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Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 22 Dec 2013 10:35:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/22/t1387726641uhx6asz4suso3cc.htm/, Retrieved Sun, 05 Dec 2021 17:19:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232537, Retrieved Sun, 05 Dec 2021 17:19:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [] [2013-12-22 15:35:26] [54713e3426a13268f2edfca2b563126c] [Current]
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Dataseries X:
0.43
0.45
0.44
0.44
0.44
0.48
0.47
0.47
0.47
0.49
0.49
0.46
0.45
0.44
0.42
0.43
0.43
0.47
0.47
0.47
0.47
0.48
0.48
0.48
0.49
0.49
0.47
0.5
0.51
0.5
0.49
0.5
0.51
0.51
0.5
0.53
0.5
0.49
0.46
0.46
0.47
0.49
0.5
0.5
0.51
0.5
0.52
0.5
0.48
0.47
0.43
0.42
0.45
0.5
0.52
0.52
0.51
0.52
0.52
0.51
0.51
0.51
0.48
0.49
0.47
0.51
0.5
0.51
0.51
0.52
0.51
0.52
0.48
0.49
0.47
0.44
0.44
0.47
0.51
0.51
0.52
0.52
0.52
0.52




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232537&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232537&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232537&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
10.4608333333333330.02065224325624580.06
20.45750.02220769727328380.06
30.50.01477097891751990.0600000000000001
40.4916666666666670.01898963034411310.06
50.48750.03695820735518830.1
60.5033333333333330.01556997888323050.05
70.4908333333333330.03058767824804720.08

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 0.460833333333333 & 0.0206522432562458 & 0.06 \tabularnewline
2 & 0.4575 & 0.0222076972732838 & 0.06 \tabularnewline
3 & 0.5 & 0.0147709789175199 & 0.0600000000000001 \tabularnewline
4 & 0.491666666666667 & 0.0189896303441131 & 0.06 \tabularnewline
5 & 0.4875 & 0.0369582073551883 & 0.1 \tabularnewline
6 & 0.503333333333333 & 0.0155699788832305 & 0.05 \tabularnewline
7 & 0.490833333333333 & 0.0305876782480472 & 0.08 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232537&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]0.460833333333333[/C][C]0.0206522432562458[/C][C]0.06[/C][/ROW]
[ROW][C]2[/C][C]0.4575[/C][C]0.0222076972732838[/C][C]0.06[/C][/ROW]
[ROW][C]3[/C][C]0.5[/C][C]0.0147709789175199[/C][C]0.0600000000000001[/C][/ROW]
[ROW][C]4[/C][C]0.491666666666667[/C][C]0.0189896303441131[/C][C]0.06[/C][/ROW]
[ROW][C]5[/C][C]0.4875[/C][C]0.0369582073551883[/C][C]0.1[/C][/ROW]
[ROW][C]6[/C][C]0.503333333333333[/C][C]0.0155699788832305[/C][C]0.05[/C][/ROW]
[ROW][C]7[/C][C]0.490833333333333[/C][C]0.0305876782480472[/C][C]0.08[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232537&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232537&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
10.4608333333333330.02065224325624580.06
20.45750.02220769727328380.06
30.50.01477097891751990.0600000000000001
40.4916666666666670.01898963034411310.06
50.48750.03695820735518830.1
60.5033333333333330.01556997888323050.05
70.4908333333333330.03058767824804720.08







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.0543899725507465
beta-0.065157757320176
S.D.0.197971844237155
T-STAT-0.329126384467692
p-value0.755399192780108

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.0543899725507465 \tabularnewline
beta & -0.065157757320176 \tabularnewline
S.D. & 0.197971844237155 \tabularnewline
T-STAT & -0.329126384467692 \tabularnewline
p-value & 0.755399192780108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232537&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0543899725507465[/C][/ROW]
[ROW][C]beta[/C][C]-0.065157757320176[/C][/ROW]
[ROW][C]S.D.[/C][C]0.197971844237155[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.329126384467692[/C][/ROW]
[ROW][C]p-value[/C][C]0.755399192780108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232537&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232537&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)
alpha0.0543899725507465
beta-0.065157757320176
S.D.0.197971844237155
T-STAT-0.329126384467692
p-value0.755399192780108







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-5.32364263004695
beta-2.05888551779943
S.D.3.85486519399386
T-STAT-0.534100523413195
p-value0.616155366046941
Lambda3.05888551779943

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -5.32364263004695 \tabularnewline
beta & -2.05888551779943 \tabularnewline
S.D. & 3.85486519399386 \tabularnewline
T-STAT & -0.534100523413195 \tabularnewline
p-value & 0.616155366046941 \tabularnewline
Lambda & 3.05888551779943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232537&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-5.32364263004695[/C][/ROW]
[ROW][C]beta[/C][C]-2.05888551779943[/C][/ROW]
[ROW][C]S.D.[/C][C]3.85486519399386[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.534100523413195[/C][/ROW]
[ROW][C]p-value[/C][C]0.616155366046941[/C][/ROW]
[ROW][C]Lambda[/C][C]3.05888551779943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232537&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232537&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-5.32364263004695
beta-2.05888551779943
S.D.3.85486519399386
T-STAT-0.534100523413195
p-value0.616155366046941
Lambda3.05888551779943



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')