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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, 07 Dec 2016 15:38:25 +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/07/t1481121731ad94hilw1q30i0o.htm/, Retrieved Tue, 07 May 2024 11:43:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298156, Retrieved Tue, 07 May 2024 11:43:36 +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] [N1895] [2016-12-07 14:38:25] [85f5800284aab30c091766186b093bb4] [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=298156&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=298156&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298156&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	5887.5	346.124649118309	910
2	6061.66666666667	374.695836241837	1090
3	5905	364.52958478266	1190
4	6137.5	577.409969369488	1620
5	6020.83333333333	402.570340174085	1410
6	6219.16666666667	483.067063541687	1550
7	6240	450.635914317453	1510
8	6308.33333333333	511.181044842295	1620
9	6230	425.398209168347	1390
10	6242.5	448.616560307149	1500

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 5887.5 & 346.124649118309 & 910 \tabularnewline
2 & 6061.66666666667 & 374.695836241837 & 1090 \tabularnewline
3 & 5905 & 364.52958478266 & 1190 \tabularnewline
4 & 6137.5 & 577.409969369488 & 1620 \tabularnewline
5 & 6020.83333333333 & 402.570340174085 & 1410 \tabularnewline
6 & 6219.16666666667 & 483.067063541687 & 1550 \tabularnewline
7 & 6240 & 450.635914317453 & 1510 \tabularnewline
8 & 6308.33333333333 & 511.181044842295 & 1620 \tabularnewline
9 & 6230 & 425.398209168347 & 1390 \tabularnewline
10 & 6242.5 & 448.616560307149 & 1500 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298156&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]5887.5[/C][C]346.124649118309[/C][C]910[/C][/ROW]
[ROW][C]2[/C][C]6061.66666666667[/C][C]374.695836241837[/C][C]1090[/C][/ROW]
[ROW][C]3[/C][C]5905[/C][C]364.52958478266[/C][C]1190[/C][/ROW]
[ROW][C]4[/C][C]6137.5[/C][C]577.409969369488[/C][C]1620[/C][/ROW]
[ROW][C]5[/C][C]6020.83333333333[/C][C]402.570340174085[/C][C]1410[/C][/ROW]
[ROW][C]6[/C][C]6219.16666666667[/C][C]483.067063541687[/C][C]1550[/C][/ROW]
[ROW][C]7[/C][C]6240[/C][C]450.635914317453[/C][C]1510[/C][/ROW]
[ROW][C]8[/C][C]6308.33333333333[/C][C]511.181044842295[/C][C]1620[/C][/ROW]
[ROW][C]9[/C][C]6230[/C][C]425.398209168347[/C][C]1390[/C][/ROW]
[ROW][C]10[/C][C]6242.5[/C][C]448.616560307149[/C][C]1500[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298156&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298156&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	5887.5	346.124649118309	910
2	6061.66666666667	374.695836241837	1090
3	5905	364.52958478266	1190
4	6137.5	577.409969369488	1620
5	6020.83333333333	402.570340174085	1410
6	6219.16666666667	483.067063541687	1550
7	6240	450.635914317453	1510
8	6308.33333333333	511.181044842295	1620
9	6230	425.398209168347	1390
10	6242.5	448.616560307149	1500

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-1593.44948817084
beta	0.331720730640737
S.D.	0.123141317601575
T-STAT	2.69382151418927
p-value	0.027334344706482

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1593.44948817084 \tabularnewline
beta & 0.331720730640737 \tabularnewline
S.D. & 0.123141317601575 \tabularnewline
T-STAT & 2.69382151418927 \tabularnewline
p-value & 0.027334344706482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298156&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1593.44948817084[/C][/ROW]
[ROW][C]beta[/C][C]0.331720730640737[/C][/ROW]
[ROW][C]S.D.[/C][C]0.123141317601575[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.69382151418927[/C][/ROW]
[ROW][C]p-value[/C][C]0.027334344706482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298156&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298156&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	-1593.44948817084
beta	0.331720730640737
S.D.	0.123141317601575
T-STAT	2.69382151418927
p-value	0.027334344706482

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-36.1426535343095
beta	4.84112133705313
S.D.	1.55667907873616
T-STAT	3.10990325699216
p-value	0.0144476088666701
Lambda	-3.84112133705313

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -36.1426535343095 \tabularnewline
beta & 4.84112133705313 \tabularnewline
S.D. & 1.55667907873616 \tabularnewline
T-STAT & 3.10990325699216 \tabularnewline
p-value & 0.0144476088666701 \tabularnewline
Lambda & -3.84112133705313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298156&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-36.1426535343095[/C][/ROW]
[ROW][C]beta[/C][C]4.84112133705313[/C][/ROW]
[ROW][C]S.D.[/C][C]1.55667907873616[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.10990325699216[/C][/ROW]
[ROW][C]p-value[/C][C]0.0144476088666701[/C][/ROW]
[ROW][C]Lambda[/C][C]-3.84112133705313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298156&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298156&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	-36.1426535343095
beta	4.84112133705313
S.D.	1.55667907873616
T-STAT	3.10990325699216
p-value	0.0144476088666701
Lambda	-3.84112133705313

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