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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 computationMon, 08 Dec 2008 17:21:40 -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/2008/Dec/09/t1228782120apvdbltgf67wo3u.htm/, Retrieved Sat, 18 May 2024 05:29:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31152, Retrieved Sat, 18 May 2024 05:29:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Run sequence plot...] [2008-12-02 22:19:27] [ed2ba3b6182103c15c0ab511ae4e6284]
F RMPD  [Standard Deviation-Mean Plot] [SD mean plot peri...] [2008-12-06 11:54:38] [ed2ba3b6182103c15c0ab511ae4e6284]
-           [Standard Deviation-Mean Plot] [] [2008-12-09 00:21:40] [c0a347e3519123f7eef62b705326dad9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92.66
94.2
94.37
94.45
94.62
94.37
93.43
94.79
94.88
94.79
94.62
94.71
93.77
95.73
95.99
95.82
95.47
95.82
94.71
96.33
96.5
96.16
96.33
96.33
95.05
96.84
96.92
97.44
97.78
97.69
96.67
98.29
98.2
98.71
98.54
98.2
96.92
99.06
99.65
99.82
99.99
100.33
99.31
101.1
101.1
100.93
100.85
100.93
99.6
101.88
101.81
102.38
102.74
102.82
101.72
103.47
102.98
102.68
102.9
103.03
101.29

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31152&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31152&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31152&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
194.32416666666670.6516616290590382.22
295.74666666666670.791308850556912.73000000000000
397.52751.032306552426083.66
499.99916666666671.205377785617694.17999999999999
5102.3341666666671.01658482035183.87000000000000

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 94.3241666666667 & 0.651661629059038 & 2.22 \tabularnewline
2 & 95.7466666666667 & 0.79130885055691 & 2.73000000000000 \tabularnewline
3 & 97.5275 & 1.03230655242608 & 3.66 \tabularnewline
4 & 99.9991666666667 & 1.20537778561769 & 4.17999999999999 \tabularnewline
5 & 102.334166666667 & 1.0165848203518 & 3.87000000000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31152&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]94.3241666666667[/C][C]0.651661629059038[/C][C]2.22[/C][/ROW]
[ROW][C]2[/C][C]95.7466666666667[/C][C]0.79130885055691[/C][C]2.73000000000000[/C][/ROW]
[ROW][C]3[/C][C]97.5275[/C][C]1.03230655242608[/C][C]3.66[/C][/ROW]
[ROW][C]4[/C][C]99.9991666666667[/C][C]1.20537778561769[/C][C]4.17999999999999[/C][/ROW]
[ROW][C]5[/C][C]102.334166666667[/C][C]1.0165848203518[/C][C]3.87000000000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31152&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31152&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
194.32416666666670.6516616290590382.22
295.74666666666670.791308850556912.73000000000000
397.52751.032306552426083.66
499.99916666666671.205377785617694.17999999999999
5102.3341666666671.01658482035183.87000000000000






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-4.27574956214814
beta0.0532237232717873
S.D.0.024060133468736
T-STAT2.21211255294767
p-value0.113877017161826

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -4.27574956214814 \tabularnewline
beta & 0.0532237232717873 \tabularnewline
S.D. & 0.024060133468736 \tabularnewline
T-STAT & 2.21211255294767 \tabularnewline
p-value & 0.113877017161826 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31152&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.27574956214814[/C][/ROW]
[ROW][C]beta[/C][C]0.0532237232717873[/C][/ROW]
[ROW][C]S.D.[/C][C]0.024060133468736[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.21211255294767[/C][/ROW]
[ROW][C]p-value[/C][C]0.113877017161826[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31152&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31152&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-4.27574956214814
beta0.0532237232717873
S.D.0.024060133468736
T-STAT2.21211255294767
p-value0.113877017161826






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-27.6689574130392
beta6.01682355593605
S.D.2.51946351274350
T-STAT2.38813680988148
p-value0.0969039503467449
Lambda-5.01682355593605

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -27.6689574130392 \tabularnewline
beta & 6.01682355593605 \tabularnewline
S.D. & 2.51946351274350 \tabularnewline
T-STAT & 2.38813680988148 \tabularnewline
p-value & 0.0969039503467449 \tabularnewline
Lambda & -5.01682355593605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31152&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-27.6689574130392[/C][/ROW]
[ROW][C]beta[/C][C]6.01682355593605[/C][/ROW]
[ROW][C]S.D.[/C][C]2.51946351274350[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.38813680988148[/C][/ROW]
[ROW][C]p-value[/C][C]0.0969039503467449[/C][/ROW]
[ROW][C]Lambda[/C][C]-5.01682355593605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31152&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31152&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-27.6689574130392
beta6.01682355593605
S.D.2.51946351274350
T-STAT2.38813680988148
p-value0.0969039503467449
Lambda-5.01682355593605


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')