Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Tue, 07 Dec 2010 10:11:36 +0000

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/2010/Dec/07/t12917165647z1m2alozx79sor.htm/, Retrieved Fri, 03 May 2024 22:10:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106119, Retrieved Fri, 03 May 2024 22:10:35 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

119

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

-     [Mean Plot] [Gilliam Schoorel] [2008-11-06 14:07:56] [666bda00bbd072dde5655a1423b1377b]
- RM D  [Variance Reduction Matrix] [VRM suiker] [2008-12-09 16:07:08] [f77c9ab3b413812d7baee6b7ec69a15d]
- RMPD    [(Partial) Autocorrelation Function] [ACF chocopasta zo...] [2008-12-18 11:31:51] [f77c9ab3b413812d7baee6b7ec69a15d]
- RMP       [Standard Deviation-Mean Plot] [Lambda chocopasta] [2008-12-18 11:59:17] [f77c9ab3b413812d7baee6b7ec69a15d]
-  M D          [Standard Deviation-Mean Plot] [Box cox transform...] [2010-12-07 10:11:36] [2fa539864aa87c5da4977c85c6885fac] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1.88
1.87
1.88
1.87
1.88
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.88
1.88
1.87
1.87
1.87
1.87
1.87
1.87
1.87
1.86
1.86
1.85
1.84
1.83
1.82
1.78
1.75
1.74
1.74
1.74
1.73
1.73
1.73
1.71
1.7
1.7
1.69
1.68
1.68
1.68
1.68
1.67
1.66
1.65
1.65
1.65

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106119&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]2 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=106119&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106119&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	2 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	1.8725	0.00452267016866636	0.00999999999999979
2	1.87166666666667	0.00389249472080753	0.00999999999999979
3	1.84916666666667	0.0277843426585856	0.09
4	1.72	0.022962419891482	0.07

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.8725 & 0.00452267016866636 & 0.00999999999999979 \tabularnewline
2 & 1.87166666666667 & 0.00389249472080753 & 0.00999999999999979 \tabularnewline
3 & 1.84916666666667 & 0.0277843426585856 & 0.09 \tabularnewline
4 & 1.72 & 0.022962419891482 & 0.07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106119&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]1.8725[/C][C]0.00452267016866636[/C][C]0.00999999999999979[/C][/ROW]
[ROW][C]2[/C][C]1.87166666666667[/C][C]0.00389249472080753[/C][C]0.00999999999999979[/C][/ROW]
[ROW][C]3[/C][C]1.84916666666667[/C][C]0.0277843426585856[/C][C]0.09[/C][/ROW]
[ROW][C]4[/C][C]1.72[/C][C]0.022962419891482[/C][C]0.07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106119&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106119&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	1.8725	0.00452267016866636	0.00999999999999979
2	1.87166666666667	0.00389249472080753	0.00999999999999979
3	1.84916666666667	0.0277843426585856	0.09
4	1.72	0.022962419891482	0.07

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	0.190820438295545
beta	-0.096278918743296
S.D.	0.0986694141305592
T-STAT	-0.975772680842108
p-value	0.432088393228807

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.190820438295545 \tabularnewline
beta & -0.096278918743296 \tabularnewline
S.D. & 0.0986694141305592 \tabularnewline
T-STAT & -0.975772680842108 \tabularnewline
p-value & 0.432088393228807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106119&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.190820438295545[/C][/ROW]
[ROW][C]beta[/C][C]-0.096278918743296[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0986694141305592[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.975772680842108[/C][/ROW]
[ROW][C]p-value[/C][C]0.432088393228807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106119&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106119&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	0.190820438295545
beta	-0.096278918743296
S.D.	0.0986694141305592
T-STAT	-0.975772680842108
p-value	0.432088393228807

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	5.13494969862847
beta	-16.110206415706
S.D.	14.0344354439198
T-STAT	-1.14790555559436
p-value	0.369785427065423
Lambda	17.110206415706

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 5.13494969862847 \tabularnewline
beta & -16.110206415706 \tabularnewline
S.D. & 14.0344354439198 \tabularnewline
T-STAT & -1.14790555559436 \tabularnewline
p-value & 0.369785427065423 \tabularnewline
Lambda & 17.110206415706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106119&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]5.13494969862847[/C][/ROW]
[ROW][C]beta[/C][C]-16.110206415706[/C][/ROW]
[ROW][C]S.D.[/C][C]14.0344354439198[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.14790555559436[/C][/ROW]
[ROW][C]p-value[/C][C]0.369785427065423[/C][/ROW]
[ROW][C]Lambda[/C][C]17.110206415706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106119&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106119&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	5.13494969862847
beta	-16.110206415706
S.D.	14.0344354439198
T-STAT	-1.14790555559436
p-value	0.369785427065423
Lambda	17.110206415706

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