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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, 02 Dec 2008 06:58:13 -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/02/t12282263999mfktdvvuj12y88.htm/, Retrieved Fri, 17 May 2024 03:21:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27802, Retrieved Fri, 17 May 2024 03:21:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Spectral Analysis] [q6] [2008-12-02 13:19:49] [74be16979710d4c4e7c6647856088456]
F RMPD    [Cross Correlation Function] [q7] [2008-12-02 13:34:43] [7ab42b4673454531c59df48fbb842b60]
F   PD      [Cross Correlation Function] [q8] [2008-12-02 13:49:39] [7ab42b4673454531c59df48fbb842b60]
F RM D          [Variance Reduction Matrix] [q8] [2008-12-02 13:58:13] [074508d5a5a3592082de3e836d27af7d] [Current]
- RMP             [Standard Deviation-Mean Plot] [q9] [2008-12-07 11:28:49] [1b742211e88d1643c42c5773474321b2]
Feedback Forum
2008-12-07 11:27:14 [Kelly Deckx] [reply] 
De tabel is juist geinterpreteerd.
2008-12-07 11:31:15 [Kelly Deckx] [reply] 
Ik ben de lamda weer vergeten te berekenen:
voor de Bel20 is dat Lambda 0.722752549375909
http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/07/t12286494592ovuihdg0srivif.htm
2008-12-07 21:06:46 [Jasmine Hendrikx] [reply] 
Evaluatie Q8:
Om deze vraag op te lossen, heeft de student gebruik gemaakt van de VRM. Dit is inderdaad een methode om de vraag op te lossen. Het klopt inderdaad dat men naar de combinatie van d en D moet kijken met de kleinste variantie en deze komt in dit geval overeen met d=1 en D=0. Er moet dus maar één keer niet-seizoenaal gedifferentieerd worden. Maar eigenlijk is dit slechts één methode en zou je ook nog gebruik moeten maken van de ACF en van Spectral Analysis om de resultaten uit de VRM te controleren. Het is namelijk zo dat deze niet altijd overeenkomen en wanneer de VRM en de ACF een verschillend resultaat geven, zou men eerder geneigd moeten zijn om de ACF te gebruiken. Je zou dus ook van de andere methodes gebruik moeten maken ter controle.

Ook is de optimale lambda niet berekend. Deze zou je kunnen berekenen via de methode Standard Deviation – Mean plot. Zo zou je de variantie kunnen stabiliseren om zo de tijdreeks meer stationair te maken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2174.56
2196.72
2350.44
2440.25
2408.64
2472.81
2407.6
2454.62
2448.05
2497.84
2645.64
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34

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=27802&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=27802&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27802&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)515314.658843016Range2522.4Trim Var.408610.27866553
V(Y[t],d=1,D=0)20960.0194712644Range706.060000000001Trim Var.11572.6074058447
V(Y[t],d=2,D=0)31430.1512466792Range954.99Trim Var.15582.4998254118
V(Y[t],d=3,D=0)87792.9376794806Range1435.58000000000Trim Var.43999.1376580002
V(Y[t],d=0,D=1)471043.514094172Range2581.52Trim Var.299050.068000244
V(Y[t],d=1,D=1)31445.0195592270Range895.73Trim Var.14834.3239794231
V(Y[t],d=2,D=1)58692.8968043434Range1341.37Trim Var.23068.0939726046
V(Y[t],d=3,D=1)162551.246281342Range2321.41Trim Var.62844.9346753913
V(Y[t],d=0,D=2)627139.686294622Range2969.94Trim Var.431788.340203656
V(Y[t],d=1,D=2)83934.1632849376Range1481.9Trim Var.42625.0325274713
V(Y[t],d=2,D=2)155611.229567614Range2179.42Trim Var.62643.4029780789
V(Y[t],d=3,D=2)393451.323213609Range3144.96Trim Var.173325.389935979

