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Author's title

Author*Unverified author*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationWed, 24 Dec 2008 08:08:19 -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/24/t12301313274e3qk7fgxuoxlj5.htm/, Retrieved Sun, 19 May 2024 12:34:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36606, Retrieved Sun, 19 May 2024 12:34:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
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 RMPD  [Spectral Analysis] [Identification an...] [2008-12-09 22:16:59] [1a689e9ccc515e1757f0522229a687e9]
-   PD    [Spectral Analysis] [Paper Cumulative ...] [2008-12-24 14:53:47] [1a689e9ccc515e1757f0522229a687e9]
-           [Spectral Analysis] [Paper Cumulative ...] [2008-12-24 14:58:31] [1a689e9ccc515e1757f0522229a687e9]
-             [Spectral Analysis] [Paper Cumulative ...] [2008-12-24 15:01:23] [1a689e9ccc515e1757f0522229a687e9]
- RM D            [Variance Reduction Matrix] [Paper Variance Re...] [2008-12-24 15:08:19] [74a138e5b32af267311b5ad4cd13bf7e] [Current]
- RM D              [(Partial) Autocorrelation Function] [Paper Autocorrela...] [2008-12-24 15:11:07] [1a689e9ccc515e1757f0522229a687e9]
-                     [(Partial) Autocorrelation Function] [Paper Autocorrela...] [2008-12-24 15:13:57] [1a689e9ccc515e1757f0522229a687e9]
- RM                    [Spectral Analysis] [Paper Cumulative ...] [2008-12-24 15:16:17] [1a689e9ccc515e1757f0522229a687e9]
-                         [Spectral Analysis] [Paper Cumulative ...] [2008-12-24 15:19:40] [1a689e9ccc515e1757f0522229a687e9]
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Dataseries X:
103,3
107,9
101
94,6
94,2
92,3
107,1
102,6
103,1
104,1
92,7
87
109,3
113,9
103,3
100,8
97,4
98,9
110,8
103,5
99,8
104,9
95,2
85,7
110
113,7
101,1
103,6
96,2
98,3
119,7
109,4
103,5
118,2
98,7
96,8
121,8
124
119,6
122,5
109,7
111,6
131,2
124,4
116,9
131,8
107,4
111
134
126,2
131,2
130,1
123,1
126,3
148,6
130,1
142,3
154,4
121,6
124,8
143,6
146,9
144,6
137,1
134,7
130,8
153,5
137,6
146,5
156,7
137,6
131,4
147,4
158,5
151,5
142,5
131,3
133,4
136,9
143,2
136,4
145,9
138,8
122,9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36606&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36606&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36606&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Variance Reduction Matrix
V(Y[t],d=0,D=0)360.176986517499Range72.8Trim Var.264.741569788967
V(Y[t],d=1,D=0)141.209653247135Range57.8Trim Var.78.4577288732394
V(Y[t],d=2,D=0)353.274629629630Range80.9Trim Var.222.263255086072
V(Y[t],d=3,D=0)1021.13325Range146.1Trim Var.588.211472837022
V(Y[t],d=0,D=1)60.2964006259781Range42Trim Var.31.1494841269841
V(Y[t],d=1,D=1)52.8301247484909Range41.4Trim Var.26.2275576036866
V(Y[t],d=2,D=1)167.587621118012Range79.3Trim Var.83.1594711792702
V(Y[t],d=3,D=1)566.930358056266Range145.9Trim Var.259.626961748634
V(Y[t],d=0,D=2)100.541635593220Range42.2Trim Var.67.666359189378
V(Y[t],d=1,D=2)91.471934541204Range45.1000000000001Trim Var.52.3729753265602
V(Y[t],d=2,D=2)286.826557773745Range77Trim Var.174.270995475113
V(Y[t],d=3,D=2)991.698120300752Range143.100000000000Trim Var.619.244149019608

