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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 05:33:06 -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/t1228307640alc2zjp0wg9dkal.htm/, Retrieved Sun, 19 May 2024 02:42:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28653, Retrieved Sun, 19 May 2024 02:42:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198

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] [NonStationaryTime...] [2008-12-03 12:33:06] [a413cf7744efd6bb212437a3916e2f23] [Current]
Feedback Forum
2008-12-08 14:46:17 [An Knapen] [reply] 
De variance reduction matrix is een methode om de graad van differentiatie te bepalen. We hebben deze graad echter nodig om de tijdreeks stationair te maken.
Uit de tabel kunnen we inderdaad afleiden dat de kleinste waarde zich voordoet bij d en D gelijk aan 1. Zowel bij de gewone als bij de getrimde variantie bekomen we dit resultaat. We zullen dus zowel seizoenaal als niet seizoenaal moeten differentiëren om zo de tijdreeks stationair te maken.
2008-12-08 18:56:04 [Sofie Sergoynne] [reply] 
De student heeft nogal een beknopt antwoord. De variance reduction matrix is een methode om de graad van differentiatie te bepalen. De tijdreeks moet idd met d=1 en D=1 worden gedifferentiëerd.
2008-12-08 19:26:18 [Ellen Van den Broeck] [reply] 
ok

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1846,5
2796,3
2895,6
2472,2
2584,4
2630,4
2663,1
3176,2
2856,7
2551,4
3088,7
2628,3
2226,2
3023,6
3077,9
3084,1
2990,3
2949,6
3014,7
3517,7
3121,2
3067,4
3174,6
2676,3
2424
3195,1
3146,6
3506,7
3528,5
3365,1
3153
3843,3
3123,2
3361,1
3481,9
2970,5
2537
3257,6
3301,3
3391,6
2933,6
3283,2
3139,7
3486,4
3202,2
3294,4
3550,3
3279,3
2678,6
3451,4
3977,1
3814,8
3310,5
3971,8
4051,9
4057,6
4391,4
3628,9
4092,2
3822,5

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28653&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28653&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'George Udny Yule' @ 72.249.76.132






Variance Reduction Matrix
V(Y[t],d=0,D=0)244089.570652542Range2544.9Trim Var.152288.609545772
V(Y[t],d=1,D=0)173028.988720047Range1712.3Trim Var.124253.398272859
V(Y[t],d=2,D=0)446186.485810647Range2783.9Trim Var.321564.717266214
V(Y[t],d=3,D=0)1344616.87419800Range4681.2Trim Var.968734.37094902
V(Y[t],d=0,D=1)90331.5437189716Range1784.1Trim Var.37167.1424390244
V(Y[t],d=1,D=1)82029.086493987Range1472.7Trim Var.43709.0487804878
V(Y[t],d=2,D=1)246166.144700483Range2534.8Trim Var.124910.749173077
V(Y[t],d=3,D=1)825470.827272727Range4966.5Trim Var.428822.527692307
V(Y[t],d=0,D=2)234151.213420635Range2243.3Trim Var.138246.342247984
V(Y[t],d=1,D=2)119928.708201681Range1468.5Trim Var.73528.411827957
V(Y[t],d=2,D=2)319628.189162210Range2487.3Trim Var.176655.885298851
V(Y[t],d=3,D=2)1029619.84801136Range4654.4Trim Var.595321.724729064

