Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationFri, 21 Apr 2017 15:17:23 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Apr/21/t1492784473wz6535f7ldcy2h4.htm/, Retrieved Mon, 13 May 2024 17:03:24 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 13 May 2024 17:03:24 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
86.88
90.65
90.68
89.64
102.62
101.84
92.51
94.29
94.68
96.94
94.03
89.65
84.9
89.07
89.8
93.22
92.23
98.41
96.63
89.8
90
92.13
93.27
90.81
85.42
88.28
88.73
90.18
92.74
96.13
94.85
94.25
96.94
101.22
98.71
95.51
93.91
98.17
97.59
99.64
107.88
108.49
100.25
99.27
101.73
101.25
97.09
94.74
94.53
93.48
96.05
106.22
98.33
99.86
93.78
88.96
83.77
89.46
86.78
88.4
87.19
92.23
95.99
104.75
105.63
108.71
96.4
93.31
93.77
98.7
95.04
95.61




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]2 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=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
193.70083333333334.833392293266315.74
291.68916666666673.550002454117813.51
393.584.6586088442405615.8
4100.0008333333334.4907550657552114.58
593.30166666666676.2922865237503122.45
697.27756.2071895631378321.52

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 93.7008333333333 & 4.8333922932663 & 15.74 \tabularnewline
2 & 91.6891666666667 & 3.5500024541178 & 13.51 \tabularnewline
3 & 93.58 & 4.65860884424056 & 15.8 \tabularnewline
4 & 100.000833333333 & 4.49075506575521 & 14.58 \tabularnewline
5 & 93.3016666666667 & 6.29228652375031 & 22.45 \tabularnewline
6 & 97.2775 & 6.20718956313783 & 21.52 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]93.7008333333333[/C][C]4.8333922932663[/C][C]15.74[/C][/ROW]
[ROW][C]2[/C][C]91.6891666666667[/C][C]3.5500024541178[/C][C]13.51[/C][/ROW]
[ROW][C]3[/C][C]93.58[/C][C]4.65860884424056[/C][C]15.8[/C][/ROW]
[ROW][C]4[/C][C]100.000833333333[/C][C]4.49075506575521[/C][C]14.58[/C][/ROW]
[ROW][C]5[/C][C]93.3016666666667[/C][C]6.29228652375031[/C][C]22.45[/C][/ROW]
[ROW][C]6[/C][C]97.2775[/C][C]6.20718956313783[/C][C]21.52[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
193.70083333333334.833392293266315.74
291.68916666666673.550002454117813.51
393.584.6586088442405615.8
4100.0008333333334.4907550657552114.58
593.30166666666676.2922865237503122.45
697.27756.2071895631378321.52







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-1.98259870737991
beta0.0736157088728777
S.D.0.167816432961566
T-STAT0.438668058745697
p-value0.683554869427546

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.98259870737991 \tabularnewline
beta & 0.0736157088728777 \tabularnewline
S.D. & 0.167816432961566 \tabularnewline
T-STAT & 0.438668058745697 \tabularnewline
p-value & 0.683554869427546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.98259870737991[/C][/ROW]
[ROW][C]beta[/C][C]0.0736157088728777[/C][/ROW]
[ROW][C]S.D.[/C][C]0.167816432961566[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.438668058745697[/C][/ROW]
[ROW][C]p-value[/C][C]0.683554869427546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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-1.98259870737991
beta0.0736157088728777
S.D.0.167816432961566
T-STAT0.438668058745697
p-value0.683554869427546







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-6.3245139098774
beta1.738742986925
S.D.3.23471030644343
T-STAT0.537526647583089
p-value0.619413904995803
Lambda-0.738742986925

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -6.3245139098774 \tabularnewline
beta & 1.738742986925 \tabularnewline
S.D. & 3.23471030644343 \tabularnewline
T-STAT & 0.537526647583089 \tabularnewline
p-value & 0.619413904995803 \tabularnewline
Lambda & -0.738742986925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.3245139098774[/C][/ROW]
[ROW][C]beta[/C][C]1.738742986925[/C][/ROW]
[ROW][C]S.D.[/C][C]3.23471030644343[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.537526647583089[/C][/ROW]
[ROW][C]p-value[/C][C]0.619413904995803[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.738742986925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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-6.3245139098774
beta1.738742986925
S.D.3.23471030644343
T-STAT0.537526647583089
p-value0.619413904995803
Lambda-0.738742986925



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')