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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 computationSun, 07 Dec 2008 12:46:08 -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/07/t1228679589z0tjsg91zcw6ul0.htm/, Retrieved Sun, 19 May 2024 12:38:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30280, Retrieved Sun, 19 May 2024 12:38:35 +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     [Standard Deviation-Mean Plot] [step 1] [2008-12-06 12:33:55] [c45c87b96bbf32ffc2144fc37d767b2e]
F RM      [Variance Reduction Matrix] [VRM] [2008-12-07 19:46:08] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F RMP       [ARIMA Backward Selection] [backward estimation] [2008-12-07 23:13:24] [c45c87b96bbf32ffc2144fc37d767b2e]
Feedback Forum
2008-12-14 20:57:41 [Michaël De Kuyer] [reply] 
Naar mijn mening heb ik gans de tweede vraag correct beantwoord. Ik stel inderdaad nog een invloed van een lange termijntrend vast in de ACF en de spectral analysis, maar als ik de tijdreeks nog een keer zou differentiëren zou ik problemen krijgen bij vraag 4.
2008-12-15 20:07:39 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
De VRM is correct berekend, goed toegelicht en de correcte waarden zijn gevonden.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3595
3914
4159
3676
3794
3446
3504
3958
3353
3480
3098
2944
3389
3497
4404
3849
3734
3060
3507
3287
3215
3764
2734
2837
2766
3851
3289
3848
3348
3682
4058
3655
3811
3341
3032
3475
3353
3186
3902
4164
3499
4145
3796
3711
3949
3740
3243
4407
4814
3908
5250
3937
4004
5560
3922
3759
4138
4634
3996
4308
4142
4429
5219
4929
5754
5592
4163
4962
5208
4755
4491
5732
5730
5024
6056
4901
5353
5578
4618
4724
5011
5298
4143
4617
4736
4214
5112
4197
4119
5104
4194
4583
3790
5557
4304
3838
4277
4951
4479
4677
4274
4782

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30280&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30280&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30280&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'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)583429.633857503Range3322Trim Var.383204.303245943
V(Y[t],d=1,D=0)442234.588118812Range3405Trim Var.251397.997957099
V(Y[t],d=2,D=0)1329020.03828283Range5754Trim Var.835913.850811486
V(Y[t],d=3,D=0)4378456.31374974Range10249Trim Var.2784587.66138917
V(Y[t],d=0,D=1)433557.533707865Range2984Trim Var.261625.893037975
V(Y[t],d=1,D=1)533776.941011236Range3198Trim Var.357844.222330412
V(Y[t],d=2,D=1)1541184.96499478Range5149Trim Var.968611.055777556
V(Y[t],d=3,D=1)4988768.3354718Range9187Trim Var.3177038.5430622
V(Y[t],d=0,D=2)1063156.60889111Range4842Trim Var.677163.62815735
V(Y[t],d=1,D=2)1389286.13568011Range5375Trim Var.910797.327791986
V(Y[t],d=2,D=2)3777610.67140351Range9134Trim Var.2526429.29126427
V(Y[t],d=3,D=2)11693437.7290090Range16804Trim Var.7205383.21935776

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 583429.633857503 & Range & 3322 & Trim Var. & 383204.303245943 \tabularnewline
V(Y[t],d=1,D=0) & 442234.588118812 & Range & 3405 & Trim Var. & 251397.997957099 \tabularnewline
V(Y[t],d=2,D=0) & 1329020.03828283 & Range & 5754 & Trim Var. & 835913.850811486 \tabularnewline
V(Y[t],d=3,D=0) & 4378456.31374974 & Range & 10249 & Trim Var. & 2784587.66138917 \tabularnewline
V(Y[t],d=0,D=1) & 433557.533707865 & Range & 2984 & Trim Var. & 261625.893037975 \tabularnewline
V(Y[t],d=1,D=1) & 533776.941011236 & Range & 3198 & Trim Var. & 357844.222330412 \tabularnewline
V(Y[t],d=2,D=1) & 1541184.96499478 & Range & 5149 & Trim Var. & 968611.055777556 \tabularnewline
V(Y[t],d=3,D=1) & 4988768.3354718 & Range & 9187 & Trim Var. & 3177038.5430622 \tabularnewline
V(Y[t],d=0,D=2) & 1063156.60889111 & Range & 4842 & Trim Var. & 677163.62815735 \tabularnewline
V(Y[t],d=1,D=2) & 1389286.13568011 & Range & 5375 & Trim Var. & 910797.327791986 \tabularnewline
V(Y[t],d=2,D=2) & 3777610.67140351 & Range & 9134 & Trim Var. & 2526429.29126427 \tabularnewline
V(Y[t],d=3,D=2) & 11693437.7290090 & Range & 16804 & Trim Var. & 7205383.21935776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30280&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]583429.633857503[/C][C]Range[/C][C]3322[/C][C]Trim Var.[/C][C]383204.303245943[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]442234.588118812[/C][C]Range[/C][C]3405[/C][C]Trim Var.[/C][C]251397.997957099[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1329020.03828283[/C][C]Range[/C][C]5754[/C][C]Trim Var.[/C][C]835913.850811486[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]4378456.31374974[/C][C]Range[/C][C]10249[/C][C]Trim Var.[/C][C]2784587.66138917[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]433557.533707865[/C][C]Range[/C][C]2984[/C][C]Trim Var.[/C][C]261625.893037975[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]533776.941011236[/C][C]Range[/C][C]3198[/C][C]Trim Var.[/C][C]357844.222330412[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]1541184.96499478[/C][C]Range[/C][C]5149[/C][C]Trim Var.[/C][C]968611.055777556[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]4988768.3354718[/C][C]Range[/C][C]9187[/C][C]Trim Var.[/C][C]3177038.5430622[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1063156.60889111[/C][C]Range[/C][C]4842[/C][C]Trim Var.[/C][C]677163.62815735[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]1389286.13568011[/C][C]Range[/C][C]5375[/C][C]Trim Var.[/C][C]910797.327791986[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]3777610.67140351[/C][C]Range[/C][C]9134[/C][C]Trim Var.[/C][C]2526429.29126427[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]11693437.7290090[/C][C]Range[/C][C]16804[/C][C]Trim Var.[/C][C]7205383.21935776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30280&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30280&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)583429.633857503Range3322Trim Var.383204.303245943
V(Y[t],d=1,D=0)442234.588118812Range3405Trim Var.251397.997957099
V(Y[t],d=2,D=0)1329020.03828283Range5754Trim Var.835913.850811486
V(Y[t],d=3,D=0)4378456.31374974Range10249Trim Var.2784587.66138917
V(Y[t],d=0,D=1)433557.533707865Range2984Trim Var.261625.893037975
V(Y[t],d=1,D=1)533776.941011236Range3198Trim Var.357844.222330412
V(Y[t],d=2,D=1)1541184.96499478Range5149Trim Var.968611.055777556
V(Y[t],d=3,D=1)4988768.3354718Range9187Trim Var.3177038.5430622
V(Y[t],d=0,D=2)1063156.60889111Range4842Trim Var.677163.62815735
V(Y[t],d=1,D=2)1389286.13568011Range5375Trim Var.910797.327791986
V(Y[t],d=2,D=2)3777610.67140351Range9134Trim Var.2526429.29126427
V(Y[t],d=3,D=2)11693437.7290090Range16804Trim Var.7205383.21935776


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')