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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 computationWed, 03 Dec 2008 00:43:18 -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/03/t12282902274z6o31scz3mbrvn.htm/, Retrieved Fri, 17 May 2024 16:03:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28553, Retrieved Fri, 17 May 2024 16:03:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact229

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Variance Reduction Matrix] [] [2008-12-03 07:43:18] [e02910eed3830f1815f587e12f46cbdb] [Current]
Feedback Forum
2008-12-06 11:04:10 [Angelique Van de Vijver] [reply] 
goede berekening van de VRM en goede vaststellingen over de variantie.
We moeten inderdaad zoeken naar de differentiatie met de kleinste variantie. Hoe kleiner de variantie, hoe meer we kunnen verklaren.
Hier heb je dus een voorbeeld waarbij de variantie en de getrimde variantie het kleinst zijn bij een verschillende differentiatie. Ik denk dat het hier beter is om de getrimde variantie te nemen omdat deze een beter beeld geeft(outliers hebben minder invloed op de getrimde variantie dan op de gewone variantie).Hier kunnen we dus best 1 keer niet-seizoenaal differentiëren(d=1;D=0).
We moeten wel opmerken dat de ACF een betrouwbaarder beeld geeft dan deze variantiereductiematrix, maar meestal komen de uitslagen van deze 2 methoden wel overeen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118,4
121,4
128,8
131,7
141,7
142,9
139,4
134,7
125,0
113,6
111,5
108,5
112,3
116,6
115,5
120,1
132,9
128,1
129,3
132,5
131,0
124,9
120,8
122,0
122,1
127,4
135,2
137,3
135,0
136,0
138,4
134,7
138,4
133,9
133,6
141,2
151,8
155,4
156,6
161,6
160,7
156,0
159,5
168,7
169,9
169,9
185,9
190,8
195,8
211,9
227,1
251,3
256,7
251,9
251,2
270,3
267,2
243,0
229,9
187,2

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=28553&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=28553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28553&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)2036.62287853107Range161.8Trim Var.1478.72807826695
V(Y[t],d=1,D=0)95.675382817066Range66.9Trim Var.32.4692452830189
V(Y[t],d=2,D=0)85.7845886267394Range49.4000000000001Trim Var.43.9691843137256
V(Y[t],d=3,D=0)198.880701754386Range74.2Trim Var.101.487796078432
V(Y[t],d=0,D=1)1089.57882535461Range116.4Trim Var.756.192386759582
V(Y[t],d=1,D=1)132.800370027752Range66.8Trim Var.37.7387804878050
V(Y[t],d=2,D=1)103.078826086957Range40.8Trim Var.66.1105384615386
V(Y[t],d=3,D=1)238.689727272728Range64.3000000000001Trim Var.156.569406207828
V(Y[t],d=0,D=2)700.993928571429Range129.1Trim Var.407.869344758065
V(Y[t],d=1,D=2)254.861378151261Range66Trim Var.129.874946236559
V(Y[t],d=2,D=2)259.754402852050Range58.3000000000001Trim Var.179.641712643678
V(Y[t],d=3,D=2)644.61922348485Range90.8000000000002Trim Var.461.682438423646

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 2036.62287853107 & Range & 161.8 & Trim Var. & 1478.72807826695 \tabularnewline
V(Y[t],d=1,D=0) & 95.675382817066 & Range & 66.9 & Trim Var. & 32.4692452830189 \tabularnewline
V(Y[t],d=2,D=0) & 85.7845886267394 & Range & 49.4000000000001 & Trim Var. & 43.9691843137256 \tabularnewline
V(Y[t],d=3,D=0) & 198.880701754386 & Range & 74.2 & Trim Var. & 101.487796078432 \tabularnewline
V(Y[t],d=0,D=1) & 1089.57882535461 & Range & 116.4 & Trim Var. & 756.192386759582 \tabularnewline
V(Y[t],d=1,D=1) & 132.800370027752 & Range & 66.8 & Trim Var. & 37.7387804878050 \tabularnewline
V(Y[t],d=2,D=1) & 103.078826086957 & Range & 40.8 & Trim Var. & 66.1105384615386 \tabularnewline
V(Y[t],d=3,D=1) & 238.689727272728 & Range & 64.3000000000001 & Trim Var. & 156.569406207828 \tabularnewline
V(Y[t],d=0,D=2) & 700.993928571429 & Range & 129.1 & Trim Var. & 407.869344758065 \tabularnewline
V(Y[t],d=1,D=2) & 254.861378151261 & Range & 66 & Trim Var. & 129.874946236559 \tabularnewline
V(Y[t],d=2,D=2) & 259.754402852050 & Range & 58.3000000000001 & Trim Var. & 179.641712643678 \tabularnewline
V(Y[t],d=3,D=2) & 644.61922348485 & Range & 90.8000000000002 & Trim Var. & 461.682438423646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28553&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]2036.62287853107[/C][C]Range[/C][C]161.8[/C][C]Trim Var.[/C][C]1478.72807826695[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]95.675382817066[/C][C]Range[/C][C]66.9[/C][C]Trim Var.[/C][C]32.4692452830189[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]85.7845886267394[/C][C]Range[/C][C]49.4000000000001[/C][C]Trim Var.[/C][C]43.9691843137256[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]198.880701754386[/C][C]Range[/C][C]74.2[/C][C]Trim Var.[/C][C]101.487796078432[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]1089.57882535461[/C][C]Range[/C][C]116.4[/C][C]Trim Var.[/C][C]756.192386759582[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]132.800370027752[/C][C]Range[/C][C]66.8[/C][C]Trim Var.[/C][C]37.7387804878050[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]103.078826086957[/C][C]Range[/C][C]40.8[/C][C]Trim Var.[/C][C]66.1105384615386[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]238.689727272728[/C][C]Range[/C][C]64.3000000000001[/C][C]Trim Var.[/C][C]156.569406207828[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]700.993928571429[/C][C]Range[/C][C]129.1[/C][C]Trim Var.[/C][C]407.869344758065[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]254.861378151261[/C][C]Range[/C][C]66[/C][C]Trim Var.[/C][C]129.874946236559[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]259.754402852050[/C][C]Range[/C][C]58.3000000000001[/C][C]Trim Var.[/C][C]179.641712643678[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]644.61922348485[/C][C]Range[/C][C]90.8000000000002[/C][C]Trim Var.[/C][C]461.682438423646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28553&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)2036.62287853107Range161.8Trim Var.1478.72807826695
V(Y[t],d=1,D=0)95.675382817066Range66.9Trim Var.32.4692452830189
V(Y[t],d=2,D=0)85.7845886267394Range49.4000000000001Trim Var.43.9691843137256
V(Y[t],d=3,D=0)198.880701754386Range74.2Trim Var.101.487796078432
V(Y[t],d=0,D=1)1089.57882535461Range116.4Trim Var.756.192386759582
V(Y[t],d=1,D=1)132.800370027752Range66.8Trim Var.37.7387804878050
V(Y[t],d=2,D=1)103.078826086957Range40.8Trim Var.66.1105384615386
V(Y[t],d=3,D=1)238.689727272728Range64.3000000000001Trim Var.156.569406207828
V(Y[t],d=0,D=2)700.993928571429Range129.1Trim Var.407.869344758065
V(Y[t],d=1,D=2)254.861378151261Range66Trim Var.129.874946236559
V(Y[t],d=2,D=2)259.754402852050Range58.3000000000001Trim Var.179.641712643678
V(Y[t],d=3,D=2)644.61922348485Range90.8000000000002Trim Var.461.682438423646


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')