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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 computationMon, 08 Dec 2008 14:09:20 -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/08/t1228770657x1p4r9vptfkk65t.htm/, Retrieved Thu, 16 May 2024 09:17:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31026, Retrieved Thu, 16 May 2024 09:17:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
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 RMP   [ARIMA Backward Selection] [] [2008-12-07 12:05:46] [74be16979710d4c4e7c6647856088456]
F RMPD      [Standard Deviation-Mean Plot] [Stefan Temmerman] [2008-12-08 21:09:20] [30f7cb12a8cb61e43b87da59ece37a2f] [Current]
Feedback Forum
2008-12-15 17:06:12 [Gert-Jan Geudens] [reply] 
Zeer goed. De transformatie is inderdaad nutteloos. Als we in de standard-deviation mean plot , 1 punt zouden toevoegen (bv. linksboven), zou dit een sterk effect hebben op de helling van de regressielijn. De transformatie is hier nutteloos.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10709
10662
10570
10297
10635
10872
10296
10383
10431
10574
10653
10805
10872
10625
10407
10463
10556
10646
10702
11353
11346
11451
11964
12574
13031
13812
14544
14931
14886
16005
17064
15168
16050
15839
15137
14954
15648
15305
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31026&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31026&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31026&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
110573.9166666667188.065490688427576
211079.9166666667673.2648348746492167
315118.41666666671064.782475295764033
415948478.2336819437281727
518824.25854.0787833577292452

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 10573.9166666667 & 188.065490688427 & 576 \tabularnewline
2 & 11079.9166666667 & 673.264834874649 & 2167 \tabularnewline
3 & 15118.4166666667 & 1064.78247529576 & 4033 \tabularnewline
4 & 15948 & 478.233681943728 & 1727 \tabularnewline
5 & 18824.25 & 854.078783357729 & 2452 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31026&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]10573.9166666667[/C][C]188.065490688427[/C][C]576[/C][/ROW]
[ROW][C]2[/C][C]11079.9166666667[/C][C]673.264834874649[/C][C]2167[/C][/ROW]
[ROW][C]3[/C][C]15118.4166666667[/C][C]1064.78247529576[/C][C]4033[/C][/ROW]
[ROW][C]4[/C][C]15948[/C][C]478.233681943728[/C][C]1727[/C][/ROW]
[ROW][C]5[/C][C]18824.25[/C][C]854.078783357729[/C][C]2452[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31026&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31026&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
110573.9166666667188.065490688427576
211079.9166666667673.2648348746492167
315118.41666666671064.782475295764033
415948478.2336819437281727
518824.25854.0787833577292452






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-129.367838705919
beta0.0545851108008287
S.D.0.0466275543659554
T-STAT1.17066210190692
p-value0.326265342989034

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -129.367838705919 \tabularnewline
beta & 0.0545851108008287 \tabularnewline
S.D. & 0.0466275543659554 \tabularnewline
T-STAT & 1.17066210190692 \tabularnewline
p-value & 0.326265342989034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31026&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-129.367838705919[/C][/ROW]
[ROW][C]beta[/C][C]0.0545851108008287[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0466275543659554[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.17066210190692[/C][/ROW]
[ROW][C]p-value[/C][C]0.326265342989034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31026&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31026&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-129.367838705919
beta0.0545851108008287
S.D.0.0466275543659554
T-STAT1.17066210190692
p-value0.326265342989034






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-9.98123080011698
beta1.70874117108411
S.D.1.24134984380792
T-STAT1.37651861770284
p-value0.262406370159459
Lambda-0.708741171084115

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -9.98123080011698 \tabularnewline
beta & 1.70874117108411 \tabularnewline
S.D. & 1.24134984380792 \tabularnewline
T-STAT & 1.37651861770284 \tabularnewline
p-value & 0.262406370159459 \tabularnewline
Lambda & -0.708741171084115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31026&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-9.98123080011698[/C][/ROW]
[ROW][C]beta[/C][C]1.70874117108411[/C][/ROW]
[ROW][C]S.D.[/C][C]1.24134984380792[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.37651861770284[/C][/ROW]
[ROW][C]p-value[/C][C]0.262406370159459[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.708741171084115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31026&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31026&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-9.98123080011698
beta1.70874117108411
S.D.1.24134984380792
T-STAT1.37651861770284
p-value0.262406370159459
Lambda-0.708741171084115


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')