Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSat, 19 Nov 2016 21:35:03 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Nov/19/t14795913543kdcu6l2gvcdkwv.htm/, Retrieved Sat, 04 May 2024 16:03:38 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 16:03:38 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
92,64
91,48
92,65
93,91
90,49
94,56
95,11
93,07
93,26
92,92
90,9
91,77
86,16
92,79
97,45
97,74
99,92
99,46
98,52
97,92
97,85
104,94
104,55
105,35
95,75
105,99
106,11
107,33
106,11
108,17
104,62
106,71
97,86
104,41
96,09
102,41
96,3
103,04
105,11
99,4
104,45
104,31
104,06
101,16
100,82
102,6
92,78
99,68
95,14
101,28
100,03
101,17
98,93
97,77
100,24
98,05
95,82
99,19
97,42
98,02
97,34
101,23
100,16
100,72
99,8
100,39
101,82
102,95
98,8
100,24
98,4
98,15




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
192.731.404648128050714.62
298.55416666666675.3531680651118519.19
3103.4633333333334.4378625758831512.42
4101.14253.6879536700895912.33
598.58833333333331.939947671896586.14
61001.61863017282015.61

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 92.73 & 1.40464812805071 & 4.62 \tabularnewline
2 & 98.5541666666667 & 5.35316806511185 & 19.19 \tabularnewline
3 & 103.463333333333 & 4.43786257588315 & 12.42 \tabularnewline
4 & 101.1425 & 3.68795367008959 & 12.33 \tabularnewline
5 & 98.5883333333333 & 1.93994767189658 & 6.14 \tabularnewline
6 & 100 & 1.6186301728201 & 5.61 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]92.73[/C][C]1.40464812805071[/C][C]4.62[/C][/ROW]
[ROW][C]2[/C][C]98.5541666666667[/C][C]5.35316806511185[/C][C]19.19[/C][/ROW]
[ROW][C]3[/C][C]103.463333333333[/C][C]4.43786257588315[/C][C]12.42[/C][/ROW]
[ROW][C]4[/C][C]101.1425[/C][C]3.68795367008959[/C][C]12.33[/C][/ROW]
[ROW][C]5[/C][C]98.5883333333333[/C][C]1.93994767189658[/C][C]6.14[/C][/ROW]
[ROW][C]6[/C][C]100[/C][C]1.6186301728201[/C][C]5.61[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
192.731.404648128050714.62
298.55416666666675.3531680651118519.19
3103.4633333333334.4378625758831512.42
4101.14253.6879536700895912.33
598.58833333333331.939947671896586.14
61001.61863017282015.61







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-21.0521230472564
beta0.243499115864706
S.D.0.193512821051385
T-STAT1.25830998970372
p-value0.276719393671575

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -21.0521230472564 \tabularnewline
beta & 0.243499115864706 \tabularnewline
S.D. & 0.193512821051385 \tabularnewline
T-STAT & 1.25830998970372 \tabularnewline
p-value & 0.276719393671575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-21.0521230472564[/C][/ROW]
[ROW][C]beta[/C][C]0.243499115864706[/C][/ROW]
[ROW][C]S.D.[/C][C]0.193512821051385[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.25830998970372[/C][/ROW]
[ROW][C]p-value[/C][C]0.276719393671575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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-21.0521230472564
beta0.243499115864706
S.D.0.193512821051385
T-STAT1.25830998970372
p-value0.276719393671575







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-42.4473160633323
beta9.45304143326148
S.D.6.05684739104222
T-STAT1.56071976441772
p-value0.193610064656849
Lambda-8.45304143326148

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -42.4473160633323 \tabularnewline
beta & 9.45304143326148 \tabularnewline
S.D. & 6.05684739104222 \tabularnewline
T-STAT & 1.56071976441772 \tabularnewline
p-value & 0.193610064656849 \tabularnewline
Lambda & -8.45304143326148 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-42.4473160633323[/C][/ROW]
[ROW][C]beta[/C][C]9.45304143326148[/C][/ROW]
[ROW][C]S.D.[/C][C]6.05684739104222[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.56071976441772[/C][/ROW]
[ROW][C]p-value[/C][C]0.193610064656849[/C][/ROW]
[ROW][C]Lambda[/C][C]-8.45304143326148[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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-42.4473160633323
beta9.45304143326148
S.D.6.05684739104222
T-STAT1.56071976441772
p-value0.193610064656849
Lambda-8.45304143326148



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')