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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 04:44:01 -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/t1259063101ip5354ua869uhno.htm/, Retrieved Fri, 26 Apr 2024 19:12:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58999, Retrieved Fri, 26 Apr 2024 19:12:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact235
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]
-    D          [Standard Deviation-Mean Plot] [] [2009-11-24 11:44:01] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]
-   PD            [Standard Deviation-Mean Plot] [] [2009-12-20 10:03:09] [5d885a68c2332cc44f6191ec94766bfa]
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Dataseries X:
100.03
100.25
99.6
100.16
100.49
99.72
100.14
98.48
100.38
101.45
98.42
98.6
100.06
98.62
100.84
100.02
97.95
98.32
98.27
97.22
99.28
100.38
99.02
100.32
99.81
100.6
101.19
100.47
101.77
102.32
102.39
101.16
100.63
101.48
101.44
100.09
100.7
100.78
99.81
98.45
98.49
97.48
97.91
96.94
98.53
96.82
95.76
95.27
97.32
96.68
97.87
97.42
97.94
99.52
100.99
99.92
101.97
101.58
99.54
100.83




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58999&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]2 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=58999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58999&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
199.810.9129174014214993.03
299.19166666666671.137979336234613.62000000000000
3101.11250.821485627606252.58
498.07833333333331.763502884670865.51
599.29833333333331.814796924944465.28999999999999

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 99.81 & 0.912917401421499 & 3.03 \tabularnewline
2 & 99.1916666666667 & 1.13797933623461 & 3.62000000000000 \tabularnewline
3 & 101.1125 & 0.82148562760625 & 2.58 \tabularnewline
4 & 98.0783333333333 & 1.76350288467086 & 5.51 \tabularnewline
5 & 99.2983333333333 & 1.81479692494446 & 5.28999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58999&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]99.81[/C][C]0.912917401421499[/C][C]3.03[/C][/ROW]
[ROW][C]2[/C][C]99.1916666666667[/C][C]1.13797933623461[/C][C]3.62000000000000[/C][/ROW]
[ROW][C]3[/C][C]101.1125[/C][C]0.82148562760625[/C][C]2.58[/C][/ROW]
[ROW][C]4[/C][C]98.0783333333333[/C][C]1.76350288467086[/C][C]5.51[/C][/ROW]
[ROW][C]5[/C][C]99.2983333333333[/C][C]1.81479692494446[/C][C]5.28999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58999&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
199.810.9129174014214993.03
299.19166666666671.137979336234613.62000000000000
3101.11250.821485627606252.58
498.07833333333331.763502884670865.51
599.29833333333331.814796924944465.28999999999999







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha34.1854280838293
beta-0.330612037898726
S.D.0.155921044670487
T-STAT-2.12038111082067
p-value0.124137590952056

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 34.1854280838293 \tabularnewline
beta & -0.330612037898726 \tabularnewline
S.D. & 0.155921044670487 \tabularnewline
T-STAT & -2.12038111082067 \tabularnewline
p-value & 0.124137590952056 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58999&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]34.1854280838293[/C][/ROW]
[ROW][C]beta[/C][C]-0.330612037898726[/C][/ROW]
[ROW][C]S.D.[/C][C]0.155921044670487[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.12038111082067[/C][/ROW]
[ROW][C]p-value[/C][C]0.124137590952056[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58999&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58999&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)
alpha34.1854280838293
beta-0.330612037898726
S.D.0.155921044670487
T-STAT-2.12038111082067
p-value0.124137590952056







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha123.930631445638
beta-26.8972276716066
S.D.11.2315310756321
T-STAT-2.39479617609416
p-value0.096324412946384
Lambda27.8972276716066

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 123.930631445638 \tabularnewline
beta & -26.8972276716066 \tabularnewline
S.D. & 11.2315310756321 \tabularnewline
T-STAT & -2.39479617609416 \tabularnewline
p-value & 0.096324412946384 \tabularnewline
Lambda & 27.8972276716066 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58999&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]123.930631445638[/C][/ROW]
[ROW][C]beta[/C][C]-26.8972276716066[/C][/ROW]
[ROW][C]S.D.[/C][C]11.2315310756321[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.39479617609416[/C][/ROW]
[ROW][C]p-value[/C][C]0.096324412946384[/C][/ROW]
[ROW][C]Lambda[/C][C]27.8972276716066[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58999&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58999&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)
alpha123.930631445638
beta-26.8972276716066
S.D.11.2315310756321
T-STAT-2.39479617609416
p-value0.096324412946384
Lambda27.8972276716066



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')