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Author's title

Author

*Unverified author*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Sun, 11 May 2008 09:14:01 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/May/11/t1210519120ru4djac155agwsf.htm/, Retrieved Sun, 19 May 2024 20:22:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=12273, Retrieved Sun, 19 May 2024 20:22:47 +0000

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Original text written by user:

tomas van gastel

IsPrivate?

No (this computation is public)

User-defined keywords

voetbal mean-deviation

Estimated Impact

210

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Standard Deviation-Mean Plot] [inkomprijzen] [2008-05-11 15:14:01] [c0c8a7bf52d32ed636461b577d7f50ce] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12273&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12273&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	14.5091666666667	0.236391752407018	0.48
2	15.1166666666667	0.391345774413772	0.76
3	16.0766666666667	0.638511526675102	1.24
4	17.0625	0.324405049842995	0.629999999999999
5	17.9216666666667	0.607615807642436	1.18
6	19.1891666666667	0.715750824256768	1.39
7	20.2541666666667	0.314106476832106	0.61
8	20.135	0.587018661620659	1.14

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 14.5091666666667 & 0.236391752407018 & 0.48 \tabularnewline
2 & 15.1166666666667 & 0.391345774413772 & 0.76 \tabularnewline
3 & 16.0766666666667 & 0.638511526675102 & 1.24 \tabularnewline
4 & 17.0625 & 0.324405049842995 & 0.629999999999999 \tabularnewline
5 & 17.9216666666667 & 0.607615807642436 & 1.18 \tabularnewline
6 & 19.1891666666667 & 0.715750824256768 & 1.39 \tabularnewline
7 & 20.2541666666667 & 0.314106476832106 & 0.61 \tabularnewline
8 & 20.135 & 0.587018661620659 & 1.14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12273&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]14.5091666666667[/C][C]0.236391752407018[/C][C]0.48[/C][/ROW]
[ROW][C]2[/C][C]15.1166666666667[/C][C]0.391345774413772[/C][C]0.76[/C][/ROW]
[ROW][C]3[/C][C]16.0766666666667[/C][C]0.638511526675102[/C][C]1.24[/C][/ROW]
[ROW][C]4[/C][C]17.0625[/C][C]0.324405049842995[/C][C]0.629999999999999[/C][/ROW]
[ROW][C]5[/C][C]17.9216666666667[/C][C]0.607615807642436[/C][C]1.18[/C][/ROW]
[ROW][C]6[/C][C]19.1891666666667[/C][C]0.715750824256768[/C][C]1.39[/C][/ROW]
[ROW][C]7[/C][C]20.2541666666667[/C][C]0.314106476832106[/C][C]0.61[/C][/ROW]
[ROW][C]8[/C][C]20.135[/C][C]0.587018661620659[/C][C]1.14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12273&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
Section	Mean	Standard Deviation	Range
1	14.5091666666667	0.236391752407018	0.48
2	15.1166666666667	0.391345774413772	0.76
3	16.0766666666667	0.638511526675102	1.24
4	17.0625	0.324405049842995	0.629999999999999
5	17.9216666666667	0.607615807642436	1.18
6	19.1891666666667	0.715750824256768	1.39
7	20.2541666666667	0.314106476832106	0.61
8	20.135	0.587018661620659	1.14

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-0.0636224251232831
beta	0.0308282556209826
S.D.	0.0307094738125842
T-STAT	1.00386792066589
p-value	0.354194827544717

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.0636224251232831 \tabularnewline
beta & 0.0308282556209826 \tabularnewline
S.D. & 0.0307094738125842 \tabularnewline
T-STAT & 1.00386792066589 \tabularnewline
p-value & 0.354194827544717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12273&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.0636224251232831[/C][/ROW]
[ROW][C]beta[/C][C]0.0308282556209826[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0307094738125842[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.00386792066589[/C][/ROW]
[ROW][C]p-value[/C][C]0.354194827544717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12273&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)
alpha	-0.0636224251232831
beta	0.0308282556209826
S.D.	0.0307094738125842
T-STAT	1.00386792066589
p-value	0.354194827544717

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-4.65702987929718
beta	1.34660769449495
S.D.	1.18368967021809
T-STAT	1.13763575739141
p-value	0.298653249086483
Lambda	-0.346607694494951

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.65702987929718 \tabularnewline
beta & 1.34660769449495 \tabularnewline
S.D. & 1.18368967021809 \tabularnewline
T-STAT & 1.13763575739141 \tabularnewline
p-value & 0.298653249086483 \tabularnewline
Lambda & -0.346607694494951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12273&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.65702987929718[/C][/ROW]
[ROW][C]beta[/C][C]1.34660769449495[/C][/ROW]
[ROW][C]S.D.[/C][C]1.18368967021809[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.13763575739141[/C][/ROW]
[ROW][C]p-value[/C][C]0.298653249086483[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.346607694494951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12273&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	-4.65702987929718
beta	1.34660769449495
S.D.	1.18368967021809
T-STAT	1.13763575739141
p-value	0.298653249086483
Lambda	-0.346607694494951

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code