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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, 16 Nov 2015 18:47:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Nov/16/t1447699688sbxkazvtuybz0zr.htm/, Retrieved Wed, 15 May 2024 03:27:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=283396, Retrieved Wed, 15 May 2024 03:27:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

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] [] [2015-11-16 18:47:43] [a231c0efc426ce58c731cc3abc4c2d25] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
85.74
86.62
86.66
87.39
87.59
88.8
88.64
89.55
89.04
88.49
89.5
89.46
90.33
90.27
91.5
92.53
93.14
93.01
92.84
92.88
93.05
93.17
93.67
94.9
95.72
96.08
97.52
98.26
98.48
98.09
98.03
98.14
98.71
98.69
98.72
98.47
99.49
99.84
100.9
101.31
100.09
99.28
99.57
101.04
101.87
101.39
100.3
99.95
99.87
100.51
100.27
100.04
99.23
99.32
99.95
100.23
101.02
99.83
99.61
100.12
99.83
100.03
100.07
100.46
100.43
100.68
101.8
101.21
100.63
100.55
99.76
98.8

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'Herman Ole Andreas Wold' @ wold.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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283396&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283396&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'Herman Ole Andreas Wold' @ wold.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
188.12333333333331.290548391775823.81
292.60751.326445180993864.63000000000001
397.90916666666671.00282744217933
4100.4191666666670.8537559618674682.59
51000.4952501663530541.78999999999999
6100.3541666666670.7561560485091483

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 88.1233333333333 & 1.29054839177582 & 3.81 \tabularnewline
2 & 92.6075 & 1.32644518099386 & 4.63000000000001 \tabularnewline
3 & 97.9091666666667 & 1.0028274421793 & 3 \tabularnewline
4 & 100.419166666667 & 0.853755961867468 & 2.59 \tabularnewline
5 & 100 & 0.495250166353054 & 1.78999999999999 \tabularnewline
6 & 100.354166666667 & 0.756156048509148 & 3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283396&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]88.1233333333333[/C][C]1.29054839177582[/C][C]3.81[/C][/ROW]
[ROW][C]2[/C][C]92.6075[/C][C]1.32644518099386[/C][C]4.63000000000001[/C][/ROW]
[ROW][C]3[/C][C]97.9091666666667[/C][C]1.0028274421793[/C][C]3[/C][/ROW]
[ROW][C]4[/C][C]100.419166666667[/C][C]0.853755961867468[/C][C]2.59[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]0.495250166353054[/C][C]1.78999999999999[/C][/ROW]
[ROW][C]6[/C][C]100.354166666667[/C][C]0.756156048509148[/C][C]3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283396&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
188.12333333333331.290548391775823.81
292.60751.326445180993864.63000000000001
397.90916666666671.00282744217933
4100.4191666666670.8537559618674682.59
51000.4952501663530541.78999999999999
6100.3541666666670.7561560485091483






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha6.13547876409685
beta-0.0536540800917637
S.D.0.0164545180051869
T-STAT-3.26075063850855
p-value0.0310588106350495

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 6.13547876409685 \tabularnewline
beta & -0.0536540800917637 \tabularnewline
S.D. & 0.0164545180051869 \tabularnewline
T-STAT & -3.26075063850855 \tabularnewline
p-value & 0.0310588106350495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283396&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]6.13547876409685[/C][/ROW]
[ROW][C]beta[/C][C]-0.0536540800917637[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0164545180051869[/C][/ROW]
[ROW][C]T-STAT[/C][C]-3.26075063850855[/C][/ROW]
[ROW][C]p-value[/C][C]0.0310588106350495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283396&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283396&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)
alpha6.13547876409685
beta-0.0536540800917637
S.D.0.0164545180051869
T-STAT-3.26075063850855
p-value0.0310588106350495






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha24.1999459172857
beta-5.31836249229966
S.D.2.15718591182974
T-STAT-2.46541684846652
p-value0.0692869228678042
Lambda6.31836249229966

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 24.1999459172857 \tabularnewline
beta & -5.31836249229966 \tabularnewline
S.D. & 2.15718591182974 \tabularnewline
T-STAT & -2.46541684846652 \tabularnewline
p-value & 0.0692869228678042 \tabularnewline
Lambda & 6.31836249229966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=283396&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]24.1999459172857[/C][/ROW]
[ROW][C]beta[/C][C]-5.31836249229966[/C][/ROW]
[ROW][C]S.D.[/C][C]2.15718591182974[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.46541684846652[/C][/ROW]
[ROW][C]p-value[/C][C]0.0692869228678042[/C][/ROW]
[ROW][C]Lambda[/C][C]6.31836249229966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=283396&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=283396&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)
alpha24.1999459172857
beta-5.31836249229966
S.D.2.15718591182974
T-STAT-2.46541684846652
p-value0.0692869228678042
Lambda6.31836249229966


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')