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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 computationWed, 03 Dec 2008 09:34:29 -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/t1228322327qxldr3lg53b7sh6.htm/, Retrieved Fri, 17 May 2024 01:03:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28776, Retrieved Fri, 17 May 2024 01:03:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact242

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] [Werkloosheid -25 ...] [2008-11-28 13:09:39] [6743688719638b0cb1c0a6e0bf433315]
-   P   [Univariate Data Series] [Unemployment unde...] [2008-12-02 17:58:48] [6743688719638b0cb1c0a6e0bf433315]
F RMP       [Variance Reduction Matrix] [Total unemploymen...] [2008-12-03 16:34:29] [9b05d7ef5dbcfba4217d280d9092f628] [Current]
- RMP         [(Partial) Autocorrelation Function] [ACF unemployment ...] [2008-12-05 11:45:36] [6743688719638b0cb1c0a6e0bf433315]
- RM          [Standard Deviation-Mean Plot] [Under 25] [2008-12-08 21:47:27] [6743688719638b0cb1c0a6e0bf433315]
F RM          [Standard Deviation-Mean Plot] [Under 25] [2008-12-08 21:47:27] [6743688719638b0cb1c0a6e0bf433315]
F RMP           [ARIMA Backward Selection] [ARMA] [2008-12-08 21:50:31] [6743688719638b0cb1c0a6e0bf433315]
- RM          [Standard Deviation-Mean Plot] [lambda] [2008-12-11 17:24:01] [6743688719638b0cb1c0a6e0bf433315]
Feedback Forum
2008-12-12 14:23:41 [9142cf052ad32d043faa9486189092cf] [reply] 
We gaan de laagste waarde zoeken in de variance reduction matrix.
We vinden deze waarde terug bij V(Y[t],d=1,D=1)
Dit wil zeggen dat we de tijdreeks een keer gewoon gaan differentiëren en een keer seizoenaal gaan differentiëren. Zoals de student vermeld heeft in zijn conclusie.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
150739
159129
157928
147768
137507
136919
136151
133001
125554
119647
114158
116193
152803
161761
160942
149470
139208
134588
130322
126611
122401
117352
112135
112879
148729
157230
157221
146681
136524
132111
125326
122716
116615
113719
110737
112093
143565
149946
149147
134339
122683
115614
116566
111272
104609
101802
94542
93051
124129
130374
123946
114971
105531
104919
104782
101281
94545
93248
84031
87486
115867

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=28776&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=28776&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28776&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)409836972.852459Range77730Trim Var.280340201.113208
V(Y[t],d=1,D=0)129311655.993220Range51418Trim Var.63367811.126485
V(Y[t],d=2,D=0)166385252.074226Range62758Trim Var.77428245.6023222
V(Y[t],d=3,D=0)369820934.49395Range91600Trim Var.144898359.596908
V(Y[t],d=0,D=1)44374587.7908163Range28215Trim Var.29140947.3997785
V(Y[t],d=1,D=1)7778138.19680851Range13366Trim Var.3827195.17770035
V(Y[t],d=2,D=1)17111965.0370028Range21883Trim Var.6884400.24878049
V(Y[t],d=3,D=1)54362755.921256Range37769Trim Var.25565462.1820513
V(Y[t],d=0,D=2)53479137.9804805Range31733Trim Var.31713071.3598485
V(Y[t],d=1,D=2)20141855.5873016Range19082Trim Var.12291410.0282258
V(Y[t],d=2,D=2)49148602.5512605Range32879Trim Var.25059109.5591398
V(Y[t],d=3,D=2)160930749.316399Range58402Trim Var.88830314.0471264

