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Description of Statistical Computation

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 computationFri, 04 Dec 2009 04:56:02 -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/04/t1259927847rk4qsyprrtgxvwd.htm/, Retrieved Sat, 27 Apr 2024 19:15:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63327, Retrieved Sat, 27 Apr 2024 19:15:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

Tree of Dependent Computations

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] [standard deviatio...] [2009-12-04 11:56:02] [1c773da0103d9327c2f1f790e2d74438] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.4816
1.4562
1.4268
1.4088
1.4016
1.3650
1.3190
1.3050
1.2785
1.3239
1.3449
1.2732
1.3322
1.4369
1.4975
1.5770
1.5553
1.5557
1.5750
1.5527
1.4748
1.4718
1.4570
1.4684
1.4227
1.3896
1.3622
1.3716
1.3419
1.3511
1.3516
1.3242
1.3074
1.2999
1.3213
1.2881
1.2611
1.2727
1.2811
1.2684
1.2650
1.2770
1.2271
1.2020
1.1938
1.2103
1.1856
1.1786
1.2015
1.2256
1.2292
1.2037
1.2165
1.2694
1.2938
1.3201
1.3014
1.3119
1.3408
1.2991

Tables (Output of Computation)





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=63327&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=63327&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63327&T=0

As an alternative you can also use a QR Code:  
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
11.3653750.06922116235792210.2084
21.496191666666670.07182071691920010.2448
31.34430.0390734273806730.1346
41.2352250.03941264506359720.102500000000000
51.267750.04972951382684680.1393

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.365375 & 0.0692211623579221 & 0.2084 \tabularnewline
2 & 1.49619166666667 & 0.0718207169192001 & 0.2448 \tabularnewline
3 & 1.3443 & 0.039073427380673 & 0.1346 \tabularnewline
4 & 1.235225 & 0.0394126450635972 & 0.102500000000000 \tabularnewline
5 & 1.26775 & 0.0497295138268468 & 0.1393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63327&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]1.365375[/C][C]0.0692211623579221[/C][C]0.2084[/C][/ROW]
[ROW][C]2[/C][C]1.49619166666667[/C][C]0.0718207169192001[/C][C]0.2448[/C][/ROW]
[ROW][C]3[/C][C]1.3443[/C][C]0.039073427380673[/C][C]0.1346[/C][/ROW]
[ROW][C]4[/C][C]1.235225[/C][C]0.0394126450635972[/C][C]0.102500000000000[/C][/ROW]
[ROW][C]5[/C][C]1.26775[/C][C]0.0497295138268468[/C][C]0.1393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63327&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63327&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.3653750.06922116235792210.2084
21.496191666666670.07182071691920010.2448
31.34430.0390734273806730.1346
41.2352250.03941264506359720.102500000000000
51.267750.04972951382684680.1393






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.106994534405879
beta0.119876154116665
S.D.0.0575906713069132
T-STAT2.08152034689470
p-value0.128815338716801

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.106994534405879 \tabularnewline
beta & 0.119876154116665 \tabularnewline
S.D. & 0.0575906713069132 \tabularnewline
T-STAT & 2.08152034689470 \tabularnewline
p-value & 0.128815338716801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63327&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.106994534405879[/C][/ROW]
[ROW][C]beta[/C][C]0.119876154116665[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0575906713069132[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.08152034689470[/C][/ROW]
[ROW][C]p-value[/C][C]0.128815338716801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63327&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63327&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.106994534405879
beta0.119876154116665
S.D.0.0575906713069132
T-STAT2.08152034689470
p-value0.128815338716801






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.81114431583423
beta2.93034017436506
S.D.1.52925837657265
T-STAT1.91618383083995
p-value0.151203607862386
Lambda-1.93034017436506

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.81114431583423 \tabularnewline
beta & 2.93034017436506 \tabularnewline
S.D. & 1.52925837657265 \tabularnewline
T-STAT & 1.91618383083995 \tabularnewline
p-value & 0.151203607862386 \tabularnewline
Lambda & -1.93034017436506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63327&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.81114431583423[/C][/ROW]
[ROW][C]beta[/C][C]2.93034017436506[/C][/ROW]
[ROW][C]S.D.[/C][C]1.52925837657265[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.91618383083995[/C][/ROW]
[ROW][C]p-value[/C][C]0.151203607862386[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.93034017436506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63327&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63327&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.81114431583423
beta2.93034017436506
S.D.1.52925837657265
T-STAT1.91618383083995
p-value0.151203607862386
Lambda-1.93034017436506


Figures (Output of Computation)

Input Parameters & R Code

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')