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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

Thu, 29 Jul 2010 13:44:36 +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/Jul/29/t1280411117n6upilmipg69bjq.htm/, Retrieved Sun, 28 Apr 2024 23:45:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=78183, Retrieved Sun, 28 Apr 2024 23:45:18 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Hoes Isabelle

Estimated Impact

180

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

-       [Standard Deviation-Mean Plot] [TIJDREEKS B - STA...] [2010-07-29 13:44:36] [35611de12c9fa8a4a915f3548e0dcd01] [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' @ 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=78183&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=78183&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=78183&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	156.25	1.70782512765993	4
2	152.25	1.25830573921179	3
3	156	1.41421356237310	3
4	148.5	6.75771164423776	15
5	142.25	2.21735578260835	5
6	141.5	2.08166599946613	5
7	129.25	9.14239210126832	19
8	125	2.94392028877595	6
9	128.25	0.5	1
10	116	7.61577310586391	15
11	107	2.44948974278318	6
12	117.75	3.30403793359983	8
13	108	4.96655480858378	11
14	106.25	2.62995563967658	6
15	108.5	2.38047614284762	5
16	109.5	10.0829889748361	23
17	123.75	3.09569593683445	7
18	127.75	4.19324854180304	9
19	118.5	7.04745817062199	17
20	123.25	7.13559154286922	16
21	117	8.52447456836295	20

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 156.25 & 1.70782512765993 & 4 \tabularnewline
2 & 152.25 & 1.25830573921179 & 3 \tabularnewline
3 & 156 & 1.41421356237310 & 3 \tabularnewline
4 & 148.5 & 6.75771164423776 & 15 \tabularnewline
5 & 142.25 & 2.21735578260835 & 5 \tabularnewline
6 & 141.5 & 2.08166599946613 & 5 \tabularnewline
7 & 129.25 & 9.14239210126832 & 19 \tabularnewline
8 & 125 & 2.94392028877595 & 6 \tabularnewline
9 & 128.25 & 0.5 & 1 \tabularnewline
10 & 116 & 7.61577310586391 & 15 \tabularnewline
11 & 107 & 2.44948974278318 & 6 \tabularnewline
12 & 117.75 & 3.30403793359983 & 8 \tabularnewline
13 & 108 & 4.96655480858378 & 11 \tabularnewline
14 & 106.25 & 2.62995563967658 & 6 \tabularnewline
15 & 108.5 & 2.38047614284762 & 5 \tabularnewline
16 & 109.5 & 10.0829889748361 & 23 \tabularnewline
17 & 123.75 & 3.09569593683445 & 7 \tabularnewline
18 & 127.75 & 4.19324854180304 & 9 \tabularnewline
19 & 118.5 & 7.04745817062199 & 17 \tabularnewline
20 & 123.25 & 7.13559154286922 & 16 \tabularnewline
21 & 117 & 8.52447456836295 & 20 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=78183&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]156.25[/C][C]1.70782512765993[/C][C]4[/C][/ROW]
[ROW][C]2[/C][C]152.25[/C][C]1.25830573921179[/C][C]3[/C][/ROW]
[ROW][C]3[/C][C]156[/C][C]1.41421356237310[/C][C]3[/C][/ROW]
[ROW][C]4[/C][C]148.5[/C][C]6.75771164423776[/C][C]15[/C][/ROW]
[ROW][C]5[/C][C]142.25[/C][C]2.21735578260835[/C][C]5[/C][/ROW]
[ROW][C]6[/C][C]141.5[/C][C]2.08166599946613[/C][C]5[/C][/ROW]
[ROW][C]7[/C][C]129.25[/C][C]9.14239210126832[/C][C]19[/C][/ROW]
[ROW][C]8[/C][C]125[/C][C]2.94392028877595[/C][C]6[/C][/ROW]
[ROW][C]9[/C][C]128.25[/C][C]0.5[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]116[/C][C]7.61577310586391[/C][C]15[/C][/ROW]
[ROW][C]11[/C][C]107[/C][C]2.44948974278318[/C][C]6[/C][/ROW]
[ROW][C]12[/C][C]117.75[/C][C]3.30403793359983[/C][C]8[/C][/ROW]
[ROW][C]13[/C][C]108[/C][C]4.96655480858378[/C][C]11[/C][/ROW]
[ROW][C]14[/C][C]106.25[/C][C]2.62995563967658[/C][C]6[/C][/ROW]
[ROW][C]15[/C][C]108.5[/C][C]2.38047614284762[/C][C]5[/C][/ROW]
[ROW][C]16[/C][C]109.5[/C][C]10.0829889748361[/C][C]23[/C][/ROW]
[ROW][C]17[/C][C]123.75[/C][C]3.09569593683445[/C][C]7[/C][/ROW]
[ROW][C]18[/C][C]127.75[/C][C]4.19324854180304[/C][C]9[/C][/ROW]
[ROW][C]19[/C][C]118.5[/C][C]7.04745817062199[/C][C]17[/C][/ROW]
[ROW][C]20[/C][C]123.25[/C][C]7.13559154286922[/C][C]16[/C][/ROW]
[ROW][C]21[/C][C]117[/C][C]8.52447456836295[/C][C]20[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=78183&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=78183&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	156.25	1.70782512765993	4
2	152.25	1.25830573921179	3
3	156	1.41421356237310	3
4	148.5	6.75771164423776	15
5	142.25	2.21735578260835	5
6	141.5	2.08166599946613	5
7	129.25	9.14239210126832	19
8	125	2.94392028877595	6
9	128.25	0.5	1
10	116	7.61577310586391	15
11	107	2.44948974278318	6
12	117.75	3.30403793359983	8
13	108	4.96655480858378	11
14	106.25	2.62995563967658	6
15	108.5	2.38047614284762	5
16	109.5	10.0829889748361	23
17	123.75	3.09569593683445	7
18	127.75	4.19324854180304	9
19	118.5	7.04745817062199	17
20	123.25	7.13559154286922	16
21	117	8.52447456836295	20

