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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

Sun, 04 Aug 2013 13:23:24 -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/2013/Aug/04/t1375637139xmy2y29vlueofia.htm/, Retrieved Sat, 04 May 2024 10:36:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=210936, Retrieved Sat, 04 May 2024 10:36:37 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Ongenae Olivier

Estimated Impact

161

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

-       [Standard Deviation-Mean Plot] [TIJDREEKS B - STA...] [2013-08-04 17:23:24] [a14baeeafb42bd31c8e1f231a0a4996d] [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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210936&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210936&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210936&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	'Gwilym Jenkins' @ jenkins.wessa.net

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	1012.5	45.1512609307466	170
2	991.666666666667	59.3653301535705	180
3	1011.66666666667	80.3213243884534	300
4	1014.16666666667	128.732162636109	410
5	1028.33333333333	143.199373623143	460
6	1012.5	168.637588821817	560
7	1004.16666666667	170.798037743323	650
8	1013.33333333333	158.133047166178	570
9	1010.83333333333	178.807073956736	620

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1012.5 & 45.1512609307466 & 170 \tabularnewline
2 & 991.666666666667 & 59.3653301535705 & 180 \tabularnewline
3 & 1011.66666666667 & 80.3213243884534 & 300 \tabularnewline
4 & 1014.16666666667 & 128.732162636109 & 410 \tabularnewline
5 & 1028.33333333333 & 143.199373623143 & 460 \tabularnewline
6 & 1012.5 & 168.637588821817 & 560 \tabularnewline
7 & 1004.16666666667 & 170.798037743323 & 650 \tabularnewline
8 & 1013.33333333333 & 158.133047166178 & 570 \tabularnewline
9 & 1010.83333333333 & 178.807073956736 & 620 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210936&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]1012.5[/C][C]45.1512609307466[/C][C]170[/C][/ROW]
[ROW][C]2[/C][C]991.666666666667[/C][C]59.3653301535705[/C][C]180[/C][/ROW]
[ROW][C]3[/C][C]1011.66666666667[/C][C]80.3213243884534[/C][C]300[/C][/ROW]
[ROW][C]4[/C][C]1014.16666666667[/C][C]128.732162636109[/C][C]410[/C][/ROW]
[ROW][C]5[/C][C]1028.33333333333[/C][C]143.199373623143[/C][C]460[/C][/ROW]
[ROW][C]6[/C][C]1012.5[/C][C]168.637588821817[/C][C]560[/C][/ROW]
[ROW][C]7[/C][C]1004.16666666667[/C][C]170.798037743323[/C][C]650[/C][/ROW]
[ROW][C]8[/C][C]1013.33333333333[/C][C]158.133047166178[/C][C]570[/C][/ROW]
[ROW][C]9[/C][C]1010.83333333333[/C][C]178.807073956736[/C][C]620[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210936&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210936&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	1012.5	45.1512609307466	170
2	991.666666666667	59.3653301535705	180
3	1011.66666666667	80.3213243884534	300
4	1014.16666666667	128.732162636109	410
5	1028.33333333333	143.199373623143	460
6	1012.5	168.637588821817	560
7	1004.16666666667	170.798037743323	650
8	1013.33333333333	158.133047166178	570
9	1010.83333333333	178.807073956736	620

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	-1602.42530569515
beta	1.70949423397854
S.D.	1.90565832575977
T-STAT	0.897062296462291
p-value	0.399485240989118

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1602.42530569515 \tabularnewline
beta & 1.70949423397854 \tabularnewline
S.D. & 1.90565832575977 \tabularnewline
T-STAT & 0.897062296462291 \tabularnewline
p-value & 0.399485240989118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210936&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1602.42530569515[/C][/ROW]
[ROW][C]beta[/C][C]1.70949423397854[/C][/ROW]
[ROW][C]S.D.[/C][C]1.90565832575977[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.897062296462291[/C][/ROW]
[ROW][C]p-value[/C][C]0.399485240989118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210936&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210936&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	-1602.42530569515
beta	1.70949423397854
S.D.	1.90565832575977
T-STAT	0.897062296462291
p-value	0.399485240989118

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	-126.17630929521
beta	18.9215229763959
S.D.	18.9992741981576
T-STAT	0.995907674106353
p-value	0.352467514146263
Lambda	-17.9215229763959

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -126.17630929521 \tabularnewline
beta & 18.9215229763959 \tabularnewline
S.D. & 18.9992741981576 \tabularnewline
T-STAT & 0.995907674106353 \tabularnewline
p-value & 0.352467514146263 \tabularnewline
Lambda & -17.9215229763959 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=210936&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-126.17630929521[/C][/ROW]
[ROW][C]beta[/C][C]18.9215229763959[/C][/ROW]
[ROW][C]S.D.[/C][C]18.9992741981576[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.995907674106353[/C][/ROW]
[ROW][C]p-value[/C][C]0.352467514146263[/C][/ROW]
[ROW][C]Lambda[/C][C]-17.9215229763959[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=210936&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=210936&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	-126.17630929521
beta	18.9215229763959
S.D.	18.9992741981576
T-STAT	0.995907674106353
p-value	0.352467514146263
Lambda	-17.9215229763959

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