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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 computationMon, 12 May 2008 12:57:08 -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/12/t1210618653ku8tfhcwzkn50kc.htm/, Retrieved Tue, 14 May 2024 07:59:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=12413, Retrieved Tue, 14 May 2024 07:59:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

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] [verkoopprijzen sc...] [2008-05-12 18:57:08] [b410a83c5378683dfcb81b0901d2d85a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24.65
25.24
25.56
25.9
25.87
25.78
25.78
25.74
25.78
25.73
24.67
24.31
24.56
25
25.38
25.99
26.22
26.19
26.22
26.22
26.61
26.72
25.46
25.48
25.59
25.88
26
26.97
27.2
27.19
27.19
27.19
27.26
26.9
26.11
25.87
26.02
26.31
26.37
26.52
26.86
26.92
26.98
26.98
27.03
26.75
26.39
26.3
26.3
26.52
26.53
26.98
27.22
27.34
27.41
27.47
27.46
27.53
27.21
26.91
26.95
26.91
27.39
27.62
27.79
27.88
27.9
28.09
28.46
28.73
27.93
27.61

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=12413&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=12413&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12413&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
125.41750.5608131271968981.59
225.83750.6588574821751532.16
326.61250.6561890664351941.67000000000000
426.61916666666670.3405465749306111.01000000000000
527.07333333333330.4237137398448781.23
627.77166666666670.5359415391004261.82

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 25.4175 & 0.560813127196898 & 1.59 \tabularnewline
2 & 25.8375 & 0.658857482175153 & 2.16 \tabularnewline
3 & 26.6125 & 0.656189066435194 & 1.67000000000000 \tabularnewline
4 & 26.6191666666667 & 0.340546574930611 & 1.01000000000000 \tabularnewline
5 & 27.0733333333333 & 0.423713739844878 & 1.23 \tabularnewline
6 & 27.7716666666667 & 0.535941539100426 & 1.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12413&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]25.4175[/C][C]0.560813127196898[/C][C]1.59[/C][/ROW]
[ROW][C]2[/C][C]25.8375[/C][C]0.658857482175153[/C][C]2.16[/C][/ROW]
[ROW][C]3[/C][C]26.6125[/C][C]0.656189066435194[/C][C]1.67000000000000[/C][/ROW]
[ROW][C]4[/C][C]26.6191666666667[/C][C]0.340546574930611[/C][C]1.01000000000000[/C][/ROW]
[ROW][C]5[/C][C]27.0733333333333[/C][C]0.423713739844878[/C][C]1.23[/C][/ROW]
[ROW][C]6[/C][C]27.7716666666667[/C][C]0.535941539100426[/C][C]1.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12413&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12413&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
125.41750.5608131271968981.59
225.83750.6588574821751532.16
326.61250.6561890664351941.67000000000000
426.61916666666670.3405465749306111.01000000000000
527.07333333333330.4237137398448781.23
627.77166666666670.5359415391004261.82






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha1.87210546380375
beta-0.0505647836471471
S.D.0.0708506754056858
T-STAT-0.713681039137831
p-value0.514854962213911

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 1.87210546380375 \tabularnewline
beta & -0.0505647836471471 \tabularnewline
S.D. & 0.0708506754056858 \tabularnewline
T-STAT & -0.713681039137831 \tabularnewline
p-value & 0.514854962213911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12413&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1.87210546380375[/C][/ROW]
[ROW][C]beta[/C][C]-0.0505647836471471[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0708506754056858[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.713681039137831[/C][/ROW]
[ROW][C]p-value[/C][C]0.514854962213911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12413&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12413&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)
alpha1.87210546380375
beta-0.0505647836471471
S.D.0.0708506754056858
T-STAT-0.713681039137831
p-value0.514854962213911






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha7.7249836805507
beta-2.55816888923882
S.D.3.87846206355962
T-STAT-0.65958332125362
p-value0.54556288103662
Lambda3.55816888923882

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 7.7249836805507 \tabularnewline
beta & -2.55816888923882 \tabularnewline
S.D. & 3.87846206355962 \tabularnewline
T-STAT & -0.65958332125362 \tabularnewline
p-value & 0.54556288103662 \tabularnewline
Lambda & 3.55816888923882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12413&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.7249836805507[/C][/ROW]
[ROW][C]beta[/C][C]-2.55816888923882[/C][/ROW]
[ROW][C]S.D.[/C][C]3.87846206355962[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.65958332125362[/C][/ROW]
[ROW][C]p-value[/C][C]0.54556288103662[/C][/ROW]
[ROW][C]Lambda[/C][C]3.55816888923882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12413&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12413&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)
alpha7.7249836805507
beta-2.55816888923882
S.D.3.87846206355962
T-STAT-0.65958332125362
p-value0.54556288103662
Lambda3.55816888923882


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')