FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 03 Dec 2008 04:34:41 -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/t1228304138ttj616b4leggowh.htm/, Retrieved Fri, 17 May 2024 01:31:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28629, Retrieved Fri, 17 May 2024 01:31:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Cross Correlation Function] [Q7 zonder aanpass...] [2008-12-03 11:21:09] [f77c9ab3b413812d7baee6b7ec69a15d]
F RMPD    [Variance Reduction Matrix] [Q8 suiker] [2008-12-03 11:34:41] [3fc0b50a130253095e963177b0139835] [Current]
Feedback Forum
2008-12-04 17:21:56 [Loïque Verhasselt] [reply] 
Q8: De student gebruikt hier het variantie reductie model maar ze vergeet de seasonal period te veranderen naar 12 omdat we werken met periodes van 12 maanden. Als we dit aanpassen krijgen we de laagste variantie bij d=2 en D=0 bij suiker omdat er geen seizoenaliteit aanwezig is in de student zijn tijdreeks. Als we dit ook aanpassen bij jam krijgen we de laagste variantie bij d=1 en D=0 opnieuw omdat er geen seizoenaliteit is. Om de lambda te vinden gebruiken we het Standaard Deviatie Mean Plot die ons voor beide tijdreeksen een correcte lambda zal geven. Na dit alles kunnen we deze 6 parameters invullen in de cross correlatie functie en krijgen we een stationair model.
2008-12-08 13:27:00 [Anouk Greeve] [reply] 
Wat mijn voorganger zegt is correct. De studente moet inderdaad de seasonal period veranderen naar 12 omdat we werken met periodes van 12 maanden. Zo krijgen we de laagste variantie bij d=2 en D=0. Als we dit ook aanpassen bij jam krijgen we de laagste variantie bij d=1 en D=0. In beide gevallen omdat er geen seizoenaliteit aanwezig is.


Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,02
100,67
100,47
100,38
100,33
100,34
100,37
100,39
100,21
100,21
100,22
100,28
100,25
100,25
100,21
100,16
100,18
100,1
99,96
99,88
99,88
99,86
99,84
99,8
99,82
99,81
99,92
100,03
99,99
100,02
100,01
100,13
100,33
100,13
99,96
100,05
99,83
99,8
100,01
100,1
100,13
100,16
100,41
101,34
101,65
101,85
102,07
102,12
102,14
102,21
102,28
102,19
102,33
102,54
102,44
102,78
102,9
103,08
102,77
102,65
102,71
103,29
102,86
103,45
103,72
103,65
103,83
104,45
105,14
105,07
105,31
105,19
105,3
105,02
105,17
105,28
105,45
105,38
105,8
105,96
105,08
105,11
105,61
105,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28629&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28629&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28629&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Variance Reduction Matrix
V(Y[t],d=0,D=0)4.27624662937464Range6.16Trim Var.3.57431375416513
V(Y[t],d=1,D=0)0.0618193652659423Range1.81000000000000Trim Var.0.0207473363774736
V(Y[t],d=2,D=0)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=3,D=0)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=0,D=1)0.0618193652659423Range1.81000000000000Trim Var.0.0207473363774736
V(Y[t],d=1,D=1)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=2,D=1)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=3,D=1)1.01822126582281Range6.93000000000012Trim Var.0.41886038732396
V(Y[t],d=0,D=2)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=1,D=2)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=2,D=2)1.01822126582281Range6.93000000000012Trim Var.0.41886038732396
V(Y[t],d=3,D=2)3.48001012658239Range12.3600000000003Trim Var.1.47514474849100

