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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Mon, 08 Dec 2008 14:43:44 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/08/t1228772733jps8f0wi4zi8q7l.htm/, Retrieved Thu, 16 May 2024 12:57:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31084, Retrieved Thu, 16 May 2024 12:57:49 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

145

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] [Arma process: eig...] [2008-12-08 21:43:44] [d6e9f26c3644bfc30f06303d9993b878] [Current]

Feedback Forum

2008-12-12 19:32:14 [Bas van Keken] [reply] 
Lambda waarde is in principe juist gevonden en is af te ronden op 2 als waarde om in te kunnen voeren in de (partial) autocorrelation software. Maar deze waarde is niet goed te gebruiken want de P-value is 0.197451813170098. Dit is vrij hoog en dient onder de 0,05 te zitten. Hier kunt u het beste de Lambda waarde 1 nemen in de bovengenoemde module. 
2008-12-13 13:36:28 [An De Koninck] [reply] 
De student komt in eerste instantie al een hoge lambdawaarde uit. Normaal dient deze onder de 5% te zitten. Als die zo is betekent dat beta significant verschillend is van nul en dat er dus iets moet gebeuren om de tijdsreeks stationair te maken.  
Hier is de lambdawaarde dus groter dan 5%. Deze parameter moet dus ingesteld worden op 1.
2008-12-15 11:50:59 [Romina Machiels] [reply] 
De vraag is juist beantwoord. 
Maar de lambda-waarde is te hoog, moet onder 5% zitten. Dus neem je best 1 als parameter.
2008-12-15 22:03:56 [df2ed12c9b09685cd516719b004050c5] [reply] 
We gebruiken de SMP om de variantie gelijk te maken en om de transformatie toe te passen.  
Standard deviation meanplot: bij een scatterplot weet je niet wanneer in de tijdreeks een outlier zich voordoet. Wat wel belangrijk is of deze outlier helemaal links of rechts ligt. Want deze kan dan de helling van de regressierechte beïnvloeden ( die door de punten van de scatterplot wordt getrokken ). Hier zien we 2 outliers, links bovenaan en rechts onderaan. Dit zorgt dus voor problemen. 
 
2de tabel: de p-waarde heeft betrekking op de beta (helling van de regressielijn). De beta wordt hier dus getoetst. Hier is de beta niet significant verschillend van 0. (p-waarde 0.197451813170098 is groter dan 5% (bij 95% betrouwbaarheid) Er is dus geen verband tussen de SE en het gemiddelde. We moeten de lambda op 1 zetten. 
 
3de tabel: Hier lezen we de lambda af ALS de beta uit de 2de tabel significant verschillend is van 0 en als in de scatterplot geen outliers zijn die de regressievergelijking beïnvloeden. 
De 2 voorwaarden zijn niet voldaan, dus we mogen deze lambda niet gebruiken. Dus we zetten de lambda op 1. 

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31084&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31084&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31084&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	14920.675	2270.81646752147	5022.8
2	15663.5	1522.95543160877	3320.6
3	16395.75	1058.28450648522	2450.2
4	16745.525	1929.98882099526	4005.5
5	17100.675	1384.09491335192	3132.3
6	17473.175	986.422084691268	2328.4
7	17795.075	1888.57073007605	4060.5
8	18595	1665.48384761506	3556.9
9	18494.95	1314.70267994960	2625.4
10	18880.825	1824.52874714724	3795.8
11	19048.075	1678.04802747120	4007.2
12	19725.575	921.533838680564	2249.80000000000
13	20093.325	1847.08909616365	4424.6
14	20550.1	1792.47663490118	3811.3
15	22711	825.47258383708	1753

