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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationWed, 29 Oct 2008 11:15:13 -0600
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/Oct/29/t1225300573s6cgmtywok3g7pb.htm/, Retrieved Sat, 18 May 2024 19:42:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19918, Retrieved Sat, 18 May 2024 19:42:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Workshop 3 - EDA ...] [2007-10-29 18:20:28] [b3bb3ec527e23fa7d74d4348b38c8499]
F R  D    [Kendall tau Correlation Matrix] [Deel 2 Q1] [2008-10-29 17:15:13] [6aa66640011d9b98524a5838bcf7301d] [Current]
-           [Kendall tau Correlation Matrix] [Q1] [2008-11-03 19:32:21] [b518240a1c80d4f939bf8b3e34f77cec]
Feedback Forum
2008-11-06 15:14:37 [Stijn Van de Velde] [reply] 
Dit antwoord is volledig juist. De berekening is correct en de bijhorende uitleg is nagenoeg volledig.
De enige toevoeging die ik kan maken is dat je als je naar de correlatie grafiek van RNR en RCF kijkt dat je een duidelijke positieve correlatie ziet. (op 1 waarde na dan). Dit bevestigd nogmaals dat deze 2 reeksen zeer sterk gecorroleerd zijn.
2008-11-11 20:39:00 [Liese Tormans] [reply] 
De student maakt gebruik van de juiste software
De Kendall Tau correlation:

Ook de conclusie van de student is correct en duidelijk omschreven.

Zoals de student vermeld heeft is het, het best dat de waardes onder de 0,05 liggen, we kunnen dan spreken van een grote betrouwbaarheid.

Op de grafiek kunnen we zien dat we een waarde hebben van 0,03 bij RNVM , RNR. Maar dat er nog een kleinere waarde is namelijk 0,01 bij RNR , RCF. We kunnen dus stellen dat RCF (cash flow eigen vermogen) de beste predictor is voor RNR. Dit heeft de student ook vermeld in zijn bespreking.

Het is ook mogelijk om dit te zien op de grafiek. De student had dit eventueel nog kunnen vermelden om zo een meerwaarde te geven aan het document.
We kunnen spreken van een positieve correlatie als er een stijgende rechte lijn (een vlakke lijn) te zien zonder al te veel pieken. Dit is het geval bij het eerste grafiekje van de twee rij bovenaan te beginnen.


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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.2	20.8	0.9	39.6	4.8
2.6	17.1	0.85	36.1	-4.2
3	22.3	0.83	34.4	1.6
3.8	25.1	0.84	33.4	5.2
4	27.7	0.85	34.8	9.2
3.5	24.9	0.83	33.7	4.6
4.1	29.5	0.83	36.3	10.6

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'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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19918&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19918&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19918&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'George Udny Yule' @ 72.249.76.132






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RCF , RNR )0.809523809523810.0107142857142857
tau( RLEZ , REV )0.3704792868174740.266379923342483
tau( RLEZ , RNR )-0.05292561240249630.873844698517373
tau( REV , RNR )0.04761904761904761

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RNVM , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , REV ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RNVM , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RCF , RLEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCF , REV ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( RCF , RNR ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RLEZ , REV ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
tau( RLEZ , RNR ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( REV , RNR ) & 0.0476190476190476 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19918&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( RNVM , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , RLEZ )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RNVM , REV )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( RNVM , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( RCF , RLEZ )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RCF , REV )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( RCF , RNR )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( RLEZ , REV )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[ROW][C]tau( RLEZ , RNR )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( REV , RNR )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19918&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19918&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:

Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RCF , RNR )0.809523809523810.0107142857142857
tau( RLEZ , REV )0.3704792868174740.266379923342483
tau( RLEZ , RNR )-0.05292561240249630.873844698517373
tau( REV , RNR )0.04761904761904761


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method='kendall')
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'tau',1,TRUE)
a<-table.element(a,'p-value',1,TRUE)
a<-table.row.end(a)
n <- length(y[,1])
n
cor.test(y[1,],y[2,],method='kendall')
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste('tau(',dimnames(t(x))[[2]][i])
dum <- paste(dum,',')
dum <- paste(dum,dimnames(t(x))[[2]][j])
dum <- paste(dum,')')
a<-table.element(a,dum,header=TRUE)
r <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,r$estimate)
a<-table.element(a,r$p.value)
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')