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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

Tue, 07 Dec 2010 19:22:00 +0000

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/2010/Dec/07/t1291749630uysv5a8ssegq01s.htm/, Retrieved Fri, 03 May 2024 19:12:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106661, Retrieved Fri, 03 May 2024 19:12:16 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

117

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] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
F   PD      [Standard Deviation-Mean Plot] [] [2010-12-07 19:22:00] [7b390cc0228d34e5578246b07143e3df] [Current]

Feedback Forum

2010-12-10 14:12:42 [Pascal Wijnen] [reply] 
De student bekomt voor zijn/haar gegevens de juiste grafiek en interpretatie. 
2010-12-13 17:56:58 [00c625c7d009d84797af914265b614f9] [reply] 
We zien duidelijk dat er geen verband bestaat tussen variantie en gemiddelde. We kunnen zeggen dat de slope (beta) gelijk is aan 0. Want de kans dat we ons vergissen bij het verwerpen van de nulhypothese(beta = 0) is zeer hoog namelijk 73%. Er moet dus geen box-cox transformatie gebeuren.

Post a new message

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	2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106661&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106661&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	2 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Standard Deviation-Mean Plot
Section	Mean	Standard Deviation	Range
1	54.6666666666667	11.9721899973786	43
2	50.5	10.2646790677369	37
3	51.0833333333333	13.4668504337897	44
4	49.75	13.7121379541167	42
5	52.6666666666667	12.4705714189508	48
6	53.0833333333333	12.1913406924254	33

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 54.6666666666667 & 11.9721899973786 & 43 \tabularnewline
2 & 50.5 & 10.2646790677369 & 37 \tabularnewline
3 & 51.0833333333333 & 13.4668504337897 & 44 \tabularnewline
4 & 49.75 & 13.7121379541167 & 42 \tabularnewline
5 & 52.6666666666667 & 12.4705714189508 & 48 \tabularnewline
6 & 53.0833333333333 & 12.1913406924254 & 33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106661&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]54.6666666666667[/C][C]11.9721899973786[/C][C]43[/C][/ROW]
[ROW][C]2[/C][C]50.5[/C][C]10.2646790677369[/C][C]37[/C][/ROW]
[ROW][C]3[/C][C]51.0833333333333[/C][C]13.4668504337897[/C][C]44[/C][/ROW]
[ROW][C]4[/C][C]49.75[/C][C]13.7121379541167[/C][C]42[/C][/ROW]
[ROW][C]5[/C][C]52.6666666666667[/C][C]12.4705714189508[/C][C]48[/C][/ROW]
[ROW][C]6[/C][C]53.0833333333333[/C][C]12.1913406924254[/C][C]33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106661&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	54.6666666666667	11.9721899973786	43
2	50.5	10.2646790677369	37
3	51.0833333333333	13.4668504337897	44
4	49.75	13.7121379541167	42
5	52.6666666666667	12.4705714189508	48
6	53.0833333333333	12.1913406924254	33

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha	18.6919922134068
beta	-0.122130501093962
S.D.	0.33073593097988
T-STAT	-0.369268923192358
p-value	0.730644321403998

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 18.6919922134068 \tabularnewline
beta & -0.122130501093962 \tabularnewline
S.D. & 0.33073593097988 \tabularnewline
T-STAT & -0.369268923192358 \tabularnewline
p-value & 0.730644321403998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106661&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]18.6919922134068[/C][/ROW]
[ROW][C]beta[/C][C]-0.122130501093962[/C][/ROW]
[ROW][C]S.D.[/C][C]0.33073593097988[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.369268923192358[/C][/ROW]
[ROW][C]p-value[/C][C]0.730644321403998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106661&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	18.6919922134068
beta	-0.122130501093962
S.D.	0.33073593097988
T-STAT	-0.369268923192358
p-value	0.730644321403998

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha	4.09200505081258
beta	-0.400772512903992
S.D.	1.45747244856086
T-STAT	-0.274977762564036
p-value	0.79695219984824
Lambda	1.40077251290399

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & 4.09200505081258 \tabularnewline
beta & -0.400772512903992 \tabularnewline
S.D. & 1.45747244856086 \tabularnewline
T-STAT & -0.274977762564036 \tabularnewline
p-value & 0.79695219984824 \tabularnewline
Lambda & 1.40077251290399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106661&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]4.09200505081258[/C][/ROW]
[ROW][C]beta[/C][C]-0.400772512903992[/C][/ROW]
[ROW][C]S.D.[/C][C]1.45747244856086[/C][/ROW]
[ROW][C]T-STAT[/C][C]-0.274977762564036[/C][/ROW]
[ROW][C]p-value[/C][C]0.79695219984824[/C][/ROW]
[ROW][C]Lambda[/C][C]1.40077251290399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106661&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106661&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	4.09200505081258
beta	-0.400772512903992
S.D.	1.45747244856086
T-STAT	-0.274977762564036
p-value	0.79695219984824
Lambda	1.40077251290399

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