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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, 26 Nov 2009 10:56:38 -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/Nov/26/t1259258233st76lj2abtwyjzc.htm/, Retrieved Mon, 29 Apr 2024 07:35:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60206, Retrieved Mon, 29 Apr 2024 07:35:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
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]
- R PD          [Standard Deviation-Mean Plot] [] [2009-11-26 17:56:38] [d1856923bab8a0db5ebd860815c7444f] [Current]
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Dataseries X:
3.68
3.72
3.77
3.92
4.12
4.03
3.93
4.03
4.24
4.13
3.87
4.26
4.46
4.56
4.58
4.85
4.84
4.51
4.37
4.23
4.23
4.25
4.41
4.28
4.42
4.39
4.44
4.62
4.64
4.34
4.22
4.01
4.11
4.06
3.82
3.76
3.83
3.79
3.92
4.04
4.02
4.03
3.96
3.7
3.54
3.37
3.39
3.49
3.3
3.14
3.31
3.3
3.26
3.43
3.6
3.76
3.57
3.59
3.66
3.85




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60206&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]4 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=60206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60206&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
13.9750.1936961068740980.58
24.464166666666670.2166043033792820.619999999999999
34.235833333333330.2889623546835170.88
43.756666666666670.2529941908543760.67
53.480833333333330.2220752670659750.71

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3.975 & 0.193696106874098 & 0.58 \tabularnewline
2 & 4.46416666666667 & 0.216604303379282 & 0.619999999999999 \tabularnewline
3 & 4.23583333333333 & 0.288962354683517 & 0.88 \tabularnewline
4 & 3.75666666666667 & 0.252994190854376 & 0.67 \tabularnewline
5 & 3.48083333333333 & 0.222075267065975 & 0.71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60206&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]3.975[/C][C]0.193696106874098[/C][C]0.58[/C][/ROW]
[ROW][C]2[/C][C]4.46416666666667[/C][C]0.216604303379282[/C][C]0.619999999999999[/C][/ROW]
[ROW][C]3[/C][C]4.23583333333333[/C][C]0.288962354683517[/C][C]0.88[/C][/ROW]
[ROW][C]4[/C][C]3.75666666666667[/C][C]0.252994190854376[/C][C]0.67[/C][/ROW]
[ROW][C]5[/C][C]3.48083333333333[/C][C]0.222075267065975[/C][C]0.71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60206&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
13.9750.1936961068740980.58
24.464166666666670.2166043033792820.619999999999999
34.235833333333330.2889623546835170.88
43.756666666666670.2529941908543760.67
53.480833333333330.2220752670659750.71







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.184729232127281
beta0.0125893816557863
S.D.0.0545789199310952
T-STAT0.230663810710804
p-value0.832411185545634

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.184729232127281 \tabularnewline
beta & 0.0125893816557863 \tabularnewline
S.D. & 0.0545789199310952 \tabularnewline
T-STAT & 0.230663810710804 \tabularnewline
p-value & 0.832411185545634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60206&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.184729232127281[/C][/ROW]
[ROW][C]beta[/C][C]0.0125893816557863[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0545789199310952[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.230663810710804[/C][/ROW]
[ROW][C]p-value[/C][C]0.832411185545634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60206&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)
alpha0.184729232127281
beta0.0125893816557863
S.D.0.0545789199310952
T-STAT0.230663810710804
p-value0.832411185545634







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.70734143282056
beta0.180679827610442
S.D.0.902564335940188
T-STAT0.200184984511083
p-value0.854137904354811
Lambda0.819320172389558

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.70734143282056 \tabularnewline
beta & 0.180679827610442 \tabularnewline
S.D. & 0.902564335940188 \tabularnewline
T-STAT & 0.200184984511083 \tabularnewline
p-value & 0.854137904354811 \tabularnewline
Lambda & 0.819320172389558 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60206&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.70734143282056[/C][/ROW]
[ROW][C]beta[/C][C]0.180679827610442[/C][/ROW]
[ROW][C]S.D.[/C][C]0.902564335940188[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.200184984511083[/C][/ROW]
[ROW][C]p-value[/C][C]0.854137904354811[/C][/ROW]
[ROW][C]Lambda[/C][C]0.819320172389558[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60206&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60206&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-1.70734143282056
beta0.180679827610442
S.D.0.902564335940188
T-STAT0.200184984511083
p-value0.854137904354811
Lambda0.819320172389558



Parameters (Session):
par1 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
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')