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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

Sat, 03 Dec 2011 05:14:48 -0500

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/2011/Dec/03/t1322907570ekv4a3cu5zftvfp.htm/, Retrieved Mon, 29 Apr 2024 05:44:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=150391, Retrieved Mon, 29 Apr 2024 05:44:51 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W83

Estimated Impact

117

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

-       [Standard Deviation-Mean Plot] [prijs haarsnit heren] [2011-12-03 10:14:48] [d0059bb5ffa81669f18ca7953f72fb2d] [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	2 seconds
R Server	'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150391&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' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150391&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150391&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' @ yule.wessa.net

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	15.6775	0.0741057802513858	0.16
2	15.79	0.0182574185835061	0.0400000000000009
3	15.86	0.0316227766016841	0.0700000000000003
4	16.0425	0.0939414711402797	0.209999999999999
5	16.235	0.0299999999999994	0.0599999999999987
6	16.2725	0.0561990510002914	0.109999999999999
7	16.49	0.095916630466254	0.219999999999999
8	16.555	0.02081665999466	0.0499999999999972
9	16.505	0.0640312423743289	0.140000000000001
10	16.605	0.10785793124909	0.23
11	16.8375	0.0330403793359985	0.0700000000000003
12	17.0075	0.0340342964277695	0.0799999999999983
13	17.385	0.243104915622865	0.530000000000001
14	17.61	0.0496655480858391	0.110000000000003
15	17.785	0.0675771164423772	0.149999999999999

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 15.6775 & 0.0741057802513858 & 0.16 \tabularnewline
2 & 15.79 & 0.0182574185835061 & 0.0400000000000009 \tabularnewline
3 & 15.86 & 0.0316227766016841 & 0.0700000000000003 \tabularnewline
4 & 16.0425 & 0.0939414711402797 & 0.209999999999999 \tabularnewline
5 & 16.235 & 0.0299999999999994 & 0.0599999999999987 \tabularnewline
6 & 16.2725 & 0.0561990510002914 & 0.109999999999999 \tabularnewline
7 & 16.49 & 0.095916630466254 & 0.219999999999999 \tabularnewline
8 & 16.555 & 0.02081665999466 & 0.0499999999999972 \tabularnewline
9 & 16.505 & 0.0640312423743289 & 0.140000000000001 \tabularnewline
10 & 16.605 & 0.10785793124909 & 0.23 \tabularnewline
11 & 16.8375 & 0.0330403793359985 & 0.0700000000000003 \tabularnewline
12 & 17.0075 & 0.0340342964277695 & 0.0799999999999983 \tabularnewline
13 & 17.385 & 0.243104915622865 & 0.530000000000001 \tabularnewline
14 & 17.61 & 0.0496655480858391 & 0.110000000000003 \tabularnewline
15 & 17.785 & 0.0675771164423772 & 0.149999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150391&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]15.6775[/C][C]0.0741057802513858[/C][C]0.16[/C][/ROW]
[ROW][C]2[/C][C]15.79[/C][C]0.0182574185835061[/C][C]0.0400000000000009[/C][/ROW]
[ROW][C]3[/C][C]15.86[/C][C]0.0316227766016841[/C][C]0.0700000000000003[/C][/ROW]
[ROW][C]4[/C][C]16.0425[/C][C]0.0939414711402797[/C][C]0.209999999999999[/C][/ROW]
[ROW][C]5[/C][C]16.235[/C][C]0.0299999999999994[/C][C]0.0599999999999987[/C][/ROW]
[ROW][C]6[/C][C]16.2725[/C][C]0.0561990510002914[/C][C]0.109999999999999[/C][/ROW]
[ROW][C]7[/C][C]16.49[/C][C]0.095916630466254[/C][C]0.219999999999999[/C][/ROW]
[ROW][C]8[/C][C]16.555[/C][C]0.02081665999466[/C][C]0.0499999999999972[/C][/ROW]
[ROW][C]9[/C][C]16.505[/C][C]0.0640312423743289[/C][C]0.140000000000001[/C][/ROW]
[ROW][C]10[/C][C]16.605[/C][C]0.10785793124909[/C][C]0.23[/C][/ROW]
[ROW][C]11[/C][C]16.8375[/C][C]0.0330403793359985[/C][C]0.0700000000000003[/C][/ROW]
[ROW][C]12[/C][C]17.0075[/C][C]0.0340342964277695[/C][C]0.0799999999999983[/C][/ROW]
[ROW][C]13[/C][C]17.385[/C][C]0.243104915622865[/C][C]0.530000000000001[/C][/ROW]
[ROW][C]14[/C][C]17.61[/C][C]0.0496655480858391[/C][C]0.110000000000003[/C][/ROW]
[ROW][C]15[/C][C]17.785[/C][C]0.0675771164423772[/C][C]0.149999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150391&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150391&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	15.6775	0.0741057802513858	0.16
2	15.79	0.0182574185835061	0.0400000000000009
3	15.86	0.0316227766016841	0.0700000000000003
4	16.0425	0.0939414711402797	0.209999999999999
5	16.235	0.0299999999999994	0.0599999999999987
6	16.2725	0.0561990510002914	0.109999999999999
7	16.49	0.095916630466254	0.219999999999999
8	16.555	0.02081665999466	0.0499999999999972
9	16.505	0.0640312423743289	0.140000000000001
10	16.605	0.10785793124909	0.23
11	16.8375	0.0330403793359985	0.0700000000000003
12	17.0075	0.0340342964277695	0.0799999999999983
13	17.385	0.243104915622865	0.530000000000001
14	17.61	0.0496655480858391	0.110000000000003
15	17.785	0.0675771164423772	0.149999999999999

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-0.38492444483641
beta	0.0273228754013954
S.D.	0.0227759345770323
T-STAT	1.1996379471931
p-value	0.251687661714873

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.38492444483641 \tabularnewline
beta & 0.0273228754013954 \tabularnewline
S.D. & 0.0227759345770323 \tabularnewline
T-STAT & 1.1996379471931 \tabularnewline
p-value & 0.251687661714873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150391&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.38492444483641[/C][/ROW]
[ROW][C]beta[/C][C]0.0273228754013954[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0227759345770323[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.1996379471931[/C][/ROW]
[ROW][C]p-value[/C][C]0.251687661714873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150391&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150391&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	-0.38492444483641
beta	0.0273228754013954
S.D.	0.0227759345770323
T-STAT	1.1996379471931
p-value	0.251687661714873

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-18.033037587408
beta	5.38099380800785
S.D.	4.72609144434864
T-STAT	1.13857166569266
p-value	0.275429485947038
Lambda	-4.38099380800785

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -18.033037587408 \tabularnewline
beta & 5.38099380800785 \tabularnewline
S.D. & 4.72609144434864 \tabularnewline
T-STAT & 1.13857166569266 \tabularnewline
p-value & 0.275429485947038 \tabularnewline
Lambda & -4.38099380800785 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=150391&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-18.033037587408[/C][/ROW]
[ROW][C]beta[/C][C]5.38099380800785[/C][/ROW]
[ROW][C]S.D.[/C][C]4.72609144434864[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.13857166569266[/C][/ROW]
[ROW][C]p-value[/C][C]0.275429485947038[/C][/ROW]
[ROW][C]Lambda[/C][C]-4.38099380800785[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=150391&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=150391&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	-18.033037587408
beta	5.38099380800785
S.D.	4.72609144434864
T-STAT	1.13857166569266
p-value	0.275429485947038
Lambda	-4.38099380800785

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 4 ;

Parameters (R input):

par1 = 4 ;

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')

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code