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 244089.570652542 & Range & 2544.9 & Trim Var. & 152288.609545772 \tabularnewline
V(Y[t],d=1,D=0) & 173028.988720047 & Range & 1712.3 & Trim Var. & 124253.398272859 \tabularnewline
V(Y[t],d=2,D=0) & 446186.485810647 & Range & 2783.9 & Trim Var. & 321564.717266214 \tabularnewline
V(Y[t],d=3,D=0) & 1344616.87419800 & Range & 4681.2 & Trim Var. & 968734.37094902 \tabularnewline
V(Y[t],d=0,D=1) & 90331.5437189716 & Range & 1784.1 & Trim Var. & 37167.1424390244 \tabularnewline
V(Y[t],d=1,D=1) & 82029.086493987 & Range & 1472.7 & Trim Var. & 43709.0487804878 \tabularnewline
V(Y[t],d=2,D=1) & 246166.144700483 & Range & 2534.8 & Trim Var. & 124910.749173077 \tabularnewline
V(Y[t],d=3,D=1) & 825470.827272727 & Range & 4966.5 & Trim Var. & 428822.527692307 \tabularnewline
V(Y[t],d=0,D=2) & 234151.213420635 & Range & 2243.3 & Trim Var. & 138246.342247984 \tabularnewline
V(Y[t],d=1,D=2) & 119928.708201681 & Range & 1468.5 & Trim Var. & 73528.411827957 \tabularnewline
V(Y[t],d=2,D=2) & 319628.189162210 & Range & 2487.3 & Trim Var. & 176655.885298851 \tabularnewline
V(Y[t],d=3,D=2) & 1029619.84801136 & Range & 4654.4 & Trim Var. & 595321.724729064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28653&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]244089.570652542[/C][C]Range[/C][C]2544.9[/C][C]Trim Var.[/C][C]152288.609545772[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]173028.988720047[/C][C]Range[/C][C]1712.3[/C][C]Trim Var.[/C][C]124253.398272859[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]446186.485810647[/C][C]Range[/C][C]2783.9[/C][C]Trim Var.[/C][C]321564.717266214[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1344616.87419800[/C][C]Range[/C][C]4681.2[/C][C]Trim Var.[/C][C]968734.37094902[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]90331.5437189716[/C][C]Range[/C][C]1784.1[/C][C]Trim Var.[/C][C]37167.1424390244[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]82029.086493987[/C][C]Range[/C][C]1472.7[/C][C]Trim Var.[/C][C]43709.0487804878[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]246166.144700483[/C][C]Range[/C][C]2534.8[/C][C]Trim Var.[/C][C]124910.749173077[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]825470.827272727[/C][C]Range[/C][C]4966.5[/C][C]Trim Var.[/C][C]428822.527692307[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]234151.213420635[/C][C]Range[/C][C]2243.3[/C][C]Trim Var.[/C][C]138246.342247984[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]119928.708201681[/C][C]Range[/C][C]1468.5[/C][C]Trim Var.[/C][C]73528.411827957[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]319628.189162210[/C][C]Range[/C][C]2487.3[/C][C]Trim Var.[/C][C]176655.885298851[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1029619.84801136[/C][C]Range[/C][C]4654.4[/C][C]Trim Var.[/C][C]595321.724729064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28653&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)244089.570652542Range2544.9Trim Var.152288.609545772
V(Y[t],d=1,D=0)173028.988720047Range1712.3Trim Var.124253.398272859
V(Y[t],d=2,D=0)446186.485810647Range2783.9Trim Var.321564.717266214
V(Y[t],d=3,D=0)1344616.87419800Range4681.2Trim Var.968734.37094902
V(Y[t],d=0,D=1)90331.5437189716Range1784.1Trim Var.37167.1424390244
V(Y[t],d=1,D=1)82029.086493987Range1472.7Trim Var.43709.0487804878
V(Y[t],d=2,D=1)246166.144700483Range2534.8Trim Var.124910.749173077
V(Y[t],d=3,D=1)825470.827272727Range4966.5Trim Var.428822.527692307
V(Y[t],d=0,D=2)234151.213420635Range2243.3Trim Var.138246.342247984
V(Y[t],d=1,D=2)119928.708201681Range1468.5Trim Var.73528.411827957
V(Y[t],d=2,D=2)319628.189162210Range2487.3Trim Var.176655.885298851
V(Y[t],d=3,D=2)1029619.84801136Range4654.4Trim Var.595321.724729064


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')