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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationWed, 03 Dec 2008 10:08:37 -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/03/t1228324213ofc6ha76ucjtq7b.htm/, Retrieved Fri, 17 May 2024 01:31:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28798, Retrieved Fri, 17 May 2024 01:31:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Variance Reduction Matrix] [Variance Reductio...] [2008-12-03 17:08:37] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- RM D    [Variance Reduction Matrix] [variance reductio...] [2010-12-26 11:39:11] [c4f608d390ad7371b1365a9b84541edb]
-    D      [Variance Reduction Matrix] [Variance Reductio...] [2010-12-29 19:51:46] [7c2d060fd17a41a80970d273bf259e67]
-           [Variance Reduction Matrix] [] [2010-12-29 20:02:21] [a2638725f7f7c6bd63902ba17eba666b]
-    D      [Variance Reduction Matrix] [vrm] [2010-12-29 21:55:45] [df61ce38492c371f14c407a12b3bb2eb]
Feedback Forum
2008-12-13 10:58:46 [Maarten Van Gucht] [reply] 
hier kunnen we bevestiging krijgen van de students voorspelling hoe we moeten differentieren (trendmatig/seizoenaal)
Uit de variantie reductie matrix tabel moet je de kleinste variantie kiezen. In dit geval wordt die bereikt door eenmaal seizoenaal te differentieren (d=0, D=1). Hoe kleiner de variantie, hoe kleiner het risico en hoe beter het model.
De getrimde varianties worden ook weergegeven. Deze zijn nodig
omdat de variantie sterk gevoelig is voor outliers. Bij de getrimde variantie worden deze gedeeltelijk verwijderd. Als er dan geen uitsluiting is qua beste methode bij de variantie, kan men nog naar de getrimde variantie kijken. de getrimde variantie is bij deze (d=0 en D=1) ook het kleinst.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92
95.9
108.8
103.4
102.1
110.1
83.2
82.7
106.8
113.7
102.5
96.6
92.1
95.6
102.3
98.6
98.2
104.5
84
73.8
103.9
106
97.2
102.6
89
93.8
116.7
106.8
98.5
118.7
90
91.9
113.3
113.1
104.1
108.7
96.7
101
116.9
105.8
99
129.4
83
88.9
115.9
104.2
113.4
112.2
100.8
107.3
126.6
102.9
117.9
128.8
87.5
93.8
122.7
126.2
124.6
116.7
115.2
111.1
129.9
113.3
118.5
137.9
103.6
101.7
127.4
137.5
128.3
118.2
117.1

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'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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28798&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28798&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'George Udny Yule' @ 72.249.76.132






Variance Reduction Matrix
V(Y[t],d=0,D=0)190.073706240487Range64.1Trim Var.124.005663461538
V(Y[t],d=1,D=0)254.730702269171Range76.8Trim Var.139.464910714286
V(Y[t],d=2,D=0)668.938684104628Range129.1Trim Var.399.502165898618
V(Y[t],d=3,D=0)1954.68550724638Range243.1Trim Var.1079.00453992597
V(Y[t],d=0,D=1)50.3935081967213Range30.9Trim Var.29.6271625544267
V(Y[t],d=1,D=1)75.2845084745763Range41.3Trim Var.47.5514744933613
V(Y[t],d=2,D=1)240.622571595558Range75.7Trim Var.156.238548621190
V(Y[t],d=3,D=1)822.713744706594Range141.6Trim Var.529.54076923077
V(Y[t],d=0,D=2)134.481930272109Range52Trim Var.80.2961018826135
V(Y[t],d=1,D=2)211.016768617021Range59.6Trim Var.126.363861788618
V(Y[t],d=2,D=2)681.403718778908Range115.3Trim Var.386.987804878049
V(Y[t],d=3,D=2)2368.92253623188Range197Trim Var.1415.74194871795

