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Author's title

Author

*Unverified author*

R Software Module

rwasp_variancereduction.wasp

Title produced by software

Variance Reduction Matrix

Date of computation

Mon, 08 Dec 2008 17:23:57 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/09/t1228782295ohp5jw0scqm7d8y.htm/, Retrieved Sun, 19 May 2024 08:56:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31158, Retrieved Sun, 19 May 2024 08:56:02 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

194

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]
F RMP     [Variance Reduction Matrix] [] [2008-12-09 00:23:57] [84e0ffed4a919a8e8c17f955a2802257] [Current]
-   PD      [Variance Reduction Matrix] [VRM] [2008-12-14 17:14:38] [7d3039e6253bb5fb3b26df1537d500b4] 

Feedback Forum

2008-12-14 10:37:21 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
De student gebruikt de juiste software maar vult de seasonal paramater verkeerd in. De student gebruikt als seasonal parameter '1', dit moet echter '12' zijn. Hierdoor bekomt de student een verkeerde oplossing. Wanneer we de parameter op 12 zetten, zien we dat de laagste variantie ligt bij d=1 & D=1. Dit zijn dus de parameters die we zullen moeten gebruiken. Ik heb een juiste berekening proberen bloggen, maar dit lukte mij echter niet. Ik heb het probleem ook in het forum gezet.
2008-12-14 15:15:37 [Chi-Kwong Man] [reply] 
Wederom verkeerde parameter geselecteerd.
2008-12-15 00:09:05 [Bob Leysen] [reply] 
Zoals hierboven al wordt aangehaald voert de student een correcte VRM uit, maar zet de seasonale parameter in op 1 en deze moet 12 zijn. Als we dit veranderen in 12, zien we dat de laagste variantie ligt bij d=1 & D=1.  
Dit zijn de parameters die we zullen gebruiken.  
We zoeken in de tabel van VRM naar de laagste waarde omdat hoe kleiner de variantie, hoe beter.  
Ik heb deze berekening gereprodcueerd, maar het lukt me niet om de berekening te bloggen.
2008-12-15 17:37:43 [Jens Peeters] [reply] 
De juiste methode maar de verkeerde parameter waardoor de resultaten fout zijn. Ik slaag er ook niet om de reproduceerde resultaten te bloggen.

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31158&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31158&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31158&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Variance Reduction Matrix
V(Y[t],d=0,D=0)	24040.7319917109	Range	708.9	Trim Var.	14891.1423067379
V(Y[t],d=1,D=0)	1855.27831616522	Range	306.2	Trim Var.	1019.21726473461
V(Y[t],d=2,D=0)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=3,D=0)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=0,D=1)	1855.27831616522	Range	306.2	Trim Var.	1019.21726473461
V(Y[t],d=1,D=1)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=2,D=1)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=3,D=1)	33224.2144269044	Range	1114.6	Trim Var.	17114.7065520862
V(Y[t],d=0,D=2)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=1,D=2)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=2,D=2)	33224.2144269044	Range	1114.6	Trim Var.	17114.7065520862
V(Y[t],d=3,D=2)	116281.440460684	Range	1939.9	Trim Var.	65727.4635102676

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 24040.7319917109 & Range & 708.9 & Trim Var. & 14891.1423067379 \tabularnewline
V(Y[t],d=1,D=0) & 1855.27831616522 & Range & 306.2 & Trim Var. & 1019.21726473461 \tabularnewline
V(Y[t],d=2,D=0) & 3601.51571083278 & Range & 388.2 & Trim Var. & 1764.07169175190 \tabularnewline
V(Y[t],d=3,D=0) & 10155.4683153647 & Range & 595.5 & Trim Var. & 5250.86267655406 \tabularnewline
V(Y[t],d=0,D=1) & 1855.27831616522 & Range & 306.2 & Trim Var. & 1019.21726473461 \tabularnewline
V(Y[t],d=1,D=1) & 3601.51571083278 & Range & 388.2 & Trim Var. & 1764.07169175190 \tabularnewline
V(Y[t],d=2,D=1) & 10155.4683153647 & Range & 595.5 & Trim Var. & 5250.86267655406 \tabularnewline
V(Y[t],d=3,D=1) & 33224.2144269044 & Range & 1114.6 & Trim Var. & 17114.7065520862 \tabularnewline
V(Y[t],d=0,D=2) & 3601.51571083278 & Range & 388.2 & Trim Var. & 1764.07169175190 \tabularnewline
V(Y[t],d=1,D=2) & 10155.4683153647 & Range & 595.5 & Trim Var. & 5250.86267655406 \tabularnewline
V(Y[t],d=2,D=2) & 33224.2144269044 & Range & 1114.6 & Trim Var. & 17114.7065520862 \tabularnewline
V(Y[t],d=3,D=2) & 116281.440460684 & Range & 1939.9 & Trim Var. & 65727.4635102676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31158&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]24040.7319917109[/C][C]Range[/C][C]708.9[/C][C]Trim Var.[/C][C]14891.1423067379[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1855.27831616522[/C][C]Range[/C][C]306.2[/C][C]Trim Var.[/C][C]1019.21726473461[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]3601.51571083278[/C][C]Range[/C][C]388.2[/C][C]Trim Var.[/C][C]1764.07169175190[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]10155.4683153647[/C][C]Range[/C][C]595.5[/C][C]Trim Var.[/C][C]5250.86267655406[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]1855.27831616522[/C][C]Range[/C][C]306.2[/C][C]Trim Var.[/C][C]1019.21726473461[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]3601.51571083278[/C][C]Range[/C][C]388.2[/C][C]Trim Var.[/C][C]1764.07169175190[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]10155.4683153647[/C][C]Range[/C][C]595.5[/C][C]Trim Var.[/C][C]5250.86267655406[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]33224.2144269044[/C][C]Range[/C][C]1114.6[/C][C]Trim Var.[/C][C]17114.7065520862[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]3601.51571083278[/C][C]Range[/C][C]388.2[/C][C]Trim Var.[/C][C]1764.07169175190[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]10155.4683153647[/C][C]Range[/C][C]595.5[/C][C]Trim Var.[/C][C]5250.86267655406[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]33224.2144269044[/C][C]Range[/C][C]1114.6[/C][C]Trim Var.[/C][C]17114.7065520862[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]116281.440460684[/C][C]Range[/C][C]1939.9[/C][C]Trim Var.[/C][C]65727.4635102676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31158&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31158&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Variance Reduction Matrix
V(Y[t],d=0,D=0)	24040.7319917109	Range	708.9	Trim Var.	14891.1423067379
V(Y[t],d=1,D=0)	1855.27831616522	Range	306.2	Trim Var.	1019.21726473461
V(Y[t],d=2,D=0)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=3,D=0)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=0,D=1)	1855.27831616522	Range	306.2	Trim Var.	1019.21726473461
V(Y[t],d=1,D=1)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=2,D=1)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=3,D=1)	33224.2144269044	Range	1114.6	Trim Var.	17114.7065520862
V(Y[t],d=0,D=2)	3601.51571083278	Range	388.2	Trim Var.	1764.07169175190
V(Y[t],d=1,D=2)	10155.4683153647	Range	595.5	Trim Var.	5250.86267655406
V(Y[t],d=2,D=2)	33224.2144269044	Range	1114.6	Trim Var.	17114.7065520862
V(Y[t],d=3,D=2)	116281.440460684	Range	1939.9	Trim Var.	65727.4635102676

Parameters (Session):

par1 = 1 ;

Parameters (R input):

par1 = 1 ;

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')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code