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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 computationTue, 09 Dec 2008 11:10:16 -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/2008/Dec/09/t1228847421i52yyo7hfcnp65q.htm/, Retrieved Fri, 24 May 2024 22:34:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31670, Retrieved Fri, 24 May 2024 22:34:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsjenske_cole@hotmail.com
Estimated Impact179

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]
F RMPD    [Standard Deviation-Mean Plot] [eigen tijdreeks sd] [2008-12-09 18:10:16] [120dfa2440e51a0cfc0f5296bc5d7460] [Current]
Feedback Forum
2008-12-14 16:38:09 [Steven Vanhooreweghe] [reply] 
hlambda is inderdaad -1.9, ik twijfel wel of er een verband is en of het dus nodig om lambda later in te vullen..

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,6
98
106,8
96,6
100,1
107,7
91,5
97,8
107,4
117,5
105,6
97,4
99,5
98
104,3
100,6
101,1
103,9
96,9
95,5
108,4
117
103,8
100,8
110,6
104
112,6
107,3
98,9
109,8
104,9
102,2
123,9
124,9
112,7
121,9
100,6
104,3
120,4
107,5
102,9
125,6
107,5
108,8
128,4
121,1
119,5
128,7
108,7
105,5
119,8
111,3
110,6
120,1
97,5
107,7
127,3
117,2
119,8
116,2
111
112,4
130,6
109,1
118,8
123,9
101,6
112,8
128
129,6
125,8
119,5

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31670&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31670&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31670&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'George Udny Yule' @ 72.249.76.132






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1102.0833333333337.0530242814803626
2102.4833333333335.7998171340033521.5
3111.1416666666678.575807232964726
4114.60833333333310.372994512266728.1
5113.4758.1144231856838529.8
6118.5916666666679.260027325046429

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 102.083333333333 & 7.05302428148036 & 26 \tabularnewline
2 & 102.483333333333 & 5.79981713400335 & 21.5 \tabularnewline
3 & 111.141666666667 & 8.5758072329647 & 26 \tabularnewline
4 & 114.608333333333 & 10.3729945122667 & 28.1 \tabularnewline
5 & 113.475 & 8.11442318568385 & 29.8 \tabularnewline
6 & 118.591666666667 & 9.2600273250464 & 29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31670&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]102.083333333333[/C][C]7.05302428148036[/C][C]26[/C][/ROW]
[ROW][C]2[/C][C]102.483333333333[/C][C]5.79981713400335[/C][C]21.5[/C][/ROW]
[ROW][C]3[/C][C]111.141666666667[/C][C]8.5758072329647[/C][C]26[/C][/ROW]
[ROW][C]4[/C][C]114.608333333333[/C][C]10.3729945122667[/C][C]28.1[/C][/ROW]
[ROW][C]5[/C][C]113.475[/C][C]8.11442318568385[/C][C]29.8[/C][/ROW]
[ROW][C]6[/C][C]118.591666666667[/C][C]9.2600273250464[/C][C]29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31670&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31670&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
1102.0833333333337.0530242814803626
2102.4833333333335.7998171340033521.5
3111.1416666666678.575807232964726
4114.60833333333310.372994512266728.1
5113.4758.1144231856838529.8
6118.5916666666679.260027325046429






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-14.3958719623575
beta0.20464181180926
S.D.0.0628237062817963
T-STAT3.25739794610871
p-value0.0311572390677195

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -14.3958719623575 \tabularnewline
beta & 0.20464181180926 \tabularnewline
S.D. & 0.0628237062817963 \tabularnewline
T-STAT & 3.25739794610871 \tabularnewline
p-value & 0.0311572390677195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31670&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-14.3958719623575[/C][/ROW]
[ROW][C]beta[/C][C]0.20464181180926[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0628237062817963[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.25739794610871[/C][/ROW]
[ROW][C]p-value[/C][C]0.0311572390677195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31670&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31670&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-14.3958719623575
beta0.20464181180926
S.D.0.0628237062817963
T-STAT3.25739794610871
p-value0.0311572390677195






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-11.5192051608253
beta2.89327556455468
S.D.0.843816593945194
T-STAT3.42879671401983
p-value0.0265649225695557
Lambda-1.89327556455468

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -11.5192051608253 \tabularnewline
beta & 2.89327556455468 \tabularnewline
S.D. & 0.843816593945194 \tabularnewline
T-STAT & 3.42879671401983 \tabularnewline
p-value & 0.0265649225695557 \tabularnewline
Lambda & -1.89327556455468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31670&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-11.5192051608253[/C][/ROW]
[ROW][C]beta[/C][C]2.89327556455468[/C][/ROW]
[ROW][C]S.D.[/C][C]0.843816593945194[/C][/ROW]
[ROW][C]T-STAT[/C][C]3.42879671401983[/C][/ROW]
[ROW][C]p-value[/C][C]0.0265649225695557[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.89327556455468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31670&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31670&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-11.5192051608253
beta2.89327556455468
S.D.0.843816593945194
T-STAT3.42879671401983
p-value0.0265649225695557
Lambda-1.89327556455468


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')