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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

Wed, 14 Dec 2016 09:55:07 +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/14/t148170594883igcimlefz1r23.htm/, Retrieved Fri, 01 Nov 2024 03:41:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299266, Retrieved Fri, 01 Nov 2024 03:41:17 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

141

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

-       [Standard Deviation-Mean Plot] [lambda] [2016-12-14 08:55:07] [037fdaa34a77b5f63489b3bcd360a80c] [Current]

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Boxplots

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=299266&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=299266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299266&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	3839.58333333333	228.885226630504	715
2	4420	135.344677834711	465
3	4589.58333333333	90.7658910037078	320
4	4695.41666666667	84.2199051545561	265
5	4999.58333333333	156.21166196329	565
6	5330	128.363829513118	415
7	5428.33333333333	103.88659317941	350
8	5371.25	148.034470801414	440
9	5192.91666666667	130.531402236002	355

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 3839.58333333333 & 228.885226630504 & 715 \tabularnewline
2 & 4420 & 135.344677834711 & 465 \tabularnewline
3 & 4589.58333333333 & 90.7658910037078 & 320 \tabularnewline
4 & 4695.41666666667 & 84.2199051545561 & 265 \tabularnewline
5 & 4999.58333333333 & 156.21166196329 & 565 \tabularnewline
6 & 5330 & 128.363829513118 & 415 \tabularnewline
7 & 5428.33333333333 & 103.88659317941 & 350 \tabularnewline
8 & 5371.25 & 148.034470801414 & 440 \tabularnewline
9 & 5192.91666666667 & 130.531402236002 & 355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299266&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]3839.58333333333[/C][C]228.885226630504[/C][C]715[/C][/ROW]
[ROW][C]2[/C][C]4420[/C][C]135.344677834711[/C][C]465[/C][/ROW]
[ROW][C]3[/C][C]4589.58333333333[/C][C]90.7658910037078[/C][C]320[/C][/ROW]
[ROW][C]4[/C][C]4695.41666666667[/C][C]84.2199051545561[/C][C]265[/C][/ROW]
[ROW][C]5[/C][C]4999.58333333333[/C][C]156.21166196329[/C][C]565[/C][/ROW]
[ROW][C]6[/C][C]5330[/C][C]128.363829513118[/C][C]415[/C][/ROW]
[ROW][C]7[/C][C]5428.33333333333[/C][C]103.88659317941[/C][C]350[/C][/ROW]
[ROW][C]8[/C][C]5371.25[/C][C]148.034470801414[/C][C]440[/C][/ROW]
[ROW][C]9[/C][C]5192.91666666667[/C][C]130.531402236002[/C][C]355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299266&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	3839.58333333333	228.885226630504	715
2	4420	135.344677834711	465
3	4589.58333333333	90.7658910037078	320
4	4695.41666666667	84.2199051545561	265
5	4999.58333333333	156.21166196329	565
6	5330	128.363829513118	415
7	5428.33333333333	103.88659317941	350
8	5371.25	148.034470801414	440
9	5192.91666666667	130.531402236002	355

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	323.449736990943
beta	-0.0388633124909219
S.D.	0.0269863561508819
T-STAT	-1.44010967148123
p-value	0.193021066572816

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 323.449736990943 \tabularnewline
beta & -0.0388633124909219 \tabularnewline
S.D. & 0.0269863561508819 \tabularnewline
T-STAT & -1.44010967148123 \tabularnewline
p-value & 0.193021066572816 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299266&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]323.449736990943[/C][/ROW]
[ROW][C]beta[/C][C]-0.0388633124909219[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0269863561508819[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.44010967148123[/C][/ROW]
[ROW][C]p-value[/C][C]0.193021066572816[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299266&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299266&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	323.449736990943
beta	-0.0388633124909219
S.D.	0.0269863561508819
T-STAT	-1.44010967148123
p-value	0.193021066572816

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	13.7075639976034
beta	-1.04311007739372
S.D.	0.922466068484617
T-STAT	-1.13078422397399
p-value	0.295395508947134
Lambda	2.04311007739372

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 13.7075639976034 \tabularnewline
beta & -1.04311007739372 \tabularnewline
S.D. & 0.922466068484617 \tabularnewline
T-STAT & -1.13078422397399 \tabularnewline
p-value & 0.295395508947134 \tabularnewline
Lambda & 2.04311007739372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299266&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]13.7075639976034[/C][/ROW]
[ROW][C]beta[/C][C]-1.04311007739372[/C][/ROW]
[ROW][C]S.D.[/C][C]0.922466068484617[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.13078422397399[/C][/ROW]
[ROW][C]p-value[/C][C]0.295395508947134[/C][/ROW]
[ROW][C]Lambda[/C][C]2.04311007739372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299266&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299266&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	13.7075639976034
beta	-1.04311007739372
S.D.	0.922466068484617
T-STAT	-1.13078422397399
p-value	0.295395508947134
Lambda	2.04311007739372

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code