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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 computationSun, 02 Nov 2008 08:24:58 -0700
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/Nov/02/t1225639527slyjru4e1jo5ez5.htm/, Retrieved Sun, 19 May 2024 11:11:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20604, Retrieved Sun, 19 May 2024 11:11:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F    D  [Mean Plot] [Hypothesis Testin...] [2008-10-30 12:52:34] [38f43994ada0e6172896e12525dcc585]
F   P     [Mean Plot] [Hypothesis Testin...] [2008-10-30 13:25:08] [38f43994ada0e6172896e12525dcc585]
F RMPD      [Pearson Correlation] [RNR - RNVM] [2008-11-01 15:02:44] [38f43994ada0e6172896e12525dcc585]
F   PD          [Pearson Correlation] [RNR - RNVM] [2008-11-02 15:24:58] [382e90e66f02be5ed86892bdc1574692] [Current]
Feedback Forum
2008-11-10 17:57:01 [Matthieu Blondeau] [reply] 
Vooreerst moeten de assen in het excel bestand verwisseld worden. De jaartallen moeten op de y-as staan en de variabelen op de x-as.

De studente heeft een correct antwoord gegeven aangezien RCF de beste predictor is, ze heeft wel niet de meest voor de hand liggende methode gebruikt. Op zich heeft de studente geen foute methode gebruikt maar de gebruikte methode neemt meer tijd in beslag. Men moet niet kijken naar de waarde op de grafiek maar naar de waarde in de tabel onder 'tau'. De waarde onderaan de grafiek geeft de betrouwbaarheid weer. Alle waarden onder de 0,05 zijn betrouwbaar.

correcte methode:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/10/t1226339794tdeumj1dp6cnlw3.htm, Retrieved Mon, 10 Nov 2008 17:56:39 +0000

2008-11-11 23:37:09 [Evelien Blockx] [reply] 
Hier heb ik inderdaad niet de snelste methode (Kendall Tau) genomen. Hiermee kan ik immers alle correlaties tegelijk berekenen.

Eerst moet ik de perioden in het excelbestand verticaal zetten.

Daarna moet ik alles invoeren in Kendall Tau.

Om de correlaties te bekijken moet je kijken in de tabel bij tau. De p-value is geen correlatiecoëfficiënt, het zegt iets over de betrouwbaarheid. Hoe kleiner, hoe meer betrouwbaar (in principe zou het onder de 0,05 moeten liggen).

Als je dan naar de correlaties gaat kijken, vind je de hoogste correlatie bij tau( RNR , RCF ). Het is een positieve correlatie van 0.80952380952381. Bovendien is de betrouwbaarheid 0,01 en dat is zeer goed.

RCF is dus de beste predictor voor RNR.

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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=20604&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=20604&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20604&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=20604&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=20604&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20604&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):
par1 = TRUE ;
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')