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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 12:34:58 -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/t12287649247rbe1541op0z89o.htm/, Retrieved Thu, 16 May 2024 10:30:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30836, Retrieved Thu, 16 May 2024 10:30:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

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] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMPD  [Standard Deviation-Mean Plot] [step 1] [2008-12-08 19:31:21] [5161246d1ccc1b670cc664d03050f084]
F RM D      [Variance Reduction Matrix] [step 2] [2008-12-08 19:34:58] [e515c0250d6233b5d2604259ab52cebe] [Current]
F RMP         [Spectral Analysis] [step 2] [2008-12-08 19:40:15] [5161246d1ccc1b670cc664d03050f084]
- RMP           [(Partial) Autocorrelation Function] [step 2] [2008-12-08 19:45:51] [5161246d1ccc1b670cc664d03050f084]
F   P             [(Partial) Autocorrelation Function] [step3] [2008-12-08 19:51:58] [5161246d1ccc1b670cc664d03050f084]
F RMPD              [ARIMA Backward Selection] [] [2008-12-08 20:09:39] [5161246d1ccc1b670cc664d03050f084]
-   PD                [ARIMA Backward Selection] [verbetering stap 5] [2008-12-15 16:48:15] [e43247bc0ab243a5af99ac7f55ba0b41]
-   P               [(Partial) Autocorrelation Function] [Assessment verbet...] [2008-12-10 15:30:12] [46c5a5fbda57fdfa1d4ef48658f82a0c]
-   P               [(Partial) Autocorrelation Function] [verbetering ] [2008-12-15 16:16:38] [e43247bc0ab243a5af99ac7f55ba0b41]
- RMP             [ARIMA Backward Selection] [Assessment verbet...] [2008-12-10 15:38:14] [46c5a5fbda57fdfa1d4ef48658f82a0c]
F RMP               [ARIMA Forecasting] [forecasting step 1] [2008-12-15 18:07:00] [5161246d1ccc1b670cc664d03050f084]
F   P           [Spectral Analysis] [step3] [2008-12-08 19:54:50] [5161246d1ccc1b670cc664d03050f084]
-   P             [Spectral Analysis] [verbetering] [2008-12-15 16:35:53] [e43247bc0ab243a5af99ac7f55ba0b41]
Feedback Forum
2008-12-15 17:08:52 [Lindsay Heyndrickx] [reply] 
De berekening is hier correct. Hier is de juiste waarde gevonden. Via deze methode kunnen we zien hoeveel keer we een gewone differentiatie en een seizonale differentiatie moeten doorvoeren. Om de waarden van d en D te bekomen moeten we in de tabel gaan zoeken naar het laagste getal (kleinste spreiding).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
83.1
89.6
105.7
110.7
110.4
109
106
100.9
114.3
101.2
109.2
111.6
91.7
93.7
105.7
109.5
105.3
102.8
100.6
97.6
110.3
107.2
107.2
108.1
97.1
92.2
112.2
111.6
115.7
111.3
104.2
103.2
112.7
106.4
102.6
110.6
95.2
89
112.5
116.8
107.2
113.6
101.8
102.6
122.7
110.3
110.5
121.6
100.3
100.7
123.4
127.1
124.1
131.2
111.6
114.2
130.1
125.9
119
133.8
107.5
113.5
134.4
126.8
135.6
139.9
129.8
131
153.1
134.1
144.1
155.9
123.3
128.1
144.3
153
149.9
150.9
141
138.9
157.4
142.9
151.7
161
138.5
135.9
151.5
164
159.1
157
142.1
144.8
152.1
154.6
148.7
157.7
146.7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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=30836&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=30836&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30836&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Variance Reduction Matrix
V(Y[t],d=0,D=0)419.920732388316Range80.9Trim Var.321.491261694734
V(Y[t],d=1,D=0)130.026578947368Range56.1Trim Var.80.22031874145
V(Y[t],d=2,D=0)332.480705487122Range81.8Trim Var.225.843957983193
V(Y[t],d=3,D=0)975.592894074583Range144.6Trim Var.624.41496844521
V(Y[t],d=0,D=1)56.2288515406163Range34.7Trim Var.34.2573765765766
V(Y[t],d=1,D=1)48.5373264486518Range31.8Trim Var.27.7250666419845
V(Y[t],d=2,D=1)151.876776373788Range63.4Trim Var.84.6977473363775
V(Y[t],d=3,D=1)527.177717554954Range112.6Trim Var.294.823096635368
V(Y[t],d=0,D=2)97.6057610350076Range45.4Trim Var.60.5361634615385
V(Y[t],d=1,D=2)114.161400625978Range51.3Trim Var.64.7614186507937
V(Y[t],d=2,D=2)364.473094567405Range98.3Trim Var.228.926728110599
V(Y[t],d=3,D=2)1289.02791097309Range162.2Trim Var.805.393886832365

