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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

Mon, 01 Dec 2008 03:51:37 -0700

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/Dec/01/t1228128800hu7dolkojlgmgzb.htm/, Retrieved Sun, 05 May 2024 14:24:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26866, Retrieved Sun, 05 May 2024 14:24:41 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

193

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

F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:31:28] [b98453cac15ba1066b407e146608df68]
- RMPD  [Standard Deviation-Mean Plot] [Q5: Standard Devi...] [2008-11-30 15:50:04] [44ec60eb6065a3f81a5f756bd5af1faf]
F    D      [Standard Deviation-Mean Plot] [SDM: Werkloosheid...] [2008-12-01 10:51:37] [924502d03698cd41cacbcd1327858815] [Current]

Feedback Forum

2008-12-15 18:48:41 [Stéphanie Claes] [reply] 
De student heeft dit goed beantwoord. De p-waarde overstijgt de drempel van 5% en de beta is niet significant verschillend van 0. Een lambda transformatie zou hier zinloos zijn 
We laten lambda in dit geval staan op 1.

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

7,8
7,6
7,5
7,6
7,5
7,3
7,6
7,5
7,6
7,9
7,9
8,1
8,2
8
7,5
6,8
6,5
6,6
7,6
8
8
7,7
7,5
7,6
7,7
7,9
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
8,1
7,9
7,3
6,9
6,6
6,7
6,9
7
7,1
7,2
7,1
6,9
7
6,8
6,4
6,7
6,7
6,4
6,3
6,2
6,5

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	'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=26866&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=26866&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26866&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	'Gwilym Jenkins' @ 72.249.127.135

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	7.65833333333333	0.223437334445796	0.8
2	7.5	0.572077553547355	1.7
3	7.61666666666667	0.220879783565357	0.8
4	7.41666666666667	0.632215920154633	1.6
5	6.68333333333333	0.327062221862594	1

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 7.65833333333333 & 0.223437334445796 & 0.8 \tabularnewline
2 & 7.5 & 0.572077553547355 & 1.7 \tabularnewline
3 & 7.61666666666667 & 0.220879783565357 & 0.8 \tabularnewline
4 & 7.41666666666667 & 0.632215920154633 & 1.6 \tabularnewline
5 & 6.68333333333333 & 0.327062221862594 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26866&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]7.65833333333333[/C][C]0.223437334445796[/C][C]0.8[/C][/ROW]
[ROW][C]2[/C][C]7.5[/C][C]0.572077553547355[/C][C]1.7[/C][/ROW]
[ROW][C]3[/C][C]7.61666666666667[/C][C]0.220879783565357[/C][C]0.8[/C][/ROW]
[ROW][C]4[/C][C]7.41666666666667[/C][C]0.632215920154633[/C][C]1.6[/C][/ROW]
[ROW][C]5[/C][C]6.68333333333333[/C][C]0.327062221862594[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26866&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26866&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	7.65833333333333	0.223437334445796	0.8
2	7.5	0.572077553547355	1.7
3	7.61666666666667	0.220879783565357	0.8
4	7.41666666666667	0.632215920154633	1.6
5	6.68333333333333	0.327062221862594	1

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	0.530900926354879
beta	-0.0184089984596246
S.D.	0.282390248882080
T-STAT	-0.0651899225716956
p-value	0.952123747937673

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.530900926354879 \tabularnewline
beta & -0.0184089984596246 \tabularnewline
S.D. & 0.282390248882080 \tabularnewline
T-STAT & -0.0651899225716956 \tabularnewline
p-value & 0.952123747937673 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26866&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.530900926354879[/C][/ROW]
[ROW][C]beta[/C][C]-0.0184089984596246[/C][/ROW]
[ROW][C]S.D.[/C][C]0.282390248882080[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.0651899225716956[/C][/ROW]
[ROW][C]p-value[/C][C]0.952123747937673[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26866&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26866&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	0.530900926354879
beta	-0.0184089984596246
S.D.	0.282390248882080
T-STAT	-0.0651899225716956
p-value	0.952123747937673

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	1.11268599061125
beta	-1.07235479521162
S.D.	5.15135567932006
T-STAT	-0.208169433828176
p-value	0.848428298185356
Lambda	2.07235479521162

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 1.11268599061125 \tabularnewline
beta & -1.07235479521162 \tabularnewline
S.D. & 5.15135567932006 \tabularnewline
T-STAT & -0.208169433828176 \tabularnewline
p-value & 0.848428298185356 \tabularnewline
Lambda & 2.07235479521162 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26866&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1.11268599061125[/C][/ROW]
[ROW][C]beta[/C][C]-1.07235479521162[/C][/ROW]
[ROW][C]S.D.[/C][C]5.15135567932006[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.208169433828176[/C][/ROW]
[ROW][C]p-value[/C][C]0.848428298185356[/C][/ROW]
[ROW][C]Lambda[/C][C]2.07235479521162[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26866&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26866&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	1.11268599061125
beta	-1.07235479521162
S.D.	5.15135567932006
T-STAT	-0.208169433828176
p-value	0.848428298185356
Lambda	2.07235479521162

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