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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

Mon, 19 Dec 2016 14:35:43 +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/19/t1482154606etcf6nldxcidcae.htm/, Retrieved Fri, 01 Nov 2024 03:36:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301350, Retrieved Fri, 01 Nov 2024 03:36:35 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

100

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

-       [Standard Deviation-Mean Plot] [Standard Deviatio...] [2016-12-19 13:35:43] [6fe662842930c5949e61d44eeb8a265b] [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=301350&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=301350&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301350&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	4568.91666666667	230.758613877766	796
2	4947.41666666667	263.123012480797	778
3	5639.41666666667	257.904654099263	1012
4	6171	175.75292574831	562
5	6451.41666666667	241.226961531453	687
6	6900.66666666667	240.000126262593	845
7	6992.41666666667	217.308937327539	605
8	7360.83333333333	280.501769021592	804
9	8258.08333333333	454.259328884711	1721
10	8512.41666666667	386.298155770274	1313

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 4568.91666666667 & 230.758613877766 & 796 \tabularnewline
2 & 4947.41666666667 & 263.123012480797 & 778 \tabularnewline
3 & 5639.41666666667 & 257.904654099263 & 1012 \tabularnewline
4 & 6171 & 175.75292574831 & 562 \tabularnewline
5 & 6451.41666666667 & 241.226961531453 & 687 \tabularnewline
6 & 6900.66666666667 & 240.000126262593 & 845 \tabularnewline
7 & 6992.41666666667 & 217.308937327539 & 605 \tabularnewline
8 & 7360.83333333333 & 280.501769021592 & 804 \tabularnewline
9 & 8258.08333333333 & 454.259328884711 & 1721 \tabularnewline
10 & 8512.41666666667 & 386.298155770274 & 1313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301350&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]4568.91666666667[/C][C]230.758613877766[/C][C]796[/C][/ROW]
[ROW][C]2[/C][C]4947.41666666667[/C][C]263.123012480797[/C][C]778[/C][/ROW]
[ROW][C]3[/C][C]5639.41666666667[/C][C]257.904654099263[/C][C]1012[/C][/ROW]
[ROW][C]4[/C][C]6171[/C][C]175.75292574831[/C][C]562[/C][/ROW]
[ROW][C]5[/C][C]6451.41666666667[/C][C]241.226961531453[/C][C]687[/C][/ROW]
[ROW][C]6[/C][C]6900.66666666667[/C][C]240.000126262593[/C][C]845[/C][/ROW]
[ROW][C]7[/C][C]6992.41666666667[/C][C]217.308937327539[/C][C]605[/C][/ROW]
[ROW][C]8[/C][C]7360.83333333333[/C][C]280.501769021592[/C][C]804[/C][/ROW]
[ROW][C]9[/C][C]8258.08333333333[/C][C]454.259328884711[/C][C]1721[/C][/ROW]
[ROW][C]10[/C][C]8512.41666666667[/C][C]386.298155770274[/C][C]1313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301350&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301350&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	4568.91666666667	230.758613877766	796
2	4947.41666666667	263.123012480797	778
3	5639.41666666667	257.904654099263	1012
4	6171	175.75292574831	562
5	6451.41666666667	241.226961531453	687
6	6900.66666666667	240.000126262593	845
7	6992.41666666667	217.308937327539	605
8	7360.83333333333	280.501769021592	804
9	8258.08333333333	454.259328884711	1721
10	8512.41666666667	386.298155770274	1313

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-8.43971350269259
beta	0.0430307060391179
S.D.	0.0167936666433678
T-STAT	2.56231750652926
p-value	0.033525864915068

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -8.43971350269259 \tabularnewline
beta & 0.0430307060391179 \tabularnewline
S.D. & 0.0167936666433678 \tabularnewline
T-STAT & 2.56231750652926 \tabularnewline
p-value & 0.033525864915068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301350&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-8.43971350269259[/C][/ROW]
[ROW][C]beta[/C][C]0.0430307060391179[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0167936666433678[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.56231750652926[/C][/ROW]
[ROW][C]p-value[/C][C]0.033525864915068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301350&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301350&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	-8.43971350269259
beta	0.0430307060391179
S.D.	0.0167936666433678
T-STAT	2.56231750652926
p-value	0.033525864915068

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-1.28297098940196
beta	0.78218442597205
S.D.	0.38794007239389
T-STAT	2.0162506573383
p-value	0.0785137210489004
Lambda	0.21781557402795

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.28297098940196 \tabularnewline
beta & 0.78218442597205 \tabularnewline
S.D. & 0.38794007239389 \tabularnewline
T-STAT & 2.0162506573383 \tabularnewline
p-value & 0.0785137210489004 \tabularnewline
Lambda & 0.21781557402795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301350&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.28297098940196[/C][/ROW]
[ROW][C]beta[/C][C]0.78218442597205[/C][/ROW]
[ROW][C]S.D.[/C][C]0.38794007239389[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.0162506573383[/C][/ROW]
[ROW][C]p-value[/C][C]0.0785137210489004[/C][/ROW]
[ROW][C]Lambda[/C][C]0.21781557402795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301350&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301350&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	-1.28297098940196
beta	0.78218442597205
S.D.	0.38794007239389
T-STAT	2.0162506573383
p-value	0.0785137210489004
Lambda	0.21781557402795

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code