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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 14:41:24 -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/t1228772536e1e39mhnmwx6zsq.htm/, Retrieved Thu, 16 May 2024 22:44:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31081, Retrieved Thu, 16 May 2024 22:44:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

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   [Standard Deviation-Mean Plot] [SMP Analysis] [2008-12-08 10:07:54] [23bfa928dab4b48567707937094f7011]
F RM D      [Variance Reduction Matrix] [VRM] [2008-12-08 21:41:24] [63302faa1e3976bf98d1de42298c0b24] [Current]
Feedback Forum
2008-12-14 23:38:27 [df2ed12c9b09685cd516719b004050c5] [reply] 
de variantie is hier zeer klein, de laagste variantie vinden we terug bij d=1 en D=0. We moeten dus 1x niet seizoenaal differentieren en 1x seizoenaal differentiëren.
2008-12-15 23:06:01 [Bonifer Spillemaeckers] [reply] 
De student geeft hier een correct antwoord.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,15
105,24
105,57
105,62
106,17
106,27
106,41
106,94
107,16
107,32
107,32
107,35
107,55
107,87
108,37
108,38
107,92
108,03
108,14
108,3
108,64
108,66
109,04
109,03
109,03
109,54
109,75
109,83
109,65
109,82
109,95
110,12
110,15
110,21
109,99
110,14
110,14
110,81
110,97
110,99
109,73
109,81
110,02
110,18
110,21
110,25
110,36
110,51
110,6
110,95
111,18
111,19
111,69
111,7
111,83
111,77
111,73
112,01
111,86
112,04

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=31081&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=31081&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31081&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)3.49598711864407Range6.89Trim Var.2.58629968553459
V(Y[t],d=1,D=0)0.0719842781998829Range1.92999999999999Trim Var.0.0221926705370106
V(Y[t],d=2,D=0)0.144462462189957Range2.61999999999998Trim Var.0.0691304298642538
V(Y[t],d=3,D=0)0.424595676691727Range3.82999999999997Trim Var.0.219769960784315
V(Y[t],d=0,D=1)0.571001019503547Range2.81Trim Var.0.386119454123113
V(Y[t],d=1,D=1)0.149173820536540Range2.83999999999999Trim Var.0.0242598780487809
V(Y[t],d=2,D=1)0.320389565217390Range3.59999999999998Trim Var.0.100875897435899
V(Y[t],d=3,D=1)0.999954040404035Range5.60999999999999Trim Var.0.459830499325236
V(Y[t],d=0,D=2)1.31826539682540Range3.69999999999997Trim Var.1.04073830645162
V(Y[t],d=1,D=2)0.439034957983192Range4.19999999999999Trim Var.0.083612903225807
V(Y[t],d=2,D=2)0.932410784313723Range5.60999999999999Trim Var.0.414859885057469
V(Y[t],d=3,D=2)2.87539450757575Range8.46999999999998Trim Var.1.39743645320197

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 3.49598711864407 & Range & 6.89 & Trim Var. & 2.58629968553459 \tabularnewline
V(Y[t],d=1,D=0) & 0.0719842781998829 & Range & 1.92999999999999 & Trim Var. & 0.0221926705370106 \tabularnewline
V(Y[t],d=2,D=0) & 0.144462462189957 & Range & 2.61999999999998 & Trim Var. & 0.0691304298642538 \tabularnewline
V(Y[t],d=3,D=0) & 0.424595676691727 & Range & 3.82999999999997 & Trim Var. & 0.219769960784315 \tabularnewline
V(Y[t],d=0,D=1) & 0.571001019503547 & Range & 2.81 & Trim Var. & 0.386119454123113 \tabularnewline
V(Y[t],d=1,D=1) & 0.149173820536540 & Range & 2.83999999999999 & Trim Var. & 0.0242598780487809 \tabularnewline
V(Y[t],d=2,D=1) & 0.320389565217390 & Range & 3.59999999999998 & Trim Var. & 0.100875897435899 \tabularnewline
V(Y[t],d=3,D=1) & 0.999954040404035 & Range & 5.60999999999999 & Trim Var. & 0.459830499325236 \tabularnewline
V(Y[t],d=0,D=2) & 1.31826539682540 & Range & 3.69999999999997 & Trim Var. & 1.04073830645162 \tabularnewline
V(Y[t],d=1,D=2) & 0.439034957983192 & Range & 4.19999999999999 & Trim Var. & 0.083612903225807 \tabularnewline
V(Y[t],d=2,D=2) & 0.932410784313723 & Range & 5.60999999999999 & Trim Var. & 0.414859885057469 \tabularnewline
V(Y[t],d=3,D=2) & 2.87539450757575 & Range & 8.46999999999998 & Trim Var. & 1.39743645320197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31081&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]3.49598711864407[/C][C]Range[/C][C]6.89[/C][C]Trim Var.[/C][C]2.58629968553459[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0719842781998829[/C][C]Range[/C][C]1.92999999999999[/C][C]Trim Var.[/C][C]0.0221926705370106[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.144462462189957[/C][C]Range[/C][C]2.61999999999998[/C][C]Trim Var.[/C][C]0.0691304298642538[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.424595676691727[/C][C]Range[/C][C]3.82999999999997[/C][C]Trim Var.[/C][C]0.219769960784315[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.571001019503547[/C][C]Range[/C][C]2.81[/C][C]Trim Var.[/C][C]0.386119454123113[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.149173820536540[/C][C]Range[/C][C]2.83999999999999[/C][C]Trim Var.[/C][C]0.0242598780487809[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.320389565217390[/C][C]Range[/C][C]3.59999999999998[/C][C]Trim Var.[/C][C]0.100875897435899[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.999954040404035[/C][C]Range[/C][C]5.60999999999999[/C][C]Trim Var.[/C][C]0.459830499325236[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1.31826539682540[/C][C]Range[/C][C]3.69999999999997[/C][C]Trim Var.[/C][C]1.04073830645162[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.439034957983192[/C][C]Range[/C][C]4.19999999999999[/C][C]Trim Var.[/C][C]0.083612903225807[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.932410784313723[/C][C]Range[/C][C]5.60999999999999[/C][C]Trim Var.[/C][C]0.414859885057469[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]2.87539450757575[/C][C]Range[/C][C]8.46999999999998[/C][C]Trim Var.[/C][C]1.39743645320197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31081&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31081&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)3.49598711864407Range6.89Trim Var.2.58629968553459
V(Y[t],d=1,D=0)0.0719842781998829Range1.92999999999999Trim Var.0.0221926705370106
V(Y[t],d=2,D=0)0.144462462189957Range2.61999999999998Trim Var.0.0691304298642538
V(Y[t],d=3,D=0)0.424595676691727Range3.82999999999997Trim Var.0.219769960784315
V(Y[t],d=0,D=1)0.571001019503547Range2.81Trim Var.0.386119454123113
V(Y[t],d=1,D=1)0.149173820536540Range2.83999999999999Trim Var.0.0242598780487809
V(Y[t],d=2,D=1)0.320389565217390Range3.59999999999998Trim Var.0.100875897435899
V(Y[t],d=3,D=1)0.999954040404035Range5.60999999999999Trim Var.0.459830499325236
V(Y[t],d=0,D=2)1.31826539682540Range3.69999999999997Trim Var.1.04073830645162
V(Y[t],d=1,D=2)0.439034957983192Range4.19999999999999Trim Var.0.083612903225807
V(Y[t],d=2,D=2)0.932410784313723Range5.60999999999999Trim Var.0.414859885057469
V(Y[t],d=3,D=2)2.87539450757575Range8.46999999999998Trim Var.1.39743645320197


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')