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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 computationFri, 05 Dec 2008 10:47:26 -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/05/t12284993267htagp47jwcomnp.htm/, Retrieved Thu, 16 May 2024 20:12:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29362, Retrieved Thu, 16 May 2024 20:12:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact220

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Variance Reduction Matrix] [step 2 uitvoer] [2008-12-05 17:47:26] [821c4b3d195be8e737cf8c9dc649d3cf] [Current]
F RMPD    [(Partial) Autocorrelation Function] [step 2 uitvoer] [2008-12-05 17:51:28] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P       [(Partial) Autocorrelation Function] [ACF] [2008-12-15 15:22:55] [415d0222c17b651a9576eaac006f530d]
F RMPD    [(Partial) Autocorrelation Function] [step 2 uitvoer] [2008-12-05 17:57:52] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMPD    [Spectral Analysis] [step 2 uitvoer] [2008-12-05 18:00:26] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P       [Spectral Analysis] [step 2] [2008-12-07 14:26:00] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P         [Spectral Analysis] [step2] [2008-12-07 14:32:03] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P       [Spectral Analysis] [step 2] [2008-12-07 14:36:58] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P         [Spectral Analysis] [step 5] [2008-12-07 14:40:19] [3a9fc6d5b5e0e816787b7dbace57e7cd]
- RMP           [(Partial) Autocorrelation Function] [step 3] [2008-12-07 14:45:29] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMPD    [Spectral Analysis] [step 2 uitvoer] [2008-12-05 18:02:42] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP       [(Partial) Autocorrelation Function] [step 3] [2008-12-07 13:14:32] [3a9fc6d5b5e0e816787b7dbace57e7cd]
F RMP         [ARIMA Backward Selection] [step 5] [2008-12-07 13:24:10] [3a9fc6d5b5e0e816787b7dbace57e7cd]
- RMPD          [ARIMA Forecasting] [ARIMA forecasting] [2008-12-09 19:14:50] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P           [ARIMA Backward Selection] [verbetering] [2008-12-16 19:23:33] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P         [(Partial) Autocorrelation Function] [verbetering] [2008-12-16 19:16:45] [3a9fc6d5b5e0e816787b7dbace57e7cd]
-   P       [Spectral Analysis] [spectral analysis] [2008-12-15 15:32:28] [415d0222c17b651a9576eaac006f530d]
F RM D    [Standard Deviation-Mean Plot] [step 3 uitvoer] [2008-12-05 18:06:39] [3a9fc6d5b5e0e816787b7dbace57e7cd]
- RMP       [(Partial) Autocorrelation Function] [acf lambda -0.3] [2008-12-05 18:09:06] [3a9fc6d5b5e0e816787b7dbace57e7cd]
- RMP         [ARIMA Backward Selection] [step 5 uitvoer] [2008-12-05 18:58:55] [3a9fc6d5b5e0e816787b7dbace57e7cd]
Feedback Forum
2008-12-15 15:21:40 [Natalie De Wilde] [reply] 
Zeer goed, we moeten inderdaad nog controleren met ACF en Spectral analysis of deze parameters de beste zijn om de tijdreeks stationair te maken.
2008-12-16 19:06:47 [Gert-Jan Geudens] [reply] 
Correct. We hebben hier niets aan toe te voegen.

CORRECTIE VAN AL ONZE VORIGE FEEDBACKS MET BETREKKING TOT DE VRM :
Voor de getrimde variantie is er GEEN logaritme gebruikt. Via een logaritme kunnen we enkel de grootste waarden verkleinen en de kleinse waarden groter maken. Trimmen is dus niet gelijk aan een logaritme toevoegen !


Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2150,3
2425,7
2642,0
2291,5
2570,7
2526,6
2266,2
1981,9
2630,3
2942,6
2713,4
2437,5
2678,9
2582,0
2780,0
2512,4
2658,4
2708,7
2518,7
2018,3
2579,3
2693,5
2468,8
2122,8
2412,8
2370,6
2642,5
2634,2
2457,5
2579,1
2505,9
1903,2
2660,2
2844,1
2607,1
2356,0
2659,9
2531,4
2845,7
2654,3
2588,2
2789,6
2533,1
1846,5
2796,3
2895,6
2472,2
2584,4
2630,4
2663,1
3176,2
2856,7
2551,4
3088,7
2628,3
2226,2
3023,6
3077,9
3084,1
2990,3
2949,6
3014,7
3517,7
3121,2
3067,4
3174,6
2676,3
2424,0
3195,1
3146,6
3506,7
3528,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29362&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]2 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=29362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29362&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Variance Reduction Matrix
V(Y[t],d=0,D=0)123017.248536776Range1682Trim Var.65695.1140649801
V(Y[t],d=1,D=0)122182.641585513Range1636.4Trim Var.73195.6640092166
V(Y[t],d=2,D=0)314860.30352795Range2634.1Trim Var.186117.117686409
V(Y[t],d=3,D=0)932262.160170503Range4553.4Trim Var.583494.491010929
V(Y[t],d=0,D=1)43658.3017259887Range926.6Trim Var.28669.7338679245
V(Y[t],d=1,D=1)34186.6396201052Range859.7Trim Var.21409.7509143686
V(Y[t],d=2,D=1)92866.6424833635Range1256.7Trim Var.61938.9956975867
V(Y[t],d=3,D=1)298270.601528822Range2244.7Trim Var.191360.834831372
V(Y[t],d=0,D=2)86379.350212766Range1541.5Trim Var.50126.3155865273
V(Y[t],d=1,D=2)75254.810962072Range1382Trim Var.38308.7816951219
V(Y[t],d=2,D=2)210350.541256038Range2188.7Trim Var.96985.771282051
V(Y[t],d=3,D=2)700663.845373736Range3950.4Trim Var.342477.382604588

