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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 computationThu, 10 Dec 2009 13:22:33 -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/2009/Dec/10/t1260476608y8b8ymx4j03h83u.htm/, Retrieved Thu, 28 Mar 2024 09:50:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65788, Retrieved Thu, 28 Mar 2024 09:50:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-11-22 12:50:05] [b98453cac15ba1066b407e146608df68]
-    D        [Standard Deviation-Mean Plot] [Shwws8_v4] [2009-11-27 21:44:00] [5f89c040fdf1f8599c99d7f78a662321]
-    D            [Standard Deviation-Mean Plot] [Paper] [2009-12-10 20:22:33] [93b66894f6318f3da4fcda772f2ffa6f] [Current]
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Dataseries X:
17
18
23.8
25.5
25.6
23.7
22
21.3
20.7
20.4
20.3
20.4
19.8
19.5
23.1
23.5
23.5
22.9
21.9
21.5
20.5
20.2
19.4
19.2
18.8
18.8
22.6
23.3
23
21.4
19.9
18.8
18.6
18.4
18.6
19.9
19.2
18.4
21.1
20.5
19.1
18.1
17
17.1
17.4
16.8
15.3
14.3
13.4
15.3
22.1
23.7
22.2
19.5
16.6
17.3
19.8
21.2
21.5
20.6
19.1
19.6
23.5
24




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=65788&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=65788&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65788&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
121.55833333333332.699985970782868.6
221.251.684420590966744.3
320.1751.884445903611024.9
417.85833333333331.977812535161626.8
519.43333333333333.1323338186590410.3

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 21.5583333333333 & 2.69998597078286 & 8.6 \tabularnewline
2 & 21.25 & 1.68442059096674 & 4.3 \tabularnewline
3 & 20.175 & 1.88444590361102 & 4.9 \tabularnewline
4 & 17.8583333333333 & 1.97781253516162 & 6.8 \tabularnewline
5 & 19.4333333333333 & 3.13233381865904 & 10.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65788&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]21.5583333333333[/C][C]2.69998597078286[/C][C]8.6[/C][/ROW]
[ROW][C]2[/C][C]21.25[/C][C]1.68442059096674[/C][C]4.3[/C][/ROW]
[ROW][C]3[/C][C]20.175[/C][C]1.88444590361102[/C][C]4.9[/C][/ROW]
[ROW][C]4[/C][C]17.8583333333333[/C][C]1.97781253516162[/C][C]6.8[/C][/ROW]
[ROW][C]5[/C][C]19.4333333333333[/C][C]3.13233381865904[/C][C]10.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65788&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65788&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
121.55833333333332.699985970782868.6
221.251.684420590966744.3
320.1751.884445903611024.9
417.85833333333331.977812535161626.8
519.43333333333333.1323338186590410.3







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha2.26200276252583
beta0.000687958180524845
S.D.0.237248396508236
T-STAT0.00289973795671561
p-value0.997868391550099

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 2.26200276252583 \tabularnewline
beta & 0.000687958180524845 \tabularnewline
S.D. & 0.237248396508236 \tabularnewline
T-STAT & 0.00289973795671561 \tabularnewline
p-value & 0.997868391550099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65788&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2.26200276252583[/C][/ROW]
[ROW][C]beta[/C][C]0.000687958180524845[/C][/ROW]
[ROW][C]S.D.[/C][C]0.237248396508236[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.00289973795671561[/C][/ROW]
[ROW][C]p-value[/C][C]0.997868391550099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65788&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65788&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)
alpha2.26200276252583
beta0.000687958180524845
S.D.0.237248396508236
T-STAT0.00289973795671561
p-value0.997868391550099







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.83125608810302
beta-0.0122961280774452
S.D.1.98838203474982
T-STAT-0.0061839867100752
p-value0.995454157881592
Lambda1.01229612807745

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.83125608810302 \tabularnewline
beta & -0.0122961280774452 \tabularnewline
S.D. & 1.98838203474982 \tabularnewline
T-STAT & -0.0061839867100752 \tabularnewline
p-value & 0.995454157881592 \tabularnewline
Lambda & 1.01229612807745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65788&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.83125608810302[/C][/ROW]
[ROW][C]beta[/C][C]-0.0122961280774452[/C][/ROW]
[ROW][C]S.D.[/C][C]1.98838203474982[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.0061839867100752[/C][/ROW]
[ROW][C]p-value[/C][C]0.995454157881592[/C][/ROW]
[ROW][C]Lambda[/C][C]1.01229612807745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65788&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65788&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)
alpha0.83125608810302
beta-0.0122961280774452
S.D.1.98838203474982
T-STAT-0.0061839867100752
p-value0.995454157881592
Lambda1.01229612807745



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')