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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationFri, 18 Nov 2016 15:21:54 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Nov/18/t147947894769jccs6e3yahg5v.htm/, Retrieved Thu, 02 May 2024 18:00:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296826, Retrieved Thu, 02 May 2024 18:00:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [PEARSON nieuwe la...] [2016-11-18 14:21:54] [d42b2dfaed369a60e2334709a5cede2f] [Current]
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Dataseries X:
11
11
11
11
10
14
9
11
11
12
10
10
10
14
12
10
9
10
12
10
13
8
12
11
11
12
12
12
11
14
11
11
11
10
12
12
10
14
12
12
11
11
11
15
11
11
10
0
10
11
12
10
12
9
10
7
9
12
11
12
13
11
11
12
9
13
11
10
8
11
12
11
8
11
8
11
7
10
9
11
11
9
11
11
13
10
14
11
11
9
10
13
9
12
11
14
12
12
7
11
11
12
12
11
11
11
15
12
12
11
12
10
11
11
9
14
11
8
11
11
13
11
11
14
11
11
11
8
10
11
11
14
12
12
12
10
13
3
10
13
7
11
12
11
11
11
12
8
14
14
12
12
14
12
10
11
11
7
13
10
11
9
13
11
10
11
12
11
11
Dataseries Y:
10
13
14
8
8
13
13
9
9
14
14
12
12
11
12
14
8
0
11
9
13
8
13
8
9
8
12
12
13
13
10
12
13
9
10
14
8
10
10
14
8
14
10
13
12
12
9
12
10
3
14
10
9
8
11
10
8
14
12
8
14
13
13
13
12
10
14
11
10
13
8
8
7
7
9
12
13
11
10
14
9
7
9
13
9
15
13
14
8
13
11
8
14
9
14
8
12
13
14
13
4
9
12
10
8
9
8
9
8
12
8
7
8
9
14
12
13
9
13
11
12
11
8
12
9
12
13
9
8
8
8
12
13
7
8
8
13
3
12
15
14
7
11
12
10
14
10
15
11
8
6
12
13
12
9
8
14
7
8
14
12
15
11
8
8
7
12
7
11




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time3 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296826&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]3 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=296826&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296826&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R ServerBig Analytics Cloud Computing Center







Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean10.952662721893510.5798816568047
Biased Variance3.607226637722777.09568992682329
Biased Standard Deviation1.899270027595542.66377362529613
Covariance0.575232459847844
Correlation0.113026930310425
Determination0.0127750869753977
T-Test1.47005002956248
p-value (2 sided)0.143429697864756
p-value (1 sided)0.0717148489323782
95% CI of Correlation[-0.0385915691007967, 0.259558234193628]
Degrees of Freedom167
Number of Observations169

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 10.9526627218935 & 10.5798816568047 \tabularnewline
Biased Variance & 3.60722663772277 & 7.09568992682329 \tabularnewline
Biased Standard Deviation & 1.89927002759554 & 2.66377362529613 \tabularnewline
Covariance & 0.575232459847844 \tabularnewline
Correlation & 0.113026930310425 \tabularnewline
Determination & 0.0127750869753977 \tabularnewline
T-Test & 1.47005002956248 \tabularnewline
p-value (2 sided) & 0.143429697864756 \tabularnewline
p-value (1 sided) & 0.0717148489323782 \tabularnewline
95% CI of Correlation & [-0.0385915691007967, 0.259558234193628] \tabularnewline
Degrees of Freedom & 167 \tabularnewline
Number of Observations & 169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296826&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]10.9526627218935[/C][C]10.5798816568047[/C][/ROW]
[ROW][C]Biased Variance[/C][C]3.60722663772277[/C][C]7.09568992682329[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]1.89927002759554[/C][C]2.66377362529613[/C][/ROW]
[ROW][C]Covariance[/C][C]0.575232459847844[/C][/ROW]
[ROW][C]Correlation[/C][C]0.113026930310425[/C][/ROW]
[ROW][C]Determination[/C][C]0.0127750869753977[/C][/ROW]
[ROW][C]T-Test[/C][C]1.47005002956248[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.143429697864756[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.0717148489323782[/C][/ROW]
[ROW][C]95% CI of Correlation[/C][C][-0.0385915691007967, 0.259558234193628][/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]167[/C][/ROW]
[ROW][C]Number of Observations[/C][C]169[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296826&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296826&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean10.952662721893510.5798816568047
Biased Variance3.607226637722777.09568992682329
Biased Standard Deviation1.899270027595542.66377362529613
Covariance0.575232459847844
Correlation0.113026930310425
Determination0.0127750869753977
T-Test1.47005002956248
p-value (2 sided)0.143429697864756
p-value (1 sided)0.0717148489323782
95% CI of Correlation[-0.0385915691007967, 0.259558234193628]
Degrees of Freedom167
Number of Observations169







Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 419.4, p-value < 2.2e-16
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 11.405, p-value = 0.003338
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 6.038, p-value = 6.619e-15
> ad.y
	Anderson-Darling normality test
data:  y
A = 3.5672, p-value = 6.041e-09

\begin{tabular}{lllllllll}
\hline
Normality Tests \tabularnewline
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 419.4, p-value < 2.2e-16
alternative hypothesis: greater
\tabularnewline
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 11.405, p-value = 0.003338
alternative hypothesis: greater
\tabularnewline
> ad.x
	Anderson-Darling normality test
data:  x
A = 6.038, p-value = 6.619e-15
\tabularnewline
> ad.y
	Anderson-Darling normality test
data:  y
A = 3.5672, p-value = 6.041e-09
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=296826&T=2

[TABLE]
[ROW][C]Normality Tests[/C][/ROW]
[ROW][C]
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 419.4, p-value < 2.2e-16
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 11.405, p-value = 0.003338
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> ad.x
	Anderson-Darling normality test
data:  x
A = 6.038, p-value = 6.619e-15
[/C][/ROW] [ROW][C]
> ad.y
	Anderson-Darling normality test
data:  y
A = 3.5672, p-value = 6.041e-09
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=296826&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296826&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 419.4, p-value < 2.2e-16
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 11.405, p-value = 0.003338
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 6.038, p-value = 6.619e-15
> ad.y
	Anderson-Darling normality test
data:  y
A = 3.5672, p-value = 6.041e-09



Parameters (Session):
par1 = TRUE ;
Parameters (R input):
R code (references can be found in the software module):
library(psychometric)
x <- x[!is.na(y)]
y <- y[!is.na(y)]
y <- y[!is.na(x)]
x <- x[!is.na(x)]
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, sub=main)
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', na.rm = T)
cxy <- as.matrix(corxy$estimate)[1,1]
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson Product Moment Correlation - Ungrouped Data',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Statistic',1,TRUE)
a<-table.element(a,'Variable X',1,TRUE)
a<-table.element(a,'Variable Y',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.element(a,mean(y))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Biased Variance',header=TRUE)
a<-table.element(a,varx)
a<-table.element(a,vary)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Biased Standard Deviation',header=TRUE)
a<-table.element(a,sqrt(varx))
a<-table.element(a,sqrt(vary))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Covariance',header=TRUE)
a<-table.element(a,cov(x,y),2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',header=TRUE)
a<-table.element(a,cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Determination',header=TRUE)
a<-table.element(a,cxy*cxy,2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-Test',header=TRUE)
a<-table.element(a,as.matrix(corxy$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,'95% CI of Correlation',header=TRUE)
a<-table.element(a,paste('[',CIr(r=cxy, n = lx, level = .95)[1],', ', CIr(r=cxy, n = lx, level = .95)[2],']',sep=''),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')
library(moments)
library(nortest)
jarque.x <- jarque.test(x)
jarque.y <- jarque.test(y)
if(lx>7) {
ad.x <- ad.test(x)
ad.y <- ad.test(y)
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Normality Tests',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('jarque.x'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('jarque.y'),'
',sep=''))
a<-table.row.end(a)
if(lx>7) {
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('ad.x'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,paste('
',RC.texteval('ad.y'),'
',sep=''))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
library(car)
bitmap(file='test2.png')
qqPlot(x,main='QQplot of variable x')
dev.off()
bitmap(file='test3.png')
qqPlot(y,main='QQplot of variable y')
dev.off()