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 14920.675 & 2270.81646752147 & 5022.8 \tabularnewline
2 & 15663.5 & 1522.95543160877 & 3320.6 \tabularnewline
3 & 16395.75 & 1058.28450648522 & 2450.2 \tabularnewline
4 & 16745.525 & 1929.98882099526 & 4005.5 \tabularnewline
5 & 17100.675 & 1384.09491335192 & 3132.3 \tabularnewline
6 & 17473.175 & 986.422084691268 & 2328.4 \tabularnewline
7 & 17795.075 & 1888.57073007605 & 4060.5 \tabularnewline
8 & 18595 & 1665.48384761506 & 3556.9 \tabularnewline
9 & 18494.95 & 1314.70267994960 & 2625.4 \tabularnewline
10 & 18880.825 & 1824.52874714724 & 3795.8 \tabularnewline
11 & 19048.075 & 1678.04802747120 & 4007.2 \tabularnewline
12 & 19725.575 & 921.533838680564 & 2249.80000000000 \tabularnewline
13 & 20093.325 & 1847.08909616365 & 4424.6 \tabularnewline
14 & 20550.1 & 1792.47663490118 & 3811.3 \tabularnewline
15 & 22711 & 825.47258383708 & 1753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31084&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]14920.675[/C][C]2270.81646752147[/C][C]5022.8[/C][/ROW]
[ROW][C]2[/C][C]15663.5[/C][C]1522.95543160877[/C][C]3320.6[/C][/ROW]
[ROW][C]3[/C][C]16395.75[/C][C]1058.28450648522[/C][C]2450.2[/C][/ROW]
[ROW][C]4[/C][C]16745.525[/C][C]1929.98882099526[/C][C]4005.5[/C][/ROW]
[ROW][C]5[/C][C]17100.675[/C][C]1384.09491335192[/C][C]3132.3[/C][/ROW]
[ROW][C]6[/C][C]17473.175[/C][C]986.422084691268[/C][C]2328.4[/C][/ROW]
[ROW][C]7[/C][C]17795.075[/C][C]1888.57073007605[/C][C]4060.5[/C][/ROW]
[ROW][C]8[/C][C]18595[/C][C]1665.48384761506[/C][C]3556.9[/C][/ROW]
[ROW][C]9[/C][C]18494.95[/C][C]1314.70267994960[/C][C]2625.4[/C][/ROW]
[ROW][C]10[/C][C]18880.825[/C][C]1824.52874714724[/C][C]3795.8[/C][/ROW]
[ROW][C]11[/C][C]19048.075[/C][C]1678.04802747120[/C][C]4007.2[/C][/ROW]
[ROW][C]12[/C][C]19725.575[/C][C]921.533838680564[/C][C]2249.80000000000[/C][/ROW]
[ROW][C]13[/C][C]20093.325[/C][C]1847.08909616365[/C][C]4424.6[/C][/ROW]
[ROW][C]14[/C][C]20550.1[/C][C]1792.47663490118[/C][C]3811.3[/C][/ROW]
[ROW][C]15[/C][C]22711[/C][C]825.47258383708[/C][C]1753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31084&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31084&T=1

As an alternative you can also use a QR Code:

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

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	14920.675	2270.81646752147	5022.8
2	15663.5	1522.95543160877	3320.6
3	16395.75	1058.28450648522	2450.2
4	16745.525	1929.98882099526	4005.5
5	17100.675	1384.09491335192	3132.3
6	17473.175	986.422084691268	2328.4
7	17795.075	1888.57073007605	4060.5
8	18595	1665.48384761506	3556.9
9	18494.95	1314.70267994960	2625.4
10	18880.825	1824.52874714724	3795.8
11	19048.075	1678.04802747120	4007.2
12	19725.575	921.533838680564	2249.80000000000
13	20093.325	1847.08909616365	4424.6
14	20550.1	1792.47663490118	3811.3
15	22711	825.47258383708	1753

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	2893.17800640192
beta	-0.0747181178000781
S.D.	0.055005639597577
T-STAT	-1.35837194779877
p-value	0.197451813170098

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 2893.17800640192 \tabularnewline
beta & -0.0747181178000781 \tabularnewline
S.D. & 0.055005639597577 \tabularnewline
T-STAT & -1.35837194779877 \tabularnewline
p-value & 0.197451813170098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31084&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2893.17800640192[/C][/ROW]
[ROW][C]beta[/C][C]-0.0747181178000781[/C][/ROW]
[ROW][C]S.D.[/C][C]0.055005639597577[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.35837194779877[/C][/ROW]
[ROW][C]p-value[/C][C]0.197451813170098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31084&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31084&T=2

As an alternative you can also use a QR Code:

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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	2893.17800640192
beta	-0.0747181178000781
S.D.	0.055005639597577
T-STAT	-1.35837194779877
p-value	0.197451813170098

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	17.0717399589404
beta	-0.997372535290324
S.D.	0.725474501147471
T-STAT	-1.37478647934944
p-value	0.192431168732869
Lambda	1.99737253529032

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 17.0717399589404 \tabularnewline
beta & -0.997372535290324 \tabularnewline
S.D. & 0.725474501147471 \tabularnewline
T-STAT & -1.37478647934944 \tabularnewline
p-value & 0.192431168732869 \tabularnewline
Lambda & 1.99737253529032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31084&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]17.0717399589404[/C][/ROW]
[ROW][C]beta[/C][C]-0.997372535290324[/C][/ROW]
[ROW][C]S.D.[/C][C]0.725474501147471[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.37478647934944[/C][/ROW]
[ROW][C]p-value[/C][C]0.192431168732869[/C][/ROW]
[ROW][C]Lambda[/C][C]1.99737253529032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31084&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31084&T=3

As an alternative you can also use a QR Code:

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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	17.0717399589404
beta	-0.997372535290324
S.D.	0.725474501147471
T-STAT	-1.37478647934944
p-value	0.192431168732869
Lambda	1.99737253529032

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 4 ;

Parameters (R input):

par1 = 4 ;

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')

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code