FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 02 Dec 2009 03:12:16 -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/Dec/02/t1259748789x30mv42xg9gv2ue.htm/, Retrieved Sun, 28 Apr 2024 06:29:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62314, Retrieved Sun, 28 Apr 2024 06:29:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordscvm
Estimated Impact190

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] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [] [2009-11-27 14:40:44] [b98453cac15ba1066b407e146608df68]
-    D    [Standard Deviation-Mean Plot] [BBWS9-Regressieta...] [2009-12-01 20:06:37] [408e92805dcb18620260f240a7fb9d53]
-    D        [Standard Deviation-Mean Plot] [W9: Regressie Model] [2009-12-02 10:12:16] [a5ada8bd39e806b5b90f09589c89554a] [Current]
-    D          [Standard Deviation-Mean Plot] [] [2009-12-07 08:23:02] [ade6aa003deff66733e677339d38f25a]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
114,1
110,3
103,9
101,6
94,6
95,9
104,7
102,8
98,1
113,9
80,9
95,7
113,2
105,9
108,8
102,3
99
100,7
115,5
100,7
109,9
114,6
85,4
100,5
114,8
116,5
112,9
102
106
105,3
118,8
106,1
109,3
117,2
92,5
104,2
112,5
122,4
113,3
100
110,7
112,8
109,8
117,3
109,1
115,9
96
99,8
116,8
115,7
99,4
94,3
91
93,2
103,1
94,1
91,8
102,7
82,6
89,1

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62314&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62314&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62314&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'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1101.3759.3225167300369133.2
2104.7083333333338.488329778245130.1
3108.87.6478160875566926.3
4109.9666666666677.7939759177239326.4
597.816666666666710.322687575814734.2

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 101.375 & 9.32251673003691 & 33.2 \tabularnewline
2 & 104.708333333333 & 8.4883297782451 & 30.1 \tabularnewline
3 & 108.8 & 7.64781608755669 & 26.3 \tabularnewline
4 & 109.966666666667 & 7.79397591772393 & 26.4 \tabularnewline
5 & 97.8166666666667 & 10.3226875758147 & 34.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62314&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]101.375[/C][C]9.32251673003691[/C][C]33.2[/C][/ROW]
[ROW][C]2[/C][C]104.708333333333[/C][C]8.4883297782451[/C][C]30.1[/C][/ROW]
[ROW][C]3[/C][C]108.8[/C][C]7.64781608755669[/C][C]26.3[/C][/ROW]
[ROW][C]4[/C][C]109.966666666667[/C][C]7.79397591772393[/C][C]26.4[/C][/ROW]
[ROW][C]5[/C][C]97.8166666666667[/C][C]10.3226875758147[/C][C]34.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62314&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62314&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
1101.3759.3225167300369133.2
2104.7083333333338.488329778245130.1
3108.87.6478160875566926.3
4109.9666666666677.7939759177239326.4
597.816666666666710.322687575814734.2






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha31.3956712282742
beta-0.216970083007641
S.D.0.0221047183652757
T-STAT-9.81555518700833
p-value0.00224766689764873

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 31.3956712282742 \tabularnewline
beta & -0.216970083007641 \tabularnewline
S.D. & 0.0221047183652757 \tabularnewline
T-STAT & -9.81555518700833 \tabularnewline
p-value & 0.00224766689764873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62314&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]31.3956712282742[/C][/ROW]
[ROW][C]beta[/C][C]-0.216970083007641[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0221047183652757[/C][/ROW]
[ROW][C]T-STAT[/C][C]-9.81555518700833[/C][/ROW]
[ROW][C]p-value[/C][C]0.00224766689764873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62314&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62314&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)
alpha31.3956712282742
beta-0.216970083007641
S.D.0.0221047183652757
T-STAT-9.81555518700833
p-value0.00224766689764873






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha14.0123312548997
beta-2.54997007684601
S.D.0.216827233212426
T-STAT-11.7603773246869
p-value0.00132134631474521
Lambda3.54997007684601

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 14.0123312548997 \tabularnewline
beta & -2.54997007684601 \tabularnewline
S.D. & 0.216827233212426 \tabularnewline
T-STAT & -11.7603773246869 \tabularnewline
p-value & 0.00132134631474521 \tabularnewline
Lambda & 3.54997007684601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62314&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]14.0123312548997[/C][/ROW]
[ROW][C]beta[/C][C]-2.54997007684601[/C][/ROW]
[ROW][C]S.D.[/C][C]0.216827233212426[/C][/ROW]
[ROW][C]T-STAT[/C][C]-11.7603773246869[/C][/ROW]
[ROW][C]p-value[/C][C]0.00132134631474521[/C][/ROW]
[ROW][C]Lambda[/C][C]3.54997007684601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62314&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62314&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)
alpha14.0123312548997
beta-2.54997007684601
S.D.0.216827233212426
T-STAT-11.7603773246869
p-value0.00132134631474521
Lambda3.54997007684601


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')