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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 16:18:33 -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/09/t1228778361knqj59i5afmhn2s.htm/, Retrieved Sat, 25 May 2024 06:29:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31120, Retrieved Sat, 25 May 2024 06:29:26 +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)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Variance Reduction Matrix] [VRM] [2008-12-08 22:53:48] [8d78428855b119373cac369316c08983]
F   P       [Variance Reduction Matrix] [VRM] [2008-12-08 23:18:33] [d6e9f26c3644bfc30f06303d9993b878] [Current]
Feedback Forum
2008-12-12 19:38:32 [Bas van Keken] [reply] 
Bij deze laagste waarde kan de differentiatie worden gevonden. Hier is het D=1, d=0. In uw geval dus 1 maal seizoenale differentiatie. Een toevoeging is dat ook de range en de trimmmed variance het laagst is.
2008-12-13 13:40:02 [An De Koninck] [reply] 
De student trekt de juiste conclusie, namelijk dat de variantie het kleinst is bij d=0 en D=1. Het is dus de bedoeling dat je deze parameters instelt bij de rest van de oefening. Zo kan je dus kijken of er sprake is van een lange termijntrend (in de autocorrelatie functie).
2008-12-15 11:52:00 [Romina Machiels] [reply] 
De vraag werd correct beantwoord.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11703.7
16283.6
16726.5
14968.9
14861.0
14583.3
15305.8
17903.9
16379.4
15420.3
17870.5
15912.8
13866.5
17823.2
17872.0
17420.4
16704.4
15991.2
16583.6
19123.5
17838.7
17209.4
18586.5
16258.1
15141.6
19202.1
17746.5
19090.1
18040.3
17515.5
17751.8
21072.4
17170.0
19439.5
19795.4
17574.9
16165.4
19464.6
19932.1
19961.2
17343.4
18924.2
18574.1
21350.6
18594.6
19823.1
20844.4
19640.2
17735.4
19813.6
22160.0
20664.3
17877.4
21211.2
21423.1
21688.7
23243.2
21490.2
22925.8
23184.8
18562.2

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'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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31120&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31120&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'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)5718533.69583607Range11539.5Trim Var.3263518.01967344
V(Y[t],d=1,D=0)4173792.96484463Range9202.5Trim Var.2810942.73934661
V(Y[t],d=2,D=0)10310496.3646873Range13394.9Trim Var.7173826.52399855
V(Y[t],d=3,D=0)30237224.1364307Range24088Trim Var.20893335.2550641
V(Y[t],d=0,D=1)926135.758494898Range5345.5Trim Var.381694.665359912
V(Y[t],d=1,D=1)2122989.45120567Range7292Trim Var.1101057.90062718
V(Y[t],d=2,D=1)6735483.85881591Range14113.4Trim Var.3331703.88212195
V(Y[t],d=3,D=1)22365247.9749613Range24801.2Trim Var.9855361.31727564
V(Y[t],d=0,D=2)1513116.01052553Range5352.00000000001Trim Var.948061.29189394
V(Y[t],d=1,D=2)3108267.81323016Range7703.8Trim Var.1860717.51273185
V(Y[t],d=2,D=2)10007314.575042Range13330.2Trim Var.5910359.58898924
V(Y[t],d=3,D=2)34578460.2232174Range26122.8Trim Var.20664011.4336092

