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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationThu, 30 Oct 2008 07:52:30 -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/30/t1225374960np8rb9r5owjaxu1.htm/, Retrieved Sun, 19 May 2024 14:12:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20041, Retrieved Sun, 19 May 2024 14:12:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact182

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Pearson Correlation] [Q3 Clothing produ...] [2007-10-20 14:22:11] [b731da8b544846036771bbf9bf2f34ce]
-    D  [Pearson Correlation] [Q3 reproduce] [2008-10-20 16:49:56] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
F    D      [Pearson Correlation] [Pearson correlati...] [2008-10-30 13:52:30] [5e9e099b83e50415d7642e10d74756e4] [Current]
F    D        [Pearson Correlation] [Pearson correlati...] [2008-10-30 13:57:31] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
-    D          [Pearson Correlation] [Pearson correlati...] [2008-10-30 13:59:14] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
-    D            [Pearson Correlation] [Pearson correlati...] [2008-10-30 14:00:51] [deb3c14ac9e4607a6d84fc9d0e0e6cc2]
Feedback Forum
2008-11-10 23:12:01 [Ilknur Günes] [reply] 
Via de Kendall tau Correlation Matrix vind je alle mogelijke correlaties terug. Je moest het dus niet allemaal afzonderlijk doen.
De cijfers geven dan de graad van toeval weer: hoe groter de cijfer, hoe meer toeval. Maw we moeten naar de kleinste cijfer kijken. In dit geval kan het 0,01 en 0,03 zijn: RCF en RNVM. Want onder 0,05 kunnen we van correlatie spreken. 0,01 is kleiner, dus RCF is de beste predictor voor RNR

2008-11-11 12:26:50 [Natascha Meeus] [reply] 
Het was makkelijker geweest als je de kendall tau techniek gebruikt. Hierdoor bekom je alle mogelijke correlaties. Alle correlaties die onder 0,05 liggen zijn zeer betrouwbaar. In dit geval kan het 0,01 en 0,03 zijn: RCF en RNVM. 0,01 is kleiner, dus RCF is de beste predictor voor RNR. Je antwoord is dus wel correct.

2008-11-11 14:56:43 [Elias Van Deun] [reply] 
Zoals de 2 vorige studenten al aangaven was de Kendall Tau Correlation een betere en gemakkelijkere techniek geweest om deze vraag op te lossen.

Eenmaal berekend, zoeken we naar de kleinste p-waarde. Dit doen we omdat dan de betrouwbaarheid het grootst is en de kans dat het verband dan op toeval berust zeer klein is.

De beste predictor voor netto rendabiliteit op EV is de variabele RCF. De variabele RCF heeft namelijk het beste en significante verband met de netto rendabiliteit. Dit betekent tevens dat de correlatie tussen RCF en de cashflow niet op toeval berust.
2008-11-11 17:45:11 [Ellen Van Ham] [reply] 
Je antwoord was wel correct, maar je moest deze opdracht eigenlijk oplossen adhv Kendall tau Correlation plot. Op de schuine as zien we de variabele met daarnaast de scatter plot. Het voordeel van deze plot is dat men de rangen met elkaar kan vergelijken. Het is ook veel robuuster voor outliers, mits de spreiding hierdoor kleiner is. De getallen zijn geen correlatiecoëfficiënt (zoals in het antwoord gezegd) maar wel de betrouwbaarheid. Hoe hoger dat getal, hoe toevalliger je het getal gevonden hebt. Als de coëfficiënt onder 0,05 is dat het beste (hoe kleiner hoe beter). Dit is het geval bij RNR.
Link: http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/31/t1225459253ljw5x8vk2vptvn6.htm

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.8
-4.2
1.6
5.2
9.2
4.6
10.6
Dataseries Y:
Download CSV
Histogram 
4.2
2.6
3
3.8
4
3.5
4.1

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20041&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20041&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20041&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'Gwilym Jenkins' @ 72.249.127.135






Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean4.542857142857143.6
Biased Variance20.56816326530610.311428571428571
Biased Standard Deviation4.535213695660450.55805785670356
Covariance2.61333333333333
Correlation0.885056581705508
Determination0.783325152820238
T-Test4.25159403195624
p-value (2 sided)0.00807948566831929
p-value (1 sided)0.00403974283415964
Degrees of Freedom5
Number of Observations7