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 123017.248536776 & Range & 1682 & Trim Var. & 65695.1140649801 \tabularnewline
V(Y[t],d=1,D=0) & 122182.641585513 & Range & 1636.4 & Trim Var. & 73195.6640092166 \tabularnewline
V(Y[t],d=2,D=0) & 314860.30352795 & Range & 2634.1 & Trim Var. & 186117.117686409 \tabularnewline
V(Y[t],d=3,D=0) & 932262.160170503 & Range & 4553.4 & Trim Var. & 583494.491010929 \tabularnewline
V(Y[t],d=0,D=1) & 43658.3017259887 & Range & 926.6 & Trim Var. & 28669.7338679245 \tabularnewline
V(Y[t],d=1,D=1) & 34186.6396201052 & Range & 859.7 & Trim Var. & 21409.7509143686 \tabularnewline
V(Y[t],d=2,D=1) & 92866.6424833635 & Range & 1256.7 & Trim Var. & 61938.9956975867 \tabularnewline
V(Y[t],d=3,D=1) & 298270.601528822 & Range & 2244.7 & Trim Var. & 191360.834831372 \tabularnewline
V(Y[t],d=0,D=2) & 86379.350212766 & Range & 1541.5 & Trim Var. & 50126.3155865273 \tabularnewline
V(Y[t],d=1,D=2) & 75254.810962072 & Range & 1382 & Trim Var. & 38308.7816951219 \tabularnewline
V(Y[t],d=2,D=2) & 210350.541256038 & Range & 2188.7 & Trim Var. & 96985.771282051 \tabularnewline
V(Y[t],d=3,D=2) & 700663.845373736 & Range & 3950.4 & Trim Var. & 342477.382604588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29362&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]123017.248536776[/C][C]Range[/C][C]1682[/C][C]Trim Var.[/C][C]65695.1140649801[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]122182.641585513[/C][C]Range[/C][C]1636.4[/C][C]Trim Var.[/C][C]73195.6640092166[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]314860.30352795[/C][C]Range[/C][C]2634.1[/C][C]Trim Var.[/C][C]186117.117686409[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]932262.160170503[/C][C]Range[/C][C]4553.4[/C][C]Trim Var.[/C][C]583494.491010929[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]43658.3017259887[/C][C]Range[/C][C]926.6[/C][C]Trim Var.[/C][C]28669.7338679245[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]34186.6396201052[/C][C]Range[/C][C]859.7[/C][C]Trim Var.[/C][C]21409.7509143686[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]92866.6424833635[/C][C]Range[/C][C]1256.7[/C][C]Trim Var.[/C][C]61938.9956975867[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]298270.601528822[/C][C]Range[/C][C]2244.7[/C][C]Trim Var.[/C][C]191360.834831372[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]86379.350212766[/C][C]Range[/C][C]1541.5[/C][C]Trim Var.[/C][C]50126.3155865273[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]75254.810962072[/C][C]Range[/C][C]1382[/C][C]Trim Var.[/C][C]38308.7816951219[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]210350.541256038[/C][C]Range[/C][C]2188.7[/C][C]Trim Var.[/C][C]96985.771282051[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]700663.845373736[/C][C]Range[/C][C]3950.4[/C][C]Trim Var.[/C][C]342477.382604588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29362&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)123017.248536776Range1682Trim Var.65695.1140649801
V(Y[t],d=1,D=0)122182.641585513Range1636.4Trim Var.73195.6640092166
V(Y[t],d=2,D=0)314860.30352795Range2634.1Trim Var.186117.117686409
V(Y[t],d=3,D=0)932262.160170503Range4553.4Trim Var.583494.491010929
V(Y[t],d=0,D=1)43658.3017259887Range926.6Trim Var.28669.7338679245
V(Y[t],d=1,D=1)34186.6396201052Range859.7Trim Var.21409.7509143686
V(Y[t],d=2,D=1)92866.6424833635Range1256.7Trim Var.61938.9956975867
V(Y[t],d=3,D=1)298270.601528822Range2244.7Trim Var.191360.834831372
V(Y[t],d=0,D=2)86379.350212766Range1541.5Trim Var.50126.3155865273
V(Y[t],d=1,D=2)75254.810962072Range1382Trim Var.38308.7816951219
V(Y[t],d=2,D=2)210350.541256038Range2188.7Trim Var.96985.771282051
V(Y[t],d=3,D=2)700663.845373736Range3950.4Trim Var.342477.382604588


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')