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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, 02 Dec 2008 08:40:16 -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/02/t1228232589n6udep72lhp58em.htm/, Retrieved Sun, 19 May 2024 10:42:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27968, Retrieved Sun, 19 May 2024 10:42:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ8 wisselkoers dollar
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Standard Deviation-Mean Plot] [Q8 wisselkoers do...] [2008-12-02 15:40:16] [9f72e095d5529918bf5b0810c01bf6ce] [Current]
Feedback Forum
2008-12-09 00:24:05 [Jessica Alves Pires] [reply] 
Ik heb de parameters gevonden, maar ik had ze best kunnen controleren adhv ACF en spectrale analyse.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.249
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684
1.457

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=27968&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=27968&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27968&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
11.13090.04972414814846910.1664
21.243333333333330.04244370246323800.1423
31.2447750.05156309682850180.1415
41.255658333333330.03886141948311230.1275
51.370633333333330.05467872003112750.1685

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.1309 & 0.0497241481484691 & 0.1664 \tabularnewline
2 & 1.24333333333333 & 0.0424437024632380 & 0.1423 \tabularnewline
3 & 1.244775 & 0.0515630968285018 & 0.1415 \tabularnewline
4 & 1.25565833333333 & 0.0388614194831123 & 0.1275 \tabularnewline
5 & 1.37063333333333 & 0.0546787200311275 & 0.1685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27968&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]1.1309[/C][C]0.0497241481484691[/C][C]0.1664[/C][/ROW]
[ROW][C]2[/C][C]1.24333333333333[/C][C]0.0424437024632380[/C][C]0.1423[/C][/ROW]
[ROW][C]3[/C][C]1.244775[/C][C]0.0515630968285018[/C][C]0.1415[/C][/ROW]
[ROW][C]4[/C][C]1.25565833333333[/C][C]0.0388614194831123[/C][C]0.1275[/C][/ROW]
[ROW][C]5[/C][C]1.37063333333333[/C][C]0.0546787200311275[/C][C]0.1685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27968&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27968&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
11.13090.04972414814846910.1664
21.243333333333330.04244370246323800.1423
31.2447750.05156309682850180.1415
41.255658333333330.03886141948311230.1275
51.370633333333330.05467872003112750.1685






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.023003645146697
beta0.0195751783294580
S.D.0.0432899122714949
T-STAT0.452187987970297
p-value0.681828223326662

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.023003645146697 \tabularnewline
beta & 0.0195751783294580 \tabularnewline
S.D. & 0.0432899122714949 \tabularnewline
T-STAT & 0.452187987970297 \tabularnewline
p-value & 0.681828223326662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27968&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.023003645146697[/C][/ROW]
[ROW][C]beta[/C][C]0.0195751783294580[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0432899122714949[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.452187987970297[/C][/ROW]
[ROW][C]p-value[/C][C]0.681828223326662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27968&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27968&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)
alpha0.023003645146697
beta0.0195751783294580
S.D.0.0432899122714949
T-STAT0.452187987970297
p-value0.681828223326662






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.14657365814413
beta0.410850645082911
S.D.1.18453659928270
T-STAT0.346845040779408
p-value0.751611668886191
Lambda0.589149354917089

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.14657365814413 \tabularnewline
beta & 0.410850645082911 \tabularnewline
S.D. & 1.18453659928270 \tabularnewline
T-STAT & 0.346845040779408 \tabularnewline
p-value & 0.751611668886191 \tabularnewline
Lambda & 0.589149354917089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27968&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.14657365814413[/C][/ROW]
[ROW][C]beta[/C][C]0.410850645082911[/C][/ROW]
[ROW][C]S.D.[/C][C]1.18453659928270[/C][/ROW]
[ROW][C]T-STAT[/C][C]0.346845040779408[/C][/ROW]
[ROW][C]p-value[/C][C]0.751611668886191[/C][/ROW]
[ROW][C]Lambda[/C][C]0.589149354917089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27968&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27968&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-3.14657365814413
beta0.410850645082911
S.D.1.18453659928270
T-STAT0.346845040779408
p-value0.751611668886191
Lambda0.589149354917089


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')