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Author*The author of this computation has been verified*
R Software Modulerwasp_correlation.wasp
Title produced by softwarePearson Correlation
Date of computationMon, 19 Dec 2016 21:58:15 +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/Dec/19/t1482181967wyayn5f7xml3pjg.htm/, Retrieved Fri, 01 Nov 2024 03:37:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301516, Retrieved Fri, 01 Nov 2024 03:37:07 +0000
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Original text written by user:
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Estimated Impact71
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Pearson Correlation] [Pearson Correlati...] [2016-12-19 20:58:15] [c3c00422a8efeb721f46880d0369ae73] [Current]
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Dataseries X:
11
11
15
15
13
14
13
15
13
15
10
11
16
17
14
13
10
10
13
17
18
17
11
15
12
15
15
12
19
13
15
13
10
13
12
15
13
18
15
12
14
11
14
9
13
13
12
16
16
15
16
16
13
13
12
11
13
15
13
14
13
15
14
14
13
11
14
17
15
15
13
12
14
11
14
18
15
18
16
12
14
14
14
14
13
12
13
16
13
14
15
13
14
17
15
13
14
17
8
15
10
15
15
14
15
18
15
19
16
17
18
13
10
14
13
12
13
12
13
16
12
14
17
14
12
14
17
13
12
14
11
17
15
10
15
16
17
15
12
15
10
13
17
17
16
15
16
16
15
16
14
17
14
12
15
14
15
14
13
16
13
14
13
13
15
13
14
13
12
Dataseries Y:
14
19
17
17
15
20
15
19
15
15
19
16
20
18
15
14
20
16
16
16
10
19
19
16
15
18
17
19
17
13
19
20
5
19
16
15
16
18
16
15
17
13
20
19
7
13
16
16
18
18
16
17
19
16
19
13
16
13
12
17
17
17
16
16
14
16
13
16
14
20
12
13
18
14
19
18
14
18
19
15
14
17
19
13
19
18
20
15
15
15
20
15
19
18
18
15
20
17
12
18
19
20
13
17
15
16
18
18
14
15
12
17
14
18
17
17
20
16
14
15
18
20
17
17
17
17
15
17
18
17
20
15
16
15
18
11
15
18
20
19
14
16
15
17
18
20
17
18
15
16
11
15
18
17
16
12
19
18
15
17
19
18
19
16
16
16
14
16
14




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301516&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]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301516&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301516&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 time4 seconds
R ServerBig Analytics Cloud Computing Center







Pearson Product Moment Correlation - Ungrouped Data
StatisticVariable XVariable Y
Mean13.994082840236716.3668639053254
Biased Variance4.360911732782476.23227478029481
Biased Standard Deviation2.088279610775932.49645243902118
Covariance0.436707523245985
Correlation0.0832724430187895
Determination0.00693429976631754
T-Test1.07986769871294
p-value (2 sided)0.28175819754922
p-value (1 sided)0.14087909877461
95% CI of Correlation[-0.0685493061981174, 0.231324535032623]
Degrees of Freedom167
Number of Observations169

\begin{tabular}{lllllllll}
\hline
Pearson Product Moment Correlation - Ungrouped Data \tabularnewline
Statistic & Variable X & Variable Y \tabularnewline
Mean & 13.9940828402367 & 16.3668639053254 \tabularnewline
Biased Variance & 4.36091173278247 & 6.23227478029481 \tabularnewline
Biased Standard Deviation & 2.08827961077593 & 2.49645243902118 \tabularnewline
Covariance & 0.436707523245985 \tabularnewline
Correlation & 0.0832724430187895 \tabularnewline
Determination & 0.00693429976631754 \tabularnewline
T-Test & 1.07986769871294 \tabularnewline
p-value (2 sided) & 0.28175819754922 \tabularnewline
p-value (1 sided) & 0.14087909877461 \tabularnewline
95% CI of Correlation & [-0.0685493061981174, 0.231324535032623] \tabularnewline
Degrees of Freedom & 167 \tabularnewline
Number of Observations & 169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301516&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]13.9940828402367[/C][C]16.3668639053254[/C][/ROW]
[ROW][C]Biased Variance[/C][C]4.36091173278247[/C][C]6.23227478029481[/C][/ROW]
[ROW][C]Biased Standard Deviation[/C][C]2.08827961077593[/C][C]2.49645243902118[/C][/ROW]
[ROW][C]Covariance[/C][C]0.436707523245985[/C][/ROW]
[ROW][C]Correlation[/C][C]0.0832724430187895[/C][/ROW]
[ROW][C]Determination[/C][C]0.00693429976631754[/C][/ROW]
[ROW][C]T-Test[/C][C]1.07986769871294[/C][/ROW]
[ROW][C]p-value (2 sided)[/C][C]0.28175819754922[/C][/ROW]
[ROW][C]p-value (1 sided)[/C][C]0.14087909877461[/C][/ROW]
[ROW][C]95% CI of Correlation[/C][C][-0.0685493061981174, 0.231324535032623][/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=301516&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301516&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
Mean13.994082840236716.3668639053254
Biased Variance4.360911732782476.23227478029481
Biased Standard Deviation2.088279610775932.49645243902118
Covariance0.436707523245985
Correlation0.0832724430187895
Determination0.00693429976631754
T-Test1.07986769871294
p-value (2 sided)0.28175819754922
p-value (1 sided)0.14087909877461
95% CI of Correlation[-0.0685493061981174, 0.231324535032623]
Degrees of Freedom167
Number of Observations169







Normality Tests
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 0.23246, p-value = 0.8903
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 66.535, p-value = 3.553e-15
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 1.7681, p-value = 0.0001527
> ad.y
	Anderson-Darling normality test
data:  y
A = 2.1565, p-value = 1.693e-05

\begin{tabular}{lllllllll}
\hline
Normality Tests \tabularnewline
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 0.23246, p-value = 0.8903
alternative hypothesis: greater
\tabularnewline
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 66.535, p-value = 3.553e-15
alternative hypothesis: greater
\tabularnewline
> ad.x
	Anderson-Darling normality test
data:  x
A = 1.7681, p-value = 0.0001527
\tabularnewline
> ad.y
	Anderson-Darling normality test
data:  y
A = 2.1565, p-value = 1.693e-05
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=301516&T=2

[TABLE]
[ROW][C]Normality Tests[/C][/ROW]
[ROW][C]
> jarque.x
	Jarque-Bera Normality Test
data:  x
JB = 0.23246, p-value = 0.8903
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 66.535, p-value = 3.553e-15
alternative hypothesis: greater
[/C][/ROW] [ROW][C]
> ad.x
	Anderson-Darling normality test
data:  x
A = 1.7681, p-value = 0.0001527
[/C][/ROW] [ROW][C]
> ad.y
	Anderson-Darling normality test
data:  y
A = 2.1565, p-value = 1.693e-05
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301516&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301516&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 = 0.23246, p-value = 0.8903
alternative hypothesis: greater
> jarque.y
	Jarque-Bera Normality Test
data:  y
JB = 66.535, p-value = 3.553e-15
alternative hypothesis: greater
> ad.x
	Anderson-Darling normality test
data:  x
A = 1.7681, p-value = 0.0001527
> ad.y
	Anderson-Darling normality test
data:  y
A = 2.1565, p-value = 1.693e-05



Parameters (Session):
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()