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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationFri, 05 Dec 2008 07:59:18 -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/t12284985214ao7ppqnlveexta.htm/, Retrieved Thu, 16 May 2024 08:20:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29358, Retrieved Thu, 16 May 2024 08:20:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [inv] [2008-12-05 14:59:18] [fa8b44cd657c07c6ee11bb2476ca3f8d] [Current]
-   P     [Standard Deviation-Mean Plot] [SDMP inv] [2008-12-21 14:54:19] [fad8a251ac01c156a8ae23a83577546f]
-   PD    [Standard Deviation-Mean Plot] [SDMP duur cons] [2008-12-21 15:01:25] [fad8a251ac01c156a8ae23a83577546f]
-   PD    [Standard Deviation-Mean Plot] [SDMP nt duur cons] [2008-12-21 15:08:14] [fad8a251ac01c156a8ae23a83577546f]
-   PD    [Standard Deviation-Mean Plot] [SDMP cons] [2008-12-21 15:14:27] [fad8a251ac01c156a8ae23a83577546f]
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Dataseries X:
93,0
99,2
112,2
112,1
103,3
108,2
90,4
72,8
111,0
117,9
111,3
110,5
94,8
100,4
132,1
114,6
101,9
130,2
84,0
86,4
122,3
120,9
110,2
112,6
102,0
105,0
130,5
115,5
103,7
130,9
89,1
93,8
123,8
111,9
118,3
116,9
103,6
116,6
141,3
107,0
125,2
136,4
91,6
95,3
132,3
130,6
131,9
118,6
114,3
111,3
126,5
112,1
119,3
142,4
101,1
97,4
129,1
136,9
129,8
123,9




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=29358&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=29358&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29358&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 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
1104.1259.606031091628519.2
293.67515.812521409735235.4
3112.6753.498928407384187.4
4110.47516.652001881655737.3
5100.62521.252823341852746.2
6116.55.9972215789202512.1
7113.2512.874393189583728.5
8104.37518.701047207754641.8
9117.7254.8937885800948411.9
10117.12517.030829887784937.7
11112.12522.09515406901144.8
12128.356.5403873483660513.7000000000000
13116.057.0811957558969815.2
14115.0520.592959962084145
15129.9255.3431420219442713

