de kleinste variantie en de hierbij gegeven d en D gebruiken om de tijdreeks stationair te maken. Hoe kleiner de variantie, hoe meer we kunnen verklaren en dus hoe kleiner het risico. Indien er veel gevaar is op outliers kan men best kijken naar de getrimde variantie. Hierbij zijn immers de 5% kleinste en de 5% grootste waarden weggeknipt waardoor de invloed van de outliers niet langer aanwezig is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.8
107.4
117.5
105.6
97.4
99.5
98
104.3
100.6
101.1
103.9
96.9
95.5
108.4
117
103.8
100.8
110.6
104
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
127
112.1
114.2
121.1
131.6
125
120.4
117.7
117.5
120.6
127.5
112.3
124.5
115.2
105.4

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=30672&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=30672&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30672&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)95.2122633053221Range36.1Trim Var.71.1241513513514
V(Y[t],d=1,D=0)119.782799770511Range45.3Trim Var.75.7598241392077
V(Y[t],d=2,D=0)328.117140758155Range74.3Trim Var.220.810350076104
V(Y[t],d=3,D=0)1004.95738030714Range139Trim Var.648.207198748044
V(Y[t],d=0,D=1)40.1340943683409Range33.6Trim Var.21.2510336538462
V(Y[t],d=1,D=1)75.0755379499217Range55.7Trim Var.39.7155158730159
V(Y[t],d=2,D=1)226.253513078471Range86Trim Var.106.195151049667
V(Y[t],d=3,D=1)741.842113871635Range166.9Trim Var.355.179153886832
V(Y[t],d=0,D=2)92.817Range39.4Trim Var.56.6287590711176
V(Y[t],d=1,D=2)143.775864406780Range71.2Trim Var.80.9414116002795
V(Y[t],d=2,D=2)415.207627118644Range103.8Trim Var.234.084753265602
V(Y[t],d=3,D=2)1354.69443436177Range194.2Trim Var.818.819064856712

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 95.2122633053221 & Range & 36.1 & Trim Var. & 71.1241513513514 \tabularnewline
V(Y[t],d=1,D=0) & 119.782799770511 & Range & 45.3 & Trim Var. & 75.7598241392077 \tabularnewline
V(Y[t],d=2,D=0) & 328.117140758155 & Range & 74.3 & Trim Var. & 220.810350076104 \tabularnewline
V(Y[t],d=3,D=0) & 1004.95738030714 & Range & 139 & Trim Var. & 648.207198748044 \tabularnewline
V(Y[t],d=0,D=1) & 40.1340943683409 & Range & 33.6 & Trim Var. & 21.2510336538462 \tabularnewline
V(Y[t],d=1,D=1) & 75.0755379499217 & Range & 55.7 & Trim Var. & 39.7155158730159 \tabularnewline
V(Y[t],d=2,D=1) & 226.253513078471 & Range & 86 & Trim Var. & 106.195151049667 \tabularnewline
V(Y[t],d=3,D=1) & 741.842113871635 & Range & 166.9 & Trim Var. & 355.179153886832 \tabularnewline
V(Y[t],d=0,D=2) & 92.817 & Range & 39.4 & Trim Var. & 56.6287590711176 \tabularnewline
V(Y[t],d=1,D=2) & 143.775864406780 & Range & 71.2 & Trim Var. & 80.9414116002795 \tabularnewline
V(Y[t],d=2,D=2) & 415.207627118644 & Range & 103.8 & Trim Var. & 234.084753265602 \tabularnewline
V(Y[t],d=3,D=2) & 1354.69443436177 & Range & 194.2 & Trim Var. & 818.819064856712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30672&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]95.2122633053221[/C][C]Range[/C][C]36.1[/C][C]Trim Var.[/C][C]71.1241513513514[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]119.782799770511[/C][C]Range[/C][C]45.3[/C][C]Trim Var.[/C][C]75.7598241392077[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]328.117140758155[/C][C]Range[/C][C]74.3[/C][C]Trim Var.[/C][C]220.810350076104[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1004.95738030714[/C][C]Range[/C][C]139[/C][C]Trim Var.[/C][C]648.207198748044[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]40.1340943683409[/C][C]Range[/C][C]33.6[/C][C]Trim Var.[/C][C]21.2510336538462[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]75.0755379499217[/C][C]Range[/C][C]55.7[/C][C]Trim Var.[/C][C]39.7155158730159[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]226.253513078471[/C][C]Range[/C][C]86[/C][C]Trim Var.[/C][C]106.195151049667[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]741.842113871635[/C][C]Range[/C][C]166.9[/C][C]Trim Var.[/C][C]355.179153886832[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]92.817[/C][C]Range[/C][C]39.4[/C][C]Trim Var.[/C][C]56.6287590711176[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]143.775864406780[/C][C]Range[/C][C]71.2[/C][C]Trim Var.[/C][C]80.9414116002795[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]415.207627118644[/C][C]Range[/C][C]103.8[/C][C]Trim Var.[/C][C]234.084753265602[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1354.69443436177[/C][C]Range[/C][C]194.2[/C][C]Trim Var.[/C][C]818.819064856712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30672&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30672&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)95.2122633053221Range36.1Trim Var.71.1241513513514
V(Y[t],d=1,D=0)119.782799770511Range45.3Trim Var.75.7598241392077
V(Y[t],d=2,D=0)328.117140758155Range74.3Trim Var.220.810350076104
V(Y[t],d=3,D=0)1004.95738030714Range139Trim Var.648.207198748044
V(Y[t],d=0,D=1)40.1340943683409Range33.6Trim Var.21.2510336538462
V(Y[t],d=1,D=1)75.0755379499217Range55.7Trim Var.39.7155158730159
V(Y[t],d=2,D=1)226.253513078471Range86Trim Var.106.195151049667
V(Y[t],d=3,D=1)741.842113871635Range166.9Trim Var.355.179153886832
V(Y[t],d=0,D=2)92.817Range39.4Trim Var.56.6287590711176
V(Y[t],d=1,D=2)143.775864406780Range71.2Trim Var.80.9414116002795
V(Y[t],d=2,D=2)415.207627118644Range103.8Trim Var.234.084753265602
V(Y[t],d=3,D=2)1354.69443436177Range194.2Trim Var.818.819064856712


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')