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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 computationThu, 18 Dec 2008 14:34:24 -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/18/t1229636267xmb3dxxfxcl563g.htm/, Retrieved Sat, 11 May 2024 07:51:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=34959, Retrieved Sat, 11 May 2024 07:51:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact190

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [verband tussen in...] [2008-12-17 19:43:34] [5e74953d94072114d25d7276793b561e]
- RMPD  [Standard Deviation-Mean Plot] [werkloosheid/invoer] [2008-12-17 21:07:30] [5e74953d94072114d25d7276793b561e]
- RMPD    [Central Tendency] [aangespast werklo...] [2008-12-18 21:18:51] [5e74953d94072114d25d7276793b561e]
- RMPD      [Univariate Explorative Data Analysis] [aangespast werklo...] [2008-12-18 21:26:12] [5e74953d94072114d25d7276793b561e]
- RMP           [Standard Deviation-Mean Plot] [aangepast werkloo...] [2008-12-18 21:34:24] [5925747fb2a6bb4cfcd8015825ee5e92] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15.59
13.17
13.30
11.60
15.18
15.87
12.58
11.43
11.26
14.36
14.47
13.24
13.03
14.43
13.98
13.62
12.20
12.24
12.07
12.30
14.59
12.96
14.14
13.92
14.24
14.10
12.91
13.69
14.11
13.99
14.53
15.36
14.95
15.95
15.25
12.67
13.86
14.65
12.41
15.33
15.31
14.84
14.75
15.83
14.83
13.00
13.92
13.94
12.54
14.41
15.18
12.99
13.06
12.81
12.95
13.23
11.80
11.69
15.33
14.89
12.92
11.27
11.55

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=34959&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=34959&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34959&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
113.50416666666671.608587513279524.61
213.290.9356378475768182.52
314.31250.970427506542633.28
414.38916666666670.9978928557290873.42
513.40666666666671.251176416115023.64

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 13.5041666666667 & 1.60858751327952 & 4.61 \tabularnewline
2 & 13.29 & 0.935637847576818 & 2.52 \tabularnewline
3 & 14.3125 & 0.97042750654263 & 3.28 \tabularnewline
4 & 14.3891666666667 & 0.997892855729087 & 3.42 \tabularnewline
5 & 13.4066666666667 & 1.25117641611502 & 3.64 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34959&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]13.5041666666667[/C][C]1.60858751327952[/C][C]4.61[/C][/ROW]
[ROW][C]2[/C][C]13.29[/C][C]0.935637847576818[/C][C]2.52[/C][/ROW]
[ROW][C]3[/C][C]14.3125[/C][C]0.97042750654263[/C][C]3.28[/C][/ROW]
[ROW][C]4[/C][C]14.3891666666667[/C][C]0.997892855729087[/C][C]3.42[/C][/ROW]
[ROW][C]5[/C][C]13.4066666666667[/C][C]1.25117641611502[/C][C]3.64[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34959&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34959&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
113.50416666666671.608587513279524.61
213.290.9356378475768182.52
314.31250.970427506542633.28
414.38916666666670.9978928557290873.42
513.40666666666671.251176416115023.64






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha4.22507384189017
beta-0.222947600888324
S.D.0.282951008302245
T-STAT-0.787937114011534
p-value0.488254280426957

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 4.22507384189017 \tabularnewline
beta & -0.222947600888324 \tabularnewline
S.D. & 0.282951008302245 \tabularnewline
T-STAT & -0.787937114011534 \tabularnewline
p-value & 0.488254280426957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34959&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.22507384189017[/C][/ROW]
[ROW][C]beta[/C][C]-0.222947600888324[/C][/ROW]
[ROW][C]S.D.[/C][C]0.282951008302245[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.787937114011534[/C][/ROW]
[ROW][C]p-value[/C][C]0.488254280426957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34959&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34959&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)
alpha4.22507384189017
beta-0.222947600888324
S.D.0.282951008302245
T-STAT-0.787937114011534
p-value0.488254280426957






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha6.58918747470148
beta-2.46657789626396
S.D.3.16868716010376
T-STAT-0.7784226626472
p-value0.493075431476563
Lambda3.46657789626396

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 6.58918747470148 \tabularnewline
beta & -2.46657789626396 \tabularnewline
S.D. & 3.16868716010376 \tabularnewline
T-STAT & -0.7784226626472 \tabularnewline
p-value & 0.493075431476563 \tabularnewline
Lambda & 3.46657789626396 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=34959&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]6.58918747470148[/C][/ROW]
[ROW][C]beta[/C][C]-2.46657789626396[/C][/ROW]
[ROW][C]S.D.[/C][C]3.16868716010376[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.7784226626472[/C][/ROW]
[ROW][C]p-value[/C][C]0.493075431476563[/C][/ROW]
[ROW][C]Lambda[/C][C]3.46657789626396[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=34959&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=34959&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)
alpha6.58918747470148
beta-2.46657789626396
S.D.3.16868716010376
T-STAT-0.7784226626472
p-value0.493075431476563
Lambda3.46657789626396


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')