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Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 05 Dec 2013 04:39:16 -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/Dec/05/t1386236396fmhkyzdig5s5o8a.htm/, Retrieved Fri, 29 Mar 2024 11:58:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230892, Retrieved Fri, 29 Mar 2024 11:58:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [spreiding] [2013-12-05 09:39:16] [14b1e901e86f0e99d3e5ae27817fa672] [Current]
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Dataseries X:
82.81
83.42
83.45
83.71
84.8
85.95
86.22
86.75
87.06
87.17
87.63
87.78
88.4
89.35
89.53
90.66
90.81
91.55
91.58
91.76
91.78
91.71
91.57
91.95
92.16
92.26
92.44
93.12
93.55
93.63
93.74
94.08
94.24
94.66
94.69
94.69
94.69
94.72
95.15
95.28
96.12
96.5
96.67
96.83
97.4
97.75
97.46
97.46
97.56
97.97
98.89
99.1
99.3
100
99.73
99.34
99.78
99.5
99.6
99.52
99.63
99.61
99.73
100.53
100.87
100.9
101.08
102.95
102.58
102.6
102.45
102.41
102.38
102.65
103.33
103.68
104.13
104.3
104.11
104.17
104.23
104.47
104.86
104.9




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230892&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]3 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=230892&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230892&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
185.56251.825412157693314.97
290.88751.175493126695813.55
393.6050.9345733105151622.53
496.33583333333331.125323859108473.06
599.19083333333330.7352730888258562.44
6101.2783333333331.268354935506713.34
7103.9341666666670.7930087507637472.52000000000001

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 85.5625 & 1.82541215769331 & 4.97 \tabularnewline
2 & 90.8875 & 1.17549312669581 & 3.55 \tabularnewline
3 & 93.605 & 0.934573310515162 & 2.53 \tabularnewline
4 & 96.3358333333333 & 1.12532385910847 & 3.06 \tabularnewline
5 & 99.1908333333333 & 0.735273088825856 & 2.44 \tabularnewline
6 & 101.278333333333 & 1.26835493550671 & 3.34 \tabularnewline
7 & 103.934166666667 & 0.793008750763747 & 2.52000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230892&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]85.5625[/C][C]1.82541215769331[/C][C]4.97[/C][/ROW]
[ROW][C]2[/C][C]90.8875[/C][C]1.17549312669581[/C][C]3.55[/C][/ROW]
[ROW][C]3[/C][C]93.605[/C][C]0.934573310515162[/C][C]2.53[/C][/ROW]
[ROW][C]4[/C][C]96.3358333333333[/C][C]1.12532385910847[/C][C]3.06[/C][/ROW]
[ROW][C]5[/C][C]99.1908333333333[/C][C]0.735273088825856[/C][C]2.44[/C][/ROW]
[ROW][C]6[/C][C]101.278333333333[/C][C]1.26835493550671[/C][C]3.34[/C][/ROW]
[ROW][C]7[/C][C]103.934166666667[/C][C]0.793008750763747[/C][C]2.52000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230892&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230892&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
185.56251.825412157693314.97
290.88751.175493126695813.55
393.6050.9345733105151622.53
496.33583333333331.125323859108473.06
599.19083333333330.7352730888258562.44
6101.2783333333331.268354935506713.34
7103.9341666666670.7930087507637472.52000000000001







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha5.18076962552681
beta-0.0423497244926031
S.D.0.017696642459024
T-STAT-2.39309375157815
p-value0.062149343446226

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 5.18076962552681 \tabularnewline
beta & -0.0423497244926031 \tabularnewline
S.D. & 0.017696642459024 \tabularnewline
T-STAT & -2.39309375157815 \tabularnewline
p-value & 0.062149343446226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230892&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]5.18076962552681[/C][/ROW]
[ROW][C]beta[/C][C]-0.0423497244926031[/C][/ROW]
[ROW][C]S.D.[/C][C]0.017696642459024[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.39309375157815[/C][/ROW]
[ROW][C]p-value[/C][C]0.062149343446226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230892&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230892&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)
alpha5.18076962552681
beta-0.0423497244926031
S.D.0.017696642459024
T-STAT-2.39309375157815
p-value0.062149343446226







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha15.2279009422819
beta-3.322940758499
S.D.1.42802038622856
T-STAT-2.32695610689073
p-value0.0674664688037899
Lambda4.322940758499

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 15.2279009422819 \tabularnewline
beta & -3.322940758499 \tabularnewline
S.D. & 1.42802038622856 \tabularnewline
T-STAT & -2.32695610689073 \tabularnewline
p-value & 0.0674664688037899 \tabularnewline
Lambda & 4.322940758499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230892&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]15.2279009422819[/C][/ROW]
[ROW][C]beta[/C][C]-3.322940758499[/C][/ROW]
[ROW][C]S.D.[/C][C]1.42802038622856[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.32695610689073[/C][/ROW]
[ROW][C]p-value[/C][C]0.0674664688037899[/C][/ROW]
[ROW][C]Lambda[/C][C]4.322940758499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230892&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230892&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)
alpha15.2279009422819
beta-3.322940758499
S.D.1.42802038622856
T-STAT-2.32695610689073
p-value0.0674664688037899
Lambda4.322940758499



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')