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Description of Statistical Computation

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, 26 Nov 2009 14:18:04 -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/Nov/26/t125927034551186g7lvfhvbw7.htm/, Retrieved Mon, 29 Apr 2024 05:32:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60426, Retrieved Mon, 29 Apr 2024 05:32:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-11-22 12:50:05] [b98453cac15ba1066b407e146608df68]
-   PD          [Standard Deviation-Mean Plot] [] [2009-11-26 21:18:04] [8551abdd6804649d94d88b1829ac2b1a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,5
110,8
104,2
88,9
89,8
90
93,9
91,3
87,8
99,7
73,5
79,2
96,9
95,2
95,6
89,7
92,8
88
101,1
92,7
95,8
103,8
81,8
87,1
105,9
108,1
102,6
93,7
103,5
100,6
113,3
102,4
102,1
106,9
87,3
93,1
109,1
120,3
104,9
92,6
109,8
111,4
117,9
121,6
117,8
124,2
106,8
102,7
116,8
113,6
96,1
85
83,2
84,9
83
79,6
83,2
83,8
82,8
71,4

Tables (Output of Computation)





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=60426&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=60426&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60426&T=0

As an alternative you can also use a QR Code:  
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
193.311.401036635483537.3
293.3756.1161671003987522
3101.6257.2070830059425226
4111.5916666666679.1960918906134331.6
588.616666666666713.572487769102045.4

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 93.3 & 11.4010366354835 & 37.3 \tabularnewline
2 & 93.375 & 6.11616710039875 & 22 \tabularnewline
3 & 101.625 & 7.20708300594252 & 26 \tabularnewline
4 & 111.591666666667 & 9.19609189061343 & 31.6 \tabularnewline
5 & 88.6166666666667 & 13.5724877691020 & 45.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60426&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]93.3[/C][C]11.4010366354835[/C][C]37.3[/C][/ROW]
[ROW][C]2[/C][C]93.375[/C][C]6.11616710039875[/C][C]22[/C][/ROW]
[ROW][C]3[/C][C]101.625[/C][C]7.20708300594252[/C][C]26[/C][/ROW]
[ROW][C]4[/C][C]111.591666666667[/C][C]9.19609189061343[/C][C]31.6[/C][/ROW]
[ROW][C]5[/C][C]88.6166666666667[/C][C]13.5724877691020[/C][C]45.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60426&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60426&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
193.311.401036635483537.3
293.3756.1161671003987522
3101.6257.2070830059425226
4111.5916666666679.1960918906134331.6
588.616666666666713.572487769102045.4






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha22.5497712638529
beta-0.133582142749644
S.D.0.177604758670407
T-STAT-0.752131551821433
p-value0.506604412768894

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 22.5497712638529 \tabularnewline
beta & -0.133582142749644 \tabularnewline
S.D. & 0.177604758670407 \tabularnewline
T-STAT & -0.752131551821433 \tabularnewline
p-value & 0.506604412768894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60426&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]22.5497712638529[/C][/ROW]
[ROW][C]beta[/C][C]-0.133582142749644[/C][/ROW]
[ROW][C]S.D.[/C][C]0.177604758670407[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.752131551821433[/C][/ROW]
[ROW][C]p-value[/C][C]0.506604412768894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60426&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60426&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha22.5497712638529
beta-0.133582142749644
S.D.0.177604758670407
T-STAT-0.752131551821433
p-value0.506604412768894






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha7.66740598414852
beta-1.19209461320186
S.D.1.95357605534948
T-STAT-0.610211519504214
p-value0.584838907091274
Lambda2.19209461320186

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 7.66740598414852 \tabularnewline
beta & -1.19209461320186 \tabularnewline
S.D. & 1.95357605534948 \tabularnewline
T-STAT & -0.610211519504214 \tabularnewline
p-value & 0.584838907091274 \tabularnewline
Lambda & 2.19209461320186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60426&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.66740598414852[/C][/ROW]
[ROW][C]beta[/C][C]-1.19209461320186[/C][/ROW]
[ROW][C]S.D.[/C][C]1.95357605534948[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.610211519504214[/C][/ROW]
[ROW][C]p-value[/C][C]0.584838907091274[/C][/ROW]
[ROW][C]Lambda[/C][C]2.19209461320186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60426&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60426&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha7.66740598414852
beta-1.19209461320186
S.D.1.95357605534948
T-STAT-0.610211519504214
p-value0.584838907091274
Lambda2.19209461320186


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 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')