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Author's title

Author

*Unverified author*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Thu, 18 Aug 2011 12:48:50 -0400

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/2011/Aug/18/t1313686221plln0hnu5asfm2h.htm/, Retrieved Wed, 15 May 2024 18:39:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=124122, Retrieved Wed, 15 May 2024 18:39:12 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

mattias debbaut

Estimated Impact

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

-       [Standard Deviation-Mean Plot] [tijdreeks B - SD ...] [2011-08-18 16:48:50] [d41d8cd98f00b204e9800998ecf8427e] [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	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=124122&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=124122&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=124122&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	'Gertrude Mary Cox' @ cox.wessa.net

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	519.166666666667	47.5697255392417	150
2	512.5	75.0908540611729	260
3	495.833333333333	97.6504278567486	300
4	503.333333333333	98.8417774511212	270
5	488.333333333333	119.683927175884	370
6	474.166666666667	140.612836174261	420
7	482.5	144.481832767999	400
8	490.833333333333	159.513939734736	460
9	482.5	138.046434875437	430

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 519.166666666667 & 47.5697255392417 & 150 \tabularnewline
2 & 512.5 & 75.0908540611729 & 260 \tabularnewline
3 & 495.833333333333 & 97.6504278567486 & 300 \tabularnewline
4 & 503.333333333333 & 98.8417774511212 & 270 \tabularnewline
5 & 488.333333333333 & 119.683927175884 & 370 \tabularnewline
6 & 474.166666666667 & 140.612836174261 & 420 \tabularnewline
7 & 482.5 & 144.481832767999 & 400 \tabularnewline
8 & 490.833333333333 & 159.513939734736 & 460 \tabularnewline
9 & 482.5 & 138.046434875437 & 430 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=124122&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]519.166666666667[/C][C]47.5697255392417[/C][C]150[/C][/ROW]
[ROW][C]2[/C][C]512.5[/C][C]75.0908540611729[/C][C]260[/C][/ROW]
[ROW][C]3[/C][C]495.833333333333[/C][C]97.6504278567486[/C][C]300[/C][/ROW]
[ROW][C]4[/C][C]503.333333333333[/C][C]98.8417774511212[/C][C]270[/C][/ROW]
[ROW][C]5[/C][C]488.333333333333[/C][C]119.683927175884[/C][C]370[/C][/ROW]
[ROW][C]6[/C][C]474.166666666667[/C][C]140.612836174261[/C][C]420[/C][/ROW]
[ROW][C]7[/C][C]482.5[/C][C]144.481832767999[/C][C]400[/C][/ROW]
[ROW][C]8[/C][C]490.833333333333[/C][C]159.513939734736[/C][C]460[/C][/ROW]
[ROW][C]9[/C][C]482.5[/C][C]138.046434875437[/C][C]430[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=124122&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=124122&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	519.166666666667	47.5697255392417	150
2	512.5	75.0908540611729	260
3	495.833333333333	97.6504278567486	300
4	503.333333333333	98.8417774511212	270
5	488.333333333333	119.683927175884	370
6	474.166666666667	140.612836174261	420
7	482.5	144.481832767999	400
8	490.833333333333	159.513939734736	460
9	482.5	138.046434875437	430

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	1203.82172550123
beta	-2.20555994168372
S.D.	0.417690716529091
T-STAT	-5.28036620016694
p-value	0.00114759572427363

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 1203.82172550123 \tabularnewline
beta & -2.20555994168372 \tabularnewline
S.D. & 0.417690716529091 \tabularnewline
T-STAT & -5.28036620016694 \tabularnewline
p-value & 0.00114759572427363 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=124122&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1203.82172550123[/C][/ROW]
[ROW][C]beta[/C][C]-2.20555994168372[/C][/ROW]
[ROW][C]S.D.[/C][C]0.417690716529091[/C][/ROW]
[ROW][C]T-STAT[/C][C]-5.28036620016694[/C][/ROW]
[ROW][C]p-value[/C][C]0.00114759572427363[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=124122&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=124122&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	1203.82172550123
beta	-2.20555994168372
S.D.	0.417690716529091
T-STAT	-5.28036620016694
p-value	0.00114759572427363

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	76.927561874508
beta	-11.6485630450876
S.D.	2.14002186028518
T-STAT	-5.44319815664653
p-value	0.000962955024760464
Lambda	12.6485630450876

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 76.927561874508 \tabularnewline
beta & -11.6485630450876 \tabularnewline
S.D. & 2.14002186028518 \tabularnewline
T-STAT & -5.44319815664653 \tabularnewline
p-value & 0.000962955024760464 \tabularnewline
Lambda & 12.6485630450876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=124122&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]76.927561874508[/C][/ROW]
[ROW][C]beta[/C][C]-11.6485630450876[/C][/ROW]
[ROW][C]S.D.[/C][C]2.14002186028518[/C][/ROW]
[ROW][C]T-STAT[/C][C]-5.44319815664653[/C][/ROW]
[ROW][C]p-value[/C][C]0.000962955024760464[/C][/ROW]
[ROW][C]Lambda[/C][C]12.6485630450876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=124122&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=124122&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	76.927561874508
beta	-11.6485630450876
S.D.	2.14002186028518
T-STAT	-5.44319815664653
p-value	0.000962955024760464
Lambda	12.6485630450876

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