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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, 01 Dec 2008 13:18:42 -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/01/t1228162762vba57389wkhkal8.htm/, Retrieved Sun, 05 May 2024 08:58:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27325, Retrieved Sun, 05 May 2024 08:58:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact241

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Standard Deviation-Mean Plot] [q5 airline data] [2008-11-28 16:40:33] [44a98561a4b3e6ab8cd5a857b48b0914]
F RMP     [(Partial) Autocorrelation Function] [q6 ACF] [2008-11-29 18:07:14] [44a98561a4b3e6ab8cd5a857b48b0914]
F RMPD        [Standard Deviation-Mean Plot] [Non stationary ti...] [2008-12-01 20:18:42] [07b7cf1321bc38017c2c7efcf91ca696] [Current]
Feedback Forum
2008-12-05 12:09:36 [Nathalie Koulouris] [reply] 
De student heeft deze vraag correct opgelost en een juiste conclusie getrokken.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99,2
99,5
99,7
99,6
100,1
100,3
100,5
100,7
100,9
101,1
101,1
101,1
101,3
100,5
100,3
100
100,1
100,2
100,5
100
100,7
101,2
101,6
101,7
101,5
101,1
101,2
101,1
101,4
101,3
101,6
102
103,2
103,4
103,6
104,8
105,2
105,1
105,1
105,7
106,2
105,9
106,1
106,5
106,7
107,1
107,5
107,9
109,2
110,1
110,2
110,4
110,5
110,8
111,2
111
111,1
111,1
111,1
111,5

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=27325&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=27325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27325&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
1100.3166666666670.6886526150697631.89999999999999
2100.6750.6210328640110911.70000000000000
3102.1833333333331.240112409372153.7
4106.250.9278518690551272.80000000000001
5110.6833333333330.6365151334819252.30000000000000

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 100.316666666667 & 0.688652615069763 & 1.89999999999999 \tabularnewline
2 & 100.675 & 0.621032864011091 & 1.70000000000000 \tabularnewline
3 & 102.183333333333 & 1.24011240937215 & 3.7 \tabularnewline
4 & 106.25 & 0.927851869055127 & 2.80000000000001 \tabularnewline
5 & 110.683333333333 & 0.636515133481925 & 2.30000000000000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27325&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]100.316666666667[/C][C]0.688652615069763[/C][C]1.89999999999999[/C][/ROW]
[ROW][C]2[/C][C]100.675[/C][C]0.621032864011091[/C][C]1.70000000000000[/C][/ROW]
[ROW][C]3[/C][C]102.183333333333[/C][C]1.24011240937215[/C][C]3.7[/C][/ROW]
[ROW][C]4[/C][C]106.25[/C][C]0.927851869055127[/C][C]2.80000000000001[/C][/ROW]
[ROW][C]5[/C][C]110.683333333333[/C][C]0.636515133481925[/C][C]2.30000000000000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27325&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
1100.3166666666670.6886526150697631.89999999999999
2100.6750.6210328640110911.70000000000000
3102.1833333333331.240112409372153.7
4106.250.9278518690551272.80000000000001
5110.6833333333330.6365151334819252.30000000000000






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha1.62898205675108
beta-0.00774981890202112
S.D.0.0342916327759484
T-STAT-0.225997372381076
p-value0.835725316230345

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 1.62898205675108 \tabularnewline
beta & -0.00774981890202112 \tabularnewline
S.D. & 0.0342916327759484 \tabularnewline
T-STAT & -0.225997372381076 \tabularnewline
p-value & 0.835725316230345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27325&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1.62898205675108[/C][/ROW]
[ROW][C]beta[/C][C]-0.00774981890202112[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0342916327759484[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.225997372381076[/C][/ROW]
[ROW][C]p-value[/C][C]0.835725316230345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27325&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)
alpha1.62898205675108
beta-0.00774981890202112
S.D.0.0342916327759484
T-STAT-0.225997372381076
p-value0.835725316230345






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha3.20248142686117
beta-0.739604647165492
S.D.4.07435218499535
T-STAT-0.181526930806139
p-value0.867526198221523
Lambda1.73960464716549

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 3.20248142686117 \tabularnewline
beta & -0.739604647165492 \tabularnewline
S.D. & 4.07435218499535 \tabularnewline
T-STAT & -0.181526930806139 \tabularnewline
p-value & 0.867526198221523 \tabularnewline
Lambda & 1.73960464716549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27325&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]3.20248142686117[/C][/ROW]
[ROW][C]beta[/C][C]-0.739604647165492[/C][/ROW]
[ROW][C]S.D.[/C][C]4.07435218499535[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.181526930806139[/C][/ROW]
[ROW][C]p-value[/C][C]0.867526198221523[/C][/ROW]
[ROW][C]Lambda[/C][C]1.73960464716549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27325&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)
alpha3.20248142686117
beta-0.739604647165492
S.D.4.07435218499535
T-STAT-0.181526930806139
p-value0.867526198221523
Lambda1.73960464716549


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')