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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 computationMon, 01 Dec 2008 05:11:38 -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/01/t1228133529bqfiv14k5mjtcfn.htm/, Retrieved Sun, 05 May 2024 11:47:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26893, Retrieved Sun, 05 May 2024 11:47:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact215

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Harrell-Davis Quantiles] [Q7 95% confidence...] [2007-10-20 15:02:46] [b731da8b544846036771bbf9bf2f34ce]
- RMPD  [Univariate Data Series] [Tijdreeks 2] [2008-10-27 17:40:40] [2d4aec5ed1856c4828162be37be304d9]
- RMPD      [Standard Deviation-Mean Plot] [Q8 tijdreeks 2 SD...] [2008-12-01 12:11:38] [d7f41258beeebb8716e3f5d39f3cdc01] [Current]
- RM          [Variance Reduction Matrix] [Q8 tijdreeks 2 VRM] [2008-12-01 12:13:35] [2d4aec5ed1856c4828162be37be304d9]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
148.8
146.7
118.8
99.4
97.6
110.2
146.6
136.4
126.2
154.9
109
128.5
144.9
136.3
134.8
103.4
106.6
119.2
149.3
150.2
142.9
163.6
98.2
138.2
143.7
132.8
149.4
128.8
98.9
106.2
140.7
133
156.4
157.7
107.9
133.6
148.1
205.6
193.1
117.5
116.4
129.5
157.1
157
158.4
161.7
116.9
161.1
155.7
160.8
145.4
111
144.8
149.2
156.6
182.5
171.3
172.7
133
148.1

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26893&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26893&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26893&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'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1126.92520.03787890244657.3
2132.320.774547364065965.4
3132.42519.366143136928458.8
4151.86666666666728.670394401743389.2
5152.59166666666719.068701549604271.5

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 126.925 & 20.037878902446 & 57.3 \tabularnewline
2 & 132.3 & 20.7745473640659 & 65.4 \tabularnewline
3 & 132.425 & 19.3661431369284 & 58.8 \tabularnewline
4 & 151.866666666667 & 28.6703944017433 & 89.2 \tabularnewline
5 & 152.591666666667 & 19.0687015496042 & 71.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26893&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]126.925[/C][C]20.037878902446[/C][C]57.3[/C][/ROW]
[ROW][C]2[/C][C]132.3[/C][C]20.7745473640659[/C][C]65.4[/C][/ROW]
[ROW][C]3[/C][C]132.425[/C][C]19.3661431369284[/C][C]58.8[/C][/ROW]
[ROW][C]4[/C][C]151.866666666667[/C][C]28.6703944017433[/C][C]89.2[/C][/ROW]
[ROW][C]5[/C][C]152.591666666667[/C][C]19.0687015496042[/C][C]71.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26893&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26893&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
1126.92520.03787890244657.3
2132.320.774547364065965.4
3132.42519.366143136928458.8
4151.86666666666728.670394401743389.2
5152.59166666666719.068701549604271.5






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-1.22416276579596
beta0.163822890379277
S.D.0.166961162864174
T-STAT0.98120357793956
p-value0.398847747792771

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -1.22416276579596 \tabularnewline
beta & 0.163822890379277 \tabularnewline
S.D. & 0.166961162864174 \tabularnewline
T-STAT & 0.98120357793956 \tabularnewline
p-value & 0.398847747792771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26893&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.22416276579596[/C][/ROW]
[ROW][C]beta[/C][C]0.163822890379277[/C][/ROW]
[ROW][C]S.D.[/C][C]0.166961162864174[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.98120357793956[/C][/ROW]
[ROW][C]p-value[/C][C]0.398847747792771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26893&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26893&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-1.22416276579596
beta0.163822890379277
S.D.0.166961162864174
T-STAT0.98120357793956
p-value0.398847747792771






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.50801013127196
beta0.92594460105623
S.D.0.999219813471286
T-STAT0.92666757461454
p-value0.422449548723832
Lambda0.0740553989437706

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.50801013127196 \tabularnewline
beta & 0.92594460105623 \tabularnewline
S.D. & 0.999219813471286 \tabularnewline
T-STAT & 0.92666757461454 \tabularnewline
p-value & 0.422449548723832 \tabularnewline
Lambda & 0.0740553989437706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26893&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.50801013127196[/C][/ROW]
[ROW][C]beta[/C][C]0.92594460105623[/C][/ROW]
[ROW][C]S.D.[/C][C]0.999219813471286[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.92666757461454[/C][/ROW]
[ROW][C]p-value[/C][C]0.422449548723832[/C][/ROW]
[ROW][C]Lambda[/C][C]0.0740553989437706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26893&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26893&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-1.50801013127196
beta0.92594460105623
S.D.0.999219813471286
T-STAT0.92666757461454
p-value0.422449548723832
Lambda0.0740553989437706


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')