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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 computationSun, 21 Dec 2008 05:53:05 -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/2008/Dec/21/t1229864060y776aum29jxbgun.htm/, Retrieved Sun, 19 May 2024 09:40:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35536, Retrieved Sun, 19 May 2024 09:40:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

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] [Werkloosheid - Jo...] [2008-12-14 16:04:23] [44ec60eb6065a3f81a5f756bd5af1faf]
- RMPD  [(Partial) Autocorrelation Function] [Werkloosheid - Jo...] [2008-12-21 11:28:19] [44ec60eb6065a3f81a5f756bd5af1faf]
-         [(Partial) Autocorrelation Function] [Werkloosheid - Jo...] [2008-12-21 12:34:50] [44ec60eb6065a3f81a5f756bd5af1faf]
- RM          [Standard Deviation-Mean Plot] [Werkloosheid - Jo...] [2008-12-21 12:53:05] [924502d03698cd41cacbcd1327858815] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21.1
21
20.4
19.5
18.6
18.8
23.7
24.8
25
23.6
22.3
21.8
20.8
19.7
18.3
17.4
17
18.1
23.9
25.6
25.3
23.6
21.9
21.4
20.6
20.5
20.2
20.6
19.7
19.3
22.8
23.5
23.8
22.6
22
21.7
20.7
20.2
19.1
19.5
18.7
18.6
22.2
23.2
23.5
21.3
20
18.7
18.9
18.3
18.4
19.9
19.2
18.5
20.9
20.5
19.4
18.1
17
17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35536&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35536&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
121.71666666666672.213115177273616.4
221.08333333333333.046557918621478.6
321.44166666666671.502397074576814.5
420.4751.738403133495064.9
518.84166666666671.224342742664473.9

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 21.7166666666667 & 2.21311517727361 & 6.4 \tabularnewline
2 & 21.0833333333333 & 3.04655791862147 & 8.6 \tabularnewline
3 & 21.4416666666667 & 1.50239707457681 & 4.5 \tabularnewline
4 & 20.475 & 1.73840313349506 & 4.9 \tabularnewline
5 & 18.8416666666667 & 1.22434274266447 & 3.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35536&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]21.7166666666667[/C][C]2.21311517727361[/C][C]6.4[/C][/ROW]
[ROW][C]2[/C][C]21.0833333333333[/C][C]3.04655791862147[/C][C]8.6[/C][/ROW]
[ROW][C]3[/C][C]21.4416666666667[/C][C]1.50239707457681[/C][C]4.5[/C][/ROW]
[ROW][C]4[/C][C]20.475[/C][C]1.73840313349506[/C][C]4.9[/C][/ROW]
[ROW][C]5[/C][C]18.8416666666667[/C][C]1.22434274266447[/C][C]3.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35536&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
121.71666666666672.213115177273616.4
221.08333333333333.046557918621478.6
321.44166666666671.502397074576814.5
420.4751.738403133495064.9
518.84166666666671.224342742664473.9






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-4.98913556231869
beta0.334791925886134
S.D.0.30457354485543
T-STAT1.09921538341436
p-value0.351976192056336

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -4.98913556231869 \tabularnewline
beta & 0.334791925886134 \tabularnewline
S.D. & 0.30457354485543 \tabularnewline
T-STAT & 1.09921538341436 \tabularnewline
p-value & 0.351976192056336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35536&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.98913556231869[/C][/ROW]
[ROW][C]beta[/C][C]0.334791925886134[/C][/ROW]
[ROW][C]S.D.[/C][C]0.30457354485543[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.09921538341436[/C][/ROW]
[ROW][C]p-value[/C][C]0.351976192056336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35536&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35536&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-4.98913556231869
beta0.334791925886134
S.D.0.30457354485543
T-STAT1.09921538341436
p-value0.351976192056336






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-11.1649512598056
beta3.88822375091385
S.D.2.81118079289622
T-STAT1.38312831417292
p-value0.260587972362049
Lambda-2.88822375091385

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -11.1649512598056 \tabularnewline
beta & 3.88822375091385 \tabularnewline
S.D. & 2.81118079289622 \tabularnewline
T-STAT & 1.38312831417292 \tabularnewline
p-value & 0.260587972362049 \tabularnewline
Lambda & -2.88822375091385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35536&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-11.1649512598056[/C][/ROW]
[ROW][C]beta[/C][C]3.88822375091385[/C][/ROW]
[ROW][C]S.D.[/C][C]2.81118079289622[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.38312831417292[/C][/ROW]
[ROW][C]p-value[/C][C]0.260587972362049[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.88822375091385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35536&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35536&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-11.1649512598056
beta3.88822375091385
S.D.2.81118079289622
T-STAT1.38312831417292
p-value0.260587972362049
Lambda-2.88822375091385


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 48 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 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')