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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 computationSat, 06 Dec 2008 03:04:30 -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/06/t12285579323q35zsh2pl8z641.htm/, Retrieved Sat, 18 May 2024 05:30:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29461, Retrieved Sat, 18 May 2024 05:30:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

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 4] [2008-10-27 17:43:09] [2d4aec5ed1856c4828162be37be304d9]
- RMPD      [Standard Deviation-Mean Plot] [Eigen tijdreeks S...] [2008-12-06 10:04:30] [d7f41258beeebb8716e3f5d39f3cdc01] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110.3
114.8
94.6
92
93.8
93.8
107.6
101
95.4
96.5
89.2
87.1
110.5
110.8
104.2
88.9
89.8
90
93.9
91.3
87.8
99.7
73.5
79.2
96.9
95.2
95.6
89.7
92.8
88
101.1
92.7
95.8
103.8
81.8
87.1
105.9
108.1
102.6
93.7
103.5
100.6
113.3
102.4
102.1
106.9
87.3
93.1
109.1
120.3
104.9
92.6
109.8
111.4
117.9
121.6
117.8
124.2
106.8
100.9

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=29461&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=29461&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29461&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
198.00833333333338.6486423877690327.7
293.311.401036635483537.3
393.3756.1161671003987522
4101.6257.2070830059425226
5111.4416666666679.3673959060459331.6

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 98.0083333333333 & 8.64864238776903 & 27.7 \tabularnewline
2 & 93.3 & 11.4010366354835 & 37.3 \tabularnewline
3 & 93.375 & 6.11616710039875 & 22 \tabularnewline
4 & 101.625 & 7.20708300594252 & 26 \tabularnewline
5 & 111.441666666667 & 9.36739590604593 & 31.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29461&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]98.0083333333333[/C][C]8.64864238776903[/C][C]27.7[/C][/ROW]
[ROW][C]2[/C][C]93.3[/C][C]11.4010366354835[/C][C]37.3[/C][/ROW]
[ROW][C]3[/C][C]93.375[/C][C]6.11616710039875[/C][C]22[/C][/ROW]
[ROW][C]4[/C][C]101.625[/C][C]7.20708300594252[/C][C]26[/C][/ROW]
[ROW][C]5[/C][C]111.441666666667[/C][C]9.36739590604593[/C][C]31.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29461&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29461&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
198.00833333333338.6486423877690327.7
293.311.401036635483537.3
393.3756.1161671003987522
4101.6257.2070830059425226
5111.4416666666679.3673959060459331.6






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha6.78429405558249
beta0.0177174379863932
S.D.0.156069872850601
T-STAT0.113522473381864
p-value0.916787111498495

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 6.78429405558249 \tabularnewline
beta & 0.0177174379863932 \tabularnewline
S.D. & 0.156069872850601 \tabularnewline
T-STAT & 0.113522473381864 \tabularnewline
p-value & 0.916787111498495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29461&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]6.78429405558249[/C][/ROW]
[ROW][C]beta[/C][C]0.0177174379863932[/C][/ROW]
[ROW][C]S.D.[/C][C]0.156069872850601[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.113522473381864[/C][/ROW]
[ROW][C]p-value[/C][C]0.916787111498495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29461&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29461&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)
alpha6.78429405558249
beta0.0177174379863932
S.D.0.156069872850601
T-STAT0.113522473381864
p-value0.916787111498495






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha0.184982981554578
beta0.421420745578844
S.D.1.86472881556393
T-STAT0.225995727669064
p-value0.835726485127381
Lambda0.578579254421156

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 0.184982981554578 \tabularnewline
beta & 0.421420745578844 \tabularnewline
S.D. & 1.86472881556393 \tabularnewline
T-STAT & 0.225995727669064 \tabularnewline
p-value & 0.835726485127381 \tabularnewline
Lambda & 0.578579254421156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29461&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.184982981554578[/C][/ROW]
[ROW][C]beta[/C][C]0.421420745578844[/C][/ROW]
[ROW][C]S.D.[/C][C]1.86472881556393[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.225995727669064[/C][/ROW]
[ROW][C]p-value[/C][C]0.835726485127381[/C][/ROW]
[ROW][C]Lambda[/C][C]0.578579254421156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29461&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29461&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)
alpha0.184982981554578
beta0.421420745578844
S.D.1.86472881556393
T-STAT0.225995727669064
p-value0.835726485127381
Lambda0.578579254421156


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')