Free Statistics

of Irreproducible Research!

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 computationThu, 03 Dec 2009 09:54:18 -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/Dec/03/t125985945212yr09013ydn8nl.htm/, Retrieved Fri, 29 Mar 2024 05:01:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62914, Retrieved Fri, 29 Mar 2024 05:01:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
- R  D      [Standard Deviation-Mean Plot] [SMP] [2009-12-03 16:54:18] [b1ac221d009d6e5c29a4ef1869874933] [Current]
Feedback Forum

Post a new message
Dataseries X:
89.6
92.8
107.6
104.6
103
106.9
56.3
93.4
109.1
113.8
97.4
72.5
82.7
88.9
105.9
100.8
94
105
58.5
87.6
113.1
112.5
89.6
74.5
82.7
90.1
109.4
96
89.2
109.1
49.1
92.9
107.7
103.5
91.1
79.8
71.9
82.9
90.1
100.7
90.7
108.8
44.1
93.6
107.4
96.5
93.6
76.5
76.7
84
103.3
88.5
99
105.9
44.7
94
107.1
104.8
102.5
77.7
85.2
91.3
106.5
92.4
97.5
107
51.1
98.6
102.2
114.3
99.4
72.5
92.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62914&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]3 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=62914&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62914&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
195.583333333333316.654119519497257.5
292.758333333333316.10174797588154.6
391.716666666666716.727105565699760.3
488.066666666666717.739495397831864.7
590.683333333333318.109055159998862.4
693.166666666666717.18552950136963.2

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 95.5833333333333 & 16.6541195194972 & 57.5 \tabularnewline
2 & 92.7583333333333 & 16.101747975881 & 54.6 \tabularnewline
3 & 91.7166666666667 & 16.7271055656997 & 60.3 \tabularnewline
4 & 88.0666666666667 & 17.7394953978318 & 64.7 \tabularnewline
5 & 90.6833333333333 & 18.1090551599988 & 62.4 \tabularnewline
6 & 93.1666666666667 & 17.185529501369 & 63.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62914&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]95.5833333333333[/C][C]16.6541195194972[/C][C]57.5[/C][/ROW]
[ROW][C]2[/C][C]92.7583333333333[/C][C]16.101747975881[/C][C]54.6[/C][/ROW]
[ROW][C]3[/C][C]91.7166666666667[/C][C]16.7271055656997[/C][C]60.3[/C][/ROW]
[ROW][C]4[/C][C]88.0666666666667[/C][C]17.7394953978318[/C][C]64.7[/C][/ROW]
[ROW][C]5[/C][C]90.6833333333333[/C][C]18.1090551599988[/C][C]62.4[/C][/ROW]
[ROW][C]6[/C][C]93.1666666666667[/C][C]17.185529501369[/C][C]63.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62914&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62914&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
195.583333333333316.654119519497257.5
292.758333333333316.10174797588154.6
391.716666666666716.727105565699760.3
488.066666666666717.739495397831864.7
590.683333333333318.109055159998862.4
693.166666666666717.18552950136963.2







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha34.2839978485242
beta-0.186941317941696
S.D.0.113333022455248
T-STAT-1.64948674174391
p-value0.174392952808147

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 34.2839978485242 \tabularnewline
beta & -0.186941317941696 \tabularnewline
S.D. & 0.113333022455248 \tabularnewline
T-STAT & -1.64948674174391 \tabularnewline
p-value & 0.174392952808147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62914&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]34.2839978485242[/C][/ROW]
[ROW][C]beta[/C][C]-0.186941317941696[/C][/ROW]
[ROW][C]S.D.[/C][C]0.113333022455248[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.64948674174391[/C][/ROW]
[ROW][C]p-value[/C][C]0.174392952808147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62914&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62914&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)
alpha34.2839978485242
beta-0.186941317941696
S.D.0.113333022455248
T-STAT-1.64948674174391
p-value0.174392952808147







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha7.3526345236181
beta-0.998612533664138
S.D.0.608494783648736
T-STAT-1.64111930044187
p-value0.176116730705619
Lambda1.99861253366414

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 7.3526345236181 \tabularnewline
beta & -0.998612533664138 \tabularnewline
S.D. & 0.608494783648736 \tabularnewline
T-STAT & -1.64111930044187 \tabularnewline
p-value & 0.176116730705619 \tabularnewline
Lambda & 1.99861253366414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62914&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]7.3526345236181[/C][/ROW]
[ROW][C]beta[/C][C]-0.998612533664138[/C][/ROW]
[ROW][C]S.D.[/C][C]0.608494783648736[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.64111930044187[/C][/ROW]
[ROW][C]p-value[/C][C]0.176116730705619[/C][/ROW]
[ROW][C]Lambda[/C][C]1.99861253366414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62914&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62914&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)
alpha7.3526345236181
beta-0.998612533664138
S.D.0.608494783648736
T-STAT-1.64111930044187
p-value0.176116730705619
Lambda1.99861253366414



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')