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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 computationTue, 18 Aug 2009 09:11:39 -0600
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/Aug/18/t1250608328wmxduye8eszaven.htm/, Retrieved Mon, 06 May 2024 21:04:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=42836, Retrieved Mon, 06 May 2024 21:04:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact224

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [grafiek opdracht ...] [2009-04-24 14:37:54] [74be16979710d4c4e7c6647856088456]
-       [Univariate Data Series] [grafiek opdracht6...] [2009-04-24 15:28:36] [74be16979710d4c4e7c6647856088456]
- RMPD    [Variability] [GuyVanHasseltOpdr...] [2009-08-18 14:30:28] [5bc2f8cd11ccb2b40c195aee8b3abfdc]
- RM D        [Standard Deviation-Mean Plot] [GuyVanHasseltStan...] [2009-08-18 15:11:39] [6998899cd6ebdd3d2c6ef8366ab46470] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.8800
1.0300
0.6900
0.7100
1.1100
1.0500
1.0300
0.6500
0.5900
0.7700
0.9000
1.2600
0.9600
0.8300
0.8700
0.7900
1.1200
0.8800
0.6400
0.6400
0.5800
0.5000
0.9900
1.0700
0.8900
0.8900
0.8300
0.8600
0.9000
1.1200
0.8800
0.8800
0.8900
0.8200
0.8800
0.8100
0.8800
0.7600
1.1300
0.8500
1.4500
1.5500
0.7100
0.8100
0.8300
0.7300
0.9000
0.9400
1.7800
0.8800
1.0400
0.8300
1.4100
0.9600
1.3000
0.8300
1.4000
0.9100
0.8700
0.9700
1.1900
1.2300
1.3300
1.1700
1.0900
0.6300
0.8900
0.6300
1.5100
0.9700
0.8400
0.9200
0.9500
0.7300
1.0200
0.7900
1.2700
0.9500
0.7500
0.5200
0.9500
0.8200
0.7600
1.2400
0.9400
1.0400
1.8100
0.9500
1.3900
0.8600
1.1500
1.5100
0.6000
0.7200
1.1000
1.6200
1.8400
1.7300
1.3600
1.0700
1.0000
1.4900
0.9000
1.4300
1.5400
0.8100
1.6100
1.3000
1.4000
1.0300
0.7900
1.1100
1.1500
1.0300
1.5900
1.1100
1.3300
0.9300
1.0700
1.1400
1.1200
0.8600
0.8200
1.0200
1.0700
1.3100
0.9800
0.8900
0.8000
0.8000
0.7800
0.9700

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' @ 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42836&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42836&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42836&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' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
10.8891666666666670.2099115975544910.67
20.82250.1983626156127390.62
30.88750.07921489758877430.31
40.9616666666666670.2754115377966490.84
51.098333333333330.303280048599950.95
61.033333333333330.2697585901685000.88
70.8958333333333330.2149189417318810.75
81.140833333333330.3708579324493491.21
91.340.3317446992109231.03
101.140.2140093455903270.8
110.9516666666666670.1599905300227790.53

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 0.889166666666667 & 0.209911597554491 & 0.67 \tabularnewline
2 & 0.8225 & 0.198362615612739 & 0.62 \tabularnewline
3 & 0.8875 & 0.0792148975887743 & 0.31 \tabularnewline
4 & 0.961666666666667 & 0.275411537796649 & 0.84 \tabularnewline
5 & 1.09833333333333 & 0.30328004859995 & 0.95 \tabularnewline
6 & 1.03333333333333 & 0.269758590168500 & 0.88 \tabularnewline
7 & 0.895833333333333 & 0.214918941731881 & 0.75 \tabularnewline
8 & 1.14083333333333 & 0.370857932449349 & 1.21 \tabularnewline
9 & 1.34 & 0.331744699210923 & 1.03 \tabularnewline
10 & 1.14 & 0.214009345590327 & 0.8 \tabularnewline
11 & 0.951666666666667 & 0.159990530022779 & 0.53 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42836&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]0.889166666666667[/C][C]0.209911597554491[/C][C]0.67[/C][/ROW]
[ROW][C]2[/C][C]0.8225[/C][C]0.198362615612739[/C][C]0.62[/C][/ROW]
[ROW][C]3[/C][C]0.8875[/C][C]0.0792148975887743[/C][C]0.31[/C][/ROW]
[ROW][C]4[/C][C]0.961666666666667[/C][C]0.275411537796649[/C][C]0.84[/C][/ROW]
[ROW][C]5[/C][C]1.09833333333333[/C][C]0.30328004859995[/C][C]0.95[/C][/ROW]
[ROW][C]6[/C][C]1.03333333333333[/C][C]0.269758590168500[/C][C]0.88[/C][/ROW]
[ROW][C]7[/C][C]0.895833333333333[/C][C]0.214918941731881[/C][C]0.75[/C][/ROW]
[ROW][C]8[/C][C]1.14083333333333[/C][C]0.370857932449349[/C][C]1.21[/C][/ROW]
[ROW][C]9[/C][C]1.34[/C][C]0.331744699210923[/C][C]1.03[/C][/ROW]
[ROW][C]10[/C][C]1.14[/C][C]0.214009345590327[/C][C]0.8[/C][/ROW]
[ROW][C]11[/C][C]0.951666666666667[/C][C]0.159990530022779[/C][C]0.53[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42836&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42836&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
10.8891666666666670.2099115975544910.67
20.82250.1983626156127390.62
30.88750.07921489758877430.31
40.9616666666666670.2754115377966490.84
51.098333333333330.303280048599950.95
61.033333333333330.2697585901685000.88
70.8958333333333330.2149189417318810.75
81.140833333333330.3708579324493491.21
91.340.3317446992109231.03
101.140.2140093455903270.8
110.9516666666666670.1599905300227790.53






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.138834550724270
beta0.372251844482342
S.D.0.129426406958583
T-STAT2.87616610265216
p-value0.0182919407262384

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.138834550724270 \tabularnewline
beta & 0.372251844482342 \tabularnewline
S.D. & 0.129426406958583 \tabularnewline
T-STAT & 2.87616610265216 \tabularnewline
p-value & 0.0182919407262384 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42836&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.138834550724270[/C][/ROW]
[ROW][C]beta[/C][C]0.372251844482342[/C][/ROW]
[ROW][C]S.D.[/C][C]0.129426406958583[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.87616610265216[/C][/ROW]
[ROW][C]p-value[/C][C]0.0182919407262384[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42836&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42836&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-0.138834550724270
beta0.372251844482342
S.D.0.129426406958583
T-STAT2.87616610265216
p-value0.0182919407262384






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-1.50961151600232
beta1.78552231728012
S.D.0.765062236723565
T-STAT2.33382623213342
p-value0.044467629075552
Lambda-0.785522317280122

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -1.50961151600232 \tabularnewline
beta & 1.78552231728012 \tabularnewline
S.D. & 0.765062236723565 \tabularnewline
T-STAT & 2.33382623213342 \tabularnewline
p-value & 0.044467629075552 \tabularnewline
Lambda & -0.785522317280122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42836&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-1.50961151600232[/C][/ROW]
[ROW][C]beta[/C][C]1.78552231728012[/C][/ROW]
[ROW][C]S.D.[/C][C]0.765062236723565[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.33382623213342[/C][/ROW]
[ROW][C]p-value[/C][C]0.044467629075552[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.785522317280122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42836&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42836&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-1.50961151600232
beta1.78552231728012
S.D.0.765062236723565
T-STAT2.33382623213342
p-value0.044467629075552
Lambda-0.785522317280122


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')