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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 computationTue, 16 Dec 2008 07:37:33 -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/16/t12294385866hvtqpssbfuzisx.htm/, Retrieved Wed, 15 May 2024 16:02:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33969, Retrieved Wed, 15 May 2024 16:02:27 +0000
QR Codes:

Original text written by user:in samenwerking met stéphanie claes, Kevin Engels, Katrien Bourdiaudhy
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [blog 1e tijdreeks...] [2008-10-13 19:23:31] [7173087adebe3e3a714c80ea2417b3eb]
-   PD  [Univariate Data Series] [tijdreeksen opnie...] [2008-10-19 17:18:46] [7173087adebe3e3a714c80ea2417b3eb]
- RMP     [Central Tendency] [tijdreeks 2 centr...] [2008-10-19 17:39:42] [7173087adebe3e3a714c80ea2417b3eb]
- RMP         [Standard Deviation-Mean Plot] [mean plot: aanvra...] [2008-12-16 14:37:33] [35348cd8592af0baf5f138bd59921307] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2400
4700
3700
2900
2800
3000
3100
3700
3000
2000
1900
1900
1800
3400
3800
2800
3100
2100
2000
2500
2400
2500
3300
3100
3700
5600
3700
2900
4000
2900
2400
3300
3800
4400
4000
3100
2700
5200
4600
3700
3200
2400
2200
3200
3100
2300
2500
2900
2700
5000
3500
3000
3800
2800
2400
2700
2800
2700
2600
3100

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33969&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33969&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12925833.5302797575672800
22733.33333333333618.4045944397912000
33650839.3720596645183200
43166.66666666667928.6679936469453000
53091.66666666667717.898870230892600

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2925 & 833.530279757567 & 2800 \tabularnewline
2 & 2733.33333333333 & 618.404594439791 & 2000 \tabularnewline
3 & 3650 & 839.372059664518 & 3200 \tabularnewline
4 & 3166.66666666667 & 928.667993646945 & 3000 \tabularnewline
5 & 3091.66666666667 & 717.89887023089 & 2600 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33969&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]2925[/C][C]833.530279757567[/C][C]2800[/C][/ROW]
[ROW][C]2[/C][C]2733.33333333333[/C][C]618.404594439791[/C][C]2000[/C][/ROW]
[ROW][C]3[/C][C]3650[/C][C]839.372059664518[/C][C]3200[/C][/ROW]
[ROW][C]4[/C][C]3166.66666666667[/C][C]928.667993646945[/C][C]3000[/C][/ROW]
[ROW][C]5[/C][C]3091.66666666667[/C][C]717.89887023089[/C][C]2600[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33969&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
12925833.5302797575672800
22733.33333333333618.4045944397912000
33650839.3720596645183200
43166.66666666667928.6679936469453000
53091.66666666667717.898870230892600






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha176.647198026451
beta0.196229409482278
S.D.0.168283287415772
T-STAT1.16606593854719
p-value0.327861238475731

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 176.647198026451 \tabularnewline
beta & 0.196229409482278 \tabularnewline
S.D. & 0.168283287415772 \tabularnewline
T-STAT & 1.16606593854719 \tabularnewline
p-value & 0.327861238475731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33969&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]176.647198026451[/C][/ROW]
[ROW][C]beta[/C][C]0.196229409482278[/C][/ROW]
[ROW][C]S.D.[/C][C]0.168283287415772[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.16606593854719[/C][/ROW]
[ROW][C]p-value[/C][C]0.327861238475731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33969&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33969&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)
alpha176.647198026451
beta0.196229409482278
S.D.0.168283287415772
T-STAT1.16606593854719
p-value0.327861238475731






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-0.499785214524136
beta0.890549126491261
S.D.0.679629423805301
T-STAT1.31034516060974
p-value0.281373700397898
Lambda0.109450873508739

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -0.499785214524136 \tabularnewline
beta & 0.890549126491261 \tabularnewline
S.D. & 0.679629423805301 \tabularnewline
T-STAT & 1.31034516060974 \tabularnewline
p-value & 0.281373700397898 \tabularnewline
Lambda & 0.109450873508739 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33969&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.499785214524136[/C][/ROW]
[ROW][C]beta[/C][C]0.890549126491261[/C][/ROW]
[ROW][C]S.D.[/C][C]0.679629423805301[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.31034516060974[/C][/ROW]
[ROW][C]p-value[/C][C]0.281373700397898[/C][/ROW]
[ROW][C]Lambda[/C][C]0.109450873508739[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33969&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33969&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-0.499785214524136
beta0.890549126491261
S.D.0.679629423805301
T-STAT1.31034516060974
p-value0.281373700397898
Lambda0.109450873508739


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')