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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSat, 10 May 2008 03:35:20 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/May/10/t1210412210ya959em4jzwtcm3.htm/, Retrieved Tue, 14 May 2024 16:34:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=12233, Retrieved Tue, 14 May 2024 16:34:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact252

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [Spreidings- en ge...] [2008-05-10 09:35:20] [f907c40368cff310b72a5f11c2582b2e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.22
101.25
101.25
101.26
101.26
101.26
101.29
101.31
101.31
101.31
101.32
101.34
101.34
101.34
101.34
101.34
101.34
101.34
101.34
101.39
102.16
102.19
102.31
102.32
102.32
102.32
102.36
102.36
102.37
102.37
102.37
102.37
103.45
103.8
103.81
103.81
103.81
103.84
103.9
103.91
103.92
103.92
103.93
104
104.51
105
105.01
105.01
105.01
105.01
105.13
105.14
105.15
105.22
105.23
105.23
105.57
106.05
106.09
106.09
106.19
106.2
106.2
106.22
106.22
106.23
106.23
106.61
106.95
107.74
107.8
107.8

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=12233&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=12233&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12233&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
1101.2816666666670.03639014185521140.120000000000005
2101.6458333333330.444797366190150.97999999999999
3102.8091666666670.6775149556663051.49000000000001
4104.230.501180424779161.20000000000000
5105.410.4263801121065561.08000000000000
6106.6991666666670.6898545081567851.61

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 101.281666666667 & 0.0363901418552114 & 0.120000000000005 \tabularnewline
2 & 101.645833333333 & 0.44479736619015 & 0.97999999999999 \tabularnewline
3 & 102.809166666667 & 0.677514955666305 & 1.49000000000001 \tabularnewline
4 & 104.23 & 0.50118042477916 & 1.20000000000000 \tabularnewline
5 & 105.41 & 0.426380112106556 & 1.08000000000000 \tabularnewline
6 & 106.699166666667 & 0.689854508156785 & 1.61 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12233&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]101.281666666667[/C][C]0.0363901418552114[/C][C]0.120000000000005[/C][/ROW]
[ROW][C]2[/C][C]101.645833333333[/C][C]0.44479736619015[/C][C]0.97999999999999[/C][/ROW]
[ROW][C]3[/C][C]102.809166666667[/C][C]0.677514955666305[/C][C]1.49000000000001[/C][/ROW]
[ROW][C]4[/C][C]104.23[/C][C]0.50118042477916[/C][C]1.20000000000000[/C][/ROW]
[ROW][C]5[/C][C]105.41[/C][C]0.426380112106556[/C][C]1.08000000000000[/C][/ROW]
[ROW][C]6[/C][C]106.699166666667[/C][C]0.689854508156785[/C][C]1.61[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12233&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12233&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
1101.2816666666670.03639014185521140.120000000000005
2101.6458333333330.444797366190150.97999999999999
3102.8091666666670.6775149556663051.49000000000001
4104.230.501180424779161.20000000000000
5105.410.4263801121065561.08000000000000
6106.6991666666670.6898545081567851.61






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-6.3533470794884
beta0.0657415025537099
S.D.0.0445359980536614
T-STAT1.47614301748662
p-value0.213947694751889

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -6.3533470794884 \tabularnewline
beta & 0.0657415025537099 \tabularnewline
S.D. & 0.0445359980536614 \tabularnewline
T-STAT & 1.47614301748662 \tabularnewline
p-value & 0.213947694751889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12233&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-6.3533470794884[/C][/ROW]
[ROW][C]beta[/C][C]0.0657415025537099[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0445359980536614[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.47614301748662[/C][/ROW]
[ROW][C]p-value[/C][C]0.213947694751889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12233&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12233&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)
alpha-6.3533470794884
beta0.0657415025537099
S.D.0.0445359980536614
T-STAT1.47614301748662
p-value0.213947694751889






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-147.178255497634
beta31.4809546773688
S.D.21.9680895752764
T-STAT1.4330310594144
p-value0.225134619792777
Lambda-30.4809546773688

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -147.178255497634 \tabularnewline
beta & 31.4809546773688 \tabularnewline
S.D. & 21.9680895752764 \tabularnewline
T-STAT & 1.4330310594144 \tabularnewline
p-value & 0.225134619792777 \tabularnewline
Lambda & -30.4809546773688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12233&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-147.178255497634[/C][/ROW]
[ROW][C]beta[/C][C]31.4809546773688[/C][/ROW]
[ROW][C]S.D.[/C][C]21.9680895752764[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.4330310594144[/C][/ROW]
[ROW][C]p-value[/C][C]0.225134619792777[/C][/ROW]
[ROW][C]Lambda[/C][C]-30.4809546773688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12233&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12233&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)
alpha-147.178255497634
beta31.4809546773688
S.D.21.9680895752764
T-STAT1.4330310594144
p-value0.225134619792777
Lambda-30.4809546773688


Figures (Output of Computation)

Input Parameters & R Code

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')