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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 computationWed, 02 Dec 2009 10:21:54 -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/02/t12597746075fhzdsg8e0m7cs2.htm/, Retrieved Sun, 28 Apr 2024 08:31:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62458, Retrieved Sun, 28 Apr 2024 08:31:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsStandard Deviation plot en Mean-plot
Estimated Impact156
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
-    D      [Standard Deviation-Mean Plot] [Workshop 9: SDM] [2009-12-02 17:21:54] [63d6214c2814604a6f6cfa44dba5912e] [Current]
-    D        [Standard Deviation-Mean Plot] [WS9.1] [2009-12-11 13:28:02] [4a2be4899cba879e4eea9daa25281df8]
-   PD          [Standard Deviation-Mean Plot] [PAPER 19] [2009-12-20 01:59:39] [4a2be4899cba879e4eea9daa25281df8]
-    D            [Standard Deviation-Mean Plot] [PAPER 20] [2009-12-20 02:03:00] [4a2be4899cba879e4eea9daa25281df8]
-   PD              [Standard Deviation-Mean Plot] [paper 1] [2009-12-20 16:05:02] [4a2be4899cba879e4eea9daa25281df8]
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Dataseries X:
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8.0
8.1




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=62458&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=62458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62458&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
17.616666666666670.2480224818744291
27.691666666666670.5230302152463141.6
36.866666666666670.2348435972120920.8
46.491666666666670.4541892543729751.5
57.20.7885544888073252.3

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 7.61666666666667 & 0.248022481874429 & 1 \tabularnewline
2 & 7.69166666666667 & 0.523030215246314 & 1.6 \tabularnewline
3 & 6.86666666666667 & 0.234843597212092 & 0.8 \tabularnewline
4 & 6.49166666666667 & 0.454189254372975 & 1.5 \tabularnewline
5 & 7.2 & 0.788554488807325 & 2.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62458&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]7.61666666666667[/C][C]0.248022481874429[/C][C]1[/C][/ROW]
[ROW][C]2[/C][C]7.69166666666667[/C][C]0.523030215246314[/C][C]1.6[/C][/ROW]
[ROW][C]3[/C][C]6.86666666666667[/C][C]0.234843597212092[/C][C]0.8[/C][/ROW]
[ROW][C]4[/C][C]6.49166666666667[/C][C]0.454189254372975[/C][C]1.5[/C][/ROW]
[ROW][C]5[/C][C]7.2[/C][C]0.788554488807325[/C][C]2.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62458&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
17.616666666666670.2480224818744291
27.691666666666670.5230302152463141.6
36.866666666666670.2348435972120920.8
46.491666666666670.4541892543729751.5
57.20.7885544888073252.3







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.306460218973018
beta0.0199722753526406
S.D.0.259280454108217
T-STAT0.077029621925549
p-value0.94344965601394

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.306460218973018 \tabularnewline
beta & 0.0199722753526406 \tabularnewline
S.D. & 0.259280454108217 \tabularnewline
T-STAT & 0.077029621925549 \tabularnewline
p-value & 0.94344965601394 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62458&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.306460218973018[/C][/ROW]
[ROW][C]beta[/C][C]0.0199722753526406[/C][/ROW]
[ROW][C]S.D.[/C][C]0.259280454108217[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.077029621925549[/C][/ROW]
[ROW][C]p-value[/C][C]0.94344965601394[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62458&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)
alpha0.306460218973018
beta0.0199722753526406
S.D.0.259280454108217
T-STAT0.077029621925549
p-value0.94344965601394







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.22073698339980
beta0.161119872428544
S.D.4.16718959053867
T-STAT0.0386639169944069
p-value0.97158739136284
Lambda0.838880127571456

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.22073698339980 \tabularnewline
beta & 0.161119872428544 \tabularnewline
S.D. & 4.16718959053867 \tabularnewline
T-STAT & 0.0386639169944069 \tabularnewline
p-value & 0.97158739136284 \tabularnewline
Lambda & 0.838880127571456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62458&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.22073698339980[/C][/ROW]
[ROW][C]beta[/C][C]0.161119872428544[/C][/ROW]
[ROW][C]S.D.[/C][C]4.16718959053867[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.0386639169944069[/C][/ROW]
[ROW][C]p-value[/C][C]0.97158739136284[/C][/ROW]
[ROW][C]Lambda[/C][C]0.838880127571456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62458&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62458&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-1.22073698339980
beta0.161119872428544
S.D.4.16718959053867
T-STAT0.0386639169944069
p-value0.97158739136284
Lambda0.838880127571456



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')