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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

Fri, 18 Mar 2016 15:54:36 +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/2016/Mar/18/t1458316493g4zwgj8orpaavm3.htm/, Retrieved Thu, 02 May 2024 07:30:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294282, Retrieved Thu, 02 May 2024 07:30:51 +0000

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No (this computation is public)

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Estimated Impact

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

-       [Standard Deviation-Mean Plot] [opgave 8 oef2 s3] [2016-03-18 15:54:36] [3b4b14340a49fc08510bf0d59f03d4db] [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	'Gwilym Jenkins' @ jenkins.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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294282&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294282&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294282&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' @ jenkins.wessa.net

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	97.0075	0.429865200858469	1.12
2	97.7816666666667	0.237174018710976	0.739999999999995
3	98.3833333333333	0.177780934315412	0.620000000000005
4	98.9225	0.225716032378577	0.769999999999996
5	99.3108333333333	0.226734058609052	0.730000000000004
6	100.064166666667	0.288332457521882	0.800000000000011
7	99.7458333333333	0.134938257485908	0.519999999999996
8	100.016666666667	0.13364357844882	0.510000000000005
9	101.365	0.645748332627278	2.06
10	103.235833333333	0.709397418214711	2.12

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 97.0075 & 0.429865200858469 & 1.12 \tabularnewline
2 & 97.7816666666667 & 0.237174018710976 & 0.739999999999995 \tabularnewline
3 & 98.3833333333333 & 0.177780934315412 & 0.620000000000005 \tabularnewline
4 & 98.9225 & 0.225716032378577 & 0.769999999999996 \tabularnewline
5 & 99.3108333333333 & 0.226734058609052 & 0.730000000000004 \tabularnewline
6 & 100.064166666667 & 0.288332457521882 & 0.800000000000011 \tabularnewline
7 & 99.7458333333333 & 0.134938257485908 & 0.519999999999996 \tabularnewline
8 & 100.016666666667 & 0.13364357844882 & 0.510000000000005 \tabularnewline
9 & 101.365 & 0.645748332627278 & 2.06 \tabularnewline
10 & 103.235833333333 & 0.709397418214711 & 2.12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294282&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]97.0075[/C][C]0.429865200858469[/C][C]1.12[/C][/ROW]
[ROW][C]2[/C][C]97.7816666666667[/C][C]0.237174018710976[/C][C]0.739999999999995[/C][/ROW]
[ROW][C]3[/C][C]98.3833333333333[/C][C]0.177780934315412[/C][C]0.620000000000005[/C][/ROW]
[ROW][C]4[/C][C]98.9225[/C][C]0.225716032378577[/C][C]0.769999999999996[/C][/ROW]
[ROW][C]5[/C][C]99.3108333333333[/C][C]0.226734058609052[/C][C]0.730000000000004[/C][/ROW]
[ROW][C]6[/C][C]100.064166666667[/C][C]0.288332457521882[/C][C]0.800000000000011[/C][/ROW]
[ROW][C]7[/C][C]99.7458333333333[/C][C]0.134938257485908[/C][C]0.519999999999996[/C][/ROW]
[ROW][C]8[/C][C]100.016666666667[/C][C]0.13364357844882[/C][C]0.510000000000005[/C][/ROW]
[ROW][C]9[/C][C]101.365[/C][C]0.645748332627278[/C][C]2.06[/C][/ROW]
[ROW][C]10[/C][C]103.235833333333[/C][C]0.709397418214711[/C][C]2.12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294282&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294282&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	97.0075	0.429865200858469	1.12
2	97.7816666666667	0.237174018710976	0.739999999999995
3	98.3833333333333	0.177780934315412	0.620000000000005
4	98.9225	0.225716032378577	0.769999999999996
5	99.3108333333333	0.226734058609052	0.730000000000004
6	100.064166666667	0.288332457521882	0.800000000000011
7	99.7458333333333	0.134938257485908	0.519999999999996
8	100.016666666667	0.13364357844882	0.510000000000005
9	101.365	0.645748332627278	2.06
10	103.235833333333	0.709397418214711	2.12

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-6.60059564806292
beta	0.0695048904801341
S.D.	0.0326378688375371
T-STAT	2.12957809304619
p-value	0.0658361997165705

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -6.60059564806292 \tabularnewline
beta & 0.0695048904801341 \tabularnewline
S.D. & 0.0326378688375371 \tabularnewline
T-STAT & 2.12957809304619 \tabularnewline
p-value & 0.0658361997165705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294282&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.60059564806292[/C][/ROW]
[ROW][C]beta[/C][C]0.0695048904801341[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0326378688375371[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.12957809304619[/C][/ROW]
[ROW][C]p-value[/C][C]0.0658361997165705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294282&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294282&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	-6.60059564806292
beta	0.0695048904801341
S.D.	0.0326378688375371
T-STAT	2.12957809304619
p-value	0.0658361997165705

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-70.6136576068924
beta	15.0649063193906
S.D.	10.405451630766
T-STAT	1.44778976001848
p-value	0.185703388999007
Lambda	-14.0649063193906

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -70.6136576068924 \tabularnewline
beta & 15.0649063193906 \tabularnewline
S.D. & 10.405451630766 \tabularnewline
T-STAT & 1.44778976001848 \tabularnewline
p-value & 0.185703388999007 \tabularnewline
Lambda & -14.0649063193906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294282&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-70.6136576068924[/C][/ROW]
[ROW][C]beta[/C][C]15.0649063193906[/C][/ROW]
[ROW][C]S.D.[/C][C]10.405451630766[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.44778976001848[/C][/ROW]
[ROW][C]p-value[/C][C]0.185703388999007[/C][/ROW]
[ROW][C]Lambda[/C][C]-14.0649063193906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294282&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294282&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	-70.6136576068924
beta	15.0649063193906
S.D.	10.405451630766
T-STAT	1.44778976001848
p-value	0.185703388999007
Lambda	-14.0649063193906

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

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code