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 419.920732388316 & Range & 80.9 & Trim Var. & 321.491261694734 \tabularnewline
V(Y[t],d=1,D=0) & 130.026578947368 & Range & 56.1 & Trim Var. & 80.22031874145 \tabularnewline
V(Y[t],d=2,D=0) & 332.480705487122 & Range & 81.8 & Trim Var. & 225.843957983193 \tabularnewline
V(Y[t],d=3,D=0) & 975.592894074583 & Range & 144.6 & Trim Var. & 624.41496844521 \tabularnewline
V(Y[t],d=0,D=1) & 56.2288515406163 & Range & 34.7 & Trim Var. & 34.2573765765766 \tabularnewline
V(Y[t],d=1,D=1) & 48.5373264486518 & Range & 31.8 & Trim Var. & 27.7250666419845 \tabularnewline
V(Y[t],d=2,D=1) & 151.876776373788 & Range & 63.4 & Trim Var. & 84.6977473363775 \tabularnewline
V(Y[t],d=3,D=1) & 527.177717554954 & Range & 112.6 & Trim Var. & 294.823096635368 \tabularnewline
V(Y[t],d=0,D=2) & 97.6057610350076 & Range & 45.4 & Trim Var. & 60.5361634615385 \tabularnewline
V(Y[t],d=1,D=2) & 114.161400625978 & Range & 51.3 & Trim Var. & 64.7614186507937 \tabularnewline
V(Y[t],d=2,D=2) & 364.473094567405 & Range & 98.3 & Trim Var. & 228.926728110599 \tabularnewline
V(Y[t],d=3,D=2) & 1289.02791097309 & Range & 162.2 & Trim Var. & 805.393886832365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30836&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]419.920732388316[/C][C]Range[/C][C]80.9[/C][C]Trim Var.[/C][C]321.491261694734[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]130.026578947368[/C][C]Range[/C][C]56.1[/C][C]Trim Var.[/C][C]80.22031874145[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]332.480705487122[/C][C]Range[/C][C]81.8[/C][C]Trim Var.[/C][C]225.843957983193[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]975.592894074583[/C][C]Range[/C][C]144.6[/C][C]Trim Var.[/C][C]624.41496844521[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]56.2288515406163[/C][C]Range[/C][C]34.7[/C][C]Trim Var.[/C][C]34.2573765765766[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]48.5373264486518[/C][C]Range[/C][C]31.8[/C][C]Trim Var.[/C][C]27.7250666419845[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]151.876776373788[/C][C]Range[/C][C]63.4[/C][C]Trim Var.[/C][C]84.6977473363775[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]527.177717554954[/C][C]Range[/C][C]112.6[/C][C]Trim Var.[/C][C]294.823096635368[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]97.6057610350076[/C][C]Range[/C][C]45.4[/C][C]Trim Var.[/C][C]60.5361634615385[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]114.161400625978[/C][C]Range[/C][C]51.3[/C][C]Trim Var.[/C][C]64.7614186507937[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]364.473094567405[/C][C]Range[/C][C]98.3[/C][C]Trim Var.[/C][C]228.926728110599[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1289.02791097309[/C][C]Range[/C][C]162.2[/C][C]Trim Var.[/C][C]805.393886832365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30836&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30836&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)419.920732388316Range80.9Trim Var.321.491261694734
V(Y[t],d=1,D=0)130.026578947368Range56.1Trim Var.80.22031874145
V(Y[t],d=2,D=0)332.480705487122Range81.8Trim Var.225.843957983193
V(Y[t],d=3,D=0)975.592894074583Range144.6Trim Var.624.41496844521
V(Y[t],d=0,D=1)56.2288515406163Range34.7Trim Var.34.2573765765766
V(Y[t],d=1,D=1)48.5373264486518Range31.8Trim Var.27.7250666419845
V(Y[t],d=2,D=1)151.876776373788Range63.4Trim Var.84.6977473363775
V(Y[t],d=3,D=1)527.177717554954Range112.6Trim Var.294.823096635368
V(Y[t],d=0,D=2)97.6057610350076Range45.4Trim Var.60.5361634615385
V(Y[t],d=1,D=2)114.161400625978Range51.3Trim Var.64.7614186507937
V(Y[t],d=2,D=2)364.473094567405Range98.3Trim Var.228.926728110599
V(Y[t],d=3,D=2)1289.02791097309Range162.2Trim Var.805.393886832365


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')