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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 computationTue, 24 Nov 2009 10:37:19 -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/24/t12590845869ghdk8k5tvhtn6x.htm/, Retrieved Mon, 26 Feb 2024 04:56:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59179, Retrieved Mon, 26 Feb 2024 04:56:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
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       [Variance Reduction Matrix] [Identifying Integ...] [2009-11-22 12:29:54] [b98453cac15ba1066b407e146608df68]
- RMPD        [Spectral Analysis] [Workshop 8 - Meth...] [2009-11-24 16:40:10] [74be16979710d4c4e7c6647856088456]
-   P           [Spectral Analysis] [Workshop 8 - Meth...] [2009-11-24 17:10:16] [1646a2766cb8c4a6f9d3b2fffef409b3]
- RMP               [Standard Deviation-Mean Plot] [Workshop 8 - Remo...] [2009-11-24 17:37:19] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
269645
267037
258113
262813
267413
267366
264777
258863
254844
254868
277267
285351
286602
283042
276687
277915
277128
277103
275037
270150
267140
264993
287259
291186
292300
288186
281477
282656
280190
280408
276836
275216
274352
271311
289802
290726
292300
278506
269826
265861
269034
264176
255198
253353
246057
235372
258556
260993
254663
250643
243422
247105
248541
245039
237080
237085
225554
226839
247934
248333
246969
245098
246263
255765
264319
268347
273046
273963
267430
271993
292710
295881




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=59179&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=59179&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59179&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
1265696.4166666679003.5167417902330507
2277853.58088.0270827259926193
32819556962.8702415024220989
426243614819.413692242356928
5242686.59212.198676458629109
6266815.33333333316654.370441054850783

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 265696.416666667 & 9003.51674179023 & 30507 \tabularnewline
2 & 277853.5 & 8088.02708272599 & 26193 \tabularnewline
3 & 281955 & 6962.87024150242 & 20989 \tabularnewline
4 & 262436 & 14819.4136922423 & 56928 \tabularnewline
5 & 242686.5 & 9212.1986764586 & 29109 \tabularnewline
6 & 266815.333333333 & 16654.3704410548 & 50783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59179&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]265696.416666667[/C][C]9003.51674179023[/C][C]30507[/C][/ROW]
[ROW][C]2[/C][C]277853.5[/C][C]8088.02708272599[/C][C]26193[/C][/ROW]
[ROW][C]3[/C][C]281955[/C][C]6962.87024150242[/C][C]20989[/C][/ROW]
[ROW][C]4[/C][C]262436[/C][C]14819.4136922423[/C][C]56928[/C][/ROW]
[ROW][C]5[/C][C]242686.5[/C][C]9212.1986764586[/C][C]29109[/C][/ROW]
[ROW][C]6[/C][C]266815.333333333[/C][C]16654.3704410548[/C][C]50783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59179&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59179&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
1265696.4166666679003.5167417902330507
2277853.58088.0270827259926193
32819556962.8702415024220989
426243614819.413692242356928
5242686.59212.198676458629109
6266815.33333333316654.370441054850783







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha29069.8948432918
beta-0.0686590941578197
S.D.0.139205937117135
T-STAT-0.493219582294442
p-value0.647708795141983

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 29069.8948432918 \tabularnewline
beta & -0.0686590941578197 \tabularnewline
S.D. & 0.139205937117135 \tabularnewline
T-STAT & -0.493219582294442 \tabularnewline
p-value & 0.647708795141983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59179&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]29069.8948432918[/C][/ROW]
[ROW][C]beta[/C][C]-0.0686590941578197[/C][/ROW]
[ROW][C]S.D.[/C][C]0.139205937117135[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.493219582294442[/C][/ROW]
[ROW][C]p-value[/C][C]0.647708795141983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59179&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59179&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)
alpha29069.8948432918
beta-0.0686590941578197
S.D.0.139205937117135
T-STAT-0.493219582294442
p-value0.647708795141983







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha32.9599645527387
beta-1.89943968558255
S.D.3.16006301407891
T-STAT-0.601076521930115
p-value0.580190493445867
Lambda2.89943968558255

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 32.9599645527387 \tabularnewline
beta & -1.89943968558255 \tabularnewline
S.D. & 3.16006301407891 \tabularnewline
T-STAT & -0.601076521930115 \tabularnewline
p-value & 0.580190493445867 \tabularnewline
Lambda & 2.89943968558255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59179&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]32.9599645527387[/C][/ROW]
[ROW][C]beta[/C][C]-1.89943968558255[/C][/ROW]
[ROW][C]S.D.[/C][C]3.16006301407891[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.601076521930115[/C][/ROW]
[ROW][C]p-value[/C][C]0.580190493445867[/C][/ROW]
[ROW][C]Lambda[/C][C]2.89943968558255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59179&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59179&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)
alpha32.9599645527387
beta-1.89943968558255
S.D.3.16006301407891
T-STAT-0.601076521930115
p-value0.580190493445867
Lambda2.89943968558255



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')