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 5718533.69583607 & Range & 11539.5 & Trim Var. & 3263518.01967344 \tabularnewline
V(Y[t],d=1,D=0) & 4173792.96484463 & Range & 9202.5 & Trim Var. & 2810942.73934661 \tabularnewline
V(Y[t],d=2,D=0) & 10310496.3646873 & Range & 13394.9 & Trim Var. & 7173826.52399855 \tabularnewline
V(Y[t],d=3,D=0) & 30237224.1364307 & Range & 24088 & Trim Var. & 20893335.2550641 \tabularnewline
V(Y[t],d=0,D=1) & 926135.758494898 & Range & 5345.5 & Trim Var. & 381694.665359912 \tabularnewline
V(Y[t],d=1,D=1) & 2122989.45120567 & Range & 7292 & Trim Var. & 1101057.90062718 \tabularnewline
V(Y[t],d=2,D=1) & 6735483.85881591 & Range & 14113.4 & Trim Var. & 3331703.88212195 \tabularnewline
V(Y[t],d=3,D=1) & 22365247.9749613 & Range & 24801.2 & Trim Var. & 9855361.31727564 \tabularnewline
V(Y[t],d=0,D=2) & 1513116.01052553 & Range & 5352.00000000001 & Trim Var. & 948061.29189394 \tabularnewline
V(Y[t],d=1,D=2) & 3108267.81323016 & Range & 7703.8 & Trim Var. & 1860717.51273185 \tabularnewline
V(Y[t],d=2,D=2) & 10007314.575042 & Range & 13330.2 & Trim Var. & 5910359.58898924 \tabularnewline
V(Y[t],d=3,D=2) & 34578460.2232174 & Range & 26122.8 & Trim Var. & 20664011.4336092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31120&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]5718533.69583607[/C][C]Range[/C][C]11539.5[/C][C]Trim Var.[/C][C]3263518.01967344[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]4173792.96484463[/C][C]Range[/C][C]9202.5[/C][C]Trim Var.[/C][C]2810942.73934661[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]10310496.3646873[/C][C]Range[/C][C]13394.9[/C][C]Trim Var.[/C][C]7173826.52399855[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]30237224.1364307[/C][C]Range[/C][C]24088[/C][C]Trim Var.[/C][C]20893335.2550641[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]926135.758494898[/C][C]Range[/C][C]5345.5[/C][C]Trim Var.[/C][C]381694.665359912[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]2122989.45120567[/C][C]Range[/C][C]7292[/C][C]Trim Var.[/C][C]1101057.90062718[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]6735483.85881591[/C][C]Range[/C][C]14113.4[/C][C]Trim Var.[/C][C]3331703.88212195[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]22365247.9749613[/C][C]Range[/C][C]24801.2[/C][C]Trim Var.[/C][C]9855361.31727564[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1513116.01052553[/C][C]Range[/C][C]5352.00000000001[/C][C]Trim Var.[/C][C]948061.29189394[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]3108267.81323016[/C][C]Range[/C][C]7703.8[/C][C]Trim Var.[/C][C]1860717.51273185[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]10007314.575042[/C][C]Range[/C][C]13330.2[/C][C]Trim Var.[/C][C]5910359.58898924[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]34578460.2232174[/C][C]Range[/C][C]26122.8[/C][C]Trim Var.[/C][C]20664011.4336092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31120&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)5718533.69583607Range11539.5Trim Var.3263518.01967344
V(Y[t],d=1,D=0)4173792.96484463Range9202.5Trim Var.2810942.73934661
V(Y[t],d=2,D=0)10310496.3646873Range13394.9Trim Var.7173826.52399855
V(Y[t],d=3,D=0)30237224.1364307Range24088Trim Var.20893335.2550641
V(Y[t],d=0,D=1)926135.758494898Range5345.5Trim Var.381694.665359912
V(Y[t],d=1,D=1)2122989.45120567Range7292Trim Var.1101057.90062718
V(Y[t],d=2,D=1)6735483.85881591Range14113.4Trim Var.3331703.88212195
V(Y[t],d=3,D=1)22365247.9749613Range24801.2Trim Var.9855361.31727564
V(Y[t],d=0,D=2)1513116.01052553Range5352.00000000001Trim Var.948061.29189394
V(Y[t],d=1,D=2)3108267.81323016Range7703.8Trim Var.1860717.51273185
V(Y[t],d=2,D=2)10007314.575042Range13330.2Trim Var.5910359.58898924
V(Y[t],d=3,D=2)34578460.2232174Range26122.8Trim Var.20664011.4336092


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')