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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 computationWed, 28 Nov 2012 09:41:45 -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/2012/Nov/28/t1354113731babtdpxf366ngur.htm/, Retrieved Fri, 29 Mar 2024 09:19:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=194166, Retrieved Fri, 29 Mar 2024 09:19:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [paper deel SA d=1] [2012-11-28 14:35:06] [d78b9afa8f7e4cb23f8a65a6f0918ac0]
- RMP     [Standard Deviation-Mean Plot] [paper deel 4: SMP] [2012-11-28 14:41:45] [4e0a07d67ff6ab1ee99ce2372e43edac] [Current]
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Dataseries X:
369.07
369.32
370.38
371.63
371.32
371.51
369.69
368.18
366.87
366.94
368.27
369.62
370.47
371.44
372.39
373.32
373.77
373.13
371.51
369.59
368.12
368.38
369.64
371.11
372.38
373.08
373.87
374.93
375.58
375.44
373.91
371.77
370.72
370.5
372.19
373.71
374.92
375.63
376.51
377.75
378.54
378.21
376.65
374.28
373.12
373.1
374.67
375.97
377.03
377.87
378.88
380.42
380.62
379.66
377.48
376.07
374.1
374.47
376.15
377.51
378.43
379.7
380.91
382.2
382.45
382.14
380.6
378.6
376.72
376.98
378.29
380.07
381.36
382.19
382.65
384.65
384.94
384.01
382.15
380.33
378.81
379.06
380.17
381.85
382.88
383.77
384.42
386.36
386.53
386.01
384.45
381.96
380.81
381.09
382.37
383.84
385.42
385.72
385.96
387.18
388.5
387.88
386.38
384.15
383.07
382.98
384.11
385.54
386.92
387.41
388.77
389.46
390.18
389.43
387.74
385.91
384.77
384.38
385.99
387.26
388.45
389.7
391.08
392.46
392.96
392.03
390.13
388.15
386.8
387.18
388.59




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=194166&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=194166&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194166&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
1369.41.638663535268354.75999999999999
2371.07251.887866159547245.64999999999998
3373.1733333333331.707514585766715.07999999999998
4375.7791666666671.839750271261315.44
5377.5216666666672.127562982011176.51999999999998
6379.75751.982359131760115.72999999999996
7381.84752.029281668696856.13
8383.70751.957698672142655.71999999999997
9385.5741666666671.770287384647595.51999999999998
10387.3516666666671.872115154448345.80000000000001

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 369.4 & 1.63866353526835 & 4.75999999999999 \tabularnewline
2 & 371.0725 & 1.88786615954724 & 5.64999999999998 \tabularnewline
3 & 373.173333333333 & 1.70751458576671 & 5.07999999999998 \tabularnewline
4 & 375.779166666667 & 1.83975027126131 & 5.44 \tabularnewline
5 & 377.521666666667 & 2.12756298201117 & 6.51999999999998 \tabularnewline
6 & 379.7575 & 1.98235913176011 & 5.72999999999996 \tabularnewline
7 & 381.8475 & 2.02928166869685 & 6.13 \tabularnewline
8 & 383.7075 & 1.95769867214265 & 5.71999999999997 \tabularnewline
9 & 385.574166666667 & 1.77028738464759 & 5.51999999999998 \tabularnewline
10 & 387.351666666667 & 1.87211515444834 & 5.80000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=194166&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]369.4[/C][C]1.63866353526835[/C][C]4.75999999999999[/C][/ROW]
[ROW][C]2[/C][C]371.0725[/C][C]1.88786615954724[/C][C]5.64999999999998[/C][/ROW]
[ROW][C]3[/C][C]373.173333333333[/C][C]1.70751458576671[/C][C]5.07999999999998[/C][/ROW]
[ROW][C]4[/C][C]375.779166666667[/C][C]1.83975027126131[/C][C]5.44[/C][/ROW]
[ROW][C]5[/C][C]377.521666666667[/C][C]2.12756298201117[/C][C]6.51999999999998[/C][/ROW]
[ROW][C]6[/C][C]379.7575[/C][C]1.98235913176011[/C][C]5.72999999999996[/C][/ROW]
[ROW][C]7[/C][C]381.8475[/C][C]2.02928166869685[/C][C]6.13[/C][/ROW]
[ROW][C]8[/C][C]383.7075[/C][C]1.95769867214265[/C][C]5.71999999999997[/C][/ROW]
[ROW][C]9[/C][C]385.574166666667[/C][C]1.77028738464759[/C][C]5.51999999999998[/C][/ROW]
[ROW][C]10[/C][C]387.351666666667[/C][C]1.87211515444834[/C][C]5.80000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=194166&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194166&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
1369.41.638663535268354.75999999999999
2371.07251.887866159547245.64999999999998
3373.1733333333331.707514585766715.07999999999998
4375.7791666666671.839750271261315.44
5377.5216666666672.127562982011176.51999999999998
6379.75751.982359131760115.72999999999996
7381.84752.029281668696856.13
8383.70751.957698672142655.71999999999997
9385.5741666666671.770287384647595.51999999999998
10387.3516666666671.872115154448345.80000000000001







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-1.55143770767423
beta0.00906890326953442
S.D.0.00795463717033457
T-STAT1.1400775516645
p-value0.287234734268755

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.55143770767423 \tabularnewline
beta & 0.00906890326953442 \tabularnewline
S.D. & 0.00795463717033457 \tabularnewline
T-STAT & 1.1400775516645 \tabularnewline
p-value & 0.287234734268755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=194166&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.55143770767423[/C][/ROW]
[ROW][C]beta[/C][C]0.00906890326953442[/C][/ROW]
[ROW][C]S.D.[/C][C]0.00795463717033457[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.1400775516645[/C][/ROW]
[ROW][C]p-value[/C][C]0.287234734268755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=194166&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194166&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-1.55143770767423
beta0.00906890326953442
S.D.0.00795463717033457
T-STAT1.1400775516645
p-value0.287234734268755







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-10.8873318633994
beta1.94005098913769
S.D.1.59486394153356
T-STAT1.21643667438629
p-value0.258487697653889
Lambda-0.940050989137687

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -10.8873318633994 \tabularnewline
beta & 1.94005098913769 \tabularnewline
S.D. & 1.59486394153356 \tabularnewline
T-STAT & 1.21643667438629 \tabularnewline
p-value & 0.258487697653889 \tabularnewline
Lambda & -0.940050989137687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=194166&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-10.8873318633994[/C][/ROW]
[ROW][C]beta[/C][C]1.94005098913769[/C][/ROW]
[ROW][C]S.D.[/C][C]1.59486394153356[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.21643667438629[/C][/ROW]
[ROW][C]p-value[/C][C]0.258487697653889[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.940050989137687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=194166&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=194166&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-10.8873318633994
beta1.94005098913769
S.D.1.59486394153356
T-STAT1.21643667438629
p-value0.258487697653889
Lambda-0.940050989137687



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')