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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 17 Dec 2009 09:27:45 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/17/t12610677448lvvgfaunq7jk69.htm/, Retrieved Tue, 30 Apr 2024 05:37:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68978, Retrieved Tue, 30 Apr 2024 05:37:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Scatterplot prijs...] [2009-12-12 17:13:39] [8733f8ed033058987ec00f5e71b74854]
- RMP     [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-12-17 16:27:45] [c6e373ff11c42d4585d53e9e88ed5606] [Current]
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Dataseries X:
96.8
87.0
96.3
107.1
115.2
106.1
89.5
91.3
97.6
100.7
104.6
94.7
101.8
102.5
105.3
110.3
109.8
117.3
118.8
131.3
125.9
133.1
147.0
145.8
164.4
149.8
137.7
151.7
156.8
180.0
180.4
170.4
191.6
199.5
218.2
217.5
205.0
194.0
199.3
219.3
211.1
215.2
240.2
242.2
240.7
255.4
253.0
218.2
203.7
205.6
215.6
188.5
202.9
214.0
230.3
230.0
241.0
259.6
247.8
270.3
289.7
322.7
315.0
320.2
329.5
360.6
382.2
435.4
464.0
468.8
403.0
351.6
252.0
188.0
146.5
152.9
148.1
165.1
177.0
206.1
244.9
228.6
253.4
241.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68978&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68978&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68978&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
198.90833333333338.194949479386528.2
2120.74166666666715.904799637692845.2
3176.526.30146902222980.5
4224.46666666666721.035871239097261.4
5225.77525.101688642943481.8
6370.22560.5800916143249179.1
7200.30833333333342.4050695582783106.9

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 98.9083333333333 & 8.1949494793865 & 28.2 \tabularnewline
2 & 120.741666666667 & 15.9047996376928 & 45.2 \tabularnewline
3 & 176.5 & 26.301469022229 & 80.5 \tabularnewline
4 & 224.466666666667 & 21.0358712390972 & 61.4 \tabularnewline
5 & 225.775 & 25.1016886429434 & 81.8 \tabularnewline
6 & 370.225 & 60.5800916143249 & 179.1 \tabularnewline
7 & 200.308333333333 & 42.4050695582783 & 106.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68978&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]98.9083333333333[/C][C]8.1949494793865[/C][C]28.2[/C][/ROW]
[ROW][C]2[/C][C]120.741666666667[/C][C]15.9047996376928[/C][C]45.2[/C][/ROW]
[ROW][C]3[/C][C]176.5[/C][C]26.301469022229[/C][C]80.5[/C][/ROW]
[ROW][C]4[/C][C]224.466666666667[/C][C]21.0358712390972[/C][C]61.4[/C][/ROW]
[ROW][C]5[/C][C]225.775[/C][C]25.1016886429434[/C][C]81.8[/C][/ROW]
[ROW][C]6[/C][C]370.225[/C][C]60.5800916143249[/C][C]179.1[/C][/ROW]
[ROW][C]7[/C][C]200.308333333333[/C][C]42.4050695582783[/C][C]106.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68978&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68978&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
SectionMeanStandard DeviationRange
198.90833333333338.194949479386528.2
2120.74166666666715.904799637692845.2
3176.526.30146902222980.5
4224.46666666666721.035871239097261.4
5225.77525.101688642943481.8
6370.22560.5800916143249179.1
7200.30833333333342.4050695582783106.9







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-7.04093320532496
beta0.17559890017554
S.D.0.0415807892601534
T-STAT4.22307761107880
p-value0.008303232229064

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -7.04093320532496 \tabularnewline
beta & 0.17559890017554 \tabularnewline
S.D. & 0.0415807892601534 \tabularnewline
T-STAT & 4.22307761107880 \tabularnewline
p-value & 0.008303232229064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68978&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-7.04093320532496[/C][/ROW]
[ROW][C]beta[/C][C]0.17559890017554[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0415807892601534[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.22307761107880[/C][/ROW]
[ROW][C]p-value[/C][C]0.008303232229064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68978&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68978&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-7.04093320532496
beta0.17559890017554
S.D.0.0415807892601534
T-STAT4.22307761107880
p-value0.008303232229064







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.7675224700593
beta1.32846057567978
S.D.0.302080312085599
T-STAT4.39770657845236
p-value0.00703694532944218
Lambda-0.328460575679782

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.7675224700593 \tabularnewline
beta & 1.32846057567978 \tabularnewline
S.D. & 0.302080312085599 \tabularnewline
T-STAT & 4.39770657845236 \tabularnewline
p-value & 0.00703694532944218 \tabularnewline
Lambda & -0.328460575679782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68978&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.7675224700593[/C][/ROW]
[ROW][C]beta[/C][C]1.32846057567978[/C][/ROW]
[ROW][C]S.D.[/C][C]0.302080312085599[/C][/ROW]
[ROW][C]T-STAT[/C][C]4.39770657845236[/C][/ROW]
[ROW][C]p-value[/C][C]0.00703694532944218[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.328460575679782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68978&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68978&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-3.7675224700593
beta1.32846057567978
S.D.0.302080312085599
T-STAT4.39770657845236
p-value0.00703694532944218
Lambda-0.328460575679782



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')