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 515314.658843016 & Range & 2522.4 & Trim Var. & 408610.27866553 \tabularnewline
V(Y[t],d=1,D=0) & 20960.0194712644 & Range & 706.060000000001 & Trim Var. & 11572.6074058447 \tabularnewline
V(Y[t],d=2,D=0) & 31430.1512466792 & Range & 954.99 & Trim Var. & 15582.4998254118 \tabularnewline
V(Y[t],d=3,D=0) & 87792.9376794806 & Range & 1435.58000000000 & Trim Var. & 43999.1376580002 \tabularnewline
V(Y[t],d=0,D=1) & 471043.514094172 & Range & 2581.52 & Trim Var. & 299050.068000244 \tabularnewline
V(Y[t],d=1,D=1) & 31445.0195592270 & Range & 895.73 & Trim Var. & 14834.3239794231 \tabularnewline
V(Y[t],d=2,D=1) & 58692.8968043434 & Range & 1341.37 & Trim Var. & 23068.0939726046 \tabularnewline
V(Y[t],d=3,D=1) & 162551.246281342 & Range & 2321.41 & Trim Var. & 62844.9346753913 \tabularnewline
V(Y[t],d=0,D=2) & 627139.686294622 & Range & 2969.94 & Trim Var. & 431788.340203656 \tabularnewline
V(Y[t],d=1,D=2) & 83934.1632849376 & Range & 1481.9 & Trim Var. & 42625.0325274713 \tabularnewline
V(Y[t],d=2,D=2) & 155611.229567614 & Range & 2179.42 & Trim Var. & 62643.4029780789 \tabularnewline
V(Y[t],d=3,D=2) & 393451.323213609 & Range & 3144.96 & Trim Var. & 173325.389935979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27802&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]515314.658843016[/C][C]Range[/C][C]2522.4[/C][C]Trim Var.[/C][C]408610.27866553[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]20960.0194712644[/C][C]Range[/C][C]706.060000000001[/C][C]Trim Var.[/C][C]11572.6074058447[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]31430.1512466792[/C][C]Range[/C][C]954.99[/C][C]Trim Var.[/C][C]15582.4998254118[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]87792.9376794806[/C][C]Range[/C][C]1435.58000000000[/C][C]Trim Var.[/C][C]43999.1376580002[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]471043.514094172[/C][C]Range[/C][C]2581.52[/C][C]Trim Var.[/C][C]299050.068000244[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]31445.0195592270[/C][C]Range[/C][C]895.73[/C][C]Trim Var.[/C][C]14834.3239794231[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]58692.8968043434[/C][C]Range[/C][C]1341.37[/C][C]Trim Var.[/C][C]23068.0939726046[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]162551.246281342[/C][C]Range[/C][C]2321.41[/C][C]Trim Var.[/C][C]62844.9346753913[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]627139.686294622[/C][C]Range[/C][C]2969.94[/C][C]Trim Var.[/C][C]431788.340203656[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]83934.1632849376[/C][C]Range[/C][C]1481.9[/C][C]Trim Var.[/C][C]42625.0325274713[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]155611.229567614[/C][C]Range[/C][C]2179.42[/C][C]Trim Var.[/C][C]62643.4029780789[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]393451.323213609[/C][C]Range[/C][C]3144.96[/C][C]Trim Var.[/C][C]173325.389935979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27802&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27802&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)515314.658843016Range2522.4Trim Var.408610.27866553
V(Y[t],d=1,D=0)20960.0194712644Range706.060000000001Trim Var.11572.6074058447
V(Y[t],d=2,D=0)31430.1512466792Range954.99Trim Var.15582.4998254118
V(Y[t],d=3,D=0)87792.9376794806Range1435.58000000000Trim Var.43999.1376580002
V(Y[t],d=0,D=1)471043.514094172Range2581.52Trim Var.299050.068000244
V(Y[t],d=1,D=1)31445.0195592270Range895.73Trim Var.14834.3239794231
V(Y[t],d=2,D=1)58692.8968043434Range1341.37Trim Var.23068.0939726046
V(Y[t],d=3,D=1)162551.246281342Range2321.41Trim Var.62844.9346753913
V(Y[t],d=0,D=2)627139.686294622Range2969.94Trim Var.431788.340203656
V(Y[t],d=1,D=2)83934.1632849376Range1481.9Trim Var.42625.0325274713
V(Y[t],d=2,D=2)155611.229567614Range2179.42Trim Var.62643.4029780789
V(Y[t],d=3,D=2)393451.323213609Range3144.96Trim Var.173325.389935979


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
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')