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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationMon, 26 Nov 2012 05:57:03 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/26/t1353927478hha8peoxe8m95mm.htm/, Retrieved Tue, 30 Apr 2024 02:32:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=193011, Retrieved Tue, 30 Apr 2024 02:32:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [spreidings en gem...] [2012-11-26 10:57:03] [0c7e7d09d2015d04a0f0c5141ec0f270] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
23.15
23.18
23.32
23.37
23.43
23.65
23.76
23.81
23.85
23.83
23.85
23.71
23.74
23.87
24.13
24.23
24.27
24.41
24.39
24.34
24.31
24.34
24.41
24.39
24.54
24.9
25.63
26.7
27.12
27.68
27.84
27.84
27.77
27.8
27.82
27.72
27.87
27.83
28.07
28.05
28.15
28.3
28.41
28.43
28.43
28.29
28.19
27.53
27.92
27.98
27.92
27.89
27.95
28.02
27.97
27.81
27.78
27.56
27.52
27.18
27.18
27.26
27.38
27.31
27.43
27.4
27.32
27.31
27.34
27.3
27.3
26.94

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'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193011&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=193011&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193011&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'Gwilym Jenkins' @ jenkins.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
123.57583333333330.2687160831311250.700000000000003
224.23583333333330.218983533350770.670000000000002
326.94666666666671.232368992361193.3
428.12916666666670.2770775517738120.899999999999999
527.79166666666670.2503028468705770.84
627.28916666666670.127810821375210.489999999999998

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 23.5758333333333 & 0.268716083131125 & 0.700000000000003 \tabularnewline
2 & 24.2358333333333 & 0.21898353335077 & 0.670000000000002 \tabularnewline
3 & 26.9466666666667 & 1.23236899236119 & 3.3 \tabularnewline
4 & 28.1291666666667 & 0.277077551773812 & 0.899999999999999 \tabularnewline
5 & 27.7916666666667 & 0.250302846870577 & 0.84 \tabularnewline
6 & 27.2891666666667 & 0.12781082137521 & 0.489999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193011&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]23.5758333333333[/C][C]0.268716083131125[/C][C]0.700000000000003[/C][/ROW]
[ROW][C]2[/C][C]24.2358333333333[/C][C]0.21898353335077[/C][C]0.670000000000002[/C][/ROW]
[ROW][C]3[/C][C]26.9466666666667[/C][C]1.23236899236119[/C][C]3.3[/C][/ROW]
[ROW][C]4[/C][C]28.1291666666667[/C][C]0.277077551773812[/C][C]0.899999999999999[/C][/ROW]
[ROW][C]5[/C][C]27.7916666666667[/C][C]0.250302846870577[/C][C]0.84[/C][/ROW]
[ROW][C]6[/C][C]27.2891666666667[/C][C]0.12781082137521[/C][C]0.489999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=193011&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193011&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
123.57583333333330.2687160831311250.700000000000003
224.23583333333330.218983533350770.670000000000002
326.94666666666671.232368992361193.3
428.12916666666670.2770775517738120.899999999999999
527.79166666666670.2503028468705770.84
627.28916666666670.127810821375210.489999999999998






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.384816959284517
beta0.0296525353260872
S.D.0.105998973484196
T-STAT0.279743608370965
p-value0.793544055864303

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.384816959284517 \tabularnewline
beta & 0.0296525353260872 \tabularnewline
S.D. & 0.105998973484196 \tabularnewline
T-STAT & 0.279743608370965 \tabularnewline
p-value & 0.793544055864303 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193011&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.384816959284517[/C][/ROW]
[ROW][C]beta[/C][C]0.0296525353260872[/C][/ROW]
[ROW][C]S.D.[/C][C]0.105998973484196[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.279743608370965[/C][/ROW]
[ROW][C]p-value[/C][C]0.793544055864303[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=193011&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193011&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-0.384816959284517
beta0.0296525353260872
S.D.0.105998973484196
T-STAT0.279743608370965
p-value0.793544055864303






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.13931388471318
beta0.891701406843111
S.D.5.03103803994423
T-STAT0.1772400446515
p-value0.867932825027038
Lambda0.108298593156889

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.13931388471318 \tabularnewline
beta & 0.891701406843111 \tabularnewline
S.D. & 5.03103803994423 \tabularnewline
T-STAT & 0.1772400446515 \tabularnewline
p-value & 0.867932825027038 \tabularnewline
Lambda & 0.108298593156889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=193011&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.13931388471318[/C][/ROW]
[ROW][C]beta[/C][C]0.891701406843111[/C][/ROW]
[ROW][C]S.D.[/C][C]5.03103803994423[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.1772400446515[/C][/ROW]
[ROW][C]p-value[/C][C]0.867932825027038[/C][/ROW]
[ROW][C]Lambda[/C][C]0.108298593156889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=193011&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=193011&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-4.13931388471318
beta0.891701406843111
S.D.5.03103803994423
T-STAT0.1772400446515
p-value0.867932825027038
Lambda0.108298593156889


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')