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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 computationSat, 01 Nov 2008 16:50:44 -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/Nov/01/t1225579922ugl1u59hv3yf7dh.htm/, Retrieved Sat, 18 May 2024 23:44:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20466, Retrieved Sat, 18 May 2024 23:44:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Pearson Correlation] [task 5 part 2 rnr...] [2008-11-01 22:11:20] [aa5573c1db401b164e448aef050955a1]
F         [Pearson Correlation] [Task 5 p2 Q1 RNR-...] [2008-11-01 22:50:44] [8a1195ff8db4df756ce44b463a631c76] [Current]
Feedback Forum
2008-11-11 12:52:46 [An De Koninck] [reply] 
Voor deze taak werd de verkeerde techniek toegepast.
Het was immers niet de bedoeling om dit te bekijken met de pearson correlatie maar met de Kendall tau Correlation Matrix.
Eerst en vooral moeten de assen verwisseld worden.
Hierna ga je in de tabel kijken wanneer de p-value het kleinste is. Hoe kleiner deze waarde, hoe beter en dus hoe betrouwbaarder.
De p-value is 0.01 (en ligt niet binnen het betrouwbaarheidsinterval). De RCF is dus de beste voorspeller voor RNR.
2008-11-11 12:56:05 [An De Koninck] [reply] 
De student heeft de verkeerde techniek gebruikt om deze oefening op te lossen.
Het was immers de bedoeling dat de Kendall tau Correlation Matrix werd gebruikt en niet de pearson correlation.
Eerst moeten de assen verwisseld worden.
Daarna is het de bedoeling dat je in de kolom naar de kleinste p-value gaat kijken. Dit is de kleinste en dus meest betrouwbare waarde.
De kleinste waarde is 0.01. De beste voorspeller voor RNR is dus RCF.

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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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20466&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20466&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20466&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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=20466&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=20466&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20466&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')