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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

Wed, 30 Nov 2011 16:51:43 -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/Nov/30/t1322690107ppts0kmpmk691n4.htm/, Retrieved Fri, 19 Apr 2024 23:33:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=149161, Retrieved Fri, 19 Apr 2024 23:33:53 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W83

Estimated Impact

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

-       [Standard Deviation-Mean Plot] [ Spreidingsgrafie...] [2011-11-30 21:51:43] [08867764ea6bb9e6f4e56ad4eb4305bb] [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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149161&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149161&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149161&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	'Gertrude Mary Cox' @ cox.wessa.net

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	117.1	2.96085573216033	6.7
2	106.225	4.79261584801731	10.4
3	98.175	2.33719917850405	5.7
4	97.075	1.60286202358989	3.10000000000001
5	99.625	4.91350180624776	9.89999999999999
6	103.425	0.94295634398771	2.2
7	101.15	5.1526692111953	12.3
8	104.95	2.75741424768448	5.30000000000001
9	108.2	1.57056253191863	3.39999999999999
10	109.775	1.38894444333338	3.2
11	110.4	4.70814896394184	10.9
12	117.825	2.74271763038049	5.8
13	115.35	3.5939764421413	8
14	104.325	2.73175767592955	5.80000000000001
15	101.95	1.7464249196573	3.59999999999999
16	106.075	2.13287755547914	4.90000000000001
17	91.225	2.18536800867345	5
18	92.975	1.15289490703475	2.7

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 117.1 & 2.96085573216033 & 6.7 \tabularnewline
2 & 106.225 & 4.79261584801731 & 10.4 \tabularnewline
3 & 98.175 & 2.33719917850405 & 5.7 \tabularnewline
4 & 97.075 & 1.60286202358989 & 3.10000000000001 \tabularnewline
5 & 99.625 & 4.91350180624776 & 9.89999999999999 \tabularnewline
6 & 103.425 & 0.94295634398771 & 2.2 \tabularnewline
7 & 101.15 & 5.1526692111953 & 12.3 \tabularnewline
8 & 104.95 & 2.75741424768448 & 5.30000000000001 \tabularnewline
9 & 108.2 & 1.57056253191863 & 3.39999999999999 \tabularnewline
10 & 109.775 & 1.38894444333338 & 3.2 \tabularnewline
11 & 110.4 & 4.70814896394184 & 10.9 \tabularnewline
12 & 117.825 & 2.74271763038049 & 5.8 \tabularnewline
13 & 115.35 & 3.5939764421413 & 8 \tabularnewline
14 & 104.325 & 2.73175767592955 & 5.80000000000001 \tabularnewline
15 & 101.95 & 1.7464249196573 & 3.59999999999999 \tabularnewline
16 & 106.075 & 2.13287755547914 & 4.90000000000001 \tabularnewline
17 & 91.225 & 2.18536800867345 & 5 \tabularnewline
18 & 92.975 & 1.15289490703475 & 2.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149161&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]117.1[/C][C]2.96085573216033[/C][C]6.7[/C][/ROW]
[ROW][C]2[/C][C]106.225[/C][C]4.79261584801731[/C][C]10.4[/C][/ROW]
[ROW][C]3[/C][C]98.175[/C][C]2.33719917850405[/C][C]5.7[/C][/ROW]
[ROW][C]4[/C][C]97.075[/C][C]1.60286202358989[/C][C]3.10000000000001[/C][/ROW]
[ROW][C]5[/C][C]99.625[/C][C]4.91350180624776[/C][C]9.89999999999999[/C][/ROW]
[ROW][C]6[/C][C]103.425[/C][C]0.94295634398771[/C][C]2.2[/C][/ROW]
[ROW][C]7[/C][C]101.15[/C][C]5.1526692111953[/C][C]12.3[/C][/ROW]
[ROW][C]8[/C][C]104.95[/C][C]2.75741424768448[/C][C]5.30000000000001[/C][/ROW]
[ROW][C]9[/C][C]108.2[/C][C]1.57056253191863[/C][C]3.39999999999999[/C][/ROW]
[ROW][C]10[/C][C]109.775[/C][C]1.38894444333338[/C][C]3.2[/C][/ROW]
[ROW][C]11[/C][C]110.4[/C][C]4.70814896394184[/C][C]10.9[/C][/ROW]
[ROW][C]12[/C][C]117.825[/C][C]2.74271763038049[/C][C]5.8[/C][/ROW]
[ROW][C]13[/C][C]115.35[/C][C]3.5939764421413[/C][C]8[/C][/ROW]
[ROW][C]14[/C][C]104.325[/C][C]2.73175767592955[/C][C]5.80000000000001[/C][/ROW]
[ROW][C]15[/C][C]101.95[/C][C]1.7464249196573[/C][C]3.59999999999999[/C][/ROW]
[ROW][C]16[/C][C]106.075[/C][C]2.13287755547914[/C][C]4.90000000000001[/C][/ROW]
[ROW][C]17[/C][C]91.225[/C][C]2.18536800867345[/C][C]5[/C][/ROW]
[ROW][C]18[/C][C]92.975[/C][C]1.15289490703475[/C][C]2.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149161&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149161&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	117.1	2.96085573216033	6.7
2	106.225	4.79261584801731	10.4
3	98.175	2.33719917850405	5.7
4	97.075	1.60286202358989	3.10000000000001
5	99.625	4.91350180624776	9.89999999999999
6	103.425	0.94295634398771	2.2
7	101.15	5.1526692111953	12.3
8	104.95	2.75741424768448	5.30000000000001
9	108.2	1.57056253191863	3.39999999999999
10	109.775	1.38894444333338	3.2
11	110.4	4.70814896394184	10.9
12	117.825	2.74271763038049	5.8
13	115.35	3.5939764421413	8
14	104.325	2.73175767592955	5.80000000000001
15	101.95	1.7464249196573	3.59999999999999
16	106.075	2.13287755547914	4.90000000000001
17	91.225	2.18536800867345	5
18	92.975	1.15289490703475	2.7

