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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_smp.wasp

Title produced by software

Standard Deviation-Mean Plot

Date of computation

Mon, 08 Dec 2008 14:36:21 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/08/t1228772260yplk35uqp6gjosw.htm/, Retrieved Thu, 16 May 2024 15:04:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31075, Retrieved Thu, 16 May 2024 15:04:09 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

181

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   [Standard Deviation-Mean Plot] [SMP Analysis] [2008-12-08 10:07:54] [23bfa928dab4b48567707937094f7011]
F    D      [Standard Deviation-Mean Plot] [SMP Aankoop wagens] [2008-12-08 21:36:21] [63302faa1e3976bf98d1de42298c0b24] [Current]

Feedback Forum

2008-12-14 22:22:08 [df2ed12c9b09685cd516719b004050c5] [reply] 
in de 2de tabel zien we dat de p-waarde (0.11) groter is dan 5% (bij 95% betrouwbaarheid) dus de beta is niet significant verschillend van 0.  
 
Wat wil zeggen dat we in de derde tabel deze lambdawaarde niet mogen gebruiken. Daarom zetten we de lambda op 1, dit heeft geen invloed op de tijdreeks. 

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Dataseries X:

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Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31075&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]3 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=31075&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31075&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	106.376666666667	0.838465409199592	2.19999999999999
2	108.3275	0.459072681906393	1.49000000000001
3	109.848333333333	0.33384354891403	1.17999999999999
4	110.331666666667	0.41721660677602	1.25999999999999
5	111.545833333333	0.45506160089164	1.44000000000001

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 106.376666666667 & 0.838465409199592 & 2.19999999999999 \tabularnewline
2 & 108.3275 & 0.459072681906393 & 1.49000000000001 \tabularnewline
3 & 109.848333333333 & 0.33384354891403 & 1.17999999999999 \tabularnewline
4 & 110.331666666667 & 0.41721660677602 & 1.25999999999999 \tabularnewline
5 & 111.545833333333 & 0.45506160089164 & 1.44000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31075&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]106.376666666667[/C][C]0.838465409199592[/C][C]2.19999999999999[/C][/ROW]
[ROW][C]2[/C][C]108.3275[/C][C]0.459072681906393[/C][C]1.49000000000001[/C][/ROW]
[ROW][C]3[/C][C]109.848333333333[/C][C]0.33384354891403[/C][C]1.17999999999999[/C][/ROW]
[ROW][C]4[/C][C]110.331666666667[/C][C]0.41721660677602[/C][C]1.25999999999999[/C][/ROW]
[ROW][C]5[/C][C]111.545833333333[/C][C]0.45506160089164[/C][C]1.44000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31075&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31075&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	106.376666666667	0.838465409199592	2.19999999999999
2	108.3275	0.459072681906393	1.49000000000001
3	109.848333333333	0.33384354891403	1.17999999999999
4	110.331666666667	0.41721660677602	1.25999999999999
5	111.545833333333	0.45506160089164	1.44000000000001

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	8.93483795970648
beta	-0.0771746242901099
S.D.	0.0348756551512439
T-STAT	-2.21285088281295
p-value	0.113798650762742

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 8.93483795970648 \tabularnewline
beta & -0.0771746242901099 \tabularnewline
S.D. & 0.0348756551512439 \tabularnewline
T-STAT & -2.21285088281295 \tabularnewline
p-value & 0.113798650762742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31075&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]8.93483795970648[/C][/ROW]
[ROW][C]beta[/C][C]-0.0771746242901099[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0348756551512439[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.21285088281295[/C][/ROW]
[ROW][C]p-value[/C][C]0.113798650762742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31075&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31075&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	8.93483795970648
beta	-0.0771746242901099
S.D.	0.0348756551512439
T-STAT	-2.21285088281295
p-value	0.113798650762742

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	65.577088223319
beta	-14.1291187488634
S.D.	7.03693945669848
T-STAT	-2.00784998020892
p-value	0.138270808877079
Lambda	15.1291187488634

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 65.577088223319 \tabularnewline
beta & -14.1291187488634 \tabularnewline
S.D. & 7.03693945669848 \tabularnewline
T-STAT & -2.00784998020892 \tabularnewline
p-value & 0.138270808877079 \tabularnewline
Lambda & 15.1291187488634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31075&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]65.577088223319[/C][/ROW]
[ROW][C]beta[/C][C]-14.1291187488634[/C][/ROW]
[ROW][C]S.D.[/C][C]7.03693945669848[/C][/ROW]
[ROW][C]T-STAT[/C][C]-2.00784998020892[/C][/ROW]
[ROW][C]p-value[/C][C]0.138270808877079[/C][/ROW]
[ROW][C]Lambda[/C][C]15.1291187488634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31075&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31075&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	65.577088223319
beta	-14.1291187488634
S.D.	7.03693945669848
T-STAT	-2.00784998020892
p-value	0.138270808877079
Lambda	15.1291187488634

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code