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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, 08 Dec 2008 12:37:16 -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/08/t1228765059h0upokc3cvgjryx.htm/, Retrieved Thu, 16 May 2024 20:12:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30841, Retrieved Thu, 16 May 2024 20:12:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact203

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [ARIMA Backward Selection] [] [2008-12-07 14:04:55] [74be16979710d4c4e7c6647856088456]
F RMPD    [Variance Reduction Matrix] [] [2008-12-08 19:37:16] [19ef54504342c1b076371d395a2ab19f] [Current]
Feedback Forum
2008-12-15 16:23:53 [Hidde Van Kerckhoven] [reply] 
ik maak hier de juiste conclusie, we moeten hier naar de kleinste range kijken...V(Y[t],d=0,D=1) 8280971.22033898 Range 14318 Trim Var. 5076501.68308875
We zien hier, dat er enkel seizoenaal gediffernteerd moet worden...
2008-12-16 07:55:45 [Glenn De Maeyer] [reply] 
We zoeken in de tabel naar de laagste variantie. De variantie geeft namenlijk aan hoeveel de spreiding is. Hoe kleiner de spreiding hoe beter.
De kleinste range (hier 14391) komt hier overeen met d=0 en D=1. Dit houdt in dat we 1 keer seizoenaal dienen te differentiëren. Door te differentiëren wensen we trend en seizoenaliteit te verwijderen uit het verloop van de reeks.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
31124
26551
30651
25859
25100
25778
20418
18688
20424
24776
19814
12738
31566
30111
30019
31934
25826
26835

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=30841&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=30841&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30841&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)34803900.2799296Range27071Trim Var.22498400.4404762
V(Y[t],d=1,D=0)47356061.1014085Range31112Trim Var.21139229.1566820
V(Y[t],d=2,D=0)128152002.578882Range56102Trim Var.57479515.637229
V(Y[t],d=3,D=0)414258448.159847Range91616Trim Var.214442076.275956
V(Y[t],d=0,D=1)8280971.22033898Range14318Trim Var.5076501.68308875
V(Y[t],d=1,D=1)11146453.9812975Range14391Trim Var.7532484.86066763
V(Y[t],d=2,D=1)32717458.9885057Range25372Trim Var.20436222.3872549
V(Y[t],d=3,D=1)113197217.081454Range47529Trim Var.74267042.734902
V(Y[t],d=0,D=2)26029136.0208333Range27070Trim Var.12664065.9123113
V(Y[t],d=1,D=2)27251999.7372803Range22710Trim Var.14358313.1951220
V(Y[t],d=2,D=2)71181686.8850242Range40196Trim Var.38673692.6641026
V(Y[t],d=3,D=2)241977628.664646Range69519Trim Var.136500049.904184

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 34803900.2799296 & Range & 27071 & Trim Var. & 22498400.4404762 \tabularnewline
V(Y[t],d=1,D=0) & 47356061.1014085 & Range & 31112 & Trim Var. & 21139229.1566820 \tabularnewline
V(Y[t],d=2,D=0) & 128152002.578882 & Range & 56102 & Trim Var. & 57479515.637229 \tabularnewline
V(Y[t],d=3,D=0) & 414258448.159847 & Range & 91616 & Trim Var. & 214442076.275956 \tabularnewline
V(Y[t],d=0,D=1) & 8280971.22033898 & Range & 14318 & Trim Var. & 5076501.68308875 \tabularnewline
V(Y[t],d=1,D=1) & 11146453.9812975 & Range & 14391 & Trim Var. & 7532484.86066763 \tabularnewline
V(Y[t],d=2,D=1) & 32717458.9885057 & Range & 25372 & Trim Var. & 20436222.3872549 \tabularnewline
V(Y[t],d=3,D=1) & 113197217.081454 & Range & 47529 & Trim Var. & 74267042.734902 \tabularnewline
V(Y[t],d=0,D=2) & 26029136.0208333 & Range & 27070 & Trim Var. & 12664065.9123113 \tabularnewline
V(Y[t],d=1,D=2) & 27251999.7372803 & Range & 22710 & Trim Var. & 14358313.1951220 \tabularnewline
V(Y[t],d=2,D=2) & 71181686.8850242 & Range & 40196 & Trim Var. & 38673692.6641026 \tabularnewline
V(Y[t],d=3,D=2) & 241977628.664646 & Range & 69519 & Trim Var. & 136500049.904184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30841&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]34803900.2799296[/C][C]Range[/C][C]27071[/C][C]Trim Var.[/C][C]22498400.4404762[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]47356061.1014085[/C][C]Range[/C][C]31112[/C][C]Trim Var.[/C][C]21139229.1566820[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]128152002.578882[/C][C]Range[/C][C]56102[/C][C]Trim Var.[/C][C]57479515.637229[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]414258448.159847[/C][C]Range[/C][C]91616[/C][C]Trim Var.[/C][C]214442076.275956[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]8280971.22033898[/C][C]Range[/C][C]14318[/C][C]Trim Var.[/C][C]5076501.68308875[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]11146453.9812975[/C][C]Range[/C][C]14391[/C][C]Trim Var.[/C][C]7532484.86066763[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]32717458.9885057[/C][C]Range[/C][C]25372[/C][C]Trim Var.[/C][C]20436222.3872549[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]113197217.081454[/C][C]Range[/C][C]47529[/C][C]Trim Var.[/C][C]74267042.734902[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]26029136.0208333[/C][C]Range[/C][C]27070[/C][C]Trim Var.[/C][C]12664065.9123113[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]27251999.7372803[/C][C]Range[/C][C]22710[/C][C]Trim Var.[/C][C]14358313.1951220[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]71181686.8850242[/C][C]Range[/C][C]40196[/C][C]Trim Var.[/C][C]38673692.6641026[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]241977628.664646[/C][C]Range[/C][C]69519[/C][C]Trim Var.[/C][C]136500049.904184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30841&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30841&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)34803900.2799296Range27071Trim Var.22498400.4404762
V(Y[t],d=1,D=0)47356061.1014085Range31112Trim Var.21139229.1566820
V(Y[t],d=2,D=0)128152002.578882Range56102Trim Var.57479515.637229
V(Y[t],d=3,D=0)414258448.159847Range91616Trim Var.214442076.275956
V(Y[t],d=0,D=1)8280971.22033898Range14318Trim Var.5076501.68308875
V(Y[t],d=1,D=1)11146453.9812975Range14391Trim Var.7532484.86066763
V(Y[t],d=2,D=1)32717458.9885057Range25372Trim Var.20436222.3872549
V(Y[t],d=3,D=1)113197217.081454Range47529Trim Var.74267042.734902
V(Y[t],d=0,D=2)26029136.0208333Range27070Trim Var.12664065.9123113
V(Y[t],d=1,D=2)27251999.7372803Range22710Trim Var.14358313.1951220
V(Y[t],d=2,D=2)71181686.8850242Range40196Trim Var.38673692.6641026
V(Y[t],d=3,D=2)241977628.664646Range69519Trim Var.136500049.904184


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 1 ;
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')