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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

Fri, 16 Dec 2016 20:48:00 +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/16/t1481917815wc8s25g8htd8xrh.htm/, Retrieved Thu, 02 May 2024 23:04:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300509, Retrieved Thu, 02 May 2024 23:04:09 +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] [Standard Deviatio...] [2016-12-16 19:48:00] [8dbd6448339a84ba150e9d534057ba9c] [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=300509&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=300509&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300509&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	4356.66666666667	197.038682558127	610
2	4712.5	321.84482426954	1130
3	4521.66666666667	218.375545306426	710
4	4630	281.81231665451	860
5	4832.5	386.714012252889	1180
6	5216.66666666667	214.065976802112	670
7	5134.16666666667	393.987963206384	1080
8	4997.5	423.129573964804	1310
9	5090	365.214756248729	1080
10	5362.5	338.287801086052	1090

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 4356.66666666667 & 197.038682558127 & 610 \tabularnewline
2 & 4712.5 & 321.84482426954 & 1130 \tabularnewline
3 & 4521.66666666667 & 218.375545306426 & 710 \tabularnewline
4 & 4630 & 281.81231665451 & 860 \tabularnewline
5 & 4832.5 & 386.714012252889 & 1180 \tabularnewline
6 & 5216.66666666667 & 214.065976802112 & 670 \tabularnewline
7 & 5134.16666666667 & 393.987963206384 & 1080 \tabularnewline
8 & 4997.5 & 423.129573964804 & 1310 \tabularnewline
9 & 5090 & 365.214756248729 & 1080 \tabularnewline
10 & 5362.5 & 338.287801086052 & 1090 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300509&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]4356.66666666667[/C][C]197.038682558127[/C][C]610[/C][/ROW]
[ROW][C]2[/C][C]4712.5[/C][C]321.84482426954[/C][C]1130[/C][/ROW]
[ROW][C]3[/C][C]4521.66666666667[/C][C]218.375545306426[/C][C]710[/C][/ROW]
[ROW][C]4[/C][C]4630[/C][C]281.81231665451[/C][C]860[/C][/ROW]
[ROW][C]5[/C][C]4832.5[/C][C]386.714012252889[/C][C]1180[/C][/ROW]
[ROW][C]6[/C][C]5216.66666666667[/C][C]214.065976802112[/C][C]670[/C][/ROW]
[ROW][C]7[/C][C]5134.16666666667[/C][C]393.987963206384[/C][C]1080[/C][/ROW]
[ROW][C]8[/C][C]4997.5[/C][C]423.129573964804[/C][C]1310[/C][/ROW]
[ROW][C]9[/C][C]5090[/C][C]365.214756248729[/C][C]1080[/C][/ROW]
[ROW][C]10[/C][C]5362.5[/C][C]338.287801086052[/C][C]1090[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300509&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300509&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	4356.66666666667	197.038682558127	610
2	4712.5	321.84482426954	1130
3	4521.66666666667	218.375545306426	710
4	4630	281.81231665451	860
5	4832.5	386.714012252889	1180
6	5216.66666666667	214.065976802112	670
7	5134.16666666667	393.987963206384	1080
8	4997.5	423.129573964804	1310
9	5090	365.214756248729	1080
10	5362.5	338.287801086052	1090

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-298.16116724808
beta	0.125313428567956
S.D.	0.0769052142749232
T-STAT	1.62945295386581
p-value	0.14186536600638

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -298.16116724808 \tabularnewline
beta & 0.125313428567956 \tabularnewline
S.D. & 0.0769052142749232 \tabularnewline
T-STAT & 1.62945295386581 \tabularnewline
p-value & 0.14186536600638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300509&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-298.16116724808[/C][/ROW]
[ROW][C]beta[/C][C]0.125313428567956[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0769052142749232[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.62945295386581[/C][/ROW]
[ROW][C]p-value[/C][C]0.14186536600638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300509&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300509&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	-298.16116724808
beta	0.125313428567956
S.D.	0.0769052142749232
T-STAT	1.62945295386581
p-value	0.14186536600638

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-12.811055857513
beta	2.1816826037105
S.D.	1.25133572976361
T-STAT	1.74348302523308
p-value	0.119410470814174
Lambda	-1.1816826037105

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -12.811055857513 \tabularnewline
beta & 2.1816826037105 \tabularnewline
S.D. & 1.25133572976361 \tabularnewline
T-STAT & 1.74348302523308 \tabularnewline
p-value & 0.119410470814174 \tabularnewline
Lambda & -1.1816826037105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300509&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-12.811055857513[/C][/ROW]
[ROW][C]beta[/C][C]2.1816826037105[/C][/ROW]
[ROW][C]S.D.[/C][C]1.25133572976361[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.74348302523308[/C][/ROW]
[ROW][C]p-value[/C][C]0.119410470814174[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.1816826037105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300509&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300509&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	-12.811055857513
beta	2.1816826037105
S.D.	1.25133572976361
T-STAT	1.74348302523308
p-value	0.119410470814174
Lambda	-1.1816826037105

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 <- '12'
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