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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

Thu, 15 Dec 2016 21:43:25 +0100

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/Dec/15/t1481836376i0d0jglfu2inim9.htm/, Retrieved Fri, 03 May 2024 07:38:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300019, Retrieved Fri, 03 May 2024 07:38:34 +0000

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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] [std dev mean plot...] [2016-12-15 20:43:25] [2c6d1bf778a41dbfbe416644f6498149] [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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300019&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300019&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300019&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	Big Analytics Cloud Computing Center

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	4162.5	658.553856429512	2450
2	4125	593.717104351896	2100
3	4120.83333333333	607.699960855884	2200
4	4150	582.31512868112	2050
5	4279.16666666667	812.25229324317	2550
6	4837.5	738.587410965259	2700
7	4995.83333333333	801.265570926091	2900
8	4862.5	718.307670215841	2500
9	5054.16666666667	719.361564587698	2550
10	5404.16666666667	788.68657599297	2800

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 4162.5 & 658.553856429512 & 2450 \tabularnewline
2 & 4125 & 593.717104351896 & 2100 \tabularnewline
3 & 4120.83333333333 & 607.699960855884 & 2200 \tabularnewline
4 & 4150 & 582.31512868112 & 2050 \tabularnewline
5 & 4279.16666666667 & 812.25229324317 & 2550 \tabularnewline
6 & 4837.5 & 738.587410965259 & 2700 \tabularnewline
7 & 4995.83333333333 & 801.265570926091 & 2900 \tabularnewline
8 & 4862.5 & 718.307670215841 & 2500 \tabularnewline
9 & 5054.16666666667 & 719.361564587698 & 2550 \tabularnewline
10 & 5404.16666666667 & 788.68657599297 & 2800 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300019&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]4162.5[/C][C]658.553856429512[/C][C]2450[/C][/ROW]
[ROW][C]2[/C][C]4125[/C][C]593.717104351896[/C][C]2100[/C][/ROW]
[ROW][C]3[/C][C]4120.83333333333[/C][C]607.699960855884[/C][C]2200[/C][/ROW]
[ROW][C]4[/C][C]4150[/C][C]582.31512868112[/C][C]2050[/C][/ROW]
[ROW][C]5[/C][C]4279.16666666667[/C][C]812.25229324317[/C][C]2550[/C][/ROW]
[ROW][C]6[/C][C]4837.5[/C][C]738.587410965259[/C][C]2700[/C][/ROW]
[ROW][C]7[/C][C]4995.83333333333[/C][C]801.265570926091[/C][C]2900[/C][/ROW]
[ROW][C]8[/C][C]4862.5[/C][C]718.307670215841[/C][C]2500[/C][/ROW]
[ROW][C]9[/C][C]5054.16666666667[/C][C]719.361564587698[/C][C]2550[/C][/ROW]
[ROW][C]10[/C][C]5404.16666666667[/C][C]788.68657599297[/C][C]2800[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300019&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300019&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	4162.5	658.553856429512	2450
2	4125	593.717104351896	2100
3	4120.83333333333	607.699960855884	2200
4	4150	582.31512868112	2050
5	4279.16666666667	812.25229324317	2550
6	4837.5	738.587410965259	2700
7	4995.83333333333	801.265570926091	2900
8	4862.5	718.307670215841	2500
9	5054.16666666667	719.361564587698	2550
10	5404.16666666667	788.68657599297	2800

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	120.355677373583
beta	0.12648357374554
S.D.	0.0457011601918537
T-STAT	2.76762281777007
p-value	0.0243860186680859

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 120.355677373583 \tabularnewline
beta & 0.12648357374554 \tabularnewline
S.D. & 0.0457011601918537 \tabularnewline
T-STAT & 2.76762281777007 \tabularnewline
p-value & 0.0243860186680859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300019&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]120.355677373583[/C][/ROW]
[ROW][C]beta[/C][C]0.12648357374554[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0457011601918537[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.76762281777007[/C][/ROW]
[ROW][C]p-value[/C][C]0.0243860186680859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300019&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300019&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	120.355677373583
beta	0.12648357374554
S.D.	0.0457011601918537
T-STAT	2.76762281777007
p-value	0.0243860186680859

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-0.82605255974385
beta	0.874743690650776
S.D.	0.299345093304082
T-STAT	2.92219151146129
p-value	0.0192266372210364
Lambda	0.125256309349224

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.82605255974385 \tabularnewline
beta & 0.874743690650776 \tabularnewline
S.D. & 0.299345093304082 \tabularnewline
T-STAT & 2.92219151146129 \tabularnewline
p-value & 0.0192266372210364 \tabularnewline
Lambda & 0.125256309349224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300019&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.82605255974385[/C][/ROW]
[ROW][C]beta[/C][C]0.874743690650776[/C][/ROW]
[ROW][C]S.D.[/C][C]0.299345093304082[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.92219151146129[/C][/ROW]
[ROW][C]p-value[/C][C]0.0192266372210364[/C][/ROW]
[ROW][C]Lambda[/C][C]0.125256309349224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300019&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300019&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	-0.82605255974385
beta	0.874743690650776
S.D.	0.299345093304082
T-STAT	2.92219151146129
p-value	0.0192266372210364
Lambda	0.125256309349224

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')

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code