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Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 10 Jan 2013 16:46:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Jan/10/t1357854446cnb8zfl0sqfp3s8.htm/, Retrieved Thu, 31 Oct 2024 23:34:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=205163, Retrieved Thu, 31 Oct 2024 23:34:16 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [] [2013-01-10 21:46:33] [44b559b455558ce8185c1073291404e7] [Current]
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Dataseries X:
98.68
99.21
99.36
100.72
102.27
102.62
102.97
102.88
102.9
103.01
103.02
103.73
104.18
103.73
103.78
103.61
103.84
103.86
104.14
104.05
104.01
104.49
104.83
104.78
104.95
105.28
105.28
105.91
106.81
106.39
107.02
106.92
107.01
106.79
107.41
107.13
107.54
108.48
108.5
108.27
109.42
110.09
109.98
109.99
109.54
108.85
106.76
107.56
106.24
108.85
111.11
111.85
110.68
106.96
106.74
105.73
105.66
104.01
106.86
108.84
110.66
106.93
103.74
101.64
102.17
101.13
100.64
100.43
99.77
99.79
99.47
99.63




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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=205163&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=205163&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=205163&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'Gertrude Mary Cox' @ cox.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1101.7808333333331.780553837910265.05
2104.1083333333330.4011083130376111.22
3106.4083333333330.8394135759654772.45999999999999
4108.7483333333331.092751600596433.33
5107.7941666666672.45341490518217.83999999999999
6102.1666666666673.4258305783437611.19

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 101.780833333333 & 1.78055383791026 & 5.05 \tabularnewline
2 & 104.108333333333 & 0.401108313037611 & 1.22 \tabularnewline
3 & 106.408333333333 & 0.839413575965477 & 2.45999999999999 \tabularnewline
4 & 108.748333333333 & 1.09275160059643 & 3.33 \tabularnewline
5 & 107.794166666667 & 2.4534149051821 & 7.83999999999999 \tabularnewline
6 & 102.166666666667 & 3.42583057834376 & 11.19 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=205163&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]101.780833333333[/C][C]1.78055383791026[/C][C]5.05[/C][/ROW]
[ROW][C]2[/C][C]104.108333333333[/C][C]0.401108313037611[/C][C]1.22[/C][/ROW]
[ROW][C]3[/C][C]106.408333333333[/C][C]0.839413575965477[/C][C]2.45999999999999[/C][/ROW]
[ROW][C]4[/C][C]108.748333333333[/C][C]1.09275160059643[/C][C]3.33[/C][/ROW]
[ROW][C]5[/C][C]107.794166666667[/C][C]2.4534149051821[/C][C]7.83999999999999[/C][/ROW]
[ROW][C]6[/C][C]102.166666666667[/C][C]3.42583057834376[/C][C]11.19[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=205163&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=205163&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
1101.7808333333331.780553837910265.05
2104.1083333333330.4011083130376111.22
3106.4083333333330.8394135759654772.45999999999999
4108.7483333333331.092751600596433.33
5107.7941666666672.45341490518217.83999999999999
6102.1666666666673.4258305783437611.19







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha14.7674341429467
beta-0.124581143432153
S.D.0.181972806298877
T-STAT-0.68461406935461
p-value0.531195996686258

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 14.7674341429467 \tabularnewline
beta & -0.124581143432153 \tabularnewline
S.D. & 0.181972806298877 \tabularnewline
T-STAT & -0.68461406935461 \tabularnewline
p-value & 0.531195996686258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=205163&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]14.7674341429467[/C][/ROW]
[ROW][C]beta[/C][C]-0.124581143432153[/C][/ROW]
[ROW][C]S.D.[/C][C]0.181972806298877[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.68461406935461[/C][/ROW]
[ROW][C]p-value[/C][C]0.531195996686258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=205163&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=205163&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)
alpha14.7674341429467
beta-0.124581143432153
S.D.0.181972806298877
T-STAT-0.68461406935461
p-value0.531195996686258







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha26.3059188476123
beta-5.58975286493004
S.D.13.7224523693304
T-STAT-0.407343579302421
p-value0.704612837702188
Lambda6.58975286493004

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 26.3059188476123 \tabularnewline
beta & -5.58975286493004 \tabularnewline
S.D. & 13.7224523693304 \tabularnewline
T-STAT & -0.407343579302421 \tabularnewline
p-value & 0.704612837702188 \tabularnewline
Lambda & 6.58975286493004 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=205163&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]26.3059188476123[/C][/ROW]
[ROW][C]beta[/C][C]-5.58975286493004[/C][/ROW]
[ROW][C]S.D.[/C][C]13.7224523693304[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.407343579302421[/C][/ROW]
[ROW][C]p-value[/C][C]0.704612837702188[/C][/ROW]
[ROW][C]Lambda[/C][C]6.58975286493004[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=205163&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=205163&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)
alpha26.3059188476123
beta-5.58975286493004
S.D.13.7224523693304
T-STAT-0.407343579302421
p-value0.704612837702188
Lambda6.58975286493004



Parameters (Session):
par1 = 50 ; par2 = 5 ; par3 = 0 ;
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')