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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 computationTue, 09 Dec 2008 07:46:14 -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/t1228834011t6luv4e9mmu0256.htm/, Retrieved Sun, 19 May 2024 10:22:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31480, Retrieved Sun, 19 May 2024 10:22:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

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] [Step2] [2008-12-09 14:46:14] [a413cf7744efd6bb212437a3916e2f23] [Current]
Feedback Forum
2008-12-14 13:20:52 [Gert-Jan Geudens] [reply] 
Goede berekening. Voor meer uitleg betreffende deze vrm, zie onze feedback op je onderstaande link :
http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/09/t1228830280i65g6t6szddcc8p.htm
2008-12-15 14:27:09 [Jonas Scheltjens] [reply] 
De student heeft hier enkel de link en de tabel gegeven zonder enige degelijke uitleg of verklaring. Aangezien het niet de taak is van de persoon die de assessments doet om deze taak voor de student te maken, verwijs ik dan ook voor de algemene en volledige uitleg voor deze Step naar Step 2 voor de unemployment data, dewelke ik zeer uitgebreid heb besproken en waar alle informatie in staat om deze vraag correct op te lossen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1846,5
2796,3
2895,6
2472,2
2584,4
2630,4
2663,1
3176,2
2856,7
2551,4
3088,7
2628,3
2226,2
3023,6
3077,9
3084,1
2990,3
2949,6
3014,7
3517,7
3121,2
3067,4
3174,6
2676,3
2424
3195,1
3146,6
3506,7
3528,5
3365,1
3153
3843,3
3123,2
3361,1
3481,9
2970,5
2537
3257,6
3301,3
3391,6
2933,6
3283,2
3139,7
3486,4
3202,2
3294,4
3550,3
3279,3
2678,6
3451,4
3977,1
3814,8
3310,5
3971,8
4051,9
4057,6
4391,4
3628,9
4092,2
3822,5

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'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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31480&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31480&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31480&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)244089.570652542Range2544.9Trim Var.152288.609545772
V(Y[t],d=1,D=0)173028.988720047Range1712.3Trim Var.124253.398272859
V(Y[t],d=2,D=0)446186.485810647Range2783.9Trim Var.321564.717266214
V(Y[t],d=3,D=0)1344616.87419800Range4681.2Trim Var.968734.37094902
V(Y[t],d=0,D=1)90331.5437189716Range1784.1Trim Var.37167.1424390244
V(Y[t],d=1,D=1)82029.086493987Range1472.7Trim Var.43709.0487804878
V(Y[t],d=2,D=1)246166.144700483Range2534.8Trim Var.124910.749173077
V(Y[t],d=3,D=1)825470.827272727Range4966.5Trim Var.428822.527692307
V(Y[t],d=0,D=2)234151.213420635Range2243.3Trim Var.138246.342247984
V(Y[t],d=1,D=2)119928.708201681Range1468.5Trim Var.73528.411827957
V(Y[t],d=2,D=2)319628.189162210Range2487.3Trim Var.176655.885298851
V(Y[t],d=3,D=2)1029619.84801136Range4654.4Trim Var.595321.724729064

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 244089.570652542 & Range & 2544.9 & Trim Var. & 152288.609545772 \tabularnewline
V(Y[t],d=1,D=0) & 173028.988720047 & Range & 1712.3 & Trim Var. & 124253.398272859 \tabularnewline
V(Y[t],d=2,D=0) & 446186.485810647 & Range & 2783.9 & Trim Var. & 321564.717266214 \tabularnewline
V(Y[t],d=3,D=0) & 1344616.87419800 & Range & 4681.2 & Trim Var. & 968734.37094902 \tabularnewline
V(Y[t],d=0,D=1) & 90331.5437189716 & Range & 1784.1 & Trim Var. & 37167.1424390244 \tabularnewline
V(Y[t],d=1,D=1) & 82029.086493987 & Range & 1472.7 & Trim Var. & 43709.0487804878 \tabularnewline
V(Y[t],d=2,D=1) & 246166.144700483 & Range & 2534.8 & Trim Var. & 124910.749173077 \tabularnewline
V(Y[t],d=3,D=1) & 825470.827272727 & Range & 4966.5 & Trim Var. & 428822.527692307 \tabularnewline
V(Y[t],d=0,D=2) & 234151.213420635 & Range & 2243.3 & Trim Var. & 138246.342247984 \tabularnewline
V(Y[t],d=1,D=2) & 119928.708201681 & Range & 1468.5 & Trim Var. & 73528.411827957 \tabularnewline
V(Y[t],d=2,D=2) & 319628.189162210 & Range & 2487.3 & Trim Var. & 176655.885298851 \tabularnewline
V(Y[t],d=3,D=2) & 1029619.84801136 & Range & 4654.4 & Trim Var. & 595321.724729064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31480&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]244089.570652542[/C][C]Range[/C][C]2544.9[/C][C]Trim Var.[/C][C]152288.609545772[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]173028.988720047[/C][C]Range[/C][C]1712.3[/C][C]Trim Var.[/C][C]124253.398272859[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]446186.485810647[/C][C]Range[/C][C]2783.9[/C][C]Trim Var.[/C][C]321564.717266214[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1344616.87419800[/C][C]Range[/C][C]4681.2[/C][C]Trim Var.[/C][C]968734.37094902[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]90331.5437189716[/C][C]Range[/C][C]1784.1[/C][C]Trim Var.[/C][C]37167.1424390244[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]82029.086493987[/C][C]Range[/C][C]1472.7[/C][C]Trim Var.[/C][C]43709.0487804878[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]246166.144700483[/C][C]Range[/C][C]2534.8[/C][C]Trim Var.[/C][C]124910.749173077[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]825470.827272727[/C][C]Range[/C][C]4966.5[/C][C]Trim Var.[/C][C]428822.527692307[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]234151.213420635[/C][C]Range[/C][C]2243.3[/C][C]Trim Var.[/C][C]138246.342247984[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]119928.708201681[/C][C]Range[/C][C]1468.5[/C][C]Trim Var.[/C][C]73528.411827957[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]319628.189162210[/C][C]Range[/C][C]2487.3[/C][C]Trim Var.[/C][C]176655.885298851[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1029619.84801136[/C][C]Range[/C][C]4654.4[/C][C]Trim Var.[/C][C]595321.724729064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31480&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31480&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)244089.570652542Range2544.9Trim Var.152288.609545772
V(Y[t],d=1,D=0)173028.988720047Range1712.3Trim Var.124253.398272859
V(Y[t],d=2,D=0)446186.485810647Range2783.9Trim Var.321564.717266214
V(Y[t],d=3,D=0)1344616.87419800Range4681.2Trim Var.968734.37094902
V(Y[t],d=0,D=1)90331.5437189716Range1784.1Trim Var.37167.1424390244
V(Y[t],d=1,D=1)82029.086493987Range1472.7Trim Var.43709.0487804878
V(Y[t],d=2,D=1)246166.144700483Range2534.8Trim Var.124910.749173077
V(Y[t],d=3,D=1)825470.827272727Range4966.5Trim Var.428822.527692307
V(Y[t],d=0,D=2)234151.213420635Range2243.3Trim Var.138246.342247984
V(Y[t],d=1,D=2)119928.708201681Range1468.5Trim Var.73528.411827957
V(Y[t],d=2,D=2)319628.189162210Range2487.3Trim Var.176655.885298851
V(Y[t],d=3,D=2)1029619.84801136Range4654.4Trim Var.595321.724729064


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')