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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 computationFri, 27 Nov 2009 12:29:24 -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/27/t1259350487qbgct6l5tmavscg.htm/, Retrieved Sun, 28 Apr 2024 19:12:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=61176, Retrieved Sun, 28 Apr 2024 19:12:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170
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] [WS8: SMP] [2009-11-27 19:29:24] [b8ce264f75295a954feffaf60221d1b0] [Current]
-   PD            [Standard Deviation-Mean Plot] [WS10; Lambda van Yt] [2009-12-10 21:48:15] [5c968c05ca472afa314d272082b56b09]
-    D              [Standard Deviation-Mean Plot] [Workshop 10] [2009-12-11 20:22:00] [b6394cb5c2dcec6d17418d3cdf42d699]
-    D              [Standard Deviation-Mean Plot] [WS 10 (7) - Lambd...] [2009-12-11 21:16:07] [aba88da643e3763d32ff92bd8f92a385]
-   PD            [Standard Deviation-Mean Plot] [ws9-1] [2009-12-11 12:17:52] [74be16979710d4c4e7c6647856088456]
-   PD            [Standard Deviation-Mean Plot] [Differentatie - H...] [2009-12-19 10:15:13] [4d62210f0915d3a20cbf115865da7cd4]
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Dataseries X:
14.3
14.2
15.9
15.3
15.5
15.1
15
12.1
15.8
16.9
15.1
13.7
14.8
14.7
16
15.4
15
15.5
15.1
11.7
16.3
16.7
15
14.9
14.6
15.3
17.9
16.4
15.4
17.9
15.9
13.9
17.8
17.9
17.4
16.7
16
16.6
19.1
17.8
17.2
18.6
16.3
15.1
19.2
17.7
19.1
18
17.5
17.8
21.1
17.2
19.4
19.8
17.6
16.2
19.5
19.9
20
17.3




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=61176&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=61176&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61176&T=0

As an alternative you can also use a 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
114.90833333333331.226568260216164.8
215.09166666666671.240570494375535
316.4251.405912838369054
417.55833333333331.345334249331464.1
518.60833333333331.509038425520364.9

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 14.9083333333333 & 1.22656826021616 & 4.8 \tabularnewline
2 & 15.0916666666667 & 1.24057049437553 & 5 \tabularnewline
3 & 16.425 & 1.40591283836905 & 4 \tabularnewline
4 & 17.5583333333333 & 1.34533424933146 & 4.1 \tabularnewline
5 & 18.6083333333333 & 1.50903842552036 & 4.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61176&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]14.9083333333333[/C][C]1.22656826021616[/C][C]4.8[/C][/ROW]
[ROW][C]2[/C][C]15.0916666666667[/C][C]1.24057049437553[/C][C]5[/C][/ROW]
[ROW][C]3[/C][C]16.425[/C][C]1.40591283836905[/C][C]4[/C][/ROW]
[ROW][C]4[/C][C]17.5583333333333[/C][C]1.34533424933146[/C][C]4.1[/C][/ROW]
[ROW][C]5[/C][C]18.6083333333333[/C][C]1.50903842552036[/C][C]4.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61176&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61176&T=1

As an alternative you can also use a QR Code:  

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

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
114.90833333333331.226568260216164.8
215.09166666666671.240570494375535
316.4251.405912838369054
417.55833333333331.345334249331464.1
518.60833333333331.509038425520364.9







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.236448299970425
beta0.0671397368737011
S.D.0.0182565289926972
T-STAT3.67757402847813
p-value0.0348159539583382

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.236448299970425 \tabularnewline
beta & 0.0671397368737011 \tabularnewline
S.D. & 0.0182565289926972 \tabularnewline
T-STAT & 3.67757402847813 \tabularnewline
p-value & 0.0348159539583382 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61176&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.236448299970425[/C][/ROW]
[ROW][C]beta[/C][C]0.0671397368737011[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0182565289926972[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.67757402847813[/C][/ROW]
[ROW][C]p-value[/C][C]0.0348159539583382[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61176&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61176&T=2

As an alternative you can also use a QR Code:  

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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.236448299970425
beta0.0671397368737011
S.D.0.0182565289926972
T-STAT3.67757402847813
p-value0.0348159539583382







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-2.01803616320853
beta0.825390570789833
S.D.0.219577385865891
T-STAT3.75899625334799
p-value0.032912669752958
Lambda0.174609429210167

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -2.01803616320853 \tabularnewline
beta & 0.825390570789833 \tabularnewline
S.D. & 0.219577385865891 \tabularnewline
T-STAT & 3.75899625334799 \tabularnewline
p-value & 0.032912669752958 \tabularnewline
Lambda & 0.174609429210167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=61176&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-2.01803616320853[/C][/ROW]
[ROW][C]beta[/C][C]0.825390570789833[/C][/ROW]
[ROW][C]S.D.[/C][C]0.219577385865891[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.75899625334799[/C][/ROW]
[ROW][C]p-value[/C][C]0.032912669752958[/C][/ROW]
[ROW][C]Lambda[/C][C]0.174609429210167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=61176&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=61176&T=3

As an alternative you can also use a QR Code:  

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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-2.01803616320853
beta0.825390570789833
S.D.0.219577385865891
T-STAT3.75899625334799
p-value0.032912669752958
Lambda0.174609429210167



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