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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 computationWed, 15 Dec 2010 17:01:32 +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/2010/Dec/15/t1292432510vzkn2gb5tj9h3s9.htm/, Retrieved Wed, 01 May 2024 22:31:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110581, Retrieved Wed, 01 May 2024 22:31:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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       [(Partial) Autocorrelation Function] [Identifying Integ...] [2009-11-22 12:16:10] [b98453cac15ba1066b407e146608df68]
-    D        [(Partial) Autocorrelation Function] [Model 1 (d = 0, D...] [2009-11-24 17:27:24] [ee7c2e7343f5b1451e62c5c16ec521f1]
-    D          [(Partial) Autocorrelation Function] [Methode 1 (D=0, d=0)] [2009-11-27 12:01:23] [76ab39dc7a55316678260825bd5ad46c]
-    D            [(Partial) Autocorrelation Function] [methode 1 (d=0 D= 0)] [2009-11-27 20:21:42] [4b453aa14d54730625f8d3de5f1f6d82]
-    D              [(Partial) Autocorrelation Function] [koffie en thee] [2009-12-16 19:04:55] [7773f496f69461f4a67891f0ef752622]
-    D                [(Partial) Autocorrelation Function] [Appelen Jonagold ...] [2009-12-17 16:51:16] [7773f496f69461f4a67891f0ef752622]
- RMPD                    [Standard Deviation-Mean Plot] [standard deviatio...] [2010-12-15 17:01:32] [c1f1b5e209adb4577289f490325e36f2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
 1.3031
 1.3241
 1.2961
 1.2865
 1.2305
 1.2101
 1.2125
 1.2350
 1.2014
 1.1992
 1.1791
 1.1832
 1.2159
 1.1922
 1.2114
 1.2614
 1.2812
 1.2786
 1.2772
 1.2815
 1.2679
 1.2765
 1.3247
 1.3191
 1.3029
 1.3234
 1.3354
 1.3651
 1.3453
 1.3534
 1.3706
 1.3638
 1.4268
 1.4485
 1.4635
 1.4587
 1.4876
 1.5189
 1.5783
 1.5633
 1.5554
 1.5757
 1.5593
 1.4660
 1.4065
 1.2759
 1.2705
 1.3954
 1.2793
 1.2694
 1.3282
 1.3230
 1.4135
 1.4042
 1.4253
 1.4322
 1.4632
 1.4713
 1.5016
 1.4318

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110581&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110581&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110581&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.302450.0159569211733760.0376000000000001
21.2220250.01255796559957070.0249000000000001
31.1907250.01121825149774540.0223
41.2202250.02931079152803630.0692000000000002
51.2796250.002075853238229250.00430000000000019
61.297050.02899856318279690.0568
71.33170.02599730755289860.0622
81.3582750.01117359237965420.0253000000000001
91.4493750.01629792931632730.0367
101.5370250.04149436708759390.0907
111.53910.04952070274137880.1097
121.3370750.0739284507705480.136
131.2999750.02993931361938680.0588
141.41880.01242390169525390.028
151.4669750.02868871729443480.0698000000000001

