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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, 12 Dec 2008 07:54:46 -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/12/t1229093737mt6deyubseddh5m.htm/, Retrieved Sun, 12 May 2024 12:06:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32819, Retrieved Sun, 12 May 2024 12:06:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183

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 RMP   [Standard Deviation-Mean Plot] [Taak 8 stap 1] [2008-12-12 12:09:36] [491a70d26f8c977398d8a0c1c87d3dd4]
-    D    [Standard Deviation-Mean Plot] [paper standard de...] [2008-12-12 14:44:37] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM D        [Variance Reduction Matrix] [paper variance re...] [2008-12-12 14:54:46] [2ba2a74112fb2c960057a572bf2825d3] [Current]
- RMP           [(Partial) Autocorrelation Function] [Paper autocorrela...] [2008-12-12 15:10:41] [491a70d26f8c977398d8a0c1c87d3dd4]
-   P             [(Partial) Autocorrelation Function] [Paper autocorrela...] [2008-12-12 15:21:21] [491a70d26f8c977398d8a0c1c87d3dd4]
- RMP           [ARIMA Backward Selection] [Paper ARIMA backw...] [2008-12-16 19:22:07] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM              [ARIMA Forecasting] [Paper Arima forec...] [2008-12-16 19:41:13] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM              [ARIMA Forecasting] [Paper Arima forec...] [2008-12-16 19:55:16] [491a70d26f8c977398d8a0c1c87d3dd4]
-  MPD              [ARIMA Forecasting] [Ws 9] [2009-12-04 14:55:47] [74be16979710d4c4e7c6647856088456]
-                     [ARIMA Forecasting] [Workshop 9-1] [2009-12-04 21:41:47] [aba88da643e3763d32ff92bd8f92a385]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103.3
101.2
107.7
110.4
101.9
115.9
89.9
88.6
117.2
123.9
100
103.6
94.1
98.7
119.5
112.7
104.4
124.7
89.1
97
121.6
118.8
114
111.5
97.2
102.5
113.4
109.8
104.9
126.1
80
96.8
117.2
112.3
117.3
111.1
102.2
104.3
122.9
107.6
121.3
131.5
89
104.4
128.9
135.9
133.3
121.3
120.5
120.4
137.9
126.1
133.2
151.1
105
119
140.4
156.6
137.1
122.7

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=32819&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=32819&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32819&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)245.071692090395Range76.6Trim Var.153.372690426275
V(Y[t],d=1,D=0)301.340017533606Range74.7Trim Var.178.729397677794
V(Y[t],d=2,D=0)790.09406231095Range130.2Trim Var.438.555837104073
V(Y[t],d=3,D=0)2390.13848370927Range223.6Trim Var.1350.82859607843
V(Y[t],d=0,D=1)70.4924822695036Range32.8Trim Var.48.5004006968641
V(Y[t],d=1,D=1)67.0002035152636Range36Trim Var.40.6390243902439
V(Y[t],d=2,D=1)206.913550724638Range59.9Trim Var.128.385480769231
V(Y[t],d=3,D=1)713.859818181818Range109.6Trim Var.457.010499325236
V(Y[t],d=0,D=2)123.194753968254Range48Trim Var.83.9386693548387
V(Y[t],d=1,D=2)128.491462184874Range43.9Trim Var.83.3078494623655
V(Y[t],d=2,D=2)421.02973262032Range84.3Trim Var.273.078448275862
V(Y[t],d=3,D=2)1532.01488636363Range154.1Trim Var.1035.61921182266

