Free Statistics

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Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationFri, 25 May 2012 08:41:21 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/25/t1337949713n5jif78qbbj5fsd.htm/, Retrieved Sat, 04 May 2024 00:35:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=167481, Retrieved Sat, 04 May 2024 00:35:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W83
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bootstrap Plot - Central Tendency] [jens vanpachtenbe...] [2012-05-24 13:19:01] [6c12ae1bbd1f05ed2de376c80d275927]
- RMPD    [Standard Deviation-Mean Plot] [jens vanpachtenbe...] [2012-05-25 12:41:21] [4080e77d9380e2af46712fd05e0afa1e] [Current]
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Dataseries X:
2.3
2.31
2.31
2.32
2.33
2.34
2.36
2.37
2.37
2.38
2.39
2.4
2.4
2.39
2.4
2.42
2.42
2.44
2.44
2.44
2.45
2.46
2.47
2.48
2.48
2.49
2.5
2.51
2.52
2.52
2.52
2.54
2.54
2.54
2.56
2.57
2.58
2.58
2.58
2.58
2.59
2.6
2.61
2.61
2.62
2.63
2.65
2.67
2.68
2.67
2.68
2.68
2.68
2.68
2.69
2.69
2.69
2.7
2.71
2.72
2.71
2.72
2.73
2.74
2.74
2.75
2.75
2.76
2.75
2.78
2.79
2.8
2.81
2.81
2.82
2.82
2.83
2.83
2.84
2.84
2.84
2.86
2.87




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 0 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167481&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167481&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167481&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 time0 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12.348333333333330.03433348043510710.1
22.434166666666670.02874917653629670.0899999999999999
32.524166666666670.0271220585613640.0899999999999999
42.608333333333330.02979729497586630.0899999999999999
52.689166666666670.01443375672974070.0500000000000003
62.751666666666670.02724746304565320.0899999999999999

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2.34833333333333 & 0.0343334804351071 & 0.1 \tabularnewline
2 & 2.43416666666667 & 0.0287491765362967 & 0.0899999999999999 \tabularnewline
3 & 2.52416666666667 & 0.027122058561364 & 0.0899999999999999 \tabularnewline
4 & 2.60833333333333 & 0.0297972949758663 & 0.0899999999999999 \tabularnewline
5 & 2.68916666666667 & 0.0144337567297407 & 0.0500000000000003 \tabularnewline
6 & 2.75166666666667 & 0.0272474630456532 & 0.0899999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167481&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]2.34833333333333[/C][C]0.0343334804351071[/C][C]0.1[/C][/ROW]
[ROW][C]2[/C][C]2.43416666666667[/C][C]0.0287491765362967[/C][C]0.0899999999999999[/C][/ROW]
[ROW][C]3[/C][C]2.52416666666667[/C][C]0.027122058561364[/C][C]0.0899999999999999[/C][/ROW]
[ROW][C]4[/C][C]2.60833333333333[/C][C]0.0297972949758663[/C][C]0.0899999999999999[/C][/ROW]
[ROW][C]5[/C][C]2.68916666666667[/C][C]0.0144337567297407[/C][C]0.0500000000000003[/C][/ROW]
[ROW][C]6[/C][C]2.75166666666667[/C][C]0.0272474630456532[/C][C]0.0899999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167481&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167481&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
12.348333333333330.03433348043510710.1
22.434166666666670.02874917653629670.0899999999999999
32.524166666666670.0271220585613640.0899999999999999
42.608333333333330.02979729497586630.0899999999999999
52.689166666666670.01443375672974070.0500000000000003
62.751666666666670.02724746304565320.0899999999999999







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.0969239738998579
beta-0.0273420923502547
S.D.0.0169142335493574
T-STAT-1.61651382372531
p-value0.181289306752818

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.0969239738998579 \tabularnewline
beta & -0.0273420923502547 \tabularnewline
S.D. & 0.0169142335493574 \tabularnewline
T-STAT & -1.61651382372531 \tabularnewline
p-value & 0.181289306752818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167481&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0969239738998579[/C][/ROW]
[ROW][C]beta[/C][C]-0.0273420923502547[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0169142335493574[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.61651382372531[/C][/ROW]
[ROW][C]p-value[/C][C]0.181289306752818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167481&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167481&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)
alpha0.0969239738998579
beta-0.0273420923502547
S.D.0.0169142335493574
T-STAT-1.61651382372531
p-value0.181289306752818







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.925255615543511
beta-2.90102981428149
S.D.2.04442974299217
T-STAT-1.41899217824704
p-value0.228902723265232
Lambda3.90102981428149

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.925255615543511 \tabularnewline
beta & -2.90102981428149 \tabularnewline
S.D. & 2.04442974299217 \tabularnewline
T-STAT & -1.41899217824704 \tabularnewline
p-value & 0.228902723265232 \tabularnewline
Lambda & 3.90102981428149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167481&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.925255615543511[/C][/ROW]
[ROW][C]beta[/C][C]-2.90102981428149[/C][/ROW]
[ROW][C]S.D.[/C][C]2.04442974299217[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.41899217824704[/C][/ROW]
[ROW][C]p-value[/C][C]0.228902723265232[/C][/ROW]
[ROW][C]Lambda[/C][C]3.90102981428149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167481&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167481&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-0.925255615543511
beta-2.90102981428149
S.D.2.04442974299217
T-STAT-1.41899217824704
p-value0.228902723265232
Lambda3.90102981428149



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')