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 190.073706240487 & Range & 64.1 & Trim Var. & 124.005663461538 \tabularnewline
V(Y[t],d=1,D=0) & 254.730702269171 & Range & 76.8 & Trim Var. & 139.464910714286 \tabularnewline
V(Y[t],d=2,D=0) & 668.938684104628 & Range & 129.1 & Trim Var. & 399.502165898618 \tabularnewline
V(Y[t],d=3,D=0) & 1954.68550724638 & Range & 243.1 & Trim Var. & 1079.00453992597 \tabularnewline
V(Y[t],d=0,D=1) & 50.3935081967213 & Range & 30.9 & Trim Var. & 29.6271625544267 \tabularnewline
V(Y[t],d=1,D=1) & 75.2845084745763 & Range & 41.3 & Trim Var. & 47.5514744933613 \tabularnewline
V(Y[t],d=2,D=1) & 240.622571595558 & Range & 75.7 & Trim Var. & 156.238548621190 \tabularnewline
V(Y[t],d=3,D=1) & 822.713744706594 & Range & 141.6 & Trim Var. & 529.54076923077 \tabularnewline
V(Y[t],d=0,D=2) & 134.481930272109 & Range & 52 & Trim Var. & 80.2961018826135 \tabularnewline
V(Y[t],d=1,D=2) & 211.016768617021 & Range & 59.6 & Trim Var. & 126.363861788618 \tabularnewline
V(Y[t],d=2,D=2) & 681.403718778908 & Range & 115.3 & Trim Var. & 386.987804878049 \tabularnewline
V(Y[t],d=3,D=2) & 2368.92253623188 & Range & 197 & Trim Var. & 1415.74194871795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28798&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]190.073706240487[/C][C]Range[/C][C]64.1[/C][C]Trim Var.[/C][C]124.005663461538[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]254.730702269171[/C][C]Range[/C][C]76.8[/C][C]Trim Var.[/C][C]139.464910714286[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]668.938684104628[/C][C]Range[/C][C]129.1[/C][C]Trim Var.[/C][C]399.502165898618[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1954.68550724638[/C][C]Range[/C][C]243.1[/C][C]Trim Var.[/C][C]1079.00453992597[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]50.3935081967213[/C][C]Range[/C][C]30.9[/C][C]Trim Var.[/C][C]29.6271625544267[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]75.2845084745763[/C][C]Range[/C][C]41.3[/C][C]Trim Var.[/C][C]47.5514744933613[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]240.622571595558[/C][C]Range[/C][C]75.7[/C][C]Trim Var.[/C][C]156.238548621190[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]822.713744706594[/C][C]Range[/C][C]141.6[/C][C]Trim Var.[/C][C]529.54076923077[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]134.481930272109[/C][C]Range[/C][C]52[/C][C]Trim Var.[/C][C]80.2961018826135[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]211.016768617021[/C][C]Range[/C][C]59.6[/C][C]Trim Var.[/C][C]126.363861788618[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]681.403718778908[/C][C]Range[/C][C]115.3[/C][C]Trim Var.[/C][C]386.987804878049[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]2368.92253623188[/C][C]Range[/C][C]197[/C][C]Trim Var.[/C][C]1415.74194871795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28798&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)190.073706240487Range64.1Trim Var.124.005663461538
V(Y[t],d=1,D=0)254.730702269171Range76.8Trim Var.139.464910714286
V(Y[t],d=2,D=0)668.938684104628Range129.1Trim Var.399.502165898618
V(Y[t],d=3,D=0)1954.68550724638Range243.1Trim Var.1079.00453992597
V(Y[t],d=0,D=1)50.3935081967213Range30.9Trim Var.29.6271625544267
V(Y[t],d=1,D=1)75.2845084745763Range41.3Trim Var.47.5514744933613
V(Y[t],d=2,D=1)240.622571595558Range75.7Trim Var.156.238548621190
V(Y[t],d=3,D=1)822.713744706594Range141.6Trim Var.529.54076923077
V(Y[t],d=0,D=2)134.481930272109Range52Trim Var.80.2961018826135
V(Y[t],d=1,D=2)211.016768617021Range59.6Trim Var.126.363861788618
V(Y[t],d=2,D=2)681.403718778908Range115.3Trim Var.386.987804878049
V(Y[t],d=3,D=2)2368.92253623188Range197Trim Var.1415.74194871795


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')