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 104.125 & 9.6060310916285 & 19.2 \tabularnewline
2 & 93.675 & 15.8125214097352 & 35.4 \tabularnewline
3 & 112.675 & 3.49892840738418 & 7.4 \tabularnewline
4 & 110.475 & 16.6520018816557 & 37.3 \tabularnewline
5 & 100.625 & 21.2528233418527 & 46.2 \tabularnewline
6 & 116.5 & 5.99722157892025 & 12.1 \tabularnewline
7 & 113.25 & 12.8743931895837 & 28.5 \tabularnewline
8 & 104.375 & 18.7010472077546 & 41.8 \tabularnewline
9 & 117.725 & 4.89378858009484 & 11.9 \tabularnewline
10 & 117.125 & 17.0308298877849 & 37.7 \tabularnewline
11 & 112.125 & 22.095154069011 & 44.8 \tabularnewline
12 & 128.35 & 6.54038734836605 & 13.7000000000000 \tabularnewline
13 & 116.05 & 7.08119575589698 & 15.2 \tabularnewline
14 & 115.05 & 20.5929599620841 & 45 \tabularnewline
15 & 129.925 & 5.34314202194427 & 13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29358&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]104.125[/C][C]9.6060310916285[/C][C]19.2[/C][/ROW]
[ROW][C]2[/C][C]93.675[/C][C]15.8125214097352[/C][C]35.4[/C][/ROW]
[ROW][C]3[/C][C]112.675[/C][C]3.49892840738418[/C][C]7.4[/C][/ROW]
[ROW][C]4[/C][C]110.475[/C][C]16.6520018816557[/C][C]37.3[/C][/ROW]
[ROW][C]5[/C][C]100.625[/C][C]21.2528233418527[/C][C]46.2[/C][/ROW]
[ROW][C]6[/C][C]116.5[/C][C]5.99722157892025[/C][C]12.1[/C][/ROW]
[ROW][C]7[/C][C]113.25[/C][C]12.8743931895837[/C][C]28.5[/C][/ROW]
[ROW][C]8[/C][C]104.375[/C][C]18.7010472077546[/C][C]41.8[/C][/ROW]
[ROW][C]9[/C][C]117.725[/C][C]4.89378858009484[/C][C]11.9[/C][/ROW]
[ROW][C]10[/C][C]117.125[/C][C]17.0308298877849[/C][C]37.7[/C][/ROW]
[ROW][C]11[/C][C]112.125[/C][C]22.095154069011[/C][C]44.8[/C][/ROW]
[ROW][C]12[/C][C]128.35[/C][C]6.54038734836605[/C][C]13.7000000000000[/C][/ROW]
[ROW][C]13[/C][C]116.05[/C][C]7.08119575589698[/C][C]15.2[/C][/ROW]
[ROW][C]14[/C][C]115.05[/C][C]20.5929599620841[/C][C]45[/C][/ROW]
[ROW][C]15[/C][C]129.925[/C][C]5.34314202194427[/C][C]13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29358&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29358&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
SectionMeanStandard DeviationRange
1104.1259.606031091628519.2
293.67515.812521409735235.4
3112.6753.498928407384187.4
4110.47516.652001881655737.3
5100.62521.252823341852746.2
6116.55.9972215789202512.1
7113.2512.874393189583728.5
8104.37518.701047207754641.8
9117.7254.8937885800948411.9
10117.12517.030829887784937.7
11112.12522.09515406901144.8
12128.356.5403873483660513.7000000000000
13116.057.0811957558969815.2
14115.0520.592959962084145
15129.9255.3431420219442713







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha54.0208961852103
beta-0.367802970978669
S.D.0.165739886865321
T-STAT-2.21915784989973
p-value0.0448883210996267

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 54.0208961852103 \tabularnewline
beta & -0.367802970978669 \tabularnewline
S.D. & 0.165739886865321 \tabularnewline
T-STAT & -2.21915784989973 \tabularnewline
p-value & 0.0448883210996267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29358&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]54.0208961852103[/C][/ROW]
[ROW][C]beta[/C][C]-0.367802970978669[/C][/ROW]
[ROW][C]S.D.[/C][C]0.165739886865321[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.21915784989973[/C][/ROW]
[ROW][C]p-value[/C][C]0.0448883210996267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29358&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29358&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)
alpha54.0208961852103
beta-0.367802970978669
S.D.0.165739886865321
T-STAT-2.21915784989973
p-value0.0448883210996267







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha20.4643769144706
beta-3.83278714720105
S.D.1.71854633310420
T-STAT-2.23024952738859
p-value0.0439807736688922
Lambda4.83278714720105

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 20.4643769144706 \tabularnewline
beta & -3.83278714720105 \tabularnewline
S.D. & 1.71854633310420 \tabularnewline
T-STAT & -2.23024952738859 \tabularnewline
p-value & 0.0439807736688922 \tabularnewline
Lambda & 4.83278714720105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29358&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]20.4643769144706[/C][/ROW]
[ROW][C]beta[/C][C]-3.83278714720105[/C][/ROW]
[ROW][C]S.D.[/C][C]1.71854633310420[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.23024952738859[/C][/ROW]
[ROW][C]p-value[/C][C]0.0439807736688922[/C][/ROW]
[ROW][C]Lambda[/C][C]4.83278714720105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29358&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29358&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)
alpha20.4643769144706
beta-3.83278714720105
S.D.1.71854633310420
T-STAT-2.23024952738859
p-value0.0439807736688922
Lambda4.83278714720105



Parameters (Session):
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')