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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, 01 Dec 2009 11:18:56 -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/01/t12596921712do3w7er8q65d9o.htm/, Retrieved Thu, 28 Mar 2024 19:02:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62162, Retrieved Thu, 28 Mar 2024 19:02:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
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       [Spectral Analysis] [Identifying Integ...] [2009-11-22 12:38:17] [b98453cac15ba1066b407e146608df68]
- RMPD        [Standard Deviation-Mean Plot] [WS 8 standard dev...] [2009-11-27 13:10:49] [12f02da0296cb21dc23d82ae014a8b71]
-   P             [Standard Deviation-Mean Plot] [WS 9 SD] [2009-12-01 18:18:56] [51d49d3536f6a59f2486a67bf50b2759] [Current]
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Dataseries X:
1901
1395
1639
1643
1751
1797
1373
1558
1555
2061
2010
2119
1985
1963
2017
1975
1589
1679
1392
1511
1449
1767
1899
2179
2217
2049
2343
2175
1607
1702
1764
1766
1615
1953
2091
2411
2550
2351
2786
2525
2474
2332
1978
1789
1904
1997
2207
2453
1948
1384
1989
2140
2100
2045
2083
2022
1950
1422
1859
2147




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=62162&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=62162&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62162&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
11733.5250.279661760418746
21783.75256.234666947028787
31974.41666666667281.433169310014804
42278.83333333333305.008743044086997
51924.08333333333257.645442890854763

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1733.5 & 250.279661760418 & 746 \tabularnewline
2 & 1783.75 & 256.234666947028 & 787 \tabularnewline
3 & 1974.41666666667 & 281.433169310014 & 804 \tabularnewline
4 & 2278.83333333333 & 305.008743044086 & 997 \tabularnewline
5 & 1924.08333333333 & 257.645442890854 & 763 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62162&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]1733.5[/C][C]250.279661760418[/C][C]746[/C][/ROW]
[ROW][C]2[/C][C]1783.75[/C][C]256.234666947028[/C][C]787[/C][/ROW]
[ROW][C]3[/C][C]1974.41666666667[/C][C]281.433169310014[/C][C]804[/C][/ROW]
[ROW][C]4[/C][C]2278.83333333333[/C][C]305.008743044086[/C][C]997[/C][/ROW]
[ROW][C]5[/C][C]1924.08333333333[/C][C]257.645442890854[/C][C]763[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62162&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62162&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
11733.5250.279661760418746
21783.75256.234666947028787
31974.41666666667281.433169310014804
42278.83333333333305.008743044086997
51924.08333333333257.645442890854763







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha72.5647838509398
beta0.101889656392078
S.D.0.0183111675621675
T-STAT5.56434514872724
p-value0.0114525914013685

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 72.5647838509398 \tabularnewline
beta & 0.101889656392078 \tabularnewline
S.D. & 0.0183111675621675 \tabularnewline
T-STAT & 5.56434514872724 \tabularnewline
p-value & 0.0114525914013685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62162&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]72.5647838509398[/C][/ROW]
[ROW][C]beta[/C][C]0.101889656392078[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0183111675621675[/C][/ROW]
[ROW][C]T-STAT[/C][C]5.56434514872724[/C][/ROW]
[ROW][C]p-value[/C][C]0.0114525914013685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62162&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62162&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)
alpha72.5647838509398
beta0.101889656392078
S.D.0.0183111675621675
T-STAT5.56434514872724
p-value0.0114525914013685







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.0529586958667381
beta0.732715119373562
S.D.0.137843756695753
T-STAT5.31554810270299
p-value0.0130042619485485
Lambda0.267284880626438

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.0529586958667381 \tabularnewline
beta & 0.732715119373562 \tabularnewline
S.D. & 0.137843756695753 \tabularnewline
T-STAT & 5.31554810270299 \tabularnewline
p-value & 0.0130042619485485 \tabularnewline
Lambda & 0.267284880626438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62162&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0529586958667381[/C][/ROW]
[ROW][C]beta[/C][C]0.732715119373562[/C][/ROW]
[ROW][C]S.D.[/C][C]0.137843756695753[/C][/ROW]
[ROW][C]T-STAT[/C][C]5.31554810270299[/C][/ROW]
[ROW][C]p-value[/C][C]0.0130042619485485[/C][/ROW]
[ROW][C]Lambda[/C][C]0.267284880626438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62162&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62162&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)
alpha0.0529586958667381
beta0.732715119373562
S.D.0.137843756695753
T-STAT5.31554810270299
p-value0.0130042619485485
Lambda0.267284880626438



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')