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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_tukeylambda.wasp
Title produced by softwareTukey lambda PPCC Plot
Date of computationFri, 15 Oct 2010 10:08:26 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Oct/15/t1287137240iuqcibqocexq4ai.htm/, Retrieved Sat, 04 May 2024 12:46:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=83384, Retrieved Sat, 04 May 2024 12:46:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Tukey lambda PPCC Plot] [Intrinsic Motivat...] [2010-10-12 12:09:04] [b98453cac15ba1066b407e146608df68]
F    D    [Tukey lambda PPCC Plot] [Intrinsic Motivat...] [2010-10-15 10:08:26] [18ef3d986e8801a4b28404e69e5bf56b] [Current]
Feedback Forum
2010-10-23 07:24:43 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De berekening werd hier door de student zeer goed uitgevoerd en ook de eindconclusie is perfect. Men zou misschien nog iets kunnen zeggen over hoe men tot deze conclusie komt. Men kan hier namelijk vaststellen dat het hoogtepunt van de grafiek bereikt wordt wanneer Lambda gelijk is aan 0,14. Op basis van deze vaststelling kan men inderdaad concluderen dat het gaat om een normaalverdeling.
2010-10-24 08:20:24 [6f5a430a34dfbeab884e51a2f2a26434] [reply] 
Het zou beter zijn moest je je antwoord beter staven. Zo kan je aan de hand van de hoogste Tukey lambda- waarden vaststellen dat het een approx; normal- verdeling is.
2010-10-25 20:57:27 [Naoual Ahidar] [reply] 
We zien duidelijk dat we met een normaalverdeling te maken hebben. Dit zien we ook aan de correlatie tabel waarin staat dat:
Approx. Normal (lambda=0.14) 0.994220675676583
Aangezien de streefwaarde hier 1 is, kunnen we dus aannemen dat de normaalverdeling de beste verdeling is om mee te werken voor deze gegevens.

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Dataset

Dataseries X:
Download CSV
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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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=83384&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=83384&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=83384&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.56242445502817
Exact Logistic (lambda=0)0.989690695953362
Approx. Normal (lambda=0.14)0.994220675676583
U-shaped (lambda=0.5)0.987562176002712
Exactly Uniform (lambda=1)0.975815658147389

\begin{tabular}{lllllllll}
\hline
Tukey Lambda - Key Values \tabularnewline
Distribution (lambda) & Correlation \tabularnewline
Approx. Cauchy (lambda=-1) & 0.56242445502817 \tabularnewline
Exact Logistic (lambda=0) & 0.989690695953362 \tabularnewline
Approx. Normal (lambda=0.14) & 0.994220675676583 \tabularnewline
U-shaped (lambda=0.5) & 0.987562176002712 \tabularnewline
Exactly Uniform (lambda=1) & 0.975815658147389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=83384&T=1

[TABLE]
[ROW][C]Tukey Lambda - Key Values[/C][/ROW]
[ROW][C]Distribution (lambda)[/C][C]Correlation[/C][/ROW]
[ROW][C]Approx. Cauchy (lambda=-1)[/C][C]0.56242445502817[/C][/ROW]
[ROW][C]Exact Logistic (lambda=0)[/C][C]0.989690695953362[/C][/ROW]
[ROW][C]Approx. Normal (lambda=0.14)[/C][C]0.994220675676583[/C][/ROW]
[ROW][C]U-shaped (lambda=0.5)[/C][C]0.987562176002712[/C][/ROW]
[ROW][C]Exactly Uniform (lambda=1)[/C][C]0.975815658147389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=83384&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=83384&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:

Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.56242445502817
Exact Logistic (lambda=0)0.989690695953362
Approx. Normal (lambda=0.14)0.994220675676583
U-shaped (lambda=0.5)0.987562176002712
Exactly Uniform (lambda=1)0.975815658147389


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
gp <- function(lambda, p)
{
(p^lambda-(1-p)^lambda)/lambda
}
sortx <- sort(x)
c <- array(NA,dim=c(201))
for (i in 1:201)
{
if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx)
}
bitmap(file='test1.png')
plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Distribution (lambda)',1,TRUE)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE)
a<-table.element(a,c[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE)
a<-table.element(a,(c[100]+c[102])/2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE)
a<-table.element(a,c[115])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE)
a<-table.element(a,c[151])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE)
a<-table.element(a,c[201])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')