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 409836972.852459 & Range & 77730 & Trim Var. & 280340201.113208 \tabularnewline
V(Y[t],d=1,D=0) & 129311655.993220 & Range & 51418 & Trim Var. & 63367811.126485 \tabularnewline
V(Y[t],d=2,D=0) & 166385252.074226 & Range & 62758 & Trim Var. & 77428245.6023222 \tabularnewline
V(Y[t],d=3,D=0) & 369820934.49395 & Range & 91600 & Trim Var. & 144898359.596908 \tabularnewline
V(Y[t],d=0,D=1) & 44374587.7908163 & Range & 28215 & Trim Var. & 29140947.3997785 \tabularnewline
V(Y[t],d=1,D=1) & 7778138.19680851 & Range & 13366 & Trim Var. & 3827195.17770035 \tabularnewline
V(Y[t],d=2,D=1) & 17111965.0370028 & Range & 21883 & Trim Var. & 6884400.24878049 \tabularnewline
V(Y[t],d=3,D=1) & 54362755.921256 & Range & 37769 & Trim Var. & 25565462.1820513 \tabularnewline
V(Y[t],d=0,D=2) & 53479137.9804805 & Range & 31733 & Trim Var. & 31713071.3598485 \tabularnewline
V(Y[t],d=1,D=2) & 20141855.5873016 & Range & 19082 & Trim Var. & 12291410.0282258 \tabularnewline
V(Y[t],d=2,D=2) & 49148602.5512605 & Range & 32879 & Trim Var. & 25059109.5591398 \tabularnewline
V(Y[t],d=3,D=2) & 160930749.316399 & Range & 58402 & Trim Var. & 88830314.0471264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28776&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]409836972.852459[/C][C]Range[/C][C]77730[/C][C]Trim Var.[/C][C]280340201.113208[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]129311655.993220[/C][C]Range[/C][C]51418[/C][C]Trim Var.[/C][C]63367811.126485[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]166385252.074226[/C][C]Range[/C][C]62758[/C][C]Trim Var.[/C][C]77428245.6023222[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]369820934.49395[/C][C]Range[/C][C]91600[/C][C]Trim Var.[/C][C]144898359.596908[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]44374587.7908163[/C][C]Range[/C][C]28215[/C][C]Trim Var.[/C][C]29140947.3997785[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]7778138.19680851[/C][C]Range[/C][C]13366[/C][C]Trim Var.[/C][C]3827195.17770035[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]17111965.0370028[/C][C]Range[/C][C]21883[/C][C]Trim Var.[/C][C]6884400.24878049[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]54362755.921256[/C][C]Range[/C][C]37769[/C][C]Trim Var.[/C][C]25565462.1820513[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]53479137.9804805[/C][C]Range[/C][C]31733[/C][C]Trim Var.[/C][C]31713071.3598485[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]20141855.5873016[/C][C]Range[/C][C]19082[/C][C]Trim Var.[/C][C]12291410.0282258[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]49148602.5512605[/C][C]Range[/C][C]32879[/C][C]Trim Var.[/C][C]25059109.5591398[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]160930749.316399[/C][C]Range[/C][C]58402[/C][C]Trim Var.[/C][C]88830314.0471264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28776&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28776&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)409836972.852459Range77730Trim Var.280340201.113208
V(Y[t],d=1,D=0)129311655.993220Range51418Trim Var.63367811.126485
V(Y[t],d=2,D=0)166385252.074226Range62758Trim Var.77428245.6023222
V(Y[t],d=3,D=0)369820934.49395Range91600Trim Var.144898359.596908
V(Y[t],d=0,D=1)44374587.7908163Range28215Trim Var.29140947.3997785
V(Y[t],d=1,D=1)7778138.19680851Range13366Trim Var.3827195.17770035
V(Y[t],d=2,D=1)17111965.0370028Range21883Trim Var.6884400.24878049
V(Y[t],d=3,D=1)54362755.921256Range37769Trim Var.25565462.1820513
V(Y[t],d=0,D=2)53479137.9804805Range31733Trim Var.31713071.3598485
V(Y[t],d=1,D=2)20141855.5873016Range19082Trim Var.12291410.0282258
V(Y[t],d=2,D=2)49148602.5512605Range32879Trim Var.25059109.5591398
V(Y[t],d=3,D=2)160930749.316399Range58402Trim Var.88830314.0471264


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')