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	12.3096865939571
beta	-0.062743392720682
S.D.	0.0376966620166795
T-STAT	-1.66442834362682
p-value	0.112432037552735

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 12.3096865939571 \tabularnewline
beta & -0.062743392720682 \tabularnewline
S.D. & 0.0376966620166795 \tabularnewline
T-STAT & -1.66442834362682 \tabularnewline
p-value & 0.112432037552735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=78183&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]12.3096865939571[/C][/ROW]
[ROW][C]beta[/C][C]-0.062743392720682[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0376966620166795[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.66442834362682[/C][/ROW]
[ROW][C]p-value[/C][C]0.112432037552735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=78183&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=78183&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	12.3096865939571
beta	-0.062743392720682
S.D.	0.0376966620166795
T-STAT	-1.66442834362682
p-value	0.112432037552735

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	12.7019989294224
beta	-2.37472256156021
S.D.	1.28169666173510
T-STAT	-1.85279608854206
p-value	0.0795100932997238
Lambda	3.37472256156021

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 12.7019989294224 \tabularnewline
beta & -2.37472256156021 \tabularnewline
S.D. & 1.28169666173510 \tabularnewline
T-STAT & -1.85279608854206 \tabularnewline
p-value & 0.0795100932997238 \tabularnewline
Lambda & 3.37472256156021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=78183&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]12.7019989294224[/C][/ROW]
[ROW][C]beta[/C][C]-2.37472256156021[/C][/ROW]
[ROW][C]S.D.[/C][C]1.28169666173510[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.85279608854206[/C][/ROW]
[ROW][C]p-value[/C][C]0.0795100932997238[/C][/ROW]
[ROW][C]Lambda[/C][C]3.37472256156021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=78183&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=78183&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	12.7019989294224
beta	-2.37472256156021
S.D.	1.28169666173510
T-STAT	-1.85279608854206
p-value	0.0795100932997238
Lambda	3.37472256156021

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