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 4.27624662937464 & Range & 6.16 & Trim Var. & 3.57431375416513 \tabularnewline
V(Y[t],d=1,D=0) & 0.0618193652659423 & Range & 1.81000000000000 & Trim Var. & 0.0207473363774736 \tabularnewline
V(Y[t],d=2,D=0) & 0.113729599518219 & Range & 2.06000000000000 & Trim Var. & 0.0412612676056348 \tabularnewline
V(Y[t],d=3,D=0) & 0.323923364197538 & Range & 3.56000000000007 & Trim Var. & 0.113031388329983 \tabularnewline
V(Y[t],d=0,D=1) & 0.0618193652659423 & Range & 1.81000000000000 & Trim Var. & 0.0207473363774736 \tabularnewline
V(Y[t],d=1,D=1) & 0.113729599518219 & Range & 2.06000000000000 & Trim Var. & 0.0412612676056348 \tabularnewline
V(Y[t],d=2,D=1) & 0.323923364197538 & Range & 3.56000000000007 & Trim Var. & 0.113031388329983 \tabularnewline
V(Y[t],d=3,D=1) & 1.01822126582281 & Range & 6.93000000000012 & Trim Var. & 0.41886038732396 \tabularnewline
V(Y[t],d=0,D=2) & 0.113729599518219 & Range & 2.06000000000000 & Trim Var. & 0.0412612676056348 \tabularnewline
V(Y[t],d=1,D=2) & 0.323923364197538 & Range & 3.56000000000007 & Trim Var. & 0.113031388329983 \tabularnewline
V(Y[t],d=2,D=2) & 1.01822126582281 & Range & 6.93000000000012 & Trim Var. & 0.41886038732396 \tabularnewline
V(Y[t],d=3,D=2) & 3.48001012658239 & Range & 12.3600000000003 & Trim Var. & 1.47514474849100 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28629&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]4.27624662937464[/C][C]Range[/C][C]6.16[/C][C]Trim Var.[/C][C]3.57431375416513[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.0618193652659423[/C][C]Range[/C][C]1.81000000000000[/C][C]Trim Var.[/C][C]0.0207473363774736[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.113729599518219[/C][C]Range[/C][C]2.06000000000000[/C][C]Trim Var.[/C][C]0.0412612676056348[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.323923364197538[/C][C]Range[/C][C]3.56000000000007[/C][C]Trim Var.[/C][C]0.113031388329983[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.0618193652659423[/C][C]Range[/C][C]1.81000000000000[/C][C]Trim Var.[/C][C]0.0207473363774736[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.113729599518219[/C][C]Range[/C][C]2.06000000000000[/C][C]Trim Var.[/C][C]0.0412612676056348[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.323923364197538[/C][C]Range[/C][C]3.56000000000007[/C][C]Trim Var.[/C][C]0.113031388329983[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]1.01822126582281[/C][C]Range[/C][C]6.93000000000012[/C][C]Trim Var.[/C][C]0.41886038732396[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]0.113729599518219[/C][C]Range[/C][C]2.06000000000000[/C][C]Trim Var.[/C][C]0.0412612676056348[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.323923364197538[/C][C]Range[/C][C]3.56000000000007[/C][C]Trim Var.[/C][C]0.113031388329983[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]1.01822126582281[/C][C]Range[/C][C]6.93000000000012[/C][C]Trim Var.[/C][C]0.41886038732396[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]3.48001012658239[/C][C]Range[/C][C]12.3600000000003[/C][C]Trim Var.[/C][C]1.47514474849100[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28629&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28629&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)4.27624662937464Range6.16Trim Var.3.57431375416513
V(Y[t],d=1,D=0)0.0618193652659423Range1.81000000000000Trim Var.0.0207473363774736
V(Y[t],d=2,D=0)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=3,D=0)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=0,D=1)0.0618193652659423Range1.81000000000000Trim Var.0.0207473363774736
V(Y[t],d=1,D=1)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=2,D=1)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=3,D=1)1.01822126582281Range6.93000000000012Trim Var.0.41886038732396
V(Y[t],d=0,D=2)0.113729599518219Range2.06000000000000Trim Var.0.0412612676056348
V(Y[t],d=1,D=2)0.323923364197538Range3.56000000000007Trim Var.0.113031388329983
V(Y[t],d=2,D=2)1.01822126582281Range6.93000000000012Trim Var.0.41886038732396
V(Y[t],d=3,D=2)3.48001012658239Range12.3600000000003Trim Var.1.47514474849100


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ;
Parameters (R input):
par1 = 1 ;
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')