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 245.071692090395 & Range & 76.6 & Trim Var. & 153.372690426275 \tabularnewline
V(Y[t],d=1,D=0) & 301.340017533606 & Range & 74.7 & Trim Var. & 178.729397677794 \tabularnewline
V(Y[t],d=2,D=0) & 790.09406231095 & Range & 130.2 & Trim Var. & 438.555837104073 \tabularnewline
V(Y[t],d=3,D=0) & 2390.13848370927 & Range & 223.6 & Trim Var. & 1350.82859607843 \tabularnewline
V(Y[t],d=0,D=1) & 70.4924822695036 & Range & 32.8 & Trim Var. & 48.5004006968641 \tabularnewline
V(Y[t],d=1,D=1) & 67.0002035152636 & Range & 36 & Trim Var. & 40.6390243902439 \tabularnewline
V(Y[t],d=2,D=1) & 206.913550724638 & Range & 59.9 & Trim Var. & 128.385480769231 \tabularnewline
V(Y[t],d=3,D=1) & 713.859818181818 & Range & 109.6 & Trim Var. & 457.010499325236 \tabularnewline
V(Y[t],d=0,D=2) & 123.194753968254 & Range & 48 & Trim Var. & 83.9386693548387 \tabularnewline
V(Y[t],d=1,D=2) & 128.491462184874 & Range & 43.9 & Trim Var. & 83.3078494623655 \tabularnewline
V(Y[t],d=2,D=2) & 421.02973262032 & Range & 84.3 & Trim Var. & 273.078448275862 \tabularnewline
V(Y[t],d=3,D=2) & 1532.01488636363 & Range & 154.1 & Trim Var. & 1035.61921182266 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32819&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]245.071692090395[/C][C]Range[/C][C]76.6[/C][C]Trim Var.[/C][C]153.372690426275[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]301.340017533606[/C][C]Range[/C][C]74.7[/C][C]Trim Var.[/C][C]178.729397677794[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]790.09406231095[/C][C]Range[/C][C]130.2[/C][C]Trim Var.[/C][C]438.555837104073[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]2390.13848370927[/C][C]Range[/C][C]223.6[/C][C]Trim Var.[/C][C]1350.82859607843[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]70.4924822695036[/C][C]Range[/C][C]32.8[/C][C]Trim Var.[/C][C]48.5004006968641[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]67.0002035152636[/C][C]Range[/C][C]36[/C][C]Trim Var.[/C][C]40.6390243902439[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]206.913550724638[/C][C]Range[/C][C]59.9[/C][C]Trim Var.[/C][C]128.385480769231[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]713.859818181818[/C][C]Range[/C][C]109.6[/C][C]Trim Var.[/C][C]457.010499325236[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]123.194753968254[/C][C]Range[/C][C]48[/C][C]Trim Var.[/C][C]83.9386693548387[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]128.491462184874[/C][C]Range[/C][C]43.9[/C][C]Trim Var.[/C][C]83.3078494623655[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]421.02973262032[/C][C]Range[/C][C]84.3[/C][C]Trim Var.[/C][C]273.078448275862[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]1532.01488636363[/C][C]Range[/C][C]154.1[/C][C]Trim Var.[/C][C]1035.61921182266[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32819&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32819&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)245.071692090395Range76.6Trim Var.153.372690426275
V(Y[t],d=1,D=0)301.340017533606Range74.7Trim Var.178.729397677794
V(Y[t],d=2,D=0)790.09406231095Range130.2Trim Var.438.555837104073
V(Y[t],d=3,D=0)2390.13848370927Range223.6Trim Var.1350.82859607843
V(Y[t],d=0,D=1)70.4924822695036Range32.8Trim Var.48.5004006968641
V(Y[t],d=1,D=1)67.0002035152636Range36Trim Var.40.6390243902439
V(Y[t],d=2,D=1)206.913550724638Range59.9Trim Var.128.385480769231
V(Y[t],d=3,D=1)713.859818181818Range109.6Trim Var.457.010499325236
V(Y[t],d=0,D=2)123.194753968254Range48Trim Var.83.9386693548387
V(Y[t],d=1,D=2)128.491462184874Range43.9Trim Var.83.3078494623655
V(Y[t],d=2,D=2)421.02973262032Range84.3Trim Var.273.078448275862
V(Y[t],d=3,D=2)1532.01488636363Range154.1Trim Var.1035.61921182266


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')