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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 computationThu, 26 Nov 2009 08:15:43 -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/26/t1259248577imzjg3bs3r82nex.htm/, Retrieved Mon, 29 Apr 2024 01:14:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60077, Retrieved Mon, 29 Apr 2024 01:14:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
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] [WS 8.7] [2009-11-26 15:15:43] [51118f1042b56b16d340924f16263174] [Current]
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Dataseries X:
1076.7
1035.9
1037
1154
1237.2
996.6
1238.2
1153.4
1268.1
1156
1144.5
1232.9
1055.2
1109.7
1079.8
1126.3
1196.8
1130.4
1183.6
1200.9
1426.6
1080.4
1325.4
1230
1125.9
1174.5
1151.9
1439.3
1344.3
1319.1
1257.6
1249.1
1397.1
1348
1548.2
1377.6
1402.9
1167.6
1392.9
1547
1420
1266.4
1280.8
1128.6
1449.5
1511.7
1548.3
1652
1650.5
1370.8
1653.3
1474.3
1418.8
1554.1
1156.6
1223.4
1337.5
1098.9
1037.6
1202.5




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=60077&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=60077&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60077&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
11144.2083333333390.74772682686271.5
21178.75833333333109.123536818959371.4
31311.05125.048067121844422.3
41397.30833333333160.150562445204523.4
51348.19166666667209.004973661278615.7

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1144.20833333333 & 90.74772682686 & 271.5 \tabularnewline
2 & 1178.75833333333 & 109.123536818959 & 371.4 \tabularnewline
3 & 1311.05 & 125.048067121844 & 422.3 \tabularnewline
4 & 1397.30833333333 & 160.150562445204 & 523.4 \tabularnewline
5 & 1348.19166666667 & 209.004973661278 & 615.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60077&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]1144.20833333333[/C][C]90.74772682686[/C][C]271.5[/C][/ROW]
[ROW][C]2[/C][C]1178.75833333333[/C][C]109.123536818959[/C][C]371.4[/C][/ROW]
[ROW][C]3[/C][C]1311.05[/C][C]125.048067121844[/C][C]422.3[/C][/ROW]
[ROW][C]4[/C][C]1397.30833333333[/C][C]160.150562445204[/C][C]523.4[/C][/ROW]
[ROW][C]5[/C][C]1348.19166666667[/C][C]209.004973661278[/C][C]615.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60077&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60077&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
11144.2083333333390.74772682686271.5
21178.75833333333109.123536818959371.4
31311.05125.048067121844422.3
41397.30833333333160.150562445204523.4
51348.19166666667209.004973661278615.7







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-297.157455931829
beta0.341697068983798
S.D.0.148197291018341
T-STAT2.30569038499839
p-value0.104442283079383

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -297.157455931829 \tabularnewline
beta & 0.341697068983798 \tabularnewline
S.D. & 0.148197291018341 \tabularnewline
T-STAT & 2.30569038499839 \tabularnewline
p-value & 0.104442283079383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60077&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-297.157455931829[/C][/ROW]
[ROW][C]beta[/C][C]0.341697068983798[/C][/ROW]
[ROW][C]S.D.[/C][C]0.148197291018341[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.30569038499839[/C][/ROW]
[ROW][C]p-value[/C][C]0.104442283079383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60077&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60077&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)
alpha-297.157455931829
beta0.341697068983798
S.D.0.148197291018341
T-STAT2.30569038499839
p-value0.104442283079383







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-18.2224496942194
beta3.23316232832730
S.D.1.11764312731550
T-STAT2.89283962770220
p-value0.0628674486147804
Lambda-2.23316232832730

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -18.2224496942194 \tabularnewline
beta & 3.23316232832730 \tabularnewline
S.D. & 1.11764312731550 \tabularnewline
T-STAT & 2.89283962770220 \tabularnewline
p-value & 0.0628674486147804 \tabularnewline
Lambda & -2.23316232832730 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60077&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-18.2224496942194[/C][/ROW]
[ROW][C]beta[/C][C]3.23316232832730[/C][/ROW]
[ROW][C]S.D.[/C][C]1.11764312731550[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.89283962770220[/C][/ROW]
[ROW][C]p-value[/C][C]0.0628674486147804[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.23316232832730[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60077&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60077&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-18.2224496942194
beta3.23316232832730
S.D.1.11764312731550
T-STAT2.89283962770220
p-value0.0628674486147804
Lambda-2.23316232832730



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')