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.30245 & 0.015956921173376 & 0.0376000000000001 \tabularnewline
2 & 1.222025 & 0.0125579655995707 & 0.0249000000000001 \tabularnewline
3 & 1.190725 & 0.0112182514977454 & 0.0223 \tabularnewline
4 & 1.220225 & 0.0293107915280363 & 0.0692000000000002 \tabularnewline
5 & 1.279625 & 0.00207585323822925 & 0.00430000000000019 \tabularnewline
6 & 1.29705 & 0.0289985631827969 & 0.0568 \tabularnewline
7 & 1.3317 & 0.0259973075528986 & 0.0622 \tabularnewline
8 & 1.358275 & 0.0111735923796542 & 0.0253000000000001 \tabularnewline
9 & 1.449375 & 0.0162979293163273 & 0.0367 \tabularnewline
10 & 1.537025 & 0.0414943670875939 & 0.0907 \tabularnewline
11 & 1.5391 & 0.0495207027413788 & 0.1097 \tabularnewline
12 & 1.337075 & 0.073928450770548 & 0.136 \tabularnewline
13 & 1.299975 & 0.0299393136193868 & 0.0588 \tabularnewline
14 & 1.4188 & 0.0124239016952539 & 0.028 \tabularnewline
15 & 1.466975 & 0.0286887172944348 & 0.0698000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110581&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]1.30245[/C][C]0.015956921173376[/C][C]0.0376000000000001[/C][/ROW]
[ROW][C]2[/C][C]1.222025[/C][C]0.0125579655995707[/C][C]0.0249000000000001[/C][/ROW]
[ROW][C]3[/C][C]1.190725[/C][C]0.0112182514977454[/C][C]0.0223[/C][/ROW]
[ROW][C]4[/C][C]1.220225[/C][C]0.0293107915280363[/C][C]0.0692000000000002[/C][/ROW]
[ROW][C]5[/C][C]1.279625[/C][C]0.00207585323822925[/C][C]0.00430000000000019[/C][/ROW]
[ROW][C]6[/C][C]1.29705[/C][C]0.0289985631827969[/C][C]0.0568[/C][/ROW]
[ROW][C]7[/C][C]1.3317[/C][C]0.0259973075528986[/C][C]0.0622[/C][/ROW]
[ROW][C]8[/C][C]1.358275[/C][C]0.0111735923796542[/C][C]0.0253000000000001[/C][/ROW]
[ROW][C]9[/C][C]1.449375[/C][C]0.0162979293163273[/C][C]0.0367[/C][/ROW]
[ROW][C]10[/C][C]1.537025[/C][C]0.0414943670875939[/C][C]0.0907[/C][/ROW]
[ROW][C]11[/C][C]1.5391[/C][C]0.0495207027413788[/C][C]0.1097[/C][/ROW]
[ROW][C]12[/C][C]1.337075[/C][C]0.073928450770548[/C][C]0.136[/C][/ROW]
[ROW][C]13[/C][C]1.299975[/C][C]0.0299393136193868[/C][C]0.0588[/C][/ROW]
[ROW][C]14[/C][C]1.4188[/C][C]0.0124239016952539[/C][C]0.028[/C][/ROW]
[ROW][C]15[/C][C]1.466975[/C][C]0.0286887172944348[/C][C]0.0698000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110581&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110581&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
11.302450.0159569211733760.0376000000000001
21.2220250.01255796559957070.0249000000000001
31.1907250.01121825149774540.0223
41.2202250.02931079152803630.0692000000000002
51.2796250.002075853238229250.00430000000000019
61.297050.02899856318279690.0568
71.33170.02599730755289860.0622
81.3582750.01117359237965420.0253000000000001
91.4493750.01629792931632730.0367
101.5370250.04149436708759390.0907
111.53910.04952070274137880.1097
121.3370750.0739284507705480.136
131.2999750.02993931361938680.0588
141.41880.01242390169525390.028
151.4669750.02868871729443480.0698000000000001






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.0568303852893355
beta0.0613340184893762
S.D.0.0427811621180943
T-STAT1.43366882648181
p-value0.175278938523717

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.0568303852893355 \tabularnewline
beta & 0.0613340184893762 \tabularnewline
S.D. & 0.0427811621180943 \tabularnewline
T-STAT & 1.43366882648181 \tabularnewline
p-value & 0.175278938523717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110581&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.0568303852893355[/C][/ROW]
[ROW][C]beta[/C][C]0.0613340184893762[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0427811621180943[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.43366882648181[/C][/ROW]
[ROW][C]p-value[/C][C]0.175278938523717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110581&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110581&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-0.0568303852893355
beta0.0613340184893762
S.D.0.0427811621180943
T-STAT1.43366882648181
p-value0.175278938523717






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-5.09932493339421
beta3.9804760767918
S.D.2.68573100253486
T-STAT1.48208293125221
p-value0.162148709688441
Lambda-2.9804760767918

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -5.09932493339421 \tabularnewline
beta & 3.9804760767918 \tabularnewline
S.D. & 2.68573100253486 \tabularnewline
T-STAT & 1.48208293125221 \tabularnewline
p-value & 0.162148709688441 \tabularnewline
Lambda & -2.9804760767918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110581&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-5.09932493339421[/C][/ROW]
[ROW][C]beta[/C][C]3.9804760767918[/C][/ROW]
[ROW][C]S.D.[/C][C]2.68573100253486[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.48208293125221[/C][/ROW]
[ROW][C]p-value[/C][C]0.162148709688441[/C][/ROW]
[ROW][C]Lambda[/C][C]-2.9804760767918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110581&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110581&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-5.09932493339421
beta3.9804760767918
S.D.2.68573100253486
T-STAT1.48208293125221
p-value0.162148709688441
Lambda-2.9804760767918


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
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')