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-1.22993552029765
beta	0.0379423259503053
S.D.	0.0437466423947709
T-STAT	0.867319727258442
p-value	0.398588252329449

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.22993552029765 \tabularnewline
beta & 0.0379423259503053 \tabularnewline
S.D. & 0.0437466423947709 \tabularnewline
T-STAT & 0.867319727258442 \tabularnewline
p-value & 0.398588252329449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149161&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.22993552029765[/C][/ROW]
[ROW][C]beta[/C][C]0.0379423259503053[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0437466423947709[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.867319727258442[/C][/ROW]
[ROW][C]p-value[/C][C]0.398588252329449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149161&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149161&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	-1.22993552029765
beta	0.0379423259503053
S.D.	0.0437466423947709
T-STAT	0.867319727258442
p-value	0.398588252329449

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-8.08089647335336
beta	1.92955497644982
S.D.	1.69530295450973
T-STAT	1.13817708588129
p-value	0.271803143705655
Lambda	-0.929554976449823

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -8.08089647335336 \tabularnewline
beta & 1.92955497644982 \tabularnewline
S.D. & 1.69530295450973 \tabularnewline
T-STAT & 1.13817708588129 \tabularnewline
p-value & 0.271803143705655 \tabularnewline
Lambda & -0.929554976449823 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=149161&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-8.08089647335336[/C][/ROW]
[ROW][C]beta[/C][C]1.92955497644982[/C][/ROW]
[ROW][C]S.D.[/C][C]1.69530295450973[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.13817708588129[/C][/ROW]
[ROW][C]p-value[/C][C]0.271803143705655[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.929554976449823[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=149161&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=149161&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	-8.08089647335336
beta	1.92955497644982
S.D.	1.69530295450973
T-STAT	1.13817708588129
p-value	0.271803143705655
Lambda	-0.929554976449823

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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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code