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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 05:05:30 -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/01/t1228133218xpsz4ljdlmmrn3l.htm/, Retrieved Sun, 05 May 2024 08:47:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26891, Retrieved Sun, 05 May 2024 08:47:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201

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 1] [2008-10-27 17:38:46] [2d4aec5ed1856c4828162be37be304d9]
- RMPD      [Standard Deviation-Mean Plot] [Q8 tijdreeks 1 SD...] [2008-12-01 12:05:30] [d7f41258beeebb8716e3f5d39f3cdc01] [Current]
- RM          [Variance Reduction Matrix] [Q8 tijdreeks 1 VRM] [2008-12-01 12:09:00] [2d4aec5ed1856c4828162be37be304d9]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.8
111.7
98.6
96.9
95.1
97
112.7
102.9
97.4
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102
106
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100
110.7
112.8
109.8
117.3
109.1
115.9
96
97.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 12 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26891&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26891&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26891&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 time12 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1101.4758.1286390452075525.3
2101.3759.3225167300369133.2
3104.7083333333338.488329778245130.1
4108.87.6478160875566926.3
5109.7833333333338.075646141706626.4

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 101.475 & 8.12863904520755 & 25.3 \tabularnewline
2 & 101.375 & 9.32251673003691 & 33.2 \tabularnewline
3 & 104.708333333333 & 8.4883297782451 & 30.1 \tabularnewline
4 & 108.8 & 7.64781608755669 & 26.3 \tabularnewline
5 & 109.783333333333 & 8.0756461417066 & 26.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26891&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]101.475[/C][C]8.12863904520755[/C][C]25.3[/C][/ROW]
[ROW][C]2[/C][C]101.375[/C][C]9.32251673003691[/C][C]33.2[/C][/ROW]
[ROW][C]3[/C][C]104.708333333333[/C][C]8.4883297782451[/C][C]30.1[/C][/ROW]
[ROW][C]4[/C][C]108.8[/C][C]7.64781608755669[/C][C]26.3[/C][/ROW]
[ROW][C]5[/C][C]109.783333333333[/C][C]8.0756461417066[/C][C]26.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26891&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26891&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
1101.4758.1286390452075525.3
2101.3759.3225167300369133.2
3104.7083333333338.488329778245130.1
4108.87.6478160875566926.3
5109.7833333333338.075646141706626.4






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha19.6525996777037
beta-0.107575685773665
S.D.0.0674148041069185
T-STAT-1.59572792947751
p-value0.208821298330654

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 19.6525996777037 \tabularnewline
beta & -0.107575685773665 \tabularnewline
S.D. & 0.0674148041069185 \tabularnewline
T-STAT & -1.59572792947751 \tabularnewline
p-value & 0.208821298330654 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26891&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]19.6525996777037[/C][/ROW]
[ROW][C]beta[/C][C]-0.107575685773665[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0674148041069185[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.59572792947751[/C][/ROW]
[ROW][C]p-value[/C][C]0.208821298330654[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26891&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26891&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)
alpha19.6525996777037
beta-0.107575685773665
S.D.0.0674148041069185
T-STAT-1.59572792947751
p-value0.208821298330654






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha8.36709241332268
beta-1.34229265658581
S.D.0.831306687166835
T-STAT-1.61467804518746
p-value0.204789849026157
Lambda2.34229265658581

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 8.36709241332268 \tabularnewline
beta & -1.34229265658581 \tabularnewline
S.D. & 0.831306687166835 \tabularnewline
T-STAT & -1.61467804518746 \tabularnewline
p-value & 0.204789849026157 \tabularnewline
Lambda & 2.34229265658581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26891&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]8.36709241332268[/C][/ROW]
[ROW][C]beta[/C][C]-1.34229265658581[/C][/ROW]
[ROW][C]S.D.[/C][C]0.831306687166835[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.61467804518746[/C][/ROW]
[ROW][C]p-value[/C][C]0.204789849026157[/C][/ROW]
[ROW][C]Lambda[/C][C]2.34229265658581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26891&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26891&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)
alpha8.36709241332268
beta-1.34229265658581
S.D.0.831306687166835
T-STAT-1.61467804518746
p-value0.204789849026157
Lambda2.34229265658581


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')