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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, 01 Dec 2008 11:43:37 -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/01/t12281574541k9o622v8faocv7.htm/, Retrieved Sun, 05 May 2024 13:54:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27141, Retrieved Sun, 05 May 2024 13:54:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212

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  [(Partial) Autocorrelation Function] [NSTS_Q5] [2008-11-30 17:55:01] [9f5bfe3b95f9ec3d2ed4c0a560a9648a]
F RMPD      [Variance Reduction Matrix] [NSTS_Q7 (bouw)] [2008-12-01 18:43:37] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]
Feedback Forum
2008-12-08 17:04:50 [Sandra Hofmans] [reply] 
Correct, het is nog beter om naar de getrimde variantie te kijken, omdat hier de outliners zijn uitgehaald.
2008-12-10 08:25:51 [Lana Van Wesemael] [reply] 
Hier had ik nog bij kunnen schrijven dat hoe kleiner de variantie en hoe kleiner het risico het risico is. Indien de tijdreeks veel outliers bevat kan men best naar de getrimde variantie kijken. Hier is de tijdreeks immers getrimd waardoor de outliers weggeknipt zijn en dus geen invloed meer hebben.
2008-12-10 10:20:14 [Peter Van Doninck] [reply] 
Correct. Er kan aan toegevoegd worden dat de getrimde variantie hier ook het kleinste is. Deze variantie houdt geen rekening met de outliërs.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
82,7
88,9
105,9
100,8
94
105
58,5
87,6
113,1
112,5
89,6
74,5
82,7
90,1
109,4
96
89,2
109,1
49,1
92,9
107,7
103,5
91,1
79,8
71,9
82,9
90,1
100,7
90,7
108,8
44,1
93,6
107,4
96,5
93,6
76,5
76,7
84
103,3
88,5
99
105,9
44,7
94
107,1
104,8
102,5
77,7
85,2
91,3
106,5
92,4
97,5
107
51,1
98,6
102,2
114,3
99,4
72,5

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=27141&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=27141&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27141&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)278.758675141243Range70.2Trim Var.164.631477987421
V(Y[t],d=1,D=0)603.569251899474Range114.2Trim Var.313.922140783745
V(Y[t],d=2,D=0)1705.31614337568Range197Trim Var.842.923680241327
V(Y[t],d=3,D=0)5605.30571428571Range346.9Trim Var.2867.81810196078
V(Y[t],d=0,D=1)41.1151019503546Range32.5Trim Var.23.0596515679442
V(Y[t],d=1,D=1)91.3079278445884Range49.4Trim Var.47.4906219512195
V(Y[t],d=2,D=1)306.013183574879Range83.4Trim Var.164.289224358974
V(Y[t],d=3,D=1)1102.76128282828Range161Trim Var.616.165519568151
V(Y[t],d=0,D=2)107.663071428571Range55.3Trim Var.53.617379032258
V(Y[t],d=1,D=2)253.064319327731Range81.7Trim Var.125.147827956989
V(Y[t],d=2,D=2)879.522994652407Range146.7Trim Var.461.255172413793
V(Y[t],d=3,D=2)3253.35984848485Range252.9Trim Var.1853.18566502463

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 278.758675141243 & Range & 70.2 & Trim Var. & 164.631477987421 \tabularnewline
V(Y[t],d=1,D=0) & 603.569251899474 & Range & 114.2 & Trim Var. & 313.922140783745 \tabularnewline
V(Y[t],d=2,D=0) & 1705.31614337568 & Range & 197 & Trim Var. & 842.923680241327 \tabularnewline
V(Y[t],d=3,D=0) & 5605.30571428571 & Range & 346.9 & Trim Var. & 2867.81810196078 \tabularnewline
V(Y[t],d=0,D=1) & 41.1151019503546 & Range & 32.5 & Trim Var. & 23.0596515679442 \tabularnewline
V(Y[t],d=1,D=1) & 91.3079278445884 & Range & 49.4 & Trim Var. & 47.4906219512195 \tabularnewline
V(Y[t],d=2,D=1) & 306.013183574879 & Range & 83.4 & Trim Var. & 164.289224358974 \tabularnewline
V(Y[t],d=3,D=1) & 1102.76128282828 & Range & 161 & Trim Var. & 616.165519568151 \tabularnewline
V(Y[t],d=0,D=2) & 107.663071428571 & Range & 55.3 & Trim Var. & 53.617379032258 \tabularnewline
V(Y[t],d=1,D=2) & 253.064319327731 & Range & 81.7 & Trim Var. & 125.147827956989 \tabularnewline
V(Y[t],d=2,D=2) & 879.522994652407 & Range & 146.7 & Trim Var. & 461.255172413793 \tabularnewline
V(Y[t],d=3,D=2) & 3253.35984848485 & Range & 252.9 & Trim Var. & 1853.18566502463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27141&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]278.758675141243[/C][C]Range[/C][C]70.2[/C][C]Trim Var.[/C][C]164.631477987421[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]603.569251899474[/C][C]Range[/C][C]114.2[/C][C]Trim Var.[/C][C]313.922140783745[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1705.31614337568[/C][C]Range[/C][C]197[/C][C]Trim Var.[/C][C]842.923680241327[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]5605.30571428571[/C][C]Range[/C][C]346.9[/C][C]Trim Var.[/C][C]2867.81810196078[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]41.1151019503546[/C][C]Range[/C][C]32.5[/C][C]Trim Var.[/C][C]23.0596515679442[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]91.3079278445884[/C][C]Range[/C][C]49.4[/C][C]Trim Var.[/C][C]47.4906219512195[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]306.013183574879[/C][C]Range[/C][C]83.4[/C][C]Trim Var.[/C][C]164.289224358974[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]1102.76128282828[/C][C]Range[/C][C]161[/C][C]Trim Var.[/C][C]616.165519568151[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]107.663071428571[/C][C]Range[/C][C]55.3[/C][C]Trim Var.[/C][C]53.617379032258[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]253.064319327731[/C][C]Range[/C][C]81.7[/C][C]Trim Var.[/C][C]125.147827956989[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]879.522994652407[/C][C]Range[/C][C]146.7[/C][C]Trim Var.[/C][C]461.255172413793[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]3253.35984848485[/C][C]Range[/C][C]252.9[/C][C]Trim Var.[/C][C]1853.18566502463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27141&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27141&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)278.758675141243Range70.2Trim Var.164.631477987421
V(Y[t],d=1,D=0)603.569251899474Range114.2Trim Var.313.922140783745
V(Y[t],d=2,D=0)1705.31614337568Range197Trim Var.842.923680241327
V(Y[t],d=3,D=0)5605.30571428571Range346.9Trim Var.2867.81810196078
V(Y[t],d=0,D=1)41.1151019503546Range32.5Trim Var.23.0596515679442
V(Y[t],d=1,D=1)91.3079278445884Range49.4Trim Var.47.4906219512195
V(Y[t],d=2,D=1)306.013183574879Range83.4Trim Var.164.289224358974
V(Y[t],d=3,D=1)1102.76128282828Range161Trim Var.616.165519568151
V(Y[t],d=0,D=2)107.663071428571Range55.3Trim Var.53.617379032258
V(Y[t],d=1,D=2)253.064319327731Range81.7Trim Var.125.147827956989
V(Y[t],d=2,D=2)879.522994652407Range146.7Trim Var.461.255172413793
V(Y[t],d=3,D=2)3253.35984848485Range252.9Trim Var.1853.18566502463


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')