FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationWed, 03 Dec 2008 03:48:46 -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/03/t12283014135e9hr6y645vtqow.htm/, Retrieved Fri, 17 May 2024 15:26:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28610, Retrieved Fri, 17 May 2024 15:26:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact290

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:10:39] [57fa5e3679c393aa19449b2f1be9928b]
-   P     [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:18:39] [57fa5e3679c393aa19449b2f1be9928b]
F           [Standard Deviation-Mean Plot] [] [2008-11-30 10:50:04] [a4ee3bef49b119f4bd2e925060c84f5e]
F             [Standard Deviation-Mean Plot] [q5 / 7] [2008-11-30 17:57:37] [4300be8b33fd3dcdacd2aa9800ceba23]
- R             [Standard Deviation-Mean Plot] [question 5] [2008-12-01 14:10:37] [379d6c32f73e3218fd773d79e4063d07]
-                 [Standard Deviation-Mean Plot] [wx] [2008-12-02 14:05:35] [98f6eecc397b06503dbf024e1e936f30]
F    D                [Standard Deviation-Mean Plot] [] [2008-12-03 10:48:46] [ba8414dd214a21fbd6c7bde748ac585f] [Current]
Feedback Forum
2008-12-06 11:30:56 [Carole Thielens] [reply] 
De student berekende enkel de lambda van 1 tijdsreeks, terwijl het de bedoeling was om lambda, d en D te zoeken voor beide tijdsreeksen uit Q7.
De waarden voor d en D kunnen gevonden worden door de variance reduction matrix. Via de partial correlation function kan gecontroleerd worden of de waarden voor d en D leidden tot een stationaire verdeling.
2008-12-08 22:05:57 [Katja van Hek] [reply] 
Hier zou er gebruik moeten gemaakt worden van VRM en ACF om zo na te gaan met welke waarden voor lambda, d en D je het model stationair kunt maken. Er is enkel voor 1 tijdreeks een lambda berekend.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.36
1.95
2.16
2.76
2.09
1.49
1.17
1.3
1.26
2.17
2.03
2.18
2.61
2.58
3.86
3.81
2.41
1.47
1.33
1.38
1.57
2.6
2.18
2.36
2.24
2.41
2.51
2.98
1.87
1.9
1.47
1.45
2.71
2.9
2.11
2.18
2.24
2.05
2.42
2.77
1.99
1.47
1.09
0.93
1.32
2.03
2.04
2.78
2.8
3.03
3.11
2.75
2.78
1.76
1.29
1.28
1.43
1.71
1.89
1.84
2.08
2.09
2.36
2.99
2.75
1.58
1.69
1.3
1.97
1.84
1.96
1.86
2.75
2.62
2.41
3.61
2.03
1.45
1.4
1.3
1.58
2.1
2.27
2.54

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=28610&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=28610&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28610&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






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.910.4949747468305831.59
22.346666666666670.8523372500828462.53
32.22750.503986381489891.53
41.92750.610039119460981.85
52.139166666666670.7005382130039641.83
62.039166666666670.474676125629281.69
72.171666666666670.6780833058680692.31

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.91 & 0.494974746830583 & 1.59 \tabularnewline
2 & 2.34666666666667 & 0.852337250082846 & 2.53 \tabularnewline
3 & 2.2275 & 0.50398638148989 & 1.53 \tabularnewline
4 & 1.9275 & 0.61003911946098 & 1.85 \tabularnewline
5 & 2.13916666666667 & 0.700538213003964 & 1.83 \tabularnewline
6 & 2.03916666666667 & 0.47467612562928 & 1.69 \tabularnewline
7 & 2.17166666666667 & 0.678083305868069 & 2.31 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28610&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]1.91[/C][C]0.494974746830583[/C][C]1.59[/C][/ROW]
[ROW][C]2[/C][C]2.34666666666667[/C][C]0.852337250082846[/C][C]2.53[/C][/ROW]
[ROW][C]3[/C][C]2.2275[/C][C]0.50398638148989[/C][C]1.53[/C][/ROW]
[ROW][C]4[/C][C]1.9275[/C][C]0.61003911946098[/C][C]1.85[/C][/ROW]
[ROW][C]5[/C][C]2.13916666666667[/C][C]0.700538213003964[/C][C]1.83[/C][/ROW]
[ROW][C]6[/C][C]2.03916666666667[/C][C]0.47467612562928[/C][C]1.69[/C][/ROW]
[ROW][C]7[/C][C]2.17166666666667[/C][C]0.678083305868069[/C][C]2.31[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28610&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28610&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:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.910.4949747468305831.59
22.346666666666670.8523372500828462.53
32.22750.503986381489891.53
41.92750.610039119460981.85
52.139166666666670.7005382130039641.83
62.039166666666670.474676125629281.69
72.171666666666670.6780833058680692.31






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.548171563388712
beta0.55223006115524
S.D.0.297175726503269
T-STAT1.85826099477598
p-value0.122243695261845

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.548171563388712 \tabularnewline
beta & 0.55223006115524 \tabularnewline
S.D. & 0.297175726503269 \tabularnewline
T-STAT & 1.85826099477598 \tabularnewline
p-value & 0.122243695261845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28610&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.548171563388712[/C][/ROW]
[ROW][C]beta[/C][C]0.55223006115524[/C][/ROW]
[ROW][C]S.D.[/C][C]0.297175726503269[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.85826099477598[/C][/ROW]
[ROW][C]p-value[/C][C]0.122243695261845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28610&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28610&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.548171563388712
beta0.55223006115524
S.D.0.297175726503269
T-STAT1.85826099477598
p-value0.122243695261845






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.78463811510061
beta1.72129971131046
S.D.1.02900816615904
T-STAT1.67277555992147
p-value0.155230505597468
Lambda-0.721299711310463

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.78463811510061 \tabularnewline
beta & 1.72129971131046 \tabularnewline
S.D. & 1.02900816615904 \tabularnewline
T-STAT & 1.67277555992147 \tabularnewline
p-value & 0.155230505597468 \tabularnewline
Lambda & -0.721299711310463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28610&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.78463811510061[/C][/ROW]
[ROW][C]beta[/C][C]1.72129971131046[/C][/ROW]
[ROW][C]S.D.[/C][C]1.02900816615904[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.67277555992147[/C][/ROW]
[ROW][C]p-value[/C][C]0.155230505597468[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.721299711310463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28610&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28610&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.78463811510061
beta1.72129971131046
S.D.1.02900816615904
T-STAT1.67277555992147
p-value0.155230505597468
Lambda-0.721299711310463


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))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')