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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 computationThu, 26 Nov 2009 12:46:52 -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/2009/Nov/26/t1259264852eofwae4mtzjkv07.htm/, Retrieved Sun, 05 May 2024 02:23:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60333, Retrieved Sun, 05 May 2024 02:23:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Standard Deviation-Mean Plot] [Identifying Integ...] [2009-11-22 12:50:05] [b98453cac15ba1066b407e146608df68]
-   PD          [Standard Deviation-Mean Plot] [Industriële produ...] [2009-11-26 19:46:52] [fcf610eda12f49b9bf9c81ec9669e97a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
25.7
24.7
24.2
23.6
24.4
22.5
19.4
18.1
18.1
20.7
19.1
18.3
16.9
17.9
20.2
21.2
23.8
24
26.6
25.3
27.6
24.7
26.6
24.4
24.6
26
24.8
24
22.7
23
24.1
24
22.7
22.6
23.1
24.4
23
22
21.3
21.5
21.3
23.2
21.8
23.3
21
22.4
20.4
19.9
21.3
18.9
15.6
12.5
7.8
5.5
4
3.3
3.7
3.1
5
6.3

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=60333&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=60333&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60333&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
124.550.8888194417315582.10000000000000
221.12.871701005791986.3
319.051.181806526749052.6
419.051.990812229551884.3
524.9251.299679447658792.8
625.8251.532699144211503.2
724.850.8386497083606082
823.450.70474581706221.40000000000000
923.20.8286535263104031.80000000000000
1021.950.7593857166596341.7
1122.41.003327796219492
1220.9251.081280105553912.5
1317.0753.842199890687638.8
145.151.990812229551884.5
154.5251.424488212189443.2

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 24.55 & 0.888819441731558 & 2.10000000000000 \tabularnewline
2 & 21.1 & 2.87170100579198 & 6.3 \tabularnewline
3 & 19.05 & 1.18180652674905 & 2.6 \tabularnewline
4 & 19.05 & 1.99081222955188 & 4.3 \tabularnewline
5 & 24.925 & 1.29967944765879 & 2.8 \tabularnewline
6 & 25.825 & 1.53269914421150 & 3.2 \tabularnewline
7 & 24.85 & 0.838649708360608 & 2 \tabularnewline
8 & 23.45 & 0.7047458170622 & 1.40000000000000 \tabularnewline
9 & 23.2 & 0.828653526310403 & 1.80000000000000 \tabularnewline
10 & 21.95 & 0.759385716659634 & 1.7 \tabularnewline
11 & 22.4 & 1.00332779621949 & 2 \tabularnewline
12 & 20.925 & 1.08128010555391 & 2.5 \tabularnewline
13 & 17.075 & 3.84219989068763 & 8.8 \tabularnewline
14 & 5.15 & 1.99081222955188 & 4.5 \tabularnewline
15 & 4.525 & 1.42448821218944 & 3.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60333&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]24.55[/C][C]0.888819441731558[/C][C]2.10000000000000[/C][/ROW]
[ROW][C]2[/C][C]21.1[/C][C]2.87170100579198[/C][C]6.3[/C][/ROW]
[ROW][C]3[/C][C]19.05[/C][C]1.18180652674905[/C][C]2.6[/C][/ROW]
[ROW][C]4[/C][C]19.05[/C][C]1.99081222955188[/C][C]4.3[/C][/ROW]
[ROW][C]5[/C][C]24.925[/C][C]1.29967944765879[/C][C]2.8[/C][/ROW]
[ROW][C]6[/C][C]25.825[/C][C]1.53269914421150[/C][C]3.2[/C][/ROW]
[ROW][C]7[/C][C]24.85[/C][C]0.838649708360608[/C][C]2[/C][/ROW]
[ROW][C]8[/C][C]23.45[/C][C]0.7047458170622[/C][C]1.40000000000000[/C][/ROW]
[ROW][C]9[/C][C]23.2[/C][C]0.828653526310403[/C][C]1.80000000000000[/C][/ROW]
[ROW][C]10[/C][C]21.95[/C][C]0.759385716659634[/C][C]1.7[/C][/ROW]
[ROW][C]11[/C][C]22.4[/C][C]1.00332779621949[/C][C]2[/C][/ROW]
[ROW][C]12[/C][C]20.925[/C][C]1.08128010555391[/C][C]2.5[/C][/ROW]
[ROW][C]13[/C][C]17.075[/C][C]3.84219989068763[/C][C]8.8[/C][/ROW]
[ROW][C]14[/C][C]5.15[/C][C]1.99081222955188[/C][C]4.5[/C][/ROW]
[ROW][C]15[/C][C]4.525[/C][C]1.42448821218944[/C][C]3.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60333&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60333&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
124.550.8888194417315582.10000000000000
221.12.871701005791986.3
319.051.181806526749052.6
419.051.990812229551884.3
524.9251.299679447658792.8
625.8251.532699144211503.2
724.850.8386497083606082
823.450.70474581706221.40000000000000
923.20.8286535263104031.80000000000000
1021.950.7593857166596341.7
1122.41.003327796219492
1220.9251.081280105553912.5
1317.0753.842199890687638.8
145.151.990812229551884.5
154.5251.424488212189443.2






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha2.34645738302519
beta-0.0434789025990702
S.D.0.0351019057364246
T-STAT-1.23864792201162
p-value0.237376057555735

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 2.34645738302519 \tabularnewline
beta & -0.0434789025990702 \tabularnewline
S.D. & 0.0351019057364246 \tabularnewline
T-STAT & -1.23864792201162 \tabularnewline
p-value & 0.237376057555735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60333&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]2.34645738302519[/C][/ROW]
[ROW][C]beta[/C][C]-0.0434789025990702[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0351019057364246[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.23864792201162[/C][/ROW]
[ROW][C]p-value[/C][C]0.237376057555735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60333&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60333&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)
alpha2.34645738302519
beta-0.0434789025990702
S.D.0.0351019057364246
T-STAT-1.23864792201162
p-value0.237376057555735






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha1.11941327545951
beta-0.295943696669336
S.D.0.240466775341677
T-STAT-1.23070514106921
p-value0.240236810448873
Lambda1.29594369666934

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 1.11941327545951 \tabularnewline
beta & -0.295943696669336 \tabularnewline
S.D. & 0.240466775341677 \tabularnewline
T-STAT & -1.23070514106921 \tabularnewline
p-value & 0.240236810448873 \tabularnewline
Lambda & 1.29594369666934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60333&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]1.11941327545951[/C][/ROW]
[ROW][C]beta[/C][C]-0.295943696669336[/C][/ROW]
[ROW][C]S.D.[/C][C]0.240466775341677[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.23070514106921[/C][/ROW]
[ROW][C]p-value[/C][C]0.240236810448873[/C][/ROW]
[ROW][C]Lambda[/C][C]1.29594369666934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60333&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60333&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)
alpha1.11941327545951
beta-0.295943696669336
S.D.0.240466775341677
T-STAT-1.23070514106921
p-value0.240236810448873
Lambda1.29594369666934


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ;
Parameters (R input):
par1 = 4 ;
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')