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 4.54285714285714 & 3.6 \tabularnewline
Biased Variance & 20.5681632653061 & 0.311428571428571 \tabularnewline
Biased Standard Deviation & 4.53521369566045 & 0.55805785670356 \tabularnewline
Covariance & 2.61333333333333 \tabularnewline
Correlation & 0.885056581705508 \tabularnewline
Determination & 0.783325152820238 \tabularnewline
T-Test & 4.25159403195624 \tabularnewline
p-value (2 sided) & 0.00807948566831929 \tabularnewline
p-value (1 sided) & 0.00403974283415964 \tabularnewline
Degrees of Freedom & 5 \tabularnewline
Number of Observations & 7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20041&T=1

[TABLE]
[ROW][C]Pearson Product Moment Correlation - Ungrouped Data[/C][/ROW]
[ROW][C]Statistic[/C][C]Variable X[/C][C]Variable Y[/C][/ROW]
[ROW][C]Mean[/C][C]4.54285714285714[/C][C]3.6[/C][/ROW]
[ROW][C]Biased Variance[/C][C]20.5681632653061[/C][C]0.311428571428571[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]4.53521369566045[/C][C]0.55805785670356[/C][/ROW]
[ROW][C]Covariance[/C][C]2.61333333333333[/C][/ROW]
[ROW][C]Correlation[/C][C]0.885056581705508[/C][/ROW]
[ROW][C]Determination[/C][C]0.783325152820238[/C][/ROW]
[ROW][C]T-Test[/C][C]4.25159403195624[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.00807948566831929[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.00403974283415964[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]5[/C][/ROW]
[ROW][C]Number of Observations[/C][C]7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20041&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20041&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:

Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean4.542857142857143.6
Biased Variance20.56816326530610.311428571428571
Biased Standard Deviation4.535213695660450.55805785670356
Covariance2.61333333333333
Correlation0.885056581705508
Determination0.783325152820238
T-Test4.25159403195624
p-value (2 sided)0.00807948566831929
p-value (1 sided)0.00403974283415964
Degrees of Freedom5
Number of Observations7


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
bitmap(file='test1.png')
histx <- hist(x, plot=FALSE)
histy <- hist(y, plot=FALSE)
maxcounts <- max(c(histx$counts, histx$counts))
xrange <- c(min(x),max(x))
yrange <- c(min(y),max(y))
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
par(mar=c(4,4,1,1))
plot(x, y, xlim=xrange, ylim=yrange, xlab=xlab, ylab=ylab)
par(mar=c(0,4,1,1))
barplot(histx$counts, axes=FALSE, ylim=c(0, maxcounts), space=0)
par(mar=c(4,0,1,1))
barplot(histy$counts, axes=FALSE, xlim=c(0, maxcounts), space=0, horiz=TRUE)
dev.off()
lx = length(x)
makebiased = (lx-1)/lx
varx = var(x)*makebiased
vary = var(y)*makebiased
corxy <- cor.test(x,y,method='pearson')
cxy <- as.matrix(corxy$estimate)[1,1]
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistic',1,TRUE)
a<-table.element(a,'Variable X',1,TRUE)
a<-table.element(a,'Variable Y',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('arithmetic_mean.htm','Mean',''),header=TRUE)
a<-table.element(a,mean(x))
a<-table.element(a,mean(y))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('biased.htm','Biased Variance',''),header=TRUE)
a<-table.element(a,varx)
a<-table.element(a,vary)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('biased1.htm','Biased Standard Deviation',''),header=TRUE)
a<-table.element(a,sqrt(varx))
a<-table.element(a,sqrt(vary))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('covariance.htm','Covariance',''),header=TRUE)
a<-table.element(a,cov(x,y),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('pearson_correlation.htm','Correlation',''),header=TRUE)
a<-table.element(a,cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('coeff_of_determination.htm','Determination',''),header=TRUE)
a<-table.element(a,cxy*cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,hyperlink('ttest_statistic.htm','T-Test',''),header=TRUE)
a<-table.element(a,as.matrix(corxy$statistic)[1,1],2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value (2 sided)',header=TRUE)
a<-table.element(a,(p2 <- as.matrix(corxy$p.value)[1,1]),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value (1 sided)',header=TRUE)
a<-table.element(a,p2/2,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degrees of Freedom',header=TRUE)
a<-table.element(a,lx-2,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of Observations',header=TRUE)
a<-table.element(a,lx,2)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')