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 360.176986517499 & Range & 72.8 & Trim Var. & 264.741569788967 \tabularnewline
V(Y[t],d=1,D=0) & 141.209653247135 & Range & 57.8 & Trim Var. & 78.4577288732394 \tabularnewline
V(Y[t],d=2,D=0) & 353.274629629630 & Range & 80.9 & Trim Var. & 222.263255086072 \tabularnewline
V(Y[t],d=3,D=0) & 1021.13325 & Range & 146.1 & Trim Var. & 588.211472837022 \tabularnewline
V(Y[t],d=0,D=1) & 60.2964006259781 & Range & 42 & Trim Var. & 31.1494841269841 \tabularnewline
V(Y[t],d=1,D=1) & 52.8301247484909 & Range & 41.4 & Trim Var. & 26.2275576036866 \tabularnewline
V(Y[t],d=2,D=1) & 167.587621118012 & Range & 79.3 & Trim Var. & 83.1594711792702 \tabularnewline
V(Y[t],d=3,D=1) & 566.930358056266 & Range & 145.9 & Trim Var. & 259.626961748634 \tabularnewline
V(Y[t],d=0,D=2) & 100.541635593220 & Range & 42.2 & Trim Var. & 67.666359189378 \tabularnewline
V(Y[t],d=1,D=2) & 91.471934541204 & Range & 45.1000000000001 & Trim Var. & 52.3729753265602 \tabularnewline
V(Y[t],d=2,D=2) & 286.826557773745 & Range & 77 & Trim Var. & 174.270995475113 \tabularnewline
V(Y[t],d=3,D=2) & 991.698120300752 & Range & 143.100000000000 & Trim Var. & 619.244149019608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36606&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]360.176986517499[/C][C]Range[/C][C]72.8[/C][C]Trim Var.[/C][C]264.741569788967[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]141.209653247135[/C][C]Range[/C][C]57.8[/C][C]Trim Var.[/C][C]78.4577288732394[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]353.274629629630[/C][C]Range[/C][C]80.9[/C][C]Trim Var.[/C][C]222.263255086072[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]1021.13325[/C][C]Range[/C][C]146.1[/C][C]Trim Var.[/C][C]588.211472837022[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]60.2964006259781[/C][C]Range[/C][C]42[/C][C]Trim Var.[/C][C]31.1494841269841[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]52.8301247484909[/C][C]Range[/C][C]41.4[/C][C]Trim Var.[/C][C]26.2275576036866[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]167.587621118012[/C][C]Range[/C][C]79.3[/C][C]Trim Var.[/C][C]83.1594711792702[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]566.930358056266[/C][C]Range[/C][C]145.9[/C][C]Trim Var.[/C][C]259.626961748634[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]100.541635593220[/C][C]Range[/C][C]42.2[/C][C]Trim Var.[/C][C]67.666359189378[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]91.471934541204[/C][C]Range[/C][C]45.1000000000001[/C][C]Trim Var.[/C][C]52.3729753265602[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]286.826557773745[/C][C]Range[/C][C]77[/C][C]Trim Var.[/C][C]174.270995475113[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]991.698120300752[/C][C]Range[/C][C]143.100000000000[/C][C]Trim Var.[/C][C]619.244149019608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36606&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36606&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)360.176986517499Range72.8Trim Var.264.741569788967
V(Y[t],d=1,D=0)141.209653247135Range57.8Trim Var.78.4577288732394
V(Y[t],d=2,D=0)353.274629629630Range80.9Trim Var.222.263255086072
V(Y[t],d=3,D=0)1021.13325Range146.1Trim Var.588.211472837022
V(Y[t],d=0,D=1)60.2964006259781Range42Trim Var.31.1494841269841
V(Y[t],d=1,D=1)52.8301247484909Range41.4Trim Var.26.2275576036866
V(Y[t],d=2,D=1)167.587621118012Range79.3Trim Var.83.1594711792702
V(Y[t],d=3,D=1)566.930358056266Range145.9Trim Var.259.626961748634
V(Y[t],d=0,D=2)100.541635593220Range42.2Trim Var.67.666359189378
V(Y[t],d=1,D=2)91.471934541204Range45.1000000000001Trim Var.52.3729753265602
V(Y[t],d=2,D=2)286.826557773745Range77Trim Var.174.270995475113
V(Y[t],d=3,D=2)991.698120300752Range143.100000000000Trim Var.619.244149019608



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 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')